Faculty Bibliography

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:

PURPOSE/OBJECTIVE:-RAVE) and to evaluate the multi relaxation components in the liver of healthy controls and chronic liver disease (CLD) patients. METHODS:components among patients (n = 3) and a control group (n = 10). RESULTS:relaxation time measurement relative to the reference on 2 different scanners. The coefficient of variation for test-retest scans performed on the same scanner was 5.7% and 2.4% for scans performed on 2 scanners. The comparison between healthy controls and CLD patients showed a significant difference (P < .05) in mono relaxation time (P = .002), stretched-exponential relaxation parameter (P = .04). The Akaike information criteria C criterion showed 2.53 ± 0.9% (2.3 ± 0.3% for CLD) of the voxels are bi-exponential while in 65.3 ± 5.8% (81.2 ± 0.06% for CLD) of the liver voxels, the stretched-exponential model was preferred. CONCLUSION/CONCLUSIONS:assessment of the liver during free breathing and can distinguish between healthy volunteers and CLD patients.

NMR relaxometry can provide information about the relaxation of the magnetization in different tissues, increasing our understanding of molecular dynamics and biochemical composition in biological systems. In general, tissues have complex and heterogeneous structures composed of multiple pools. As a result, bulk magnetization returns to its original state with different relaxation times, in a multicomponent relaxation. Recovering the distribution of relaxation times in each voxel is a difficult inverse problem; it is usually unstable and requires long acquisition time, especially on clinical scanners. MRI can also be viewed as an inverse problem, especially when compressed sensing (CS) is used. The solution of these two inverse problems, CS and relaxometry, can be obtained very efficiently in a synergistically combined manner, leading to a more stable multicomponent relaxometry obtained with short scan times. In this paper, we will discuss the details of this technique from the viewpoint of inverse problems.

PURPOSE/OBJECTIVE:) mapping of knee cartilage. METHODS:-mapping of knee cartilage, including spatio-temporal finite differences, wavelets, dictionary from principal component analysis, and exponential decay models, and also low rank and low rank plus sparse models (L+S). Complex-valued fitting was used and Marchenko-Pastur principal component analysis filtering also tested. RESULTS:mapping, with median normalized absolute deviation below 10% up to AF of 6. CONCLUSION/CONCLUSIONS:mapping of knee cartilage, being it is a good alternative to Cartesian sampling for reducing scan time and/or improving image and mapping quality. The methods exponential decay models, spatio-temporal finite differences, and low rank obtained the best results for radial sampling patterns.

BACKGROUND:Quantification of dynamic biomechanical strain in articular cartilage in vivo; in situ using noninvasive MRI techniques is desirable and may potentially be used to assess joint pathology. PURPOSE/OBJECTIVE:To demonstrate the use of static mechanical loading and continuous 3D-MRI acquisition of the human knee joint in vivo to measure the strain in the tibiofemoral articular cartilage. STUDY TYPE/METHODS:Prospective. SUBJECTS/METHODS:Five healthy human volunteers (four women, one man; age 25.6 ± 1.7) underwent MRI at rest, under static mechanical loading condition, and during recovery. FIELD STRENGTH/SEQUENCE/UNASSIGNED:A field strength of 3T was used. The sequence used was 3D-continuous golden angle radial sparse parallel (GRASP) MRI and compressed sensing (CS) reconstruction. ASSESSMENT/RESULTS:Tibiofemoral cartilage deformation maps under loading and during recovery were calculated using an optical flow algorithm. The corresponding Lagrangian strain was calculated in the articular cartilage. STATISTICAL TESTS/UNASSIGNED:Range of displacement and strain in each subject, and the resulting mean and standard deviation, were calculated. RESULTS:During the loading condition, the cartilage displacement in the direction of loading ranged from a minimum of -673.6 ± 121.9 μm to a maximum of 726.5 ± 169.5 μm. Corresponding strain ranged from a minimum of -7.0 ± 4.2% to a maximum of 5.4 ± 1.6%. During the recovery condition, the cartilage displacement in the same direction reduced to a minimum of -613.0 ± 129.5 μm and a maximum of 555.7 ± 311.4 μm. The corresponding strain range reduced to a minimum of -1.6 ± 7.5% to a maximum of 4.2 ± 2.6%. DATA CONCLUSION/UNASSIGNED:This study shows the feasibility of using static mechanical loading with continuous GRASP-MRI acquisition to measure the strain in the articular cartilage. By measuring strain during the loading and recovery phases, dynamic strain information in the articular cartilage might be able to be investigated. LEVEL OF EVIDENCE/METHODS:2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019.

An improvement of the monotone fast iterative shrinkage-thresholding algorithm (MFISTA) for faster convergence is proposed. Our motivation is to reduce the reconstruction time of compressed sensing problems in magnetic resonance imaging. The proposed modification introduces an extra term, which is a multiple of the proximal-gradient step, into the so-called momentum formula used for the computation of the next iterate in MFISTA. In addition, the modified algorithm selects the next iterate as a possibly-improved point obtained by any other procedure, such as an arbitrary shift, a line search, or other methods. As an example, an arbitrary-length shift in the direction from the previous iterate to the output of the proximal-gradient step is considered. The resulting algorithm accelerates MFISTA in a manner that varies with the iterative steps. Convergence analysis shows that the proposed modification provides improved theoretical convergence bounds, and that it has more flexibility in its parameters than the original MFISTA. Since such problems need to be studied in the context of functions of several complex variables, a careful extension of FISTA-like methods to complex variables is provided.