Multishot Diffusion-weighted Imaging Research Articles

Motion-Compensated Multishot Pancreatic Diffusion-Weighted Imaging With Deep Learning-Based Denoising.

Pancreatic diffusion-weighted imaging (DWI) has numerous clinical applications, but conventional single-shot methods suffer from off resonance-induced artifacts like distortion and blurring while cardiovascular motion-induced phase inconsistency leads to quantitative errors and signal loss, limiting its utility. Multishot DWI (msDWI) offers reduced image distortion and blurring relative to single-shot methods but increases sensitivity to motion artifacts. Motion-compensated diffusion-encoding gradients (MCGs) reduce motion artifacts and could improve motion robustness of msDWI but come with the cost of extended echo time, further reducing signal. Thus, a method that combines msDWI with MCGs while minimizing the echo time penalty and maximizing signal would improve pancreatic DWI. In this work, we combine MCGs generated via convex-optimized diffusion encoding (CODE), which reduces the echo time penalty of motion compensation, with deep learning (DL)-based denoising to address residual signal loss. We hypothesize this method will qualitatively and quantitatively improve msDWI of the pancreas. This prospective institutional review board-approved study included 22 patients who underwent abdominal MR examinations from August 22, 2022 and May 17, 2023 on 3.0 T scanners. Following informed consent, 2-shot spin-echo echo-planar DWI (b = 0, 800 s/mm2) without (M0) and with (M1) CODE-generated first-order gradient moment nulling was added to their clinical examinations. DL-based denoising was applied to the M1 images (M1 + DL) off-line. ADC maps were reconstructed for all 3 methods. Blinded pair-wise comparisons of b = 800 s/mm2 images were done by 3 subspecialist radiologists. Five metrics were compared: pancreatic boundary delineation, motion artifacts, signal homogeneity, perceived noise, and diagnostic preference. Regions of interest of the pancreatic head, body, and tail were drawn, and mean ADC values were computed. Repeated analysis of variance and post hoc pairwise t test with Bonferroni correction were used for comparing mean ADC values. Bland-Altman analysis compared mean ADC values. Reader preferences were tabulated and compared using Wilcoxon signed rank test with Bonferroni correction and Fleiss κ. M1 was significantly preferred over M0 for perceived motion artifacts and signal homogeneity (P < 0.001). M0 was significantly preferred over M1 for perceived noise (P < 0.001), but DL-based denoising (M1 + DL) reversed this trend and was significantly favored over M0 (P < 0.001). ADC measurements from M0 varied between different regions of the pancreas (P = 0.001), whereas motion correction with M1 and M1 + DL resulted in homogeneous ADC values (P = 0.24), with values similar to those reported for ssDWI with motion correction. ADC values from M0 were significantly higher than M1 in the head (bias 16.6%; P < 0.0001), body (bias 11.0%; P < 0.0001), and tail (bias 8.6%; P = 0.001). A small but significant bias (2.6%) existed between ADC values from M1 and M1 + DL. CODE-generated motion compensating gradients improves multishot pancreatic DWI as interpreted by expert readers and eliminated ADC variation throughout the pancreas. DL-based denoising mitigated signal losses from motion compensation while maintaining ADC consistency. Integrating both techniques could improve the accuracy and reliability of multishot pancreatic DWI.

Comparative analysis of image quality and diagnostic performance among SS-EPI, MS-EPI, and rFOV DWI in bladder cancer.

To compare image quality and diagnostic performance among SS-EPI diffusion weighted imaging (DWI), multi-shot (MS) EPI DWI, and reduced field-of-view (rFOV) DWI for muscle-invasive bladder cancer (MIBC). This retrospective study included 73 patients with bladder cancer who underwent multiparametric MRI in our referral center between August 2020 and February 2023. Qualitative image assessment was performed in 73; and quantitative assessment was performed in 66 patients with maximum lesion diameter > 10mm. The diagnostic performance of the imaging finding of muscle invasion was evaluated in 47 patients with pathological confirmation of MIBC. T2-weighted imaging, SS-EPI DWI, MS-EPI DWI, rFOV DWI, and dynamic contrast-enhanced imaging were acquired with 3T-MRI. Qualitative image assessment was performed by three readers who rated anatomical distortion, clarity of bladder wall, and lesion conspicuity using a four-point scale. Quantitative assessment included calculation of SNR and CNR, and grading of the presence of muscle layer invasion according to the VI-RADS diagnostic criteria. Wilcoxon matched pairs signed rank test was used to compare qualitative and quantitative image quality. McNemar test and receiver-operating characteristic analysis were used to compare diagnostic performance. Anatomical distortion was less in MS-EPI DWI, rFOV DWI, and SS-EPI DWI, in that order with significant difference. Clarity of bladder wall was greater for MS-EPI DWI, SS-EPI DWI, and rFOV DWI, in that order. There were significant differences between any two combinations of the three DWI types, except between SS-EPI DWI and MS-EPI in Reader 1. Lesion conspicuity, diagnostic performance, SNR and CNR were not significantly different among the three DWI types. Among the three DWI sequences evaluated, MS-EPI DWI showed the least anatomical distortion and superior bladder wall delineation but no improvement in diagnostic performance for MIBC. MS-EPI DWI may be considered for additional imaging if SS-EPI DWI is of poor quality.

Comparison of single shot and multishot diffusion-weighted imaging in 5-T magnetic resonance imaging for brain disease diagnosis.

Diffusion-weighted imaging (DWI) with single-shot echo-planar imaging (ssEPI) is a valuable tool for detecting acute brain lesions but does suffer from image distortions. Multishot echo-planar imaging (msEPI) is a technique for reducing such distortions. This study aimed to compare the image quality and diagnostic efficacy of ssEPI- and msEPI-DWI at 5.0 T for brain disease detection. This study retrospectively reviewed images of 107 consecutive patients with suspected brain diseases who underwent ssEPI- and msEPI-DWI at 5.0 T at the First Affiliated Hospital of University of Science and Technology of China from August 2023 to September 2023. Two radiologists independently graded image quality and measured the image distortion. Signal-to-noise ratio, contrast-to-noise ratio, and apparent diffusion coefficient (ADC) were calculated and compared between ssEPI- and msEPI-DWI. Image quality scores were compared using the Wilcoxon test and other continuous variables by the paired t-test. The diagnostic accuracy of ADC values in distinguishing lesions from normal-appearing tissues was measured with the area under the curve (AUC). Image quality evaluation and distortion analysis revealed that msEPI-DWI significantly outperformed ssEPI-DWI (two-sided P<0.001). No significant difference was observed in signal-to-noise ratio, contrast-to-noise ratio, or ADC values between msEPI- and ssEPI-DWI (two-sided P≥0.601). The ADC values of msEPI- and ssEPI-DWI showed strong correlations for both lesions (r=0.97) and contralateral normal tissues (r=0.91) (two-sided P<0.001). Compared to those of the contralateral white matter, ADC values of low-grade gliomas (LGGs) were significantly higher [ssEPI-DWI: 1,119.9±273.1 vs. 805.1±73.9; msEPI-DWI: 1,196.2±355.6 vs. 757.3±98.0 (unit: ×10-6 mm2/s)], while the ADC values of acute cerebral infarction (ACI) lesions were significantly lower [ssEPI-DWI: 603.9±273.2 vs. 888.9±212.0; msEPI-DWI: 538.0±281.2 vs. 905.0±188.9 (unit: ×10-6 mm2/s)] (two-sided P≤0.003). The AUCs for detecting LGGs were excellent for both ssEPI-DWI [AUC =0.934; 95% confidence interval (CI): 0.84-1.00] and msEPI-DWI (AUC =0.944; 95% CI: 0.86-1.00) (two-sided P<0.001; two-sided DeLong test: P=0.833). As compared to ssEPI-DWI, msEPI-DWI, when performed at 5.0 T, demonstrated superior image quality and less anatomical distortion in a wide spectrum of brain diseases and showed promising diagnostic performance for LGGs and ACI. In the future, msEPI-DWI at 5.0 T could become clinically routine in the diagnosis and grading of brain disorders.

Accelerated multi-b-value multi-shot diffusion-weighted imaging based on EPI with keyhole and a low-rank tensor constraint

PurposeMulti-Shot (MS) Echo-Planar Imaging (EPI) may improve the in-plane resolution of multi-b-value DWI, yet it also considerably increases the scan time. Here we explored the combination of EPI with Keyhole (EPIK) and a calibrationless reconstruction algorithm for acceleration of multi-b-value MS-DWI. MethodsWe firstly analyzed the impact of nonuniform phase accrual in EPIK on the reconstructed image. Based on insights gained from the analysis, we developed a calibrationless reconstruction algorithm based on a Space-Contrast-Coil Locally Low-Rank Tensor (SCC-LLRT) constraint for reconstruction of EPIK-acquired data. We compared the algorithm with a modified SPatial-Angular Locally Low-Rank (SPA-LLR) algorithm through simulations, phantoms, and in vivo study. We then compared EPIK with uniformly undersampled EPI for accelerating multi-b-value DWI in 6 healthy subjects. ResultsThrough theoretical derivations, we found that the reconstruction of EPIK with a SENSE-encoding-based algorithm, such as SPA-LLR, may cause additional aliasing artifacts due to the frequency-dependent distortion of the coil sensitivity. Results from simulations, phantoms, and in vivo study verified the theoretical finding by showing that the calibrationless SCC-LLRT algorithm reduced aliasing artifacts compared with SPA-LLR. Finally, EPIK with SCC-LLRT substantially reduced the ghosting artifacts compared with uniform undersampled multi-b-value DWI, decreasing the fitting errors in ADC (0.05 ± 0.01 vs 0.10 ± 0.01, P < 0.001) and IVIM mapping (0.026 ± 0.004 vs 0.06 ± 0.006, P < 0.001). ConclusionThe SCC-LLRT algorithm reduced the aliasing artifacts of EPIK by using a calibrationless modeling of the multi-coil data. The dense sampling of k-space center offers EPIK a potential to improve image quality for acceleration of multi-b-value MS-DWI.

Robust multishot diffusion-weighted imaging of the abdomen with region-based shot rejection.

Diffusion-weighted (DW) imaging provides a useful clinical contrast, but is susceptible to motion-induced dephasing caused by the application of strong diffusion gradients. Phase navigators are commonly used to resolve shot-to-shot motion-induced phase in multishot reconstructions, but poor phase estimates result in signal dropout and Apparent Diffusion Coefficient (ADC) overestimation. These artifacts are prominent in the abdomen, a region prone to involuntary cardiac and respiratory motion. To improve the robustness of DW imaging in the abdomen, region-based shot rejection schemes that selectively weight regions where the shot-to-shot phase is poorly estimated were evaluated. Spatially varying weights for each shot, reflecting both the accuracy of the estimated phase and the degree of subvoxel dephasing, were estimated from the phase navigator magnitude images. The weighting was integrated into a multishot reconstruction using different formulations and phase navigator resolutions and tested with different phase navigator resolutions in multishot DW-echo Planar Imaging acquisitions of the liver and pancreas, using conventional monopolar and velocity-compensated diffusion encoding. Reconstructed images and ADC estimates were compared qualitatively. The proposed region-based shot rejection reduces banding and signal dropout artifacts caused by physiological motion in the liver and pancreas. Shot rejection allows conventional monopolar diffusion encoding to achieve median ADCs in the pancreas comparable to motion-compensated diffusion encoding, albeit with a greater spread of ADCs. Region-based shot rejection is a linear reconstruction that improves the motion robustness of multi-shot DWI and requires no sequence modifications.

Highly accelerated multi-shot intravoxel incoherent motion diffusion-weighted imaging in brain enabled by parametric POCS-based multiplexed sensitivity encoding.

Recently, intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) has also been demonstrated as an imaging tool for applications in neurological and neurovascular diseases. However, the use of single-shot diffusion-weighted echo-planar imaging for IVIM DWI acquisition leads to suboptimal data quality: for instance, geometric distortion and deteriorated image quality at high spatial resolution. Although the recently commercialized multi-shot acquisition methods, such as multiplexed sensitivity encoding (MUSE), can attain high-resolution and high-quality DWI with signal-to-noise ratio (SNR) performance superior to that of the conventional parallel imaging method, the prolonged scan time associated with multi-shot acquisition is impractical for routine IVIM DWI. This study proposes an acquisition and reconstruction framework based on parametric-POCSMUSE to accelerate the four-shot IVIM DWI with 70% reduction of total scan time (13 min 8 s versus 4 min 8 s). First, the four-shot IVIM DWI scan with 17 b values was accelerated by acquiring only one segment per b value except for b values of 0 and 600 s/mm2 . Second, an IVIM-estimation scheme was integrated into the parametric-POCSMUSE to enable joint reconstruction of multi-b images from under-sampled four-shot IVIM DWI data. In vivo experiments on both healthy subjects and patients show that the proposed framework successfully produced multi-b DW images with significantly higher SNRs and lower reconstruction errors than did the conventional acceleration method based on parallel imaging. In addition, the IVIM quantitative maps estimated from the data produced by the proposed framework showed quality comparable to that of fully sampled MUSE-reconstructed images, suggesting that the proposed framework can enable highly accelerated multi-shot IVIM DWI without sacrificing data quality. In summary, the proposed framework can make multi-shot IVIM DWI feasible in a routine MRI examination, with reasonable scan time and improved geometric fidelity.

High-resolution multi-shot diffusion imaging of structural networks in healthy neurocognitive aging

Healthy neurocognitive aging has been associated with the microstructural degradation of white matter pathways that connect distributed gray matter regions, assessed by diffusion-weighted imaging (DWI). However, the relatively low spatial resolution of standard DWI has limited the examination of age-related differences in the properties of smaller, tightly curved white matter fibers, as well as the relatively more complex microstructure of gray matter. Here, we capitalize on high-resolution multi-shot DWI, which allows spatial resolutions < 1 mm3 to be achieved on clinical 3T MRI scanners. We assessed whether traditional diffusion tensor-based measures of gray matter microstructure and graph theoretical measures of white matter structural connectivity assessed by standard (1.5 mm3 voxels, 3.375 μl volume) and high-resolution (1 mm3 voxels, 1μl volume) DWI were differentially related to age and cognitive performance in 61 healthy adults 18–78 years of age. Cognitive performance was assessed using an extensive battery comprising 12 separate tests of fluid (speed-dependent) cognition. Results indicated that the high-resolution data had larger correlations between age and gray matter mean diffusivity, but smaller correlations between age and structural connectivity. Moreover, parallel mediation models including both standard and high-resolution measures revealed that only the high-resolution measures mediated age-related differences in fluid cognition. These results lay the groundwork for future studies planning to apply high-resolution DWI methodology to further assess the mechanisms of both healthy aging and cognitive impairment.

Multishot Diffusion-Weighted MRI of the Breasts in the Supine vs. Prone Position.

Diffusion-weighted imaging (DWI) may allow for breast cancer screening MRI without a contrast injection. Multishot methods improve prone DWI of the breasts but face different challenges in the supine position. To establish a multishot DWI (msDWI) protocol for supine breast MRI and to evaluate the performance of supine vs. prone msDWI. Prospective. Protocol optimization: 10 healthy women (ages 22-56), supine vs. prone: 24 healthy women (ages 22-62) and five women (ages 29-61) with breast tumors. 3-T, protocol optimization msDWI: free-breathing (FB) 2-shots, FB 4-shots, respiratory-triggered (RT) 2-shots, RT 4-shots, supine vs. prone: RT 4-shot msDWI, T2-weighted fast-spin echo. Protocol optimization and supine vs. prone: three observers performed an image quality assessment of sharpness, aliasing, distortion (vs. T2), perceived SNR, and overall image quality (scale of 1-5). Apparent diffusion coefficients (ADCs) in fibroglandular tissue (FGT) and breast tumors were measured. Effect of study variables on dichotomized ratings (4/5 vs. 1/2/3) and FGT ADCs were assessed with mixed-effects logistic regression. Interobserver agreement utilized Gwet's agreement coefficient (AC). Lesion ADCs were assessed by Bland-Altman analysis and concordance correlation (ρc ). P value <0.05 was considered statistically significant. Protocol optimization: 4-shots significantly improved sharpness and distortion; RT significantly improved sharpness, aliasing, perceived SNR, and overall image quality. FGT ADCs were not significantly different between shots (P=0.812), FB vs. RT (P=0.591), or side (P=0.574). Supine vs. prone: supine images were rated significantly higher for sharpness, aliasing, and overall image quality. FGT ADCs were significantly higher supine; lesion ADCs were highly correlated (ρc =0.92). Based on image quality, supine msDWI outperformed prone msDWI. Lesion ADCs were highly correlated between the two positions, while FGT ADCs were higher in the supine position. 2. Stage 1.

Technical note: Revised projections onto convex sets reconstruction of multi-shot diffusion-weighted imaging.

High-resolution diffusion-weighted imaging (DWI) is usually achieved through multi-shot acquisitions and parallel imaging-based reconstructions. Multiple POCS (projections onto convex sets) based algorithms have been proposed for DWI reconstructions. However, the slow convergence of POCS and the suboptimal quality of the reconstructed images limit their applications. In this study, a revised POCS algorithm for multi-shot DWI reconstruction is proposed based on FISTA (fast iterative shrinkage-thresholding algorithm) to achieve faster convergence and higher accuracy. In FISTA, the next iteration is computed based on two previous iterations, instead of only the previous one, to improve the convergence speed. This scheme is adopted into the relevant POCS-based algorithms, including POCSENSE (POCS-based sensitivity-encoding), POCSMUSE (POCS-based multiplexed sensitivity-encoding), iPOCSMUSE (iterative POCSMUSE), and POCS-ICE (POCS-enhanced inherent correction of motion-induced phase errors) to address the slow convergence problem. Simulations and in vivo experiments were performed to evaluate the performance of the proposed method. Experimental results show that the proposed method enables faster convergence compared to the original POCS. For example, for a spiral DWI simulation using eight-shot interleaves and having SNR of 20dB, the iteration number needed for the revised POCS-ICE decreases by about 70% to achieve approximately the same nRMSE level as POCS-ICE. Additionally, it improves image quality in terms of fewer artifacts compared with the original POCS. The revised DWI reconstruction methods can achieve higher convergence rates than the original POCS-based algorithms and higher image quality with the same iteration numbers. As such, the proposed method can serve as a practical and efficient reconstruction method for multi-shot DWI.

Multiple B-Value Model-Based Residual Network (MORN) for Accelerated High-Resolution Diffusion-Weighted Imaging.

Single-Shot Echo Planar Imaging (SSEPI) based Diffusion Weighted Imaging (DWI) has shortcomings such as low resolution and severe distortions. In contrast, Multi-Shot EPI (MSEPI) provides optimal spatial resolution but increases scan time. This study proposed a Multiple b-value mOdel-based Residual Network (MORN) model to reconstruct multiple b-value high-resolution DWI from undersampled k-space data simultaneously. We incorporated Parallel Imaging (PI) into a residual U-net to reconstruct multiple b-value multi-coil data with the supervision of MUltiplexed Sensitivity-Encoding (MUSE) reconstructed Multi-Shot DWI (MSDWI). Moreover, asymmetric concatenations among different b-values and the combined loss to back propagate helped the feature transfer. After training and validation of the MORN in a dataset of 32 healthy cases, additional assessments were performed on 6 patients with different tumor types. The experimental results demonstrated that the MORN model outperformed conventional PI reconstruction (i.e. SENSE) and two state-of-the-art deep learning methods (SENSE-GAN and VSNet) in terms of PSNR (Peak Signal-to-Noise Ratio), SSIM (Structual SIMilarity) and apparent diffusion coefficient maps. In addition, using the pre-trained model under DWI, the MORN achieved consistent fractional anisotrophy and mean diffusivity reconstructed from multiple diffusion directions. Hence, the proposed method shows potential in clinical application according to the observations on tumor patients as well as images of multiple diffusion directions.

Reduced field-of-view and multi-shot DWI acquisition techniques: Prospective evaluation of image quality and distortion reduction in prostate cancer imaging

ObjectivesTo prospectively compare image quality and apparent diffusion coefficient (ADC) quantification for reduced field-of-view (rFOV)- and multi-shot echo-planar imaging (msEPI)-based diffusion weighted imaging (DWI), using single-shot echo-planar-imaging (ssEPI) DWI as the reference. MethodsUnder IRB approval and after informed consent, msEPI, rFOV, and ssEPI DWI acquisitions were prospectively added to clinical prostate MRI exams at 3.0 T. Image distortion was quantitatively evaluated by root-mean-squared displacement (dr.m.s.). Histogram-based quantitative ADC parameters were compared in a sub-set of patients for proven sites of prostate cancer and matched non-cancerous prostate. Three radiologists also independently evaluated the DWI sequences for subjective image quality and distortion/artifact on a 5-point Likert scale. ResultsTwenty-five patients were included (15 with proven sites of cancer). Average dr.m.s. demonstrated a small but statistically significant reduction in distortion for both rFOV and msEPI relative to ssEPI. Quantitative ADC parameters for prostate tumors demonstrated no significant difference across the 3 DWI acquisitions and each acquisition demonstrated a statistically significant decrease in mean ADC for tumor compared to normal prostate. Qualitative reader assessment demonstrated favorable image quality for rFOV and msEPI, more notable for msEPI. ConclusionsrFOV and msEPI DWI techniques achieved reduction in image distortion, improvement in image quality, and maintained reproducible ADC quantification compared to the standard ssEPI.

Deep learning based multiplexed sensitivity-encoding (DL-MUSE) for high-resolution multi-shot DWI

PurposeA phase correction method for high-resolution multi-shot (MSH) diffusion weighted imaging (DWI) is proposed. The efficacy and generalization capability of the method were validated on both healthy volunteers and patients. Theory and methodsConventionally, inter-shot phase variations for MSH echo-planar imaging (EPI) DWI are corrected by model-based algorithms. However, many acquisition imperfections are hard to measure accurately for conventional model-based methods, making the phase estimation and artifacts suppression unreliable. We propose a deep learning multiplexed sensitivity-encoding (DL-MUSE) framework to improve the phase estimations based on convolutional neural network (CNN) reconstruction. Aliasing-free single-shot (SSH) DW images, which have been used routinely in clinical settings, were used for training before the aliasing correction of MSH-DWI images. A dual-channel U-net comprising multiple convolutional layers was used for the phase estimation of MSH-EPI. The network was trained on a dataset containing 30 healthy volunteers and tested on another dataset of 52 healthy subjects and 15 patients with lesions or tumors with different shot numbers (4, 6 and 8). To further validate the generalization capability of our network, we acquired a dataset with different numbers of shots, TEs, partial Fourier factors, resolutions, ETLs, FOVs, coil numbers, and image orientations from two sites. We also compared the reconstruction performance of our proposed method with that of the conventional MUSE and SSH-EPI qualitatively and quantitatively. ResultsOur results show that DL-MUSE is capable of correcting inter-shot phase errors with high and robust performance. Compared to conventional model-based MUSE, our method, by applying deep learning-based phase corrections, showed reduced distortion, noise level, and signal loss in high b-value DWIs. The improvements of image quality become more evident as the shot number increases from 4 to 8, especially in those central regions of the images, where g-factor artifacts are severe. Furthermore, the proposed method could provide the information about the orientation of the white matter with better consistency and achieve finer fibers delineation compared to the SSH-EPI method. Besides, the experiments on volunteers and patients from two different sites demonstrated the generalizability of our proposed method preliminarily. ConclusionA deep learning-based reconstruction algorithm for MSH-EPI images, which helps improve image quality greatly, was proposed. Results from healthy volunteers and tumor patients demonstrated the feasibility and generalization performances of our method for high-resolution MSH-EPI DWI, which can be used for routine clinical applications as well as neuroimaging research.

High-dimensional fast convolutional framework (HICU) for calibrationless MRI.

To present a computational procedure for accelerated, calibrationless magnetic resonance image (Cl-MRI) reconstruction that is fast, memory efficient, and scales to high-dimensional imaging. Cl-MRI methods can enable high acceleration rates and flexible sampling patterns, but their clinical application is limited by computational complexity and large memory footprint. The proposed computational procedure, HIgh-dimensional fast convolutional framework (HICU), provides fast, memory-efficient recovery of unsampled k-space points. For demonstration, HICU is applied to 6 2D T2-weighted brain, 7 2D cardiac cine, 5 3D knee, and 1 multi-shot diffusion weighted imaging (MSDWI) datasets. The 2D imaging results show that HICU can offer 1-2 orders of magnitude computation speedup compared to other Cl-MRI methods without sacrificing imaging quality. The 2D cine and 3D imaging results show that the computational acceleration techniques included in HICU yield computing time on par with SENSE-based compressed sensing methods with up to 3dB improvement in signal-to-error ratio and better perceptual quality. The MSDWI results demonstrate the feasibility of HICU for a challenging multi-shot echo-planar imaging application. The presented method, HICU, offers efficient computation and scalability as well as extendibility to a wide variety of MRI applications.

Multishot Diffusion-Weighted MRI of the Breast With Multiplexed Sensitivity Encoding (MUSE) and Shot Locally Low-Rank (Shot-LLR) Reconstructions.

Diffusion-weighted imaging (DWI) has shown promise to screen for breast cancer without a contrast injection, but image distortion and low spatial resolution limit standard single-shot DWI. Multishot DWI methods address these limitations but introduce shot-to-shot phase variations requiring correction during reconstruction. To investigate the performance of two multishot DWI reconstruction methods, multiplexed sensitivity encoding (MUSE) and shot locally low-rank (shot-LLR), compared to single-shot DWI in the breast. Prospective. A total of 45 women who consented to have multishot DWI added to a clinically indicated breast MRI. Single-shot DWI reconstructed by parallel imaging, multishot DWI with four or eight shots reconstructed by MUSE and shot-LLR, 3D T2 -weighted imaging, and contrast-enhanced MRI at 3T. Three blinded observers scored images for 1) general image quality (perceived signal-to-noise ratio [SNR], ghosting, distortion), 2) lesion features (discernment and morphology), and 3) perceived resolution. Apparent diffusion coefficient (ADC) of the lesion was also measured and compared between methods. Image quality features and perceived resolution were assessed with a mixed-effects logistic regression. Agreement among observers was estimated with a Krippendorf's alpha using linear weighting. Lesion feature ratings were visualized using histograms, and correlation coefficients of lesion ADC between different methods were calculated. MUSE and shot-LLR images were rated to have significantly better perceived resolution (P < 0.001), higher SNR (P < 0.005), and a lower level of distortion (P < 0.05) with respect to single-shot DWI. Shot-LLR showed reduced ghosting artifacts with respect to both MUSE (P < 0.001) and single-shot DWI (P < 0.001). Eight-shot DWI had improved perceived SNR and perceived resolution with respect to four-shot DWI (P < 0.005). Multishot DWI enables increased resolution and improved image quality with respect to single-shot DWI in the breast. Shot-LLR reconstructs multishot DWI with minimal ghosting artifacts. The improvement of multishot DWI in image quality increases with an increased number of shots. 2 TECHNICAL EFFICACY STAGE: 2.

Reliability and validity of multi-shot DWI in diagnosis of cervical spondylotic myelopathy: a study based on 3-T MRI.

To investigate whether multi-shot diffusion-weighted imaging (ms-DWI) could be applied in diagnosis and quantitative evaluation of cervical spondylotic myelopathy (CSM). Thirty-three normal volunteers and 78 patients with CSM were included in this study. The apparent diffusion coefficient (ADC) values were measured at C2-C7 levels on sagittal section ADC map. The intraclass correlation coefficient (ICC) and Bland and Altman plot and Spearman coefficient were used to quantify the reproducibility of test and retest and inter-rater reliability. Pearson correlations were calculated to compare lADC and rADC versus mJOA and NDI scores. Receiver operating characteristic curve and the area under the curve (AUC) were applied to evaluate the diagnostic reliability. There was no statistically significant difference between ADC values obtained from normal volunteers at C2-C7 levels (P < 0.05). The ICC and spearman coefficient of lADC and rADC indicated excellent test-retest and inter-rater reliability. The mean lADC and rADC were significantly higher from patients than that from volunteers (all P < 0.01). The lADC had moderate to good correlations with mJOA and NDI (all P < 0.0001). Moreover, rADC had good to excellent correlations with mJOA and NDI (all P < 0.0001). Comparing AUCs, rADC was significantly superior in diagnosis which participants were CSM than lADC (P = 0.0118). The ms-DWI could be applied in diagnosis and quantitive assessment of CSM according to lADC and rADC. A new parameter, rADC, could be served as a diagnostic indice for CSM, which may quantitively reflect the severity of CSM. These slides can be retrieved under Electronic Supplementary Material.

Phase-constrained reconstruction of high-resolution multi-shot diffusion weighted image

Diffusion weighted imaging (DWI) is a unique examining method in tumor diagnosis, acute stroke evaluation. Single-shot echo planar imaging is currently conventional method for DWI. However, single-shot DWI suffers from image distortion, blurring and low spatial resolution. Although multi-shot DWI improves image resolution, it brings phase variations among different shots at the same time. In this paper, we introduce a smooth phase constraint of each shot image into multi-shot navigator-free DWI reconstruction by imposing the low-rankness of Hankel matrix constructed from the k-space data. Furthermore, we exploit the partial sum minimization of singular values to constrain the low-rankness of Hankel matrix. Results on brain imaging data show that the proposed method outperforms the state-of-the-art methods in terms of artifacts removal and our method potentially has the ability to reconstruct high number of shot of DWI.

Cholesteatoma: multishot echo-planar vs non echo-planar diffusion-weighted MRI for the prediction of middle ear and mastoid cholesteatoma.

Objective:We aimed to compare a newer readout-segmented echoplanar imaging (RS-EPI) technique with the established single shot turbo spin echo (SS-TSE) non-EPI diffusion-weighted imaging (DWI) in detecting surgically validated cholesteatoma.Methods:We retrospectively reviewed 358 consecutive MRI studies in 285 patients in which both RS-EPI and non-EPI DWI sequences were performed. Each diffusion sequence was reviewed independently and scored negative, indeterminate or positive for cholesteatoma in isolation and after reviewing the T 1W sequence. Average artefacts scores were evaluated and the lesion size measured as a distortion indicator. The imaging scores were correlated with surgical validation, clinical and imaging follow-up.Results:There were 239 middle ear and central mastoid tract and 34 peripheral mastoid lesions. 102 tympanomastoid operations were performed. The positive predictive value ( PPV), post-operative PPV, primary PPV, negative predictive value were 93%, 95%, 87.5%, 70% for RS-EPI and 92.5%, 93.6%, 90%, 79% for non-EPI DWI. There was good agreement between the two techniques (k = 0.75). Non-EPI DWI is less susceptible to skull base artefacts although the mean cholesteatoma measurement difference was only 0.53 mm.Conclusion:RS-EPI has comparable PPV with non-EPI DWI in both primary and post-operative cholesteatoma but slightly lower negative predictive value. When there is a mismatch, non-EPI DWI better predicts the presence of cholesteatoma. There is good agreement between the sequences for cholesteatoma diagnosis. The T 1W sequence is very important in downgrading indeterminate DWI signal lesions to a negative score.Advances in knowledge:This is, to our knowledge, the first study to compare a multishot EPI DWI technique with the established non- EPI DWI in cholesteatoma diagnosis.

Technical Note: Clustering-based motion compensation scheme for multishot diffusion tensor imaging.

To extend image reconstruction using image-space sampling function (IRIS) to address large-scale motion in multishot diffusion-weighted imaging (DWI). A clustered IRIS (CIRIS) algorithm that would extend IRIS was proposed to correct for large-scale motion. For DWI, CIRIS initially groups the shots into clusters without intracluster large-scale motion and reconstructs each cluster by using IRIS. Then, CIRIS registers these cluster images and combines the registered images by using a weighted average to correct for voxel mismatch caused by intercluster large-scale motion. For diffusion tensor imaging (DTI), CIRIS further reduces the effect of motion on diffusion directions by treating motion-induced direction changes as additional diffusion directions. CIRIS also introduces the detection and rejection of motion-corrupted data to avoid corresponding image degradation. The proposed method was evaluated by simulation and in vivo diffusion datasets. Experiments demonstrated that CIRIS can reduce motion-induced blurring and artifacts in DWI and provide more accurate DTI estimations in the presence of large-scale motion, compared with IRIS. The proposed method presents a novel approach to correct for large-scale in-plane motion for multishot DWI and is expected to benefit the practical application of high-resolution diffusion imaging.

Motion-robust reconstruction of multishot diffusion-weighted images without phase estimation through locally low-rank regularization.

The goal of this work is to propose a motion robust reconstruction method for diffusion-weighted MRI that resolves shot-to-shot phase mismatches without using phase estimation. Assuming that shot-to-shot phase variations are slowly varying, spatial-shot matrices can be formed using a local group of pixels to form columns, in which each column is from a different shot (excitation). A convex model with a locally low-rank constraint on the spatial-shot matrices is proposed. In vivo brain and breast experiments were performed to evaluate the performance of the proposed method. The proposed method shows significant benefits when the motion is severe, such as for breast imaging. Furthermore, the resulting images can be used for reliable phase estimation in the context of phase-estimation-based methods to achieve even higher image quality. We introduced the shot-locally low-rank method, a reconstruction technique for multishot diffusion-weighted MRI without explicit phase estimation. In addition, its motion robustness can be beneficial to neuroimaging and body imaging.

Assessment of uveal melanomas using advanced diffusion-weighted imaging techniques: value of reduced field of view DWI ("zoomed DWI") and readout-segmented DWI (RESOLVE).

Background Diffusion-weighted imaging (DWI) has become an important tool for lesion characterization. Advanced techniques of DWI may be used to improve image quality. Purpose To evaluate multi-shot segmented DWI (rs-EPI, RESOLVE) and reduced field-of view DWI (rFOV-EPI, “zoomed” EPI) in patients with ocular melanoma and to compare image quality and diagnostic performance in differentiation of melanoma from retinal detachment. Material and Methods In this prospective and IRB-approved trial, we examined 26 patients using methods including conventional single-shot echo-planar DWI (ss-EPI), rs-EPI, and rFOV-EPI. Subjective image quality was compared using a four-point score and the maximum tumor length was measured in all sequences. Apparent diffusion coefficient (ADC) measurements were performed using a region-of interest analysis. Tumor delineation and differences in ADC values between melanomas and retinal detachments were compared. Results Diffusion restriction was markedly reduced in melanomas in all applied image techniques. Subjective image quality was significantly higher for rFOV-EPI (score = 3.5 ± 0.5) compared with rs-EPI (score = 3.3 ± 0.6) and ss-EPI (score = 2.5 ± 0.9). Regarding tumor diameter measurements, rFOV-EPI showed the best agreement compared with high-resolution conventional sequences. ADC measurements of the tumor and retinal detachment differed significantly ( P &lt; 0.001) with the rFOV-EPI performing best (sensitivities and specificities compared with T1-weighted ss-EPI 61%/82%; rFOV-EPI 86%/92%; rs-EPI 79%/92%, respectively). Conclusion rFOV-EPI showed improved image quality compared with ss-EPI and rs-EPI, the most accurate tumor delineation and the best differentiation from retinal detachments in our patients.