XD-GRASP: Golden-angle radial MRI with reconstruction of extra motion-state dimensions using compressed sensing

Respiratory motion-corrected, compressively sampled dynamic MR image reconstruction by exploiting multiple sparsity constraints and phase correlation-based data binning

INTRODUCTION

Cardiac magnetic resonance imaging (cMRI) is a standard method that is clinically used to evaluate the function of the human heart. Respiratory motion during a cMRI scan causes blurring artefacts in the reconstructed images. In conventional MRI, breath holding is used to avoid respiratory motion artefacts, which may be difficult for cardiac patients.

MATERIALS AND METHODS

This paper proposes a method in which phase correlation-based binning, followed by image registration-based sparsity along with spatio-temporal sparsity, is incorporated into the standard low rank + sparse (L+S) reconstruction for free-breathing cardiac cine MRI. The proposed method is validated on clinical data and simulated free-breathing cardiac cine data for different acceleration factors (AFs). The reconstructed images are analysed using visual assessment, artefact power (AP) and root-mean-square error (RMSE). The results of the proposed method are compared with the contemporary motion-corrected compressed sensing (MC-CS) method given in the literature.

RESULTS

Our results show that the proposed method successfully reconstructs the motion-corrected images from respiratory motion-corrupted, compressively sampled cardiac cine MR data, e.g., there is 26% and 24% improvement in terms of AP and RMSE values, respectively, at AF = 4 and 20% and 16.04% improvement in terms of AP and RMSE values, respectively, at AF = 8 in the reconstruction results from the proposed method for the cardiac phantom cine data.

CONCLUSION

The proposed method achieves significant improvement in the AP and RMSE values at different AFs for both the phantom and in vivo data.

Deep Learning to Automate Reference-Free Image Quality Assessment of Whole-Heart MR Images

Purpose

To develop and characterize an algorithm that mimics human expert visual assessment to quantitatively determine the quality of three-dimensional (3D) whole-heart MR images.

Materials and Methods

In this study, 3D whole-heart cardiac MRI scans from 424 participants (average age, 57 years ± 18 [standard deviation]; 66.5% men) were used to generate an image quality assessment algorithm. A deep convolutional neural network for image quality assessment (IQ-DCNN) was designed, trained, optimized, and cross-validated on a clinical database of 324 (training set) scans. On a separate test set (100 scans), two hypotheses were tested: (a) that the algorithm can assess image quality in concordance with human expert assessment as assessed by human-machine correlation and intra- and interobserver agreement and (b) that the IQ-DCNN algorithm may be used to monitor a compressed sensing reconstruction process where image quality progressively improves. Weighted κ values, agreement and disagreement counts, and Krippendorff α reliability coefficients were reported.

Results

Regression performance of the IQ-DCNN was within the range of human intra- and interobserver agreement and in very good agreement with the human expert (R ² = 0.78, κ = 0.67). The image quality assessment during compressed sensing reconstruction correlated with the cost function at each iteration and was successfully applied to rank the results in very good agreement with the human expert.

Conclusion

The proposed IQ-DCNN was trained to mimic expert visual image quality assessment of 3D whole-heart MR images. The results from the IQ-DCNN were in good agreement with human expert reading, and the network was capable of automatically comparing different reconstructed volumes.Supplemental material is available for this article.© RSNA, 2020.

4D-MRI driven MR-guided online adaptive radiotherapy for abdominal stereotactic body radiation therapy on a high field MR-Linac: Implementation and initial clinical experience

•

This is the first clinical use of 4D-MRI in an online radiation therapy workflow.

•

Eleven patients received SBRT on an Elekta MR-Linac using ATP and ATS workflows.

•

A parallel contour editing approach was successfully utilized in the ATS workflow.

Background and purpose

In this report, we describe our implementation and initial clinical experience using 4D-MRI driven MR-guided online adaptive radiotherapy (MRgOART) for abdominal stereotactic body radiotherapy (SBRT) on the Elekta Unity MR-Linac.

Materials and methods

Eleven patients with abdominal malignancies were treated with free-breathing SBRT in three to five fractions on a 1.5 T MR-Linac. Online adaptive plans were generated using Adapt-To-Position (ATP) or Adapt-To-Shape (ATS) workflows based on motion averaged or mid-position images derived from a pre-beam 4D-MRI. A high performance server positioned on the local MR-Linac machine network was utilized for 4D-MR image reconstruction. A parallel contour editing approach was employed in the ATS workflow. Intravoxel incoherent motion (IVIM) and T2 mapping sequences were acquired during adaptive planning in both ATP and ATS workflows for treatment response monitoring. Adaptive plans were delivered under real-time cine image motion monitoring.

Results

The shortest 4D-MRI time-to-image was the motion averaged image, followed by mid position and respiratory binned images. In this cohert of patients, 50% of treatments utilized the ATS workflow; the remaining treatments utilized the ATP workflow. Mid-position images were utilized as daily planning images for two of the eleven patients. The mean daily adaptive plan secondary dose calculation and ArcCheck 3D Gamma passing rates were 97.5% (92.1–100.0%) and 99.3% (96.2–100.0%), respectively. The median overall treatment times for abdominal SBRT was 46 and 62 min for ATP and ATS workflows, respectively.

Conclusion

We have successfully implemented and utilized a 4D-MRI driven MRgOART process with ATP and ATS workflows for free-breathing abdominal SBRT on a 1.5 T Elekta Unity MR-Linac.

Highly accelerated, real-time phase-contrast MRI using radial k-space sampling and GROG-GRASP reconstruction: a feasibility study in pediatric patients with congenital heart disease

Retrospective electrocardiogram-gated, 2D phase-contrast (PC) flow MRI is routinely used in clinical evaluation of valvular/vascular disease in pediatric patients with congenital heart disease (CHD). In patients not requiring general anesthesia, clinical standard PC is conducted with free breathing for several minutes per slice with averaging. In younger patients under general anesthesia, clinical standard PC is conducted with breath-holding. One approach to overcome this limitation is using either navigator gating or self-navigation of respiratory motion, at the expense of lengthening scan times. An alternative approach is using highly accelerated, free-breathing, real-time PC (rt-PC) MRI, which to date has not been evaluated in CHD patients. The purpose of this study was to develop a 38.4-fold accelerated 2D rt-PC pulse sequence using radial k-space sampling and compressed sensing with 1.5 × 1.5 × 6.0 mm³ nominal spatial resolution and 40 ms nominal temporal resolution, and evaluate whether it is capable of accurately measuring flow in 17 pediatric patients (aortic valve, pulmonary valve, right and left pulmonary arteries) compared with clinical standard 2D PC (either breath-hold or free breathing). For clinical translation, we implemented an integrated reconstruction pipeline capable of producing DICOMs of the order of 2 min per time series (46 frames). In terms of association, forward volume, backward volume, regurgitant fraction, and peak velocity at peak systole measured with standard PC and rt-PC were strongly correlated (R² > 0.76; P < 0.001). Compared with clinical standard PC, in terms of agreement, forward volume (mean difference = 1.4% (3.0% of mean)) and regurgitant fraction (mean difference = -2.5%) were in good agreement, whereas backward volume (mean difference = -1.1 mL (28.2% of mean)) and peak-velocity at peak systole (mean difference = -21.3 cm/s (17.2% of mean)) were underestimated by rt-PC. This study demonstrates that the proposed rt-PC with the said spatial resolution and temporal resolution produces relatively accurate forward volumes and regurgitant fractions but underestimates backward volumes and peak velocities at peak systole in pediatric patients with CHD.

Clinical Implementation of a Free-Breathing, Motion-Robust Dynamic Contrast-Enhanced MRI Protocol to Evaluate Pleural Tumors

OBJECTIVE. The purpose of this study was to develop a motion insensitive clinical dynamic contrast-enhanced MRI (DCE-MRI) protocol to assess the response of pleural tumors in clinical trials. MATERIALS AND METHODS. Thirty-two patients with pleura-based lesions were administered contrast material and imaged with gradient-recalled echo DCE-MRI sequence variants: either a traditional cartesian k-space acquisition (FLASH), a time-resolved imaging with stochastic trajectories acquisition (TWIST), or a radial stack-of-stars acquisition (radial) sequence in addition to other standard-of-care imaging sequences. Each image acquisition's sensitivity to motion was evaluated by comparing the motion of the thoracic border in 3D throughout the acquisition. One-way ANOVA was used to compare the image quality between different acquisitions. The 95% CIs were calculated for mean thoracic border displacement. The effects of motion on kinetic parameter estimation were explored with simulations according to clinically acquired data. RESULTS. Radial was the most motion-robust sequence with subvoxel mean displacement in the superior-inferior direction (0.4 ± 1.2 [SD] mm). FLASH showed intermediate displacement (4.6 ± 2.0 mm), whereas TWIST was most sensitive to motion (6.4 ± 3.4 mm). Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the images acquired with the radial sequence were on par or better than the FLASH and TWIST sequences when reconstructed with an improved density compensation algorithm. Simulations showed that motion on scans showing pleural-based lesions can lead to markedly inaccurate kinetic parameter estimation and inappropriate kinetic model convergence within a nested model analysis. CONCLUSION. A practical radial k-space trajectory sequence that provides motion-insensitive pharmacokinetic parameters was incorporated as part of the DCE-MRI protocol of pleural tumors. Validation and usefulness in clinical trials assessing response to therapy is needed.

Respiratory motion compensation using data binning in dynamic contrast enhanced golden-angle radial MRI

GRASP (Golden-Angle Radial Sparse Parallel MRI) is a data acquisition and reconstruction technique that combines parallel imaging and golden-angle radial sampling. The continuously acquired free breathing Dynamic Contrast Enhanced (DCE) golden-angle radial MRI data of liver and abdomen has artifacts due to respiratory motion, resulting in low vessel-tissue contrast that makes GRASP reconstructed images less suitable for diagnosis. In this paper, DCE golden-angle radial MRI data of abdomen and liver perfusion is sorted into different motion states using the self-gating property of radial acquisition and then reconstructed using GRASP. Three methods of amplitude-based data binning namely uniform binning, adaptive binning and optimal binning are applied on the DCE golden-angle radial data to extract different motion states and a comparison is performed with the conventional GRASP reconstruction. Also, a comparison among the amplitude-based data binning techniques is performed and benefits of each of these binning techniques are discussed from a clinical perspective. The image quality assessment in terms of hepatic vessel clarity, liver edge sharpness, contrast enhancement clarity and streaking artifacts is performed by a certified radiologist. The results show that DCE golden-angle radial trajectories benefit from all the three types of amplitude-based data binning methods providing improved reconstruction results. The choice of binning technique depends upon the clinical application e.g. uniform and adaptive binning are helpful for a detailed analysis of lesion characteristic and contrast enhancement in different motion states while optimal binning can be used when clinical analysis requires a single image per contrast enhancement phase with no motion blurring artifacts.

Assessment of Hepatic Perfusion Using GRASP MRI: Bringing Liver MRI on a New Level

PURPOSE

The aim of this study was to demonstrate the feasibility of hepatic perfusion imaging using dynamic contrast-enhanced (DCE) golden-angle radial sparse parallel (GRASP) magnetic resonance imaging (MRI) for characterizing liver parenchyma and hepatocellular carcinoma (HCC) before and after transarterial chemoembolization (TACE) as a potential alternative to volume perfusion computed tomography (VPCT).

METHODS AND MATERIALS

Between November 2017 and September 2018, 10 patients (male = 8; mean age, 66.5 ± 8.6 years) with HCC were included in this prospective, institutional review board-approved study. All patients underwent DCE GRASP MRI with high spatiotemporal resolution after injection of liver-specific MR contrast agent before and after TACE. In addition, VPCT was acquired before TACE serving as standard of reference. From the dynamic imaging data of DCE MRI and VPCT, perfusion maps (arterial liver perfusion [mL/100 mL/min], portal liver perfusion [mL/100 mL/min], hepatic perfusion index [%]) were calculated using a dual-input maximum slope model and compared with assess perfusion measures, lesion characteristics, and treatment response using Wilcoxon signed-rank test. To evaluate interreader agreement for measurement repeatability, the interclass correlation coefficient (ICC) was calculated.

RESULTS

Perfusion maps could be successfully generated from all DCE MRI and VPCT data. The ICC was excellent for all perfusion maps (ICC ≥ 0.88; P ≤ 0.001). Image analyses revealed perfusion parameters for DCE MRI and VPCT within the same absolute range for tumor and liver tissue. Dynamic contrast-enhanced MRI further enabled quantitative assessment of treatment response showing a significant decrease (P ≤ 0.01) of arterial liver perfusion and hepatic perfusion index in the target lesion after TACE.

CONCLUSIONS

Dynamic contrast-enhanced GRASP MRI allows for a reliable and robust assessment of hepatic perfusion parameters providing quantitative results comparable to VPCT and enables characterization of HCC before and after TACE, thus posing the potential to serve as an alternative to VPCT.

A super-resolution framework for the reconstruction of T2-weighted (T2w) time-resolved (TR) 4DMRI using T1w TR-4DMRI as the guidance

PURPOSE

The purpose of this study was to develop T2-weighted (T2w) time-resolved (TR) four-dimensional magnetic resonance imaging (4DMRI) reconstruction technique with higher soft-tissue contrast for multiple breathing cycle motion assessment by building a super-resolution (SR) framework using the T1w TR-4DMRI reconstruction as guidance.

METHODS

The multi-breath T1w TR-4DMRI was reconstructed by deforming a high-resolution (HR: 2 × 2 × 2 mm³ ) volumetric breath-hold (BH, 20s) three-dimensional magnetic resonance imaging (3DMRI) image to a series of low-resolution (LR: 5 × 5 × 5 mm³ ) 3D cine images at a 2Hz frame rate in free-breathing (FB, 40 s) using an enhanced Demons algorithm, namely [T1^BH →FB] reconstruction. Within the same imaging session, respiratory-correlated (RC) T2w 4DMRI (2 × 2 × 2 mm³ ) was acquired based on an internal navigator to gain HR T2w (T2^HR ) in three states (full exhalation and mid and full inhalation) in ~5 min. Minor binning artifacts in the RC-4DMRI were automatically identified based on voxel intensity correlation (VIC) between consecutive slices as outliers (VIC < VIC^mean -σ) and corrected by deforming the artifact slices to interpolated slices from the adjacent slices iteratively until no outliers were identified. A T2^HR image with minimal deformation (<1 cm at the diaphragm) from the T1^BH image was selected for multi-modal B-Spline deformable image registration (DIR) to establish the T2^HR -T1^BH voxel correspondence. Two approaches to reconstruct T2w TR-4DMRI were investigated: (A) T2^HR →[T1^BH →FB]: to deform T2w HR to T1w BH only as T1w TR-4DMRI was reconstructed, and combine the two displacement vector fields (DVFs) to reconstruct T2w TR-4DMRI, and (B) [T2^HR ←T1^BH ]→FB: to deform T1w BH to T2w HR first and apply the deformed T1w BH to reconstruct T2w TR-4DMRI. The reconstruction times were similar, 8-12 min per volume. To validate the two methods, T2w- and T1w-mapped 4D XCAT digital phantoms were utilized with three synthetic spherical tumors (ϕ = 2.0, 3.0, and 4.0 cm) in the lower or mid lobes as the ground truth to evaluate the tumor location (the center of mass, COM), size (volume ratio, %V), and shape (Dice index). Six lung cancer patients were scanned under an IRB-approved protocol and the T2w TR-4DMRI images reconstructed from the two methods were compared based on the preservation of the three tumor characteristics. The local tumor-contained image quality was also characterized using the VIC and structure similarity (SSIM) indexes.

RESULTS

In the 4D digital phantom, excellent tumor alignment after T2^HR -T1^HR DIR is achieved: ∆COM = 0.8 ± 0.5 mm, %V = 1.06 ± 0.02, and Dice = 0.91 ± 0.03, in both deformation directions using the DIR-target image as the reference. In patients, binning artifacts are corrected with improved image quality: average VIC increases from 0.92 ± 0.03 to 0.95 ± 0.01. Both T2w TR-4DMRI reconstruction methods produce similar tumor alignment errors ∆COM = 2.9 ± 0.6 mm. However, method B ([T2^HR ←T1^BH ]→FB) produces superior results in preserving more T2w tumor features with a higher %V = 0.99 ± 0.03, Dice = 0.81 ± 0.06, VIC = 0.85 ± 0.06, and SSIM = 0.65 ± 0.10 in the T2w TR-4DMRI images.

CONCLUSIONS

This study has demonstrated the feasibility of T2w TR-4DMRI reconstruction with high soft-tissue contrast and adequately-preserved tumor position, size, and shape in multiple breathing cycles. The T2w-centric DIR (method B) produces a superior solution for the SR-based framework of T2w TR-4DMRI reconstruction with highly preserved tumor characteristics and local image features, which are useful for tumor delineation and motion management in radiation therapy.

Impact of temporal resolution and motion correction for dynamic contrast-enhanced MRI of the liver using an accelerated golden-angle radial sequence

This paper presents a prospective study evaluating the impact on image quality and quantitative dynamic contrast-enhanced (DCE)-MRI perfusion parameters when varying the number of respiratory motion states when using an eXtra-Dimensional Golden-Angle Radial Sparse Parallel (XD-GRASP) MRI sequence. DCE acquisition was performed using a 3D stack-of-stars gradient-echo golden-angle radial acquisition in free-breathing with 100 spokes per motion state and temporal resolution of 6 s/volume, and using a non-rigid motion compensation to align different motion states. Parametric analysis was conducted using a dual-input single-compartment model. Nonparametric analysis was performed on the time-intensity curves. A total of 22 hepatocellular carcinomas (size: 11-52 mm) were evaluated. XD-GRASP reconstructed with increasing number of spokes for each motion state increased the signal-to-noise ratio (SNR) (p < 0.05) but decreased temporal resolution (0.04 volume/s vs 0.17 volume/s for one motion state) (p < 0.05). A visual scoring by an experienced radiologist show no change between increasing number of motion states with same number of spokes using the Likert score. The normalized maximum intensity time ratio, peak enhancement ratio and tumor arterial fraction increased with decreasing number of motion states (p < 0.05) while the transfer constant from the portal venous plasma to the surrounding tissue significantly decreased (p < 0.05). These same perfusion parameters show a significant difference in case of tumor displacement more than 1 cm (p < 0.05) whereas in the opposite case there was no significant variation. While a higher number of motion states and higher number of spokes improves SNR, the resulting lower temporal resolution can influence quantitative parameters that capture rapid signal changes. Finally, fewer displacement compensation is advantageous with lower number of motion state due to the higher temporal resolution. XD-GRASP can be used to perform quantitative perfusion measures in the liver, but the number of motion states may significantly alter some quantitative parameters.

Free-breathing fat and R2 * quantification in the liver using a stack-of-stars multi-echo acquisition with respiratory-resolved model-based reconstruction

Purpose

To develop a free‐breathing hepatic fat and R2∗ quantification method by extending a previously described stack‐of‐stars model‐based fat‐water separation technique with additional modeling of the transverse relaxation rate R2∗.

Methods

The proposed technique combines motion‐robust radial sampling using a stack‐of‐stars bipolar multi‐echo 3D GRE acquisition with iterative model‐based fat‐water separation. Parallel‐Imaging and Compressed‐Sensing principles are incorporated through modeling of the coil‐sensitivity profiles and enforcement of total‐variation (TV) sparsity on estimated water, fat, and R2∗ parameter maps. Water and fat signals are used to estimate the confounder‐corrected proton‐density fat fraction (PDFF). Two strategies for handling respiratory motion are described: motion‐averaged and motion‐resolved reconstruction. Both techniques were evaluated in patients (n = 14) undergoing a hepatobiliary research protocol at 3T. PDFF and R2∗ parameter maps were compared to a breath‐holding Cartesian reference approach.

Results

Linear regression analyses demonstrated strong (r > 0.96) and significant (P ≪ .01) correlations between radial and Cartesian PDFF measurements for both the motion‐averaged reconstruction (slope: 0.90; intercept: 0.07%) and the motion‐resolved reconstruction (slope: 0.90; intercept: 0.11%). The motion‐averaged technique overestimated hepatic R2∗ values (slope: 0.35; intercept: 30.2 1/s) compared to the Cartesian reference. However, performing a respiratory‐resolved reconstruction led to better R2∗ value consistency (slope: 0.77; intercept: 7.5 1/s).

Conclusions

The proposed techniques are promising alternatives to conventional Cartesian imaging for fat and R2∗ quantification in patients with limited breath‐holding capabilities. For accurate R2∗ estimation, respiratory‐resolved reconstruction should be used.

3D whole-heart isotropic sub-millimeter resolution coronary magnetic resonance angiography with non-rigid motion-compensated PROST

BACKGROUND

To enable free-breathing whole-heart sub-millimeter resolution coronary magnetic resonance angiography (CMRA) in a clinically feasible scan time by combining low-rank patch-based undersampled reconstruction (3D-PROST) with a highly accelerated non-rigid motion correction framework.

METHODS

Non-rigid motion corrected CMRA combined with 2D image-based navigators has been previously proposed to enable 100% respiratory scan efficiency in modestly undersampled acquisitions. Achieving sub-millimeter isotropic resolution with such techniques still requires prohibitively long acquisition times. We propose to combine 3D-PROST reconstruction with a highly accelerated non-rigid motion correction framework to achieve sub-millimeter resolution CMRA in less than 10 min. Ten healthy subjects and eight patients with suspected coronary artery disease underwent 4-5-fold accelerated free-breathing whole-heart CMRA with 0.9 mm3 isotropic resolution. Vessel sharpness, vessel length and image quality obtained with the proposed non-rigid (NR) PROST approach were compared against translational correction only (TC-PROST) and a previously proposed NR motion-compensated technique (non-rigid SENSE) in healthy subjects. For the patient study, image quality scoring and visual comparison with coronary computed tomography angiography (CCTA) were performed.

RESULTS

Average scan times [min:s] were 6:01 ± 0:59 (healthy subjects) and 8:29 ± 1:41 (patients). In healthy subjects, vessel sharpness of the left anterior descending (LAD) and right (RCA) coronary arteries were improved with the proposed non-rigid PROST (LAD: 51.2 ± 8.8%, RCA: 61.2 ± 9.1%) in comparison to TC-PROST (LAD: 43.8 ± 5.1%, P = 0.051, RCA: 54.3 ± 8.3%, P = 0.218) and non-rigid SENSE (LAD: 46.1 ± 5.8%, P = 0.223, RCA: 56.7 ± 9.6%, P = 0.50), although differences were not statistically significant. The average visual image quality score was significantly higher for NR-PROST (LAD: 3.2 ± 0.6, RCA: 3.3 ± 0.7) compared with TC-PROST (LAD: 2.1 ± 0.6, P = 0.018, RCA: 2.0 ± 0.7, P = 0.014) and non-rigid SENSE (LAD: 2.3 ± 0.5, P = 0.008, RCA: 2.5 ± 0.7, P = 0.016). In patients, the proposed approach showed good delineation of the coronaries, in agreement with CCTA, with image quality scores and vessel sharpness similar to that of healthy subjects.

CONCLUSIONS

We demonstrate the feasibility of combining high undersampling factors with non-rigid motion-compensated reconstruction to obtain high-quality sub-millimeter isotropic CMRA images in ~ 8 min. Validation in a larger cohort of patients with coronary artery disease is now warranted.

Extreme MRI: Large-scale volumetric dynamic imaging from continuous non-gated acquisitions

PURPOSE

To develop a framework to reconstruct large-scale volumetric dynamic MRI from rapid continuous and non-gated acquisitions, with applications to pulmonary and dynamic contrast-enhanced (DCE) imaging.

THEORY AND METHODS

The problem considered here requires recovering 100 gigabytes of dynamic volumetric image data from a few gigabytes of k-space data, acquired continuously over several minutes. This reconstruction is vastly under-determined, heavily stressing computing resources as well as memory management and storage. To overcome these challenges, we leverage intrinsic three-dimensional (3D) trajectories, such as 3D radial and 3D cones, with ordering that incoherently cover time and k-space over the entire acquisition. We then propose two innovations: (a) A compressed representation using multiscale low-rank matrix factorization that constrains the reconstruction problem, and reduces its memory footprint. (b) Stochastic optimization to reduce computation, improve memory locality, and minimize communications between threads and processors. We demonstrate the feasibility of the proposed method on DCE imaging acquired with a golden-angle ordered 3D cones trajectory and pulmonary imaging acquired with a bit-reversed ordered 3D radial trajectory. We compare it with "soft-gated" dynamic reconstruction for DCE and respiratory-resolved reconstruction for pulmonary imaging.

RESULTS

The proposed technique shows transient dynamics that are not seen in gating-based methods. When applied to datasets with irregular, or non-repetitive motions, the proposed method displays sharper image features.

CONCLUSIONS

We demonstrated a method that can reconstruct massive 3D dynamic image series in the extreme undersampling and extreme computation setting.

Pseudo-spiral sampling and compressed sensing reconstruction provides flexibility of temporal resolution in accelerated aortic 4D flow MRI: A comparison with k-t principal component analysis

INTRODUCTION

Time-resolved three-dimensional phase contrast MRI (4D flow) of aortic blood flow requires acceleration to reduce scan time. Two established techniques for highly accelerated 4D flow MRI are k-t principal component analysis (k-t PCA) and compressed sensing (CS), which employ either regular or random k-space undersampling. The goal of this study was to gain insights into the quantitative differences between k-t PCA- and CS-derived aortic blood flow, especially for high temporal resolution CS 4D flow MRI.

METHODS

The scan protocol consisted of both k-t PCA and CS accelerated 4D flow MRI, as well as a 2D flow reference scan through the ascending aorta acquired in 15 subjects. 4D flow scans were accelerated with factor R = 8. For CS accelerated scans, we used a pseudo-spiral Cartesian sampling scheme, which could additionally be reconstructed at higher temporal resolution, resulting in R = 13. 4D flow data were compared with the 2D flow scan in terms of flow, peak flow and stroke volume. A 3D peak systolic voxel-wise velocity and wall shear stress (WSS) comparison between k-t PCA and CS 4D flow was also performed.

RESULTS

The mean difference in flow/peak flow/stroke volume between the 2D flow scan and the 4D flow CS with R = 8 and R = 13 was 4.2%/9.1%/3.0% and 5.3%/7.1%/1.9%, respectively, whereas for k-t PCA with R = 8 the difference was 9.7%/25.8%/10.4%. In the voxel-by-voxel 4D flow comparison we found 13.6% and 3.5% lower velocity and WSS values of k-t PCA compared with CS with R = 8, and 15.9% and 5.5% lower velocity and WSS values of k-t PCA compared with CS with R = 13.

CONCLUSION

Pseudo-spiral accelerated 4D flow acquisitions in combination with CS reconstruction provides a flexible choice of temporal resolution. We showed that our proposed strategy achieves better agreement in flow values with 2D reference scans compared with using k-t PCA accelerated acquisitions.

Feasibility of free-breathing T1-weighted 3D radial VIBE for fetal MRI in various anomalies

RATIONALE AND OBJECTIVES

In magnetic resonance (MR) fetal imaging, the image quality acquired by the traditional Cartesian-sampled breath-hold T1-weighted (T1W) sequence may be degraded by motion artifacts arising from both mother and fetus. The radial VIBE sequence is reported to be a viable alternative to conventional Cartesian acquisition for both pediatric and adult MR, yielding better image quality. This study evaluated the role of radial VIBE in fetal MR imaging and compared its image quality and motion artifacts with those of the Cartesian T1W sequence.

MATERIALS AND METHODS

We included 246 pregnant women with 50 lesions on 1.5-T MR imaging. Image quality and lesion conspicuity were evaluated by two radiologists, blinded to the acquisition schemes used, using a five-point scale, where a higher score indicated a better trajectory method. Mixed-model analysis of variance and interobserver variability assessment were performed.

RESULTS

The radial VIBE sequence showed a significantly better performance than conventional T1W imaging in the head and neck, fetal body, and placenta region: 3.92 ± 0.88 vs 3 ± 0.74, p < 0.001, 3.8 ± 0.94 vs 3.15 ± 0.87, p < 0.001, and 4.17 ± 0.63 vs 3.12 ± 0.72, p < 0.001, respectively. Additionally, fewer motion artifacts were observed in all regions with the radial VIBE sequence (p < 0.01). Of 50 lesions, 49 presented better lesion conspicuity on radial VIBE images than on T1W images (4.34 ± 0.91 vs 3.48 ± 1.46, p < 0.001).

CONCLUSION

For fetal imaging, the radial VIBE sequences yielded better image quality and lesion conspicuity, with fewer motion artifacts, than conventional breath-hold Cartesian-sampled T1W sequences.

Bi-Linear Modeling of Data Manifolds for Dynamic-MRI Recovery

This paper puts forth a novel bi-linear modeling framework for data recovery via manifold-learning and sparse-approximation arguments and considers its application to dynamic magnetic-resonance imaging (dMRI). Each temporal-domain MR image is viewed as a point that lies onto or close to a smooth manifold, and landmark points are identified to describe the point cloud concisely. To facilitate computations, a dimensionality reduction module generates low-dimensional/compressed renditions of the landmark points. Recovery of high-fidelity MRI data is realized by solving a non-convex minimization task for the linear decompression operator and affine combinations of landmark points which locally approximate the latent manifold geometry. An algorithm with guaranteed convergence to stationary solutions of the non-convex minimization task is also provided. The aforementioned framework exploits the underlying spatio-temporal patterns and geometry of the acquired data without any prior training on external data or information. Extensive numerical results on simulated as well as real cardiac-cine MRI data illustrate noteworthy improvements of the advocated machine-learning framework over state-of-the-art reconstruction techniques.

From Compressed-Sensing to Artificial Intelligence-Based Cardiac MRI Reconstruction

Cardiac magnetic resonance (CMR) imaging is an important tool for the non-invasive assessment of cardiovascular disease. However, CMR suffers from long acquisition times due to the need of obtaining images with high temporal and spatial resolution, different contrasts, and/or whole-heart coverage. In addition, both cardiac and respiratory-induced motion of the heart during the acquisition need to be accounted for, further increasing the scan time. Several undersampling reconstruction techniques have been proposed during the last decades to speed up CMR acquisition. These techniques rely on acquiring less data than needed and estimating the non-acquired data exploiting some sort of prior information. Parallel imaging and compressed sensing undersampling reconstruction techniques have revolutionized the field, enabling 2- to 3-fold scan time accelerations to become standard in clinical practice. Recent scientific advances in CMR reconstruction hinge on the thriving field of artificial intelligence. Machine learning reconstruction approaches have been recently proposed to learn the non-linear optimization process employed in CMR reconstruction. Unlike analytical methods for which the reconstruction problem is explicitly defined into the optimization process, machine learning techniques make use of large data sets to learn the key reconstruction parameters and priors. In particular, deep learning techniques promise to use deep neural networks (DNN) to learn the reconstruction process from existing datasets in advance, providing a fast and efficient reconstruction that can be applied to all newly acquired data. However, before machine learning and DNN can realize their full potentials and enter widespread clinical routine for CMR image reconstruction, there are several technical hurdles that need to be addressed. In this article, we provide an overview of the recent developments in the area of artificial intelligence for CMR image reconstruction. The underlying assumptions of established techniques such as compressed sensing and low-rank reconstruction are briefly summarized, while a greater focus is given to recent advances in dictionary learning and deep learning based CMR reconstruction. In particular, approaches that exploit neural networks as implicit or explicit priors are discussed for 2D dynamic cardiac imaging and 3D whole-heart CMR imaging. Current limitations, challenges, and potential future directions of these techniques are also discussed.

MRSIGMA: Magnetic Resonance SIGnature MAtching for real-time volumetric imaging

PURPOSE

To propose a real-time 3D MRI technique called MR SIGnature MAtching (MRSIGMA) for high-resolution volumetric imaging and motion tracking with very low imaging latency.

METHODS

MRSIGMA consists of two steps: (1) offline learning of a database of possible 3D motion states and corresponding motion signature ranges and (2) online matching of new motion signatures acquired in real time with prelearned motion states. Specifically, the offline learning step (non-real-time) reconstructs motion-resolved 4D images representing different motion states and assigns a unique motion range to each state. The online matching step (real-time) acquires motion signatures only and selects one of the prelearned 3D motion states for each newly acquired signature, which generates 3D images efficiently in real time. The MRSIGMA technique was evaluated on 15 golden-angle stack-of-stars liver data sets, and the performance of respiratory motion tracking with the online-generated real-time 3D MRI was compared with the corresponding 2D projections acquired in real time.

RESULTS

The total latency of generating each 3D image during online matching was about 300 ms, including acquisition of the motion signature data (~138 ms) and corresponding matching process (~150 ms). Linear correlation assessment suggested excellent correlation (R²  = 0.948) between motion displacement measured from the online-generated real-time 3D images and the 2D real-time projections.

CONCLUSION

This proof-of-concept study demonstrates the feasibility of MRSIGMA for high-resolution real-time volumetric imaging, which shifts the acquisition and reconstruction burden to an offline learning step and leaves fast online matching for online imaging with very low imaging latency. The MRSIGMA technique can potentially be used for real-time motion tracking in MRI-guided radiation therapy.

MRI Techniques to Decrease Imaging Times in Children

Long acquisition times can limit the use of MRI in pediatric patients, and the use of sedation or general anesthesia is frequently necessary to facilitate diagnostic examinations. The use of sedation or anesthesia has disadvantages including increased cost and imaging time and potential risks to the patient. Reductions in imaging time may decrease or eliminate the need for sedation or general anesthesia. Over the past decade, a number of imaging techniques that can decrease imaging time have become commercially available. These products have been used increasingly in clinical practice and include parallel imaging, simultaneous multisection imaging, radial k-space acquisition, compressed sensing MRI reconstruction, and automated protocol selection software. The underlying concepts, supporting data, current clinical applications, and available products for each of these strategies are reviewed in this article. In addition, emerging techniques that are still under investigation may provide further reductions in imaging time, including artificial intelligence-based reconstruction, gradient-controlled aliasing sampling and reconstruction, three-dimensional MR spectroscopy, and prospective motion correction. The preliminary results for these techniques are also discussed. ^©RSNA, 2020 See discussion on this article by Greer and Vasanawala.

Newly Developed Methods for Reducing Motion Artifacts in Pediatric Abdominal MRI: Tips and Pearls

OBJECTIVE. The purpose of this article is to review established and emerging methods for reducing motion artifacts in pediatric abdominal MRI. CONCLUSION. Clearly understanding the strengths and limitations of motion reduction methods can enable practitioners of pediatric abdominal MRI to select and combine the appropriate techniques and potentially reduce the need for sedation and anesthesia.

Left ventricular extracellular volume expansion does not predict recurrence of atrial fibrillation following catheter ablation

INTRODUCTION

A recent study reported that diffuse left ventricular (LV) fibrosis is a predictor of atrial fibrillation (AF) recurrence following catheter ablation, by measuring postcontrast cardiac T1 (an error prone metric as per the 2017 Society for Cardiovascular Magnetic Resonance consensus statement) using an inversion-recovery pulse sequence (an error prone method in arrhythmia) in AF ablation candidates. The purpose of this study was to verify the prior study, by measuring extracellular volume (ECV) fraction (an accurate metric) using a saturation-recovery pulse sequence (accurate method in arrhythmia).

METHODS AND RESULTS

This study examined 100 AF patients (mean age = 62 ± 11 years, 69 males and 31 females, 67 paroxysmal [pAF] and 33 persistent [peAF]) who underwent a preablation cardiovascular magnetic resonance (CMR) exam. LV ECV and left atrial (LA) and LV functional parameters were quantified using standard analysis methods. During an average follow-up period of 457 ± 261 days with 4 ± 3 rhythm checks per patient, 72 patients maintained sinus rhythm. Between those who maintained sinus rhythm (n = 72) and those who reverted to AF (n = 28), the only clinical characteristic that was significantly different was age (60 ± 12 years vs 66 ± 9 years); for CMR metrics, neither mean LV ECV (25.1 ± 3.3% vs 24.7 ± 3.7%), native LV T1 (1093.8 ± 73.5 ms vs 1070.2 ± 115.9 ms), left ventricular ejection fraction (54.1 ± 11.2% vs 55.7 ± 7.1%), nor LA end diastolic volume/body surface area (42.4 ± 14.8 mL/m2 vs 43.4 ± 19.6 mL/m2 ) were significantly different (P ≥ .23). According to Cox regression tests, none of the clinical and imaging variables predict AF recurrence.

CONCLUSION

Neither LV ECV nor other CMR metrics predict recurrence of AF following catheter ablation.

FPGA-based hardware accelerator for SENSE (a parallel MR image reconstruction method)

SENSE (Sensitivity Encoding) is a parallel MRI (pMRI) technique that allows accelerated data acquisition using multiple receiver coils and reconstructs the artifact-free images from the acquired under-sampled data. However, an increasing number of receiver coils has raised the computational demands of pMRI techniques to an extent where the reconstruction time on general purpose computers becomes impractically long for real-time MRI. Field Programmable Gate Arrays (FPGAs) have recently emerged as a viable hardware platform for accelerating pMRI algorithms (e.g. SENSE). However, recent efforts to accelerate SENSE using FPGAs have been focused on a fixed number of receiver coils (L=8) and acceleration factor (A_f=2). This paper presents a novel 32-bit floating-point FPGA-based hardware accelerator for SENSE (HW-ACC-SENSE); having an ability to work in coordination with an on-chip ARM processor performing reconstructions for different values of L and A_f. Moreover, the proposed design provides flexibility to integrate multiple units of HW-ACC-SENSE with an on-chip ARM processor, for low-latency image reconstruction. The VIVADO High-Level-Synthesis (HLS) tool has been used to design and implement the HW-ACC-SENSE on the Xilinx FPGA development board (ZCU102). A series of experiments has been performed on in-vivo datasets acquired using 8, 12 and 30 receiver coil elements. The performance of the proposed architecture is compared with the single thread and multi-thread CPU-based implementations of SENSE. The results show that the proposed design withstands the reconstruction quality of the SENSE algorithm while demonstrating a maximum speed-gain up to 298× over the CPU counterparts in our experiments.

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

PURPOSE

The 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.

METHODS

Motion-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.

RESULTS

For 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.

CONCLUSION

Motion-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.

Six-dimensional quantitative DCE MR Multitasking of the entire abdomen: Method and application to pancreatic ductal adenocarcinoma

PURPOSE

To develop a quantitative DCE MRI technique enabling entire-abdomen coverage, free-breathing acquisition, 1-second temporal resolution, and T₁ -based quantification of contrast agent concentration and kinetic modeling for the characterization of pancreatic ductal adenocarcinoma (PDAC).

METHODS

Segmented FLASH readouts following saturation-recovery preparation with randomized 3D Cartesian undersampling was used for incoherent data acquisition. MR Multitasking was used to reconstruct 6-dimensional images with 3 spatial dimensions, 1 T₁ recovery dimension for dynamic T₁ quantification, 1 respiratory dimension to resolve respiratory motion, and 1 DCE time dimension to capture the contrast kinetics. Sixteen healthy subjects and 14 patients with pathologically confirmed PDAC were recruited for the in vivo studies, and kinetic parameters v_p , K^trans , v_e , and K_ep were evaluated for each subject. Intersession repeatability of Multitasking DCE was assessed in 8 repeat healthy subjects. One-way unbalanced analysis of variance was performed between control and patient groups.

RESULTS

In vivo studies demonstrated that v_p , K^trans , and K_ep of PDAC were significantly lower compared with nontumoral regions in the patient group (P = .002, .003, .004, respectively) and normal pancreas in the control group (P = .011, <.001, <.001, respectively), while v_e was significantly higher than nontumoral regions (P < .001) and healthy pancreas (P < .001). The kinetic parameters showed good in vivo repeatability (interclass correlation coefficient: v_p , 0.95; K^trans , 0.98; v_e , 0.96; K_ep , 0.99).

CONCLUSION

The proposed Multitasking DCE is promising for the quantification of vascular properties of PDAC. Quantitative DCE parameters were repeatable in vivo and showed significant differences between normal pancreas and both tumor and nontumoral regions in patients with PDAC.

The noise navigator: a surrogate for respiratory-correlated 4D-MRI for motion characterization in radiotherapy

Respiratory-correlated 4D-MRI can characterize respiratory-induced motion of tumors and organs-at-risk for radiotherapy treatment planning and is a necessity for image guidance of moving tumors treated on an MRI-linac. Essential for 4D-MRI generation is a robust respiratory surrogate signal. We investigated the feasibility of the noise navigator as respiratory surrogate signal for 4D-MRI generation. The noise navigator is based on the respiratory-induced modulation of the thermal noise variance measured by the receive coils during MR acquisition and thus is inherently present and synchronized with MRI data acquisition. Additionally, the noise navigator can be combined with any rectilinear readout strategy (e.g. radial and cartesian) and is independent of MR image contrast and imaging orientation. For radiotherapy applications, the noise navigator provides a robust respiratory signal for patients scanned with an elevated coil setup. This is particularly attractive for widely used cartesian sequences where currently a non-interfering self-navigation means is lacking for MRI-based simulation and MRI-guided radiotherapy. The feasibility of 4D-MRI generation with the noise navigator as respiratory surrogate signal was demonstrated for both cartesian and radial readout strategies in radiotherapy setup on four healthy volunteers and two radiotherapy patients on a dedicated 1.5 T MRI scanner and two radiotherapy patients on a 1.5 T MRI-linac system. Moreover, the respiratory-correlated 4D-MR images showed liver motion comparable to a reference 2D cine MRI series for the volunteers. For 2D cartesian cine MRI acquisitions, both the noise navigator and respiratory bellows were benchmarked against an image navigator. Respiratory phase detection based on the noise navigator agreed 1.4 times better with the image navigator than the respiratory bellows did. For a 3D Stack-of-Stars acquisitions, the noise navigator was compared to radial self-navigation and a 1.7 times higher respiratory phase detection agreement was observed than for the respiratory bellows compared to radial self-navigation.

Computational MRI with Physics-based Constraints: Application to Multi-contrast and Quantitative Imaging

Compressed sensing takes advantage of low-dimensional signal structure to reduce sampling requirements far below the Nyquist rate. In magnetic resonance imaging (MRI), this often takes the form of sparsity through wavelet transform, finite differences, and low rank extensions. Though powerful, these image priors are phenomenological in nature and do not account for the mechanism behind the image formation. On the other hand, MRI signal dynamics are governed by physical laws, which can be explicitly modeled and used as priors for reconstruction. These explicit and implicit signal priors can be synergistically combined in an inverse problem framework to recover sharp, multi-contrast images from highly accelerated scans. Furthermore, the physics-based constraints provide a recipe for recovering quantitative, bio-physical parameters from the data. This article introduces physics-based modeling constraints in MRI and shows how they can be used in conjunction with compressed sensing for image reconstruction and quantitative imaging. We describe model-based quantitative MRI, as well as its linear subspace approximation. We also discuss approaches to selecting user-controllable scan parameters given knowledge of the physical model. We present several MRI applications that take advantage of this framework for the purpose of multi-contrast imaging and quantitative mapping.

Compressed Sensing: From Research to Clinical Practice with Deep Neural Networks

Compressed sensing (CS) reconstruction methods leverage sparse structure in underlying signals to recover high-resolution images from highly undersampled measurements. When applied to magnetic resonance imaging (MRI), CS has the potential to dramatically shorten MRI scan times, increase diagnostic value, and improve overall patient experience. However, CS has several shortcomings which limit its clinical translation such as: 1) artifacts arising from inaccurate sparse modelling assumptions, 2) extensive parameter tuning required for each clinical application, and 3) clinically infeasible reconstruction times. Recently, CS has been extended to incorporate deep neural networks as a way of learning complex image priors from historical exam data. Commonly referred to as unrolled neural networks, these techniques have proven to be a compelling and practical approach to address the challenges of sparse CS. In this tutorial, we will review the classical compressed sensing formulation and outline steps needed to transform this formulation into a deep learning-based reconstruction framework. Supplementary open source code in Python will be used to demonstrate this approach with open databases. Further, we will discuss considerations in applying unrolled neural networks in the clinical setting.

Cardiovascular ultrashort echo time to map fibrosis-promises and challenges

Increased collagen, or fibrosis, is an important marker of disease and may improve identification of patients at risk. In addition, fibrosis imaging may play an increasing role in guiding therapy and monitoring its effectiveness. MRI is the most frequently used modality to detect, visualize and quantify fibrosis non-invasively. However, standard MRI techniques used to phenotype cardiac fibrosis such as delayed enhancement and extracellular volume determination by T1 mapping, require the administration of gadolinium-based contrast and are particularly difficult to use in patients with cardiac devices such as pacemakers and automatic defibrillators. Therefore, such methods are limited in the serial evaluation of cardiovascular fibrosis as part of chronic disease monitoring. A method to directly measure collagen amount could be of great clinical benefit. In the current review we will discuss the potential of a novel MR technique, ultrashort echo time (UTE) MR, for fibrosis imaging. Although UTE imaging is successfully applied in other body areas such as musculoskeletal applications, there is very limited experience so far in the heart. We will review the established methods and currently available literature, discuss the technical considerations and challenges, show preliminary in vivo images and provide a future outlook on potential applications of cardiovascular UTE.

Respiration resolved imaging with continuous stable state 2D acquisition using linear frequency SWEEP

PURPOSE

To investigate the potential of continuous radiofrequency (RF) shifting (SWEEP) as a technique for creating densely sampled data while maintaining a stable signal state for dynamic imaging.

METHODS

We present a method where a continuous stable state of magnetization is swept smoothly across the anatomy of interest, creating an efficient approach to dense multiple 2D slice imaging. This is achieved by introducing a linear frequency offset to successive RF pulses shifting the excited slice by a fraction of the slice thickness with each successive repeat times (TR). Simulations and in vivo imaging were performed to assess how this affects the measured signal. Free breathing, respiration resolved 4D volumes in fetal/placental imaging is explored as potential application of this method.

RESULTS

The SWEEP method maintained a stable signal state over a full acquisition reducing artifacts from unstable magnetization. Simulations demonstrated that the effects of SWEEP on slice profiles was of the same order as that produced by physiological motion observed with conventional methods. Respiration resolved 4D data acquired with this method shows reduced respiration artifacts and resilience to non-rigid and non-cyclic motion.

CONCLUSIONS

The SWEEP method is presented as a technique for improved acquisition efficiency of densely sampled short-TR 2D sequences. Using conventional slice excitation the number of RF pulses required to enter a true steady state is excessively high when using short-TR 2D acquisitions, SWEEP circumvents this limitation by creating a stable signal state that is preserved between slices.

Motion Compensated Dynamic MRI Reconstruction With Local Affine Optical Flow Estimation

This paper proposes a novel framework to reconstruct dynamic magnetic resonance imaging (DMRI) with motion compensation (MC). Specifically, by combining the intensity-based optical flow constraint with the traditional compressed sensing scheme, we are able to jointly reconstruct the DMRI sequences and estimate the interframe motion vectors. Then, the DMRI reconstruction can be refined through MC with the estimated motion field. By employing the coarse-to-fine multi-scale resolution strategy, we are able to update the motion field in different spatial scales. The estimated motion vectors need to be interpolated to the finest resolution scale to compensate the DMRI reconstruction. Moreover, the proposed framework is capable of handling a wide class of prior information (regularizations) for DMRI reconstruction, such as sparsity, low rank, and total variation. The formulated optimization problem is solved by a primal-dual algorithm with linesearch due to its efficiency when dealing with non-differentiable problems. Experiments on various DMRI datasets validate the reconstruction quality improvement using the proposed scheme in comparison to several state-of-the-art algorithms.

SANTIS: Sampling-Augmented Neural neTwork with Incoherent Structure for MR image reconstruction

PURPOSE

To develop and evaluate a novel deep learning-based reconstruction framework called SANTIS (Sampling-Augmented Neural neTwork with Incoherent Structure) for efficient MR image reconstruction with improved robustness against sampling pattern discrepancy.

METHODS

With a combination of data cycle-consistent adversarial network, end-to-end convolutional neural network mapping, and data fidelity enforcement for reconstructing undersampled MR data, SANTIS additionally utilizes a sampling-augmented training strategy by extensively varying undersampling patterns during training, so that the network is capable of learning various aliasing structures and thereby removing undersampling artifacts more effectively and robustly. The performance of SANTIS was demonstrated for accelerated knee imaging and liver imaging using a Cartesian trajectory and a golden-angle radial trajectory, respectively. Quantitative metrics were used to assess its performance against different references. The feasibility of SANTIS in reconstructing dynamic contrast-enhanced images was also demonstrated using transfer learning.

RESULTS

Compared to conventional reconstruction that exploits image sparsity, SANTIS achieved consistently improved reconstruction performance (lower errors and greater image sharpness). Compared to standard learning-based methods without sampling augmentation (e.g., training with a fixed undersampling pattern), SANTIS provides comparable reconstruction performance, but significantly improved robustness, against sampling pattern discrepancy. SANTIS also achieved encouraging results for reconstructing liver images acquired at different contrast phases.

CONCLUSION

By extensively varying undersampling patterns, the sampling-augmented training strategy in SANTIS can remove undersampling artifacts more robustly. The novel concept behind SANTIS can particularly be useful for improving the robustness of deep learning-based image reconstruction against discrepancy between training and inference, an important, but currently less explored, topic.

Surveillance of abdominal aortic aneurysm using accelerated 3D non-contrast black-blood cardiovascular magnetic resonance with compressed sensing (CS-DANTE-SPACE)

BACKGROUND

3D non-contrast high-resolution black-blood cardiovascular magnetic resonance (CMR) (DANTE-SPACE) has been used for surveillance of abdominal aortic aneurysm (AAA) and validated against computed tomography (CT) angiography. However, it requires a long scan time of more than 7 min. We sought to develop an accelerated sequence applying compressed sensing (CS-DANTE-SPACE) and validate it in AAA patients undergoing surveillance.

METHODS

Thirty-eight AAA patients (all males, 73 ± 6 years) under clinical surveillance were recruited for this study. All patients were scanned with DANTE-SPACE (scan time 7:10 min) and CS-DANTE-SPACE (scan time 4:12 min, a reduction of 41.4%). Nine 9 patients were scanned more than 2 times. In total, 50 pairs of images were available for comparison. Two radiologists independently evaluated the image quality on a 1-4 scale, and measured the maximal diameter of AAA, the intra-luminal thrombus (ILT) and lumen area, ILT-to-muscle signal intensity ratio, and the ILT-to-lumen contrast ratio. The sharpness of the aneurysm inner/outer boundaries was quantified.

RESULTS

CS-DANTE-SPACE achieved comparable image quality compared with DANTE-SPACE (3.15 ± 0.67 vs. 3.03 ± 0.64, p = 0.06). There was excellent agreement between results from the two sequences for diameter/area and ILT ratio measurements (ICCs> 0.85), and for quantifying growth rate (3.3 ± 3.1 vs. 3.3 ± 3.4 mm/year, ICC = 0.95.) CS-DANTE-SPACE showed a higher ILT-to-lumen contrast ratio (p = 0.01) and higher sharpness than DANTE-SPACE (p = 0.002). Both sequences had excellent inter-reader reproducibility for quantitative measurements (ICC > 0.88).

CONCLUSION

CS-DANTE-SPACE can reduce scan time while maintaining image quality for AAA imaging. It is a promising tool for the surveillance of patients with AAA disease in the clinical setting.

Impact of (k,t) sampling on DCE MRI tracer kinetic parameter estimation in digital reference objects

Purpose

To evaluate the impact of (k,t) data sampling on the variance of tracer‐kinetic parameter (TK) estimation in high‐resolution whole‐brain dynamic contrast enhanced magnetic resonance imaging (DCE‐MRI) using digital reference objects. We study this in the context of TK model constraints, and in the absence of other constraints.

Methods

Three anatomically and physiologically realistic brain‐tumor digital reference objects were generated. Data sampling strategies included uniform and variable density; zone‐based, lattice, pseudo‐random, and pseudo‐radial; with 50‐time frames and 4‐fold to 25‐fold undersampling. In all cases, we assume a fully sampled first time frame, and prior knowledge of the arterial input function. TK parameters were estimated by indirect estimation (i.e., image‐time‐series reconstruction followed by model fitting), and direct estimation from the under‐sampled data. We evaluated methods based on the Cramér‐Rao bound and Monte‐Carlo simulations, over the range of signal‐to‐noise ratio (SNR) seen in clinical brain DCE‐MRI.

Results

Lattice‐based sampling provided the lowest SDs, followed by pseudo‐random, pseudo‐radial, and zone‐based. This ranking was consistent for the Patlak and extended Tofts model. Pseudo‐random sampling resulted in 19% higher averaged SD compared to lattice‐based sampling. Zone‐based sampling resulted in substantially higher SD at undersampling factors above 10. CRB analysis showed only a small difference between uniform and variable density for both lattice‐based and pseudo‐random sampling up to undersampling factors of 25.

Conclusion

Lattice sampling provided the lowest SDs, although the differences between sampling schemes were not substantial at low undersampling factors. The differences between lattice‐based and pseudo‐random sampling strategies with both uniform and variable density were within the range of error induced by other sources, at up to 25‐fold undersampling.

Respiratory-Correlated (RC) vs. Time-Resolved (TR) Four-Dimensional Magnetic Resonance Imaging (4DMRI) for Radiotherapy of Thoracic and Abdominal Cancer

Recent technological and clinical advancements of both respiratory-correlated (RC) and time-resolved (TR) four-dimensional magnetic resonance imaging (4DMRI) techniques are reviewed in light of tumor/organ motion simulation, monitoring, and assessment in radiotherapy. For radiotherapy of thoracic and abdominal cancer, respiratory-induced tumor motion, and motion variation due to breathing irregularities are the major uncertainties in treatment. RC-4DMRI is developed to assess tumor motion for treatment planning, whereas TR-4DMRI is developed to assess both motion and motion variation for treatment planning, delivery and assessment. RC-4DMRI is reconstructed to provide one-breathing-cycle motion, similar to 4D computed tomography (4DCT), the current clinical standard, but with higher soft-tissue contrast, no ionizing radiation, and less binning artifacts due to the use of an internal respiratory surrogate. Recent studies have shown that its spatial resolution has reached or exceeded that of 4DCT and scanning time becomes clinically acceptable. TR-4DMRI is recently developed with an adequate spatiotemporal resolution to assess tumor motion and motion variations for treatment simulation, delivery and assessment. The super-resolution approach is most promising since it can image any organ/body motion, whereas RC-4D MRI are limited to resolve only respiration-induced motion and some TR-4DMRI approaches may more or less depend on RC-4DMRI. TR-4DMRI provides multi-breath motion data that are useful not only in MR-guided radiotherapy but also for building a patient-specific motion model to guide radiotherapy treatment using an non-MR-equipped linear accelerator. Based on 4DMRI motion data, motion-corrected dynamic contrast imaging and diffusion-weighted imaging have also been reported, aiming to facilitate tumor delineation for more accurate radiotherapy targeting. Both RC- and TR-4DMRI have been evaluated for potential clinical applications, such as delineation of tumor volumes, where sufficiently high spatial resolution and large field-of-view are required. The 4DMRI techniques are promising to play a role in motion assessment in radiotherapy treatment planning, delivery, assessment, and adaptation.

Deep learning within a priori temporal feature spaces for large-scale dynamic MR image reconstruction: Application to 5-D cardiac MR Multitasking

High spatiotemporal resolution dynamic magnetic resonance imaging (MRI) is a powerful clinical tool for imaging moving structures as well as to reveal and quantify other physical and physiological dynamics. The low speed of MRI necessitates acceleration methods such as deep learning reconstruction from under-sampled data. However, the massive size of many dynamic MRI problems prevents deep learning networks from directly exploiting global temporal relationships. In this work, we show that by applying deep neural networks inside a priori calculated temporal feature spaces, we enable deep learning reconstruction with global temporal modeling even for image sequences with >40,000 frames. One proposed variation of our approach using dilated multi-level Densely Connected Network (mDCN) speeds up feature space coordinate calculation by 3000x compared to conventional iterative methods, from 20 minutes to 0.39 seconds. Thus, the combination of low-rank tensor and deep learning models not only makes large-scale dynamic MRI feasible but also practical for routine clinical application.

Free-Breathing & Ungated Cardiac MRI Using Iterative SToRM (i-SToRM)

We introduce a local manifold regularization approach to recover dynamic MRI data from highly undersampled measurements. The proposed scheme relies on the manifold structure of local image patches at the same spatial location in a free-breathing cardiac MRI dataset; this approach is a generalization of the SmooThness Regularization on Manifolds (SToRM) scheme that exploits the global manifold structure of images in the dataset. Since the manifold structure of the patches varies depending on the spatial location and is often considerably simpler than the global one, this approach significantly reduces the data demand, facilitating the recovery from shorter scans. Since the navigator-based estimation of manifold structure pursued in SToRM is not feasible in this setting, a reformulation of SToRM is introduced. Specifically, the regularization term of the cost function involves the sum of robust distances between images sub-patches in the dataset. The optimization algorithm alternates between updating the images and estimating the manifold structure of the image patches. The utility of the proposed scheme is demonstrated in the context of in-vivo prospective free-breathing cardiac CINE MRI imaging with multichannel acquisitions and simulated phantoms. The new framework facilitates a reduction in scan time, as compared to the SToRM strategy.

Iterative motion-compensation reconstruction ultra-short TE (iMoCo UTE) for high-resolution free-breathing pulmonary MRI

PURPOSE

To develop a high-scanning efficiency, motion-corrected imaging strategy for free-breathing pulmonary MRI by combining an iterative motion-compensation reconstruction with a ultrashort echo time (UTE) acquisition called iMoCo UTE.

METHODS

An optimized golden-angle ordering radial UTE sequence was used to continuously acquire data for 5 minutes. All readouts were grouped to different respiratory motion states based on self-navigator signals, and then motion-resolved data was reconstructed by XD golden-angle radial sparse parallel reconstruction. One state from the motion-resolved images was selected as a reference, and then motion fields from the other states to the reference were derived via nonrigid registration. Finally, all motion-resolved data and motion fields were reconstructed by using an iterative motion-compensation (MoCo) reconstruction with a total generalized variation sparse constraint.

RESULTS

The iMoCo UTE strategy was evaluated in volunteers and nonsedated pediatric patient (4-6 years old) studies. Images reconstructed with iMoCo UTE provided sharper anatomical lung structures and higher apparent SNR and contrast-to-noise ratio compared to using other motion-correction strategies, such as soft-gating, motion-resolved reconstruction, and nonrigid MoCo. iMoCo UTE also showed promising results in an infant study.

CONCLUSION

The proposed iMoCo UTE combines self-navigation, motion modeling, and a compressed sensing reconstruction to increase scan efficiency and SNR and to reduce respiratory motion in lung MRI. This proposed strategy shows improvements in free-breathing lung MRI scans, especially in very challenging application situations such as pediatric MRI studies.

Reconstruction techniques for cardiac cine MRI

The present survey describes the state-of-the-art techniques for dynamic cardiac magnetic resonance image reconstruction. Additionally, clinical relevance, main challenges, and future trends of this image modality are outlined. Thus, this paper aims to provide a general vision about cine MRI as the standard procedure in functional evaluation of the heart, focusing on technical methodologies.

Model-based myocardial T1 mapping with sparsity constraints using single-shot inversion-recovery radial FLASH cardiovascular magnetic resonance

BACKGROUND

This study develops a model-based myocardial T1 mapping technique with sparsity constraints which employs a single-shot inversion-recovery (IR) radial fast low angle shot (FLASH) cardiovascular magnetic resonance (CMR) acquisition. The method should offer high resolution, accuracy, precision and reproducibility.

METHODS

The proposed reconstruction estimates myocardial parameter maps directly from undersampled k-space which is continuously measured by IR radial FLASH with a 4 s breathhold and retrospectively sorted based on a cardiac trigger signal. Joint sparsity constraints are imposed on the parameter maps to further improve T1 precision. Validations involved studies of an experimental phantom and 8 healthy adult subjects.

RESULTS

In comparison to an IR spin-echo reference method, phantom experiments with T1 values ranging from 300 to 1500 ms revealed good accuracy and precision at simulated heart rates between 40 and 100 bpm. In vivo T1 maps achieved better precision and qualitatively better preservation of image features for the proposed method than a real-time CMR approach followed by pixelwise fitting. Apart from good inter-observer reproducibility (0.6% of the mean), in vivo results confirmed good intra-subject reproducibility (1.05% of the mean for intra-scan and 1.17, 1.51% of the means for the two inter-scans, respectively) of the proposed method.

CONCLUSION

Model-based reconstructions with sparsity constraints allow for single-shot myocardial T1 maps with high spatial resolution, accuracy, precision and reproducibility within a 4 s breathhold. Clinical trials are warranted.

Low-Rank Tensor Models for Improved Multi-Dimensional MRI: Application to Dynamic Cardiac T 1 Mapping

Multi-dimensional, multi-contrast magnetic resonance imaging (MRI) has become increasingly available for comprehensive and time-efficient evaluation of various pathologies, providing large amounts of data and offering new opportunities for improved image reconstructions. Recently, a cardiac phase-resolved myocardial T 1 mapping method has been introduced to provide dynamic information on tissue viability. Improved spatio-temporal resolution in clinically acceptable scan times is highly desirable but requires high acceleration factors. Tensors are well-suited to describe inter-dimensional hidden structures in such multi-dimensional datasets. In this study, we sought to utilize and compare different tensor decomposition methods, without the use of auxiliary navigator data. We explored multiple processing approaches in order to enable high-resolution cardiac phase-resolved myocardial T 1 mapping. Eight different low-rank tensor approximation and processing approaches were evaluated using quantitative analysis of accuracy and precision in T 1 maps acquired in six healthy volunteers. All methods provided comparable T 1 values. However, the precision was significantly improved using local processing, as well as a direct tensor rank approximation. Low-rank tensor approximation approaches are well-suited to enable dynamic T 1 mapping at high spatio-temporal resolutions.

MR fingerprinting for water T1 and fat fraction quantification in fat infiltrated skeletal muscles

PURPOSE

To develop a fast MR fingerprinting (MRF) sequence for simultaneous estimation of water T1 (T1_H2O ) and fat fraction (FF) in fat infiltrated skeletal muscles.

METHODS

The MRF sequence for T1_H2O and FF quantification (MRF T1-FF) comprises a 1400 radial spokes echo train, following nonselective inversion, with varying echo and repetition time, as well as prescribed flip angle. Undersampled frames were reconstructed at different acquisition time-points by nonuniform Fourier transform, and a bi-component model based on Bloch simulations applied to adjust the signal evolution and extract T1_H2O and FF. The sequence was validated on a multi-vial phantom, in three healthy volunteers and five patients with neuromuscular diseases. We evaluated the agreement between MRF T1-FF parameters and reference values and confounding effects due to B0 and B1 inhomogeneities.

RESULTS

In phantom, T1_H2O and FF were highly correlated with references values measured with multi-inversion time inversion recovery-stimulated echo acquisition mode and Dixon, respectively (R² > 0.99). In vivo, T1_H2O and FF determined by the MRF T1-FF sequence were also correlated with reference values (R² = 0.98 and 0.97, respectively). The precision on T1_H2O was better than 5% for muscles where FF was less than 0.4. Both T1_H2O and FF values were not confounded by B0 nor B1 inhomogeneities.

CONCLUSION

The MRF T1-FF sequence derived T1_H2O and FF values in voxels containing a mixture of water and fat protons. This method can be used to comprehend and characterize the effects of tissue water compartmentation and distribution on muscle T1 values in patients affected by chronic fat infiltration.

Manifold recovery using kernel low-rank regularization: application to dynamic imaging

We introduce a novel kernel low-rank algorithm to recover free-breathing and ungated dynamic MRI data from highly undersampled measurements. The image frames in the free breathing and ungated dataset are assumed to be points on a bandlimited manifold. We show that the non-linear features of these images satisfy annihilation conditions, which implies that the kernel matrix derived from the dataset is low-rank. We penalize the nuclear norm of the feature matrix to recover the images from highly undersampled measurements. The regularized optimization problem is solved using an iterative reweighted least squares (IRLS) algorithm, which alternates between the update of the Laplacian matrix of the manifold and the recovery of the signals from the noisy measurements. To improve computational efficiency, we use a two step algorithm using navigator measurements. Specifically, the Laplacian matrix is estimated from the navigators using the IRLS scheme, followed by the recovery of the images using a quadratic optimization. We show the relation of this two step algorithm with our recent SToRM approach, thus reconciling SToRM and manifold regularization methods with algorithms that rely on explicit lifting of data to a high dimensional space. The IRLS based estimation of the Laplacian matrix is a systematic and noise-robust alternative to current heuristic strategies based on exponential maps. We also approximate the Laplacian matrix using a few eigen vectors, which results in a fast and memory efficient algorithm. The proposed scheme is demonstrated on several patients with different breathing patterns and cardiac rates.

Motion robust respiratory-resolved 3D radial flow MRI and its application in neonatal congenital heart disease

PURPOSE

To test and implement a motion-robust and respiratory-resolved 3D Radial Flow framework that addresses the need for rapid, high resolution imaging in neonatal patients with congenital heart disease.

METHODS

A 4-point velocity encoding and 3D radial trajectory with double-golden angle ordering was combined with bulk motion correction (from projection center of mass) and respiration phase detection (from principal component analysis of heartbeat-averaged data) to create motion-robust 3D velocity cardiac time-averaged data. This framework was tested in a whole-chest digital phantom with simulated bulk and realistic physiological motion. In vivo imaging was performed in 20 congenital heart disease infants under feed-and-sleep with submillimeter isotropic resolution in ~3 min. Flows were validated against clinical 2D PCMRI and whole-heart visualizations of blood flow were performed.

RESULTS

The proposed framework resolved all simulated digital phantom motion states (mean ± standard error: rotation - azimuthal = 0.29 ± 0.02°; translation - T_y = 1.29 ± 0.12 mm, T_z = -0.27 ± 0.13 mm; rotation+translation - polar = 0.49 ± 0.16°, T_x = -2.47 ± 0.51 mm, T_z = 5.78 ± 1.33 mm). Measured timing errors of peak expiration across all signal-to-noise ratio values were 22% of the true respiratory period (range = [404-489 ± 298-334] ms). For in vivo imaging, motion correction improved 3D Radial Flow measurements (no correction: R² = 0.62, root mean square error = 0.80 L/min/m² , Bland-Altman bias [limits of agreement] = -0.21 [-1.40, 0.94] L/min/m² ; motion corrected, expiration: R² = 0.90, root mean square error = 0.46 L/min/m² , bias [limits of agreement] = 0.06 [-0.49, 0.62] L/min/m² ). Respiratory-resolved 3D velocity visualizations were achieved in various neonatal pathologies pre- and postsurgical correction.

CONCLUSION

3D cardiac flow may be visualized and accurately quantified in neonatal subjects using the proposed framework. This technique may enable more comprehensive hemodynamic studies in small infants.

Natively fat-suppressed 5D whole-heart MRI with a radial free-running fast-interrupted steady-state (FISS) sequence at 1.5T and 3T

PURPOSE

To implement, optimize, and test fast interrupted steady-state (FISS) for natively fat-suppressed free-running 5D whole-heart MRI at 1.5 tesla (T) and 3T.

METHODS

FISS was implemented for fully self-gated free-running cardiac- and respiratory-motion-resolved radial imaging of the heart at 1.5T and 3T. Numerical simulations and phantom scans were performed to compare fat suppression characteristics and to determine parameter ranges (number of readouts [NR] per FISS module and TR) for effective fat suppression. Subsequently, free-running FISS data were collected in 10 healthy volunteers and images were reconstructed with compressed sensing. All acquisitions were compared with a continuous balanced steady-state free precession version of the same sequence, and both fat suppression and scan times were analyzed.

RESULTS

Simulations demonstrate a variable width and location of suppression bands in FISS that were dependent on TR and NR. For a fat suppression bandwidth of 100 Hz and NR ≤ 8, simulations demonstrated that a TR between 2.2 ms and 3.0 ms is required at 1.5T, whereas a range of 3.0 ms to 3.5 ms applies at 3T. Fat signal increases with NR. These findings were corroborated in phantom experiments. In volunteers, fat SNR was significantly decreased using FISS compared with balanced steady-state free precession (P < 0.05) at both field strengths. After protocol optimization, high-resolution (1.1 mm³ ) 5D whole-heart free-running FISS can be performed with effective fat suppression in under 8 min at 1.5T and 3T at a modest scan time increase compared to balanced steady-state free precession.

CONCLUSION

An optimal FISS parameter range was determined enabling natively fat-suppressed 5D whole-heart free-running MRI with a single continuous scan at 1.5T and 3T, demonstrating potential for cardiac imaging and noncontrast angiography.

MRI in cardio-oncology: A review of cardiac complications in oncologic care

From detailed characterization of cardiac abnormalities to the assessment of cancer treatment-related cardiac dysfunction, cardiac MRI is playing a growing role in the evaluation of cardiac pathology in oncology patients. Current guidelines are now incorporating the use of MRI for the comprehensive multidisciplinary approach to cancer management, and innovative applications of MRI in research are expanding its potential to provide a powerful noninvasive tool in the arsenal against cancer. This review focuses on the application of cardiac MRI to diagnose and manage cardiovascular complications related to cancer and its treatment. Following an introduction to current cardiac MRI methods and principles, this review is divided into two sections: functional cardiovascular analysis and anatomical or tissue characterization related to cancer and cancer therapeutics. Level of Evidence: 5 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2019;50:1349-1366.

Efficient triple-VENC phase-contrast MRI for improved velocity dynamic range

PURPOSE

To evaluate the utility of an efficient triple velocity-encoding (VENC) 4D flow MRI implementation to improve velocity unwrapping of 4D flow MRI data with the same scan time as an interleaved dual-VENC acquisition.

METHODS

A balanced 7-point acquisition was used to derive 3 sets of 4D flow images corresponding to 3 different VENCs. These 3 datasets were then used to unwrap the aliased lowest VENC into a minimally aliased, triple-VENC dataset. Triple-VENC MRI was evaluated and compared with dual-VENC MRI over 3 different VENC ranges (50-150, 60-150, and 60-180 cm/s) in vitro in a steadily rotating phantom as well as in a pulsatile flow phantom. In vivo, triple-VENC data of the thoracic aorta were also evaluated in 3 healthy volunteers (2 males, 26-44 years old) with VENC = 50/75/150 cm/s. Two triple-VENC (triconditional and biconditional) and 1 dual-VENC unwrapping algorithms were quantitatively assessed through comparison to a reference, unaliased, single-VENC scan.

RESULTS

Triple-VENC 4D flow constant rotation phantom results showed high correlation with the analytical solution (intraclass correlation coefficient = 0.984-0.995, P < .001) and up to a 61% reduction in velocity noise compared with the corresponding single-VENC scans (VENC = 150, 180 cm/s). Pulsatile flow phantom experiments demonstrated good agreement between triple-VENC and single-VENC acquisitions (peak flow < 0.8% difference; peak velocity < 11.7% difference). Triconditional triple-VENC unwrapping consistently outperformed dual-VENC unwrapping, correctly unwrapping more than 83% and 46%-66% more voxels in vitro and in vivo, respectively.

CONCLUSION

Triple-VENC 4D flow MRI adds no additional scan time to dual-VENC MRI and has the potential for improved unwrapping to extend the velocity dynamic range beyond dual-VENC methods.

GRASP-Pro: imProving GRASP DCE-MRI through self-calibrating subspace-modeling and contrast phase automation

PURPOSE

To propose a highly accelerated, high-resolution dynamic contrast-enhanced MRI (DCE-MRI) technique called GRASP-Pro (golden-angle radial sparse parallel imaging with imProved performance) through a joint sparsity and self-calibrating subspace constraint with automated selection of contrast phases.

METHODS

GRASP-Pro reconstruction enforces a combination of an explicit low-rank subspace-constraint and a temporal sparsity constraint. The temporal basis used to construct the subspace is learned from an intermediate reconstruction step using the low-resolution portion of radial k-space, which eliminates the need for generating the basis using auxiliary data or a physical signal model. A convolutional neural network was trained to generate the contrast enhancement curve in the artery, from which clinically relevant contrast phases are automatically selected for evaluation. The performance of GRASP-Pro was demonstrated for high spatiotemporal resolution DCE-MRI of the prostate and was compared against standard GRASP in terms of overall image quality, image sharpness, and residual streaks and/or noise level.

RESULTS

Compared to GRASP, GRASP-Pro reconstructed dynamic images with enhanced sharpness, less residual streaks and/or noise, and finer delineation of the prostate without prolonging reconstruction time. The image quality improvement reached statistical significance (P < 0.05) in all the assessment categories. The neural network successfully generated contrast enhancement curves in the artery, and corresponding peak enhancement indexes correlated well with that from the manual selection.

CONCLUSION

GRASP-Pro is a promising method for rapid and continuous DCE-MRI. It enables superior reconstruction performance over standard GRASP and allows reliable generation of artery enhancement curve to guide the selection of desired contrast phases for improving the efficiency of GRASP MRI workflow.