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.