Purpose To develop an MR multitasking‐based multidimensional assessment of cardiovascular system (MT‐MACS) with electrocardiography‐free and navigator‐free data acquisition for a comprehensive evaluation of thoracic aortic diseases. Methods The MT‐MACS technique adopts a low‐rank tensor image model with a cardiac time dimension for phase‐resolved cine imaging and a T2‐prepared inversion‐recovery dimension for multicontrast assessment. Twelve healthy subjects and 2 patients with thoracic aortic diseases were recruited for the study at 3 T, and both qualitative (image quality score) and quantitative (contrast‐to‐noise ratio between lumen and wall, lumen and wall area, and aortic strain index) analyses were performed in all healthy subjects. The overall image quality was scored based on a 4‐point scale: 3, excellent; 2, good; 1, fair; and 0, poor. Statistical analysis was used to test the measurement agreement between MT‐MACS and its corresponding 2D references. Results The MT‐MACS images reconstructed from acquisitions as short as 6 minutes demonstrated good or excellent image quality for bright‐blood (2.58 ± 0.46), dark‐blood (2.58 ± 0.50), and gray‐blood (2.17 ± 0.53) contrast weightings, respectively. The contrast‐to‐noise ratios for the three weightings were 49.2 ± 12.8, 20.0 ± 5.8 and 2.8 ± 1.8, respectively. There were good agreements in the lumen and wall area (intraclass correlation coefficient = 0.993, P < .001 for lumen; intraclass correlation coefficient = 0.969, P < .001 for wall area) and strain (intraclass correlation coefficient = 0.947, P < .001) between MT‐MACS and conventional 2D sequences. Conclusion The MT‐MACS technique provides high‐quality, multidimensional images for a comprehensive assessment of the thoracic aorta. Technical feasibility was demonstrated in healthy subjects and patients with thoracic aortic diseases. Further clinical validation is warranted.

The aim of this study is to simultaneously quantify T1/T2 across three slices of the left-ventricular myocardium without breath-holds or ECG monitoring, all within a 3 min scan. Radial simultaneous multi-slice (SMS) encoding, self-gating, and image reconstruction was incorporated into the cardiovascular magnetic resonance (CMR) Multitasking framework to simultaneously image three short-axis slices. A T2prep-IR FLASH sequence with two flip angles was designed and implemented to allow B1+-robust T1 and T2 mapping. The proposed Multitasking-SMS method was validated in a standardized phantom and 10 healthy volunteers, comparing T1 and T2 measurements and scan-rescan repeatability against corresponding reference methods in one layer of phantom vials and in 16 American Heart Association (AHA) myocardial segments. In phantom, Multitasking-SMS T1/T2 measurements showed substantial correlation (R2 &gt; 0.996) and excellent agreement [intraclass correlation coefficients (ICC) ≥ 0.999)] with reference measurements. In healthy volunteers, Multitasking-SMS T1/T2 maps reported similar myocardial T1/T2 values (1,215 ± 91.0/41.5 ± 6.3 ms) to the reference myocardial T1/T2 values (1,239 ± 67.5/42.7 ± 4.1 ms), with P = 0.347 and P = 0.296, respectively. Bland–Altman analyses also demonstrated good in vivo repeatability in both the multitasking and references, with segment-wise coefficients of variation of 4.7% (multitasking T1), 8.9% (multitasking T2), 2.4% [modified look-locker inversion recovery (MOLLI)], and 4.6% (T2-prep FLASH), respectively. In summary, multitasking-SMS is feasible for free-breathing, non-ECG, myocardial T1/T2 quantification in 16 AHA segments over 3 short-axis slices in 3 min. The method shows the great potential for reducing exam time for quantitative CMR without ECG or breath-holds.

Objective. To develop and test the feasibility of a novel Single ProjectIon DrivEn Real-time Multi-contrast (SPIDERM) MR imaging technique that can generate real-time 3D images on-the-fly with flexible contrast weightings and a low latency. Approach. In SPIDERM, a “prep” scan is first performed, with sparse k-space sampling periodically interleaved with the central k-space line (navigator data), to learn a subject-specific model, incorporating a spatial subspace and a linear transformation between navigator data and subspace coordinates. A “live” scan is then performed by repeatedly acquiring the central k-space line only to dynamically determine subspace coordinates. With the “prep”-learned subspace and “live” coordinates, real-time 3D images are generated on-the-fly with computationally efficient matrix multiplication. When implemented based on a multi-contrast pulse sequence, SPIDERM further allows for data-driven image contrast regeneration to convert real-time contrast-varying images into contrast-frozen images at user’s discretion while maintaining motion states. Both digital phantom and in-vivo experiments were performed to evaluate the technical feasibility of SPIDERM. Main results. The elapsed time from the input of the central k-space line to the generation of real-time contrast-frozen 3D images was approximately 45 ms, permitting a latency of 55 ms or less. Motion displacement measured from SPIDERM and reference images showed excellent correlation (R^2≥0.983). Geometric variation from the ground truth in the digital phantom was acceptable as demonstrated by pancreas contour analysis (Dice≥0.84, mean surface distance≤0.95 mm). Quantitative image quality metrics showed good consistency between reference images and contrast-varying SPIDREM images in in-vivo studies (mean NMRSE=0.141, PSNR=30.12, SSIM=0.88). Significance. SPIDERM is capable of generating real-time multi-contrast 3D images with a low latency. An imaging framework based on SPIDERM has the potential to serve as a standalone package for MR guided radiation therapy by offering adaptive simulation through a “prep” scan and real-time image guidance through a “live” scan.

Purpose:(1) To investigate the effect of internal localized movement on 3DMR intracranial vessel wall imaging and (2) to develop a novel motion-compensation approach combining volumetric navigator (vNav) and self-gating (SG) to simultaneously compensate for bulk and localized movements. Methods: A 3D variable-flip-angle turbo spin-echo (ie, SPACE) sequence was modified to incorporate vNav and SG modules. The SG signals from the center k-space line are acquired at the beginning of each TR to detect localized motion-affected TRs. The vNavs from low-resolution 3D EPI are acquired to identify bulk head motion. Fifteen healthy subjects and 3 stroke patients were recruited in this study.Overall image quality (0-poor to 4-excellent) and vessel wall sharpness were compared among the scenarios with and without bulk and/or localized motion and/or the proposed compensation strategies. Results: Localized motion reduced wall sharpness, which was significantly mitigated by SG (ie, outer boundary of basilar artery: 0.68 ± 0.27 vs 0.86 ± 0.17; P = .037). When motion occurred, the overall image quality and vessel wall sharpness obtained with vNav-SG SPACE were significantly higher than those obtained with conventional SPACE (ie, basilarartery outer boundary sharpness: 0.73 ± 0.24 vs 0.94 ± 0.24; P = .033), yet comparable to those obtained in motion-free scans (ie, basilarartery outer boundary sharpness: 0.94 ± 0.24 vs 0.96 ± 0.31; P = .815). Conclusion:Localized movements can induce considerable artifacts in intracranial vessel wall imaging. The vNav-SG approach is capable of compensating for both bulk and localized motions. K E Y W O R D S intracranial vessel wall, motion compensation, self-gating, vessel wall imaging, volumetric navigators 638 | HU et al. How to cite this article: Hu Z, van der Kouwe A, Han F, et al. Motion-compensated 3D turbo spin echo for more robust MR intracranial vessel wall imaging.