TY - JOUR AB - Building deep learning models that can rapidly segment whole slide images (WSIs) using only a handful of training samples remains an open challenge in computational pathology. The difficulty lies in the histological images themselves: many morphological structures within a slide are closely related and very similar in appearance, making it difficult to distinguish between them. However, a skilled pathologist can quickly identify the relevant phenotypes. Through years of training, they have learned to organize visual features into a hierarchical taxonomy (e.g., identifying carcinoma versus healthy tissue, or distinguishing regions within a tumor as cancer cells, the microenvironment,...). Thus, each region is associated with multiple labels representing different tissue types. Pathologists typically deal with this by analyzing the specimen at multiple scales and comparing visual features between different magnifications. Inspired by this multi-scale diagnostic workflow, we introduce the Navigator, a vision model that navigates through WSIs like a domain expert: it searches for the region of interest at a low scale, zooms in gradually, and localizes ever finer microanatomical classes. As a result, the Navigator can detect coarse-grained patterns at lower resolution and fine-grained features at higher resolution. In addition, to deal with sparsely annotated samples, we train the Navigator with a novel semi-supervised framework called S5CL v2. The proposed model improves the F1 score by up to 8% on various datasets including our challenging new TCGA-COAD-30CLS and Erlangen cohorts. AU - Tran, M. AU - Wagner, S. AU - Weichert, W.* AU - Matek, C. AU - Boxberg, M.* AU - Peng, T. C1 - 73290 C2 - 56986 CY - 445 Hoes Lane, Piscataway, Nj 08855-4141 Usa SP - 2002-2015 TI - Navigating through whole slide images with hierarchy, multi-object, and multi-scale data. JO - IEEE Trans. Med. Imaging VL - 44 IS - 5 PB - Ieee-inst Electrical Electronics Engineers Inc PY - 2025 SN - 0278-0062 ER - TY - JOUR AB - Ultra-wideband raster-scan optoacoustic mesoscopy (RSOM) is a novel modality that has demonstrated unprecedented ability to visualize epidermal and dermal structures in-vivo. However, an automatic and quantitative analysis of three-dimensional RSOM datasets remains unexplored. In this work we present our framework: Deep Learning RSOM Analysis Pipeline (DeepRAP), to analyze and quantify morphological skin features recorded by RSOM and extract imaging biomarkers for disease characterization. DeepRAP uses a multi-network segmentation strategy based on convolutional neural networks with transfer learning. This strategy enabled the automatic recognition of skin layers and subsequent segmentation of dermal microvasculature with an accuracy equivalent to human assessment. DeepRAP was validated against manual segmentation on 25 psoriasis patients under treatment and our biomarker extraction was shown to characterize disease severity and progression well with a strong correlation to physician evaluation and histology. In a unique validation experiment, we applied DeepRAP in a time series sequence of occlusion-induced hyperemia from 10 healthy volunteers. We observe how the biomarkers decrease and recover during the occlusion and release process, demonstrating accurate performance and reproducibility of DeepRAP. Furthermore, we analyzed a cohort of 75 volunteers and defined a relationship between aging and microvascular features in-vivo. More precisely, this study revealed that fine microvascular features in the dermal layer have the strongest correlation to age. The ability of our newly developed framework to enable the rapid study of human skin morphology and microvasculature in-vivo promises to replace biopsy studies, increasing the translational potential of RSOM. AU - He, H. AU - Paetzold, J.C.* AU - Borner, N.* AU - Riedel, E. AU - Gerl, S.* AU - Schneider, S.* AU - Fisher, C. AU - Ezhov, I.* AU - Shit, S.* AU - Li, H.* AU - Rückert, D.* AU - Aguirre, J.* AU - Biedermann, T.* AU - Darsow, U.* AU - Menze, B.* AU - Ntziachristos, V. C1 - 69838 C2 - 55273 CY - 445 Hoes Lane, Piscataway, Nj 08855-4141 Usa SP - 2074-2085 TI - Machine learning analysis of human skin by optoacoustic mesoscopy for automated extraction of psoriasis and aging biomarkers. JO - IEEE Trans. Med. Imaging VL - 43 IS - 6 PB - Ieee-inst Electrical Electronics Engineers Inc PY - 2024 SN - 0278-0062 ER - TY - JOUR AB - Optical coherence tomography angiography (OCTA) is a non-invasive imaging modality that can acquire high-resolution volumes of the retinal vasculature and aid the diagnosis of ocular, neurological and cardiac diseases. Segmenting the visible blood vessels is a common first step when extracting quantitative biomarkers from these images. Classical segmentation algorithms based on thresholding are strongly affected by image artifacts and limited signal-to-noise ratio. The use of modern, deep learning-based segmentation methods has been inhibited by a lack of large datasets with detailed annotations of the blood vessels. To address this issue, recent work has employed transfer learning, where a segmentation network is trained on synthetic OCTA images and is then applied to real data. However, the previously proposed simulations fail to faithfully model the retinal vasculature and do not provide effective domain adaptation. Because of this, current methods are unable to fully segment the retinal vasculature, in particular the smallest capillaries. In this work, we present a lightweight simulation of the retinal vascular network based on space colonization for faster and more realistic OCTA synthesis. We then introduce three contrast adaptation pipelines to decrease the domain gap between real and artificial images. We demonstrate the superior segmentation performance of our approach in extensive quantitative and qualitative experiments on three public datasets that compare our method to traditional computer vision algorithms and supervised training using human annotations. Finally, we make our entire pipeline publicly available, including the source code, pretrained models, and a large dataset of synthetic OCTA images. AU - Kreitner, L.* AU - Paetzold, J.C. AU - Rauch, N.* AU - Chen, C.* AU - Hagag, A.M.* AU - Fayed, A.E.* AU - Sivaprasad, S.* AU - Rausch, S.* AU - Weichsel, J.* AU - Menze, B.H.* AU - Harders, M.* AU - Knier, B.* AU - Rueckert, D.* AU - Menten, M.J.* C1 - 70999 C2 - 55856 CY - 445 Hoes Lane, Piscataway, Nj 08855-4141 Usa SP - 2061-2073 TI - Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations. JO - IEEE Trans. Med. Imaging VL - 43 IS - 6 PB - Ieee-inst Electrical Electronics Engineers Inc PY - 2024 SN - 0278-0062 ER - TY - JOUR AB - Deep unsupervised approaches are gathering increased attention for applications such as pathology detection and segmentation in medical images since they promise to alleviate the need for large labeled datasets and are more generalizable than their supervised counterparts in detecting any kind of rare pathology. As the Unsupervised Anomaly Detection (UAD) literature continuously grows and new paradigms emerge, it is vital to continuously evaluate and benchmark new methods in a common framework, in order to reassess the state-of-the-art (SOTA) and identify promising research directions. To this end, we evaluate a diverse selection of cutting-edge UAD methods on multiple medical datasets, comparing them against the established SOTA in UAD for brain MRI. Our experiments demonstrate that newly developed feature-modeling methods from the industrial and medical literature achieve increased performance compared to previous work and set the new SOTA in a variety of modalities and datasets. Additionally, we show that such methods are capable of benefiting from recently developed self-supervised pre-training algorithms, further increasing their performance. Finally, we perform a series of experiments in order to gain further insights into some unique characteristics of selected models and datasets. Our code can be found under https://github.com/iolag/UPD_study/. AU - Lagogiannis, I.* AU - Meissen, F.* AU - Kaissis, G. AU - Rueckert, D.* C1 - 70350 C2 - 55401 CY - 445 Hoes Lane, Piscataway, Nj 08855-4141 Usa SP - 241-252 TI - Unsupervised pathology detection: A deep dive Into the state of the art. JO - IEEE Trans. Med. Imaging VL - 43 IS - 1 PB - Ieee-inst Electrical Electronics Engineers Inc PY - 2024 SN - 0278-0062 ER - TY - JOUR AB - Cryo-electron tomography (cryo-ET) allows to visualize the cellular context at macromolecular level. To date, the impossibility of obtaining a reliable ground truth is limiting the application of deep learning-based image processing algorithms in this field. As a consequence, there is a growing demand of realistic synthetic datasets for training deep learning algorithms. In addition, besides assisting the acquisition and interpretation of experimental data, synthetic tomograms are used as reference models for cellular organization analysis from cellular tomograms. Current simulators in cryo-ET focus on reproducing distortions from image acquisition and tomogram reconstruction, however, they can not generate many of the low order features present in cellular tomograms. Here we propose several geometric and organization models to simulate low order cellular structures imaged by cryo-ET. Specifically, clusters of any known cytosolic or membrane bound macromolecules, membranes with different geometries as well as different filamentous structures such as microtubules or actin-like networks. Moreover, we use parametrizable stochastic models to generate a high diversity of geometries and organizations to simulate representative and generalized datasets, including very crowded environments like those observed in native cells. These models have been implemented in a multiplatform open-source Python package, including scripts to generate cryo-tomograms with adjustable sizes and resolutions. In addition, these scripts provide also distortion-free density maps besides the ground truth in different file formats for efficient access and advanced visualization. We show that such a realistic synthetic dataset can be readily used to train generalizable deep learning algorithms. AU - Martinez-Sanchez, A.* AU - Lamm, L. AU - Jasnin, M. AU - Phelippeau, H.* C1 - 70718 C2 - 55558 CY - 445 Hoes Lane, Piscataway, Nj 08855-4141 Usa SP - 3742-3754 TI - Simulating the cellular context in synthetic datasets for cryo-electron tomography. JO - IEEE Trans. Med. Imaging VL - 43 IS - 11 PB - Ieee-inst Electrical Electronics Engineers Inc PY - 2024 SN - 0278-0062 ER - TY - JOUR AB - Weakly supervised object detection (WSup-OD) increases the usefulness and interpretability of image classification algorithms without requiring additional supervision. The successes of multiple instance learning in this task for natural images, however, do not translate well to medical images due to the very different characteristics of their objects (i.e. pathologies). In this work, we propose Weakly Supervised ROI Proposal Networks (WSRPN), a new method for generating bounding box proposals on the fly using a specialized region of interest-attention (ROI-attention) module. WSRPN integrates well with classic backbone-head classification algorithms and is end-to-end trainable with only image-label supervision. We experimentally demonstrate that our new method outperforms existing methods in the challenging task of disease localization in chest X-ray images. Code: https://anonymous.4open.science/r/WSRPN-DCA1. AU - Müller, P.* AU - Meissen, F.* AU - Kaissis, G. AU - Rueckert, D.* C1 - 71441 C2 - 56112 CY - 445 Hoes Lane, Piscataway, Nj 08855-4141 Usa SP - 221-231 TI - Weakly supervised object detection in chest X-rays with differentiable ROI proposal networks and soft ROI pooling. JO - IEEE Trans. Med. Imaging VL - 44 IS - 1 PB - Ieee-inst Electrical Electronics Engineers Inc PY - 2024 SN - 0278-0062 ER - TY - JOUR AB - Fluorescence molecular endoscopy (FME) is emerging as a “red-flag” technique with potential to deliver earlier, faster, and more personalized detection of disease in the gastrointestinal tract, including cancer, and to gain insights into novel drug distribution, dose finding, and response prediction. However, to date, the performance of FME systems is assessed mainly by endoscopists during a procedure, leading to arbitrary, potentially biased, and heavily subjective assessment. This approach significantly affects the repeatability of the procedures and the interpretation or comparison of the acquired data, representing a major bottleneck towards the clinical translation of the technology. Herein, we propose a robust methodology for FME performance assessment and quality control that is based on a novel multi-parametric rigid standard. This standard enables the characterization of an FME system’s sensitivity through a single acquisition, performance comparison of multiple systems, and, for the first time, quality control of a system as a function of time and number of usages. We show the photostability of the standard experimentally and demonstrate how it can be used to characterize the performance of an FME system. Moreover, we showcase how the standard can be employed for quality control of a system. In this study, we find that the use of composite fluorescence standards before endoscopic procedures can ensure that an FME system meets the performance criteria and that components prone to performance degradation are replaced in time, avoiding disruption of clinical endoscopy logistics. This will help overcome a major barrier for the translation of FME into the clinics. AU - Tenditnaya, A. AU - Gabriels, R.Y.* AU - Hooghiemstra, W.T.R.* AU - Klemm, U. AU - Nagengast, W.B.* AU - Ntziachristos, V. AU - Gorpas, D. C1 - 70716 C2 - 55559 CY - 445 Hoes Lane, Piscataway, Nj 08855-4141 Usa SP - 3710-3718 TI - Performance assessment and quality control of fluorescence molecular endoscopy with a multi-parametric rigid standard. JO - IEEE Trans. Med. Imaging VL - 43 IS - 11 PB - Ieee-inst Electrical Electronics Engineers Inc PY - 2024 SN - 0278-0062 ER - TY - JOUR AB - Biophysical modeling, particularly involving partial differential equations (PDEs), offers significant potential for tailoring disease treatment protocols to individual patients. However, the inverse problem-solving aspect of these models presents a substantial challenge, either due to the high computational requirements of model-based approaches or the limited robustness of deep learning (DL) methods. We propose a novel framework that leverages the unique strengths of both approaches in a synergistic manner. Our method incorporates a DL ensemble for initial parameter estimation, facilitating efficient downstream evolutionary sampling initialized with this DL-based prior. We showcase the effectiveness of integrating a rapid deep-learning algorithm with a high-precision evolution strategy in estimating brain tumor cell concentrations from magnetic resonance images. The DL-Prior plays a pivotal role, significantly constraining the effective sampling-parameter space. This reduction results in a fivefold convergence acceleration and a Dice-score of 95%. AU - Weidner, J.* AU - Ezhov, I.* AU - Balcerak, M.* AU - Metz, M.C.* AU - Litvinov, S.* AU - Kaltenbach, S.* AU - Feiner, L.F.* AU - Lux, L.* AU - Kofler, F. AU - Lipkova, J.* AU - Latz, J.* AU - Rueckert, D.* AU - Menze, B.* AU - Wiestler, B.* C1 - 72591 C2 - 56626 CY - 445 Hoes Lane, Piscataway, Nj 08855-4141 Usa SP - 1297-1307 TI - A learnable prior improves inverse tumor growth modeling. JO - IEEE Trans. Med. Imaging VL - 44 IS - 3 PB - Ieee-inst Electrical Electronics Engineers Inc PY - 2024 SN - 0278-0062 ER - TY - JOUR AB - Motion represents one of the major challenges in magnetic resonance imaging (MRI). Since the MR signal is acquired in frequency space, any motion of the imaged object leads to complex artefacts in the reconstructed image in addition to other MR imaging artefacts. Deep learning has been frequently proposed for motion correction at several stages of the reconstruction process. The wide range of MR acquisition sequences, anatomies and pathologies of interest, and motion patterns (rigid vs. deformable and random vs. regular) makes a comprehensive solution unlikely. To facilitate the transfer of ideas between different applications, this review provides a detailed overview of proposed methods for learning-based motion correction in MRI together with their common challenges and potentials. This review identifies differences and synergies in underlying data usage, architectures, training and evaluation strategies. We critically discuss general trends and outline future directions, with the aim to enhance interaction between different application areas and research fields. AU - Spieker, V. AU - Eichhorn, H. AU - Hammernik, K. AU - Rueckert, D.* AU - Preibisch, C.* AU - Karampinos, D.C.* AU - Schnabel, J.A. C1 - 68831 C2 - 53692 TI - Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review. JO - IEEE Trans. Med. Imaging PY - 2023 SN - 0278-0062 ER - TY - JOUR AB - Chemical staining of the blood smears is one of the crucial components of blood analysis. It is an expensive, lengthy and sensitive process, often prone to produce slight variations in colour and seen structures due to a lack of unified protocols across laboratories. Even though the current developments in deep generative modeling offer an opportunity to replace the chemical process with a digital one, there are specific safety-ensuring requirements due to the severe consequences of mistakes in a medical setting. Therefore digital staining system would profit from an additional confidence estimation quantifying the quality of the digitally stained white blood cell. To this aim, during the staining generation, we disentangle the latent space of the Generative Adversarial Network, obtaining separate representation s of the white blood cell and the staining. We estimate the generated image's confidence of white blood cell structure and staining quality by corrupting these representations with noise and quantifying the information retained between multiple outputs. We show that confidence estimated in this way correlates with image quality measured in terms of LPIPS values calculated for the generated and ground truth stained images. We validate our method by performing digital staining of images captured with a Differential Inference Contrast microscope on a dataset composed of white blood cells of 24 patients. The high absolute value of the correlation between our confidence score and LPIPS demonstrates the effectiveness of our method, opening the possibility of predicting the quality of generated output and ensuring trustworthiness in medical safety-critical setup. AU - Tomczak, A.* AU - Ilic, S.* AU - Marquardt, G.* AU - Engel, T.* AU - Navab, N.* AU - Albarqouni, S. C1 - 70215 C2 - 55079 SP - 3895-3906 TI - Digital staining of white blood cells with confidence estimation. JO - IEEE Trans. Med. Imaging VL - 42 IS - 12 PY - 2023 SN - 0278-0062 ER - TY - JOUR AB - Image contrast in multispectral optoacoustic tomography (MSOT) can be severely reduced by electrical noise and interference in the acquired optoacoustic signals. Previously employed signal processing techniques have proven insufficient to remove the effects of electrical noise because they typically rely on simplified models and fail to capture complex characteristics of signal and noise. Moreover, they often involve time-consuming processing steps that are unsuited for real-time imaging applications. In this work, we develop and demonstrate a discriminative deep learning approach to separate electrical noise from optoacoustic signals prior to image reconstruction. The proposed deep learning algorithm is based on two key features. First, it learns spatiotemporal correlations in both noise and signal by using the entire optoacoustic sinogram as input. Second, it employs training on a large dataset of experimentally acquired pure noise and synthetic optoacoustic signals. We validated the ability of the trained model to accurately remove electrical noise on synthetic data and on optoacoustic images of a phantom and the human breast. We demonstrate significant enhancements of morphological and spectral optoacoustic images reaching 19% higher blood vessel contrast and localized spectral contrast at depths of more than 2 cm for images acquired in vivo. We discuss how the proposed denoising framework is applicable to clinical multispectral optoacoustic tomography and suitable for real-time operation. AU - Dehner, C. AU - Olefir, I. AU - Basak, K. AU - Jüstel, D. AU - Ntziachristos, V. C1 - 66708 C2 - 53109 SP - 3182-3193 TI - Deep-learning-based electrical noise removal enables high spectral optoacoustic contrast in deep tissue. JO - IEEE Trans. Med. Imaging VL - 41 IS - 11 PY - 2022 SN - 0278-0062 ER - TY - JOUR AB - Signals acquired by optoacoustic tomography systems have broadband frequency content that encodes information about structures on different physical scales. Concurrent processing and rendering of such broadband signals may result in images with poor contrast and fidelity due to a bias towards low frequency contributions from larger structures. This problem cannot be addressed by filtering different frequency bands and reconstructing them individually, as this procedure leads to artefacts due to its incompatibility with the entangled frequency content of signals generated by structures of different sizes. Here we introduce frequency-band model-based (fbMB) reconstruction to separate frequency-band-specific optoacoustic image components during image formation, thereby enabling structures of all sizes to be rendered with high fidelity. In order to disentangle the overlapping frequency content of image components, fbMB uses soft priors to achieve an optimal trade-off between localization of the components in frequency bands and their structural integrity. We demonstrate that fbMB produces optoacoustic images with improved contrast and fidelity, which reveal anatomical structures in in vivo images of mice in unprecedented detail. These enhancements further improve the accuracy of spectral unmixing in small vasculature. By offering a precise treatment of the frequency components of optoacoustic signals, fbMB improves the quality, accuracy, and quantification of optoacoustic images and provides a method of choice for optoacoustic reconstructions. AU - Longo, A. AU - Jüstel, D. AU - Ntziachristos, V. C1 - 66709 C2 - 53108 SP - 3373-3384 TI - Disentangling the frequency content in optoacoustics. JO - IEEE Trans. Med. Imaging VL - 41 IS - 11 PY - 2022 SN - 0278-0062 ER - TY - JOUR AB - Segmenting the fine structure of the mouse brain on magnetic resonance (MR) images is critical for delineating morphological regions, analyzing brain function, and understanding their relationships. Compared to a single MRI modality, multimodal MRI data provide complementary tissue features that can be exploited by deep learning models, resulting in better segmentation results. However, multimodal mouse brain MRI data is often lacking, making automatic segmentation of mouse brain fine structure a very challenging task. To address this issue, it is necessary to fuse multimodal MRI data to produce distinguished contrasts in different brain structures. Hence, we propose a novel disentangled and contrastive GAN-based framework, named MouseGAN++, to synthesize multiple MR modalities from single ones in a structure-preserving manner, thus improving the segmentation performance by imputing missing modalities and multi-modality fusion. Our results demonstrate that the translation performance of our method outperforms the state-of-the-art methods. Using the subsequently learned modality-invariant information as well as the modality-translated images, MouseGAN++ can segment fine brain structures with averaged dice coefficients of 90.0% (T2w) and 87.9% (T1w), respectively, achieving around +10% performance improvement compared to the state-of-the-art algorithms. Our results demonstrate that MouseGAN++, as a simultaneous image synthesis and segmentation method, can be used to fuse cross-modality information in an unpaired manner and yield more robust performance in the absence of multimodal data. We release our method as a mouse brain structural segmentation tool for free academic usage at https://github.com/yu02019. AU - Yu, Z.* AU - Han, X.* AU - Zhang, S.* AU - Feng, J.* AU - Peng, T. AU - Zhang, X.Y.* C1 - 67060 C2 - 53437 CY - 445 Hoes Lane, Piscataway, Nj 08855-4141 Usa SP - 1197-1209 TI - MouseGAN++: Unsupervised disentanglement and contrastive representation for multiple MRI modalities synthesis and structural segmentation of mouse brain. JO - IEEE Trans. Med. Imaging VL - 42 IS - 4 PB - Ieee-inst Electrical Electronics Engineers Inc PY - 2022 SN - 0278-0062 ER - TY - JOUR AB - Optoacoustic signals are typically reconstructed into images using inversion algorithms applied in the time-domain. However, time-domain reconstructions can be computationally intensive and therefore slow when large amounts of raw data are collected from an optoacoustic scan. Here we consider a fast weighted ω-κ (FWOK) algorithm operating in the frequency domain to accelerate the inversion in raster-scan optoacoustic mesoscopy (RSOM), while seamlessly incorporating impulse response correction with minimum computational burden. We investigate the FWOK performance with RSOM measurements from phantoms and mice in vivo and obtained 360-fold speed improvement over inversions based on the back-projection algorithm in the time-domain. This previously unexplored inversion of in vivo optoacoustic data with impulse response correction in frequency domain reconstructions points to a promising strategy of accelerating optoacoustic imaging computations, toward video-rate tomography. AU - Mustafa, Q. AU - Omar, M. AU - Prade, L. AU - Mohajerani, P. AU - Stylogiannis, A. AU - Ntziachristos, V. AU - Zakian Dominguez, C.M. C1 - 62227 C2 - 50745 CY - 445 Hoes Lane, Piscataway, Nj 08855-4141 Usa SP - 3349-3357 TI - In vivo three-dimensional Raster Scan Optoacoustic Mesoscopy using Frequency Domain Inversion. JO - IEEE Trans. Med. Imaging VL - 40 IS - 12 PB - Ieee-inst Electrical Electronics Engineers Inc PY - 2021 SN - 0278-0062 ER - TY - JOUR AB - The impulse response of optoacoustic (photoacoustic) tomographic imaging system depends on several system components, the characteristics of which can influence the quality of reconstructed images. The effect of these system components on reconstruction quality have not been considered in detail so far. Here we combine sparse measurements of the total impulse response (TIR) with a geometric acoustic model to obtain a full characterization of the TIR of a handheld optoacoustic tomography system with concave limited-view acquisition geometry. We then use this synthetic TIR to reconstruct data from phantoms and healthy human volunteers, demonstrating improvements in image resolution and fidelity. The higher accuracy of optoacoustic tomographic reconstruction with TIR correction further improves the diagnostic capability of handheld optoacoustic tomographic systems. AU - Chowdhury, S.P. AU - Prakash, J. AU - Karlas, A. AU - Jüstel, D. AU - Ntziachristos, V. C1 - 60295 C2 - 49215 CY - 445 Hoes Lane, Piscataway, Nj 08855-4141 Usa SP - 3218-3230 TI - A synthetic total impulse response characterization method for correction of hand-held optoacoustic images. JO - IEEE Trans. Med. Imaging VL - 39 IS - 10 PB - Ieee-inst Electrical Electronics Engineers Inc PY - 2020 SN - 0278-0062 ER - TY - JOUR AB - Iterative model-based algorithms are known to enable more accurate and quantitative optoacoustic (photoacoustic) tomographic reconstructions than standard back-projection methods. However, three-dimensional (3D) model-based inversion is often hampered by high computational complexity and memory overhead. Parallel implementations on a graphics processing unit (GPU) have been shown to efficiently reduce the memory requirements by on-the-fly calculation of the actions of the optoacoustic model matrix, but the high complexity still makes these approaches impractical for large 3D optoacoustic datasets. Herein, we show that the computational complexity of 3D model-based iterative inversion can be significantly reduced by splitting the model matrix into two parts: one maximally sparse matrix containing only one entry per voxel-transducer pair and a second matrix corresponding to cyclic convolution. We further suggest reconstructing the images by multiplying the transpose of the model matrix calculated in this manner with the acquired signals, which is equivalent to using a very large regularization parameter in the iterative inversion method. The performance of these two approaches is compared to that of standard back-projection and a recently introduced GPU-based model-based method using datasets from in vivo experiments. The reconstruction time was accelerated by approximately an order of magnitude with the new iterative method, while multiplication with the transpose of the matrix is shown to be as fast as standard back-projection. AU - Ding, L AU - Razansky, D.* AU - Deán-Ben, X.L.* C1 - 60118 C2 - 49240 CY - 445 Hoes Lane, Piscataway, Nj 08855-4141 Usa SP - 2931-2940 TI - Model-based reconstruction of large three-dimensional optoacoustic datasets. JO - IEEE Trans. Med. Imaging VL - 39 IS - 9 PB - Ieee-inst Electrical Electronics Engineers Inc PY - 2020 SN - 0278-0062 ER - TY - JOUR AB - Optoacoustic tomography systems have attained unprecedented volumetric imaging speeds, thus enabling insights into rapid biological dynamics and marking a milestone in the clinical translation of this modality. Fast imaging performance often comes at the cost of limited field-of-view, which may hinder potential applications looking at larger tissue volumes. The imaged field-of-view can potentially be expanded via scanning and using additional hardware to track the position of the imaging probe. However, this approach turns impractical for high-resolution volumetric scans performed in a freehand mode along arbitrary trajectories. We have developed an accurate framework for spatial compounding of time-lapse optoacoustic data. The method exploits the frequency-domain properties of vascular networks in optoacoustic images and estimates the relative motion and orientation of the imaging probe. This allows rapidly combining sequential volumetric frames into large area scans without additional tracking hardware. The approach is universally applicable for compounding volumetric data acquired with calibrated scanning systems but also in a freehand mode with up to six degrees of freedom. Robust performance is demonstrated for whole-body mouse imaging with spiral volumetric optoacoustic tomography and for freehand visualization of vascular networks in humans using volumetric imaging probes. The newly introduced capability for angiographic observations at multiple spatial and temporal scales is expected to greatly facilitate the use of optoacoustic imaging technology in pre-clinical research and clinical diagnostics. The technique can equally benefit other biomedical imaging modalities, such as scanning fluorescence microscopy, optical coherence tomography or ultrasonography, thus optimizing their trade-offs between fast imaging performance and field-of-view. AU - Knauer, N. AU - Deán-Ben, X.L.* AU - Razansky, D.* C1 - 57880 C2 - 47969 CY - 445 Hoes Lane, Piscataway, Nj 08855-4141 Usa SP - 1160-1169 TI - Spatial compounding of volumetric data enables freehand optoacoustic angiography of large-scale vascular networks. JO - IEEE Trans. Med. Imaging VL - 39 IS - 4 PB - Ieee-inst Electrical Electronics Engineers Inc PY - 2020 SN - 0278-0062 ER - TY - JOUR AB - Optoacoustic (photoacoustic) mesoscopy offers unique capabilities in skin imaging and resolves skin features associated with detection, diagnosis, and management of disease. A critical first step in the quantitative analysis of clinical optoacoustic images is to identify the skin surface in a rapid, reliable, and automated manner. Nevertheless, most common edge- and surface-detection algorithms cannot reliably detect the skin surface on 3D raster-scan optoacoustic mesoscopy (RSOM) images, due to discontinuities and diffuse interfaces in the image. We present herein a novel dynamic programming approach that extracts the skin boundary as a 2D surface in one single step, as opposed to consecutive extraction of several independent 1D contours. A domain-specific energy function is introduced, taking into account the properties of volumetric optoacoustic mesoscopy images. The accuracy of the proposed method is validated on scans of the volar forearm of 19 volunteers with different skin complexions, for which the skin surface has been traced manually to provide a reference. In addition, the robustness and the limitations of the method are demonstrated on data where the skin boundaries are low-contrast or ill-defined. The automatic skin surface detection method can improve the speed and accuracy in the analysis of quantitative features seen on the RSOM images and accelerate the clinical translation of the technique. Our method can likely be extended to identify other types of surfaces in the RSOM and other imaging modalities. AU - Nitkunanantharajah, S. AU - Zahnd, G.* AU - Olivo, M.* AU - Navab, N.* AU - Mohajerani, P.* AU - Ntziachristos, V. C1 - 56638 C2 - 47124 CY - 445 Hoes Lane, Piscataway, Nj 08855-4141 Usa SP - 458-467 TI - Skin surface detection in 3D optoacoustic mesoscopy based on dynamic programming, JO - IEEE Trans. Med. Imaging VL - 39 IS - 2 PB - Ieee-inst Electrical Electronics Engineers Inc PY - 2020 SN - 0278-0062 ER - TY - JOUR AB - Label free imaging of oxygenation distribution in tissues is highly desired in numerous biomedical applications, but is still elusive, in particular in sub-epidermal measurements. Eigenspectra multispectral optoacoustic tomography (eMSOT) and its Bayesian-based implementation have been introduced to offer accurate label-free blood oxygen saturation (sO(2)) maps in tissues. The method uses the eigenspectra model of light fluence in tissue to account for the spectral changes due to the wavelength dependent attenuation of light with tissue depth. eMSOT relies on the solution of an inverse problem bounded by a number of ad hoc hand-engineered constraints. Despite the quantitative advantage offered by eMSOT, both the non-convex nature of the optimization problem and the possible sub-optimality of the constraints may lead to reduced accuracy. We present herein a neural network architecture that is able to learn how to solve the inverse problem of eMSOT by directly regressing from a set of input spectra to the desired fluence values. The architecture is composed of a combination of recurrent and convolutional layers and uses both spectral and spatial features for inference. We train an ensemble of such networks using solely simulated data and demonstrate how this approach can improve the accuracy of sO(2) computation over the original eMSOT, not only in simulations but also in experimental datasets obtained from blood phantoms and small animals (mice) in vivo. The use of a deep-learning approach in optoacoustic sO(2) imaging is confirmed herein for the first time on ground truth sO(2) values experimentally obtained in vivo and ex vivo. AU - Olefir, I. AU - Tzoumas, S.* AU - Restivo, C. AU - Mohajerani, P. AU - Xing, L.* AU - Ntziachristos, V. C1 - 60797 C2 - 49564 CY - 445 Hoes Lane, Piscataway, Nj 08855-4141 Usa SP - 3643-3654 TI - Deep learning-based spectral unmixing for optoacoustic imaging of tissue oxygen saturation. JO - IEEE Trans. Med. Imaging VL - 39 IS - 11 PB - Ieee-inst Electrical Electronics Engineers Inc PY - 2020 SN - 0278-0062 ER - TY - JOUR AB - The recently developed optoacoustic tomography systems have attained volumetric frame rates exceeding 100 Hz, thus opening up new venues for studying previously invisible biological dynamics. Further gains in temporal resolution can potentially be achieved via partial data acquisition, though a priori knowledge on the acquired data is essential for rendering accurate reconstructions using compressed sensing approaches. In this work, we suggest a machine learning method based on principal component analysis for high-frame-rate volumetric cardiac imaging using only a few tomographic optoacoustic projections. The method is particularly effective for discerning periodic motion, as demonstrated herein by non-invasive imaging of a beating mouse heart. A training phase enables efficiently compressing the heart motion information, which is subsequently used as prior information for image reconstruction from sparse sampling at a higher frame rate. It is shown that image quality is preserved with a 64-fold reduction in the data flow. We demonstrate that, under certain conditions, the volumetric motion could effectively be captured by relying on time-resolved data from a single optoacoustic detector. Feasibility of capturing transient (non-periodic) events not registered in the training phase is further demonstrated by visualizing perfusion of a contrast agent in vivo. The suggested approach can be used to significantly boost the temporal resolution of optoacoustic imaging and facilitate development of more affordable and data efficient systems. AU - Özbek, A. AU - Dean-Ben, X.L. AU - Razansky, D. C1 - 60296 C2 - 49214 CY - 445 Hoes Lane, Piscataway, Nj 08855-4141 Usa SP - 3250-3255 TI - Compressed optoacoustic sensing of volumetric cardiac motion. JO - IEEE Trans. Med. Imaging VL - 39 IS - 10 PB - Ieee-inst Electrical Electronics Engineers Inc PY - 2020 SN - 0278-0062 ER - TY - JOUR AB - IEEE This paper addresses digital staining and classification of the unstained white blood cell images obtained with a differential contrast microscope. We have data coming from multiple domains that are partially labeled and partially matching across the domains. Using unstained images removes time-consuming staining procedures and could facilitate and automatize comprehensive diagnostics. To this aim, we propose a method that translates unstained images to realistically looking stained images preserving the inter-cellular structures, crucial for the medical experts to perform classification. We achieve better structure preservation by adding auxiliary tasks of segmentation and direct reconstruction. Segmentation enforces that the network learns to generate correct nucleus and cytoplasm shape, while direct reconstruction enforces reliable translation between the matching images across domains. Besides, we build a robust domain agnostic latent space by injecting the target domain label directly to the generator, i.e., bypassing the encoder. It allows the encoder to extract features independently of the target domain and enables an automated domain invariant classification of the white blood cells. We validated our method on a large dataset composed of leukocytes of 24 patients, achieving state-of-the-art performance on both digital staining and classification tasks. AU - Tomczak, A.* AU - Ilic, S.* AU - Marquardt, G.* AU - Engel, T.* AU - Förster, F.* AU - Navab, N.* AU - Albarqouni, S. C1 - 60922 C2 - 49747 CY - 445 Hoes Lane, Piscataway, Nj 08855-4141 Usa SP - 2897-2910 TI - Multi-task multi-domain learning for digital staining and classification of leukocytes. JO - IEEE Trans. Med. Imaging VL - 40 IS - 10 PB - IEEE PY - 2020 SN - 0278-0062 ER - TY - JOUR AB - Raster-scan optoacoustic mesoscopy (RSOM) offers high-resolution non-invasive insights into skin pathophysiology, which holds promise for disease diagnosis and monitoring in dermatology and other fields. However, RSOM is quite vulnerable to verticalmotion of the skin, which can depend on the part of the body being imaged. Motion correction algorithms have already been proposed, but they are not fully automated, they depend on anatomical segmentation pre-processing steps that might not be performed successfully, and they are not site-specific. Here, we determined for the first time the magnitude of themicrometric vertical skin displacements at different sites on the body that affect RSOM. The quantification of motion allowed us to develop a site-specific correction algorithm. The algorithm is fully automated and does not need prior anatomical information. We found that the magnitude of the vertical motion depends strongly on the site of imaging and is caused by breathing, heart beating, and arterial pulsation. The developed algorithm resulted in more than 2-fold improvement in the signal-to-noise ratio of the reconstructed images at every site tested. Proposing an effective automatedmotion correction algorithm paves the way for realizing the full clinical potential of RSOM. AU - Aguirre Bueno, J. AU - Berezhnoi, A. AU - He, H.* AU - Schwarz, M. AU - Hindelang, B.* AU - Omar, M. AU - Ntziachristos, V. C1 - 55428 C2 - 46257 CY - 445 Hoes Lane, Piscataway, Nj 08855-4141 Usa SP - 1340-1346 TI - Motion quantification and automated correction in clinical RSOM. JO - IEEE Trans. Med. Imaging VL - 38 IS - 6 PB - Ieee-inst Electrical Electronics Engineers Inc PY - 2019 SN - 0278-0062 ER - TY - JOUR AB - Three-dimensional freehand imaging techniques are gaining wider adoption due to their flexibility and cost efficiency. Typical examples for such a combination of a tracking system with an imaging device are freehand SPECT or freehand 3D ultrasound. However, the quality of the resulting image data is heavily dependent on the skill of the human operator and on the level of noise of the tracking data. The latter aspect can introduce blur or strong artifacts, which can significantly hamper the interpretation of image data. Unfortunately, the most commonly used tracking systems to date, i.e., optical and electromagnetic, present a trade-off between invading the surgeon's workspace (due to line-of-sight requirements) and higher levels of noise and sensitivity due to the interference of surrounding metallic objects. In this paper, we propose a novel approach for total variation regularization of data from tracking systems (which we term pose signals) based on a variational formulation in the manifold of Euclidean transformations. The performance of the proposed approach was evaluated using synthetic data as well as real ultrasound sweeps executed on both a Lego phantom and human anatomy, showing significant improvement in terms of tracking data quality and compounded ultrasound images. Source code can be found at http://github.com/IFL-CAMP/pose_regularization. AU - Esposito, M.* AU - Hennersperger, C.* AU - Göbl, R.* AU - Demaret, L. AU - Storath, M.* AU - Navab, N.* AU - Baust, M.* AU - Weinmann, A. C1 - 57179 C2 - 47588 CY - 445 Hoes Lane, Piscataway, Nj 08855-4141 Usa SP - 2245-2258 TI - Total variation regularization of pose signals with an application to 3D freehand ultrasound. JO - IEEE Trans. Med. Imaging VL - 38 IS - 10 PB - Ieee-inst Electrical Electronics Engineers Inc PY - 2019 SN - 0278-0062 ER - TY - JOUR AB - X-ray grating interferometry is a powerful emerging tool in biomedical imaging, providing access to three complementary image modalities. In addition to the conventional attenuation modality, interferometry provides a phase modality, which visualizes soft tissue structures, and a dark-field modality, which relates to the number and size of sub-resolution scattering objects. A particularly strong dark-field signal originates from the alveoli or air sacs in the lung. Dark-field lung radiographs in animal models have already shown increased sensitivity in diagnosing lung diseases, such as lung cancer or emphysema, compared to conventional X-ray chest radiography. However, to date, X-ray dark-field lung imaging has either averaged information over several breaths or has been captured during a breath hold. In this paper, we demonstrate the first time-resolved dark-field imaging of a breath cycle in a mechanically ventilated mouse, in vivo, which was obtained using a grating interferometer. We achieved a time resolution of 0.1 s, visualizing the changes in the dark-field, phase, and attenuation images during inhalation and exhalation. These measurements show that the dark-field signal depends on the air volume and, hence, the alveolar dimensions of the lung. Conducting this type of scan with animal disease models would help to locate the optimum breath point for single-image diagnostic dark-field imaging and could indicate if the changes in the dark-field signal during breath provide a diagnostically useful complementary measure. AU - Gradl, R.* AU - Morgan, K.S.* AU - Dierolf, M.* AU - Jud, C.* AU - Hehn, L.* AU - Günther, B.* AU - Möller, W. AU - Kutschke, D. AU - Yang, L. AU - Stöger, T. AU - Pfeiffer, D.* AU - Gleich, B.* AU - Achterhold, K.* AU - Schmid, O. AU - Pfeiffer, F.* C1 - 54313 C2 - 45486 CY - 445 Hoes Lane, Piscataway, Nj 08855-4141 Usa SP - 649-656 TI - Dynamic in vivo chest x-ray dark field imaging in mice. JO - IEEE Trans. Med. Imaging VL - 38 IS - 2 PB - Ieee-inst Electrical Electronics Engineers Inc PY - 2019 SN - 0278-0062 ER - TY - JOUR AB - Glioblastoma (GBM) is a highly invasive brain tumor, whose cells infiltrate surrounding normal brain tissue beyond the lesion outlines visible in the current medical scans. These infiltrative cells are treated mainly by radiotherapy. Existing radiotherapy plans for brain tumors derive from population studies and scarcely account for patient-specific conditions. Here, we provide a Bayesian machine learning framework for the rational design of improved, personalized radiotherapy plans using mathematical modeling and patient multimodal medical scans. Our method, for the first time, integrates complementary information from high-resolution MRI scans and highly specific FET-PET metabolic maps to infer tumor cell density in GBM patients. The Bayesian framework quantifies imaging and modeling uncertainties and predicts patient-specific tumor cell density with credible intervals. The proposed methodology relies only on data acquired at a single time point and, thus, is applicable to standard clinical settings. An initial clinical population study shows that the radiotherapy plans generated from the inferred tumor cell infiltration maps spare more healthy tissue thereby reducing radiation toxicity while yielding comparable accuracy with standard radiotherapy protocols. Moreover, the inferred regions of high tumor cell densities coincide with the tumor radioresistant areas, providing guidance for personalized dose-escalation. The proposed integration of multimodal scans and mathematical modeling provides a robust, non-invasive tool to assist personalized radiotherapy design. AU - Lipkova, J.* AU - Angelikopoulos, P.* AU - Wu, S.* AU - Alberts, E.* AU - Wiestler, B.* AU - Diehl, C.* AU - Preibisch, C.* AU - Pyka, T.* AU - Combs, S.E. AU - Hadjidoukas, P.* AU - van Leemput, K.* AU - Koumoutsakos, P.* AU - Lowengrub, J.* AU - Menze, B.* C1 - 56788 C2 - 47324 CY - 445 Hoes Lane, Piscataway, Nj 08855-4141 Usa SP - 1875-1884 TI - Personalized radiotherapy design for glioblastoma: Integrating mathematical tumor models, multimodal scans, and bayesian inference. JO - IEEE Trans. Med. Imaging VL - 38 IS - 8 PB - Ieee-inst Electrical Electronics Engineers Inc PY - 2019 SN - 0278-0062 ER - TY - JOUR AB - IEEE Optoacoustic (photoacoustic) endoscopy has shown potential to reveal complementary contrast to optical endoscopy methods, indicating clinical relevance. However operational parameters for accurate optoacoustic endoscopy must be specified for optimal performance. Recent support from the EU Horizon 2020 program ESOTRAC to develop a next-generation optoacoustic esophageal endoscope directs the interrogation of the optimal frequency required for accurate implementation. We simulated the frequency response of the esophagus wall and then validated the simulation results with experimental measurements of pig esophagus. Phantoms and fresh pig esophagus samples were measured using two detectors with central frequencies of 15 or 50 MHz, and the imaging performance of both detectors was compared. We analyzed the frequency bandwidth of optoacoustic signals in relation to morphological layer structures of the esophagus and found the 50 MHz detector to differentiate layer structures better than the 15 MHz detector. Furthermore, we identify the necessary detection bandwidth for visualizing esophagus morphology and selecting ultrasound transducers for future optoacoustic endoscopy of the esophagus. AU - He, H. AU - Bühler, A. AU - Bozhko, D. AU - Jian, X.* AU - Cui, Y.* AU - Ntziachristos, V. C1 - 52499 C2 - 44018 SP - 1162-1167 TI - Importance of ultrawide bandwidth for optoacoustic esophagus imaging. JO - IEEE Trans. Med. Imaging VL - 37 IS - 5 PY - 2018 SN - 0278-0062 ER - TY - JOUR AB - The quantification of hemoglobin oxygen saturation (sO(2)) with multispectral optoacoustic (OA) (photoacoustic) tomography (MSOT) is a complex spectral unmixing problem, since the OA spectra of hemoglobin are modified with tissue depth due to depth (location) and wavelength dependencies of optical fluence in tissue. In a recent work, a method termed eigenspectra MSOT (eMSOT) was proposed for addressing the dependence of spectra on fluence and quantifying blood sO(2) in deep tissue. While eMSOT offers enhanced sO(2) quantification accuracy over conventional unmixing methods, its performance may be compromised by noise and image reconstruction artifacts. In this paper, we propose a novel Bayesian method to improve eMSOT performance in noisy environments. We introduce a spectral reliability map, i.e., a method that can estimate the level of noise superimposed onto the recorded OA spectra. Using this noise estimate, we formulate eMSOT as a Bayesian inverse problem where the inversion constraints are based on probabilistic graphical models. Results based on numerical simulations indicate that the proposed method offers improved accuracy and robustness under high noise levels due the adaptive nature of the Bayesian method. AU - Olefir, I. AU - Tzoumas, S.* AU - Yang, H. AU - Ntziachristos, V. C1 - 53293 C2 - 44566 CY - 445 Hoes Lane, Piscataway, Nj 08855-4141 Usa SP - 2070-2079 TI - A bayesian approach to eigenspectra optoacoustic tomography. JO - IEEE Trans. Med. Imaging VL - 37 IS - 9 PB - Ieee-inst Electrical Electronics Engineers Inc PY - 2018 SN - 0278-0062 ER - TY - JOUR AB - Accurate extraction of physical and biochemical parameters from optoacoustic images is often impeded due to the use of unrigorous inversion schemes, incomplete tomographic detection coverage or other experimental factors that cannot be readily accounted for during the image acquisition and reconstruction process. For instance, inaccurate assumptions in the physical forward model may lead to negative optical absorption values in the reconstructed images. Any artifacts present in the single wavelength optoacoustic images can be significantly aggravated when performing a two-step reconstruction consisting in acoustic inversion and spectral unmixing aimed at rendering the distributions of spectrally-distinct absorbers. We investigate a number of algorithmic strategies with non-negativity constraints imposed at the different phases of the reconstruction process. Performance is evaluated in cross-sectional multispectral optoacoustic tomography (MSOT) recordings from tissue-mimicking phantoms and in vivo mice embedded with varying concentrations of contrast agents. Additional in vivo validation is subsequently performed with molecular imaging data involving subcutaneous tumors labeled with genetically-expressed iRFP proteins and organ perfusion by optical contrast agents. It is shown that constrained reconstruction is essential for reducing the critical image artifacts associated with inaccurate modeling assumptions. Furthermore, imposing the non-negativity constraint directly on the unmixed distribution of the probe of interest was found to maintain the most robust and accurate reconstruction performance in all experiments. AU - Ding, L AU - Dean-Ben, X.L. AU - Burton, N.C.B.* AU - Sobol, R.W.* AU - Ntziachristos, V. AU - Razansky, D. C1 - 50975 C2 - 42549 CY - Piscataway SP - 1676-1685 TI - Constrained inversion and spectral unmixing in multispectral optoacoustic tomography. JO - IEEE Trans. Med. Imaging VL - 36 IS - 8 PB - Ieee-inst Electrical Electronics Engineers Inc PY - 2017 SN - 0278-0062 ER - TY - JOUR AB - Optimal optoacoustic tomographic sampling is often hindered by the frequency-dependent directivity of ultrasound sensors, which can only be accounted for with an accurate three-dimensional (3D) model. Herein, we introduce a 3D modelbased reconstruction method applicable to optoacoustic imaging systems employing detection elements with arbitrary size and shape. The computational complexity and memory requirements are mitigated by introducing an efficient graphics processing unit (GPU)-based implementation of the iterative inversion. On-the-fly calculation of the entries of the model-matrix via a small look-up table avoids otherwise unfeasible storage of matrices typically occupying more than 300GB of memory. Superior imaging performance of the suggested method with respect to standard optoacoustic image reconstruction methods is first validated quantitatively using tissue-mimicking phantoms. Significant improvements in the spatial resolution, contrast to noise ratio and overall 3D image quality are also reported in real tissues by imaging the finger of a healthy volunteer with a hand-held volumetric optoacoustic imaging system. AU - Ding, L AU - Dean-Ben, X.L. AU - Razansky, D. C1 - 51367 C2 - 42914 CY - Piscataway SP - 1858-1867 TI - Efficient three-dimensional model-based reconstruction scheme for arbitrary optoacoustic acquisition geometries. JO - IEEE Trans. Med. Imaging VL - 36 IS - 9 PB - Ieee-inst Electrical Electronics Engineers Inc PY - 2017 SN - 0278-0062 ER - TY - JOUR AB - The high complementarity of ultrasonography and optoacoustic tomography has prompted the development of combined approaches that utilize the same transducer array for detecting both optoacoustic and pulse-echo ultrasound responses from tissues. Yet, due to the fundamentally different physical contrast and image formation mechanisms, the development of detection technology optimally suited for image acquisition in both modalities remains a major challenge. Herein, we introduce a multi-segment detector array approach incorporating array segments of linear and concave geometry to optimally support both ultrasound and optoacoustic image acquisition. The various image rendering strategies are tested and optimized in numerical simulations and calibrated tissue-mimicking phantom experiments. We subsequently demonstrate real-time hybrid optoacoustic ultrasound (OPUS) image acquisition in a healthy volunteer. The new approach enables the acquisition of highquality anatomical data by both modalities complemented by functional information on blood oxygenation status provided by the multispectral optoacoustic tomography. AU - Mercep, E. AU - Dean-Ben, X.L. AU - Razansky, D. C1 - 51371 C2 - 42947 CY - Piscataway SP - 2129-2137 TI - Combined pulse-echo ultrasound and multispectral optoacoustic tomography with a multi-segment detector array. JO - IEEE Trans. Med. Imaging VL - 36 IS - 10 PB - Ieee-inst Electrical Electronics Engineers Inc PY - 2017 SN - 0278-0062 ER - TY - JOUR AB - Optoacoustic (photoacoustic) dermoscopy offers two principal advantages over conventional optical imaging applied in dermatology. First, it yields high-resolution cross-sectional images of the skin at depths not accessible to other non-invasive optical imaging methods. Second, by resolving absorption spectra at multiple wavelengths, it enables label-free three-dimensional visualization of morphological and functional features. However, the relation of pulse energy to generated bandwidth and imaging depth remains poorly defined. In this study, we apply computer models to investigate the optoacoustic frequency response generated by simulated skin. We relate our simulation results to experimental measurements of the detection bandwidth as a function of optical excitation energy in phantoms and human skin. Using raster-scan optoacoustic mesoscopy (RSOM), we further compare the performance of two broadband ultrasonic detectors (bandwidth of 20-180 MHz and 10-90 MHz) in acquiring optoacoustic readouts. Based on the findings of this study, we propose energy ranges required for skin imaging with considerations of laser safety standards. AU - Schwarz, M. AU - Soliman, D. AU - Omar, M. AU - Bühler, A. AU - Ovsepian, S.V. AU - Aguirre, J. AU - Ntziachristos, V. C1 - 51369 C2 - 42915 SP - 1287-1296 TI - Optoacoustic dermoscopy of the human skin: Tuning excitation energy for optimal detection bandwidth with fast and deep imaging in vivo. JO - IEEE Trans. Med. Imaging VL - 36 IS - 6 PY - 2017 SN - 0278-0062 ER - TY - JOUR AB - Magnetic particle imaging (MPI) is an emerging medical imaging modality which is based on the non-linear response of magnetic nanoparticles to an applied magnetic field. It is an important feature of MPI that even fast dynamic processes can be captured for 3D volumes. The high temporal resolution in turn leads to large amounts of data which have to be handled efficiently. But as the system matrix of MPI is non-sparse, the image reconstruction gets computationally demanding. Therefore, currently only basic image reconstruction methods such as Tikhonov regularization are used. However, Tikhonov regularization is known to oversmooth edges in the reconstructed image and to have only a limited noise reducing effect. In this work, we develop an efficient edge preserving and noise reducing reconstruction method for MPI. As regularization model, we propose to use the nonnegative fused lasso model, and we devise a discretization that is adapted to the acquisition geometry of the preclinical MPI scanner considered in this work. We develop a customized solver based on a generalized forward-backward scheme which is particularly suitable for the dense and not well-structured system matrices in MPI. Already a non-optimized prototype implementation processes a 3D volume within a few seconds so that processing several frames per second seems amenable. We demonstrate the improvement in reconstruction quality over the state-of-the-art method in an experimental medical setup for an in-vitro angioplasty of a stenosis. AU - Storath, M.* AU - Brandt, C.* AU - Hofmann, M.* AU - Knopp, T.* AU - Salamon, J.* AU - Weber, A.* AU - Weinmann, A. C1 - 50503 C2 - 42319 CY - Piscataway SP - 74-85 TI - Edge preserving and noise reducing reconstruction for magnetic particle imaging. JO - IEEE Trans. Med. Imaging VL - 36 IS - 1 PB - Ieee-inst Electrical Electronics Engineers Inc PY - 2017 SN - 0278-0062 ER - TY - JOUR AB - In this paper, we consider combined TV denoising and diffusion tensor fitting in DTI using the affine-invariant Riemannian metric on the space of diffusion tensors. Instead of first fitting the diffusion tensors, and then denoising them, we define a suitable TV type energy functional which incorporates the measured DWIs (using an inverse problem setup) and which measures the nearness of neighboring tensors in the manifold. To approach this functional, we propose generalized forwardbackward splitting algorithms which combine an explicit and several implicit steps performed on a decomposition of the functional.We validate the performance of the derived algorithms on synthetic and real DTI data. In particular, we work on real 3D data. To our knowledge, the present paper describes the first approach to TV regularization in a combined manifold and inverse problem setup. AU - Baust, M.* AU - Weinmann, A. AU - Wieczorek, M.* AU - Lasser, T.* AU - Storath, M.* AU - Navab, N.* C1 - 48982 C2 - 41501 CY - Piscataway SP - 1972-1989 TI - Combined tensor fitting and TV regularization in diffusion tensor imaging based on a Riemannian manifold approach. JO - IEEE Trans. Med. Imaging VL - 35 IS - 8 PB - Ieee-inst Electrical Electronics Engineers Inc PY - 2016 SN - 0278-0062 ER - TY - JOUR AB - Analytical (closed-form) inversion schemes have been the standard approach for image reconstruction in optoacoustic tomography due to their fast reconstruction abilities and low memory requirements. Yet, the need for quantitative imaging and artifact reduction has led to the development of more accurate inversion approaches, which rely on accurate forward modeling of the optoacoustic wave generation and propagation. In this way, multiple experimental factors can be incorporated, such as the exact detection geometry, spatio-temporal response of the transducers, and acoustic heterogeneities. The modelbased inversion commonly results in very large sparse matrix formulations that require computationally extensive and memory demanding regularization schemes for image reconstruction, hindering their effective implementation in real-time imaging applications. Herein, we introduce a new discretization procedure for efficient model-based reconstructions in two-dimensional optoacoustic tomography that allows for parallel implementation on a graphics processing unit (GPU) with a relatively low numerical complexity. By on-the-fly calculation of the model matrix in each iteration of the inversion procedure, the new approach results in imaging frame rates exceeding 10Hz, thus enabling real-time image rendering using the model-based approach. AU - Ding, L AU - Dean-Ben, X.L. AU - Razansky, D. C1 - 48116 C2 - 39916 CY - Piscataway SP - 1883-1891 TI - Real-time model-based inversion in cross-sectional optoacoustic tomography. JO - IEEE Trans. Med. Imaging VL - 35 IS - 8 PB - Ieee-inst Electrical Electronics Engineers Inc PY - 2016 SN - 0278-0062 ER - TY - JOUR AB - High fidelity optoacoustic (photoacoustic) tomography requires dense spatial sampling of optoacoustic signals using point acoustic detectors. However, in practice, spatial resolution of the images is often limited by limited sampling either due to coarse multi-element arrays or time in raster scan measurements. Herein, we investigate a method that integrates information from multiple optoacoustic images acquired at sub-diffraction steps into one high resolution image by means of an iterative registration algorithm. Experimental validations performed in target phantoms and ex-vivo tissue samples confirm that the suggested approach renders significant improvements in terms of optoacoustic image resolution and quality without introducing significant alterations into the signal acquisition hardware or inversion algorithms. AU - He, H. AU - Mandal, S. AU - Bühler, A. AU - Dean-Ben, X.L. AU - Razansky, D. AU - Ntziachristos, V. C1 - 47281 C2 - 39201 CY - Piscataway SP - 812-818 TI - Improving optoacoustic image quality via geometric pixel super-resolution approach. JO - IEEE Trans. Med. Imaging VL - 35 IS - 3 PB - Ieee-inst Electrical Electronics Engineers Inc PY - 2016 SN - 0278-0062 ER - TY - JOUR AB - The concept of sparsity is extensively exploited in the fields of data acquisition and image processing, contributing to better signal-to-noise and spatio-temporal performance of the various imaging methods. In the field of optoacoustic tomography, the image reconstruction problem is often characterized by computationally extensive inversion of very large datasets, for instance when acquiring volumetric multispectral data with high temporal resolution. In this article we seek to accelerate accurate model-based optoacoustic inversions by identifying various sources of sparsity in the forward and inverse models as well as in the single- and multi-frame representation of the projection data. These sources of sparsity are revealed through appropriate transformations in the signal, model and image domains and are subsequently exploited for expediting image reconstruction. The sparsity-based inversion scheme was tested with experimental data, offering reconstruction speed enhancement by a factor of 40 to 700 times as compared with the conventional iterative model-based inversions while preserving similar image quality. The demonstrated results pave the way for achieving real-time performance of model-based reconstruction in multi-dimensional optoacoustic imaging. AU - Lutzweiler, C. AU - Tzoumas, S. AU - Rosenthal, A. AU - Ntziachristos, V. AU - Razansky, D. C1 - 47178 C2 - 39137 CY - Piscataway SP - 674-684 TI - High-throughput sparsity-based inversion scheme for optoacoustic tomography. JO - IEEE Trans. Med. Imaging VL - 35 IS - 2 PB - Ieee-inst Electrical Electronics Engineers Inc PY - 2016 SN - 0278-0062 ER - TY - JOUR AB - Segmentation of biomedical images is essential for studying and characterizing anatomical structures as well as for detection and evaluation of tissue pathologies. Segmentation has been further shown to enhance the reconstruction performance in many tomographic imaging modalities by accounting for heterogeneities in the excitation field and tissue properties in the imaged region. This is particularly relevant in optoacoustic tomography, where discontinuities in the optical and acoustic tissue properties, if not properly accounted for, may result in deterioration of the imaging performance. Efficient segmentation of optoacoustic images is often hampered by the relatively low intrinsic contrast of large anatomical structures, which is further impaired by the limited angular coverage of some commonly employed tomographic imaging configurations. Herein, we analyze the performance of active contour models for boundary segmentation in cross-sectional optoacoustic tomography. The segmented mask is employed to construct a two compartment model for the acoustic and optical parameters of the imaged tissues, which is subsequently used to improve accuracy of the image reconstruction routines. The performance of the suggested segmentation and modeling approach are showcased in tissuemimicking phantoms and small animal imaging experiments. AU - Mandal, S. AU - Dean-Ben, X.L. AU - Razansky, D. C1 - 48515 C2 - 41108 CY - Piscataway SP - 2209-2217 TI - Visual quality enhancement in optoacoustic tomography using active contour segmentation priors. JO - IEEE Trans. Med. Imaging VL - 35 IS - 10 PB - Ieee-inst Electrical Electronics Engineers Inc PY - 2016 SN - 0278-0062 ER - TY - JOUR AB - The imaging performance of fluorescence molecular tomography (FMT) improves when information from the underlying anatomy is incorporated into the inversion scheme, in the form of priors. The requirement for incorporation of priors has recently driven the development of hybrid FMT systems coupled to other modalities, such as X-ray CT and MRI. A critical methodological aspect in this case relates to the particular method selected to incorporate prior information obtained from the anatomical imaging modality into the FMT inversion. We propose herein a new approach for utilizing prior information, which preferentially minimizes residual errors associated with measurements that better describe the anatomical segments considered. This preferential minimization was realized using a weighted least square (WLS) approach, where the weights were optimized using a Mamdani-type fuzzy inference system. The method of priors introduced herein was deployed as a two-step structured regularization approach and was verified with experimental measurements from phantoms as well as ex vivo and in vivo animal studies. The results demonstrate accurate performance and minimization of reconstruction bias, without requiring user input for setting the regularization parameters. As such, the proposed method offers significant progress in incorporation of anatomical priors in FMT and, as a result, in realization of the full potential of hybrid FMT. AU - Mohajerani, P. AU - Ntziachristos, V. C1 - 46771 C2 - 37803 CY - Piscataway SP - 381-390 TI - An inversion scheme for hybrid fluorescence ‎molecular tomography using a fuzzy inference system. JO - IEEE Trans. Med. Imaging VL - 35 IS - 2 PB - Ieee-inst Electrical Electronics Engineers Inc PY - 2016 SN - 0278-0062 ER - TY - JOUR AB - Statistical sub-pixel detection via the adaptive matched filter (AMF) has been shown to improve the molecular imaging sensitivity and specificity of optoacoustic (photoacoustic) imaging. Applied to multispectral optoacoustic tomography (MSOT), AMF assumes that the spatially-varying tissue spectra follow a multivariate Gaussian distribution, that the spectrum of the target molecule is precisely known and that the molecular target lies in "low probability" within the data. However, when these assumptions are violated, AMF may result in considerable performance degradation. The objective of this work is to develop a robust statistical detection framework that is appropriately suited to the characteristics of MSOT molecular imaging. Using experimental imaging data, we perform a statistical characterization of MSOT tissue images and conclude to a detector that is based on the t-distribution. More importantly, we introduce a method for estimating the covariance matrix of the background-tissue statistical distribution, which enables robust detection performance independently of the molecular target size or intensity. The performance of the statistical detection framework is assessed through simulations and experimental in vivo measurements and compared to previously used methods. AU - Tzoumas, S. AU - Kravtsiv, A. AU - Gao, Y.* AU - Bühler, A. AU - Ntziachristos, V. C1 - 48916 C2 - 41470 CY - Piscataway SP - 2534-2545 TI - Statistical molecular target detection framework for multispectral optoacoustic tomography. JO - IEEE Trans. Med. Imaging VL - 35 IS - 12 PB - Ieee-inst Electrical Electronics Engineers Inc PY - 2016 SN - 0278-0062 ER - TY - JOUR AB - Raster-scan optoacoustic mesoscopy (RSOM) comes with high potential for in vivo diagnostic imaging in dermatology, since it allows for high resolution imaging of the natural chromophores melanin, and hemoglobin at depths of several millimeters. We have applied ultra-wideband RSOM, in the 10 MHz to 160 MHz frequency band, to image healthy human skin at distinct locations. We analyzed the anatomical information contained at different frequency ranges of the optoacoustic (photoacoustic) signals in relation to resolving features of different skin layers in vivo. We further compared results obtained from glabrous and hairy skin and identify that frequencies above 60 MHz are necessary for revealing the epidermal thickness, a prerequisite for determining the invasion depth of melanoma in future studies. By imaging a benign nevus we show that the applied RSOM system provides strong contrast of melanin-rich structures. We further identify the spectral bands responsible for imaging the fine structures in the stratum corneum, assessing dermal papillae, and resolving microvascular structures in the horizontal plexus. AU - Schwarz, M. AU - Omar, M. AU - Bühler, A. AU - Aguirre Bueno, J. AU - Ntziachristos, V. C1 - 32659 C2 - 35200 CY - Piscataway SP - 672-677 TI - Implications of ultrasound frequency in optoacoustic mesoscopy of the skin. JO - IEEE Trans. Med. Imaging VL - 34 IS - 2 PB - Ieee-inst Electrical Electronics Engineers Inc PY - 2015 SN - 0278-0062 ER - TY - JOUR AB - Optoacoustic (photoacoustic) imaging based on cylindrically focused one-dimensional transducer arrays comes with powerful characteristics in visualizing optical contrast. Parallel reading of multiple detectors arranged around a tissue crosssection enables capturing data for generating images of this plane within micro-seconds. Dedicated small animals scanners and handheld systems using one-dimensional cylindrically focused ultrasound transducer arrays have demonstrated real-time crosssectional imaging and high in-plane resolution. Yet, the resolution achieved along the axis perpendicular to the focal plane, i.e. the elevation resolution, is determined by the focusing capacities of the detector and is typically lower than the in-plane resolution. Herein, we investigated whether deconvolution of the sensitivity field of the transducer could lead to tangible image improvements. We showcase the findings on experimental measurements from phantoms and animals and discuss the features and the limitations of the approach in improving resolution along the elevation dimension. AU - Bühler, A. AU - Dean-Ben, X.L. AU - Razansky, D. AU - Ntziachristos, V. C1 - 27497 C2 - 32699 CY - Piscataway SP - 814-821 TI - Volumetric optoacoustic imaging with multi-bandwidth deconvolution. JO - IEEE Trans. Med. Imaging VL - 33 IS - 4 PB - IEEE PY - 2014 SN - 0278-0062 ER - TY - JOUR AB - The implementation of hybrid fluorescence molecular tomography (FMT) and X-ray computed tomography (CT) has been shown to be a necessary development, not only for combining anatomical with functional and molecular contrast, but also for generating optical images of high fidelity and quantification accuracy. FMT affords highly sensitive three-dimensional imaging of fluorescence bio-distribution throughout animal bodies but in stand-alone form it offers images of low resolution. It was shown that FMT accuracy significantly improves by considering anatomical priors from X-ray computed tomography (CT). Conversely, X-ray CT generally suffers from low soft tissue contrast. Therefore utilization of X-ray CT data as prior information to the fluorescence inversion problem is challenging in applications where different internal organs are not clearly differentiated. Instead, we combined herein FMT with emerging X-ray phase-contrast computed tomography (PCCT). PCCT relies on the phase shift differences in tissue to deliver anatomical images of biological samples with superior soft tissue contrast than conventional absorption-based CT. We demonstrate for the first time hybrid FMT-PCCT imaging of different animal models, where FMT and PCCT scans were performed in vivo and ex vivo, respectively. The results show that FMT-PCCT expands the potential and utility of FMT in imaging lesions which show otherwise low or no contrast in conventional CT, while retaining the cost benefits and technical simplicity of CT and single hybrid devices. The results point to the most accurate hybrid FMT performance to date. AU - Mohajerani, P. AU - Hipp, A.* AU - Willner, M.* AU - Marschner, M.* AU - Trajkovic-Arsic, M.* AU - Ma, X. AU - Burton, N.C.* AU - Klemm, U. AU - Radrich, K. AU - Ermolayev, V. AU - Tzoumas, S. AU - Siveke, J.* AU - Bech, M.* AU - Pfeiffer, F.* AU - Ntziachristos, V. C1 - 30995 C2 - 34064 CY - Piscataway SP - 1434-1446 TI - FMT-PCCT: Hybrid fluorescence molecular tomography-X-ray phase-contrast CT imaging of mouse models. JO - IEEE Trans. Med. Imaging VL - 33 IS - 7 PB - Ieee-inst Electrical Electronics Engineers Inc PY - 2014 SN - 0278-0062 ER - TY - JOUR AB - Multispectral Optoacoustic Tomography (MSOT) utilizes broadband ultrasound detection for imaging biologically-relevant optical absorption features at a range of scales. Due to the multiscale and multispectral features of the technology, MSOT comes with distinct requirements in implementation and data analysis. In this work, we investigate the interplay between scale, which depends on ultrasonic detection frequency, and optical multispectral spectral analysis, two dimensions that are unique to MSOT and represent a previously unexplored challenge. We show that ultrasound frequency-dependent artifacts suppress multispectral features and complicate spectral analysis. In response, we employ a wavelet decomposition to perform spectral unmixing on a per-scale basis (or per ultrasound frequency band) and showcase imaging of fine-scale features otherwise hidden by low frequency components. We explain the proposed algorithm by means of simple simulations and demonstrate improved performance in imaging data of blood vessels in human subjects. AU - Taruttis, A. AU - Rosenthal, A. AU - Kacprowicz, M. AU - Burton, N.C. AU - Ntziachristos, V. C1 - 30996 C2 - 34065 CY - Piscataway SP - 1194-1202 TI - Multiscale multispectral optoacoustic tomography by a stationary wavelet transform prior to unmixing. JO - IEEE Trans. Med. Imaging VL - 33 IS - 5 PB - Ieee-inst Electrical Electronics Engineers Inc PY - 2014 SN - 0278-0062 ER - TY - JOUR AB - Detection of intrinsic or extrinsically administered chromophores and photo-absorbing nanoparticles has been achieved by Multi-Spectral Optoacoustic Tomography (MSOT). The detection sensitivity of MSOT depends not only on the signal to noise ratio considerations, as in conventional optoacoustic (photoacoustic) tomography implementations, but also on the ability to resolve the molecular targets of interest from the absorbing tissue background by means of spectral un-mixing or sub-pixel detection methods. However, it is not known which un-mixing methods are optimally suited for the characteristics of multispectral optoacoustic images. In this work we investigated the performance of different sub-pixel detection methods, typically used in remote sensing hyperspectral imaging, within the context of MSOT. A quantitative comparison of the different algorithmic approaches was carried out in an effort to identify methods that operate optimally under the particulars of MSOT applications. We find that statistical sub-pixel detection methods can demonstrate a unique detection performance with up to five times enhanced sensitivity as compared to linear un-mixing approximations, under the condition that the optical agent of interest is sparsely present within the tissue volume, as common when using targeted agents and reporter genes. AU - Tzoumas, S. AU - Deliolanis, N. AU - Morscher, S. AU - Ntziachristos, V. C1 - 27501 C2 - 32701 CY - Piscataway SP - 48-60 TI - Un-mixing molecular agents from absorbing tissue in multispectral optoacoustic tomography. JO - IEEE Trans. Med. Imaging VL - 33 IS - 1 PB - IEEE PY - 2014 SN - 0278-0062 ER - TY - JOUR AB - Optoacoustic (photoacoustic) imaging uniquely visualizes optical contrast in high resolution and comes with very attractive characteristics for clinical imaging applications. In this paper we showcase the performance of a scanner based on a 24 MHz central-frequency 128 element array, developed for applications in dermatology. We perform system characterization to examine the imaging performance achieved. We then showcase its imaging ability on healthy tissue and cancer. Finally we image burns and human lesions in vivo and gain insights on the benefits and challenges of this approach as it is considered for diagnostic and treatment follow-up applications in dermatology and beyond. AU - Vionnet, L. AU - Gateau, J. AU - Schwarz, M. AU - Bühler, A. AU - Ermolayev, V. AU - Ntziachristos, V. C1 - 28189 C2 - 33002 CY - Piscataway SP - 535-545 TI - 24MHz scanner for optoacoustic imaging of skin and burn. JO - IEEE Trans. Med. Imaging VL - 33 IS - 2 PB - IEEE PY - 2014 SN - 0278-0062 ER - TY - JOUR AB - Image quality in 3-D optoacoustic (photoacoustic) tomography is greatly influenced by both the measurement system, in particular the number and spatial arrangement of ultrasound sensors, and the ability to account for the spatio-temporal response of the sensor element(s) in the reconstruction algorithm. Herein we present a reconstruction procedure based on the inversion of a time-domain forward model incorporating the spatial impulse response due to the shape of the transducer, which is subsequently applied in a tomographic system based on a translation-rotation scan of a linear detector array. The proposed method was also adapted to cope with the data-intensive requirements of high-resolution volumetric optoacoustic imaging. The processing of 2.10(4) individual signals resulted in well-resolved images of both similar to 200 mu m absorbers in phantoms and complex vascular structures in biological tissue. The results reported herein demonstrate that the introduced model-based methodology exhibits a better contrast and resolution than standard back-projection and model-based algorithms that assume point detectors. Moreover, the capability of handling large datasets anticipates that model-based methods incorporating the sensor properties can become standard practice in volumetric optoacoustic image formation. AU - Araque Caballero, M.A. AU - Gateau, J. AU - Dean-Ben, X.L. AU - Ntziachristos, V. C1 - 28023 C2 - 32907 CY - Piscataway SP - 433-443 TI - Model-based optoacoustic image reconstruction of large three-dimensional tomographic datasets acquired with an array of directional detectors. JO - IEEE Trans. Med. Imaging VL - 33 IS - 2 PB - IEEE PY - 2013 SN - 0278-0062 ER - TY - JOUR AB - Optoacoustic tomography provides a unique possibility for ultra-high-speed 3-D imaging by acquiring complete volumetric datasets from interrogation of tissue by a single nanosecond-duration laser pulse. Yet, similarly to ultrasound, optoacoustics is a time-resolved imaging method, thus, fast 3-D imaging implies real-time acquisition and processing of high speed data from hundreds of detectors simultaneously, which presents significant technological challenges. Herein we present a highly efficient graphical processing unit (GPU) framework for real-time reconstruction and visualization of 3-D tomographic optoacoustic data. By utilizing a newly developed 3-D optoacoustic scanner, which simultaneously acquires signals with a handheld 256-element spherical ultrasonic array system, we further demonstrate tracking of deep tissue human vasculature rendered at a rate of 10 volumetric frames per second. The flexibility provided by the handheld hardware design, combined with the real-time operation, makes the developed platform highly usable for both clinical imaging practice and small animal research applications. AU - Dean-Ben, X.L. AU - Ozbek, A. AU - Razansky, D. C1 - 26009 C2 - 32020 SP - 2050-2055 TI - Volumetric real-time tracking of peripheral human vasculature with GPU-accelerated three-dimensional optoacoustic tomography. JO - IEEE Trans. Med. Imaging VL - 33 IS - 11 PB - Institute of Electrical and Electronics Engineers Inc. PY - 2013 SN - 0278-0062 ER - TY - JOUR AB - Optoacoustic tomography has recently demonstrated powerful performance in small animal imaging and initial clinical trials in terms of the high spatial resolution, versatile contrast, and dynamic imaging capabilities it can provide. Yet, the current optoacoustic image reconstruction methods are usually based on inaccurate forward modelling approaches or otherwise demand a high computational cost, which imposes certain practical limitations and hinders image quantification. Herein, we introduce a new method for accelerating optoacoustic reconstructions, based on angular image discretization of the forward model solution. The method is particularly suitable for accurate image reconstruction with arbitrary meshes and space-dependent resolution, while it can also readily account for small speed of sound variations without compromising the calculation speed. It is further anticipated that the new approach will greatly facilitate development of high performance 3-D optoacoustic reconstruction methods. AU - Dean-Ben, X.L. AU - Ntziachristos, V. AU - Razansky, D. C1 - 7453 C2 - 29718 SP - 1154-1162 TI - Acceleration of optoacoustic model-based reconstruction using angular image discretization. JO - IEEE Trans. Med. Imaging VL - 31 IS - 5 PB - Institute of Electrical and Electronics Engineers Inc. PY - 2012 SN - 0278-0062 ER - TY - JOUR AB - In many practical optoacoustic imaging implementations, dimensionality of the tomographic problem is commonly reduced into two dimensions or 1-D scanning geometries in order to simplify technical implementation, improve imaging speed or increase signal-to-noise ratio. However, this usually comes at a cost of significantly reduced quality of the tomographic data, out-of-plane image artifacts, and overall loss of image contrast and spatial resolution. Quantitative optoacoustic image reconstruction implies therefore collection of point 3-D (volumetric) data from as many locations around the object as possible. Here, we propose and validate an accurate model-based inversion algorithm for 3-D optoacoustic image reconstruction. Superior performance versus commonly-used backprojection inversion algorithms is showcased by numerical simulations and phantom experiments. AU - Dean-Ben, X.L. AU - Bühler, A. AU - Ntziachristos, V. AU - Razansky, D. C1 - 10546 C2 - 30286 SP - 1922-1928 TI - Accurate model-based reconstruction algorithm for three-dimensional optoacoustic tomography. JO - IEEE Trans. Med. Imaging VL - 31 IS - 10 PB - Institute of Electrical and Electronics Engineers PY - 2012 SN - 0278-0062 ER - TY - JOUR AB - The use of model-based algorithms in tomographic imaging offers many advantages over analytical inversion methods. However, the relatively high computational complexity of model-based approaches often restricts their efficient implementation. In practice, many modern imaging modalities, such as computed-tomography, positron-emission tomography, or optoacoustic tomography, normally use a very large number of pixels/voxels for image reconstruction. Consequently, the size of the forward-model matrix hinders the use of many inversion algorithms. In this paper, we present a new framework for model-based tomographic reconstructions, which is based on a wavelet-packet representation of the imaged object and the acquired projection data. The frequency localization property of the wavelet-packet base leads to an approximately separable model matrix, for which reconstruction at each spatial frequency band is independent and requires only a fraction of the projection data. Thus, the large model matrix is effectively separated into a set of smaller matrices, facilitating the use of inversion schemes whose complexity is highly nonlinear with respect to matrix size. The performance of the new methodology is demonstrated for the case of 2-D optoacoustic tomography for both numerically generated and experimental data. AU - Rosenthal, A. AU - Jetzfellner, T. AU - Razansky, D. AU - Ntziachristos, V. C1 - 8181 C2 - 30053 SP - 1346-1357 TI - Efficient framework for model-based tomographic image reconstruction using wavelet packets. JO - IEEE Trans. Med. Imaging VL - 31 IS - 7 PB - Institute of Electrical and Electronics Engineers PY - 2012 SN - 0278-0062 ER - TY - JOUR AB - A method is presented to reduce artefacts produced in optoacoustic tomography images due to internal reflection or scattering of the acoustic waves. It is based on weighting the tomographic contribution of each detector with the probability that a signal affected by acoustic mismatches is measured at that position. The correction method does not require a priori knowledge of the acoustic or optical properties of the imaged sample. Performance tests were made with agar phantoms that included air gaps for mimicking strong acoustic reflections as well as with an acoustically heterogeneous adult Zebrafish. The results obtained with the method proposed show a clear reduction of the artefacts with respect to the original images reconstructed with filtered back-projection algorithm. This performance is directly related to in-vivo small animal imaging applications involving imaging in the presence of bones, lungs, and other highly mismatched organs. AU - Dean-Ben, X.L. AU - Ma, R. AU - Razansky, D. AU - Ntziachristos, V. C1 - 6088 C2 - 28043 SP - 401-408 TI - Statistical approach for optoacoustic image reconstruction in the presence of strong acoustic heterogeneities. JO - IEEE Trans. Med. Imaging VL - 30 IS - 2 PB - IEEE-Inst Electrical Electrical Electronics Engineers Inc. PY - 2011 SN - 0278-0062 ER - TY - JOUR AB - no Abstract AU - Wang, G.* AU - Bresler, Y.* AU - Ntziachristos, V. C1 - 5744 C2 - 28541 CY - Piscataway, NJ SP - 1013-1016 TI - Compressive sensing for biomedical imaging. JO - IEEE Trans. Med. Imaging VL - 30 IS - 5 PB - IEEE PY - 2011 SN - 0278-0062 ER - TY - JOUR AB - Fluorescence molecular tomography (FMT) allows in vivo localization and quantification of fluorescence biodistributions in whole animals. The ill-posed nature of the tomographic reconstruction problem, however, limits the attainable resolution. Improvements in resolution and overall imaging performance can be achieved by forming image priors from geometric information obtained by a secondary anatomical or functional high-resolution imaging modality such as X-ray computed tomography or magnetic resonance imaging. A particular challenge in using image priors is to avoid the use of assumptions that may bias the solution and reduced the accuracy of the inverse problem. This is particularly relevant in FMT inversions where there is not an evident link between secondary geometric information and the underlying fluorescence biodistribution. We present here a new, two step approach to incorporating structural priors into the FMT inverse problem. By using the anatomic information to define a low dimensional inverse problem, we obtain a solution which we then use to determine the parameters defining a spatially varying regularization matrix for the full resolution problem. The regularization term is thus customized for each data set and is guided by the data rather than depending only on user defined a priori assumptions. Results are presented for both simulated and experimental data sets, and show significant improvements in image quality as compared to traditional regularization techniques. AU - Hyde, D.* AU - Miller, E.L.* AU - Brooks, D.H.* AU - Ntziachristos, V. C1 - 2239 C2 - 27127 SP - 365-374 TI - Data specific spatially varying regularization for multimodal fluorescence molecular tomography. JO - IEEE Trans. Med. Imaging VL - 29 IS - 2 PB - IEEE-Inst Electrical Electrical Electronics Engineers Inc. PY - 2010 SN - 0278-0062 ER - TY - JOUR AB - We present a fast model-based inversion algorithm for quantitative two- and three-dimensional optoacoustic tomography. The algorithm is based on an accurate and efficient forward model, which eliminates the need for regularization in the inversion process while providing modeling flexibility essential for quantitative image formation. The resulting image-reconstruction method eliminates stability problems encountered in previously published model-based techniques and, thus, enables performing image reconstruction in real time. Our model-based framework offers a generalization of the forward solution to more comprehensive optoacoustic propagation models, such as including detector frequency response, without changing the inversion procedure. The reconstruction speed and other algorithmic performances are demonstrated using numerical simulation studies and experimentally on tissue-mimicking optically heterogeneous phantoms and small animals. In the experimental examples, the model-based reconstructions manifested correctly the effect of light attenuation through the objects and did not suffer from the artifacts which usually afflict the commonly used filtered backprojection algorithms, such as negative absorption values. AU - Rosenthal, A. AU - Razansky, D. AU - Ntziachristos, V. C1 - 1623 C2 - 27216 SP - 1275-1285 TI - Fast semi-analytical model-based acoustic inversion for quantitative optoacoustic tomography. JO - IEEE Trans. Med. Imaging VL - 29 IS - 6 PB - IEEE-Inst Electrical Electrical Electronics Engineers Inc. PY - 2010 SN - 0278-0062 ER - TY - JOUR AB - A hybrid imaging system for simultaneous fluorescence tomography and X-ray computed tomography (XCT) of small animals has been developed and presented. The system capitalizes on the imaging power of a 360 degrees-projection free-space fluorescence tomography system, implemented within a microcomputed tomography scanner. Image acquisition is based on techniques that automatically adjust a series of imaging parameters to offer a high dynamic range dataset. Image segmentation further allows the incorporation of structural priors in the optical reconstruction problem to improve the imaging performance. The functional system characteristics are showcased, and images from a brain imaging study are shown, which are reconstructed using XCT-derived priors into the optical forward problem. AU - Schulz, R.B. AU - Ale, A.B.F. AU - Sarantopoulos, A. AU - Freyer, M. AU - Soehngen, E. AU - Zientkowska, M. AU - Ntziachristos, V. C1 - 95 C2 - 27128 SP - 465-473 TI - Hybrid system for simultaneous fluorescence and X-Ray computed tomography. JO - IEEE Trans. Med. Imaging VL - 29 IS - 2 PB - IEEE-Inst Electrical Electrical Electronics Engineers Inc. PY - 2010 SN - 0278-0062 ER - TY - JOUR AB - We report on a new quantification methodology of optoacoustic tomographic reconstructions under heterogeneous illumination conditions representative of realistic whole-body imaging scenarios. Our method relies on the differences in the spatial characteristics of the absorption coefficient and the optical energy density within the medium. By using sparse-representation based decomposition, we exploit these different characteristics to extract both the absorption coefficient and the photon density within the imaged object from the optoacoustic image. In contrast to previous methods, this algorithm is not based on the solution of theoretical light transport equations and it does not require explicit knowledge of the illumination geometry or the optical properties of the object and other unknown or loosely defined experimental parameters, leading to highly robust performance. The method was successfully examined with numerically and experimentally generated data and was found to be ideally suited for practical implementations in tomographic schemes of varying complexity, including multiprojection illumination systems and multispectral optoacoustic tomography (MSOT) studies of tissue biomarkers. AU - Rosenthal, A. AU - Razansky, D. AU - Ntziachristos, V. C1 - 1837 C2 - 26711 SP - 1997-2006 TI - Quantitative optoacoustic signal extraction using sparse signal representation. JO - IEEE Trans. Med. Imaging VL - 28 IS - 12 PY - 2009 SN - 0278-0062 ER -