Three‐dimensional simultaneous brain T₁, T₂, and ADC mapping with MR Multitasking

Abstract

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.

Three‐dimensional whole‐brain simultaneous T1, T2, and T1ρquantification using MR Multitasking: Method and initial clinical experience in tissue characterization of multiple sclerosis

“…In addition, it has been shown that IR-TSE could lead to T1 underestimation in the brain compared to the traditional "gold standard" IR-SE. 28 Second, T2-preparations might lead to T2 underestimation due to B1 inhomogeneities, 33,69 while ME-SE was likely to cause T2 overestimation due to stimulated echo contamination. 70 Last, reference T1ρ mapping was subject to T1 contamination during the FLASH readouts despite the implementation of two-shot acquisition to allow fewer phase encoding lines per shot, which could also be the reason of blurriness in reference T1ρ maps but could be improved in future works by either implementing centric readout mode in both phase and partition encoding directions or using more shots at the expense of a longer scan time.…”

Magnetic resonance multitasking for multidimensional assessment of cardiovascular system: Development and feasibility study on the thoracic aorta

“…Currently, there are various low‐rank strategies available for reconstruction of the multidimensional arrays, either implicitly or explicitly 21,25‐29 . Magnetic resonance multitasking, based on the description of previous works, 21‐22,30,31 uses a mixed strategy that reconstructs the image tensor by directly recovering each of its factor matrices. Basically, in this work, image reconstruction can be divided into five steps:…”

“… Generate ungated images, which are reconstructed using explicit low‐rank matrix imaging with only one time dimension representing elapsed time, 21,22 for image‐based cardiac phase and respiratory position identification by means of a modified T 1 recovery–aware k ‐means clustering approach, 21 placing the corresponding images into 14 cardiac bins and 6 respiratory bins. Predetermine the temporal basis functions in (along the inversion‐recovery dimension) from a training dictionary of inversion‐recovery signals with different T 1 , T 2 , and B 1 inhomogeneity values, which is generated according to the Bloch equations ahead of time 21,31 Apply small‐scale low‐rank tensor completion to recover missing elements from a frequently sampled subset of k‐space (“auxiliary data”), which will be undersampled because it is impossible to acquire every combination of cardiac phase, respiratory phase, and inversion‐recovery time point: where is the collected auxiliary data; represents the undersampling pattern of the auxiliary data set; denotes the mode‐n flattening of the completed auxiliary tensor; and is the nuclear norm that promotes low‐rankness of each unfolded matrix.…”

“…After the FLASH readouts, a short gap of fixed duration allows T 1 recovery toward thermal equilibrium. Both phase and partition encodings for the imaging data were collected with randomized ordering according to a variable‐density Gaussian distribution, to achieve incoherent undersampling of the k‐space 30,31 . The auxiliary data collected at the center k‐space ( = = 0) was interleaved with the imaging data every 9 readouts.…”

“…Thus, our image tensor  can be expressed in matrix form as where I (1) denotes mode-1 unfolding of the tensor  into a matrix; the factor matrix U x contains basis images; C (1) denotes the unfolded core tensor; V t c , W t r , and Z t T1 contain temporal basis functions for each time dimension; and the ⊗ operator denotes the Kronecker product.24 Currently, there are various low-rank strategies available for reconstruction of the multidimensional arrays, either implicitly or explicitly. 21,[25][26][27][28][29] Magnetic resonance multitasking, based on the description of previous works, [21][22]30,31 uses a mixed strategy that reconstructs the image tensor by directly recovering each of its factor matrices. Basically, in this work, image reconstruction can be divided into five steps:…”