A multi-scale residual network for accelerated radial MR parameter mapping

A deep learning MR parameter mapping framework which combines accelerated radial data acquisition with a multi-scale residual network (MS-ResNet) for image reconstruction is proposed. The proposed supervised learning strategy uses input image patches from multi-contrast images with radial undersampling artifacts and target image patches from artifact-free multi-contrast images. Subspace filtering is used during pre-processing to denoise input patches. For each anatomy and relaxation parameter, an individual network is trained. in vivo T₁ mapping results are obtained on brain and abdomen datasets and in vivo T₂ mapping results are obtained on brain and knee datasets. Quantitative results for the T₂ mapping of the knee show that MS-ResNet trained using either fully sampled or undersampled data outperforms conventional model-based compressed sensing methods. This is significant because obtaining fully sampled training data is not possible in many applications. in vivo brain and abdomen results for T₁ mapping and in vivo brain results for T₂ mapping demonstrate that MS-ResNet yields contrast-weighted images and parameter maps that are comparable to those achieved by model-based iterative methods while offering two orders of magnitude reduction in reconstruction times. The proposed approach enables recovery of high-quality contrast-weighted images and parameter maps from highly accelerated radial data acquisitions. The rapid image reconstructions enabled by the proposed approach makes it a good candidate for routine clinical use.     

High-performance rapid MR parameter mapping using model-based deep adversarial learning

PurposeTo develop and evaluate a deep adversarial learning-based image reconstruction approach for rapid and efficient MR parameter mapping.MethodsThe proposed method provides an image reconstruction framework by combining the end-to-end convolutional neural network (CNN) mapping, adversarial learning, and MR physical models. The CNN performs direct image-to-parameter mapping by transforming a series of undersampled images directly into MR parameter maps. Adversarial learning is used to improve image sharpness and enable better texture restoration during the image-to-parameter conversion. An additional pathway concerning the MR signal model is added between the estimated parameter maps and undersampled k-space data to ensure the data consistency during network training. The proposed framework was evaluated on T₂ mapping of the brain and the knee at an acceleration rate R = 8 and was compared with other state-of-the-art reconstruction methods. Global and regional quantitative assessments were performed to demonstrate the reconstruction performance of the proposed method.ResultsThe proposed adversarial learning approach achieved accurate T₂ mapping up to R = 8 in brain and knee joint image datasets. Compared to conventional reconstruction approaches that exploit image sparsity and low-rankness, the proposed method yielded lower errors and higher similarity to the reference and better image sharpness in the T₂ estimation. The quantitative metrics were normalized root mean square error of 3.6% for brain and 7.3% for knee, structural similarity index of 85.1% for brain and 83.2% for knee, and tenengrad measures of 9.2% for brain and 10.1% for the knee. The adversarial approach also achieved better performance for maintaining greater image texture and sharpness in comparison to the CNN approach without adversarial learning.ConclusionThe proposed framework by incorporating the efficient end-to-end CNN mapping, adversarial learning, and physical model enforced data consistency is a promising approach for rapid and efficient reconstruction of quantitative MR parameters.     

On the R2⁎ relaxometry in complex multi-peak multi-Echo chemical shift-based water-fat quantification: Applications to the neuromuscular diseases

PurposeInvestigation of the feasibility of the R₂^⁎ mapping techniques by using latest theoretical models corrected for confounding factors and optimized for signal to noise ratio.Theory and methodsThe improvement of the performance of state of the art magnetic resonance imaging (MRI) relaxometry algorithms is challenging because of a non-negligible bias and still unresolved numerical instabilities. Here, R₂^⁎ mapping reconstructions, including complex fitting with multi-spectral fat-correction by using single-decay and double-decay formulation, are deeply studied in order to investigate and identify optimal configuration parameters and minimize the occurrence of numerical artifacts. The effects of echo number, echo spacing, and fat/water relaxation model type are evaluated through both simulated and in-vivo data. We also explore the stability and feasibility of the fat/water relaxation model by analyzing the impact of high percentage of fat infiltrations and local transverse relaxation differences among biological species.ResultsThe main limits of the MRI relaxometry are the presence of bias and the occurrence of artifacts, which significantly affect its accuracy. Chemical-shift complex R₂^⁎-correct single-decay reconstructions exhibit a large bias in presence of a significant difference in the relaxation rates of fat and water and with fat concentration larger than 30%. We find that for fat-dominated tissues or in patients affected by extensive iron deposition, MRI reconstructions accounting for multi-exponential relaxation time provide accurate R₂^⁎ measurements and are less prone to numerical artifacts.ConclusionsComplex fitting and fat-correction with multi-exponential decay formulation outperforms the conventional single-decay approximation in various diagnostic scenarios. Although it still lacks of numerical stability, which requires model enhancement and support from spectroscopy, it offers promising perspectives for the development of relaxometry as a reliable tool to improve tissue characterization and monitoring of neuromuscular disorders.     

A non-invasive technique for 3-dimensional assessment of articular cartilage thickness based on MRI part 2: Validation using CT arthrography

Established methods for the measurement of articular cartilage thickness are invasive and cannot be sequentially applied in living subjects. In the present study, the distribution of cartilage thickness throughout entire joint surfaces was determined from MR images obtained with a fat-suppressed gradient-echo sequence at a resolution of 0.31 × 0.31 × 2.00 mm³, and compared to that derived from CT arthrography. A minimal distance algorithm was employed to produce 3D cartilage thickness maps of seven cadaveric human knee joints. The mean amount of deviation of the cartilage volumes was 5.6% (±4.6), statistical analysis showing that there was high agreement between the two methods (r = 0.995, slope = 1.037, y-intercept = -90.5 mm³). The 3D thickness maps yielded a striking agreement between the two methods, the maximum values generally yielding a deviation of none or one thickness interval of 0.5 mm. This investigation shows that accurate 3D assessment of articular cartilage thickness can be performed with MRI, this technique having the advantage that it is suitable for investigating living subjects.     

Four-angle method for practical ultra-high-resolution magnetic resonance mapping of brain longitudinal relaxation time and apparent proton density

Changes in longitudinal relaxation time (T₁) and proton density (PD) are sensitive indicators of microstructural alterations associated with various central nervous system diseases as well as brain maturation and aging. In this work, we introduce a new approach for rapid and accurate high-resolution (HR) or ultra HR (UHR) mapping of T₁ and apparent PD (APD) of the brain with correction of radiofrequency field, B₁, inhomogeneities. The four-angle method (FAM) uses four spoiled-gradient recalled-echo (SPGR) images acquired at different flip angles (FA) and short repetition times (TRs). The first two SPGR images are acquired at low-spatial resolution and used to accurately map the active B₁⁺ field with the recently introduced steady-state double angle method (SS-DAM). The estimated B₁⁺ map is used in conjunction with the two other SPGR images, acquired at HR or UHR, to map T₁ and APD. The method is evaluated with numerical, phantom, and in-vivo imaging measurements. Furthermore, we investigated imaging acceleration methods to further shorten the acquisition time. Our results indicate that FAM provides an accurate method for simultaneous HR or UHR mapping of T₁ and APD in human brain in clinical high-field MRI. Derived parameter maps without B₁⁺correction suffer from large inaccuracies, but this issue is well-corrected through use of the SS-DAM. Furthermore, the use of SPGR imaging with short TR and phased-array coil acquisition permits substantial imaging acceleration and enables robust HR or UHR T₁ and APD mapping in a clinically acceptable time frame, with whole brain coverage obtained in less than 2 min or 5 min, respectively. The method exhibits high reproducibility and benefits from the use of the conventional SPGR sequence, available in all preclinical and clinical MRI machines, and very simple modeling to address a critical outstanding issue in neuroimaging.     

Learning-based compressed sensing for infrared image super resolution

This paper presents an infrared image super-resolution method based on compressed sensing (CS). First, the reconstruction model under the CS framework is established and a Toeplitz matrix is selected as the sensing matrix. Compared with traditional learning-based methods, the proposed method uses a set of sub-dictionaries instead of two coupled dictionaries to recover high resolution (HR) images. And Toeplitz sensing matrix allows the proposed method time-efficient. Second, all training samples are divided into several feature spaces by using the proposed adaptive k-means classification method, which is more accurate than the standard k-means method. On the basis of this approach, a complex nonlinear mapping from the HR space to low resolution (LR) space can be converted into several compact linear mappings. Finally, the relationships between HR and LR image patches can be obtained by multi-sub-dictionaries and HR infrared images are reconstructed by the input LR images and multi-sub-dictionaries. The experimental results show that the proposed method is quantitatively and qualitatively more effective than other state-of-the-art methods.     

Simultaneous quantification of SPIO and gadolinium contrast agents using MR fingerprinting

PurposeDevelop a magnetic resonance fingerprinting (MRF) methodology with R₂¹ quantification, intended for use with simultaneous contrast agent concentration mapping, particularly gadolinium (Gd) and iron labelled CD8+ T cells.MethodsVariable-density spiral SSFP MRF was used, modified to allow variable TE, and with an exp.(−TE·R₂¹) dictionary modulation. In vitro phantoms containing SPIO labelled cells and/or gadolinium were used to validate parameter maps, probe undersampling capacity, and verify dual quantification capabilities. A C57BL/6 mouse was imaged using MRF to demonstrate acceptable in vivo resolution and signal at 8× undersampling necessary for a 25-min scan.ResultsStrong agreement was found between conventional and MRF-derived values for R₁, R₂, and R₂¹. Expanded MRF allowed quantification of iron-loaded CD8+ T cells. Results were robust to 8× undersampling and enabled recreation of relaxation profiles for both a Gd agent and iron labelled cells simultaneously. In vivo data demonstrated sufficient SNR in undersampled data for parameter mapping to visualise key features.ConclusionMRF can be expanded to include R₁, R₂, and R₂¹ mapping required for simultaneous quantification of gadolinium and SPIO in vitro, allowing for potential implementation of a variety of future in vivo studies using dual MR contrast agents, including molecular imaging of labelled cells.     

Magnetic resonance T1ρ imaging of osteoarthritis: a rabbit ACL transection model

Objective

The objective of this study was to develop quantitative T_1ρ-weighted magnetic resonance imaging methodology for the detection and characterization of cartilage degeneration in a rabbit anterior cruciate ligament (ACL) transection model.

Methods

The right knee ACLs of 18 adult female New Zealand white rabbits were transected. The left knee joint served as a sham control. The rabbits were euthanized at 3 (Group 1), 6 (Group 2) and 12 (Group 3) weeks postoperatively. High-resolution 3D fat-saturated spoiled gradient echo images and T_1ρ-weighted images were obtained in both the sagittal and axial planes at 3 T using a quadrature wrist coil. Following MR analysis, histological slides from the lateral femoral condyle cartilage were graded using the Mankin grading system.

Results

For all three groups, the average overall T_1ρ values were significantly higher in the ACL-transected knee compared to control knee, and the percentage differences in T_1ρ values between ACL-transected and control increased with the duration of time after transection. The average Mankin score for ACL-transected knees was higher than that for control for each time point, but this difference was statistically significant only for all groups combined.

Conclusions

This study demonstrates the feasibility of using T_1ρ-weighted imaging as a useful tool in the detection and quantification of cartilage damage in all knee compartments in an ACL-transected rabbit model of cartilage degeneration.     

Examiner repeatability of patellar cartilage T2 values

Aim

The purpose of this study was to evaluate the intra- and interexaminer resegmentation precision of patellar cartilage T₂ mapping measurements in healthy subjects.

Materials and Methods

T₂-weighted images of patellar cartilage for 10 subjects were acquired. Two individuals manually segmented patellar cartilage at each slice location twice, once on each of two separate days. Bulk average and zonal T₂ values for the superficial, middle, and deep layers of cartilage were calculated. The root mean square (RMS) and coefficient of variation (COV) were calculated using the repeated measurements of each slice of each subject by each examiner.

Results

The intraexaminer bulk T₂ differences were 0.2±1.0 ms, with an RMS error of 0.7 ms and a COV of 1.9%. The differences of interexaminer bulk T₂ values was 1.0±1.4 ms, with an RMS error of 1.2 ms and a COV of 3.3%. The superficial zone of cartilage had the highest zonal variability of T₂ values. The average interexaminer T₂ values for the superficial, middle and deep zones were 42.2±5.6, 38.1±5.3 and 31.9±4.6 ms, respectively.

Conclusion

The interexaminer variability of calculated T₂ values highlights the difficulty of interpreting significant differences of T₂ values which are similar in magnitude. The repeatability measurements of patellar cartilage T₂ values were less than reported intersession T₂ repeatability.     

Feasibility of T1 mapping with histogram analysis for the diagnosis and staging of liver fibrosis: Preclinical results

PurposeTo compare the diagnostic accuracy of parameters derived from the histogram analysis of precontrast, 10-min hepatobiliary phase (HBP) and 20-min HBP T1 maps for staging liver fibrosis (LF).MethodsLF was induced in New Zealand white rabbits by subcutaneous injections of carbon tetrachloride for 4–16 weeks (n = 120), and 20 rabbits injected with saline served as controls. Precontrast, 10-min and 20-min HBP modified Look-Locker inversion recovery (MOLLI) T1 mapping was performed. Histogram analysis of T1 maps was performed, and the mean, median, skewness, kurtosis, entropy, inhomogeneity and 10th/25th/75th/90th percentiles of T1_native, T1_10min and T1_20min were derived. Quantitative histogram parameters were compared. For significant parameters, further receiver operating characteristic (ROC) analyses were performed to evaluate the potential diagnostic performance in differentiating LF stages.ResultsFinally, 17, 20, 21, 21 and 20 rabbits were included for the F0, F1, F2, F3, and F4 pathological grades of fibrosis, respectively. The mean/75th of T1_native, entropy of T1_10min and entropy/mean/median/10th of T1_20min demonstrated a significant good correlation with the LF stage (|r| = 0.543–0.866, all P < 0.05). The 75th of T1_native, entropy_10min, and entropy_20min were the three most reliable imaging markers in reflecting the stage of LF. The area under the ROC curve of entropy_20min was larger than that of entropy_10min (P < 0.05 for LF ≥ F2, ≥F3, and ≥ F4) and the 75th of T1_native (P < 0.05 for LF ≥ F2 and ≥ F3) for staging LF.ConclusionMagnetic resonance histogram analysis of T1 maps, particularly the entropy derived from 20-min HBP T1 mapping, is promising for predicting the LF stage.     

Fractional order vs. exponential fitting in UTE MR imaging of the patellar tendon

PurposeQuantification of the T₂¹ relaxation time constant is relevant in various magnetic resonance imaging applications. Mono- or bi-exponential models are typically used to determine these parameters. However, in case of complex, heterogeneous tissues these models could lead to inaccurate results. We compared a model, provided by the fractional-order extension of the Bloch equation with the conventional models.MethodsAxial 3D ultra-short echo time (UTE) scans were acquired using a 3.0 T MRI and a 16-channel surface coil. After image registration, voxel-wise T₂¹ was quantified with mono-exponential, bi-exponential and fractional-order fitting. We evaluated all three models repeatability and the bias of their derived parameters by fitting at various noise levels. To investigate the effect of the SNR for the different models, a Monte-Carlo experiment with 1000 repeats was performed for different noise levels for one subject. For a cross-sectional investigation, we used the mean fitted values of the ROIs in five volunteers.ResultsComparing the mono-exponential and the fractional order T₂¹ maps, the fractional order fitting method yielded enhanced contrast and an improved delineation of the different tissues. In the case of the bi-exponential method, the long T₂¹ component map demonstrated the anatomy clearly with high contrast. Simulations showed a nonzero bias of the parameters for all three mathematical models. ROI based fitting showed that the T₂¹ values were different depending on the applied method, and they differed most for the patellar tendon in all subjects.ConclusionsIn high SNR cases, the fractional order and bi-exponential models are both performing well with low bias. However, in all observed cases, one of the bi-exponential components has high standard deviation in T₂¹. The bi-exponential model is suitable for T₂¹ mapping, but we recommend using the fractional order model for cases of low SNR.     

Deriving new soft tissue contrasts from conventional MR images using deep learning

Versatile soft tissue contrast in magnetic resonance imaging is a unique advantage of the imaging modality. However, the versatility is not fully exploited. In this study, we propose a deep learning-based strategy to derive more soft tissue contrasts from conventional MR images obtained in standard clinical MRI. Two types of experiments are performed. First, MR images corresponding to different pulse sequences are predicted from one or more images already acquired. As an example, we predict T_1ρ weighted knee image from T₂ weighted image and/or T₁ weighted image. Furthermore, we estimate images corresponding to alternative imaging parameter values. In a representative case, variable flip angle images are predicted from a single T₁ weighted image, whose accuracy is further validated in quantitative T₁ map subsequently derived. To accomplish these tasks, images are retrospectively collected from 56 subjects, and self-attention convolutional neural network models are trained using 1104 knee images from 46 subjects and tested using 240 images from 10 other subjects. High accuracy has been achieved in resultant qualitative images as well as quantitative T₁ maps. The proposed deep learning method can be broadly applied to obtain more versatile soft tissue contrasts without additional scans or used to normalize MR data that were inconsistently acquired for quantitative analysis.     

Evaluation of B0-correction of relative CBF maps using tagging distance dependent Z-spectrum (TADDZ)

Arterial spin labeling (ASL) MRI, based on endogenous contrast from blood water, is used in research and diagnosis of cerebral vascular conditions. However, artifacts due to imperfect imaging conditions such as B₀-inhomogeneity (ΔB₀) could lead to variations in the quantification of relative cerebral blood flow (CBF). In this study, we evaluate a new approach using tagging distance dependent Z-spectrum (TADDZ) data, similar to the ΔB₀ corrections in the chemical exchange saturation transfer (CEST) experiments, to remove the imaging plane B₀ inhomogeneity induced CBF artifacts in ASL MRI. Our results indicate that imaging-plane B₀-inhomogeneity can lead to variations and errors in the relative CBF maps especially under small tagging distances. Along with an acquired B₀ map, TADDZ data helps to eliminate B₀-inhomogeneity induced artifacts in the resulting relative CBF maps. We demonstrated the effective use of TADDZ data to reduce variation while subjected to systematic changes in ΔB₀. In addition, TADDZ corrected ASL MRI, with improved consistency, was shown to outperform conventional ASL MRI by differentiating the subtle CBF difference in Alzheimer's disease (AD) mice brains with different APOE genotypes.     

Assessment of the patellofemoral cartilage: Correlation of knee pain score with magnetic resonance cartilage grading and magnetization transfer ratio asymmetry of glycosaminoglycan chemical exchange saturation transfer

PurposeBiochemical imaging of glycosaminoglycan chemical exchange saturation transfer (gagCEST) could predict the depletion of glycosaminoglycans (GAG) in early osteoarthritis. The purpose of this study was to evaluate the relationship between the magnetization transfer ratio asymmetry (MTR_asym) of gagCEST images and visual analog scale (VAS) pain scores in the knee joint.Materials and methodsThis retrospective study was approved by the institutional review board. A phantom study was performed using hyaluronic acid to validate the MTR_asym values of gagCEST images. Knee magnetic resonance (MR) images of 22 patients (male, 9; female, 13; mean age, 50.3 years; age range; 25–79 years) with knee pain were included in this study. The MR imaging (MRI) protocol involved standard knee MRI as well as gagCEST imaging, which allowed region-of-interest analyses of the patellar facet and femoral trochlea. The MTR_asym at 1.0 ppm was calculated at each region. The cartilages of the patellar facets and femoral trochlea were graded according to the Outerbridge classification system. Data regarding the VAS scores of knee pain were collected from the electronic medical records of the patients. Statistical analysis was performed using Spearman's correlation.ResultsThe results of the phantom study revealed excellent correlation between the MTR_asym values and the concentration of GAGs (r = 0.961; p = 0.003). The cartilage grades on the MR images showed significant negative correlation with the MTR_asym values in the patellar facet and femoral trochlea (r = −0.460; p = 0.031 and r = −0.543; p = 0.009, respectively). The VAS pain scores showed significant negative correlation with the MTR_asym values in the patellar facet and femoral trochlea (r = −0.435; p = 0.043 and r = −0.671; p = 0.001, respectively).ConclusionThe pain scores were associated with the morphological and biochemical changes in articular cartilages visualized on knee MR images. The biochemical changes, visualized in terms of the MTR_asym values of the gagCEST images, exhibited greater correlation with the pain scores than the morphological changes visualized on conventional MR images; these results provide evidence supporting the theory regarding the association of patellofemoral osteoarthritis with knee pain scores.     

MRI magic-angle effect in femorotibial cartilages of the red kangaroo

ObjectiveKangaroo knee cartilages are robust tissues that can support knee flexion and endure high levels of compressive stress. This study aimed to develop a detailed understanding of the collagen architecture in kangaroo knee cartilages and thus obtain insights into the biophysical basis of their function.DesignCylindrical/square plugs from femoral and tibial hyaline cartilage and tibial fibrocartilage were excised from the knees of three adult red kangaroos. Multi-slice, multi-echo MR images were acquired at the sample orientations 0° and 55° (“magic angle”) with respect to the static magnetic field. Maps of the transverse relaxation rate constant (R₂) and depth profiles of R₂ and its anisotropic component (R₂^A) were constructed from the data.ResultsThe R₂^A profiles confirmed the classic three-zone organisation of all cartilage samples. Femoral hyaline cartilage possessed a well-developed, thick superficial zone. Tibial hyaline cartilage possessed a very thick radial zone (80% relative thickness) that exhibited large R₂^A values consistent with highly ordered collagen. The R₂^A profile of tibial fibrocartilage exhibited a unique region near the bone (bottom 5–10%) consistent with elevated proteoglycan content (“attachment sub-zone”).ConclusionsOur observations suggest that the well-developed superficial zone of femoral hyaline cartilage is suitable for supporting knee flexion; the thick and well-aligned radial zone of tibial hyaline cartilage is adapted to endure high compressive stress; while the innermost part of the radial zone of tibial fibrocartilage may facilitate anchoring of the collagen fibres to withstand high shear deformation. These findings may inspire new designs for cartilage tissue engineering.     

Mixture-Based Probabilistic Graphical Models for the Label Ranking Problem

The goal of the Label Ranking (LR) problem is to learn preference models that predict the preferred ranking of class labels for a given unlabeled instance. Different well-known machine learning algorithms have been adapted to deal with the LR problem. In particular, fine-tuned instance-based algorithms (e.g., k-nearest neighbors) and model-based algorithms (e.g., decision trees) have performed remarkably well in tackling the LR problem. Probabilistic Graphical Models (PGMs, e.g., Bayesian networks) have not been considered to deal with this problem because of the difficulty of modeling permutations in that framework. In this paper, we propose a Hidden Naive Bayes classifier (HNB) to cope with the LR problem. By introducing a hidden variable, we can design a hybrid Bayesian network in which several types of distributions can be combined: multinomial for discrete variables, Gaussian for numerical variables, and Mallows for permutations. We consider two kinds of probabilistic models: one based on a Naive Bayes graphical structure (where only univariate probability distributions are estimated for each state of the hidden variable) and another where we allow interactions among the predictive attributes (using a multivariate Gaussian distribution for the parameter estimation). The experimental evaluation shows that our proposals are competitive with the start-of-the-art algorithms in both accuracy and in CPU time requirements.     

Rapid high-resolution volumetric T1 mapping using a highly accelerated stack-of-stars Look Locker technique

PurposeTo develop a fast volumetric T₁ mapping technique.Materials and methodsA stack-of-stars (SOS) Look Locker technique based on the acquisition of undersampled radial data (>30× relative to Nyquist) and an efficient multi-slab excitation scheme is presented. A principal-component based reconstruction is used to reconstruct T₁ maps. Computer simulations were performed to determine the best choice of partitions per slab and degree of undersampling. The technique was validated in phantoms against reference T₁ values measured with a 2D Cartesian inversion-recovery spin-echo technique. The SOS Look Locker technique was tested in brain (n = 4) and prostate (n = 5). Brain T₁ mapping was carried out with and without k_z acceleration and results between the two approaches were compared. Prostate T₁ mapping was compared to standard techniques. A reproducibility study was conducted in brain and prostate. Statistical analyses were performed using linear regression and Bland Altman analysis.ResultsPhantom T₁ values showed excellent correlations between SOS Look Locker and the inversion-recovery spin-echo reference (r² = 0.9965; p < 0.0001) and between SOS Look Locker with slab-selective and non-slab selective inversion pulses (r² = 0.9999; p < 0.0001). In vivo results showed that full brain T₁ mapping (1 mm³) with k_z acceleration is achieved in 4 min 21 s. Full prostate T₁ mapping (0.9 × 0.9 × 4 mm³) is achieved in 2 min 43 s. T₁ values for brain and prostate were in agreement with literature values. A reproducibility study showed coefficients of variation in the range of 0.18–0.2% (brain) and 0.15–0.18% (prostate).ConclusionA rapid volumetric T₁ mapping technique was developed. The technique enables high-resolution T₁ mapping with adequate anatomical coverage in a clinically acceptable time.     

Targeted radiofrequency field mapping using 3D reduced field-of-view-catalyzed double-angle method

Purpose

The aim of this study was to develop a targeted volumetric radiofrequency field (B₁⁺) mapping technique to provide region-of-interest B₁⁺ information.

Materials and Methods

Targeted B₁⁺ maps were acquired using three-dimensional (3D) reduced field-of-view (FOV) inner-volume turbo spin echo-catalyzed double-angle method (DAM). Targeted B₁⁺ maps were compared with full-FOV B₁⁺ maps acquired using 3D catalyzed DAM in a phantom and in the brain of a healthy volunteer. In addition, targeted volumetric abdomeninal B₁⁺ mapping was demonstrated in the abdomen of another healthy volunteer.

Results

The targeted reduced-FOV images demonstrated no aliasing artifacts in all experiments. Close match between targeted B₁⁺ map and reference full-FOV B₁⁺ map in the same region was observed, with percentage root-mean-squared error <0.4% in the phantom and <0.8% in the healthy volunteer brain. The abdominal B₁⁺ maps showed small B₁⁺ variation in the kidneys and liver from the healthy volunteer.

Conclusion

The proposed 3D reduced-FOV catalyzed DAM provides a rapid, simple and accurate method for targeted volumetric B₁⁺ mapping and can be easily implemented for applications related to radiofrequency field mapping in small targeted regions.     

Smoothly Clipped Absolute Deviation (SCAD) regularization for compressed sensing MRI Using an augmented Lagrangian scheme

PurposeCompressed sensing (CS) provides a promising framework for MR image reconstruction from highly undersampled data, thus reducing data acquisition time. In this context, sparsity-promoting regularization techniques exploit the prior knowledge that MR images are sparse or compressible in a given transform domain. In this work, a new regularization technique was introduced by iterative linearization of the non-convex smoothly clipped absolute deviation (SCAD) norm with the aim of reducing the sampling rate even lower than it is required by the conventional l₁ norm while approaching an l₀ norm.Materials and MethodsThe CS-MR image reconstruction was formulated as an equality-constrained optimization problem using a variable splitting technique and solved using an augmented Lagrangian (AL) method developed to accelerate the optimization of constrained problems. The performance of the resulting SCAD-based algorithm was evaluated for discrete gradients and wavelet sparsifying transforms and compared with its l₁-based counterpart using phantom and clinical studies. The k-spaces of the datasets were retrospectively undersampled using different sampling trajectories. In the AL framework, the CS-MRI problem was decomposed into two simpler sub-problems, wherein the linearization of the SCAD norm resulted in an adaptively weighted soft thresholding rule with a sparsity enhancing effect.ResultsIt was demonstrated that the proposed regularization technique adaptively assigns lower weights on the thresholding of gradient fields and wavelet coefficients, and as such, is more efficient in reducing aliasing artifacts arising from k-space undersampling, when compared to its l₁-based counterpart.ConclusionThe SCAD regularization improves the performance of l₁-based regularization technique, especially at reduced sampling rates, and thus might be a good candidate for some applications in CS-MRI.