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Objective: To consider potential clinical needs, technical solutions and research promises of ultrahigh-field strength cardiovascular MR (CMR).Methods: A literature review is given, surveying advantages and disadvantages of CMR at ultrahigh fields (UHF). Key concepts, emerging technologies, practical considerations and applications of UHF CMR are provided. Examples of UHF CMR imaging strategies and their added value are demonstrated, including the numerous unsolved problems. A concluding section explores future directions in UHF CMR.Results: UHF CMR can be regarded as one of the most challenging MRI applications. Image quality achievable at UHF is not always exclusively defined by signal-to-noise considerations. Some of the inherent advantages of UHF MRI are offset by practical challenges. But UHF CMR can boast advantages over its kindred lower field counterparts by trading the traits of high magnetic fields for increased temporal and/or spatial resolution.Conclusions: CMR at ultrahigh-field strengths is a powerful motivator, since speed and signal may be invested to overcome the fundamental constraints that continue to hamper traditional CMR. If practical challenges can be overcome, UHF CMR will help to open the door to new approaches for basic science and clinical research. [ABSTRACT FROM AUTHOR]

Deep-learning-based MR image reconstruction in settings where large fully sampled dataset collection is infeasible requires methods that effectively use both under-sampled and fully sampled datasets. This paper evaluates a weakly supervised, multi-coil, physics-guided approach to MR image reconstruction, leveraging both dataset types, to improve both the quality and robustness of reconstruction. A physics-guided end-to-end variational network (VarNet) is pretrained in a self-supervised manner using a 4 × under-sampled dataset following the self-supervised learning via data undersampling (SSDU) methodology. The pre-trained weights are transferred to another VarNet, which is fine-tuned using a smaller, fully sampled dataset by optimizing multi-scale structural similarity (MS-SSIM) loss in image space. The proposed methodology is compared with fully self-supervised and fully supervised training. Reconstruction quality improvements in SSIM, PSNR, and NRMSE when abundant training data is available (the high-data regime), and enhanced robustness when training data is scarce (the low-data regime) are demonstrated using weak supervision for knee and brain MR image reconstructions at 8 × and 10 × acceleration, respectively. Multi-coil physics-guided MR image reconstruction using both under-sampled and fully sampled datasets is achievable with transfer learning and fine-tuning. This methodology can provide improved reconstruction quality in the high-data regime and improved robustness in the low-data regime at high acceleration rates., (© 2024. The Author(s), under exclusive licence to European Society for Magnetic Resonance in Medicine and Biology (ESMRMB).)

Stroke is a leading cause of mortality and disability. Emergent diagnosis and intervention are critical, and predicated upon initial brain imaging; however, existing clinical imaging modalities are generally costly, immobile, and demand highly specialized operation and interpretation. Low-energy microwaves have been explored as a low-cost, small form factor, fast, and safe probe for tissue dielectric properties measurements, with both imaging and diagnostic potential. Nevertheless, challenges inherent to microwave reconstruction have impeded progress, hence conduction of microwave imaging remains an elusive scientific aim. Herein, we introduce a dedicated experimental framework comprising a robotic navigation system to translate blood-mimicking phantoms within a human head model. An 8-element ultra-wideband array of modified antipodal Vivaldi antennas was developed and driven by a two-port vector network analyzer spanning 0.6-9.0 GHz at an operating power of 1 mW. Complex scattering parameters were measured, and dielectric signatures of hemorrhage were learned using a dedicated deep neural network for prediction of hemorrhage classes and localization. An overall sensitivity and specificity for detection >0.99 was observed, with Rayleigh mean localization error of 1.65 mm. The study establishes the feasibility of a robust experimental model and deep learning solution for ultra-wideband microwave stroke detection., (© 2024. The Author(s).)

Purpose: Echo modulation curve (EMC) modeling enables accurate quantification of T 2 relaxation times in multi-echo spin-echo (MESE) imaging. The standard EMC-T 2 mapping framework, however, requires sufficient echoes and cumbersome pixel-wise dictionary-matching steps. This work proposes a deep learning version of EMC-T 2 mapping, called DeepEMC-T 2 mapping, to efficiently estimate accurate T 2 maps from fewer echoes., Methods: DeepEMC-T 2 mapping was developed using a modified U-Net to estimate both T 2 and proton density (PD) maps directly from MESE images. The network implements several new features to improve the accuracy of T 2 /PD estimation. A total of 67 MESE datasets acquired in axial orientation were used for network training and evaluation. An additional 57 datasets acquired in coronal orientation with different scan parameters were used to evaluate the generalizability of the framework. The performance of DeepEMC-T 2 mapping was evaluated in seven experiments., Results: Compared to the reference, DeepEMC-T 2 mapping achieved T 2 estimation errors from 1% to 11% and PD estimation errors from 0.4% to 1.5% with ten/seven/five/three echoes, which are more accurate than standard EMC-T 2 mapping. By incorporating datasets acquired with different scan parameters and orientations for joint training, DeepEMC-T 2 exhibits robust generalizability across varying imaging protocols. Increasing the echo spacing and including longer echoes improve the accuracy of parameter estimation. The new features proposed in DeepEMC-T 2 mapping all enabled more accurate T 2 estimation., Conclusions: DeepEMC-T 2 mapping enables simplified, efficient, and accurate T 2 quantification directly from MESE images without dictionary matching. Accurate T 2 estimation from fewer echoes allows for increased volumetric coverage and/or higher slice resolution without prolonging total scan times., (© 2024 International Society for Magnetic Resonance in Medicine.)

Due to limitations in current motion tracking technologies and increasing interest in alternative sensors for motion tracking both inside and outside the MRI system, in this study we share our preliminary experience with three alternative sensors utilizing diverse technologies and interactions with tissue to monitor motion of the body surface, respiratory-related motion of major organs, and non-respiratory motion of deep-seated organs. These consist of (1) a Pilot-Tone RF transmitter combined with deep learning algorithms for tracking liver motion, (2) a single-channel ultrasound transducer with deep learning for monitoring bladder motion, and (3) a 3D Time-of-Flight camera for observing the motion of the anterior torso surface. Additionally, we demonstrate the capability of these sensors to simultaneously capture motion data outside the MRI environment, which is particularly relevant for procedures like radiation therapy, where motion status could be related to previously characterized cyclical anatomical data. Our findings indicate that the ultrasound sensor can track motion in deep-seated organs (bladder) as well as respiratory-related motion. The Time-of-Flight camera offers ease of interpretation and performs well in detecting surface motion (respiration). The Pilot-Tone demonstrates efficacy in tracking bulk respiratory motion and motion of major organs (liver). Simultaneous use of all three sensors could provide complementary motion information outside the MRI bore, providing potential value for motion tracking during position-sensitive treatments such as radiation therapy.

Magnetic resonance imaging (MRI) has experienced remarkable advancements in the integration of artificial intelligence (AI) for image acquisition and reconstruction. The availability of raw k-space data is crucial for training AI models in such tasks, but public MRI datasets are mostly restricted to DICOM images only. To address this limitation, the fastMRI initiative released brain and knee k-space datasets, which have since seen vigorous use. In May 2023, fastMRI was expanded to include biparametric (T2- and diffusion-weighted) prostate MRI data from a clinical population. Biparametric MRI plays a vital role in the diagnosis and management of prostate cancer. Advances in imaging methods, such as reconstructing under-sampled data from accelerated acquisitions, can improve cost-effectiveness and accessibility of prostate MRI. Raw k-space data, reconstructed images and slice, volume and exam level annotations for likelihood of prostate cancer are provided in this dataset for 47468 slices corresponding to 1560 volumes from 312 patients. This dataset facilitates AI and algorithm development for prostate image reconstruction, with the ultimate goal of enhancing prostate cancer diagnosis., (© 2024. The Author(s).)

Over the past few decades, daily exposure to radiofrequency (RF) fields has been increasing due to the rapid development of wireless and medical imaging technologies. Under extreme circumstances, exposure to very strong RF energy can lead to heating of body tissue, even resulting in tissue injury. The presence of implanted devices, moreover, can amplify RF effects on surrounding tissue. Therefore, it is important to understand the interactions of RF fields with tissue in the presence of implants, in order to establish appropriate wireless safety protocols, and also to extend the benefits of medical imaging to increasing numbers of people with implanted medical devices. This study explored the neurological effects of RF exposure in rodents implanted with neuronal recording electrodes. We exposed freely moving and anesthetized rats and mice to 950 MHz RF energy while monitoring their brain activity, temperature, and behavior. We found that RF exposure could induce fast onset firing of single neurons without heat injury. In addition, brain implants enhanced the effect of RF stimulation resulting in reversible behavioral changes. Using an optical temperature measurement system, we found greater than tenfold increase in brain temperature in the vicinity of the implant. On the one hand, our results underline the importance of careful safety assessment for brain-implanted devices, but on the other hand, we also show that metal implants may be used for neurostimulation if brain temperature can be kept within safe limits., (© 2023 Bioelectromagnetics Society.)

Purpose: To introduce a method for the estimation of the ideal current patterns (ICP) that yield optimal signal-to-noise ratio (SNR) for realistic heterogeneous tissue models in MRI., Theory and Methods: The ICP were calculated for different surfaces that resembled typical radiofrequency (RF) coil formers. We constructed numerical electromagnetic (EM) bases to accurately represent EM fields generated by RF current sources located on the current-bearing surfaces. Using these fields as excitations, we solved the volume integral equation and computed the EM fields in the sample. The fields were appropriately weighted to calculate the optimal SNR and the corresponding ICP. We demonstrated how to qualitatively use ICP to guide the design of a coil array to maximize SNR inside a head model., Results: In agreement with previous analytic work, ICP formed large distributed loops for voxels in the middle of the sample and alternated between a single loop and a figure-eight shape for a voxel 3-cm deep in the sample's cortex. For the latter voxel, a surface quadrature loop array inspired by the shape of the ICP reached 87 . 5 % $$ 87.5\% $$ of the optimal SNR at 3T, whereas a single loop placed above the voxel reached only 55 . 7 % $$ 55.7\% $$ of the optimal SNR. At 7T, the performance of the two designs decreased to 79 . 7 % $$ 79.7\% $$ and 49 . 8 % $$ 49.8\% $$ , respectively, suggesting that loops could be suboptimal at ultra-high field MRI., Conclusion: ICP can be calculated for human tissue models, potentially guiding the design of application-specific RF coil arrays., (© 2023 International Society for Magnetic Resonance in Medicine.)

In March 2022, the first ISMRM Workshop on Low-Field MRI was held virtually. The goals of this workshop were to discuss recent low field MRI technology including hardware and software developments, novel methodology, new contrast mechanisms, as well as the clinical translation and dissemination of these systems. The virtual Workshop was attended by 368 registrants from 24 countries, and included 34 invited talks, 100 abstract presentations, 2 panel discussions, and 2 live scanner demonstrations. Here, we report on the scientific content of the Workshop and identify the key themes that emerged. The subject matter of the Workshop reflected the ongoing developments of low-field MRI as an accessible imaging modality that may expand the usage of MRI through cost reduction, portability, and ease of installation. Many talks in this Workshop addressed the use of computational power, efficient acquisitions, and contemporary hardware to overcome the SNR limitations associated with low field strength. Participants discussed the selection of appropriate clinical applications that leverage the unique capabilities of low-field MRI within traditional radiology practices, other point-of-care settings, and the broader community. The notion of "image quality" versus "information content" was also discussed, as images from low-field portable systems that are purpose-built for clinical decision-making may not replicate the current standard of clinical imaging. Speakers also described technical challenges and infrastructure challenges related to portability and widespread dissemination, and speculated about future directions for the field to improve the technology and establish clinical value., (© 2023 International Society for Magnetic Resonance in Medicine. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

Purpose: To describe an inversion-recovery T 1 -weighted radial stack-of-stars 3D gradient echo (GRE) sequence with comparable image quality to conventional MP-RAGE and to demonstrate how the radial acquisition scheme can be utilized for additional retrospective motion correction to improve robustness to head motion., Methods: The proposed sequence, named MP-RAVE, has been derived from a previously described radial stack-of-stars 3D GRE sequence (RAVE) and includes a 180° inversion recovery pulse that is generated once for every stack of radial views. The sequence is combined with retrospective 3D motion correction to improve robustness. The effectiveness has been evaluated in phantoms and healthy volunteers and compared to conventional MP-RAGE acquisition., Results: MP-RAGE and MP-RAVE anatomical images were rated "good" to "excellent" in overall image quality, with artifact level between "mild" and "no artifacts", and with no statistically significant difference between methods. During head motion, MP-RAVE showed higher inherent robustness with artifacts confined to local brain regions. In combination with motion correction, MP-RAVE provided noticeably improved image quality during different head motion and showed statistically significant improvement in image sharpness., Conclusion: MP-RAVE provides comparable image quality and contrast to conventional MP-RAGE with improved robustness to head motion. In combination with retrospective 3D motion correction, MP-RAVE can be a useful alternative to MP-RAGE, especially in non-cooperative or pediatric patients., (© 2023 International Society for Magnetic Resonance in Medicine.)

Background MRI is a powerful diagnostic tool with a long acquisition time. Recently, deep learning (DL) methods have provided accelerated high-quality image reconstructions from undersampled data, but it is unclear if DL image reconstruction can be reliably translated to everyday clinical practice. Purpose To determine the diagnostic equivalence of prospectively accelerated DL-reconstructed knee MRI compared with conventional accelerated MRI for evaluating internal derangement of the knee in a clinical setting. Materials and Methods A DL reconstruction model was trained with images from 298 clinical 3-T knee examinations. In a prospective analysis, patients clinically referred for knee MRI underwent a conventional accelerated knee MRI protocol at 3 T followed by an accelerated DL protocol between January 2020 and February 2021. The equivalence of the DL reconstruction of the images relative to the conventional images for the detection of an abnormality was assessed in terms of interchangeability. Each examination was reviewed by six musculoskeletal radiologists. Analyses pertaining to the detection of meniscal or ligament tears and bone marrow or cartilage abnormalities were based on four-point ordinal scores for the likelihood of an abnormality. Additionally, the protocols were compared with use of four-point ordinal scores for each aspect of image quality: overall image quality, presence of artifacts, sharpness, and signal-to-noise ratio. Results A total of 170 participants (mean age ± SD, 45 years ± 16; 76 men) were evaluated. The DL-reconstructed images were determined to be of diagnostic equivalence with the conventional images for detection of abnormalities. The overall image quality score, averaged over six readers, was significantly better ( P < .001) for the DL than for the conventional images. Conclusion In a clinical setting, deep learning reconstruction enabled a nearly twofold reduction in scan time for a knee MRI and was diagnostically equivalent with the conventional protocol. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Roemer in this issue.

Purpose: To explore the limits of deep learning-based brain MRI reconstruction and identify useful acceleration ranges for general-purpose imaging and potential screening., Materials and Methods: In this retrospective study conducted from 2019 through 2021, a model was trained for reconstruction on 5847 brain MR images. Performance was evaluated across a wide range of accelerations (up to 100-fold along a single phase-encoded direction for two-dimensional [2D] sections) on the fastMRI test set collected at New York University, consisting of 558 image volumes. In a sample of 69 volumes, reconstructions were classified by radiologists for identification of two clinical thresholds: (a) general-purpose diagnostic imaging and (b) potential use in a screening protocol. A Monte Carlo procedure was developed to estimate reconstruction error with only undersampled data. The model was evaluated on both in-domain and out-of-domain data. The 95% CIs were calculated using the percentile bootstrap method., Results: Radiologists rated 100% of 69 volumes as having sufficient image quality for general-purpose imaging at up to 4× acceleration and 65 of 69 volumes (94%) as having sufficient image quality for screening at up to 14× acceleration. The Monte Carlo procedure estimated ground truth peak signal-to-noise ratio and mean squared error with coefficients of determination greater than 0.5 at 2× to 20× acceleration levels. Out-of-distribution experiments demonstrated the model's ability to produce images substantially distinct from the training set, even at 100× acceleration., Conclusion: For 2D brain images using deep learning-based reconstruction, maximum acceleration for potential screening was three to four times higher than that for diagnostic general-purpose imaging. Keywords: MRI Reconstruction, High Acceleration, Deep Learning, Screening, Out of Distribution Supplemental material is available for this article. © RSNA, 2022., Competing Interests: Disclosures of conflicts of interest: A.R. Payment for expert testimony (medicolegal). M.J.M. Employed by NYU and Facebook. T.M. No relevant relationships. E.L. Support from NYU Langone Health, Department of Radiology (neuroradiology fellowship) and GME for registration fee for ASNR ($950) where author gave a 5-minute oral presentation of a portion of this research. A.S. No relevant relationships. F.K. NIH (NIBIB) grant; NIH grants R01EB024532 and P41EB017183; U.S. patent US20170309019A1; Subtle Medical stock options. D.K.S. Royalties for intellectual property portfolio on parallel MRI (concluded 2021) from GE and Bruker; fees for service as scientific advisor (Q.Bio 2020–2021, Ezra 2022), in the general area of accelerated imaging but not directly related to this manuscript; U.S. patent 10671939, “System, method and computer-accessible medium for learning an optimized variational network for medical imaging reconstruction;” stock options in Ezra for service as scientific advisor, in the general area of accelerated imaging but not directly related to this manuscript; NIH grant P41EB017183. Y.W.L. NIH grant P41EB017183., (© 2022 by the Radiological Society of North America, Inc.)

Purpose: To develop a 3D MR technique to simultaneously acquire proton multiparametric maps (T 1 , T 2 , and proton density) and sodium density weighted images over the whole brain., Methods: We implemented a 3D stack-of-stars MR pulse sequence which consists of interleaved proton ( 1 H) and sodium ( 23 Na) excitations, tailored slice encoding gradients that can encode the same slice for both nuclei, and simultaneous readout with different radial trajectories ( 1 H, full-radial; 23 Na, center-out radial). The receive chain of our 7T scanner was modified to enable simultaneous acquisition of 1 H and 23 Na signal. A heuristically optimized flip angle train was implemented for proton MR fingerprinting (MRF). The SNR and the accuracy of proton T 1 and T 2 were evaluated in phantoms. Finally, in vivo application of the method was demonstrated in five healthy subjects., Results: The SNR for the simultaneous measurement was almost identical to that for the single-nucleus measurements (<2% change). The proton T 1 and T 2 maps remained similar to the results from a reference 2D MRF technique (normalized RMS error in T 1 ≈ 4.2% and T 2 ≈ 11.3%). Measurements in healthy subjects corroborated these results and demonstrated the feasibility of our method for in vivo application. The in vivo T 1 values measured using our method were lower than the results measured by other conventional techniques., Conclusions: With the 3D simultaneous implementation, we were able to acquire sodium and proton density weighted images in addition to proton T 1 , T 2 , and B 1 + from 1 H MRF that covers the whole brain volume within 21 min., (© 2021 International Society for Magnetic Resonance in Medicine.)

Deep neural networks (DNNs) show promise in image-based medical diagnosis, but cannot be fully trusted since they can fail for reasons unrelated to underlying pathology. Humans are less likely to make such superficial mistakes, since they use features that are grounded on medical science. It is therefore important to know whether DNNs use different features than humans. Towards this end, we propose a framework for comparing human and machine perception in medical diagnosis. We frame the comparison in terms of perturbation robustness, and mitigate Simpson's paradox by performing a subgroup analysis. The framework is demonstrated with a case study in breast cancer screening, where we separately analyze microcalcifications and soft tissue lesions. While it is inconclusive whether humans and DNNs use different features to detect microcalcifications, we find that for soft tissue lesions, DNNs rely on high frequency components ignored by radiologists. Moreover, these features are located outside of the region of the images found most suspicious by radiologists. This difference between humans and machines was only visible through subgroup analysis, which highlights the importance of incorporating medical domain knowledge into the comparison., (© 2022. The Author(s).)

Purpose: The paper introduces a classical model to describe the dynamics of large spin-1/2 ensembles associated with nuclei bound in large molecule structures, commonly referred to as the semi-solid spin pool, and their magnetization transfer (MT) to spins of nuclei in water., Theory and Methods: Like quantum-mechanical descriptions of spin dynamics and like the original Bloch equations, but unlike existing MT models, the proposed model is based on the algebra of angular momentum in the sense that it explicitly models the rotations induced by radiofrequency (RF) pulses. It generalizes the original Bloch model to non-exponential decays, which are, for example, observed for semi-solid spin pools. The combination of rotations with non-exponential decays is facilitated by describing the latter as Green's functions, comprised in an integro-differential equation., Results: Our model describes the data of an inversion-recovery magnetization-transfer experiment with varying durations of the inversion pulse substantially better than established models. We made this observation for all measured data, but in particular for pulse durations smaller than 300 μs. Furthermore, we provide a linear approximation of the generalized Bloch model that reduces the simulation time by approximately a factor 15,000, enabling simulation of the spin dynamics caused by a rectangular RF-pulse in roughly 2 μs., Conclusion: The proposed theory unifies the original Bloch model, Henkelman's steady-state theory for MT, and the commonly assumed rotation induced by hard pulses (i.e., strong and infinitesimally short applications of RF-fields) and describes experimental data better than previous models., (© 2021 International Society for Magnetic Resonance in Medicine.)

As the use of Radio Frequency (RF) technologies increases, the impact of RF radiation on neurological function continues to receive attention. Whether RF radiation can modulate ongoing neuronal activity by non-thermal mechanisms has been debated for decades. However, the interactions between radiated energy and metal-based neural probes during experimentation could impact neural activity, making interpretation of the results difficult. To address this problem, we modified a miniature 1-photon Ca 2+ imaging device to record interference-free neural activity and compared the results to those acquired using metal-containing silicon probes. We monitored the neuronal activity of awake rodent-brains under RF energy exposure (at 950 MHz) and in sham control paradigms. Spiking activity was reliably affected by RF energy in metal containing systems. However, we did not observe neuronal responses using metal-free optical recordings at induced local electric field strengths up to 230 V/m. Our results suggest that RF exposure higher than levels that are allowed by regulatory limits in real-life scenarios do not affect neuronal activity., Competing Interests: Competing interests The authors declare no competing interests.

Objective: The outcome of arthroscopic treatment for femoroacetabular impingement (FAI) depends on the preoperative status of the hip cartilage. Quantitative T2 can detect early biochemical cartilage changes, but its routine implementation is challenging. Furthermore, intrinsic T2 variability between patients makes it difficult to define a threshold to identify cartilage lesions. To address this, we propose a normalized T2-index as a new method to evaluate cartilage in FAI., Design: We retrospectively analyzed magnetic resonance imaging (MRI) data of 18 FAI patients with arthroscopically confirmed cartilage defects. Cartilage T2 maps were reconstructed from multi-spin-echo 3-T data using the echo-modulation-curve (EMC) model-based technique. The central femoral cartilage, assumed healthy in early-stage FAI, was used as the normalization reference to define a T2-index. We investigated the ability of the T2-index to detect surgically confirmed cartilage lesions., Results: The average T2-index was 1.14 ± 0.1 and 1.13 ± 0.1 for 2 separated segmentations. Using T2-index >1 as the threshold for damaged cartilage, accuracy was 88% and 100% for the 2 segmentations. We found moderate intraobserver repeatability, although separate segmentations yielded comparable accuracy. Damaged cartilage could not be identified using nonnormalized average T2 values., Conclusions: This preliminary study confirms the importance of normalizing T2 values to account for interpatient variability and suggests that the T2-index is a promising biomarker for the detection of cartilage lesions in FAI. Future work is needed to confirm that combining T2-index with morphologic MRI and other quantitative biomarkers could improve cartilage assessment in FAI.

Purpose: To develop a protocol for abdominal imaging on a prototype 0.55 T scanner and to benchmark the image quality against conventional 1.5 T exam., Methods: In this prospective IRB-approved HIPAA-compliant study, 10 healthy volunteers were recruited and imaged. A commercial MRI system was modified to operate at 0.55 T (LF) with two different gradient performance levels. Each subject underwent non-contrast abdominal examinations on the 0.55 T scanner utilizing higher gradients (LF-High), lower adjusted gradients (LF-Adjusted), and a conventional 1.5 T scanner. The following pulse sequences were optimized: fat-saturated T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and Dixon T1-weighted imaging (T1WI). Three readers independently evaluated image quality in a blinded fashion on a 5-point Likert scale, with a score of 1 being non-diagnostic and 5 being excellent. An exact paired sample Wilcoxon signed-rank test was used to compare the image quality., Results: Diagnostic image quality (overall image quality score ≥ 3) was achieved at LF in all subjects for T2WI, DWI, and T1WI with no more than one unit lower score than 1.5 T. The mean difference in overall image quality score was not significantly different between LF-High and LF-Adjusted for T2WI (95% CI - 0.44 to 0.44; p = 0.98), DWI (95% CI - 0.43 to 0.36; p = 0.92), and for T1 in- and out-of-phase imaging (95%C I - 0.36 to 0.27; p = 0.91) or T1 fat-sat (water only) images (95% CI - 0.24 to 0.18; p = 1.0)., Conclusion: Diagnostic abdominal MRI can be performed on a prototype 0.55 T scanner, either with conventional or with reduced gradient performance, within an acquisition time of 10 min or less., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Augmented reality (AR) and virtual reality (VR) are burgeoning technologies that have the potential to greatly enhance patient care. Visualizing patient-specific three-dimensional (3D) imaging data in these enhanced virtual environments may improve surgeons' understanding of anatomy and surgical pathology, thereby allowing for improved surgical planning, superior intra-operative guidance, and ultimately improved patient care. It is important that radiologists are familiar with these technologies, especially since the number of institutions utilizing VR and AR is increasing. This article gives an overview of AR and VR and describes the workflow required to create anatomical 3D models for use in AR using the Microsoft HoloLens device. Case examples in urologic oncology (prostate cancer and renal cancer) are provided which depict how AR has been used to guide surgery at our institution., (© 2021. The Author(s).)

Purpose: To describe an approach for detection of respiratory signals using a transmitted radiofrequency (RF) reference signal called Pilot-Tone (PT) and to use the PT signal for creation of motion-resolved images based on 3D stack-of-stars imaging under free-breathing conditions., Methods: This work explores the use of a reference RF signal generated by a small RF transmitter, placed outside the MR bore. The reference signal is received in parallel to the MR signal during each readout. Because the received PT amplitude is modulated by the subject's breathing pattern, a respiratory signal can be obtained by detecting the strength of the received PT signal over time. The breathing-induced PT signal modulation can then be used for reconstructing motion-resolved images from free-breathing scans. The PT approach was tested in volunteers using a radial stack-of-stars 3D gradient echo (GRE) sequence with golden-angle acquisition., Results: Respiratory signals derived from the proposed PT method were compared to signals from a respiratory cushion sensor and k-space-center-based self-navigation under different breathing conditions. Moreover, the accuracy was assessed using a modified acquisition scheme replacing the golden-angle scheme by a zero-angle acquisition. Incorporating the PT signal into eXtra-Dimensional (XD) motion-resolved reconstruction led to improved image quality and clearer anatomical depiction of the lung and liver compared to k-space-center signal and motion-averaged reconstruction, when binned into 6, 8, and 10 motion states., Conclusion: PT is a novel concept for tracking respiratory motion. Its small dimension (8 cm), high sampling rate, and minimal interaction with the imaging scan offers great potential for resolving respiratory motion., (© 2020 International Society for Magnetic Resonance in Medicine.)

Purpose: To develop and evaluate a neural network-based method for Gibbs artifact and noise removal., Methods: A convolutional neural network (CNN) was designed for artifact removal in diffusion-weighted imaging data. Two implementations were considered: one for magnitude images and one for complex images. Both models were based on the same encoder-decoder structure and were trained by simulating MRI acquisitions on synthetic non-MRI images., Results: Both machine learning methods were able to mitigate artifacts in diffusion-weighted images and diffusion parameter maps. The CNN for complex images was also able to reduce artifacts in partial Fourier acquisitions., Conclusions: The proposed CNNs extend the ability of artifact correction in diffusion MRI. The machine learning method described here can be applied on each imaging slice independently, allowing it to be used flexibly in clinical applications., (© 2020 International Society for Magnetic Resonance in Medicine.)

Objective: Global Maxwell Tomography (GMT) is a recently introduced volumetric technique for noninvasive estimation of electrical properties (EP) from magnetic resonance measurements. Previous work evaluated GMT using ideal radiofrequency (RF) excitations. The aim of this simulation study was to assess GMT performance with a realistic RF coil., Methods: We designed a transmit-receive RF coil with 8 decoupled channels for 7T head imaging. We calculated the RF transmit field ( B 1 + ) inside heterogeneous head models for different RF shimming approaches, and used them as input for GMT to reconstruct EP for all voxels., Results: Coil tuning/decoupling remained relatively stable when the coil was loaded with different head models. Mean error in EP estimation changed from [Formula: see text] to [Formula: see text] and from [Formula: see text] to [Formula: see text] for relative permittivity and conductivity, respectively, when changing head model without re-tuning the coil. Results slightly improved when an SVD-based RF shimming algorithm was applied, in place of excitation with one coil at a time. Despite errors in EP, RF transmit field ( B 1 + ) and absorbed power could be predicted with less than [Formula: see text] error over the entire head. GMT could accurately detect a numerically inserted tumor., Conclusion: This work demonstrates that GMT can reliably reconstruct EP in realistic simulated scenarios using a tailored 8-channel RF coil design at 7T. Future work will focus on construction of the coil and optimization of GMT's robustness to noise, to enable in-vivo GMT experiments., Significance: GMT could provide accurate estimations of tissue EP, which could be used as biomarkers and could enable patient-specific estimation of RF power deposition, which is an unsolved problem for ultra-high-field magnetic resonance imaging.

Competing Interests: Dr. Goldberg reports personal fees from Eyeclick, personal fees from NESTRE, other from Oxford University Press, other from Penguin Random House Press, other from Elsevier, outside the submitted work. Dr. Podell has nothing to disclose. Dr. Sodickson reports personal fees and other from Q Bio, personal fees from Bruker, non-financial support and other from Siemens, non-financial support and other from Facebook, outside the submitted work; . Dr. Fieremans reports personal fees from General Electric Company, other from MicSi, Inc., outside the submitted work.

OBJECTIVE. Deep learning (DL) image reconstruction has the potential to disrupt the current state of MRI by significantly decreasing the time required for MRI examinations. Our goal was to use DL to accelerate MRI to allow a 5-minute comprehensive examination of the knee without compromising image quality or diagnostic accuracy. MATERIALS AND METHODS. A DL model for image reconstruction using a variational network was optimized. The model was trained using dedicated multisequence training, in which a single reconstruction model was trained with data from multiple sequences with different contrast and orientations. After training, data from 108 patients were retrospectively undersampled in a manner that would correspond with a net 3.49-fold acceleration of fully sampled data acquisition and a 1.88-fold acceleration compared with our standard twofold accelerated parallel acquisition. An interchangeability study was performed, in which the ability of six readers to detect internal derangement of the knee was compared for clinical and DL-accelerated images. RESULTS. We found a high degree of interchangeability between standard and DL-accelerated images. In particular, results showed that interchanging the sequences would produce discordant clinical opinions no more than 4% of the time for any feature evaluated. Moreover, the accelerated sequence was judged by all six readers to have better quality than the clinical sequence. CONCLUSION. An optimized DL model allowed acceleration of knee images that performed interchangeably with standard images for detection of internal derangement of the knee. Importantly, readers preferred the quality of accelerated images to that of standard clinical images.

Purpose: To advance research in the field of machine learning for MR image reconstruction with an open challenge., Methods: We provided participants with a dataset of raw k-space data from 1,594 consecutive clinical exams of the knee. The goal of the challenge was to reconstruct images from these data. In order to strike a balance between realistic data and a shallow learning curve for those not already familiar with MR image reconstruction, we ran multiple tracks for multi-coil and single-coil data. We performed a two-stage evaluation based on quantitative image metrics followed by evaluation by a panel of radiologists. The challenge ran from June to December of 2019., Results: We received a total of 33 challenge submissions. All participants chose to submit results from supervised machine learning approaches., Conclusions: The challenge led to new developments in machine learning for image reconstruction, provided insight into the current state of the art in the field, and highlighted remaining hurdles for clinical adoption., (© 2020 International Society for Magnetic Resonance in Medicine.)

Purpose: This study reports on the development of a novel 3D procedure planning technique to provide pre-ablation treatment planning for partial gland prostate cryoablation (cPGA)., Methods: Twenty men scheduled for partial gland cryoablation (cPGA) underwent pre-operative image segmentation and 3D modeling of the prostatic capsule, index lesion, urethra, rectum, and neurovascular bundles based upon multi-parametric MRI data. Pre-treatment 3D planning models were designed including virtual 3D cryotherapy probes to predict and plan cryotherapy probe configuration needed to achieve confluent treatment volume. Treatment efficacy was measured with 6 month post-operative MRI, serum prostate specific antigen (PSA) at 3 and 6 months, and treatment zone biopsy results at 6 months. Outcomes from 3D planning were compared to outcomes from a series of 20 patients undergoing cPGA using traditional 2D planning techniques., Results: Forty men underwent cPGA. The median age of the cohort undergoing 3D treatment planning was 64.8 years with a median pretreatment PSA of 6.97 ng/mL. The Gleason grade group (GGG) of treated index lesions in this cohort included 1 (5%) GGG1, 11 (55%) GGG2, 7 (35%) GGG3, and 1 (5%) GGG4. Two (10%) of these treatments were post-radiation salvage therapies. The 2D treatment cohort included 20 men with a median age of 68.5 yrs., median pretreatment PSA of 6.76 ng/mL. The Gleason grade group (GGG) of treated index lesions in this cohort included 3 (15%) GGG1, 8 (40%) GGG2, 8 (40%) GGG3, 1 (5%) GGG4. Two (10%) of these treatments were post-radiation salvage therapies. 3D planning predicted the same number of cryoprobes for each group, however a greater number of cryoprobes was used in the procedure for the prospective 3D group as compared to that with 2D planning (4.10 ± 1.37 and 3.25 ± 0.44 respectively, p = 0.01). At 6 months post cPGA, the median PSA was 1.68 ng/mL and 2.38 ng/mL in the 3D and 2D cohorts respectively, with a larger decrease noted in the 3D cohort (75.9% reduction noted in 3D cohort and 64.8% reduction 2D cohort, p 0.48). In-field disease detection was 1/14 (7.1%) on surveillance biopsy in the 3D cohort and 3/14 (21.4%) in the 2D cohort, p = 0.056) In the 3D cohort, 6 month biopsy was not performed in 4 patients (20%) due to undetectable PSA, negative MRI, and negative MRI Axumin PET. For the group with traditional 2D planning, treatment zone biopsy was positive in 3/14 (21.4%) of the patients, p = 0.056., Conclusions: 3D prostate cancer models derived from mpMRI data provide novel guidance for planning confluent treatment volumes for cPGA and predicted a greater number of treatment probes than traditional 2D planning methods. This study prompts further investigation into the use of 3D treatment planning techniques as the increase of partial gland ablation treatment protocols develop.

The current coronavirus disease 2019 (COVID-19) crisis continues to grow and has resulted in marked changes to clinical operations. In parallel with clinical preparedness, universities have shut down most scientific research activities. Radiology researchers are currently grappling with these challenges that will continue to affect current and future imaging research. The purpose of this article is to describe the collective experiences of a diverse international group of academic radiology research programs in managing their response to the COVID-19 pandemic. The acute response at six distinct institutions will be described first, exploring common themes, challenges, priorities, and practices. This will be followed by reflections about the future of radiology research in the wake of the COVID-19 pandemic., (© RSNA, 2020.)

Purpose: To study the effects of magnetization transfer (MT, in which a semi-solid spin pool interacts with the free pool), in the context of magnetic resonance fingerprinting (MRF)., Methods: Simulations and phantom experiments were performed to study the impact of MT on the MRF signal and its potential influence on T 1 and T 2 estimation. Subsequently, an MRF sequence implementing off-resonance MT pulses and a dictionary with an MT dimension, generated by incorporating a two-pool model, were used to estimate the fractional pool size in addition to the B 1 + , T 1 , and T 2 values. The proposed method was evaluated in the human brain., Results: Simulations and phantom experiments showed that an MRF signal obtained from a cross-linked bovine serum sample is influenced by MT. Using a dictionary based on an MT model, a better match between simulations and acquired MR signals can be obtained (NRMSE 1.3% vs. 4.7%). Adding off-resonance MT pulses can improve the differentiation of MT from T 1 and T 2 . In vivo results showed that MT affects the MRF signals from white matter (fractional pool-size ~16%) and gray matter (fractional pool-size ~10%). Furthermore, longer T 1 (~1060 ms vs. ~860 ms) and T 2 values (~47 ms vs. ~35 ms) can be observed in white matter if MT is accounted for., Conclusion: Our experiments demonstrated a potential influence of MT on the quantification of T 1 and T 2 with MRF. A model that encompasses MT effects can improve the accuracy of estimated relaxation parameters and allows quantification of the fractional pool size., (© 2019 International Society for Magnetic Resonance in Medicine.)

Purpose: The goal of this work is to demonstrate a method for the simultaneous acquisition of proton multiparametric maps (T 1 , T 2 , and proton density) and sodium density images in 1 single scan. We hope that the development of such capabilities will help to ease the implementation of sodium MRI in clinical trials and provide more opportunities for researchers to investigate metabolism through sodium MRI., Methods: We developed a sequence based on magnetic resonance fingerprinting (MRF), which contains interleaved proton ( 1 H) and sodium ( 23 Na) excitations followed by a simultaneous center-out radial readout for both nuclei. The receive chain of a 7T scanner was modified to enable simultaneous acquisition of 1 H and 23 Na signal. The obtained signal-to-noise ratio (SNR) was evaluated, and the accuracy of both proton T 1 , T 2 , and B 1 + and sodium density maps were verified in phantoms. Finally, the method was demonstrated in 2 healthy subjects., Results: The SNR obtained using the simultaneous measurement was almost identical to single-nucleus measurements (<1% change). Similarly, the proton T 1 and T 2 maps remained stable (normalized root mean square error in T 1 ≈ 2.2%, in T 2 ≈ 1.4%, and B 1 + ≈ 5.4%), which indicates that the proposed sequence and hardware have no significant effects on the signal from either nucleus. In vivo measurements corroborated these results and demonstrated the feasibility of our method for in vivo application., Conclusions: With the proposed approach, we were able to simultaneously acquire sodium density images in addition to proton T 1 , T 2 , and B 1 + maps as well as proton density images., (© 2019 International Society for Magnetic Resonance in Medicine.)

A publicly available dataset containing k-space data as well as Digital Imaging and Communications in Medicine image data of knee images for accelerated MR image reconstruction using machine learning is presented., (2020 by the Radiological Society of North America, Inc.)

Purpose: To revisit the "loopole," an unusual coil topology whose unbalanced current distribution captures both loop and electric dipole properties, which can be advantageous in ultra-high-field MRI., Methods: Loopole coils were built by deliberately breaking the capacitor symmetry of traditional loop coils. The corresponding current distribution, transmit efficiency, and signal-to-noise ratio (SNR) were evaluated in simulation and experiments in comparison to those of loops and electric dipoles at 7 T (297 MHz)., Results: The loopole coil exhibited a hybrid current pattern, comprising features of both loops and electric dipole current patterns. Depending on the orientation relative to B 0 , the loopole demonstrated significant performance boost in either the transmit efficiency or SNR at the center of a dielectric sample when compared to a traditional loop. Modest improvements were observed when compared to an electric dipole., Conclusion: The loopole can achieve high performance by supporting both divergence-free and curl-free current patterns, which are both significant contributors to the ultimate intrinsic performance at ultra-high field. While electric dipoles exhibit similar hybrid properties, loopoles maintain the engineering advantages of loops, such as geometric decoupling and reduced resonance frequency dependence on sample loading., Competing Interests: Conflicts of Interest The authors have no conflicts of interest to declare.

Objective: In this paper, we introduce global Maxwell tomography (GMT), a novel volumetric technique that estimates electric conductivity and permittivity by solving an inverse scattering problem based on magnetic resonance measurements., Methods: GMT relies on a fast volume integral equation solver, MARIE, for the forward path, and a novel regularization method, match regularization, designed specifically for electrical property estimation from noisy measurements. We performed simulations with three different tissue-mimicking numerical phantoms of different complexity, using synthetic transmit sensitivity maps with realistic noise levels as the measurements. We performed an experiment at 7 T using an eight-channel coil and a uniform phantom., Results: We showed that GMT could estimate relative permittivity and conductivity from noisy magnetic resonance measurements with an average error as low as 0.3% and 0.2%, respectively, over the entire volume of the numerical phantom. Voxel resolution did not affect GMT performance and is currently limited only by the memory of the graphics processing unit. In the experiment, GMT could estimate electrical properties within 5% of the values measured with a dielectric probe., Conclusion: This work demonstrated the feasibility of GMT with match regularization, suggesting that it could be effective for accurate in vivo electrical property estimation. GMT does not rely on any symmetry assumption for the electromagnetic field, and can be generalized to estimate also the spin magnetization, at the expense of increased computational complexity., Significance: GMT could provide insight into the distribution of electromagnetic fields inside the body, which represents one of the key ongoing challenges for various diagnostic and therapeutic applications.

Following the success of deep learning in a wide range of applications, neural network-based machine learning techniques have received interest as a means of accelerating magnetic resonance imaging (MRI). A number of ideas inspired by deep learning techniques from computer vision and image processing have been successfully applied to non-linear image reconstruction in the spirit of compressed sensing for both low dose computed tomography and accelerated MRI. The additional integration of multi-coil information to recover missing k-space lines in the MRI reconstruction process, is still studied less frequently, even though it is the de-facto standard for currently used accelerated MR acquisitions. This manuscript provides an overview of the recent machine learning approaches that have been proposed specifically for improving parallel imaging. A general background introduction to parallel MRI is given that is structured around the classical view of image space and k-space based methods. Both linear and non-linear methods are covered, followed by a discussion of recent efforts to further improve parallel imaging using machine learning, and specifically using artificial neural networks. Image-domain based techniques that introduce improved regularizers are covered as well as k-space based methods, where the focus is on better interpolation strategies using neural networks. Issues and open problems are discussed as well as recent efforts for producing open datasets and benchmarks for the community.

Purpose: The optimization and analysis of spin ensemble trajectories in the hybrid state-a state in which the direction of the magnetization adiabatically follows the steady state while the magnitude remains in a transient state., Methods: Numerical optimizations were performed to find spin ensemble trajectories that minimize the Cramér-Rao bound for T 1 -encoding, T 2 -encoding, and their weighted sum, respectively, followed by a comparison between the Cramér-Rao bounds obtained with our optimized spin-trajectories, Look-Locker sequences, and multi-spin-echo methods. Finally, we experimentally tested our optimized spin trajectories with in vivo scans of the human brain., Results: After a nonrecurring inversion segment on the southern half of the Bloch sphere, all optimized spin trajectories pursue repetitive loops on the northern hemisphere in which the beginning of the first and the end of the last loop deviate from the others. The numerical results obtained in this work align well with intuitive insights gleaned directly from the governing equation. Our results suggest that hybrid-state sequences outperform traditional methods. Moreover, hybrid-state sequences that balance T 1 - and T 2 -encoding still result in near optimal signal-to-noise efficiency for each relaxation time. Thus, the second parameter can be encoded at virtually no extra cost., Conclusions: We provided new insights into the optimal encoding processes of spin relaxation times in order to guide the design of robust and efficient pulse sequences. We found that joint acquisitions of T 1 and T 2 in the hybrid state are substantially more efficient than sequential encoding techniques., (© 2019 International Society for Magnetic Resonance in Medicine.)

Low-dose x-ray CT is a major research area with high clinical impact. Compressed sensing using view-based sparse sampling and sparsity-promoting regularization has shown promise in simulations, but these methods can be difficult to implement on diagnostic clinical CT scanners since the x-ray beam cannot be switched on and off rapidly enough. An alternative to view-based sparse sampling is interrupted-beam sparse sampling. SparseCT is a recently-proposed interrupted-beam scheme that achieves sparse sampling by blocking a portion of the beam using a multislit collimator (MSC). The use of an MSC necessitates a number of modifications to the standard compressed sensing reconstruction pipeline. In particular, we find that SparseCT reconstruction is feasible within a model-based image reconstruction framework that incorporates data fidelity weighting to consider penumbra effects and source jittering to consider the effect of partial source obstruction. Here, we present these modifications and demonstrate their application in simulations and real-world prototype scans. In simulations compared to conventional low-dose acquisitions, SparseCT is able to achieve smaller normalized root-mean square differences and higher structural similarity measures on two reduction factors. In prototype experiments, we successfully apply our reconstruction modifications and maintain image resolution at quarter-dose reduction level. The SparseCT design requires only small hardware modifications to current diagnostic clinical scanners, opening up new possibilities for CT dose reduction.

Purpose: SparseCT, an undersampling scheme for compressed sensing (CS) computed tomography (CT), has been proposed to reduce radiation dose by acquiring undersampled projection data from clinical CT scanners (Koesters et al. in, SparseCT: Interrupted-Beam Acquisition and Sparse Reconstruction for Radiation Dose Reduction; 2017). SparseCT partially blocks the x-ray beam with a multislit collimator (MSC) to perform a multidimensional undersampling along the view and detector row dimensions. SparseCT undersamples the projection data within each view and moves the MSC along the z-direction during gantry rotation to change the undersampling pattern. It enables reconstruction of images from undersampled data using CS algorithms. The purpose of this work is to design the spacing and width of the MSC slits and the MSC motion patterns based on beam separation, undersampling efficiency, and image quality. The development and testing of a SparseCT prototype with the designed MSC will be described in a following paper., Methods: We chose a few initial MSC designs based on the guidance from two metrics: beam separation and undersampling efficiency. Both beam separation and undersampling efficiency were measured from numerically simulated photon distribution with MSC taken into consideration. Beam separation measures the separation between x-ray beams from consecutive slits, taking into account penumbra effects on both sides of each slit. Undersampling efficiency measures the dose-weighted similarity between penumbra undersampling and binary undersampling, in other words, the effective contribution of the incident dose to the signal to noise ratio of the projection data. We then compared the initially chosen MSC designs in terms of their reconstruction image quality. SparseCT projections were simulated from fully sampled patient projection data according to the MSC design and motion pattern, reconstructed iteratively using a sparsity-enforcing penalized weighted least squares cost function with ordered subsets/momentum algorithm, and compared visually and quantitatively., Results: Simulated photon distributions indicate that the size of the penumbra is dominated by the size of the focal spot. Therefore, a wider MSC slit and a smaller focal spot lead to increased beam separation and undersampling efficiency. For fourfold undersampling with a 1.2 mm focal spot, a minimum MSC slit width of three detector rows (projected to the detector surface) is needed for beam separation; for threefold undersampling, a minimum slit width of four detector rows is needed. Simulations of SparseCT projection and reconstruction indicate that the motion pattern of the MSC does not have a visible impact on image quality. An MSC slit width of three or four detector rows yields similar image quality., Conclusion: The MSC is the key component of the SparseCT method. Simulations of MSC designs incorporating x-ray beam penumbra effects showed that for threefold and fourfold dose reductions, an MSC slit width of four detector rows provided reasonable beam separation, undersampling efficiency, and image quality., (© 2019 American Association of Physicists in Medicine.)

Background: The value of dynamic contrast-enhanced (DCE) sequences in prostate MRI compared with noncontrast MRI is controversial., Purpose: To evaluate the population net benefit of risk stratification using DCE-MRI for detection of high-grade prostate cancer (HGPCA), with or without high spatiotemporal resolution DCE imaging., Study Type: Decision curve analysis., Population: Previously published patient studies on MRI for HGPCA detection, one using DCE with golden-angle radial sparse parallel (GRASP) images and the other using standard DCE-MRI., Field Strength/sequence: GRASP or standard DCE-MRI at 3 T., Assessment: Each study reported the proportion of lesions with HGPCA in each Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) category (1-5), before and after reclassification of peripheral zone lesions from PI-RADS 3-4 based on contrast-enhanced images. This additional risk stratifying information was translated to population net benefit, when biopsy was hypothetically performed for: all lesions, no lesions, PI-RADS ≥3 (using NC-MRI), and PI-RADS ≥4 on DCE., Statistical Tests: Decision curve analysis was performed for both GRASP and standard DCE-MRI data, translating the avoidance of unnecessary biopsies and detection of HGPCA to population net benefit. We standardized net benefit values for HGPCA prevalence and graphically summarized the comparative net benefit of biopsy strategies., Results: For a clinically relevant range of risk thresholds for HGPCA (>11%), GRASP DCE-MRI with biopsy of PI-RADS ≥4 lesions provided the highest net benefit, while biopsy of PI-RADS ≥3 lesions provided highest net benefit at low personal risk thresholds (2-11%). In the same range of risk thresholds using standard DCE-MRI, the optimal strategy was biopsy for all lesions (0-15% risk threshold) or PI-RADS ≥3 on NC-MRI (16-33% risk threshold)., Data Conclusion: GRASP DCE-MRI may potentially enable biopsy of PI-RADS ≥4 lesions, providing relatively preserved detection of HGPCA and avoidance of unnecessary biopsies compared with biopsy of all PI-RADS ≥3 lesions. J. Magn. Reson. Imaging 2019;49:1400-1408., (© 2019 International Society for Magnetic Resonance in Medicine.)

Purpose: To investigate how high-permittivity materials (HPMs) can improve SNR when placed between MR detectors and the imaged body., Methods: We used a simulation framework based on dyadic Green's functions to calculate the electromagnetic field inside a uniform dielectric sphere at 7 Tesla, with and without a surrounding layer of HPM. SNR-optimizing (ideal) current patterns were expressed as the sum of signal-optimizing (signal-only) current patterns and dark mode current patterns that minimize sample noise while contributing nothing to signal. We investigated how HPM affects the shape and amplitude of these current patterns, sample noise, and array SNR., Results: Ideal and signal-only current patterns were identical for a central voxel. HPMs introduced a phase shift into these patterns, compensating for signal propagation delay in the HPMs. For an intermediate location within the sphere, dark mode current patterns were present and illustrated the mechanisms by which HPMs can reduce sample noise. High-amplitude signal-only current patterns were observed for HPM configurations that shield the electromagnetic field from the sample. For coil arrays, these configurations corresponded to poor SNR in deep regions but resulted in large SNR gains near the surface due to enhanced fields in the vicinity of the HPM. For very high relative permittivity values, HPM thicknesses corresponding to even multiples of λ/4 resulted in coil SNR gains throughout the sample., Conclusion: HPMs affect both signal sensitivity and sample noise. Lower amplitude signal-only optimal currents corresponded to higher array SNR performance and could guide the design of coils integrated with HPM., (© 2018 International Society for Magnetic Resonance in Medicine.)

Background: Patient-specific 3D models are being used increasingly in medicine for many applications including surgical planning, procedure rehearsal, trainee education, and patient education. To date, experiences on the use of 3D models to facilitate patient understanding of their disease and surgical plan are limited. The purpose of this study was to investigate in the context of renal and prostate cancer the impact of using 3D printed and augmented reality models for patient education., Methods: Patients with MRI-visible prostate cancer undergoing either robotic assisted radical prostatectomy or focal ablative therapy or patients with renal masses undergoing partial nephrectomy were prospectively enrolled in this IRB approved study (n = 200). Patients underwent routine clinical imaging protocols and were randomized to receive pre-operative planning with imaging alone or imaging plus a patient-specific 3D model which was either 3D printed, visualized in AR, or viewed in 3D on a 2D computer monitor. 3D uro-oncologic models were created from the medical imaging data. A 5-point Likert scale survey was administered to patients prior to the surgical procedure to determine understanding of the cancer and treatment plan. If randomized to receive a pre-operative 3D model, the survey was completed twice, before and after viewing the 3D model. In addition, the cohort that received 3D models completed additional questions to compare usefulness of the different forms of visualization of the 3D models. Survey responses for each of the 3D model groups were compared using the Mann-Whitney and Wilcoxan rank-sum tests., Results: All 200 patients completed the survey after reviewing their cases with their surgeons using imaging only. 127 patients completed the 5-point Likert scale survey regarding understanding of disease and surgical procedure twice, once with imaging and again after reviewing imaging plus a 3D model. Patients had a greater understanding using 3D printed models versus imaging for all measures including comprehension of disease, cancer size, cancer location, treatment plan, and the comfort level regarding the treatment plan (range 4.60-4.78/5 vs. 4.06-4.49/5, p < 0.05)., Conclusions: All types of patient-specific 3D models were reported to be valuable for patient education. Out of the three advanced imaging methods, the 3D printed models helped patients to have the greatest understanding of their anatomy, disease, tumor characteristics, and surgical procedure.

Background: Computed tomography (CT) and spirometry are the current standard methods for assessing lung anatomy and pulmonary ventilation, respectively. However, CT provides limited ventilation information and spirometry only provides global measures of lung ventilation. Thus, a method that can enable simultaneous examination of lung anatomy and ventilation is of clinical interest., Purpose: To develop and test a 4D respiratory-resolved sparse lung MRI (XD-UTE: eXtra-Dimensional Ultrashort TE imaging) approach for simultaneous evaluation of lung anatomy and pulmonary ventilation., Study Type: Prospective., Population: In all, 23 subjects (11 volunteers and 12 patients, mean age = 63.6 ± 8.4)., Field Strength/sequence: 3T MR; a prototype 3D golden-angle radial UTE sequence, a Cartesian breath-hold volumetric-interpolated examination (BH-VIBE) sequence., Assessment: All subjects were scanned using the 3D golden-angle radial UTE sequence during normal breathing. Ten subjects underwent an additional scan during alternating normal and deep breathing. Respiratory-motion-resolved sparse reconstruction was performed for all the acquired data to generate dynamic normal-breathing or deep-breathing image series. For comparison, BH-VIBE was performed in 12 subjects. Lung images were visually scored by three experienced chest radiologists and were analyzed by two observers who segmented the left and right lung to derive ventilation parameters in comparison with spirometry., Statistical Tests: Nonparametric paired two-tailed Wilcoxon signed-rank test; intraclass correlation coefficient, Pearson correlation coefficient., Results: XD-UTE achieved significantly improved image quality compared both with Cartesian BH-VIBE and radial reconstruction without motion compensation (P < 0.05). The global ventilation parameters (a sum of the left and right lung measures) were in good correlation with spirometry in the same subjects (correlation coefficient = 0.724). There were excellent correlations between the results obtained by two observers (intraclass correlation coefficient ranged from 0.8855-0.9995)., Data Conclusion: Simultaneous evaluation of lung anatomy and ventilation using XD-UTE is demonstrated, which have shown good potential for improved diagnosis and management of patients with heterogeneous lung diseases., Level of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:411-422., (© 2018 International Society for Magnetic Resonance in Medicine.)

Purpose: Although deep learning has shown great promise for MR image reconstruction, an open question regarding the success of this approach is the robustness in the case of deviations between training and test data. The goal of this study is to assess the influence of image contrast, SNR, and image content on the generalization of learned image reconstruction, and to demonstrate the potential for transfer learning., Methods: Reconstructions were trained from undersampled data using data sets with varying SNR, sampling pattern, image contrast, and synthetic data generated from a public image database. The performance of the trained reconstructions was evaluated on 10 in vivo patient knee MRI acquisitions from 2 different pulse sequences that were not used during training. Transfer learning was evaluated by fine-tuning baseline trainings from synthetic data with a small subset of in vivo MR training data., Results: Deviations in SNR between training and testing led to substantial decreases in reconstruction image quality, whereas image contrast was less relevant. Trainings from heterogeneous training data generalized well toward the test data with a range of acquisition parameters. Trainings from synthetic, non-MR image data showed residual aliasing artifacts, which could be removed by transfer learning-inspired fine-tuning., Conclusion: This study presents insights into the generalization ability of learned image reconstruction with respect to deviations in the acquisition settings between training and testing. It also provides an outlook for the potential of transfer learning to fine-tune trainings to a particular target application using only a small number of training cases., (© 2018 International Society for Magnetic Resonance in Medicine.)