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

Abstract

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. 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. 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±7.7mm fat and 7.4±11.2mm water in local-MAP-STAPLE, and 16.4±8.5mm fat and 7.2±11.8mm 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. 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. 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.