Accelerated two‐dimensional phase‐contrast for cardiovascular MRI using deep learning‐based reconstruction with complex difference estimation. Issue 1 (12th September 2022)

• Record Type: Journal Article
• Authors: Oscanoa, Julio A.; Middione, Matthew J.; Syed, Ali B.; Sandino, Christopher M.; Vasanawala, Shreyas S.; Ennis, Daniel B.
• Is Part Of: Magnetic resonance in medicine. Volume 89:Issue 1(2023)
• Page Start: 356
• Page End: 369
• Publication Date: 2022-09-12
• Subjects: accuracy -- blood flow -- complex difference -- deep learning -- phase contrast -- precision -- velocity; Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.29441
• Languages: English
• ISSNs: 0740-3194

Abstract

Abstract : 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 (6–11 × $$ \times $$ ) 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 9 × $$ \times $$ 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 9 × $$ \times $$ acceleration. Clinical feasibility was demonstrated using a prospective clinical deployment of our 8 × $$ \times $$ undersampled acquisition and CD‐DL reconstruction in a cohort of pediatric patients.