Current Magnetic Resonance Imaging Research Articles

P.109 Machine learning based approach to improving the prediction of neurological deterioration in mild Degenerative Cervical Myelopathy

Background: Degenerative cervical myelopathy (DCM) is the most common form of atraumatic spinal cord injury globally, yet clinical guidelines remain unclear on surgical recommendations for patients with mild forms of DCM. This is in part due to limitations in current MR imaging interpretation and complex mechanisms of neurological deterioration. Supervised machine learning (ML) models can help to identify clinical and imaging indicators of deterioration within mild DCM patients. Methods: 127 MRI scans (T2w, Diffusion Tensor Imaging, and Magnetization transfer scans) accompanied by a series of clinical tests underwent a semi-automated analysis to derive quantitative metrics. Random forest classifier, Support Vector Machine, and Logistic Regression models were trained and tested to predict 6-month neurological deterioration within patients. Results: The ML models performed, on average, better than previous studies with a balanced accuracy ranging between 70-75%. “Advanced” imaging metrics such as diffusion tensor imaging and magnetization transfer scans played an important role in improving model accuracy but only when used near the maximally compressed disc level, suggesting that limited yet targetted imaging metrics support ML model performance. Conclusions: The inclusion of specific, targeted imaging and clinical metrics support ML model performance in predicting neurological deterioration within mild DCM patients.

A Dual-Gene Reporter-Amplifier Architecture for Enhancing the Sensitivity of Molecular MRI by Water Exchange.

The development of genetic reporters for magnetic resonance imaging (MRI) is essential for investigating biological functions in vivo. However, current MRI reporters have low sensitivity, making it challenging to create significant contrast against the tissue background, especially when only a small fraction of cells express the reporter. To overcome this limitation, we developed an approach for amplifying the sensitivity of molecular MRI by combining a chemogenetic contrast mechanism with a biophysical approach to increase water diffusion through the co-expression of a dual-gene construct comprising an organic anion transporting polypeptide, Oatp1b3, and a water channel, Aqp1. We first show that the expression of Aqp1 amplifies MRI contrast in cultured cells engineered to express Oatp1b3. We demonstrate that the contrast amplification is caused by Aqp1-driven increase in water exchange, which provides the gadolinium ions internalized by Oatp1b3-expressing cells with access to a larger water pool compared with exchange-limited conditions. We further show that our methodology allows cells to be detected using approximately 10-fold lower concentrations of gadolinium than that in the Aqp1-free scenario. Finally, we show that our approach enables the imaging of mixed-cell cultures containing a low fraction of Oatp1b3-labeled cells that are undetectable on the basis of Oatp1b3 expression alone.

Human Brain Magnetic Resonance Imaging Studies for Psychiatric Disorders: The Current Progress and Future Directions.

With the prevalence of psychiatric disorders and the limitations of the diagnostic scheme and treatment options of these disorders, magnetic resonance imaging (MRI) studies play a significant role in uncovering the pathological basis of psychiatric disorders and potentially using biological markers in clinical settings. The use of MRI in clinical research has grown over the past three decades, and current MRI research continues to provide an avenue to guide the development of diagnostic approaches and therapeutic solutions. However, the current shortcomings of MRI studies derive not only from technical limitations (i.e., the range of contrasts that MRI probes or sensors can create) but also from confounding factors in the current methodological approaches of case-control studies for psychiatric disorders. Thus, by reviewing the recent literature on MRI research on psychiatric disorders, we explain the current progress and limitations of brain MRI methodologies used to study psychiatric disorders. We consider the growing use of cross-disorder methods to identify shared and disease-specific pathological features across psychiatric disorders. In addition, we need to outline healthy developmental and aging changes of the brain and investigate the disorder difference as a deviation of the trajectory. Although these methods have provided us with new insights, the demarcation between psychiatric disorders based on a definitive set of pathologies remains limited. This challenge of disease stratification is further complicated by the presence of multiple different sets of disorder pathologies within a single disorder and the different progressive timelines of different disorders. As such, we introduce the ongoing research projects in Japan, namely, the Brain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/MINDS) and the Strategic International Brain Science Research Promotion Program (Brain/MINDS Beyond). These collaborative research initiatives across Japan use neuroimaging and travel-subject harmonization to conduct nationwide MRI studies capable of providing large-scale coherent results, which may address the current limitations of MRI psychiatric disorder research.

Advanced MRI metrics improve the prediction of baseline disease severity for individuals with degenerative cervical myelopathy

BACKGROUND CONTEXTDegenerative cervical myelopathy (DCM) is the most common form of atraumatic spinal cord injury globally. Degeneration of spinal discs, bony osteophyte growth and ligament pathology results in physical compression of the spinal cord contributing to damage of white matter tracts and grey matter cellular populations. This results in an insidious neurological and functional decline in patients which can lead to paralysis. Magnetic resonance imaging (MRI) confirms the diagnosis of DCM and is a prerequisite to surgical intervention, the only known treatment for this disorder. Unfortunately, there is a weak correlation between features of current commonly acquired MRI scans (“community MRI, cMRI”) and the degree of disability experienced by a patient. PURPOSEThis study examines the predictive ability of current MRI sequences relative to “advanced MRI” (aMRI) metrics designed to detect evidence of spinal cord injury secondary to degenerative myelopathy. We hypothesize that the utilization of higher fidelity aMRI scans will increase the effectiveness of machine learning models predicting DCM severity and may ultimately lead to a more efficient protocol for identifying patients in need of surgical intervention. STUDY DESIGN/SETTINGSingle institution analysis of imaging registry of patients with DCM. PATIENT SAMPLEA total of 296 patients in the cMRI group and 228 patients in the aMRI group. OUTCOME MEASURESPhysiologic measures: accuracy of machine learning algorithms to detect severity of DCM assessed clinically based on the modified Japanese Orthopedic Association (mJOA) scale. METHODSPatients enrolled in the Canadian Spine Outcomes Research Network registry with DCM were screened and 296 cervical spine MRIs acquired in cMRI were compared with 228 aMRI acquisitions. aMRI acquisitions consisted of diffusion tensor imaging, magnetization transfer, T2-weighted, and T2*-weighted images. The cMRI group consisted of only T2-weighted MRI scans. Various machine learning models were applied to both MRI groups to assess accuracy of prediction of baseline disease severity assessed clinically using the mJOA scale for cervical myelopathy. RESULTSThrough the utilization of Random Forest Classifiers, disease severity was predicted with 41.8% accuracy in cMRI scans and 73.3% in the aMRI scans. Across different predictive model variations tested, the aMRI scans consistently produced higher prediction accuracies compared to the cMRI counterparts. CONCLUSIONSaMRI metrics perform better in machine learning models at predicting disease severity of patients with DCM. Continued work is needed to refine these models and address DCM severity class imbalance concerns, ultimately improving model confidence for clinical implementation.

Resting-state functional magnetic resonance imaging reveals functional connectivity alteration in the experimental autoimmune encephalomyelitis model of multiple sclerosis.

Multiple sclerosis (MS) is an autoimmune degenerative disease targeting white matter in the central nervous system. The most common animal model that mimics MS is experimental autoimmune encephalomyelitis (EAE) and it plays a crucial role in pharmacological research, from the identification of a therapeutic target to the in vivo validation of efficacy. Magnetic resonance imaging (MRI) is largely used to detect MS lesions, and resting-state functional MRI (rsfMRI) to investigate alterations in the brain functional connectivity (FC). MRI was mainly used in EAE studies to detect lesions in the spinal cord and brain. The current longitudinal MRI study aims to validate rsfMRI as a biomarker of the disease progression in the myelin oligodendrocyte glycoprotein 35-55 induced EAE animal model of MS. MR images were acquired 14, 25, and 50 days postimmunization. Seed-based analysis was used to investigate the whole-brain FC with some predefined areas, such as the thalamic regions, cerebellum, motor and somatosensory cortex. When compared with the control group, the EAE group exhibited a slightly altered FC and a decreasing trend in the total number of activated voxels along the disease progression. The most interesting result regards the whole-brain FC with the cerebellum. A hyperconnectivity behavior was found at an early phase and a significant reduced connectivity at a late phase. Moreover, we found a negative correlation between the total number of activated voxels during the late phase and the cumulative disease index. The results obtained provide a clinically relevant experimental platform that may be pivotal for the elucidation of the key mechanisms of accumulation of irreversible disability, as well as the development of innovative therapies for MS. Moreover, the negative correlation between the disease severity and the size of the activated area suggests a possible research pathway to follow for the resolution of the clinico-radiological paradox.

ART and AutoART ECAP measurements and cochlear nerve anatomy as predictors in adult cochlear implant recipients.

The purpose of this retrospective study is to compare the results of electrically evoked compound action potential (ECAP) measurements using automatic auditory response telemetry (AutoART) with those obtained by ART in adults. The study also aimed to evaluate the predictive value of intraoperative ART and AutoART ECAPs for speech intelligibility (SI) and hearing success (HS), and to determine if cochlear nerve (CN) cross-sectional area (CSA) obtained preoperatively by magnetic resonance imaging (MRI) scans could predict ART and AutoART ECAPs and SI and HS outcome. The study analyzed and correlated ART and AutoART ECAP thresholds at electrodes E2, E6, and E10, as well as averaged ECAP thresholds over electrodes E1-E12, using data from 32 implants. Correlations were also examined for ART and AutoART ECAP slopes. In addition, averaged ART and AutoART ECAP thresholds and slopes over all 12 electrodes for each participant were correlated with CN CSA measured from MRI sequences. SI of the monosyllabic Freiburg Speech Test at 65 dB sound pressure level was examined along with averaged ART and AutoART thresholds and slopes over all 12 electrodes. A parallel analysis was performed for HS, derived from the difference between baseline and 6-month SI. Finally, correlations between CN CSA and SI, as well as CN CSA and HS were examined. The results of the study showed a significant positive correlation between ART and AutoART ECAP thresholds and as well as slopes for E2, E6, E10 and averaged thresholds and slopes of E1-E12. However, no significant correlation was observed between ART and AutoART averaged ECAP thresholds and slopes and either SI and HS or CN CSA. Furthermore, no significant correlation was found between CN CSA and SI and HS. While AutoART is a reliable and safe program for measuring ECAPs in adults, the study found no preoperative prognostic information on intraoperative ECAP results using parameters extracted from current MRI sequences or pre-/intraoperative information on subsequent hearing outcome using ECAP and CN CSA.

Current MR imaging of cartilage in the context of knee osteoarthritis (part2) : Cartilage pathologies and their assessment

High-quality magnetic resonance (MR) imaging is essential for the precise assessment of the knee joint and plays a key role in the diagnostics, treatment and prognosis. Intact cartilage tissue is characterized by asmooth surface, uniform tissue thickness and an organized zonal structure, which are manifested as depth-dependent signal intensity variations. Cartilage pathologies are identifiable through alterations in signal intensity and morphology and should be communicated based on a precise terminology. Cartilage pathologies can show hyperintense and hypointense signal alterations. Cartilage defects are assessed based on their depth and should be described in terms of their location and extent. The following symptom constellations are of overarching clinical relevance in image reading and interpretation: symptom constellations associated with rapidly progressive forms of joint degeneration and unfavorable prognosis, accompanying symptom constellations mostly in connection with destabilizing meniscal lesions and subchondral insufficiency fractures (accelerated osteoarthritis) as well as symptoms beyond the "typical" degeneration, especially when adiscrepancy is observed between (minor) structural changes and (major) synovitis and effusion (inflammatory arthropathy).

Current MR imaging of cartilage in the context of knee osteoarthritis (part1) : Principles and sequences

Magnetic resonance imaging (MRI) is the clinical method of choice for cartilage imaging in the context of degenerative and nondegenerative joint diseases. The MRI-based definitions of osteoarthritis rely on the detection of osteophytes, cartilage pathologies, bone marrow edema and meniscal lesions but currently ascientific consensus is lacking. In the clinical routine proton density-weighted, fat-suppressed 2D turbo spin echo sequences with echo times of 30-40 ms are predominantly used, which are sufficiently sensitive and specific for the assessment of cartilage. The additionally acquired T1-weighted sequences are primarily used for evaluating other intra-articular and periarticular structures. Diagnostically relevant artifacts include magic angle and chemical shift artifacts, which can lead to artificial signal enhancement in cartilage or incorrect representations of the subchondral lamina and its thickness. Although scientifically validated, high-resolution 3D gradient echo sequences (for cartilage segmentation) and compositional MR sequences (for quantification of physical tissue parameters) are currently reserved for scientific research questions. The future integration of artificial intelligence techniques in areas such as image reconstruction (to reduce scan times while maintaining image quality), image analysis (for automated identification of cartilage defects), and image postprocessing (for automated segmentation of cartilage in terms of volume and thickness) will significantly improve the diagnostic workflow and advance the field further.

Hierarchical neural architecture search with adaptive global–local feature learning for Magnetic Resonance Image reconstruction

Neural architecture search (NAS) has been introduced into the design of deep neural network architectures for Magnetic Resonance Imaging (MRI) reconstruction since NAS-based methods can acquire the complex network architecture automatically without professional designing experience and improve the model’s generalization ability. However, current NAS-based MRI reconstruction methods suffer from a lack of efficient operators in the search space, which leads to challenges in effectively recovering high-frequency details. This limitation is primarily due to the prevalent use of convolution operators in the current search space, which struggle to capture both global and local features of MR images simultaneously, resulting in insufficient information utilization. To address this issue, a generative adversarial network (GAN) based model is proposed to reconstruct the MR image from under-sampled K-space data. Firstly, parameterized global and local feature learning modules at multiple scales are added into the search space to improve the capability of recovering high-frequency details. Secondly, to mitigate the increased search time caused by the augmented search space, a hierarchical NAS is designed to learn the global–local feature learning modules that enable the reconstruction network to learn global and local information of MR images at different scales adaptively. Thirdly, to reduce the number of network parameters and computational complexity, the standard operations in global–local feature learning modules are replaced with lightweight operations. Finally, experiments on several publicly available brain MRI image datasets evaluate the performance of the proposed method. Compared to the state-of-the-art MRI reconstruction methods, the proposed method yields better reconstruction results in terms of peak signal-to-noise ratio and structural similarity at a lower computational cost. Additionally, our reconstruction results are validated through a brain tumor classification task, affirming the practicability of the proposed method. Our code is available at https://github.com/wwHwo/HNASMRI.

A Novel MR Imaging Sequence of 3D-ZOOMit Real Inversion-Recovery Imaging Improves Endolymphatic Hydrops Detection in Patients with Ménière Disease.

The detection rate of premortem MR imaging endolymphatic hydrops is lower than that of postmortem endolymphatic hydrops in Ménière disease, indicating that current MR imaging techniques may underestimate endolymphatic hydrops. Therefore, we prospectively investigated whether a novel high-resolution MR imaging technique, the 3D zoomed imaging technique with parallel transmission real inversion-recovery (3D-ZOOMit real IR), would improve the detection of endolymphatic hydrops compared with conventional 3D TSE inversion-recovery with real reconstruction. Fifty patients with definite unilateral Ménière disease were enrolled and underwent 3D-ZOOMit real IR and 3D TSE inversion-recovery with real reconstruction 6 hours after IV gadolinium injection. The endo- and perilymph spaces were scored separately. The contrast-to-noise ratio, SNR, and signal intensity ratio of the 2 sequences were respectively calculated and compared. The presence of endolymphatic hydrops was evaluated. The endolymphatic space in the cochlea and vestibule was better visualized with 3D-ZOOMit real IR than with conventional 3D TSE inversion-recovery with real reconstruction (P < .001). There were differences between the 2 sequences in the evaluation of no cochlear hydrops and cochlear hydrops (both, P < .017). All contrast-to-noise ratio, SNR, and signal intensity ratio values of 3D-ZOOMit real IR images were statistically higher than those of conventional 3D TSE inversion-recovery with real reconstruction (all, P < .001). The 3D-ZOOMit real IR sequences are superior to conventional 3D TSE inversion-recovery with real reconstruction sequences in visualizing the endolymphatic space, detecting endolymphatic hydrops, and discovering contrast permeability.

Progres of deep learning based quantitative MRI reconstruction methods

磁共振成像是一种应用广泛的无创医学成像方法，因其丰富的软组织对比度可以成像人体几乎所有内部结构，包括器官、骨骼、肌肉和血管，已成为临床医学影像诊断的利器。然而磁共振成像存在两大公认的瓶颈：成像速度慢、扫描操作烦琐。深度学习给磁共振成像带来莫大的契机，给传统磁共振加速成像带来新的可能。鉴于该领域的快速发展性质，本文旨在总结文献中报道的大量深度学习和磁共振图像重建相结合的方法，以更好地了解该领域。本文简单介绍磁共振成像在多通道线圈接收的并行加速和压缩感知加速下的深度学习重建方法，其中单对比度图像可通过多通道接收线圈提供的冗余度用于加速，多对比度图像可额外使用不同对比度图像这一维度用于加速，动态图像与多对比度图像类似可额外使用时间维度用于加速，本文也将介绍深度学习在这些方面的发展。随着磁共振成像近年来由定性多对比度成像向定量多参数成像的发展，其中定量成像的图像中可内含多对比度图像，如何借用深度学习提供的能力将定性多对比度图像映射到参数图也是一个难点，近年来这一方向也获得了较快的发展，文中也将针对这方面内容进行调研并介绍。针对上述内容，分别介绍国际研究现状和国内研究现状，拟更好地总结国内外研究的发展的异同和趋势。最后对深度学习助力定量磁共振成像方面进行了展望。;Magnetic resonance imaging(MRI) is a commonly-used non-invasive medical imaging method. Due to richable soft tissue-related contrast features of human body, medical imaging diagnosis is beneficial from more internal structures-contextual image in clinic, including its organs, bones, muscles, and blood vessels. However, two sorts of bottlenecks of MRI like slow scanning speed and labor-intensive scanning operation are required to be resolved beyond the constraints of hardware and existing techniques. Current MRI has been facilitating in terms of the emerging deep learning based medical imaging technique. To optimize its acquisition time, parallel imaging and compressed sensing combined with the use of multi-array coils, conventional MRI mainly depends on the hardware improvements and new compactable sequence design. First, literature review of deep learning-based MR reconstruction methods are analyzed, including such deep learning issues originated from 1) integration of parallel imaging and compressed sensing of MRI and 2) acceleration of multi-contrast images and dynamic images of single-contrast image. Deep learning-based reconstruction models is oriented to get their generalizability incorporated with multiple datasets, in which MRI data are challenged for multiple factors like its relevance of centers, models, and field strengths. Two decadal development of quantitative MRI technique can provide pixel-level characterization of intrinsic tissue quantitative parameters, such as T1, T2, and ADC. Generally, quantitative MRI is often required to get multiple weighted images under different parameters, and quantitative tissue parameters can be generated via signal model-required pixel-level nonlinear fitting. Compared to the acquisition of contrast images, acquisition and reconstruction duration has become more longer. To accelerate the acquisition and reconstruction or facilitate accurate mapping, extension ability of deep learning has been developing for the reconstruction of MRI fast imaging. Apart from conventional single parameter mapping method, simultaneous multi-parameter mapping techniques have leaked out, such as MR fingerprinting. Compared to single parameter mapping, multi-parameter mapping can be synchronized and accelerated, and more efficient acquisition and inherent coregistration can be used to deal with such complicated reconstruction. Deep learning technique can be as a multifaceted tool to simplify the reconstruction and speed up the acquisition. However, it is still challenged for such constraints like deep learning algorithms and large amounts of training data. Deep learning methods are required to be melted into magnetic resonance physical models and traditional reconstruction algorithms to a certain extent, and to improve the interpretability of the model and alleviate the impact on large datasets, such methods of data enhancement, weakly supervised learning, unsupervised learning, and transfer learning can be involved in as well. A quantitative MRI-relevant unsupervised network can be potentially used for training in terms of different parameter mapping sequences, which can greatly shrink the amount of data samples further.

Effects of gradient high-field static magnetic fields on diabetic mice.

Although 9.4 T magnetic resonance imaging (MRI) has been tested in healthy volunteers, its safety in diabetic patients is unclear. Furthermore, the effects of high static magnetic fields (SMFs), especially gradient vs. uniform fields, have not been investigated in diabetics. Here, we investigated the consequences of exposure to 1.0-9.4 T high SMFs of different gradients (>10 T/m vs. 0-10 T/m) on type 1 diabetic (T1D) and type 2 diabetic (T2D) mice. We found that 14 h of prolonged treatment of gradient (as high as 55.5 T/m) high SMFs (1.0-8.6 T) had negative effects on T1D and T2D mice, including spleen, hepatic, and renal tissue impairment and elevated glycosylated serum protein, blood glucose, inflammation, and anxiety, while 9.4 T quasi-uniform SMFs at 0-10 T/m did not induce the same effects. In regular T1D mice (blood glucose ≥16.7 mmol/L), the >10 T/m gradient high SMFs increased malondialdehyde ( P<0.01) and decreased superoxide dismutase ( P<0.05). However, in the severe T1D mice (blood glucose ≥30.0 mmol/L), the >10 T/m gradient high SMFs significantly increased tissue damage and reduced survival rate. In vitro cellular studies showed that gradient high SMFs increased cellular reactive oxygen species and apoptosis and reduced MS-1 cell number and proliferation. Therefore, this study showed that prolonged exposure to high-field (1.0-8.6 T) >10 T/m gradient SMFs (35-1 380 times higher than that of current clinical MRI) can have negative effects on diabetic mice, especially mice with severe T1D, whereas 9.4 T high SMFs at 0-10 T/m did not produce the same effects, providing important information for the future development and clinical application of SMFs, especially high-field MRI.

Bio-orthogonal Metabolic Fluorine Labeling Enables Deep-Tissue Visualization of Tumor Cells In Vivo by 19F Magnetic Resonance Imaging.

The high resolution, deep penetration, and negligible biological background of 19F magnetic resonance imaging (MRI) makes it a potential means for imaging various biological targets in vivo. However, the limited targeting strategies of current 19F MRI probes significantly restrict their applications for in vivo tracking of low-abundance targets and specific biological processes, which greatly stimulates the investigations on new targeting methods for 19F MRI. Herein, we report a strategy, termed as bio-orthogonal metabolic fluorine labeling, for selective cellular 19F labeling, which permits in vivo imaging of tumor cells with high specificity. This strategy exploits the display of azido groups on the cell surface via selective uptake and metabolic engineering of tetra-acetylated N-azidoacetylmannosamine (Ac4ManAz) by cancer cells and subsequent rapid and specific bio-orthogonal ligation between azido and cyclootynyl groups to incorporate 19F-containing moieties on the surface of cancer cells. We validated the feasibility of this method on the cellular level with A549 and HepG2 cells and further illustrated the application of this method for in vivo deep-tissue visualization of cancer cells with A549 tumor-bearing BALB/c mice using hot spot 19F MRI. Our strategy expands the arsenal for targeted 19F MRI and provides a promising method for imaging biological targets in living subjects with high tissue penetration and low biological background.

Magnetic resonance imaging for non-invasive measurement of plastic ingestion in marine wildlife

Monitoring plastic ingestion by marine wildlife is important for both characterizing the extent of plastic pollution in the environment and understanding its effect on species and ecosystems. Current methods to detect plastic in the digestive system of animals are slow and invasive, such that the number of animals that can be screened is limited. In this article, magnetic resonance imaging (MRI) is investigated as a possible technology to perform rapid, non-invasive detection of plastic ingestion. Standard MRI methods were able to directly measure one type of plastic in a fulmar stomach and another type was able to be indirectly detected. In addition to MRI, other standard nuclear magnetic resonance (NMR) measurements were made. Different types of plastic were tested, and distinctive NMR signal characteristics were found in common for each type, allowing them to be distinguished from one another. The NMR results indicate specialized MRI sequences could be used to directly image several types of plastic. Although current commercial MRI technology is not suitable for field use, existing single-sided MRI research systems could be adapted for use outside the laboratory and become an important tool for future monitoring of wild animals.

Diagnostic Accuracy of Magnetic Resonance Imaging With 3-Dimensional T2-SPACE Techniques for Preganglionic Injury of the Brachial Plexus

An accurate diagnosis of the site and severity of a brachial plexus injury is imperative for selecting the appropriate management. Conventional magnetic resonance imaging (MRI) does not allow for the precise interpretation of preganglionic injuries (pre-GIs), especially intravertebral canal injuries. We developed 4 MRI sequences of conventional 1.5-tesla 3-dimensional T2-weighted turbo spin echo sampling perfection with the application of optimized contrasts using different flip angles evolution (T2-SPACE) images to clearly visualize each component of the brachial plexus. The purpose of this study was to introduce basic normal and pathologic findings of our current MRI approach, focusing on its diagnostic accuracy for pre-GIs. We initially examined 119 patients with brachial plexus injuries who underwent surgical exploration by MRI using 4 sequences of the 1.5-tesla 3-dimensional T2-SPACE technique. We obtained coronal, transverse, coronal oblique, and coronal cuts of T2 short time inversion recovery. The images of 595 roots were interpreted by multiple-image synchronizing techniques of the 4 views to precisely interpret the presence of spinal cord edema, numbers of anterior and posterior rootlets, sites of ganglions, meningeal cysts, and the "black line sign" (displaced ruptured dura or bundles of ruptured rootlets). We assessed the accuracy, sensitivity, and specificity of these abnormal findings with regard to diagnosing pre-GIs by comparing surgical exploration. The absence or decreased numbers of anterior and posterior rootlets and displacement of ganglions were definitive evidence of pre-GIs and the other findings, like spinal cord edema, meningeal cysts, and black line signs, were predictive signs. The synchronizing techniques of the four 1.5-tesla 3-dimensional T2-SPACE images provided high diagnostic accuracy of pre-GIs. Diagnostic II.

3D Enhancement Color Maps in the Characterization of Intracranial Atherosclerotic Plaques.

High-resolution MR imaging allows the identification of culprit symptomatic plaques after the administration of gadolinium. Current high-resolution MR imaging methods are limited by 2D multiplanar views and manual sampling of ROIs. We analyzed a new 3D method to objectively quantify gadolinium plaque enhancement. Patients with stroke due to intracranial atherosclerotic disease underwent 7T high-resolution MR imaging. 3D segmentations of the plaque and its parent vessel were generated. Signal intensity probes were automatically extended from the lumen into the plaque and the vessel wall to generate 3D enhancement color maps. Plaque gadolinium (Gd) uptake was quantified from 3D color maps as gadolinium uptake = (µPlaque T1 + Gd -µPlaque T1/SDPlaque T1). Additional metrics of enhancement such as enhancement ratio, variance, and plaque-versus-parent vessel enhancement were also calculated. Conventional 2D measures of enhancement were collected for comparison. Thirty-six culprit and 44 nonculprit plaques from 36 patients were analyzed. Culprit plaques had higher gadolinium uptake than nonculprit plaques (P < .001). Gadolinium uptake was the most accurate metric for identifying culprit plaques (OR, 3.9; 95% CI 2.1-8.3). Gadolinium uptake was more sensitive (86% versus 70%) and specific (71% versus 68%) in identifying culprit plaques than conventional 2D measurements. A multivariate model, including gadolinium uptake and plaque burden, identified culprit plaques with an 83% sensitivity and 86% specificity. The new 3D color map method of plaque-enhancement analysis is more accurate for identifying culprit plaques than conventional 2D methods. This new method generates a new set of metrics that could potentially be used to assess disease progression.

Current MRI Methods for the Assessment of Pathologic Prostate: A Brief Review for Clinicians.

The application of Magnetic Resonance Imaging (MRI) have helped in different modalities for prostate cancer management, from early detection to treatment planning and follow-up, the evolution of MRI techniques allows to obtain not only anatomical but also functional information to take advantage of prostate cancer detection and staging while supplying prognostic and predictive biomarkers. This review presents conventional and advanced MRI techniques (known as multiparametric MRI) that allow functional and quantitative assessment of the normal prostate gland and its correlation with prostate cancer. Additional topics include the epidemiology of prostate cancer following the Global Burden Diseases Cancer Collaboration 2018, the clinical anatomy of the prostate gland, and the lower urinary tract; we also mention some aspects of the diagnosis performance in ultrasound. We end the review with a brief explanation about the anatomical foundation of external-beam planning radiotherapy.

Multi-Coil MRI Reconstruction Challenge-Assessing Brain MRI Reconstruction Models and Their Generalizability to Varying Coil Configurations.

Deep-learning-based brain magnetic resonance imaging (MRI) reconstruction methods have the potential to accelerate the MRI acquisition process. Nevertheless, the scientific community lacks appropriate benchmarks to assess the MRI reconstruction quality of high-resolution brain images, and evaluate how these proposed algorithms will behave in the presence of small, but expected data distribution shifts. The multi-coil MRI (MC-MRI) reconstruction challenge provides a benchmark that aims at addressing these issues, using a large dataset of high-resolution, three-dimensional, T1-weighted MRI scans. The challenge has two primary goals: (1) to compare different MRI reconstruction models on this dataset and (2) to assess the generalizability of these models to data acquired with a different number of receiver coils. In this paper, we describe the challenge experimental design and summarize the results of a set of baseline and state-of-the-art brain MRI reconstruction models. We provide relevant comparative information on the current MRI reconstruction state-of-the-art and highlight the challenges of obtaining generalizable models that are required prior to broader clinical adoption. The MC-MRI benchmark data, evaluation code, and current challenge leaderboard are publicly available. They provide an objective performance assessment for future developments in the field of brain MRI reconstruction.

The relationship of cerebrospinal fluid neurofilament levels with magnetic resonance imaging lesion location and disease activity in multiple sclerosis.

Background and purposeNeurofilament light chain (NfL) is an accepted biomarker of disease activity in multiple sclerosis (MS), but its relationship with magnetic resonance imaging (MRI) activity particularly in reference to lesion location and recurrent activity is not well understood.MethodsIn 139 MS patients who underwent lumbar punctures with follow‐up in 25, the relationship between cerebrospinal fluid (CSF) NfL and cranial MRI based on lesion location and lesion number was evaluated. Spearman rank correlation was used to assess the association between CSF NfL and MRI lesion location and lesion counts at baseline and follow‐up at 1 year. Multiple linear regression analysis was performed to assess which lesion location was most strongly associated with CSF NfL values.ResultsThe associations between baseline CSF NfL and lesion location and follow‐up lesions were modest, whilst those between baseline MRI and follow‐up CSF NfL were greater: periventricular (r = 0.31, p = 0.141), juxtacortical (r = 0.47, p = 0.022), infratentorial (r = 0.71, p ≤ 0.001) and cord lesions (r = 0.60, p = 0.002). All associations, however, improved following adjustment for disease duration and type of MS. Modelling revealed 53% of (log) CSF NfL could be explained by variance in baseline MRI lesion location.ConclusionsBaseline CSF NfL did not correlate with current or future MRI activity and lesion location. However, baseline MRI activity explained around 53% of the variation in the follow‐up CSF NfL, suggesting that the relationship between MRI and CSF NfL is mainly precedent rather than an association, that is one occurring before the other.

Synthetic MR: Physical principles, clinical implementation, and new developments

Current clinical MR imaging practices rely on the qualitative assessment of images for diagnosis and treatment planning. While contrast in MR images is dependent on the spin parameters of the imaged tissue, pixel values on MR images are relative and are not scaled to represent any tissue properties. Synthetic MR is a fully featured imaging workflow consisting of efficient multi-parameter mapping acquisition, synthetic image generation, and volume quantitation of brain tissues. As the application becomes more widely available on multiple vendors and scanner platforms, it has also gained widespread adoption as clinicians begin to recognize the benefits of rapid quantitation. This review will provide details about the sequence with a focus on the physical principles behind its relaxometry mechanisms. It will present an overview of the products in their current form and some potential issues when implementing it in the clinic. It will conclude by highlighting some recent advances of the technique, including a 3D mapping method and its associated applications.