Automated shape-independent assessment of the spatial distribution of proton density fat fraction in vertebral bone marrow

• Published in: Zeitschrift für Medizinische Physik
• Main Authors: Tobias Haueise, Norbert Stefan, Tim J. Schulz, Fritz Schick, Andreas L. Birkenfeld, Jürgen Machann
• Format: Article
• Language: English
• Published: Elsevier 2024-08-01
• Subjects: Bone marrow; CSE MRI; Deep learning; PDFF; Segmentation
• Online Access: http://www.sciencedirect.com/science/article/pii/S0939388922001374

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.