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

PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: Region-based shot rejection is a linear reconstruction that improves the motion robustness of multi-shot DWI and requires no sequence modifications.

Distortionless, free-breathing, and respiratory resolved 3D diffusion weighted imaging of the abdomen.

PURPOSE: Abdominal imaging is frequently performed with breath holds or respiratory triggering to reduce the effects of respiratory motion. Diffusion weighted sequences provide a useful clinical contrast but have prolonged scan times due to low signal-to-noise ratio (SNR), and cannot be completed in a single breath hold. Echo-planar imaging (EPI) is the most commonly used trajectory for diffusion weighted imaging but it is susceptible to off-resonance artifacts. A respiratory resolved, three-dimensional (3D) diffusion prepared sequence that obtains distortionless diffusion weighted images during free-breathing is presented. Techniques to address the myriad of challenges including: 3D shot-to-shot phase correction, respiratory binning, diffusion encoding during free-breathing, and robustness to off-resonance are described. METHODS: A twice-refocused, M1-nulled diffusion preparation was combined with an RF-spoiled gradient echo readout and respiratory resolved reconstruction to obtain free-breathing diffusion weighted images in the abdomen. Cartesian sampling permits a sampling density that enables 3D shot-to-shot phase navigation and reduction of transient fat artifacts. Theoretical properties of a region-based shot rejection are described. The region-based shot rejection method was evaluated with free-breathing (normal and exaggerated breathing), and respiratory triggering. The proposed sequence was compared in vivo with multishot DW-EPI. RESULTS: The proposed sequence exhibits no evident distortion in vivo when compared to multishot DW-EPI, robustness to B0 and B1 field inhomogeneities, and robustness to motion from different respiratory patterns. CONCLUSION: Acquisition of distortionless, diffusion weighted images is feasible during free-breathing with a b-value of 500 s/mm2, scan time of 6 min, and a clinically viable reconstruction time.

Reproducibility of Quantitative Double-Echo Steady-State T2 Mapping of Knee Cartilage.

BACKGROUND: Cartilage T2 can detect joints at risk of developing osteoarthritis. The quantitative double-echo steady state (qDESS) sequence is attractive for knee cartilage T2 mapping because of its acquisition time of under 5 minutes. Understanding the reproducibility errors associated with qDESS T2 is essential to profiling the technical performance of this biomarker. PURPOSE: To examine the combined acquisition and segmentation reproducibility of knee cartilage qDESS T2 using two different regional analysis schemes: 1) manual segmentation of subregions loaded during common activities and 2) automatic subregional segmentation. STUDY TYPE: Prospective. SUBJECTS: 11 uninjured participants (age: 28 ± 3 years; 8 (73%) female). FIELD STRENGTH/SEQUENCE: 3-T, qDESS. ASSESSMENT: Test-retest T2 maps were acquired twice on the same day and with a 1-week interval between scans. For each acquisition, average cartilage T2 was calculated in four manually segmented regions encompassing tibiofemoral contact areas during common activities and 12 automatically segmented regions from the deep-learning open-source framework for musculoskeletal MRI analysis (DOSMA) encompassing medial and lateral anterior, central, and posterior tibiofemoral regions. Test-retest T2 values from matching regions were used to evaluate reproducibility. STATISTICAL TESTS: Coefficients of variation (%CV), root-mean-square-average-CV (%RMSA-CV), and intraclass correlation coefficients (ICCs) assessed test-retest T2 reproducibility. The median of test-retest standard deviations was used for T2 precision. Bland-Altman (BA) analyses examined test-retest biases. The smallest detectable difference (SDD) was defined as the BA limit of agreement of largest magnitude. Significance was accepted for P < 0.05. RESULTS: All cartilage regions across both segmentation schemes demonstrated intraday and interday qDESS T2 CVs and RMSA-CVs of ≤5%. T2 ICC values >0.75 were observed in the majority of regions but were more variable in interday tibial comparisons. Test-retest T2 precision was <1.3 msec. The T2 SDD was 3.8 msec. DATA CONCLUSION: Excellent CV and RMSA-CV reproducibility may suggest that qDESS T2 increases or decreases >5% (3.8 msec) could represent changes to cartilage composition. TECHNICAL EFFICACY: Stage 2.

A method for measuring B0 field inhomogeneity using quantitative double-echo in steady-state.

PURPOSE: To develop and validate a method for B 0 $$ {B}_0 $$ mapping for knee imaging using the quantitative Double-Echo in Steady-State (qDESS) exploiting the phase difference ( Δ Î¸ $$ \Delta \theta $$ ) between the two echoes acquired. Contrary to a two-gradient-echo (2-GRE) method, Δ Î¸ $$ \Delta \theta $$ depends only on the first echo time. METHODS: Bloch simulations were applied to investigate robustness to noise of the proposed methodology and all imaging studies were validated with phantoms and in vivo simultaneous bilateral knee acquisitions. Two phantoms and five healthy subjects were scanned using qDESS, water saturation shift referencing (WASSR), and multi-GRE sequences. Δ B 0 $$ \Delta {B}_0 $$ maps were calculated with the qDESS and the 2-GRE methods and compared against those obtained with WASSR. The comparison was quantitatively assessed exploiting pixel-wise difference maps, Bland-Altman (BA) analysis, and Lin's concordance coefficient ( ρ c $$ {\rho}_c $$ ). For in vivo subjects, the comparison was assessed in cartilage using average values in six subregions. RESULTS: The proposed method for measuring Δ B 0 $$ \Delta {B}_0 $$ inhomogeneities from a qDESS acquisition provided Δ B 0 $$ \Delta {B}_0 $$ maps that were in good agreement with those obtained using WASSR. Δ B 0 $$ \Delta {B}_0 $$ ρ c $$ {\rho}_c $$ values were ≥ $$ \ge $$ 0.98 and 0.90 in phantoms and in vivo, respectively. The agreement between qDESS and WASSR was comparable to that of a 2-GRE method. CONCLUSION: The proposed method may allow B0 correction for qDESS T 2 $$ {T}_2 $$ mapping using an inherently co-registered Δ B 0 $$ \Delta {B}_0 $$ map without requiring an additional B0 measurement sequence. More generally, the method may help shorten knee imaging protocols that require an auxiliary Δ B 0 $$ \Delta {B}_0 $$ map by exploiting a qDESS acquisition that also provides T 2 $$ {T}_2 $$ measurements and high-quality morphological imaging.

Noise2Recon: Enabling SNR-robust MRI reconstruction with semi-supervised and self-supervised learning.

PURPOSE: To develop a method for building MRI reconstruction neural networks robust to changes in signal-to-noise ratio (SNR) and trainable with a limited number of fully sampled scans. METHODS: We propose Noise2Recon, a consistency training method for SNR-robust accelerated MRI reconstruction that can use both fully sampled (labeled) and undersampled (unlabeled) scans. Noise2Recon uses unlabeled data by enforcing consistency between model reconstructions of undersampled scans and their noise-augmented counterparts. Noise2Recon was compared to compressed sensing and both supervised and self-supervised deep learning baselines. Experiments were conducted using retrospectively accelerated data from the mridata three-dimensional fast-spin-echo knee and two-dimensional fastMRI brain datasets. All methods were evaluated in label-limited settings and among out-of-distribution (OOD) shifts, including changes in SNR, acceleration factors, and datasets. An extensive ablation study was conducted to characterize the sensitivity of Noise2Recon to hyperparameter choices. RESULTS: In label-limited settings, Noise2Recon achieved better structural similarity, peak signal-to-noise ratio, and normalized-RMS error than all baselines and matched performance of supervised models, which were trained with 14 × $$ 14\times $$ more fully sampled scans. Noise2Recon outperformed all baselines, including state-of-the-art fine-tuning and augmentation techniques, among low-SNR scans and when generalizing to OOD acceleration factors. Augmentation extent and loss weighting hyperparameters had negligible impact on Noise2Recon compared to supervised methods, which may indicate increased training stability. CONCLUSION: Noise2Recon is a label-efficient reconstruction method that is robust to distribution shifts, such as changes in SNR, acceleration factors, and others, with limited or no fully sampled training data.

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

BACKGROUND: 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. PURPOSE: To establish a multishot DWI (msDWI) protocol for supine breast MRI and to evaluate the performance of supine vs. prone msDWI. STUDY TYPE: Prospective. POPULATION: 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. FIELD STRENGTH/SEQUENCE: 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. ASSESSMENT: 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. STATISTICAL TESTS: 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. RESULTS: 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). DATA CONCLUSION: 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. EVIDENCE LEVEL: 2. TECHNICAL EFFICACY: Stage 1.

Generalizability of Deep Learning Segmentation Algorithms for Automated Assessment of Cartilage Morphology and MRI Relaxometry.

BACKGROUND: Deep learning (DL)-based automatic segmentation models can expedite manual segmentation yet require resource-intensive fine-tuning before deployment on new datasets. The generalizability of DL methods to new datasets without fine-tuning is not well characterized. PURPOSE: Evaluate the generalizability of DL-based models by deploying pretrained models on independent datasets varying by MR scanner, acquisition parameters, and subject population. STUDY TYPE: Retrospective based on prospectively acquired data. POPULATION: Overall test dataset: 59 subjects (26 females); Study 1: 5 healthy subjects (zero females), Study 2: 8 healthy subjects (eight females), Study 3: 10 subjects with osteoarthritis (eight females), Study 4: 36 subjects with various knee pathology (10 females). FIELD STRENGTH/SEQUENCE: A 3-T, quantitative double-echo steady state (qDESS). ASSESSMENT: Four annotators manually segmented knee cartilage. Each reader segmented one of four qDESS datasets in the test dataset. Two DL models, one trained on qDESS data and another on Osteoarthritis Initiative (OAI)-DESS data, were assessed. Manual and automatic segmentations were compared by quantifying variations in segmentation accuracy, volume, and T2 relaxation times for superficial and deep cartilage. STATISTICAL TESTS: Dice similarity coefficient (DSC) for segmentation accuracy. Lin's concordance correlation coefficient (CCC), Wilcoxon rank-sum tests, root-mean-squared error-coefficient-of-variation to quantify manual vs. automatic T2 and volume variations. Bland-Altman plots for manual vs. automatic T2 agreement. A P value < 0.05 was considered statistically significant. RESULTS: DSCs for the qDESS-trained model, 0.79-0.93, were higher than those for the OAI-DESS-trained model, 0.59-0.79. T2 and volume CCCs for the qDESS-trained model, 0.75-0.98 and 0.47-0.95, were higher than respective CCCs for the OAI-DESS-trained model, 0.35-0.90 and 0.13-0.84. Bland-Altman 95% limits of agreement for superficial and deep cartilage T2 were lower for the qDESS-trained model, ±2.4 msec and ±4.0 msec, than the OAI-DESS-trained model, ±4.4 msec and ±5.2 msec. DATA CONCLUSION: The qDESS-trained model may generalize well to independent qDESS datasets regardless of MR scanner, acquisition parameters, and subject population. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 1.

A joint linear reconstruction for multishot diffusion weighted non-Carr-Purcell-Meiboom-Gill fast spin echo with full signal.

PURPOSE: Diffusion weighted Fast Spin Echo (DW-FSE) is a promising approach for distortionless DW imaging that is robust to system imperfections such as eddy currents and off-resonance. Due to non-Carr-Purcell-Meiboom-Gill (CPMG) magnetization, most DW-FSE sequences discard a large fraction of the signal ( 2 - 2 × $$ \sqrt{2}-2\times $$ ), reducing signal-to-noise ratio (SNR) efficiency compared to DW-EPI. The full FSE signal can be preserved by quadratically incrementing the transmit phase of the refocusing pulses, but this method of resolving non-CPMG magnetization has only been applied to single-shot DW-FSE due to challenges associated with image reconstruction. We present a joint linear reconstruction for multishot quadratic phase increment data that addresses these challenges and corrects ghosting from both shot-to-shot phase and intrashot signal oscillations. Multishot imaging reduces T2 blur and joint reconstruction of shots improves g-factor performance. A thorough analysis on the condition number of the proposed linear system is described. METHODS: A joint multishot reconstruction is derived from the non-CPMG signal model. Multishot quadratic phase increment DW-FSE was tested in a standardized diffusion phantom and compared to single-shot DW-FSE and DW-EPI in vivo in the brain, cervical spine, and prostate. The pseudo multiple replica technique was applied to generate g-factor and SNR maps. RESULTS: The proposed joint shot reconstruction eliminates ghosting from shot-to-shot phase and intrashot oscillations. g-factor performance is improved compared to previously proposed reconstructions, permitting efficient multishot imaging. apparent diffusion coefficient estimates in phantom experiments and in vivo are comparable to those obtained with conventional methods. CONCLUSION: Multi-shot non-CPMG DW-FSE data with full signal can be jointly reconstructed using a linear model.

Volumetric and multispectral DWI near metallic implants using a non-linear phase Carr-Purcell-Meiboom-Gill diffusion preparation.

PURPOSE: DWI near metal implants has not been widely explored due to substantial challenges associated with through-slice and in-plane distortions, the increased encoding requirement of different spectral bins, and limited SNR. There is no widely adopted clinical protocol for DWI near metal since the commonly used EPI trajectory fails completely due to distortion from extreme off-resonance ranging from 2 to 20 kHz. We present a sequence that achieves DWI near metal with moderate b-values (400-500 s/mm2 ) and volumetric coverage in clinically feasible scan times. THEORY AND METHODS: Multispectral excitation with Cartesian sampling, view angle tilting, and kz phase encoding reduce in-plane and through-plane off-resonance artifacts, and Carr-Purcell-Meiboom-Gill (CPMG) spin-echo refocusing trains counteract T2* effects. The effect of random phase on the refocusing train is eliminated using a stimulated echo diffusion preparation. Root-flipped Shinnar-Le Roux refocusing pulses permits preparation of a high spectral bandwidth, which improves imaging times by reducing the number of excitations required to cover the desired spectral range. B1 sensitivity is reduced by using an excitation that satisfies the CPMG condition in the preparation. A method for ADC quantification insensitive to background gradients is presented. RESULTS: Non-linear phase refocusing pulses reduces the peak B1 by 46% which allows RF bandwidth to be doubled. Simulations and phantom experiments show that a non-linear phase CPMG pulse pair reduces B1 sensitivity. Application in vivo demonstrates complementary contrast to conventional multispectral acquisitions and improved visualization compared to DW-EPI. CONCLUSION: Volumetric and multispectral DW imaging near metal can be achieved with a 3D encoded sequence.

Circumventing the curse of dimensionality in magnetic resonance fingerprinting through a deep learning approach.

Magnetic resonance fingerprinting (MRF) is a rapidly developing approach for fast quantitative MRI. A typical drawback of dictionary-based MRF is an explosion of the dictionary size as a function of the number of reconstructed parameters, according to the "curse of dimensionality", which determines an explosion of resource requirements. Neural networks (NNs) have been proposed as a feasible alternative, but this approach is still in its infancy. In this work, we design a deep learning approach to MRF using a fully connected network (FCN). In the first part we investigate, by means of simulations, how the NN performance scales with the number of parameters to be retrieved in comparison with the standard dictionary approach. Four MRF sequences were considered: IR-FISP, bSSFP, IR-FISP-B1 , and IR-bSSFP-B1 , the latter two designed to be more specific for B1+ parameter encoding. Estimation accuracy, memory usage, and computational time required to perform the estimation task were considered to compare the scalability capabilities of the dictionary-based and the NN approaches. In the second part we study optimal training procedures by including different data augmentation and preprocessing strategies during training to achieve better accuracy and robustness to noise and undersampling artifacts. The study is conducted using the IR-FISP MRF sequence exploiting both simulations and in vivo acquisitions. Results demonstrate that the NN approach outperforms the dictionary-based approach in terms of scalability capabilities. Results also allow us to heuristically determine the optimal training strategy to make an FCN able to predict T1 , T2 , and M0 maps that are in good agreement with those obtained with the original dictionary approach. k-SVD denoising is proposed and found to be critical as a preprocessing step to handle undersampled data.

Clinical utility of accelerated MAVRIC-SL with robust-PCA compared to conventional MAVRIC-SL in evaluation of total hip arthroplasties.

OBJECTIVE: To compare the diagnostic performance of a conventional metal artifact suppression sequence MAVRIC-SL (multi-acquisition variable-resonance image combination selective) and a novel 2.6-fold faster sequence employing robust principal component analysis (RPCA), in the MR evaluation of hip implants at 3 T. MATERIALS AND METHODS: Thirty-six total hip implants in 25 patients were scanned at 3 T using a conventional MAVRIC-SL proton density-weighted sequence and an RPCA MAVRIC-SL proton density-weighted sequence. Comparison was made of image quality, geometric distortion, visualization around acetabular and femoral components, and conspicuity of abnormal imaging findings using the Wilcoxon signed-rank test and a non-inferiority test. Abnormal findings were correlated with subsequent clinical management and intraoperative findings if the patient underwent subsequent surgery. RESULTS: Mean scores for conventional MAVRIC-SL were better than RPCA MAVRIC-SL for all qualitative parameters (p < 0.05), although the probability of RPCA MAVRIC-SL being clinically useful was non-inferior to conventional MAVRIC-SL (within our accepted 10% difference, p < 0.05), except for visualization around the acetabular component. Abnormal imaging findings were seen in 25 hips, and either equally visible or visible but less conspicuous on RPCA MAVRIC-SL in 21 out of 25 cases. In 4 cases, a small joint effusion was queried on MAVRIC-SL but not RPCA MAVRIC-SL, but the presence or absence of a small effusion did not affect subsequent clinical management and patient outcome. CONCLUSION: While the overall image quality is reduced, RPCA MAVRIC-SL allows for significantly reduced scan time and maintains almost equal diagnostic performance.

RUN-UP: Accelerated multishot diffusion-weighted MRI reconstruction using an unrolled network with U-Net as priors.

PURPOSE: To accelerate and improve multishot diffusion-weighted MRI reconstruction using deep learning. METHODS: An unrolled pipeline containing recurrences of model-based gradient updates and neural networks was introduced for accelerating multishot DWI reconstruction with shot-to-shot phase correction. The network was trained to predict results of jointly reconstructed multidirection data using single-direction data as input. In vivo brain and breast experiments were performed for evaluation. RESULTS: The proposed method achieves a reconstruction time of 0.1 second per image, over 100-fold faster than a shot locally low-rank reconstruction. The resultant image quality is comparable to the target from the joint reconstruction with a peak signal-to-noise ratio of 35.3 dB, a normalized root-mean-square error of 0.0177, and a structural similarity index of 0.944. The proposed method also improves upon the locally low-rank reconstruction (2.9 dB higher peak signal-to-noise ratio, 29% lower normalized root-mean-square error, and 0.037 higher structural similarity index). With training data from the brain, this method also generalizes well to breast diffusion-weighted imaging, and fine-tuning further reduces aliasing artifacts. CONCLUSION: A proposed data-driven approach enables almost real-time reconstruction with improved image quality, which improves the feasibility of multishot DWI in a wide range of clinical and neuroscientific studies.

Diffusion-weighted double-echo steady-state with a three-dimensional cones trajectory for non-contrast-enhanced breast MRI.

The image quality limitations of echo-planar diffusion-weighted imaging (DWI) are an obstacle to its widespread adoption in the breast. Steady-state DWI is an alternative DWI method with more robust image quality but its contrast for imaging breast cancer is not well-understood. The aim of this study was to develop and evaluate diffusion-weighted double-echo steady-state imaging with a three-dimensional cones trajectory (DW-DESS-Cones) as an alternative to conventional DWI for non-contrast-enhanced MRI in the breast. This prospective study included 28 women undergoing clinically indicated breast MRI and six asymptomatic volunteers. In vivo studies were performed at 3 T and included DW-DESS-Cones, DW-DESS-Cartesian, DWI, and CE-MRI acquisitions. Phantom experiments (diffusion phantom, High Precision Devices) and simulations were performed to establish framework for contrast of DW-DESS-Cones in comparison to DWI in the breast. Motion artifacts of DW-DESS-Cones were measured with artifact-to-noise ratio in volunteers and patients. Lesion-to-fibroglandular tissue signal ratios were measured, lesions were categorized as hyperintense or hypointense, and an image quality observer study was performed in DW-DESS-Cones and DWI in patients. Effect of DW-DESS-Cones method on motion artifacts was tested by mixed-effects generalized linear model. Effect of DW-DESS-Cones on signal in phantom was tested by quadratic regression. Correlation was calculated between DW-DESS-Cones and DWI lesion-to-fibroglandular tissue signal ratios. Inter-observer agreement was assessed with Gwet's AC. Simulations predicted hyperintensity of lesions with DW-DESS-Cones but at a 3% to 67% lower degree than with DWI. Motion artifacts were reduced with DW-DESS-Cones versus DW-DESS-Cartesian (p < 0.05). Lesion-to-fibroglandular tissue signal ratios were not correlated between DW-DESS-Cones and DWI (r = 0.25, p = 0.38). Concordant hyperintensity/hypointensity was observed between DW-DESS-Cones and DWI in 11/14 lesions. DW-DESS-Cones improved sharpness, distortion, and overall image quality versus DWI. DW-DESS-Cones may be able to eliminate motion artifacts in the breast allowing for investigation of higher degrees of steady-state diffusion weighting. Malignant breast lesions in DW-DESS-Cones demonstrated hyperintensity with respect to surrounding tissue without an injection of contrast. LEVEL OF EVIDENCE: 2. TECHNICAL EFFICACY STAGE: 1.

Prospective Deployment of Deep Learning in MRI: A Framework for Important Considerations, Challenges, and Recommendations for Best Practices.

Artificial intelligence algorithms based on principles of deep learning (DL) have made a large impact on the acquisition, reconstruction, and interpretation of MRI data. Despite the large number of retrospective studies using DL, there are fewer applications of DL in the clinic on a routine basis. To address this large translational gap, we review the recent publications to determine three major use cases that DL can have in MRI, namely, that of model-free image synthesis, model-based image reconstruction, and image or pixel-level classification. For each of these three areas, we provide a framework for important considerations that consist of appropriate model training paradigms, evaluation of model robustness, downstream clinical utility, opportunities for future advances, as well recommendations for best current practices. We draw inspiration for this framework from advances in computer vision in natural imaging as well as additional healthcare fields. We further emphasize the need for reproducibility of research studies through the sharing of datasets and software. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 2.

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

BACKGROUND: 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. PURPOSE: 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. STUDY TYPE: Prospective. POPULATION: A total of 45 women who consented to have multishot DWI added to a clinically indicated breast MRI. FIELD STRENGTH/SEQUENCES: 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. ASSESSMENT: 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. STATISTICAL TESTS: 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. RESULTS: 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). DATA CONCLUSION: 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. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 2.

Non-contrast MRI of synovitis in the knee using quantitative DESS.

OBJECTIVES: To determine whether synovitis graded by radiologists using hybrid quantitative double-echo in steady-state (qDESS) images can be utilized as a non-contrast approach to assess synovitis in the knee, compared against the reference standard of contrast-enhanced MRI (CE-MRI). METHODS: Twenty-two knees (11 subjects) with moderate to severe osteoarthritis (OA) were scanned using CE-MRI, qDESS with a high diffusion weighting (qDESSHigh), and qDESS with a low diffusion weighting (qDESSLow). Four radiologists graded the overall impression of synovitis, their diagnostic confidence, and regional grading of synovitis severity at four sites (suprapatellar pouch, intercondylar notch, and medial and lateral peripatellar recesses) in the knee using a 4-point scale. Agreement between CE-MRI and qDESS, inter-rater agreement, and intra-rater agreement were assessed using a linearly weighted Gwet's AC2. RESULTS: Good agreement was seen between CE-MRI and both qDESSLow (AC2 = 0.74) and qDESSHigh (AC2 = 0.66) for the overall impression of synovitis, but both qDESS sequences tended to underestimate the severity of synovitis compared to CE-MRI. Good inter-rater agreement was seen for both qDESS sequences (AC2 = 0.74 for qDESSLow, AC2 = 0.64 for qDESSHigh), and good intra-rater agreement was seen for both sequences as well (qDESSLow AC2 = 0.78, qDESSHigh AC2 = 0.80). Diagnostic confidence was moderate to high for qDESSLow (mean = 2.36) and slightly less than moderate for qDESSHigh (mean = 1.86), compared to mostly high confidence for CE-MRI (mean = 2.73). CONCLUSIONS: qDESS shows potential as an alternative MRI technique for assessing the severity of synovitis without the use of a gadolinium-based contrast agent. KEY POINTS: The use of the quantitative double-echo in steady-state (qDESS) sequence for synovitis assessment does not require the use of a gadolinium-based contrast agent. Preliminary results found that low diffusion-weighted qDESS (qDESSLow) shows good agreement to contrast-enhanced MRI for characterization of the severity of synovitis, with a relative bias towards underestimation of severity. Preliminary results also found that qDESSLow shows good inter- and intra-rater agreement for the depiction of synovitis, particularly for readers experienced with the sequence.

Diagnostic Accuracy of Quantitative Multicontrast 5-Minute Knee MRI Using Prospective Artificial Intelligence Image Quality Enhancement.

BACKGROUND. Potential approaches for abbreviated knee MRI, including prospective acceleration with deep learning, have achieved limited clinical implementation. OBJECTIVE. The objective of this study was to evaluate the interreader agreement between conventional knee MRI and a 5-minute 3D quantitative double-echo steady-state (qDESS) sequence with automatic T2 mapping and deep learning super-resolutionaugmentation and to compare the diagnostic performance of the two methods regarding findings from arthroscopic surgery. METHODS. Fifty-one patients with knee pain underwent knee MRI that included an additional 3D qDESS sequence with automatic T2 mapping. Fourier interpolation was followed by prospective deep learning super resolution to enhance qDESS slice resolution twofold. A musculoskeletal radiologist and a radiology resident performed independent retrospective evaluations of articular cartilage, menisci, ligaments, bones, extensor mechanism, and synovium using conventional MRI. Following a 2-month washout period, readers reviewed qDESS images alone followed by qDESS with the automatic T2 maps. Interreader agreement between conventional MRI and qDESS was computed using percentage agreement and Cohen kappa. The sensitivity and specificity of conventional MRI, qDESS alone, and qDESS plus T2 mapping were compared with arthroscopic findings using exact McNemar tests. RESULTS. Conventional MRI and qDESS showed 92% agreement in evaluating all tissues. Kappa was 0.79 (95% CI, 0.76-0.81) across all imaging findings. In 43 patients who underwent arthroscopy, sensitivity and specificity were not significantly different (p = .23 to > .99) between conventional MRI (sensitivity, 58-93%; specificity, 27-87%) and qDESS alone (sensitivity, 54-90%; specificity, 23-91%) for cartilage, menisci, ligaments, and synovium. For grade 1 cartilage lesions, sensitivity and specificity were 33% and 56%, respectively, for conventional MRI; 23% and 53% for qDESS (p = .81); and 46% and 39% for qDESS with T2 mapping (p = .80). For grade 2A lesions, values were 27% and 53% for conventional MRI, 26% and 52% for qDESS (p = .02), and 58% and 40% for qDESS with T2 mapping (p < .001). CONCLUSION. The qDESS method prospectively augmented with deep learning showed strong interreader agreement with conventional knee MRI and near-equivalent diagnostic performance regarding arthroscopy. The ability of qDESS to automatically generate T2 maps increases sensitivity for cartilage abnormalities. CLINICAL IMPACT. Using prospective artificial intelligence to enhance qDESS image quality may facilitate an abbreviated knee MRI protocol while generating quantitative T2 maps.

Zero echo time pediatric musculoskeletal magnetic resonance imaging: initial experience.

BACKGROUND: Projection radiography (XR) is often supplemented by both CT (to evaluate osseous structures with ionizing radiation) and MRI (for marrow and soft-tissue assessment). Zero echo time (ZTE) MR imaging produces a "CT-like" osseous contrast that might obviate CT. OBJECTIVE: This study investigated our institution's initial experience in implementing an isotropic ZTE MR imaging sequence for pediatric musculoskeletal examinations. MATERIALS AND METHODS: Pediatric patients referred for extremity MRI at 3 tesla (T) underwent ZTE MR imaging to yield images with contrast similar to that of CT. A radiograph-like image was also created with ray-sum image processing. We assessed ZTE-CT/XR anatomical image quality (Sanat) from 0 (nondiagnostic) to 5 (outstanding). Further, we made image comparisons on a 5-point scale (Scomp) (range of -2 = conventional CT/XR greater anatomical delineation to +2 = ZTE-CT/XR greater anatomical delineation; 0=same) for three cohorts: (1) ZTE-XR to conventional radiography, (2) ZTE-CT to conventional CT and (3) pathological lesion assessment on ZTE-XR to conventional radiography. We measured cortical thickness of ZTE-XR and ZTE-CT and compared these with conventional imaging. We calculated confidence interval of proportions, Wilcoxon rank sum test and intraclass correlation coefficients for inter-reader agreement. RESULTS: Cohorts 1, 2 and 3 consisted of 40, 20 and 35 cases, respectively (age range 0.6-23.0 years). ZTE-CT versus CT and ZTE-XR versus radiography of cortical thicknesses were not significantly different (P=0.55 and P=0.31, respectively). Cortical delineation was rated diagnostic or better (score of 3, 4 or 5) in all cases (confidence interval of proportions = 100%) for ZTE-CT/XR. Similarly, intramedullary cavity delineation was rated diagnostic or better in all cases for ZTE-CT, and ZTE-XR was at least diagnostic in 58-63% of cases. For cohort 2, cortex and intramedullary cavity Scomp for ZTE-CT was comparable to those of conventional CT, with confidence interval of proportion (sum of score of -1 to +2) of 93-100% and 95%, respectively. Pathology visualized on ZTE-CT/XR was comparable; Scomp confidence interval of proportions was 95%/97-100%, with improved delineation of non-displaced fractures on ZTE-XR. Readers had moderate to near-perfect intraclass correlation coefficient (range=0.60-0.93). CONCLUSION: Implementation of a diagnostic-quality ZTE MRI sequence in the pediatric population is feasible and can be performed as a complementary pulse sequence to enhance musculoskeletal MRI studies. Compared to conventional CT, ZTE has comparable cortical delineation, intramedullary cavity and pathology visualization. While not intended as a replacement for conventional radiography, ZTE-XR provides similar visualization of pathology.

Rapid Knee MRI Acquisition and Analysis Techniques for Imaging Osteoarthritis.

Osteoarthritis (OA) of the knee is a major source of disability that has no known treatment or cure. Morphological and compositional MRI is commonly used for assessing the bone and soft tissues in the knee to enhance the understanding of OA pathophysiology. However, it is challenging to extend these imaging methods and their subsequent analysis techniques to study large population cohorts due to slow and inefficient imaging acquisition and postprocessing tools. This can create a bottleneck in assessing early OA changes and evaluating the responses of novel therapeutics. The purpose of this review article is to highlight recent developments in tools for enhancing the efficiency of knee MRI methods useful to study OA. Advances in efficient MRI data acquisition and reconstruction tools for morphological and compositional imaging, efficient automated image analysis tools, and hardware improvements to further drive efficient imaging are discussed in this review. For each topic, we discuss the current challenges as well as potential future opportunities to alleviate these challenges. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 3.

Utility of deep learning super-resolution in the context of osteoarthritis MRI biomarkers.

BACKGROUND: Super-resolution is an emerging method for enhancing MRI resolution; however, its impact on image quality is still unknown. PURPOSE: To evaluate MRI super-resolution using quantitative and qualitative metrics of cartilage morphometry, osteophyte detection, and global image blurring. STUDY TYPE: Retrospective. POPULATION: In all, 176 MRI studies of subjects at varying stages of osteoarthritis. FIELD STRENGTH/SEQUENCE: Original-resolution 3D double-echo steady-state (DESS) and DESS with 3× thicker slices retrospectively enhanced using super-resolution and tricubic interpolation (TCI) at 3T. ASSESSMENT: A quantitative comparison of femoral cartilage morphometry was performed for the original-resolution DESS, the super-resolution, and the TCI scans in 17 subjects. A reader study by three musculoskeletal radiologists assessed cartilage image quality, overall image sharpness, and osteophytes incidence in all three sets of scans. A referenceless blurring metric evaluated blurring in all three image dimensions for the three sets of scans. STATISTICAL TESTS: Mann-Whitney U-tests compared Dice coefficients (DC) of segmentation accuracy for the DESS, super-resolution, and TCI images, along with the image quality readings and blurring metrics. Sensitivity, specificity, and diagnostic odds ratio (DOR) with 95% confidence intervals compared osteophyte detection for the super-resolution and TCI images, with the original-resolution as a reference. RESULTS: DC for the original-resolution (90.2 ± 1.7%) and super-resolution (89.6 ± 2.0%) were significantly higher (P < 0.001) than TCI (86.3 ± 5.6%). Segmentation overlap of super-resolution with the original-resolution (DC = 97.6 ± 0.7%) was significantly higher (P < 0.0001) than TCI overlap (DC = 95.0 ± 1.1%). Cartilage image quality for sharpness and contrast levels, and the through-plane quantitative blur factor for super-resolution images, was significantly (P < 0.001) better than TCI. Super-resolution osteophyte detection sensitivity of 80% (76-82%), specificity of 93% (92-94%), and DOR of 32 (22-46) was significantly higher (P < 0.001) than TCI sensitivity of 73% (69-76%), specificity of 90% (89-91%), and DOR of 17 (13-22). DATA CONCLUSION: Super-resolution appears to consistently outperform naïve interpolation and may improve image quality without biasing quantitative biomarkers. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:768-779.