Quantitative cardiovascular magnetic resonance (CMR) imaging can be used to characterize fibrosis, oedema, ischaemia, inflammation and other disease conditions. However, the need to reduce artefacts arising from body motion through a combination of electrocardiography (ECG) control, respiration control, and contrast-weighting selection makes CMR exams lengthy. Here, we show that physiological motions and other dynamic processes can be conceptualized as multiple time dimensions that can be resolved via low-rank tensor imaging, allowing for motion-resolved quantitative imaging with up to four time dimensions. This continuous-acquisition approach, which we name cardiovascular MR multitasking, captures — rather than avoids — motion, relaxation and other dynamics to efficiently perform quantitative CMR without the use of ECG triggering or breath holds. We demonstrate that CMR multitasking allows for T1 mapping, T1-T2 mapping and time-resolved T1 mapping of myocardial perfusion without ECG information and/or in free-breathing conditions. CMR multitasking may provide a foundation for the development of setup-free CMR imaging for the quantitative evaluation of cardiovascular health.

To develop a 3D whole-brain simultaneous T1/T2/T1ρ quantification method with MR Multitasking that provides high quality, co-registered multiparametric maps in 9 min. Methods: MR Multitasking conceptualizes T1/T2/T1ρ relaxations as different time dimensions, simultaneously resolving all three dimensions with a low-rank tensor image model. The proposed method was validated on a phantom and in healthy volunteers, comparing quantitative measurements against corresponding reference methods and evaluating the scan-rescan repeatability. Initial clinical validation was performed in agematched relapsing-remitting multiple sclerosis (RRMS) patients to examine the feasibility of quantitative tissue characterization and to compare with the healthy control cohort. The feasibility of synthesizing six contrast-weighted images was also examined. Results: Our framework produced high quality, co-registered T1/T2/T1ρ maps that closely resemble the reference maps. Multitasking T1/T2/T1ρ measurements showed substantial agreement with reference measurements on the phantom and in healthy controls. Bland-Altman analysis indicated good in vivo repeatability of all three parameters. In RRMS patients, lesions were conspicuously delineated on all three maps and on four synthetic weighted images (T2-weighted, T2-FLAIR, double inversion recovery, and a novel "T1ρ-FLAIR" contrast). T1 and T2 showed significant differences for normal appearing white matter between patients and controls, while T1ρ showed significant differences for normal appearing white matter, cortical gray matter, and deep gray matter. The combination of three parameters significantly improved the differentiation between RRMS patients and healthy controls, compared to using any single parameter alone. Conclusion: MR Multitasking simultaneously quantifies whole-brain T1/T2/T1ρ and is clinically promising for quantitative tissue characterization of neurological diseases, such as MS.

Purpose To develop a simultaneous T1, T2, and ADC mapping method that provides co‐registered, distortion‐free images and enables multiparametric quantification of 3D brain coverage in a clinically feasible scan time with the MR Multitasking framework. Methods The T1/T2/diffusion weighting was generated by a series of T2 preparations and diffusion preparations. The underlying multidimensional image containing 3 spatial dimensions, 1 T1 weighting dimension, 1 T2‐preparation duration dimension, 1 b‐value dimension, and 1 diffusion direction dimension was modeled as a 5‐way low‐rank tensor. A separate real‐time low‐rank model incorporating time‐resolved phase correction was also used to compensate for both inter‐shot and intra‐shot phase inconsistency induced by physiological motion. The proposed method was validated on both phantom and 16 healthy subjects. The quantification of T1/T2/ADC was evaluated for each case. Three post‐surgery brain tumor patients were scanned for demonstration of clinical feasibility. Results Multitasking T1/T2/ADC maps were perfectly co‐registered and free from image distortion. Phantom studies showed substantial quantitative agreement (R2=0.999) with reference protocols for T1/T2/ADC. In vivo studies showed nonsignificant T1 (P = .248), T2 (P = .97), ADC (P = .328) differences among the frontal, parietal, and occipital regions. Although Multitasking showed significant differences of T1 (P = .03), T2 (P < .001), and ADC (P = .001) biases against the references, the mean bias estimates were small (ΔT1% < 5%, ΔT2% < 7%, ΔADC% < 5%), with all intraclass correlation coefficients greater than 0.82 indicating “excellent” agreement. Patient studies showed that Multitasking T1/T2/ADC maps were consistent with the clinical qualitative images. Conclusion The Multitasking approach simultaneously quantifies T1/T2/ADC with substantial agreement with the references and is promising for clinical applications.