Objective: The purpose of this paper is to prove that computer-vision techniques allow synthesizing water-fat separation maps for local specific absorption rate (SAR) estimation, when patient-specific water-fat images are not available. Methods: We obtained ground truth head models by using patient-specific water-fat images. We obtained two different label-fusion water-fat models generating a water-fat multiatlas and applying the STAPLE and local-MAP-STAPLE label-fusion methods. We also obtained patch-based water-fat models applying a local group-wise weighted combination of the multiatlas. Electromagnetic (EM) simulations were performed, and B1+ magnitude and 10 g averaged SAR maps were generated. Results: We found local approaches provide a high DICE overlap (72.6 ± 10.2% fat and 91.6 ± 1.5% water in local-MAP-STAPLE, and 68.8 ± 8.2% fat and 91.1 ± 1.0% water in patch-based), low Hausdorff distances (18.6 ± 7.7 mm fat and 7.4 ± 11.2 mm water in local-MAP-STAPLE, and 16.4 ± 8.5 mm fat and 7.2 ± 11.8 mm water in patch-based) and a low error in volume estimation (15.6 ± 34.4% fat and 5.6 ± 4.1% water in the local-MAP-STAPLE, and 14.0 ± 17.7% fat and 4.7 ± 2.8% water in patch-based). The positions of the peak 10 g-averaged local SAR hotspots were the same for every model. Conclusion: We have created patient-specific head models using three different computer-vision-based water-fat separation approaches and compared the predictions of B1+ field and SAR distributions generated by simulating these models. Our results prove that a computer-vision approach can be used for patient-specific water-fat separation, and utilized for local SAR estimation in high-field MRI. Significance: Computer-vision approaches can be used for patient-specific water-fat separation and for patient specific local SAR estimation, when water-fat images of the patient are not available.
Bibliographical noteFunding Information:
Manuscript received January 3, 2018; revised May 24, 2018; accepted July 6, 2018. Date of publication July 16, 2018; date of current version February 18, 2019. This work was supported by Consejería de Edu-cación, Juventud y Deporte de la Comunidad de Madrid, through the Madrid-MIT M+Visión Consortium, and project DPI2015-68664-C4-2-R of the Spanish Ministry of Economy and Innovation. (Corresponding author: Angel Torrado-Carvajal.) A. Torrado-Carvajal was with the Medical Image Analysis and Biometry Lab, Universidad Rey Juan Carlos, Madrid 28933, Spain. He is now with the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02115 USA (e-mail:, atorradocarvajal@ mgh.harvard.edu).
- Computer vision
- SAR management
- head models
- magnetic resonance imaging
- multi-atlas segmentation
- water-fat separation
PubMed: MeSH publication types
- Journal Article
- Research Support, Non-U.S. Gov't