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.     

Neural network enhanced 3D turbo spin echo for MR intracranial vessel wall imaging

PurposeTo improve the signal-to-noise ratio (SNR) and image sharpness for whole brain isotropic 0.5 mm three-dimensional (3D) T₁ weighted (T₁w) turbo spin echo (TSE) intracranial vessel wall imaging (IVWI) at 3 T.MethodsThe variable flip angle (VFA) method enables useful optimization across scan efficiency, SNR and relaxation induced point spread function (PSF) for TSE imaging. A convolutional neural network (CNN) was developed to retrospectively enhance the acquired TSE image with PSF blurring. The previously developed VFA method to increase SNR at the expense of blur can be combined with the presented PSF correction to yield long echo train length (ETL) scan while the acquired image remains high SNR and sharp. The overall approach can enable an optimized solution for accelerated whole brain high-resolution 3D T₁w TSE IVWI. Its performance was evaluated on healthy volunteers and patients.ResultsThe PSF blurred image acquired by a long ETL scan can be enhanced by CNN to restore similar sharpness as a short ETL scan, which outperforms the traditional linear PSF enhancement approach. For accelerated whole brain IVWI on volunteers, the optimized isotropic 0.5 mm 3D T₁w TSE sequence with CNN based PSF enhancement provides sufficient flow suppression and improved image quality. Preliminary results on patients further demonstrated its improved delineation for intracranial vessel wall and plaque morphology.ConclusionThe CNN enhanced VFA TSE imaging enables an overall image quality improvement for high-resolution 3D T₁w IVWI, and may provide a better tradeoff across scan efficiency, SNR and PSF for 3D TSE acquisitions.     

A multi-scale variational neural network for accelerating motion-compensated whole-heart 3D coronary MR angiography

PurposeTo enable fast reconstruction of undersampled motion-compensated whole-heart 3D coronary magnetic resonance angiography (CMRA) by learning a multi-scale variational neural network (MS-VNN) which allows the acquisition of high-quality 1.2 × 1.2 × 1.2 mm isotropic volumes in a short and predictable scan time.MethodsEighteen healthy subjects and one patient underwent free-breathing 3D CMRA acquisition with variable density spiral-like Cartesian sampling, combined with 2D image navigators for translational motion estimation/compensation. The proposed MS-VNN learns two sets of kernels and activation functions for the magnitude and phase images of the complex-valued data. For the magnitude, a multi-scale approach is applied to better capture the small calibre of the coronaries. Ten subjects were considered for training and validation. Prospectively undersampled motion-compensated data with 5-fold and 9-fold accelerations, from the remaining 9 subjects, were used to evaluate the framework. The proposed approach was compared to Wavelet-based compressed-sensing (CS), conventional VNN, and to an additional fully-sampled (FS) scan.ResultsThe average acquisition time (m:s) was 4:11 for 5-fold, 2:34 for 9-fold acceleration and 18:55 for fully-sampled. Reconstruction time with the proposed MS-VNN was ~14 s. The proposed MS-VNN achieves higher image quality than CS and VNN reconstructions, with quantitative right coronary artery sharpness (CS:43.0%, VNN:43.9%, MS-VNN:47.0%, FS:50.67%) and vessel length (CS:7.4 cm, VNN:7.7 cm, MS-VNN:8.8 cm, FS:9.1 cm) comparable to the FS scan.ConclusionThe proposed MS-VNN enables 5-fold and 9-fold undersampled CMRA acquisitions with comparable image quality that the corresponding fully-sampled scan. The proposed framework achieves extremely fast reconstruction time and does not require tuning of regularization parameters, offering easy integration into clinical workflow.     

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.     

Free-running cardiac magnetic resonance fingerprinting: Joint T1/T2 map and Cine imaging

PurposeTo develop and evaluate a novel non-ECG triggered 2D magnetic resonance fingerprinting (MRF) sequence allowing for simultaneous myocardial T₁ and T₂ mapping and cardiac Cine imaging.MethodsCardiac MRF (cMRF) has been recently proposed to provide joint T₁/T₂ myocardial mapping by triggering the acquisition to mid-diastole and relying on a subject-dependent dictionary of MR signal evolutions to generate the maps. In this work, we propose a novel “free-running” (non-ECG triggered) cMRF framework for simultaneous myocardial T₁ and T₂ mapping and cardiac Cine imaging in a single scan. Free-running cMRF is based on a transient state bSSFP acquisition with tiny golden angle radial readouts, varying flip angle and multiple adiabatic inversion pulses. The acquired data is retrospectively gated into several cardiac phases, which are reconstructed with an approach that combines parallel imaging, low rank modelling and patch-based high-order tensor regularization. Free-running cMRF was evaluated in a standardized phantom and ten healthy subjects. Comparison with reference spin-echo, MOLLI, SASHA, T₂-GRASE and Cine was performed.ResultsT₁ and T₂ values obtained with the proposed approach were in good agreement with reference phantom values (ICC(A,1) > 0.99). Reported values for myocardium septum T₁ were 1043 ± 48 ms, 1150 ± 100 ms and 1160 ± 79 ms for MOLLI, SASHA and free-running cMRF respectively and for T₂ of 51.7 ± 4.1 ms and 44.6 ± 4.1 ms for T₂-GRASE and free-running cMRF respectively. Good agreement was observed between free-running cMRF and conventional Cine 2D ejection fraction (bias = −0.83%).ConclusionThe proposed free-running cardiac MRF approach allows for simultaneous assessment of myocardial T₁ and T₂ and Cine imaging in a single scan.     

Quantitative T2 mapping using accelerated 3D stack-of-spiral gradient echo readout

PurposeTo develop a rapid T₂ mapping protocol using optimized spiral acquisition, accelerated reconstruction, and model fitting.Materials and methodsA T₂-prepared stack-of-spiral gradient echo (GRE) pulse sequence was applied. A model-based approach joined with compressed sensing was compared with the two methods applied separately for accelerated reconstruction and T₂ mapping. A 2-parameter-weighted fitting method was compared with 2- or 3-parameter models for accurate T₂ estimation under the influences of noise and B₁ inhomogeneity. The performance was evaluated using both digital phantoms and healthy volunteers. Mitigating partial voluming with cerebrospinal fluid (CSF) was also tested.ResultsSimulations demonstrates that the 2-parameter-weighted fitting approach was robust to a large range of B₁ scales and SNR levels. With an in-plane acceleration factor of 5, the model-based compressed sensing-incorporated method yielded around 8% normalized errors compared to references. The T₂ estimation with and without CSF nulling was consistent with literature values.ConclusionThis work demonstrated the feasibility of a T₂ quantification technique with 3D high-resolution and whole-brain coverage in 2–3 min. The proposed iterative reconstruction method, which utilized the model consistency, data consistency and spatial sparsity jointly, provided reasonable T₂ estimation. The technique also allowed mitigation of CSF partial volume effect.     

Accelerating quantitative MR imaging with the incorporation of B1 compensation using deep learning

Quantitative magnetic resonance imaging (MRI) attracts attention due to its support to quantitative image analysis and data driven medicine. However, the application of quantitative MRI is severely limited by the long data acquisition time required by repetitive image acquisition and measurement of field map. Inspired by recent development of artificial intelligence, we propose a deep learning strategy to accelerate the acquisition of quantitative MRI, where every quantitative T₁ map is derived from two highly undersampled variable-contrast images with radiofrequency field inhomogeneity automatically compensated. In a multi-step framework, variable-contrast images are first jointly reconstructed from incoherently undersampled images using convolutional neural networks; then T₁ map and B₁ map are predicted from reconstructed images employing deep learning. Thus, the acceleration includes undersampling in every input image, a reduction in the number of variable contrast images, as well as a waiver of B₁ map measurement. The strategy is validated in T₁ mapping of cartilage. Acquired with a consistent imaging protocol, 1224 image sets from 51 subjects are used for the training of the prediction models, and 288 image sets from 12 subjects are used for testing. High degree of acceleration is achieved with image fidelity well maintained. The proposed method can be broadly applied to quantify other tissue properties (e.g. T₂, T_1ρ) as well.     

Incorporating prior knowledge via volumetric deep residual network to optimize the reconstruction of sparsely sampled MRI

For sparse sampling that accelerates magnetic resonance (MR) image acquisition, non-linear reconstruction algorithms have been developed, which incorporated patient specific a prior information. More generic a prior information could be acquired via deep learning and utilized for image reconstruction. In this study, we developed a volumetric hierarchical deep residual convolutional neural network, referred to as T-Net, to provide a data-driven end-to-end mapping from sparsely sampled MR images to fully sampled MR images, where cartilage MR images were acquired using an Ultra-short TE sequence and retrospectively undersampled using pseudo-random Cartesian and radial acquisition schemes. The network had a hierarchical architecture that promoted the sparsity of feature maps and increased the receptive field, which were valuable for signal synthesis and artifact suppression. Relatively dense local connections and global shortcuts were established to facilitate residual learning and compensate for details lost in hierarchical processing. Additionally, volumetric processing was adopted to fully exploit spatial continuity in three-dimensional space. Data consistency was further enforced. The network was trained with 336 three-dimensional images (each consisting of 32 slices) and tested by 24 images. The incorporation of a priori information acquired via deep learning facilitated high acceleration factors (as high as 8) while maintaining high image fidelity (quantitatively evaluated using the structural similarity index measurement). The proposed T-Net had an improved performance as compared to several state-of-the-art networks.     

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.     

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.     

A brain phantom for motion-corrected PROPELLER showing image contrast and construction similar to those of in vivo MRI

PurposeA fast spin-echo sequence based on the Periodically Rotated Overlapping Parallel Lines with Enhanced Reconstruction (PROPELLER) technique is a magnetic resonance (MR) imaging data acquisition and reconstruction method for correcting motion during scans. Previous studies attempted to verify the in vivo capabilities of motion-corrected PROPELLER in real clinical situations. However, such experiments are limited by repeated, stray head motion by research participants during the prescribed and precise head motion protocol of a PROPELLER acquisition. Therefore, our purpose was to develop a brain phantom set for motion-corrected PROPELLER.Materials and methodsThe profile curves of the signal intensities on the in vivo T₂-weighted image (T₂WI) and 3-D rapid prototyping technology were used to produce the phantom. In addition, we used a homemade driver system to achieve in-plane motion at the intended timing. We calculated the Pearson's correlation coefficient (R²) between the signal intensities of the in vivo T₂WI and the phantom T₂WI and clarified the rotation precision of the driver system. In addition, we used the phantom set to perform initial experiments to show the rotational angle and frequency dependences of PROPELLER.ResultsThe in vivo and phantom T₂WIs were visually congruent, with a significant correlation (R²) of 0.955 (p < .001). The rotational precision of the driver system was within 1 degree of tolerance. The experiment on the rotational angle dependency showed image discrepancies between the rotational angles. The experiment on the rotational frequency dependency showed that the reconstructed images became increasingly blurred by the corruption of the blades as the number of motions increased.ConclusionsIn this study, we developed a phantom that showed image contrasts and construction similar to the in vivo T₂WI. In addition, our homemade driver system achieved precise in-plane motion at the intended timing. Our proposed phantom set could perform systematic experiments with a real clinical MR image, which to date has not been possible in in vivo studies. Further investigation should focus on the improvement of the motion-correction algorithm in PROPELLER using our phantom set for what would traditionally be considered problematic patients (children, emergency patients, elderly, those with dementia, and so on).     

Improved acceleration of phase-contrast flow imaging with magnitude difference regularization

PurposeTo develop a regularized image reconstruction algorithm for improved scan acceleration of phase-contrast (PC) flow MRI.MethodsBased on the magnitude similarity between bipolar-encoded k-space data, magnitude-difference regularization was incorporated into the conventional compressed sensing (CS) reconstruction. The gradient of the magnitude regularization was derived so the reconstruction problem can be solved using non-linear conjugate gradient with backtracking line search. Phase contrast flow data obtained in the peripheral arteries of healthy and patient subjects were retrospectively undersampled for testing the proposed reconstruction method. Three-dimensional velocity-encoded PC flow MRI was performed with prospective 4-fold undersampling for measuring arotic flow velocity in a healthy volunteer.ResultsIn the femoral arteries of healthy volunteers, the root-mean-square (RMS) errors of mean velocities were 0.56 ± 0.09 cm/s with CS-only reconstruction and 0.46 ± 0.08 cm/s with addition of magnitude regularization for three-fold acceleration; 1.34 ± 0.17 cm/s (CS only) and 1.08 ± 0.15 cm/s (magnitude regularized) for four-fold acceleration. In the iliac arteries of the patient, the RMS errors of mean velocities were 0.72 ± 0.12 cm/s and 0.56 ± 0.10 for three-fold acceleration, and 1.75 ± 0.21 and 1.24 ± 0.19 cm/s for four-fold acceleration (in the order of CS-only and magnitude regularized reconstructions). In the popliteal arteries, the RMS errors were 0.61 ± 0.10 cm/s and 0.42 ± 0.11 for three-fold acceleration, and 1.41 ± 0.19 and 1.12 ± 0.17 cm/s for four-fold acceleration. The maximum through-plane mean flow velocities were measured as 63.2 cm/s and 84.5 cm/s in ascending and descending aortas, respectively.ConclusionThe addition of magnitude-difference regularization into conventional CS reconstruction improves the accuracy of image reconstruction using highly undersampled phase-contrast flow MR data.     

Improving dynamic contrast-enhanced MRI of the lung using motion-weighted sparse reconstruction: Initial experiences in patients

PurposeThe purpose of this study was to evaluate the performance of motion-weighted Golden-angle RAdial Sparse Parallel MRI (motion-weighted GRASP) for free-breathing dynamic contrast-enhanced MRI (DCE-MRI) of the lung.MethodsMotion-weighted GRASP incorporates a soft-gating motion compensation algorithm into standard GRASP reconstruction, so that motion-corrupted motion k-space (e.g., k-space acquired in inspiratory phases) contributes less to the final reconstructed images. Lung MR data from 20 patients (mean age = 57.9 ± 13.5) with known pulmonary lesions were retrospectively collected for this study. Each subject underwent a free-breathing DCE-MR scan using a fat-statured T₁-weighted stack-of-stars golden-angle radial sequence and a post-contrast breath-hold MR scan using a Cartesian volumetric-interpolated imaging sequence (BH-VIBE). Each radial dataset was reconstructed using GRASP without motion compensation and motion-weighted GRASP. All MR images were visually evaluated by two experienced radiologists blinded to reconstruction and acquisition schemes independently. In addition, the influence of motion-weighted reconstruction on dynamic contrast-enhancement patterns was also investigated.ResultsFor image quality assessment, motion-weighted GRASP received significantly higher visual scores than GRASP (P < 0.05) for overall image quality (3.68 vs. 3.39), lesion conspicuity (3.54 vs. 3.18) and overall artifact level (3.53 vs. 3.15). There was no significant difference (P > 0.05) between the breath-hold BH-VIBE and motion-weighted GRASP images. For assessment of temporal fidelity, motion-weighted GRASP maintained a good agreement with respect to GRASP.ConclusionMotion-weighted GRASP achieved better reconstruction performance in free-breathing DCE-MRI of the lung compared to standard GRASP, and it may enable improved assessment of pulmonary lesions.     

Three-dimensional black-blood T2 mapping with compressed sensing and data-driven parallel imaging in the carotid artery

PurposeTo develop a 3D black-blood T₂ mapping sequence with a combination of compressed sensing (CS) and parallel imaging (PI) for carotid wall imaging.Materials and methodsA 3D black-blood fast-spin-echo (FSE) sequence for T₂ mapping with CS and PI was developed and validated. Phantom experiments were performed to assess T₂ accuracy using a Eurospin Test Object, with different combination of CS and PI acceleration factors. A 2D multi-echo FSE sequence was used as a reference to evaluate the accuracy. The concordance correlation coefficient and Bland-Altman statistics were calculated. Twelve volunteers were scanned twice to determine the repeatability of the sequence and the intraclass correlation coefficient (ICC) was reported. Wall-lumen sharpness was calculated for different CS and PI combinations. Six patients with carotid stenosis > 50% were scanned with optimised sequence. The T₂ maps were compared with multi-contrast images.ResultsPhantom scans showed good correlation in T₂ measurement between current and reference sequence (r = 0.991). No significant difference was found between different combination of CS and PI accelerations (p = 0.999). Volunteer scans showed good repeatability of T₂ measurement (ICC: 0.93, 95% CI 0.84–0.97). The mean T₂ of the healthy wall was 48.0 ± 9.5 ms. Overall plaque T₂ values from patients were 54.9 ± 12.2 ms. Recent intraplaque haemorrhage and fibrous tissue have higher T₂ values than the mean plaque T₂ values (88.1 ± 6.8 ms and 62.7 ± 9.3 ms, respectively).ConclusionThis study demonstrates the feasibility of combining CS and PI for accelerating 3D T₂ mapping in the carotid artery, with accurate T₂ measurements and good repeatability.     

Feasibility of high-resolution magnetic resonance imaging of the liver using deep learning reconstruction based on the deep learning denoising technique

PurposeTo evaluate the feasibility of High-resolution (HR) magnetic resonance imaging (MRI) of the liver using deep learning reconstruction (DLR) based on a deep learning denoising technique compared with standard-resolution (SR) imaging.Materials and methodsThis retrospective study included patients who underwent abdominal MRI including both HR imaging using DLR and SR imaging between April 1 and August 31, 2019. DLR was applied to all HR images using 12 different strength levels of noise reduction to determine the optimal denoised level for HR images. The mean signal-to-noise ratio (SNR) was then compared between the original HR images without DLR and the optimal denoised HR images with DLR and SR images. The mean image noise, sharpness and overall image quality were also compared. Statistical analyses were performed with the Friedman and Dunn-Bonferroni post-hoc test.ResultsIn total, 49 patients were analyzed (median age, 71 years; 25 women). In quantitative analysis, the mean SNRs on the original HR images without DLR were significantly lower than those on the SR images in all sequences (p < 0.01). Conversely, the mean SNRs on optimal denoised HR images were significantly higher than those on the SR images in all sequences (p < 0.01). In the qualitative analysis, the mean scores for the image noise and overall image quality were significantly higher on optimal denoised HR images than on the SR images in all sequences (p < 0.01) except for the mean image noise score in in-phase (IP) images.ConclusionsThe use of a deep learning-based noise reduction technique substantially and successfully improved the SNR and image quality in HR imaging of the liver. Denoised HR imaging using the DLR technique appears feasible for use in liver MR examinations compared with SR imaging.     

Recovery of accurate T2 from historical 1.5 tesla proton density and T2-weighted images: Application to 7-year T2 changes in multiple sclerosis brain

ObjectiveTo determine accurate quantitative transverse relaxation times (T₂) using retrospective clinical images and apply it to examine 7-year changes in multiple sclerosis (MS) brain.MethodsA method for T₂ mapping from retrospective proton density (PD) and T₂-weighted fast spin echo images was recently introduced, but requires measurement of flip angles. We examined whether 1.5 T flip angle variation in brain can be predicted, thus enabling T₂ analysis of historical PD and T₂-weighted images without a concurrent flip angle map. After method validation in healthy volunteers, retrospective longitudinal T₂ analysis was performed in 14 MS subjects over seven years. Changes in patient T₂ values were compared with brain atrophy, T₂ lesion load and disability score in MS.ResultsSimilar flip angle maps across volunteers enabled retrospective T₂ from PD and T₂-weighted images even when different refocusing angles were used. Over seven years, significant T₂ changes of 2–4% were observed when using T₂ modelling and the 7-year effect size for globus pallidus T₂ was 0.56, which was more significant than brain atrophy. No significant T₂ results were found when using exponential fit, which cannot account for refocusing angle variation. Moreover, change is T₂ in globus pallidus and internal capsule correlated with MS disability score over time when using T₂ modelling.ConclusionsAccurate quantitative T₂ can be extracted from standard clinical 1.5 T MRI exams that include PD and T₂-weighted imaging even when no flip angle map is available. This method was applied retrospectively to examine seven year changes in MS.     

Improved nerve conspicuity with water-weighting and denoising in two-point Dixon magnetic resonance neurography

BackgroundT₂-weighted, two-point Dixon fast-spin-echo (FSE) is an effective technique for magnetic resonance neurography (MRN) that can provide quantitative assessment of muscle denervation. Low signal-to-noise ratio and inadequate fat suppression, however, can impede accurate interpretation.PurposeTo quantify effects of principal component analysis (PCA) denoising on tissue signal intensities and fat fraction (FF) and to determine qualitative image quality improvements from both denoising and water-weighting (WW) algorithms to improve nerve conspicuity and fat suppression.Study typeProspective.SubjectsTwenty-one subjects undergoing MR neurography evaluation (11/10 male/female, mean age = 46.3±13.7 years) with 60 image volumes. Twelve subjects (23 image volumes) were determined to have muscle denervation based on diffusely elevated T₂ signal intensity.Field strength/sequence3 T, 2D, two-point Dixon FSE.AssessmentQualitative assessment included overall image quality, nerve conspicuity, fat suppression, pulsation and ringing artifacts by 3 radiologists separately on a three-point scale (1 = poor, 2 = average, 3 = excellent). Quantitative measurements for FF and signal intensity relative to normal muscle were made for nerve, abnormal muscle and subcutaneous fat.Statistical testsLinear and ordinal regression models were used for quantitative and qualitative comparisons, respectively; 95% confidence intervals (CIs) and p-values for pairwise comparisons were adjusted using the Holm-Bonferroni method. Inter-rater agreement was assessed using Gwet's agreement coefficient (AC₂).ResultsSimulations showed PCA-denoising reduced FF error from 2.0% to 1.0%, and from 7.6% to 3.1% at noise levels of 10% and 30%, respectively. In human subjects, PCA-denoising did not change signal levels and FF quantitatively. WW decreased fat signal significantly (−83.6%, p < 0.001). Nerve conspicuity was improved by WW (odds ratio, OR = 5.8, p < 0.001). Fat suppression was improved by both PCA (OR = 3.6, p < 0.001) and WW (OR = 2.2, p < 0.001). Overall image quality was improved by PCA + WW (OR = 1.7, p = 0.04).ConclusionsWW and PCA-denoising improved nerve conspicuity and fat suppression in MR neurography. Denoising can potentially provide improved accuracy of FF maps for assessing fat-infiltrated muscle.     

Myocardial T1-mapping and extracellular volume in pulmonary arterial hypertension: A systematic review and meta-analysis

IntroductionElevated myocardial T₁-mapping and extracellular volume (ECV) measured on cardiac MR (CMR) imaging is associated with myocardial abnormalities such as oedema or fibrosis. This meta-analysis aims to provide a summary of T₁-mapping and ECV values in pulmonary arterial hypertension (PAH) and compare their values with controls.MethodsWe searched CENTRAL, MEDLINE, Embase, and Web of Science in August 2020. We included CMR studies reporting T₁-mapping or ECV values in adults with any type of PAH. We calculated the mean difference of T₁-values and ECV between PAH and controls.ResultsWe included 12 studies with 674 participants. T₁-values were significantly higher in PAH with the highest mean difference (MD) recorded at the RV insertion points (RVIP) (108 milliseconds (ms), 95% confidence intervals (CI) 89 to 128), followed by the RV free wall (MD 91 ms, 95% CI 56 to 126). The pooled mean T₁-value in PAH at the RVIP was 1084, 95% CI (1071 to 1097) measured using 1.5 Tesla Siemens systems. ECV was also higher in PAH with an MD of 7.5%, 95% CI (5.9 to 9.1) at the RV free wall.ConclusionT₁ mapping values in PAH patients are on average 9% higher than healthy controls when assessed under the same conditions including the same MRI system, magnetic field strength or sequence used for acquisition. The highest T₁ and ECV values are at the RVIP. T₁ mapping and ECV values in PH are higher than the values reported in cardiomyopathies and were associated with poor RV function and RV dilatation.     

Simultaneous measurement of total water content and myelin water fraction in brain at 3 T using a T2 relaxation based method

PurposeThis work demonstrates the in vivo application of a T₂ relaxation based total water content (TWC) measurement technique at 3 T in healthy human brain, and evaluates accuracy using simulations that model brain tissue. The benefit of using T₂ relaxation is that it provides simultaneous measurements of myelin water fraction, which correlates to myelin content.MethodsT₂ relaxation data was collected from 10 healthy human subjects with a gradient and spin echo (GRASE) sequence, along with inversion recovery for T₁ mapping. Voxel-wise T₂ distributions were calculated by fitting the T₂ relaxation data with a non-negative least squares algorithm incorporating B₁⁺ inhomogeneity corrections. TWC was the sum of the signals in the T₂ distribution, corrected for T₁ relaxation and receiver coil inhomogeneity, relative to either an external water standard or cerebrospinal fluid (CSF). Simulations were performed to determine theoretical errors in TWC.ResultsTWC values measured in healthy human brain relative to both external and CSF standards agreed with literature values. Simulations demonstrated that TWC could be measured to within 3–4% accuracy.ConclusionIn vivo TWC measurement using T₂ relaxation at 3 T works well and provides a valuable tool for studying neurological diseases with both myelin and water changes.     

基于支撑先验与深度图像先验的无预训练磁共振图像重建方法

基于深度学习的磁共振成像(magnetic resonance imaging, MRI)方法需要大规模、高质量的病患数据样本集进行预训练.然而,由于病患隐私及设备等因素限制,获取大规模、高质量的磁共振数据集在实际临床应用中面临挑战.本文提出一种新的基于深度学习的欠采样磁共振图像重建方法,该方法无需预训练、不依赖训练数据集,而是充分利用待重建的目标MR图像的结构先验和支撑先验,并将其引入深度图像先验(deep image prior, DIP)框架,从而削减对训练数据集的依赖,提升学习效率.基于参考图像与目标图像的相似性,采用高分辨率参考图像作为深度网络输入,将结构先验信息引入网络;将参考图像在小波域中幅值大的系数索引集作为目标图像的已知支撑集,构造正则化约束项,将网络训练转化为网络参数的最优化求解过程.实验结果表明,本文方法可由欠采样k空间数据重建得到更精确的磁共振图像,且在保留组织特征、细节纹理方面具有明显优势.