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OBJECTIVE:To investigate changes in bone metabolism by positron emission tomography (PET), as well as spatial relationships between bone metabolism and magnetic resonance imaging (MRI) quantitative markers of early cartilage degradation, in anterior cruciate ligament (ACL)-reconstructed knees. DESIGN:Both knees of 15 participants with unilateral reconstructed ACL tears and unaffected contralateral knees were scanned using a simultaneous 3.0T PET-MRI system following injection of 18F-sodium fluoride (18F-NaF). The maximum pixel standardized uptake value (SUVmax) in the subchondral bone and the average T2 relaxation time in cartilage were measured in each knee in eight knee compartments. We tested differences in SUVmax and cartilage T2 relaxation times between the ACL-injured knee and the contralateral control knee as well as spatial relationships between these bone and cartilage changes. RESULTS:Significantly increased subchondral bone 18F-NaF SUVmax and cartilage T2 times were observed in the ACL-reconstructed knees (median [inter-quartile-range (IQR)]: 5.0 [5.8], 36.8 [3.6] ms) compared to the contralateral knees (median [IQR]: 1.9 [1.4], 34.4 [3.8] ms). A spatial relationship between the two markers was also seen. Using the contralateral knee as a control, we observed a significant correlation of r = 0.59 between the difference in subchondral bone SUVmax (between injured and contralateral knees) and the adjacent cartilage T2 (between the two knees) [P

Purpose: To rapidly calculate and validate subject-specific field maps based on the three-dimensional shape of the bilateral breast volume.Materials and Methods: Ten healthy female volunteers were scanned at 3 Tesla using a multi-echo sequence that provides water, fat, in-phase, out-of-phase, and field map images. A shape-specific binary mask was automatically generated to calculate a computed field map using a dipole field model. The measured and computed field maps were compared by visualizing the spatial distribution of the difference field map, the mean absolute error, and the 80% distribution widths of frequency histograms.Results: The 10 computed field maps had a mean absolute error of 38 Hz (0.29 ppm) compared with the measured field maps. The average 80% distribution widths for the histograms of all of the computed, measured, and difference field maps are 205 Hz, 233 Hz, and 120 Hz, respectively.Conclusion: The computed field maps had substantial overall agreement with the measured field maps, indicating that breast MRI field maps can be computed based on the air-tissue interfaces. These estimates may provide a predictive model for field variations and thus have the potential to improve applications in breast MRI. [ABSTRACT FROM AUTHOR]

Purpose: Diffusion-weighted imaging (DWI) suffers from geometric distortion and chemical shift artifacts due to the commonly used Echo Planar Imaging (EPI) trajectory. Even with fat suppression in DWI, severe B 0 and B 1 variations can result in residual fat, which becomes both a source of image artifacts and a confounding factor in diffusion-weighted contrast in distinguishing benign and malignant tissues. This work presents a method for acquiring distortion-free diffusion-weighted images using spatiotemporal acquisition and joint reconstruction. Water-fat separation is performed by chemical-shift encoding., Methods: Spatiotemporal acquisition is employed to obtain distortion-free images at a series of echo times. Chemical-shift encoding is used for water-fat separation. Reconstruction and separation are performed jointly in the spat-spectral domain. To address the shot-to-shot motion-induced phase in DWI, an Fast Spin Echo (FSE)-based phase navigator is incorporated into the sequence to obtain distortion-free phase information. The proposed method was validated in phantoms and in vivo for the brain, head and neck, and breast., Results: The proposed method enables the acquisition of distortion-free diffusion-weighted images in the presence of B 0 field inhomogenieties commonly observed in the body. Water and fat components are separated with no obvious spectral leakage artifacts. The estimated Apparent Diffusion Coefficient (ADC) is comparable to that of multishot DW-EPI., Conclusion: Distortion-free, water-fat separated diffusion-weighted images in body can be obtained through the utilization of spatiotemporal acquisition and joint reconstruction methods., (© 2024 International Society for Magnetic Resonance in Medicine.)

Purpose: Comprehensive assessment of image quality requires accounting for spatial variations in (i) intensity artifact, (ii) geometric distortion, (iii) signal-to-noise ratio (SNR), and (iv) spatial resolution, among other factors. This work presents an ensemble of methods to meet this need, from phantom design to image analysis, and applies it to the scenario of imaging near metal., Methods: A modular phantom design employing a gyroid lattice is developed to enable the co-registered volumetric quantitation of image quality near a metallic hip implant. A method for measuring spatial resolution by means of local point spread function (PSF) estimation is presented and the relative fitness of gyroid and cubic lattices is examined. Intensity artifact, geometric distortion, and SNR maps are also computed. Results are demonstrated with 2D-FSE and MAVRIC-SL scan protocols on a 3T MRI scanner., Results: The spatial resolution method demonstrates a worst-case error of 0.17 pixels for measuring in-plane blurring up to 3 pixels (full width at half maximum). The gyroid outperforms a cubic lattice design for the local PSF estimation task. The phantom supports four configurations toggling the presence/absence of both metal and structure with good spatial correspondence for co-registered analysis of the four quality factors. The marginal scan time to evaluate one scan protocol amounts to five repetitions. The phantom design can be fabricated in 2 days at negligible material cost., Conclusion: The phantom and associated analysis methods can elucidate complex image quality trade-offs involving intensity artifact, geometric distortion, SNR, and spatial resolution. The ensemble of methods is suitable for benchmarking imaging performance near metal., (© 2024 International Society for Magnetic Resonance in Medicine.)

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., (© 2024 International Society for Magnetic Resonance in Medicine.)

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/mm 2 , scan time of 6 min, and a clinically viable reconstruction time., (© 2024 International Society for Magnetic Resonance in Medicine.)

Background: Cartilage T 2 can detect joints at risk of developing osteoarthritis. The quantitative double-echo steady state (qDESS) sequence is attractive for knee cartilage T 2 mapping because of its acquisition time of under 5 minutes. Understanding the reproducibility errors associated with qDESS T 2 is essential to profiling the technical performance of this biomarker., Purpose: To examine the combined acquisition and segmentation reproducibility of knee cartilage qDESS T 2 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 T 2 maps were acquired twice on the same day and with a 1-week interval between scans. For each acquisition, average cartilage T 2 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 T 2 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 T 2 reproducibility. The median of test-retest standard deviations was used for T 2 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 T 2 CVs and RMSA-CVs of ≤5%. T 2 ICC values >0.75 were observed in the majority of regions but were more variable in interday tibial comparisons. Test-retest T 2 precision was <1.3 msec. The T 2 SDD was 3.8 msec., Data Conclusion: Excellent CV and RMSA-CV reproducibility may suggest that qDESS T 2 increases or decreases >5% (3.8 msec) could represent changes to cartilage composition., Technical Efficacy: Stage 2., (© 2024 International Society for Magnetic Resonance in Medicine.)

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., (© 2023 International Society for Magnetic Resonance in Medicine.)

Purpose: [Formula: see text] mapping is a powerful tool for studying osteoarthritis (OA) changes and bilateral imaging may be useful in investigating the role of between-knee asymmetry in OA onset and progression. The quantitative double-echo in steady-state (qDESS) can provide fast simultaneous bilateral knee [Formula: see text] and high-resolution morphometry for cartilage and meniscus. The qDESS uses an analytical signal model to compute [Formula: see text] relaxometry maps, which require knowledge of the flip angle (FA). In the presence of [Formula: see text] inhomogeneities, inconsistencies between the nominal and actual FA can affect the accuracy of [Formula: see text] measurements. We propose a pixel-wise [Formula: see text] correction method for qDESS [Formula: see text] mapping exploiting an auxiliary [Formula: see text] map to compute the actual FA used in the model., Methods: The technique was validated in a phantom and in vivo with simultaneous bilateral knee imaging. [Formula: see text] measurements of femoral cartilage (FC) of both knees of six healthy participants were repeated longitudinally to investigate the association between [Formula: see text] variation and [Formula: see text]., Results: The results showed that applying the [Formula: see text] correction mitigated [Formula: see text] variations that were driven by [Formula: see text] inhomogeneities. Specifically, [Formula: see text] left-right symmetry increased following the [Formula: see text] correction ([Formula: see text] = 0.74 > [Formula: see text] = 0.69). Without the [Formula: see text] correction, [Formula: see text] values showed a linear dependence with [Formula: see text]. The linear coefficient decreased using the [Formula: see text] correction (from 24.3 ± 1.6 ms to 4.1 ± 1.8) and the correlation was not statistically significant after the application of the Bonferroni correction (p value > 0.01)., Conclusion: The study showed that [Formula: see text] correction could mitigate variations driven by the sensitivity of the qDESS [Formula: see text] mapping method to [Formula: see text], therefore, increasing the sensitivity to detect real biological changes. The proposed method may improve the robustness of bilateral qDESS [Formula: see text] mapping, allowing for an accurate and more efficient evaluation of OA pathways and pathophysiology through longitudinal and cross-sectional studies., (© 2023. The Author(s), under exclusive licence to European Society for Magnetic Resonance in Medicine and Biology (ESMRMB).)

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 (ρ c ). 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 (ρ c  = 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., (© 2022 International Society for Magnetic Resonance in Medicine.)

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., (© 2022 International Society for Magnetic Resonance in Medicine.)

We systematically evaluate the training methodology and efficacy of two inpainting-based pretext tasks of context prediction and context restoration for medical image segmentation using self-supervised learning (SSL). Multiple versions of self-supervised U-Net models were trained to segment MRI and CT datasets, each using a different combination of design choices and pretext tasks to determine the effect of these design choices on segmentation performance. The optimal design choices were used to train SSL models that were then compared with baseline supervised models for computing clinically-relevant metrics in label-limited scenarios. We observed that SSL pretraining with context restoration using 32 × 32 patches and Poission-disc sampling, transferring only the pretrained encoder weights, and fine-tuning immediately with an initial learning rate of 1 × 10-3 provided the most benefit over supervised learning for MRI and CT tissue segmentation accuracy ( p < 0.001). For both datasets and most label-limited scenarios, scaling the size of unlabeled pretraining data resulted in improved segmentation performance. SSL models pretrained with this amount of data outperformed baseline supervised models in the computation of clinically-relevant metrics, especially when the performance of supervised learning was low. Our results demonstrate that SSL pretraining using inpainting-based pretext tasks can help increase the robustness of models in label-limited scenarios and reduce worst-case errors that occur with supervised learning.

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., (© 2022 International Society for Magnetic Resonance in Medicine.)

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/mm 2 ) 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. B 1 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 B 1 by 46% which allows RF bandwidth to be doubled. Simulations and phantom experiments show that a non-linear phase CPMG pulse pair reduces B 1 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., (© 2022 International Society for Magnetic Resonance in Medicine.)

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-B 1 , and IR-bSSFP-B 1 , the latter two designed to be more specific for B 1 + 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 T 1 ,  T 2 , and M 0 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., (© 2022 John Wiley & Sons, Ltd.)

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., (© 2021. ISS.)

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 (qDESS High ), and qDESS with a low diffusion weighting (qDESS Low ). 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 qDESS Low (AC2 = 0.74) and qDESS High (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 qDESS Low , AC2 = 0.64 for qDESS High ), and good intra-rater agreement was seen for both sequences as well (qDESS Low AC2 = 0.78, qDESS High AC2 = 0.80). Diagnostic confidence was moderate to high for qDESS Low (mean = 2.36) and slightly less than moderate for qDESS High (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 (qDESS Low ) 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 qDESS Low shows good inter- and intra-rater agreement for the depiction of synovitis, particularly for readers experienced with the sequence., (© 2021. European Society of Radiology.)

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 (S anat ) from 0 (nondiagnostic) to 5 (outstanding). Further, we made image comparisons on a 5-point scale (S comp ) (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 S comp 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; S comp 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., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Cartilage transmits and redistributes biomechanical loads in the knee joint during exercise. Exercise-induced loading alters cartilage hydration and is detectable using quantitative magnetic resonance imaging (MRI), where T 2 relaxation time (T 2 ) is influenced by cartilage collagen composition, fiber orientation, and changes in the extracellular matrix. This study characterized short-term transient responses of healthy knee cartilage to running-induced loading using bilateral scans and image registration. Eleven healthy female recreational runners (33.73 ± 4.22 years) and four healthy female controls (27.25 ± 1.38 years) were scanned on a 3T GE MRI scanner with quantitative 3D double-echo in steady-state before running over-ground (runner group) or resting (control group) for 40 min. Subjects were scanned immediately post-activity at 5-min intervals for 60 min. T 2 times were calculated for femoral, tibial, and patellar cartilage at each time point and analyzed using a mixed-effects model and Bonferroni post hoc. There were immediate decreases in T 2 (mean ± SEM) post-run in superficial femoral cartilage of at least 3.3% ± 0.3% (p = .002) between baseline and Time 0 that remained for 25 min, a decrease in superficial tibial cartilage T 2 of 2.9% ± 0.4% (p = .041) between baseline and Time 0, and a decrease in superficial patellar cartilage T 2 of 3.6% ± 0.3% (p = .020) 15 min post-run. There were decreases in the medial posterior region of superficial femoral cartilage T 2 of at least 5.3 ± 0.2% (p = .022) within 5 min post-run that remained at 60 min post-run. These results increase understanding of transient responses of healthy cartilage to repetitive, exercise-induced loading and establish preliminary recommendations for future definitive studies of cartilage response to running., (© 2021 Orthopaedic Research Society. Published by Wiley Periodicals LLC.)

Purpose: Almost one in four lumpectomies fails to fully remove cancerous tissue from the breast, requiring reoperation. This high failure rate suggests that existing lumpectomy guidance methods are inadequate for allowing surgeons to consistently identify the proper volume of tissue for excision. Current guidance techniques either provide little information about the tumor position or require surgeons to frequently switch between making incisions and manually probing for a marker placed at the lesion site. This article explores the feasibility of thermo-acoustic ultrasound (TAUS) to enable hands-free localization of metallic biopsy markers throughout surgery, which would allow for continuous visualization of the lesion site in the breast without the interruption of surgery. In a TAUS-based localization system, microwave excitations would be transmitted into the breast, and the amplification in microwave absorption around the metallic markers would generate acoustic signals from the marker sites through the thermo-acoustic effect. Detection and ranging of these signals by multiple acoustic receivers on the breast could then enable marker localization through acoustic multilateration., Methods: Physics simulations were used to characterize the TAUS signals generated from different markers by microwave excitations. First, electromagnetic simulations determined the spatial pattern of the amplification in microwave absorption around the markers. Then, acoustic simulations characterized the acoustic fields generated from these markers at various acoustic frequencies. TAUS-based one-dimensional (1D) ranging of two metallic markers-including a biopsy marker that is FDA-approved for clinical use-immersed in saline was also performed using a bench-top setup. To perform TAUS acquisitions, a microwave applicator was driven by 2.66 GHz microwave signals that were amplitude-modulated by chirps at the desired acoustic excitation frequencies, and the resulting TAUS signal from the markers was detected by an ultrasonic transducer., Results: The simulation results show that the geometry of the marker strongly impacts the quantity and spatial pattern of both the microwave absorption around the marker and the resulting TAUS signal generated from the marker. The simulated TAUS signal maps and acoustic frequency responses also make clear that the marker geometry plays an important role in determining the overall system response. Using the bench-top setup, TAUS detection and 1D localization of the markers were successfully demonstrated for multiple different combinations of microwave applicator and metallic marker. These initial results indicate that TAUS-based localization of biopsy markers is feasible., Conclusions: Through microwave excitations and acoustic detection, TAUS can be used to localize metallic biopsy markers. With further development, TAUS opens new avenues to enable a more intuitive lumpectomy guidance system that could help to achieve better lumpectomy outcomes., (© 2021 American Association of Physicists in Medicine.)

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., (© 2020 International Society for Magnetic Resonance in Medicine.)

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.

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., (© 2020 International Society for Magnetic Resonance in Medicine.)

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 T 2 -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., (© 2020 International Society for Magnetic Resonance in Medicine.)

Diffusion tensor imaging (DTI) measures water diffusion in skeletal muscle tissue and allows for muscle assessment in a broad range of neuromuscular diseases. However, current DTI measurements, typically performed using pulsed gradient spin echo (PGSE) diffusion encoding, are limited to the assessment of non-contracted musculature, therefore providing limited insight into muscle contraction mechanisms and contraction abnormalities. In this study, we propose the use of an oscillating gradient spin echo (OGSE) diffusion encoding strategy for DTI measurements to mitigate the effect of signal voids in contracted muscle and to obtain reliable diffusivity values. Two OGSE sequences with encoding frequencies of 25 and 50 Hz were tested in the lower leg of five healthy volunteers with relaxed musculature and during active dorsiflexion and plantarflexion, and compared with a conventional PGSE approach. A significant reduction of areas of signal voids using OGSE compared with PGSE was observed in the tibialis anterior for the scans obtained in active dorsiflexion and in the soleus during active plantarflexion. The use of PGSE sequences led to unrealistically elevated axial diffusivity values in the tibialis anterior during dorsiflexion and in the soleus during plantarflexion, while the corresponding values obtained using the OGSE sequences were significantly reduced. Similar findings were seen for radial diffusivity, with significantly higher diffusivity measured in plantarflexion in the soleus muscle using the PGSE sequence. Our preliminary results indicate that DTI with OGSE diffusion encoding is feasible in human musculature and allows to quantitatively assess diffusion properties in actively contracting skeletal muscle. OGSE holds great potential to assess microstructural changes occurring in the skeletal muscle during contraction, and for non-invasive assessment of contraction abnormalities in patients with muscle diseases., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Mazzoli, Moulin, Kogan, Hargreaves and Gold.)

Background: To assess diagnostic accuracy of quantitative double-echo in steady-state (qDESS) MRI for detecting synovitis in knee osteoarthritis (OA)., Methods: Patients with different degrees of radiographic knee OA were included prospectively. All underwent MRI with both qDESS and contrast-enhanced T1-weighted magnetic resonance imaging (CE-MRI). A linear combination of the two qDESS images can be used to create an image that displays contrast between synovium and the synovial fluid. Synovitis on both qDESS and CE-MRI was assessed semi-quantitatively, using a whole-knee synovitis sum score, indicating no/equivocal, mild, moderate, and severe synovitis. The correlation between sum scores of qDESS and CE-MRI (reference standard) was determined using Spearman's rank correlation coefficient and intraclass correlation coefficient for absolute agreement. Receiver operating characteristic analysis was performed to assess the diagnostic performance of qDESS for detecting different degrees of synovitis, with CE-MRI as reference standard., Results: In the 31 patients included, very strong correlation was found between synovitis sum scores on qDESS and CE-MRI (ρ = 0.96, p < 0.001), with high absolute agreement (0.84 (95%CI 0.14-0.95)). Mean sum score (SD) values on qDESS 5.16 (3.75) were lower than on CE-MRI 7.13 (4.66), indicating systematically underestimated synovitis severity on qDESS. For detecting mild synovitis or higher, high sensitivity and specificity were found for qDESS (1.00 (95%CI 0.80-1.00) and 0.909 (0.571-1.00), respectively). For detecting moderate synovitis or higher, sensitivity and specificity were good (0.727 (95%CI 0.393-0.927) and 1.00 (0.800-1.00), respectively)., Conclusion: qDESS MRI is able to, however with an underestimation, detect synovitis in patients with knee OA.

Purpose: To organize a multi-institute knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthritis progression., Materials and Methods: A dataset partition consisting of three-dimensional knee MRI from 88 retrospective patients at two time points (baseline and 1-year follow-up) with ground truth articular (femoral, tibial, and patellar) cartilage and meniscus segmentations was standardized. Challenge submissions and a majority-vote ensemble were evaluated against ground truth segmentations using Dice score, average symmetric surface distance, volumetric overlap error, and coefficient of variation on a holdout test set. Similarities in automated segmentations were measured using pairwise Dice coefficient correlations. Articular cartilage thickness was computed longitudinally and with scans. Correlation between thickness error and segmentation metrics was measured using the Pearson correlation coefficient. Two empirical upper bounds for ensemble performance were computed using combinations of model outputs that consolidated true positives and true negatives., Results: Six teams ( T 1 - T 6 ) submitted entries for the challenge. No differences were observed across any segmentation metrics for any tissues ( P = .99) among the four top-performing networks ( T 2 , T 3 , T 4 , T 6 ). Dice coefficient correlations between network pairs were high (> 0.85). Per-scan thickness errors were negligible among networks T 1 - T 4 ( P = .99), and longitudinal changes showed minimal bias (< 0.03 mm). Low correlations (ρ < 0.41) were observed between segmentation metrics and thickness error. The majority-vote ensemble was comparable to top-performing networks ( P = .99). Empirical upper-bound performances were similar for both combinations (P = .99)., Conclusion: Diverse networks learned to segment the knee similarly, where high segmentation accuracy did not correlate with cartilage thickness accuracy and voting ensembles did not exceed individual network performance.See also the commentary by Elhalawani and Mak in this issue. Keywords: Cartilage, Knee, MR-Imaging, Segmentation © RSNA, 2020 Supplemental material is available for this article., Competing Interests: Disclosures of Conflicts of Interest: A.D.D. Activities related to the present article: grants and travel support from the National Science Foundation, the National Institute of Arthritis and Musculoskeletal and Skin Diseases, the National Institute of Biomedical Imaging and Bioengineering, GE Healthcare, and Philips. Activities not related to the present article: grants from the National Institutes of Health. Other relationships: disclosed no relevant relationships. F.C. disclosed no relevant relationships. C. Iriondo disclosed no relevant relationships. A.M. disclosed no relevant relationships. S.J. disclosed no relevant relationships. U.B. disclosed no relevant relationships. M.P. Activities related to the present article: grant from the Independent Research Fund Denmark. Activities not related to the present article: disclosed no relevant relationships. Other relationships: disclosed no relevant relationships. C. Igel Activities related to the present article: grant from the Danish Council for Independent Research. Activities not related to the present article: disclosed no relevant relationships. Other relationships: disclosed no relevant relationships. E.B.D. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: stockholder in Biomediq and Cerebriu. Other relationships: disclosed no relevant relationships. S.G. disclosed no relevant relationships. M.Y. disclosed no relevant relationships. X.L. disclosed no relevant relationships. C.M.D. Activities related to the present article: grant from the National Institute of Arthritis and Musculoskeletal and Skin Diseases. Activities not related to the present article: disclosed no relevant relationships. Other relationships: disclosed no relevant relationships. V.J. disclosed no relevant relationships. R.R. disclosed no relevant relationships. G.E.G. Activities related to the present article: grants from the National Institutes of Health. Activities not related to the present article: board member for HeartVista; consultant for Canon; grants from GE Healthcare. Other relationships: disclosed no relevant relationships. B.A.H. Activities related to the present article: grant from the National Institutes of Health. Activities not related to the present article: royalties from patents licensed by Siemens and GE Healthcare; stockholder in LVIS. Other relationships: disclosed no relevant relationships. V.P. disclosed no relevant relationships. A.S.C. Activities related to the present article: grants from the National Institutes of Health, GE Healthcare, and Philips. Activities not related to the present article: board member for BrainKey and Chondrometrics; consultant for Skope, Subtle Medical, Chondrometrics, Image Analysis Group, Edge Analytics, ICM, and Culvert Engineering; stockholder in Subtle Medical, LVIS, and BrainKey; travel support from Paracelsus Medical Private University. Other relationships: disclosed no relevant relationships., (2021 by the Radiological Society of North America, Inc.)

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., (© 2020 International Society for Magnetic Resonance in Medicine.)

This work presents an analytical relationship between gradient-spoiled and RF-spoiled steady-state signals. The two echoes acquired in double-echo in steady-state scans are shown to lie on a line in the signal plane, where the two axes represent the amplitudes of each echo. The location along the line depends on the amount of spoiling and the diffusivity. The line terminates in a point corresponding to an RF-spoiled signal. In addition to the main contribution of demonstrating this signal relationship, we also include the secondary contribution of preliminary results from an example application of the relationship, in the form of a heuristic denoising method when both types of scans are performed. This is investigated in simulations, phantom scans, and in vivo scans. For the signal model, the main topic of this study, simulations confirmed its accuracy and explored its dependency on signal parameters and image noise. For the secondary topic of its preliminary application to reduce noise, simulations demonstrated the denoising method giving a reduction in noise-induced standard deviation of about 30%. The relative effect of the method on the signals is shown to depend on the slope of the described line, which is demonstrated to be zero at the Ernst angle. The phantom scans show a similar effect as the simulations. In vivo scans showed a slightly lower average improvement of about 28%.

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., (© 2019 International Society for Magnetic Resonance in Medicine.)

Purpose: Identifying small lung lesions during minimally invasive thoracic surgery can be challenging. We describe 3-dimensional mixed-reality visualization technology that may facilitate noninvasive nodule localization., Description: A software application and medical image processing pipeline were developed for the Microsoft HoloLens to incorporate patient-specific data and provide a mixed-reality tool to explore and manipulate chest anatomy with a custom-designed user interface featuring gesture and voice recognition., Evaluation: A needs assessment between engineering and clinical disciplines identified the potential utility of mixed-reality technology in facilitating safe and effective resection of small lung nodules. Through an iterative process, we developed a prototype employing a wearable headset that allows the user to (1) view a patient's original preoperative imaging; (2) manipulate a 3-dimensional rendering of that patient's chest anatomy including the bronchial, osseus, and vascular structures; and (3) simulate lung deflation and surgical instrument placement., Conclusions: Mixed-reality visualization during surgical planning may facilitate accurate and rapid identification of small lung lesions during minimally invasive surgeries and reduce the need for additional invasive preoperative localization procedures., (Copyright © 2020 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.)

Objectives: To assess the discriminative power of a 5-min quantitative double-echo steady-state (qDESS) sequence for simultaneous T 2 measurements of cartilage and meniscus, and structural knee osteoarthritis (OA) assessment, in a clinical OA population, using radiographic knee OA as reference standard., Methods: Fifty-three subjects were included and divided over three groups based on radiographic and clinical knee OA: 20 subjects with no OA (Kellgren-Lawrence grade (KLG) 0), 18 with mild OA (KLG2), and 15 with moderate OA (KLG3). All patients underwent a 5-min qDESS scan. We measured T 2 relaxation times in four cartilage and four meniscus regions of interest (ROIs) and performed structural OA evaluation with the MRI Osteoarthritis Knee Score (MOAKS) using qDESS with multiplanar reformatting. Between-group differences in T 2 values and MOAKS were calculated using ANOVA. Correlations of the reference standard (i.e., radiographic knee OA) with T 2 and MOAKS were assessed with correlation analyses for ordinal variables., Results: In cartilage, mean T 2 values were 36.1 ± SD 4.3, 40.6 ± 5.9, and 47.1 ± 4.3 ms for no, mild, and moderate OA, respectively (p < 0.001). In menisci, mean T 2 values were 15 ± 3.6, 17.5 ± 3.8, and 20.6 ± 4.7 ms for no, mild, and moderate OA, respectively (p < 0.001). Statistically significant correlations were found between radiographic OA and T 2 and between radiographic OA and MOAKS in all ROIs (p < 0.05)., Conclusion: Quantitative T 2 and structural assessment of cartilage and meniscus, using a single 5-min qDESS scan, can distinguish between different grades of radiographic OA, demonstrating the potential of qDESS as an efficient tool for OA imaging., Key Points: • Quantitative T 2 values of cartilage and meniscus as well as structural assessment of the knee with a single 5-min quantitative double-echo steady-state (qDESS) scan can distinguish between different grades of knee osteoarthritis (OA). • Quantitative and structural qDESS-based measurements correlate significantly with the reference standard, radiographic degree of OA, for all cartilage and meniscus regions. • By providing quantitative measurements and diagnostic image quality in one rapid MRI scan, qDESS has great potential for application in large-scale clinical trials in knee OA.

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., (© 2019 International Society for Magnetic Resonance in Medicine.)

A 61-year-old man presented with chronic dorsal foot pain of 9 years that worsened with ambulation. Conventional diagnostic imaging and medical workup were unrevealing, and ankle arthrodesis had been recommended by an orthopedic surgeon for pain relief. Instead, the patient participated in a clinical imaging trial designed for identifying pain generators using whole-body fluorodeoxyglucose (18F-FDG) positron emission tomography/magnetic resonance imaging (PET/MRI). The scan revealed not only high 18F-FDG uptake at the site of pain, but also a hematoma and an inflamed, fibrotic, ruptured plantaris muscle. The fibrotic plantaris likely altered biomechanics with walking, explaining why symptoms worsened with activity. A simple tenotomy of the plantaris tendon was performed to decouple ankle movement from the plantaris injury, resulting in pain relief. This case illustrates the potential of whole-body 18F-FDG PET/MRI to better localize pain generators.

Purpose: To investigate a computationally efficient method for optimizing the Cramér-Rao Lower Bound (CRLB) of quantitative sequences without using approximations or an analytical expression of the signal., Methods: Automatic differentiation was applied to Bloch simulations and used to optimize several quantitative sequences without the need for approximations or an analytical expression. The results were validated with in vivo measurements and comparisons to prior art. Multi-echo spin echo and DESPO T 1 were used as benchmarks to verify the CRLB implementation. The CRLB of the Magnetic Resonance Fingerprinting (MRF) sequence, which has a complicated analytical formulation, was also optimized using automatic differentiation., Results: The sequence parameters obtained for multi-echo spin echo and DESPO T 1 matched results obtained using conventional methods. In vivo, MRF scans demonstrate that the CRLB optimization obtained with automatic differentiation can improve performance in presence of white noise. For MRF, the CRLB optimization converges in 1.1 CPU hours for N TR = 400 and has O ( N TR ) asymptotic runtime scaling for the calculation of the CRLB objective and gradient., Conclusions: Automatic differentiation can be used to optimize the CRLB of quantitative sequences without using approximations or analytical expressions. For MRF, the runtime is computationally efficient and can be used to investigate confounding factors as well as MRF sequences with a greater number of repetitions., (© 2019 International Society for Magnetic Resonance in Medicine.)

Background: Clinical knee MRI protocols require upwards of 15 minutes of scan time., Purpose/hypothesis: To compare the imaging appearance of knee abnormalities depicted with a 5-minute 3D double-echo in steady-state (DESS) sequence with separate echo images, with that of a routine clinical knee MRI protocol. A secondary goal was to compare the imaging appearance of knee abnormalities depicted with 5-minute DESS paired with a 2-minute coronal proton-density fat-saturated (PDFS) sequence., Study Type: Prospective., Subjects: Thirty-six consecutive patients (19 male) referred for a routine knee MRI., Field Strength/sequences: DESS and PDFS at 3T., Assessment: Five musculoskeletal radiologists evaluated all images for the presence of internal knee derangement using DESS, DESS+PDFS, and the conventional imaging protocol, and their associated diagnostic confidence of the reading., Statistical Tests: Differences in positive and negative percent agreement (PPA and NPA, respectively) and 95% confidence intervals (CIs) for DESS and DESS+PDFS compared with the conventional protocol were calculated and tested using exact McNemar tests. The percentage of observations where DESS or DESS+PDFS had equivalent confidence ratings to DESS+Conv were tested with exact symmetry tests. Interreader agreement was calculated using Krippendorff's alpha., Results: DESS had a PPA of 90% (88-92% CI) and NPA of 99% (99-99% CI). DESS+PDFS had increased PPA of 99% (95-99% CI) and NPA of 100% (99-100% CI) compared with DESS (both P < 0.001). DESS had equivalent diagnostic confidence to DESS+Conv in 94% of findings, whereas DESS+PDFS had equivalent diagnostic confidence in 99% of findings (both P < 0.001). All readers had moderate concordance for all three protocols (Krippendorff's alpha 47-48%)., Data Conclusion: Both 1) 5-minute 3D-DESS with separated echoes and 2) 5-minute 3D-DESS paired with a 2-minute coronal PDFS sequence depicted knee abnormalities similarly to a routine clinical knee MRI protocol, which may be a promising technique for abbreviated knee MRI., Level of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018., (© 2018 International Society for Magnetic Resonance in Medicine.)

Purpose: To accelerate imaging near metallic implants with multi-spectral imaging (MSI) techniques by exploiting a signal model in the spectral dimension., Methods: MSI techniques resolve metal-induced field perturbations by acquiring separate 3D spatial encodings at multiple excitation frequencies, which are referred to as spectral bins. The proposed model-based reconstruction exploits the correlation between spectral bins in image reconstruction by enforcing a signal model to describe the signal profile across bins. This work evaluates the accuracy of the MSI signal model in simulations and in vivo experiments. The proposed model-based reconstruction was evaluated in 6 subjects at an overall undersampling factor of 17.4 and compared with model-free parallel imaging and compressed sensing (PI & CS). The quality of reconstructed images was evaluated using normalized RMS error (nRMSE) and structural similarity index (SSIM) comparisons, with paired Wilcoxon tests in 6 subjects used to determine whether there was a significant difference in the metrics., Results: Both simulations and in vivo experiments show that the proposed signal model can represent the MSI signal profiles in the spectral dimension compactly and accurately. In the in vivo experiments, the model-based reconstruction significantly improved image quality over model-free PI & CS, with P < 0.05 for both nRMSE and SSIM at 17.4× acceleration., Conclusion: This work presents the feasibility of using a model-based reconstruction to accelerate MSI techniques for faster MR imaging near metal., (© 2018 International Society for Magnetic Resonance in Medicine.)

Background: View-sharing (VS) increases spatiotemporal resolution in dynamic contrast-enhanced (DCE) MRI by sharing high-frequency k-space data across temporal phases. This temporal sharing results in respiratory motion within any phase to propagate artifacts across all shared phases. Compressed sensing (CS) eliminates the need for VS by recovering missing k-space data from pseudorandom undersampling, reducing temporal blurring while maintaining spatial resolution., Purpose: To evaluate a CS reconstruction algorithm on undersampled DCE-MRI data for image quality and hepatocellular carcinoma (HCC) detection., Study Type: Retrospective., Subjects: Fifty consecutive patients undergoing MRI for HCC screening (29 males, 21 females, 52-72 years)., Field Strength/sequence: 3.0T MRI. Multiphase 3D-SPGR T 1 -weighted sequence undersampled in arterial phases with a complementary Poisson disc sampling pattern reconstructed with VS and CS algorithms., Assessment: VS and CS reconstructions evaluated by blinded assessments of image quality and anatomic delineation on Likert scales (1-4 and 1-5, respectively), and HCC detection by OPTN/UNOS criteria including a diagnostic confidence score (1-5). Blinded side-by-side reconstruction comparisons for lesion depiction and overall series preference (-3-3)., Statistical Analysis: Two-tailed Wilcoxon signed rank tests for paired nonparametric analyses with Bonferroni-Holm multiple-comparison corrections. McNemar's test for differences in lesion detection frequency and transplantation eligibility., Results: CS compared with VS demonstrated significantly improved contrast (mean 3.6 vs. 2.9, P < 0.0001) and less motion artifact (mean 3.6 vs. 3.2, P = 0.006). CS compared with VS demonstrated significantly improved delineations of liver margin (mean 4.5 vs. 3.8, P = 0.0002), portal veins (mean 4.5 vs. 3.7, P < 0.0001), and hepatic veins (mean 4.6 vs. 3.5, P < 0.0001), but significantly decreased delineation of hepatic arteries (mean 3.2 vs. 3.7, P = 0.004). No significant differences were seen in the other assessments., Data Conclusion: Applying a CS reconstruction to data acquired for a VS reconstruction significantly reduces motion artifacts in a clinical DCE protocol for HCC screening., Level of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:984-993., (© 2018 International Society for Magnetic Resonance in Medicine.)

Purpose: To improve the homogeneity and consistency of apparent diffusion coefficient (ADC) estimates in cartilage from the double-echo in steady-state (DESS) sequence by applying SNR-weighted regularization during post-processing., Methods: An estimation method that linearizes ADC estimates from DESS is used in conjunction with a smoothness constraint to suppress noise-induced variation in ADC estimates. Simulations, phantom scans, and in vivo scans are used to demonstrate how the method reduces ADC variability. Conventional diffusion-weighted echo-planar imaging (DW EPI) maps are acquired for comparison of mean and standard deviation (SD) of the ADC estimate., Results: Simulations and phantom scans demonstrated that the SNR-weighted regularization can produce homogenous ADC maps at varying levels of SNR, whereas non-regularized maps only estimate ADC accurately at high SNR levels. The in vivo maps showed that the SNR-weighted regularization produced ADC maps with similar heterogeneity to maps produced with standard DW EPI, but without the distortion of such reference scans., Conclusion: A linear approximation of a simplified model of the relationship between DESS signals allows for fast SNR-weighted regularization of ADC maps that reduces estimation error in relatively short T 2 tissue such as cartilage., (© International Society for Magnetic Resonance in Medicine.)

Purpose: Printed magnetic ink creates predictable B 0 field perturbations based on printed shape and magnetic susceptibility. This can be exploited for contrast in MR imaging techniques that are sensitized to off-resonance. The purpose of this work was to characterize the susceptibility variations of magnetic ink and demonstrate its application for creating MR-visible skin markings., Methods: The magnetic susceptibility of the ink was estimated by comparing acquired and simulated B 0 field maps of a custom-built phantom. The phantom was also imaged using a 3D gradient echo sequence with a presaturation pulse tuned to different frequencies, which adjusts the range of suppressed frequencies. Healthy volunteers with a magnetic ink pattern pressed to the skin or magnetic ink temporary flexible adhesives applied to the skin were similarly imaged., Results: The volume-average magnetic susceptibility of the ink was estimated to be 131 ± 3 parts per million across a 1-mm isotropic voxel (13,100 parts per million assuming a 10-μm thickness of printed ink). Adjusting the saturation frequency highlights different off-resonant regions created by the ink patterns; for example, if tuned to suppress fat, fat suppression will fail near the ink due to the off-resonance. This causes magnetic ink skin markings placed over a region with underlying subcutaneous fat to be visible on MR images., Conclusion: Patterns printed with magnetic ink can be imaged and identified with MRI. Temporary flexible skin adhesives printed with magnetic ink have the potential to be used as skin markings that are visible both by eye and on MR images., (© 2018 International Society for Magnetic Resonance in Medicine.)

Purpose: To develop a super-resolution technique using convolutional neural networks for generating thin-slice knee MR images from thicker input slices, and compare this method with alternative through-plane interpolation methods., Methods: We implemented a 3D convolutional neural network entitled DeepResolve to learn residual-based transformations between high-resolution thin-slice images and lower-resolution thick-slice images at the same center locations. DeepResolve was trained using 124 double echo in steady-state (DESS) data sets with 0.7-mm slice thickness and tested on 17 patients. Ground-truth images were compared with DeepResolve, clinically used tricubic interpolation, and Fourier interpolation methods, along with state-of-the-art single-image sparse-coding super-resolution. Comparisons were performed using structural similarity, peak SNR, and RMS error image quality metrics for a multitude of thin-slice downsampling factors. Two musculoskeletal radiologists ranked the 3 data sets and reviewed the diagnostic quality of the DeepResolve, tricubic interpolation, and ground-truth images for sharpness, contrast, artifacts, SNR, and overall diagnostic quality. Mann-Whitney U tests evaluated differences among the quantitative image metrics, reader scores, and rankings. Cohen's Kappa (κ) evaluated interreader reliability., Results: DeepResolve had significantly better structural similarity, peak SNR, and RMS error than tricubic interpolation, Fourier interpolation, and sparse-coding super-resolution for all downsampling factors (p < .05, except 4 × and 8 × sparse-coding super-resolution downsampling factors). In the reader study, DeepResolve significantly outperformed (p < .01) tricubic interpolation in all image quality categories and overall image ranking. Both readers had substantial scoring agreement (κ = 0.73)., Conclusion: DeepResolve was capable of resolving high-resolution thin-slice knee MRI from lower-resolution thicker slices, achieving superior quantitative and qualitative diagnostic performance to both conventionally used and state-of-the-art methods., (© 2018 International Society for Magnetic Resonance in Medicine.)

Purpose: Magnetic resonance (MR) guidance for biopsy procedures requires high intrinsic soft-tissue contrast. However, artifacts induced by the metallic needle can reduce its localization and require low-susceptibility needle materials with poorer cutting performance. In a proof of concept, we demonstrate the feasibility of 2D multispectral imaging (2DMSI) for both needle tracking and for needle artifact reduction for more precise needle localization and to enable the usage of needle materials with higher susceptibility., Method: We applied 2DMSI for imaging of MR-compatible biopsy needles, conventional stainless-steel needles, and mixed-material needles and compared it to conventional techniques. In addition, we exploited intrinsic off-resonance information for passive needle tracking., Results: 2DMSI achieved a stronger reduction of the needle artifact compared to conventional techniques. For the mixed-material needles, the artifact was reduced to a level below that for MR-compatible needles with conventional imaging. The passive tracking also improved the ability to pinpoint the needle., Conclusion: 2DMSI is promising for both needle tracking and artifact-reduced imaging of biopsy needles for a more precise needle localization. 2DMSI may be particularly promising for needles inducing large distortions or for targeting of small lesions. In addition, it may enable the use of needle materials with higher susceptibility and potentially better sampling performance. Magn Reson Med 80:655-661, 2018. © 2017 International Society for Magnetic Resonance in Medicine., (© 2017 International Society for Magnetic Resonance in Medicine.)

Purpose: To demonstrate and evaluate the scan time and quantitative accuracy of simultaneous bilateral-knee imaging compared with single-knee acquisitions., Methods: Hardware modifications and safety testing was performed to enable MR imaging with two 16-channel flexible coil arrays. Noise covariance and sensitivity-encoding g-factor maps for the dual-coil-array configuration were computed to evaluate coil cross-talk and noise amplification. Ten healthy volunteers were imaged on a 3T MRI scanner with both dual-coil-array bilateral-knee and single-coil-array single-knee configurations. Two experienced musculoskeletal radiologists compared the relative image quality between blinded image pairs acquired with each configuration. Differences in T 2 relaxation time measurements between dual-coil-array and single-coil-array acquisitions were compared with the standard repeatability of single-coil-array measurements using a Bland-Altman analysis., Results: The mean g-factors for the dual-coil-array configuration were low for accelerations up to 6 in the right-left direction, and minimal cross-talk was observed between the two coil arrays. Image quality ratings of various joint tissues showed no difference in 89% (95% confidence interval: 85-93%) of rated image pairs, with only small differences ("slightly better" or "slightly worse") in image quality observed. The T 2 relaxation time measurements between the dual-coil-array configuration and the single-coil configuration showed similar limits of agreement and concordance correlation coefficients (limits of agreement: -0.93 to 1.99 ms; CCC: 0.97 (95% confidence interval: 0.96-0.98)), to the repeatability of single-coil-array measurements (limits of agreement: -2.07 to 1.96 ms; CCC: 0.97 (95% confidence interval: 0.95-0.98))., Conclusion: A bilateral coil-array setup can image both knees simultaneously in similar scan times as conventional unilateral knee scans, with comparable image quality and quantitative accuracy. This has the potential to improve the value of MRI knee evaluations. Magn Reson Med 80:529-537, 2018. © 2017 International Society for Magnetic Resonance in Medicine., (© 2017 International Society for Magnetic Resonance in Medicine.)

Purpose: To explore the temperature-induced signal change in two-dimensional multi-spectral imaging (2DMSI) for fast thermometry near metallic hardware to enable MR-guided focused ultrasound surgery (MRgFUS) in patients with implanted metallic hardware., Method: 2DMSI was optimized for temperature sensitivity and applied to monitor focus ultrasound surgery (FUS) sonications near metallic hardware in phantoms and ex vivo porcine muscle tissue. Further, we evaluated its temperature sensitivity for in vivo muscle in patients without metallic hardware. In addition, we performed a comparison of temperature sensitivity between 2DMSI and conventional proton-resonance-frequency-shift (PRFS) thermometry at different distances from metal devices and different signal-to-noise ratios (SNR)., Results: 2DMSI thermometry enabled visualization of short ultrasound sonications near metallic hardware. Calibration using in vivo muscle yielded a constant temperature sensitivity for temperatures below 43 °C. For an off-resonance coverage of ± 6 kHz, we achieved a temperature sensitivity of 1.45%/K, resulting in a minimum detectable temperature change of ∼2.5 K for an SNR of 100 with a temporal resolution of 6 s per frame., Conclusion: The proposed 2DMSI thermometry has the potential to allow MR-guided FUS treatments of patients with metallic hardware and therefore expand its reach to a larger patient population. Magn Reson Med 80:259-271, 2018. © 2017 International Society for Magnetic Resonance in Medicine., (© 2017 International Society for Magnetic Resonance in Medicine.)

Purpose: To mitigate artifacts from through-plane flow at the locations of steady-state stopbands in balanced steady-state free precession (SSFP) using partial dephasing., Methods: A 60° range in the phase accrual during a TR was created over the voxel by slightly unbalancing the slice-select dephaser. The spectral profiles of SSFP with partial dephasing for various constant flow rates and during pulsatile flow were simulated to determine if partial dephasing decreases through-plane flow artifacts originating near SSFP dark bands while maintaining on-resonant signal. Simulations were then validated in a flow phantom. Lastly, phase-cycled SSFP cardiac cine images were acquired with and without partial dephasing in six subjects., Results: Partial dephasing decreased the strength and non-linearity of the dependence of the signal at the stopbands on the through-plane flow rate. It thus mitigated hyper-enhancement from out-of-slice signal contributions and transient-related artifacts caused by variable flow both in the phantom and in vivo. In six volunteers, partial dephasing noticeably decreased artifacts in all of the phase-cycled cardiac cine datasets., Conclusion: Partial dephasing can mitigate the flow artifacts seen at the stopbands in balanced SSFP while maintaining the sequence's desired signal. By mitigating hyper-enhancement and transient-related artifacts originating from the stopbands, partial dephasing facilitates robust multiple-acquisition phase-cycled SSFP in the heart. Magn Reson Med 79:2944-2953, 2018. © 2017 International Society for Magnetic Resonance in Medicine., (© 2017 International Society for Magnetic Resonance in Medicine.)

Chronic sciatica is a major cause of disability worldwide, but accurate diagnosis of the causative pathology remains challenging. In this report, the feasibility of an 18 F-FDG PET/MRI approach for improved diagnosis of chronic sciatica is presented. Methods: 18 F-FDG PET/MRI was performed on 9 chronic sciatica patients and 5 healthy volunteers (healthy controls). Region-of-interest analysis using SUV max was performed, and 18 F-FDG uptake in lesions was compared with that in the corresponding areas in healthy controls. Results: Significantly increased 18 F-FDG uptake was observed in detected lesions in all patients and was correlated with pain symptoms. 18 F-FDG-avid lesions not only were found in impinged spinal nerves but also were associated with nonspinal causes of pain, such as facet joint degeneration, pars defect, or presumed scar neuroma. Conclusion: The feasibility of 18 F-FDG PET/MRI for diagnosing pain generators in chronic sciatica was demonstrated, revealing various possible etiologies., (© 2018 by the Society of Nuclear Medicine and Molecular Imaging.)