Matthew J. Middione^{1}, Julio A. Oscanoa^{1,2}, Ali B. Syed^{1}, Shreyas S. Vasanawala^{1}, and Daniel B. Ennis^{1,3}

^{1}Department of Radiology, Stanford University, Stanford, CA, United States, ^{2}Department of Bioengineering, Stanford University, Stanford, CA, United States, ^{3}Cardiovascular Institute, Stanford University, Stanford, CA, United States

We have previously demonstrated a Deep Learning (DL) reconstruction of highly accelerated 2D PC-MRI datasets. We trained a DL reconstruction by retrospectively undersampling fully-sampled 2D PC-MRI datasets to enable up to 9x accelerated images with <5% error in accuracy and precision. Herein, we compare the accuracy and precision of 2D PC-MRI measurements for our prospectively deployed sequence and DL reconstruction. In this initial feasibility study, we show that our DL reconstruction provides <5% error in the accuracy of peak velocity and total flow relative to 2x parallel imaging and better accuracy and precision compared to 8x compressed sensing.

Recently, Deep Learning (DL) based frameworks have been developed to reconstruct highly accelerated MRI acquisitions

The

2D PC-MRI datasets (n=3) were prospectively acquired using a custom ECG-gated spoiled gradient echo sequence with the following imaging parameters: field-of-view=360mm

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**Table 1. **Patient demographics and 2D PC-MRI imaging parameters used to train, evaluate, and test the retrospectively undersampled 2D PC-MRI DL-based network (n=194).

**Figure 1. (A)** The DL reconstruction pipeline uses an unrolled network architecture that iteratively alternates between a Data Consistency (DC) update and a CNN-based denoising step. **(B)** The CNN was comprised of a Rectified Linear Unit (ReLU) pre‐activation layer and a 2D spatial and 1D temporal (2+1)D convolutional layer, as described in the original DL-ESPIRiT methodology^{7}. **(C)** The first velocity encoding and a complex difference (CD) image are reconstructed using PC-DL and CD-DL, respectively. The final velocity image is generated phase difference processing (PD).

**Figure 2. **Qualitative results from a single volunteer showing the magnitude (top) and phase (bottom) for conventional 2x PI as well as 8x CS and the proposed DL method. Overall, image quality is preserved for the CS and DL reconstruction methods, with a small artifact in the aorta of the magnitude images, visible as the aortic valve closes, and a slight exaggeration of pulsatility artifacts, visible both anteriorly and posteriorly to the chest wall in the phase images.

**Table 2. **Quantitative comparison of peak velocity and total flow. Comparisons were made for 2x PI scans as well as 8x accelerated scans reconstructed with CS and the proposed DL method. 8x DL shows higher accuracy (lower mean % difference) and higher precision (smaller standard deviation) relative to 8x CS. Furthermore, accuracy is within 5% for 8x DL for both peak velocity and total flow, whereas accuracy is within 5% for 8x CS for total flow only.

**Figure 3. **Qualitative results from a single volunteer showing the magnitude (top) and phase (middle) images for 8x DL reconstructed images for **(A) **reduced scan time (20 seconds vs. 5 seconds), **(B) **increased temporal resolution (100ms vs. 30ms) and **(C) **increased spatial resolution (1.9x2.3mm^{2} vs. 1.3x1.4mm^{2}).

DOI: https://doi.org/10.58530/2022/0078