Individualized SAR calculations using computer vision‐based MR segmentation and a fast electromagnetic solver

• Published in: Magnetic resonance in medicine Vol. 85; no. 1; pp. 429 - 443
• Main Authors: Milshteyn, Eugene, Guryev, Georgy, Torrado‐Carvajal, Angel, Adalsteinsson, Elfar, White, Jacob K., Wald, Lawrence L., Guerin, Bastien
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.01.2021
• Subjects: Algorithms; Computer Simulation; Computer vision; Computers; electromagnetic; Electromagnetic Fields; Heterogeneity; Humans; Integral equations; Magnetic Resonance Imaging; patient‐specific; Safety margins; SAR; Segmentation; Soft tissues; Three dimensional models; Workflow; patient-specific; segmentation; SAR; electromagnetic
• ISSN: 0740-3194; 1522-2594; 1522-2594
• DOI: 10.1002/mrm.28398

Purpose We propose a fast, patient‐specific workflow for on‐line specific absorption rate (SAR) supervision. An individualized electromagnetic model is created while the subject is on the table, followed by rapid SAR estimates for that individual. Our goal is an improved correspondence between the patient and model, reducing reliance on general anatomical body models. Methods A 3D fat‐water 3T acquisition (~2 minutes) is automatically segmented using a computer vision algorithm (~1 minute) into what we found to be the most important electromagnetic tissue classes: air, bone, fat, and soft tissues. We then compute the individual’s EM field exposure and global and local SAR matrices using a fast electromagnetic integral equation solver. We assess the approach in 10 volunteers and compare to the SAR seen in a standard generic body model (Duke). Results The on‐the‐table workflow averaged 7′44″. Simulation of the simplified Duke models confirmed that only air, bone, fat, and soft tissue classes are needed to estimate global and local SAR with an error of 6.7% and 2.7%, respectively, compared to the full model. In contrast, our volunteers showed a 16.0% and 20.3% population variability in global and local SAR, respectively, which was mostly underestimated by the Duke model. Conclusion Timely construction and deployment of a patient‐specific model is computationally feasible. The benefit of resolving the population heterogeneity compared favorably to the modest modeling error incurred. This suggests that individualized SAR estimates can improve electromagnetic safety in MRI and possibly reduce conservative safety margins that account for patient‐model mismatch, especially in non‐standard patients.