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.

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.

PurposeTo develop a 3D multitasking multi‐echo (MT‐ME) technique for the comprehensive characterization of liver tissues with 5‐min free‐breathing acquisition; whole‐liver coverage; a spatial resolution of 1.5 × 1.5 × 6 mm3; and simultaneous quantification of T1, water‐specific T1 (T1w), proton density fat fraction (PDFF), and .MethodsSix‐echo bipolar spoiled gradient echo readouts following inversion recovery preparation was performed to generate T1, water/fat, and contrast. MR multitasking was used to reconstruct the MT‐ME images with 3 spatial dimensions: 1 T1 recovery dimension, 1 multi‐echo dimension, and 1 respiratory dimension. A basis function–based approach was developed for T1w quantification, followed by the estimation of and T1‐corrected PDFF. The intrasession repeatability and agreement against references of MT‐ME measurements were tested on a phantom and 15 clinically healthy subjects. In addition, 4 patients with confirmed liver diseases were recruited, and the agreement between MT‐ME measurements and references was assessed.ResultsMT‐ME produced high‐quality, coregistered T1, T1w, PDFF, and maps with good intrasession repeatability and substantial agreement with references on phantom and human studies. The intra‐class coefficients of T1, T1w, PDFF, and from the repeat MT‐ME measurements on clinically healthy subjects were 0.989, 0.990, 0.999, and 0.988, respectively. The intra‐class coefficients of T1, PDFF, and between the MT‐ME and reference measurements were 0.924, 0.987, and 0.975 in healthy subjects and 0.980, 0.999, and 0.998 in patients. The T1w was independent to PDFF (R = −0.029, P = .904).ConclusionThe proposed MT‐ME technique quantifies T1, T1w, PDFF, and simultaneously and is clinically promising for the comprehensive characterization of liver tissue properties.

Purpose To develop a new technique that enables simultaneous quantification of whole‐brain T1, T2, T2∗, as well as susceptibility and synthesis of six contrast‐weighted images in a single 9.1‐minute scan. Methods The technique uses hybrid T2‐prepared inversion‐recovery pulse modules and multi‐echo gradient‐echo readouts to collect k‐space data with various T1, T2, and T2∗ weightings. The underlying image is represented as a six‐dimensional low‐rank tensor consisting of three spatial dimensions and three temporal dimensions corresponding to T1 recovery, T2 decay, and multi‐echo behaviors, respectively. Multiparametric maps were fitted from reconstructed image series. The proposed method was validated on phantoms and healthy volunteers, by comparing quantitative measurements against corresponding reference methods. The feasibility of generating six contrast‐weighted images was also examined. Results High quality, co‐registered T1, T2, and T2∗ susceptibility maps were generated that closely resembled the reference maps. Phantom measurements showed substantial consistency (R2 > 0.98) with the reference measurements. Despite the significant differences of T1 (p < .001), T2 (p = .002), and T2∗ (p = 0.008) between our method and the references for in vivo studies, excellent agreement was achieved with all intraclass correlation coefficients greater than 0.75. No significant difference was found for susceptibility (p = .900). The framework is also capable of synthesizing six contrast‐weighted images. Conclusion The MR Multitasking–based 3D brain mapping of T1, T2, T2∗, and susceptibility agrees well with the reference and is a promising technique for multicontrast and quantitative imaging.

Purpose To develop a free‐breathing, non‐electrocardiogram technique for simultaneous myocardial T1, T2, T2*, and fat‐fraction (FF) mapping in a single scan. Methods The MR Multitasking framework is adapted to quantify T1, T2, T2*, and FF simultaneously. A variable TR scheme is developed to preserve temporal resolution and imaging efficiency. The underlying high‐dimensional image is modeled as a low‐rank tensor, which allows accelerated acquisition and efficient reconstruction. The accuracy and/or repeatability of the technique were evaluated on static and motion phantoms, 12 healthy volunteers, and 3 patients by comparing to the reference techniques. Results In static and motion phantoms, T1/T2/T2*/FF measurements showed substantial consistency (R > 0.98) and excellent agreement (intraclass correlation coefficient > 0.93) with reference measurements. In human subjects, the proposed technique yielded repeatable T1, T2, T2*, and FF measurements that agreed with those from references. Conclusions The proposed free‐breathing, non‐electrocardiogram, motion‐resolved Multitasking technique allows simultaneous quantification of myocardial T1, T2, T2*, and FF in a single 2.5‐min scan.

To develop a low-dose Multitasking DCE technique (LD-MT-DCE) for breast imaging, enabling dynamic T 1 mapping-based quantitative characterization of tumor blood flow and vascular properties with whole-breast coverage, a spatial resolution of 0.9 × 0.9 × 1.1 mm 3 , and a temporal resolution of 1.4 seconds using a 20% gadolinium dose (0.02 mmol/kg). Methods: Magnetic resonance Multitasking was used to reconstruct 5D images with three spatial dimensions, one T 1 recovery dimension for dynamic T 1 quantification, and one DCE dimension for contrast kinetics. Kinetic parameters F p , v p , K trans , and v e were estimated from dynamic T 1 maps using the two-compartment exchange model. The LD-MT-DCE repeatability and agreement against standard-dose MT-DCE were evaluated in 20 healthy subjects. In 7 patients with triple-negative breast cancer, LD-MT-DCE image quality and diagnostic results were compared with that of standarddose clinical DCE in the same imaging session. One-way unbalanced analysis of variance with Tukey test was performed to evaluate the statistical significance of the kinetic parameters between control and patient groups. Results: The LD-MT-DCE technique was repeatable, agreed with standard-dose MT-DCE, and showed excellent image quality. The diagnosis using LD-MT-DCE matched well with clinical results. The values of F p , v p , and K trans were significantly different between malignant tumors and normal breast tissue (P < .001, < .001, and < .001, respectively), and between malignant and benign tumors (P = .020, .003, and < .001, respectively). Conclusion: The LD-MT-DCE technique was repeatable and showed excellent image quality and equivalent diagnosis compared with standard-dose clinical DCE.