This study demonstrates that an optimal 12-fold, accelerated, real-time cine MRI pulse sequence using radial k-space sampling and CS produces good to excellent visual scores and relatively accurate LV functional parameters in patients at 1.5T and 3T. Magn Reson Med 79:2745-2751, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

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Compressed sensing (CS) is a promising method for accelerating cardiac perfusion MRI to achieve clinically acceptable image quality with high spatial resolution (1.6 × 1.6 × 8 mm 3 ) and extensive myocardial coverage (6-8 slices per heartbeat). A major disadvantage of CS is its relatively lengthy processing time (~8 min per slice with 64 frames using a graphics processing unit), thereby making it impractical for clinical translation. The purpose of this study was to implement and test whether an image reconstruction pipeline including a neural network is capable of reconstructing 6.4-fold accelerated, non-Cartesian (radial) cardiac perfusion k-space data at least 10 times faster than CS, without significant loss in image quality. We implemented a 3D (2D + time) U-Net and trained it with 132 2D + time datasets (coil combined, zero filled as input; CS reconstruction as reference) with 64 time frames from 28 patients (8448 2D images in total). For testing, we used 56 2D + time coil-combined, zerofilled datasets (3584 2D images in total) from 12 different patients as input to our trained U-Net, and compared the resulting images with CS reconstructed images using quantitative metrics of image quality and visual scores (conspicuity of wall enhancement, noise, artifacts; each score ranging from 1 (worst) to 5 (best), with 3 defined as clinically acceptable) evaluated by readers. Including pre-and postprocessing steps, compared with CS, U-Net significantly reduced the reconstruction time by 14.4-fold (32.1 ± 1.4 s for U-Net versus 461.3 ± 16.9 s for CS, p < 0.001), while maintaining high data fidelity (structural similarity index = 0.914 ± 0.023, normalized root mean square error = 1.7 ± 0.3%, identical mean edge sharpness of 1.2 mm). The median visual summed score was not significantly different (p = 0.053) between CS (14; interquartile range (IQR) = 0.5) and U-Net (12; IQR = 0.5). This study shows that the proposed pipeline with a U-Net is capable of reconstructing 6.4-fold accelerated, non-Cartesian cardiac perfusion k-space data 14.4 times faster than CS, without significant loss in data fidelity or image quality.

Purpose To develop an accelerated cardiac perfusion pulse sequence and test whether it is capable of increasing spatial coverage, generating high‐quality images, and enabling quantification of myocardial blood flow (MBF). Methods We implemented an accelerated first‐pass cardiac perfusion pulse sequence by combining radial k‐space sampling, compressed sensing (CS), and k‐space weighted image contrast (KWIC) filtering. The proposed and clinical standard pulse sequences were evaluated in a randomized order in 13 patients at rest. For visual analysis, 3 readers graded the conspicuity of wall enhancement, artifact, and noise level on a 5‐point Likert scale (overall score index = sum of 3 individual scores). Resting MBF was calculated using a Fermi function model with and without KWIC filtering. Mean visual scores and MBF values were compared between sequences using appropriate statistical tests. Results The proposed pulse sequence produced greater spatial coverage (6–8 slices) with higher spatial resolution (1.6 × 1.6 × 8 mm3) and shorter readout duration (78 ms) compared to clinical standard (3–4 slices, 3 × 3 × 8 mm3, 128 ms, respectively). The overall image score index between accelerated (11.1 ± 1.3) and clinical standard (11.2 ± 1.3) was not significantly different (P = 0.64). Mean resting MBF values with KWIC filtering (0.9–1.2 mL/g/min across different slices) were significantly lower (P < 0.0001) than those without KWIC filtering (3.1–4.3 mL/g/min) and agreed better with values reported in literature. Conclusion An accelerated, first‐pass cardiac perfusion pulse sequence with radial k‐space sampling, CS, and KWIC filtering is capable of increasing spatial coverage, generating high‐quality images, and enabling quantification of MBF.