Computer-Vision Techniques for Water-Fat Separation in Ultra High-Field MRI Local Specific Absorption Rate Estimation

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.