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Purpose: This study investigates the implications of all degrees of freedom of within‐scan patient head motion on patient safety. Methods: Electromagnetic simulations were performed by displacing and/or rotating a virtual body model inside an 8‐channel transmit array to simulate 6 degrees of freedom of motion. Rotations of up to 20° and displacements of up to 20 mm including off‐axis axial/coronal translations were investigated, yielding 104 head positions. Quadrature excitation, RF shimming, and multi‐spoke parallel‐transmit excitation pulses were designed for axial slice‐selection at 7T, for seven slices across the head. Variation of whole‐head specific absorption rate (SAR) and 10‐g averaged local SAR of the designed pulses, as well as the change in the maximum eigenvalue (worst‐case pulse) were investigated by comparing off‐center positions to the central position. Results: In their respective worst‐cases, patient motion increased the eigenvalue‐based local SAR by 42%, whole‐head SAR by 60%, and the 10‐g averaged local SAR by 210%. Local SAR was observed to be more sensitive to displacements along right–left and anterior–posterior directions than displacement in the superior–inferior direction and rotation. Conclusion: This is the first study to investigate the effect of all 6 degrees of freedom of motion on safety of practical pulses. Although the results agree with the literature for overlapping cases, the results demonstrate higher increases (up to 3.1‐fold) in local SAR for off‐axis displacement in the axial plane, which had received less attention in the literature. This increase in local SAR could potentially affect the local SAR compliance of subjects, unless realistic within‐scan patient motion is taken into account during pulse design. [ABSTRACT FROM AUTHOR]

Purpose: Introduce a novel coil design using an electrically long transversely oriented slot in a conductive sheet. Theory and Methods: Theoretical considerations, numerical simulations, and experimental measurements are presented for transverse slot antennas as compared with electric dipole antennas. Results: Simulations show improved central and average transmit and receive efficiency, as well as larger coverage in the transverse plane, for a single slot as compared to a single dipole element. Experiments on a body phantom confirm the simulation results for a slot antenna relative to a dipole, demonstrating a large region of relatively high sensitivity and homogeneity. Images in a human subject also show a large imaging volume for a single slot and six slot antenna array. High central transmit efficiency was observed for slot arrays relative to dipole arrays. Conclusion: Transverse slots can exhibit improved sensitivity and larger field of view compared with traditional conductive dipoles. Simulations and experiments indicate high potential for slot antennas in high field MRI. Magn Reson Med 80:1233–1242, 2018. © 2018 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. [ABSTRACT FROM AUTHOR]

Background: There is growing interest in detecting cerebro-cerebellar circuits, which requires adequate blood oxygenation level dependent contrast and signal-to-noise ratio (SNR) throughout the brain. Although 7T scanners offer increased SNR, coverage of commercial head coils is currently limited to the cerebrum.Purpose: To improve cerebellar functional MRI (fMRI) at 7T with high permittivity material (HPM) pads extending the sensitivity of a commercial coil.Study Type: Simulations were used to determine HPM pad configuration and assess radiofrequency (RF) safety. In vivo experiments were performed to evaluate RF field distributions and SNR and assess improvements of cerebellar fMRI.Subjects: Eight healthy volunteers enrolled in a prospective motor fMRI study with and without HPM.Field Strength/sequence: Gradient echo (GRE) echo planar imaging for fMRI, turbo FLASH for flip angle mapping, GRE sequence for SNR maps, and T1 -weighted MPRAGE were acquired with and without HPM pads at 7T.Assessment: Field maps, SNR maps, and anatomical images were evaluated for coverage. Simulation results were used to assess SAR levels of the experiment. Activation data from fMRI experiments were compared with and without HPM pads. STATISTICAL TESTS: fMRI data were analyzed using FEAT FSL for each subject followed by group level analysis using paired t-test of acquisitions with and without HPM.Results: Simulations showed 52% improvement in transmit efficiency in cerebellum with HPM and SAR levels well below recommended limits. Experiments showed 27% improvement in SNR in cerebellum and improvement in coverage on T1 -weighted images. fMRI showed greater cerebellar activation in individual subjects with the HPM pad present (Z > = 4), especially in inferior slices of cerebellum, with 59% average increase in number of activated voxels in the cerebellum. Group-level analysis showed improved functional activation (Z > = 2.3) in cerebellar regions with HPM pads without loss of measured activation elsewhere.Data Conclusion: HPM pads can improve cerebellar fMRI at 7T with a commonly-used head coil without compromising RF safety.Level Of Evidence: 2 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2018;48:431-440. [ABSTRACT FROM AUTHOR]

Purpose: To explore the use of polyvinylpyrrolidone (PVP) for simulated materials with tissue‐equivalent dielectric properties. Methods: PVP and salt were used to control, respectively, relative permittivity and electrical conductivity in a collection of 63 samples with a range of solute concentrations. Their dielectric properties were measured with a commercial probe and fitted to a 3D polynomial in order to establish an empirical recipe. The material's thermal properties and MR spectra were measured. Results: The empirical polynomial recipe (available at https://www.amri.ninds.nih.gov/cgi-bin/phantomrecipe) provides the PVP and salt concentrations required for dielectric materials with permittivity and electrical conductivity values between approximately 45 and 78, and 0.1 to 2 siemens per meter, respectively, from 50 MHz to 4.5 GHz. The second‐ (solute concentrations) and seventh‐ (frequency) order polynomial recipe provided less than 2.5% relative error between the measured and target properties. PVP side peaks in the spectra were minor and unaffected by temperature changes. Conclusion: PVP‐based phantoms are easy to prepare and nontoxic, and their semitransparency makes air bubbles easy to identify. The polymer can be used to create simulated material with a range of dielectric properties, negligible spectral side peaks, and long T2 relaxation time, which are favorable in many MR applications. Magn Reson Med 80:413–419, 2018. © 2017 International Society for Magnetic Resonance in Medicine. [ABSTRACT FROM AUTHOR]

RF safety in parallel transmission (pTx) is generally ensured by imposing specific absorption rate (SAR) limits during pTx RF pulse design. There is increasing interest in using temperature to ensure safety in MRI. In this work, we present a local temperature correlation matrix formalism and apply it to impose strict constraints on maximum absolute temperature in pTx RF pulse design for head and hip regions. Electromagnetic field simulations were performed on the head and hip of virtual body models. Temperature correlation matrices were calculated for four different exposure durations ranging between 6 and 24 min using simulated fields and body-specific constants. Parallel transmission RF pulses were designed using either SAR or temperature constraints, and compared with each other and unconstrained RF pulse design in terms of excitation fidelity and safety. The use of temperature correlation matrices resulted in better excitation fidelity compared with the use of SAR in parallel transmission RF pulse design (for the 6 min exposure period, 8.8% versus 21.0% for the head and 28.0% versus 32.2% for the hip region). As RF exposure duration increases (from 6 min to 24 min), the benefit of using temperature correlation matrices on RF pulse design diminishes. However, the safety of the subject is always guaranteed (the maximum temperature was equal to 39°C). This trend was observed in both head and hip regions, where the perfusion rates are very different. [ABSTRACT FROM AUTHOR]

Purpose Develop a practical comprehensive package for proactive management of parallel radiofrequency (RF) transmission. Methods With a constrained optimization framework and predictive models from a prescan based multichannel calibration, we presented a method supporting design and optimization of parallel RF excitation pulses that accurately obey the forward/reflected peak and average power limits of the RF power amplifiers in parallel transmit imaging experiments and Bloch simulations. Moreover, local SAR limits were incorporated into the parallel RF excitation pulses using electromagnetic field simulations. Virtual transmit coils concept for minimization of reflected power (effecting subject-specific matching) was additionally demonstrated by leveraging experimentally calibrated power models. Results Incorporation of experimentally calibrated power prediction models resulted in accurate compliance with prescribed hardware and global specific absorption rate (SAR) limits. Incorporation of spatial average 10 g SAR models, facilitated by simplifying numerical approximations, provided assurance of patient safety. RF pulses designed with various constraints demonstrated excellent excitation fidelity-the normalized root-mean-square error of the simulated excitation profiles was 2.6% for the fully constrained pulses, comparable to that of the unconstrained pulses. An RF shimming example showed a reduction of the reflected-to-forward power ratio to 1.7% from a conventional approach's 8.1%. Conclusion Using the presented RF pulse design method, effective proactive management of the multifaceted power and SAR limits was demonstrated in experimental and simulation studies. Magn Reson Med 76:20-31, 2016. © 2015 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

The cover image, by Cem M. Deniz et al., is based on the Research Article Parallel transmission RF pulse design with strict temperature constraints, DOI: 10.1002/nbm.3694. [ABSTRACT FROM AUTHOR]

Purpose: Accurately predicting the expected duration of time until total knee replacement (time-to-TKR) is crucial for patient management and health care planning. Predicting when surgery may be needed, especially within shorter windows like 3 years, allows clinicians to plan timely interventions and health care systems to allocate resources more effectively. Existing models lack the precision for such time-based predictions. A survival analysis model for predicting time-to-TKR was developed using features from medical images and clinical measurements., Methods: From the Osteoarthritis Initiative dataset, all knees with clinical variables, MRI scans, radiographs, and quantitative and semiquantitative assessments from images were identified. This resulted in 895 knees that underwent TKR within the 9-year follow-up period, as specified by the Osteoarthritis Initiative study design, and 786 control knees that did not undergo TKR (right-censored, indicating their status beyond the 9-year follow-up is unknown). These knees were used for model training and testing. Additionally, 518 and 164 subjects from the Multi-Center Osteoarthritis Study and internal hospital data were used for external testing, respectively. Deep learning models were utilized to extract features from radiographs and MR scans. Extracted features, clinical variables, and image assessments were used in survival analysis with Lasso Cox feature selection and a random survival forest model to predict time-to-TKR., Results: The proposed model exhibited strong discrimination power by integrating self-supervised deep learning features with clinical variables (eg, age, body mass index, pain score) and image assessment measurements (eg, Kellgren-Lawrence grade, joint space narrowing, bone marrow lesion size, cartilage morphology) from multiple modalities. The model achieved an area under the curve of 94.5 (95% CI, 94.0-95.1) for predicting the time-to-TKR., Conclusions: The proposed model demonstrated the potential of self-supervised learning and multimodal data fusion in accurately predicting time-to-TKR that may assist physicians to develop personalize treatment strategies., Competing Interests: Please see ICMJE form(s) for author conflicts of interest. These have been provided as supplementary materials. There is no conflict of interest to declare for any of the authors., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Radiological Society of North America.)

Although human's ability to visually understand the structure of the World plays a crucial role in perceiving the World and making appropriate decisions, human perception does not solely rely on vision but amalgamates the information from acoustic, verbal, and visual stimuli. An active area of research has been revolving around designing an efficient framework that adapts to multiple modalities and ideally improves the performance of existing tasks. While numerous frameworks have proved effective on natural datasets like ImageNet, a limited number of studies have been carried out in the biomedical domain. In this work, we extend the available frameworks for natural data to biomedical data by leveraging the abundant, unstructured multi-modal data available as radiology images and reports. We attempt to answer the question, "For multi-modal learning, self-supervised learning and joint learning using both learning strategies, which one improves the visual representation for downstream chest radiographs classification tasks the most?". Our experiments indicated that in limited labeled data settings with 1% and 10% labeled data, the joint learning with multi-modal and self-supervised models outperforms self-supervised learning and is at par with multi-modal learning. Additionally, we found that multi-modal learning is generally more robust on out-of-distribution datasets. The code is publicly available online.

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 use convolutional neural networks (CNNs) for fully automated MRI segmentation of the glenohumeral joint and evaluate the accuracy of three-dimensional (3D) MRI models created with this method., Materials and Methods: Shoulder MR images of 100 patients (average age, 44 years; range, 14-80 years; 60 men) were retrospectively collected from September 2013 to August 2018. CNNs were used to develop a fully automated segmentation model for proton density-weighted images. Shoulder MR images from an additional 50 patients (mean age, 33 years; range, 16-65 years; 35 men) were retrospectively collected from May 2014 to April 2019 to create 3D MRI glenohumeral models by transfer learning using Dixon-based sequences. Two musculoskeletal radiologists performed measurements on fully and semiautomated segmented 3D MRI models to assess glenohumeral anatomy, glenoid bone loss (GBL), and their impact on treatment selection. Performance of the CNNs was evaluated using Dice similarity coefficient (DSC), sensitivity, precision, and surface-based distance measurements. Measurements were compared using matched-pairs Wilcoxon signed rank test., Results: The two-dimensional CNN model for the humerus and glenoid achieved a DSC of 0.95 and 0.86, a precision of 95.5% and 87.5%, an average precision of 98.6% and 92.3%, and a sensitivity of 94.8% and 86.1%, respectively. The 3D CNN model, for the humerus and glenoid, achieved a DSC of 0.95 and 0.86, precision of 95.1% and 87.1%, an average precision of 98.7% and 91.9%, and a sensitivity of 94.9% and 85.6%, respectively. There was no difference between glenoid and humeral head width fully and semiautomated 3D model measurements ( P value range, .097-.99)., Conclusion: CNNs could potentially be used in clinical practice to provide rapid and accurate 3D MRI glenohumeral bone models and GBL measurements. Supplemental material is available for this article. © RSNA, 2020., (2020 by the Radiological Society of North America, Inc.)

Background The methods for assessing knee osteoarthritis (OA) do not provide enough comprehensive information to make robust and accurate outcome predictions. Purpose To develop a deep learning (DL) prediction model for risk of OA progression by using knee radiographs in patients who underwent total knee replacement (TKR) and matched control patients who did not undergo TKR. Materials and Methods In this retrospective analysis that used data from the OA Initiative, a DL model on knee radiographs was developed to predict both the likelihood of a patient undergoing TKR within 9 years and Kellgren-Lawrence (KL) grade. Study participants included a case-control matched subcohort between 45 and 79 years. Patients were matched to control patients according to age, sex, ethnicity, and body mass index. The proposed model used a transfer learning approach based on the ResNet34 architecture with sevenfold nested cross-validation. Receiver operating characteristic curve analysis and conditional logistic regression assessed model performance for predicting probability and risk of TKR compared with clinical observations and two binary outcome prediction models on the basis of radiographic readings: KL grade and OA Research Society International (OARSI) grade. Results Evaluated were 728 participants including 324 patients (mean age, 64 years ± 8 [standard deviation]; 222 women) and 324 control patients (mean age, 64 years ± 8; 222 women). The prediction model based on DL achieved an area under the receiver operating characteristic curve (AUC) of 0.87 (95% confidence interval [CI]: 0.85, 0.90), outperforming a baseline prediction model by using KL grade with an AUC of 0.74 (95% CI: 0.71, 0.77; P < .001). The risk for TKR increased with probability that a person will undergo TKR from the DL model (odds ratio [OR], 7.7; 95% CI: 2.3, 25; P < .001), KL grade (OR, 1.92; 95% CI: 1.17, 3.13; P = .009), and OARSI grade (OR, 1.20; 95% CI: 0.41, 3.50; P = .73). Conclusion The proposed deep learning model better predicted risk of total knee replacement in osteoarthritis than did binary outcome models by using standard grading systems. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Richardson in this issue.

Purpose: To determine the feasibility of using finite element analysis applied to 3-T magnetic resonance (MR) images of proximal femur microarchitecture for detection of lower bone strength in subjects with fragility fractures compared with control subjects without fractures., Materials and Methods: This prospective study was institutional review board approved and HIPAA compliant. Written informed consent was obtained. Postmenopausal women with (n = 22) and without (n = 22) fragility fractures were matched for age and body mass index. All subjects underwent standard dual-energy x-ray absorptiometry. Images of proximal femur microarchitecture were obtained by using a high-spatial-resolution three-dimensional fast low-angle shot sequence at 3 T. Finite element analysis was applied to compute elastic modulus as a measure of strength in the femoral head and neck, Ward triangle, greater trochanter, and intertrochanteric region. The Mann-Whitney test was used to compare bone mineral density T scores and elastic moduli between the groups. The relationship (R(2)) between elastic moduli and bone mineral density T scores was assessed., Results: Patients with fractures showed lower elastic modulus than did control subjects in all proximal femur regions (femoral head, 8.51-8.73 GPa vs 9.32-9.67 GPa; P = .04; femoral neck, 3.11-3.72 GPa vs 4.39-4.82 GPa; P = .04; Ward triangle, 1.85-2.21 GPa vs 3.98-4.13 GPa; P = .04; intertrochanteric region, 1.62-2.18 GPa vs 3.86-4.47 GPa; P = .006-.007; greater trochanter, 0.65-1.21 GPa vs 1.96-2.62 GPa; P = .01-.02), but no differences in bone mineral density T scores. There were weak relationships between elastic moduli and bone mineral density T scores in patients with fractures (R(2) = 0.25-0.31, P = .02-.04), but not in control subjects. CONCLUSION Finite element analysis applied to high-spatial-resolution 3-T MR images of proximal femur microarchitecture can allow detection of lower elastic modulus, a marker of bone strength, in subjects with fragility fractures compared with control subjects. MR assessment of proximal femur strength may provide information about bone quality that is not provided by dual-energy x-ray absorptiometry.