Faculty Bibliography

This work proposes an alternating learning approach to learn the sampling pattern (SP) and the parameters of variational networks (VN) in accelerated parallel magnetic resonance imaging (MRI). We investigate four variations of the learning approach, that alternates between improving the SP, using bias-accelerated subset selection, and improving parameters of the VN, using ADAM. The variations include the use of monotone or non-monotone alternating steps and systematic reduction of learning rates. The algorithms learn an effective pair to be used in future scans, including an SP that captures fewer k-space samples in which the generated undersampling artifacts are removed by the VN reconstruction. The quality of the VNs and SPs obtained by the proposed approaches is compared against different methods, including other kinds of joint learning methods and state-of-art reconstructions, on two different datasets at various acceleration factors (AF). We observed improvements visually and in three different figures of merit commonly used in deep learning (RMSE, SSIM, and HFEN) on AFs from 2 to 20 with brain and knee joint datasets when compared to the other approaches. The improvements ranged from 1% to 62% over the next best approach tested with VNs. The proposed approach has shown stable performance, obtaining similar learned SPs under different initial training conditions. We observe that the improvement is not only due to the learned sampling density, it is also due to the learned position of samples in k-space. The proposed approach was able to learn effective pairs of SPs and reconstruction VNs, improving 3D Cartesian accelerated parallel MRI applications.

Quantitative MRI can detect early biochemical changes in cartilage, but its bilateral use in clinical routines is challenging. The aim of this prospective study was to demonstrate the feasibility of magnetic resonance fingerprinting for bilateral simultaneous T₁ , T₂ , and T_1ρ mapping of the hip joint. The study population consisted of six healthy volunteers with no known trauma or pain in the hip. Monoexponential T₁ , T₂ , and T_1ρ relaxation components were assessed in femoral lateral, superolateral, and superomedial, and inferior, as well as acetabular, superolateral, and superomedial subregions in left and right hip cartilage. Aligned ranked nonparametric factorial analysis was used to assess the side's impact on the subregions. Kruskal-Wallis and Wilcoxon tests were used to compare subregions, and coefficient of variation to assess repeatability. Global averages of T₁ (676.0 ± 45.4 and 687.6 ± 44.5 ms), T₂ (22.5 ± 2.6 and 22.1 ± 2.5 ms), and T_1ρ (38.2 ± 5.5 and 38.2 ± 5.5 ms) were measured in the left and right hip, and articular cartilage, respectively. The Kruskal-Wallis test showed a significant difference between different subregions' relaxation times regardless of the hip side (p < 0.001 for T₁ , p = 0.012 for T₂ , and p < 0.001 for T_1ρ ). The Wilcoxon test showed that T₁ of femoral layers was significantly (p < 0.003) higher than that for acetabular cartilage. The experiments showed excellent repeatability with CV_rms of 1%, 2%, and 4% for T₁ , T₂ , and T_1ρ, respectively. It was concluded that bilateral T₁ , T₂ , and T_1ρ relaxation times, as well as B₁ ⁺ maps, can be acquired simultaneously from hip joints using the proposed MRF sequence.

In this study, a fast data-driven optimization approach, named bias-accelerated subset selection (BASS), is proposed for learning efficacious sampling patterns (SPs) with the purpose of reducing scan time in large-dimensional parallel MRI. BASS is applicable when Cartesian fully-sampled k-space measurements of specific anatomy are available for training and the reconstruction method for undersampled measurements is specified; such information is used to define the efficacy of any SP for recovering the values at the non-sampled k-space points. BASS produces a sequence of SPs with the aim of finding one of a specified size with (near) optimal efficacy. BASS was tested with five reconstruction methods for parallel MRI based on low-rankness and sparsity that allow a free choice of the SP. Three datasets were used for testing, two of high-resolution brain images ([Formula: see text]-weighted images and, respectively, [Formula: see text]-weighted images) and another of knee images for quantitative mapping of the cartilage. The proposed approach has low computational cost and fast convergence; in the tested cases it obtained SPs up to 50 times faster than the currently best greedy approach. Reconstruction quality increased by up to 45% over that provided by variable density and Poisson disk SPs, for the same scan time. Optionally, the scan time can be nearly halved without loss of reconstruction quality. Quantitative MRI and prospective accelerated MRI results show improvements. Compared with greedy approaches, BASS rapidly learns effective SPs for various reconstruction methods, using larger SPs and larger datasets; enabling better selection of sampling-reconstruction pairs for specific MRI problems.

BACKGROUND:Noninvasive measurement of internal dynamic strain can be potentially useful to characterize spine intervertebral disc (IVD) in the setting of injury or degenerative disease. PURPOSE/OBJECTIVE:To develop and demonstrate a noninvasive technique to quantify three-dimensional (3D) internal dynamic strains in the IVD using a combination of static mechanical loading of the IVD using a magnetic resonance imaging (MRI)-compatible ergometer. STUDY TYPE/METHODS:Prospective. SUBJECTS/METHODS:Silicone gel phantom studies were conducted to assess strain variation with load and repeatability. Mechanical testing was done on the phantoms to confirm MR results. Eight healthy human volunteers (four men and four woman, age = 29 ± 5 years) underwent MRI using a rest, static loading, and recovery paradigm. Repeatability tests were conducted in three subjects. FIELD STRENGTH/SEQUENCE/UNASSIGNED:MRI (3 T) with 3D continuous golden-angle radial sparse parallel (GRASP) and compressed sensing (CS) reconstruction. ASSESSMENT/RESULTS:CS reconstruction of the images, motion deformation, and Lagrangian strain maps were calculated for five IVD segments from L1/L2 to L5/S1. STATISTICAL TESTS/UNASSIGNED:Ranges of displacement and strain in each subject and the resulting mean and standard deviation were calculated. Student t-tests were used to calculate changes in strain from loading to recovery. The correlation coefficient (CC) in the repeatability study was calculated. RESULTS:The most compressive strain experienced by the IVD segments under loaded conditions was in the L4/L5 segment (-7.5 ± 2.9%). The change in minimum strain from load to recovery was the most for the L4/L5 segment (-7.5% to -5.0%, P = 0.026) and the least for the L1/L2 segment (-4.4% to -3.9%, P = 0.51). In vivo repeatability in three subjects shows strong correlation between scans in subjects done 6 months apart, with CCs equal to 0.86, 0.94, and 0.94 along principal directions. DATA CONCLUSION/UNASSIGNED:This study shows the feasibility of using static mechanical loading with continuous GRASP-MRI acquisition with CS reconstruction to measure 3D internal dynamic strains in the spine IVD. LEVEL OF EVIDENCE/METHODS:2 TECHNICAL EFFICACY STAGE: 1.

PURPOSE/OBJECTIVE: METHODS:measured using TB-SL MRF in Bloch simulations, model agar phantoms, and in vivo experiments to those with a self-compensated spin-lock preparation module (SC-SL). The TB-SL MRF repeatability was evaluated in maps acquired in the lower leg skeletal muscle of 12 diabetic peripheral neuropathy patients, scanned two times each during visits separated by about 30 days. RESULTS:= 31.7 ± 3.2 ms in skeletal muscle across patients. Bland-Altman analysis demonstrated low bias between TB-SL and SC-SL MRF and between TB-SL MRF maps acquired in two visits. The coefficient of variation was less than 3% for all measurements. CONCLUSION/CONCLUSIONS:

BACKGROUND:mapping is useful to quantify various neurologic disorders, but data are currently time-consuming to acquire. PURPOSE/OBJECTIVE:mapping of the human brain with acceleration factors (AFs) of 2, 5, and 10. STUDY TYPE/METHODS:Retrospective. SUBJECTS/METHODS:imaging of the whole brain. FIELD STRENGTH/SEQUENCE/UNASSIGNED:preparation module on a clinical 3T scanner. ASSESSMENT/RESULTS:estimation errors were assessed as a function of AF. STATISTICAL TESTS/UNASSIGNED:estimation errors, respectively. Linear regression plots, Bland-Altman plots, and Pearson correlation coefficients (CC) are shown. RESULTS:estimates. DATA CONCLUSION/UNASSIGNED:mapping of the brain. LEVEL OF EVIDENCE/METHODS:2. TECHNICAL EFFICACY STAGE/UNASSIGNED:1.

The Fast Proximal Gradient Method (FPGM) and the Monotone FPGM (MFPGM) for minimization of nonsmooth convex functions are introduced and applied to tomographic image reconstruction. Convergence properties of the sequence of objective function values are derived, including a O(1 / k²) non-asymptotic bound. The presented theory broadens current knowledge and explains the convergence behavior of certain methods that are known to present good practical performance. Numerical experimentation involving computerized tomography image reconstruction shows the methods to be competitive in practical scenarios. Experimental comparison with Algebraic Reconstruction Techniques are performed uncovering certain behaviors of accelerated Proximal Gradient algorithms that apparently have not yet been noticed when these are applied to tomographic image reconstruction.

In this study we use undersampled MRI acquisition methods to obtain accelerated 3D mono and biexponential spin-lattice relaxation time in the rotating frame (T_1ρ) mapping of knee cartilage, reducing the usual long scan time. We compare the accelerated T_1ρ maps obtained by deep learning-based variational network (VN) and compressed sensing (CS). Both methods were compared with spatial (S) and spatio-temporal (ST) filters. Complex-valued fitting was used for T_1ρ parameters estimation. We tested with seven in vivo and six synthetic datasets, with acceleration factors (AF) from 2 to 10. Median normalized absolute deviation (MNAD), analysis of variance (ANOVA), and coefficient of variation (CV) were used for analysis. The methods CS-ST, VN-S, and VN-ST performed well for accelerating monoexponential T_1ρ mapping, with MNAD around 5% for AF = 2, which increases almost linearly with the AF to an MNAD of 13% for AF = 8, with all methods. For biexponential mapping, the VN-ST was the best method starting with MNAD of 7.4% for AF = 2 and reaching MNAD of 13.1% for AF = 8. The VN was able to produce 3D-T_1ρ mapping of knee cartilage with lower error than CS. The best results were obtained by VN-ST, improving CS-ST method by nearly 7.5%.

PURPOSE/OBJECTIVE:To implement a novel technique for simultaneous, quantitative multiparametric mapping of the knee articular cartilage. METHODS:relaxation time (P = .02) in medial femoral cartilage. CONCLUSION/CONCLUSIONS: