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Automated shape-independent assessment of the spatial distribution of proton density fat fraction in vertebral bone marrow.
Z. Med. Phys., DOI: 10.1016/j.zemedi.2022.12.004 (2023)
This work proposes a method for automatic standardized assessment of bone marrow volume and spatial distribution of the proton density fat fraction (PDFF) in vertebral bodies. Intra- and interindividual variability in size and shape of vertebral bodies is a challenge for comparable interindividual evaluation and monitoring of changes in the composition and distribution of bone marrow due to aging and/or intervention. Based on deep learning image segmentation, bone marrow PDFF of single vertebral bodies is mapped to a cylindrical template and corrected for the inclination with respect to the horizontal plane. The proposed technique was applied and tested in a cohort of 60 healthy (30 males, 30 females) individuals. Obtained bone marrow volumes and mean PDFF values are comparable to former manual and (semi-)automatic approaches. Moreover, the proposed method allows shape-independent characterization of the spatial PDFF distribution inside vertebral bodies.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Bone Marrow ; Cse Mri ; Deep Learning ; Pdff ; Segmentation
ISSN (print) / ISBN
0939-3889
e-ISSN
1876-4436
Publisher
Elsevier
Non-patent literature
Publications
Reviewing status
Peer reviewed