Anette Karlsson1,2, Johannes Rosander3, Thobias Romu1,2, Joakim Tallberg2, Anders Grönqvist2,4, Magnus Borga1,2, Olof Dahlqvist Leinhard2,5. 1. Department of Biomedical Engineering (IMT), Linköping University, Linköping, Sweden. 2. Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden. 3. Advanced MR Analytics (AMRA) AB, Linköping, Sweden. 4. Department of Radiation Physics and Department of Medical and Health Sciences, Linköping University, Linköping, Sweden. 5. Department of Medical and Health Sciences (IMH), Linköping University, Linköping, Sweden.
Abstract
PURPOSE: To develop and demonstrate a rapid whole-body magnetic resonance imaging (MRI) method for automatic quantification of total and regional skeletal muscle volume. MATERIALS AND METHODS: The method was based on a multi-atlas segmentation of intensity corrected water-fat separated image volumes. Automatic lean muscle tissue segmentations were achieved by nonrigid registration of atlas datasets with 10 different manually segmented muscle groups. Ten subjects scanned at 1.5 T and 3.0 T were used as atlases, initial validation and optimization. Further validation used 11 subjects scanned at 3.0 T. The automated and manual segmentations were compared using intraclass correlation, true positive volume fractions, and delta volumes. RESULTS: For the 1.5 T datasets, the intraclass correlation, true positive volume fractions (mean ± standard deviation, SD), and delta volumes (mean ± SD) were 0.99, 0.91 ± 0.02, -0.10 ± 0.70L (whole body), 0.99, 0.93 ± 0.02, 0.01 ± 0.07L (left anterior thigh), and 0.98, 0.80 ± 0.07, -0.08 ± 0.15L (left abdomen). The corresponding values at 3.0 T were 0.97, 0.92 ± 0.03, -0.17 ± 1.37L (whole body), 0.99, 0.93 ± 0.03, 0.03 ± 0.08L (left anterior thigh), and 0.89, 0.90 ± 0.04, -0.03 ± 0.42L (left abdomen). The validation datasets showed similar results. CONCLUSION: The method accurately quantified the whole-body skeletal muscle volume and the volume of separate muscle groups independent of field strength and image resolution.
PURPOSE: To develop and demonstrate a rapid whole-body magnetic resonance imaging (MRI) method for automatic quantification of total and regional skeletal muscle volume. MATERIALS AND METHODS: The method was based on a multi-atlas segmentation of intensity corrected water-fat separated image volumes. Automatic lean muscle tissue segmentations were achieved by nonrigid registration of atlas datasets with 10 different manually segmented muscle groups. Ten subjects scanned at 1.5 T and 3.0 T were used as atlases, initial validation and optimization. Further validation used 11 subjects scanned at 3.0 T. The automated and manual segmentations were compared using intraclass correlation, true positive volume fractions, and delta volumes. RESULTS: For the 1.5 T datasets, the intraclass correlation, true positive volume fractions (mean ± standard deviation, SD), and delta volumes (mean ± SD) were 0.99, 0.91 ± 0.02, -0.10 ± 0.70L (whole body), 0.99, 0.93 ± 0.02, 0.01 ± 0.07L (left anterior thigh), and 0.98, 0.80 ± 0.07, -0.08 ± 0.15L (left abdomen). The corresponding values at 3.0 T were 0.97, 0.92 ± 0.03, -0.17 ± 1.37L (whole body), 0.99, 0.93 ± 0.03, 0.03 ± 0.08L (left anterior thigh), and 0.89, 0.90 ± 0.04, -0.03 ± 0.42L (left abdomen). The validation datasets showed similar results. CONCLUSION: The method accurately quantified the whole-body skeletal muscle volume and the volume of separate muscle groups independent of field strength and image resolution.
Authors: Leon Lenchik; Laura Heacock; Ashley A Weaver; Robert D Boutin; Tessa S Cook; Jason Itri; Christopher G Filippi; Rao P Gullapalli; James Lee; Marianna Zagurovskaya; Tara Retson; Kendra Godwin; Joey Nicholson; Ponnada A Narayana Journal: Acad Radiol Date: 2019-08-10 Impact factor: 3.173
Authors: Michael S Middleton; William Haufe; Jonathan Hooker; Magnus Borga; Olof Dahlqvist Leinhard; Thobias Romu; Patrik Tunón; Gavin Hamilton; Tanya Wolfson; Anthony Gamst; Rohit Loomba; Claude B Sirlin Journal: Radiology Date: 2017-03-09 Impact factor: 11.105