Accelerated two‐dimensional phase‐contrast for cardiovascular MRI using deep learning‐based reconstruction with complex difference estimation

Purpose To develop and validate a deep learning‐based reconstruction framework for highly accelerated two‐dimensional (2D) phase contrast (PC‐MRI) data with accurate and precise quantitative measurements. Methods We propose a modified DL‐ESPIRiT reconstruction framework for 2D PC‐MRI, comprised of an unrolled neural network architecture with a Complex Difference estimation (CD‐DL). CD‐DL was trained on 155 fully sampled 2D PC‐MRI pediatric clinical datasets. The fully sampled data () was retrospectively undersampled (6–11) and reconstructed using CD‐DL and a parallel imaging and compressed sensing method (PICS). Measurements of peak velocity and total flow were compared to determine the highest acceleration rate that provided accuracy and precision within . Feasibility of CD‐DL was demonstrated on prospectively undersampled datasets acquired in pediatric clinical patients () and compared to traditional parallel imaging (PI) and PICS. Results The retrospective evaluation showed that 9 accelerated 2D PC‐MRI images reconstructed with CD‐DL provided accuracy and precision (bias, [95 confidence intervals]) within . CD‐DL showed higher accuracy and precision compared to PICS for measurements of peak velocity (2.8 [, 4.5] vs. 3.9 [, 4.9]) and total flow (1.8 [, 3.4] vs. 2.9 [, 6.9]). The prospective feasibility study showed that CD‐DL provided higher accuracy and precision than PICS for measurements of peak velocity and total flow. Conclusion In a retrospective evaluation, CD‐DL produced quantitative measurements of 2D PC‐MRI peak velocity and total flow with error in both accuracy and precision for up to 9 acceleration. Clinical feasibility was demonstrated using a prospective clinical deployment of our 8 undersampled acquisition and CD‐DL reconstruction in a cohort of pediatric patients.