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Gromann, L.B.* ; Scherer, K.* ; Yaroshenko, A.* ; Bölükbas, D.A. ; Hellbach, K.* ; Meinel, F.G.* ; Braunagel, M.* ; Eickelberg, O. ; Reiser, M.F.* ; Pfeiffer, F.* ; Meiners, S. ; Herzen, J.*

First experiences with in-vivo x-ray dark-field imaging of lung cancer in mice.

Proc. SPIE 10132:101325L (2017)

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Purpose: The purpose of the present study was to evaluate if x-ray dark-field imaging can help to visualize lung cancer in mice. Materials and Methods: The experiments were performed using mutant mice with high-grade adenocarcinomas. Eight animals with pulmonary carcinoma and eight control animals were imaged in radiography mode using a prototype small-animal x-ray dark-field scanner and three of the cancerous ones additionally in CT mode. After imaging, the lungs were harvested for histological analysis. To determine their diagnostic value, x-ray dark-field and conventional attenuation images were analyzed by three experienced readers in a blind assessment. Results radiographic imaging: The lung nodules were much clearer visualized on the dark-field radiographs compared to conventional radiographs. The loss of air-tissue interfaces in the tumor leads to a significant loss of x-ray scattering, reflected in a strong dark-field signal change. The difference between tumor and healthy tissue in terms of x-ray attenuation is significantly less pronounced. Furthermore, the signal from the overlaying structures on conventional radiographs complicates the detection of pulmonary carcinoma. Results CT imaging: The very first in-vivo CT-imaging results are quite promising as smaller tumors are often better visible in the dark-field images. However the imaging quality is still quite low, especially in the attenuation images due to un-optimized scanning parameters. Conclusion: We found a superior diagnostic performance of dark-field imaging compared to conventional attenuation based imaging, especially when it comes to the detection of small lung nodules. These results support the motivation to further develop this technique and translate it towards a clinical environment.

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