Combined T2 -preparation and multidimensional outer volume suppression for coronary artery imaging with 3D cones trajectories.

PURPOSE: To develop a modular magnetization preparation sequence for combined T2 -preparation and multidimensional outer volume suppression (OVS) for coronary artery imaging. METHODS: A combined T2 -prepared 1D OVS sequence with fat saturation was defined to contain a 90°-60 180°60 composite nonselective tip-down pulse, two 180°Y hard pulses for refocusing, and a -90° spectral-spatial sinc tip-up pulse. For 2D OVS, 2 modules were concatenated, selective in X and then Y. Bloch simulations predicted robustness of the sequence to B0 and B1 inhomogeneities. The proposed sequence was compared with a T2 -prepared 2D OVS sequence proposed by Luo et al, which uses a spatially selective 2D spiral tip-up. The 2 sequences were compared in phantom studies and in vivo coronary artery imaging studies with a 3D cones trajectory. RESULTS: Phantom results demonstrated superior OVS for the proposed sequence compared with the Luo sequence. In studies on 15 healthy volunteers, the proposed sequence had superior image edge profile acutance values compared with the Luo sequence for the right (P < .05) and left (P < .05) coronary arteries, suggesting superior vessel sharpness. The proposed sequence also had superior signal-to-noise ratio (P < .05) and passband-to-stopband ratio (P < .05). Reader scores and reader preference indicated superior coronary image quality of the proposed sequence for both the right (P < .05) and left (P < .05) coronary arteries. CONCLUSION: The proposed sequence with concatenated 1D spatially selective tip-ups and integrated fat saturation has superior image quality and suppression compared with the Luo sequence with 2D spatially selective tip-up.

Deep residual network for off-resonance artifact correction with application to pediatric body MRA with 3D cones.

PURPOSE: To enable rapid imaging with a scan time-efficient 3D cones trajectory with a deep-learning off-resonance artifact correction technique. METHODS: A residual convolutional neural network to correct off-resonance artifacts (Off-ResNet) was trained with a prospective study of pediatric MRA exams. Each exam acquired a short readout scan (1.18 ms ± 0.38) and a long readout scan (3.35 ms ± 0.74) at 3 T. Short readout scans, with longer scan times but negligible off-resonance blurring, were used as reference images and augmented with additional off-resonance for supervised training examples. Long readout scans, with greater off-resonance artifacts but shorter scan time, were corrected by autofocus and Off-ResNet and compared with short readout scans by normalized RMS error, structural similarity index, and peak SNR. Scans were also compared by scoring on 8 anatomical features by two radiologists, using analysis of variance with post hoc Tukey's test and two one-sided t-tests. Reader agreement was determined with intraclass correlation. RESULTS: The total scan time for long readout scans was on average 59.3% shorter than short readout scans. Images from Off-ResNet had superior normalized RMS error, structural similarity index, and peak SNR compared with uncorrected images across ±1 kHz off-resonance (P < .01). The proposed method had superior normalized RMS error over -677 Hz to +1 kHz and superior structural similarity index and peak SNR over ±1 kHz compared with autofocus (P < .01). Radiologic scoring demonstrated that long readout scans corrected with Off-ResNet were noninferior to short readout scans (P < .05). CONCLUSION: The proposed method can correct off-resonance artifacts from rapid long-readout 3D cones scans to a noninferior image quality compared with diagnostically standard short readout scans.