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There is a clinical need for alternatives to gadolinium contrast‐enhanced magnetic resonance imaging (MRI) to facilitate early detection and assessment of femoral head ischemia in pediatric patients with Legg‐Calvé‐Perthes disease (LCPD), a juvenile form of idiopathic osteonecrosis of the femoral head. The purpose of this study was to determine if intravoxel incoherent motion (IVIM), a noncontrast‐enhanced MRI method to simultaneously measure tissue perfusion and diffusion, can detect femoral head ischemia using a piglet model of LCPD. Twelve 6‐week‐old piglets underwent unilateral hip surgery to induce complete femoral head ischemia. The unoperated, contralateral femoral head served as a perfused control. The bilateral hips of the piglets were imaged in vivo at 3T MRI using IVIM and contrast‐enhanced MRI 1 week after surgery. Median apparent diffusion coefficient (ADC) and IVIM parameters (diffusion coefficient: Ds; perfusion coefficient: Df; perfusion fraction: f; and perfusion flux: f*Df) were compared between regions of interest comprising the epiphyseal bone marrow of the ischemic and control femoral heads. Contrast‐enhanced MRI confirmed complete femoral head ischemia in 11/12 piglets. IVIM perfusion fraction (f) and flux (f*Df) were significantly decreased in the ischemic versus control femoral heads: on average, f decreased 47 ± 27% (Δf = −0.055 ± 0.034; p = 0.0003) and f*Df decreased 50 ± 27% (Δf*Df = −0.59 ± 0.49 × 10−3 mm2/s; p = 0.0026). In contrast, IVIM diffusion coefficient (Ds) and ADC were significantly increased in the ischemic versus control femoral heads: on average, Ds increased 78 ± 21% (ΔDs = 0.60 ± 0.14 × 10−3 mm2/s; p < 0.0001) and ADC increased 60 ± 36% (ΔADC = 0.50 ± 0.23 × 10−3 mm2/s; p < 0.0001). In conclusion, IVIM is sensitive in detecting bone marrow ischemia in a piglet model of LCPD. [ABSTRACT FROM AUTHOR]

Purpose: Simultaneous multi‐slice acquisitions are essential for modern neuroimaging research, enabling high temporal resolution functional and high‐resolution q‐space sampling diffusion acquisitions. Recently, deep learning reconstruction techniques have been introduced for unaliasing these accelerated acquisitions, and robust artificial‐neural‐networks for k‐space interpolation (RAKI) have shown promising capabilities. This study systematically examines the impacts of hyperparameter selections for RAKI networks, and introduces a novel technique for training data generation which is analogous to the split‐slice formalism used in slice‐GRAPPA. Methods: RAKI networks were developed with variable hyperparameters and with and without split‐slice training data generation. Each network was trained and applied to five different datasets including acquisitions harmonized with Human Connectome Project lifespan protocol. Unaliasing performance was assessed through L1 errors computed between unaliased and calibration frequency‐space data. Results: Split‐slice training significantly improved network performance in nearly all hyperparameter configurations. Best unaliasing results were achieved with three layer RAKI networks using at least 64 convolutional filters with receptive fields of 7 voxels, 128 single‐voxel filters in the penultimate RAKI layer, batch normalization, and no training dropout with the split‐slice augmented training dataset. Networks trained without the split‐slice technique showed symptoms of network over‐fitting. Conclusions: Split‐slice training for simultaneous multi‐slice RAKI networks positively impacts network performance. Hyperparameter tuning of such reconstruction networks can lead to further improvements in unaliasing performance. [ABSTRACT FROM AUTHOR]

Objective: Digital breast tomosynthesis (DBT) has significantly improved cancer detection capabilities through its identification of subtle findings often imperceptible on 2D digital mammography, particularly architectural distortion (AD). The purpose of this study was to analyze of suspicious AD detected on screening DBT to evaluate the incidence of malignancy and to determine other patient or imaging characteristics in these cases as possible predictors of malignancy. Methods: This was an IRB approved retrospective analysis of subjects with AD detected on DBT screening mammography who were given a biopsy recommendation between January 1, 2016, and June 30, 2018. Univariate analysis of various imaging characteristics and patient high-risk factors was performed for statistical correlation with diagnosis of malignancy. Results: In the 218 DBT-detected AD findings with a final BI-RADS assessment of 4 or 5 on diagnostic workup, 94 (43.1%) yielded malignancy, 57 (26.2%) were classified as high-risk, and 67 (30.7%) were benign. There was a strong statistically significant association with malignancy in the cases with an US correlate (P < 0.0001). There was a statistically significant inverse correlation between malignancy and one-view findings (P = 0.0002). The presence of AD on 2D (P = 0.005) or synthetic 2D views (P = 0.002) showed statistically significant correlations with malignancy, whereas breast density or high-risk factors (P = 0.316) did not. Conclusion: AD detected on DBT that persists on further workup and has no explainable cause should be considered suspicious for malignancy. Identification of the AD on both standard mammographic views and the presence of an US correlate significantly increase the probability of malignancy. [ABSTRACT FROM AUTHOR]

Purpose : Due to host‐mediated adverse reaction to metallic debris, there is an increasing need for noninvasive assessment of the soft tissue surrounding large joint arthroplasties. Quantitative T2 mapping can be beneficial for tissue characterization and early diagnosis of tissue pathology but current T2 mapping techniques lack the capability to image near metal hardware. A novel multi‐spectral T2 mapping technique is proposed to address this unmet need. Methods : A T2 mapping pulse sequence based on routinely implemented 3D multi‐spectral imaging (3D‐MSI) pulse sequences is described and demonstrated. The 3D‐MSI pulse sequence is altered to acquire images at 2 echo times. Phantom and knee experiments were performed to assess the quantitative capabilities of the sequence in comparison to a commercially available T2 mapping sequence. The technique was demonstrated for use within a clinical protocol in 2 total hip arthroplasty (THA) cases to assess T2 variations within the periprosthetic joint space. Results : The proposed multi‐spectral T2 mapping technique agreed, within experimental errors, with T2 values derived from a commercially available clinical standard of care T2 mapping sequence. The same level of agreement was observed in quantitative phantoms and in vivo experiments. In THA cases, the method was able to assess variations of T2 within the synovial envelope immediately adjacent to implant interfaces. Conclusions : The proposed 3D‐MSI T2 mapping sequence was successfully demonstrated in assessing tissue T2 variations near metal implants. [ABSTRACT FROM AUTHOR]

Purpose The need for diffusion-weighted-imaging (DWI) near metallic implants is becoming increasingly relevant for a variety of clinical diagnostic applications. Conventional DWI methods are significantly hindered by metal-induced image artifacts. A novel approach relying on multispectral susceptibility artifact reduction techniques is presented to address this unmet need. Methods DWI near metal implants is achieved through a combination of several advanced MRI acquisition technologies. Previously described approaches to Carr-Purcell-Meiboom-Gill spin-echo train DWI sequences using the periodically rotated overlapping parallel lines with enhanced reconstruction are combined with multispectral-imaging metal artifact reduction principles to provide DWI with substantially reduced artifact levels. The presented methods are applied to limited sets of slices over areas of sarcoma risk near six implanted devices. Results Using the presented methods, DWI assessment without bulk image distortions is demonstrated in the immediate vicinity of metallic interfaces. In one subject, the apparent diffusion coefficient was reduced in a region of suspected sarcoma directly adjacent to fixation hardware. Conclusions An initial demonstration of minimal-artifact multispectral DWI in the near vicinity of metallic hardware is described and successfully demonstrated on clinical subjects. Magn Reson Med 79:987-993, 2018. © 2017 International Society for Magnetic Resonance in Medicine. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this work is to introduce a single model-based deep network that can provide high-quality reconstructions from undersampled parallel MRI data acquired with multiple sequences, acquisition settings, and field strengths., Methods: A single unrolled architecture, which offers good reconstructions for multiple acquisition settings, is introduced. The proposed scheme adapts the model to each setting by scaling the convolutional neural network (CNN) features and the regularization parameter with appropriate weights. The scaling weights and regularization parameter are derived using a multilayer perceptron model from conditional vectors, which represents the specific acquisition setting. The perceptron parameters and the CNN weights are jointly trained using data from multiple acquisition settings, including differences in field strengths, acceleration, and contrasts. The conditional network is validated using datasets acquired with different acquisition settings., Results: The comparison of the adaptive framework, which trains a single model using the data from all the settings, shows that it can offer consistently improved performance for each acquisition condition. The comparison of the proposed scheme with networks that are trained independently for each acquisition setting shows that it requires less training data per acquisition setting to offer good performance., Conclusion: The Ada-MoDL framework enables the use of a single model-based unrolled network for multiple acquisition settings. In addition to eliminating the need to train and store multiple networks for different acquisition settings, this approach reduces the training data needed for each acquisition setting., (© 2023 International Society for Magnetic Resonance in Medicine.)

Purpose: To evaluate two settings (noise reduction of 50% or 75%) of a deep learning (DL) reconstruction model relative to each other and to conventional MR image reconstructions on clinical orthopedic MRI datasets., Materials and Methods: This retrospective study included 54 patients who underwent two-dimensional fast spin-echo MRI for hip ( n = 22; mean age, 44 years ± 13 [standard deviation]; nine men) or shoulder ( n = 32; mean age, 56 years ± 17; 17 men) conditions between March 2019 and June 2020. MR images were reconstructed with conventional methods and the vendor-provided and commercially available DL model applied with 50% and 75% noise reduction settings (DL 50 and DL 75, respectively). Quantitative analytics, including relative anatomic edge sharpness, relative signal-to-noise ratio (rSNR), and relative contrast-to-noise ratio (rCNR) were computed for each dataset. In addition, the image sets were randomized, blinded, and presented to three board-certified musculoskeletal radiologists for ranking based on overall image quality and diagnostic confidence. Statistical analysis was performed with a nonparametric hypothesis comparing derived quantitative metrics from each reconstruction approach. In addition, inter- and intrarater agreement analysis was performed on the radiologists' rankings., Results: Both denoising settings of the DL reconstruction showed improved edge sharpness, rSNR, and rCNR relative to the conventional reconstructions. The reader rankings demonstrated strong agreement, with both DL reconstructions outperforming the conventional approach (Gwet agreement coefficient = 0.98). However, there was lower agreement between the readers on which DL reconstruction denoising setting produced higher-quality images (Gwet agreement coefficient = 0.31 for DL 50 and 0.35 for DL 75)., Conclusion: The vendor-provided DL MRI reconstruction showed higher edge sharpness, rSNR, and rCNR in comparison with conventional methods; however, optimal levels of denoising may need to be further assessed. Keywords: MRI Reconstruction Method, Deep Learning, Image Analysis, Signal-to-Noise Ratio, MR-Imaging, Neural Networks, Hip, Shoulder, Physics, Observer Performance, Technology Assessment Supplemental material is available for this article. © RSNA, 2021., Competing Interests: Disclosures of Conflicts of Interest: K.M.K. institution received a grant from GE Healthcare. M.S. disclosed no relevant relationships. V.E.A. disclosed no relevant relationships. S.B. disclosed no relevant relationships. R.A. disclosed no relevant relationships. A.S.N. institution received funding from GE Healthcare for work in neuroimaging MRI technology development and dissemination; is an inventor on patents including MRI technology focusing on multispectral imaging and magnetic field measurement and modulation; is a scientific advisor for and holds stock in Vasognosis, a start-up company focused on neurovascular imaging applications. R.M.L. is employed by and holds stock options in GE Healthcare; GE Healthcare has patents pending on the algorithms used in this work, but no money has been received. G.M. is employed by GE Healthcare; has been issued U.S. patent no. US10635943B1. S.S.K. is employed by GE Healthcare; received royalties from the Medical College of Wisconsin for a licensed patent unrelated to this work that was filed in 2015. D.V. disclosed no relevant relationships. M.R.S. disclosed no relevant relationships. S.F. disclosed no relevant relationships. R.M. disclosed no relevant relationships., (2021 by the Radiological Society of North America, Inc.)

Purpose: Due to host-mediated adverse reaction to metallic debris, there is an increasing need for noninvasive assessment of the soft tissue surrounding large joint arthroplasties. Quantitative T 2 mapping can be beneficial for tissue characterization and early diagnosis of tissue pathology but current T 2 mapping techniques lack the capability to image near metal hardware. A novel multi-spectral T 2 mapping technique is proposed to address this unmet need., Methods: A T 2 mapping pulse sequence based on routinely implemented 3D multi-spectral imaging (3D-MSI) pulse sequences is described and demonstrated. The 3D-MSI pulse sequence is altered to acquire images at 2 echo times. Phantom and knee experiments were performed to assess the quantitative capabilities of the sequence in comparison to a commercially available T 2 mapping sequence. The technique was demonstrated for use within a clinical protocol in 2 total hip arthroplasty (THA) cases to assess T 2 variations within the periprosthetic joint space., Results: The proposed multi-spectral T 2 mapping technique agreed, within experimental errors, with T 2 values derived from a commercially available clinical standard of care T 2 mapping sequence. The same level of agreement was observed in quantitative phantoms and in vivo experiments. In THA cases, the method was able to assess variations of T 2 within the synovial envelope immediately adjacent to implant interfaces., Conclusions: The proposed 3D-MSI T 2 mapping sequence was successfully demonstrated in assessing tissue T 2 variations near metal implants., (© 2019 International Society for Magnetic Resonance in Medicine.)

Recent work on blind compressed sensing (BCS) has shown that exploiting sparsity in dictionaries that are learnt directly from the data at hand can outperform compressed sensing (CS) that uses fixed dictionaries. A challenge with BCS however is the large computational complexity during its optimization, which limits its practical use in several MRI applications. In this paper, we propose a novel optimization algorithm that utilize variable splitting strategies to significantly improve the convergence speed of the BCS optimization. The splitting allows us to efficiently decouple the sparse coefficient, and dictionary update steps from the data fidelity term, resulting in subproblems that take closed form analytical solutions, which otherwise require slower iterative conjugate gradient algorithms. Through experiments on multi coil parametric MRI data, we demonstrate the superior performance of BCS over conventional CS schemes, while achieving convergence speed up factors of over 10 fold over the previously proposed implementation of the BCS algorithm.