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 (n=29$$ n=29 $$) was retrospectively undersampled (6-11x$$ \times $$) 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 +/- 5%$$ \pm 5\% $$. Feasibility of CD-DL was demonstrated on prospectively undersampled datasets acquired in pediatric clinical patients (n=5$$ n=5 $$) and compared to traditional parallel imaging (PI) and PICS. Results The retrospective evaluation showed that 9x$$ \times $$ accelerated 2D PC-MRI images reconstructed with CD-DL provided accuracy and precision (bias, [95%$$ \% $$ confidence intervals]) within +/- 5%$$ \pm 5\% $$. CD-DL showed higher accuracy and precision compared to PICS for measurements of peak velocity (2.8%$$ \% $$ [-2.9$$ -2.9 $$, 4.5] vs. 3.9%$$ \% $$ [-11.0$$ -11.0 $$, 4.9]) and total flow (1.8%$$ \% $$ [-3.9$$ -3.9 $$, 3.4] vs. 2.9%$$ \% $$ [-7.1$$ -7.1 $$, 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 <= 5%$$ \le 5\% $$ error in both accuracy and precision for up to 9x$$ \times $$ acceleration. Clinical feasibility was demonstrated using a prospective clinical deployment of our 8x$$ \times $$ undersampled acquisition and CD-DL reconstruction in a cohort of pediatric patients.