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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.

Cardiovascular disease is one of the leading causes of death worldwide. Epicardial adipose tissue (EAT) has emerged as an independent predictor of high cardiometabolic risk. Cardiovascular MRI has proven to be a feasible and reproducible method to assess EAT quantitatively. We present a novel approach for the automated quantification of EAT using "a priori" anatomical information. We extracted a region of interest (ROI) in the end-diastolic heart phase followed by a GVF-snake algorithm to smooth it. For the EAT and endocardial boundary detection, a Law's texture filter is applied. Left and right ventricle are localized using spatial prior information. Then, thresholding is applied to quantify the cardiac muscle. For the EAT, it is differentiated from the paracardial fat by K-cosine curvature analysis. Results for 10 morbidly obese patients show no significant differences between manual and automatic quantification with a remarkable time and effort saving between them.