Detection of diffusely abnormal white matter in multiple sclerosis on multiparametric brain MRI using semi-supervised deep learning.

In addition to focal lesions, diffusely abnormal white matter (DAWM) is seen on brain MRI of multiple sclerosis (MS) patients and may represent early or distinct disease processes. The role of MRI-observed DAWM is understudied due to a lack of automated assessment methods. Supervised deep learning (DL) methods are highly capable in this domain, but require large sets of labeled data.

Repeat it without me: Crowdsourcing the T mapping common ground via the ISMRM reproducibility challenge.

Purpose: T mapping is a widely used quantitative MRI technique, but its tissue-specific values remain inconsistent across protocols, sites, and vendors. The ISMRM Reproducible Research and Quantitative MR study groups jointly launched a challenge to assess the reproducibility of a well-established inversion-recovery T mapping technique, using acquisition details from a seminal T mapping paper on a standardized phantom and in human brains.

Methods: The challenge used the acquisition protocol from Barral et al.

Artificial Intelligence Assistive Software Tool for Automated Detection and Quantification of Amyloid-Related Imaging Abnormalities.

Importance: Amyloid-related imaging abnormalities (ARIA) are brain magnetic resonance imaging (MRI) findings associated with the use of amyloid-β-directed monoclonal antibody therapies in Alzheimer disease (AD). ARIA monitoring is important to inform treatment dosing decisions and might be improved through assistive software.

Objective: To assess the clinical performance of an artificial intelligence (AI)-based software tool for assisting radiological interpretation of brain MRI scans in patients monitored for ARIA.

Identifying the white matter pathways involved in multiple sclerosis-related tremor using diffusion tensor imaging.

Background: Tremor affects up to 45% of patients with Multiple Sclerosis (PwMS). Current understanding is based on insights from other neurological disorders, thus, not fully addressing the distinctive aspects of MS pathology.

Objective: To characterize the brain white matter (WM) correlates of MS-related tremor using diffusion tensor imaging (DTI).

Functional assessment of the dural lymphatic vessels using dynamic contrast MRI in multiple sclerosis.

Background And Purpose: The discovery of glymphatic function in the human brain has generated interest in waste clearance mechanisms in neurological disorders such as multiple sclerosis (MS). However, noninvasive in vivo functional assessment is currently lacking. This work studies the feasibility of a novel intravenous dynamic contrast MRI method to assess the dural lymphatics, a purported pathway contributing to glymphatic clearance.

Characterizing the time course of cerebrovascular reactivity in multiple sclerosis.

Background And Purpose: Changes in cerebral perfusion occur early in relapsing and progressive multiple sclerosis (MS) patients, though whether cerebral blood flow (CBF) can be altered by therapy is unknown. We sought to characterize the time course of change in CBF (cerebral vascular reactivity [CVR]), following intravenous (IV) acetazolamide (ACZ) in whole brain and within various gray and white matter brain regions in MS patients.

Methods: We enrolled five relapsing MS patients on injectable therapies.

Cerebrovascular Effects of Lower Body Negative Pressure at 3T MRI: Implications for Long-Duration Space Travel.

Background: Optic disc edema develops in most astronauts during long-duration spaceflight. It is hypothesized to result from weightlessness-induced venous congestion of the head and neck and is an unresolved health risk of space travel.

Purpose: Determine if short-term application of lower body negative pressure (LBNP) could reduce internal jugular vein (IJV) expansion associated with the supine posture without negatively impacting cerebral perfusion or causing IJV flow stasis.

Quantitative Assessment of In Vivo Human Anterior Cruciate Ligament Autograft Remodeling: A 3-Dimensional UTE-T2* Imaging Study.

Background: The timing of return to play after anterior cruciate ligament (ACL) reconstruction is still controversial due to uncertainty of true ACL graft state at the time of RTP. Recent work utilizing ultra-short echo T2* (UTE-T2*) magnetic resonance imaging (MRI) as a scanner-independent method to objectively and non-invasively assess the status of in vivo ACL graft remodeling has produced promising results.

Purpose/hypothesis: The purpose of this study was to prospectively and noninvasively investigate longitudinal changes in T2* within ACL autografts at incremental time points up to 12 months after primary ACL reconstruction in human patients.

Deep learning segmentation of gadolinium-enhancing lesions in multiple sclerosis.

Objective: The aim of this study is to assess the performance of deep learning convolutional neural networks (CNNs) in segmenting gadolinium-enhancing lesions using a large cohort of multiple sclerosis (MS) patients.

Methods: A three-dimensional (3D) CNN model was trained for segmentation of gadolinium-enhancing lesions using multispectral magnetic resonance imaging data (MRI) from 1006 relapsing-remitting MS patients. The network performance was evaluated for three combinations of multispectral MRI used as input: (U5) fluid-attenuated inversion recovery (FLAIR), T2-weighted, proton density-weighted, and pre- and post-contrast T1-weighted images; (U2) pre- and post-contrast T1-weighted images; and (U1) only post-contrast T1-weighted images.

Deep Learning for Predicting Enhancing Lesions in Multiple Sclerosis from Noncontrast MRI.

Background Enhancing lesions on MRI scans obtained after contrast material administration are commonly thought to represent disease activity in multiple sclerosis (MS); it is desirable to develop methods that can predict enhancing lesions without the use of contrast material. Purpose To evaluate whether deep learning can predict enhancing lesions on MRI scans obtained without the use of contrast material. Materials and Methods This study involved prospective analysis of existing MRI data.

Are multi-contrast magnetic resonance images necessary for segmenting multiple sclerosis brains? A large cohort study based on deep learning.

Background: Magnetic resonance images with multiple contrasts or sequences are commonly used for segmenting brain tissues, including lesions, in multiple sclerosis (MS). However, acquisition of images with multiple contrasts increases the scan time and complexity of the analysis, possibly introducing factors that could compromise segmentation quality.

Objective: To investigate the effect of various combinations of multi-contrast images as input on the segmented volumes of gray (GM) and white matter (WM), cerebrospinal fluid (CSF), and lesions using a deep neural network.

Deep-Learning-Based Neural Tissue Segmentation of MRI in Multiple Sclerosis: Effect of Training Set Size.

Background: The dependence of deep-learning (DL)-based segmentation accuracy of brain MRI on the training size is not known.

Purpose: To determine the required training size for a desired accuracy in brain MRI segmentation in multiple sclerosis (MS) using DL.

Study Type: Retrospective analysis of MRI data acquired as part of a multicenter clinical trial.

Serial Metabolic Evaluation of Perihematomal Tissues in the Intracerebral Hemorrhage Pig Model.

Purpose: Perihematomal edema (PHE) occurs in patients with intracerebral hemorrhage (ICH) and is often used as surrogate of secondary brain injury. PHE resolves over time, but little is known about the functional integrity of the tissues that recover from edema. In a pig ICH model, we aimed to assess metabolic integrity of perihematoma tissues by using non-invasive magnetic resonance spectroscopy (MRS).

Brain and lesion segmentation in multiple sclerosis using fully convolutional neural networks: A large-scale study.

Objective: To investigate the performance of deep learning (DL) based on fully convolutional neural network (FCNN) in segmenting brain tissues in a large cohort of multiple sclerosis (MS) patients.

Methods: We developed a FCNN model to segment brain tissues, including T2-hyperintense MS lesions. The training, validation, and testing of FCNN were based on ~1000 magnetic resonance imaging (MRI) datasets acquired on relapsing-remitting MS patients, as a part of a phase 3 randomized clinical trial.

MRI acoustic noise-modulated computer animations for patient distraction and entertainment with application in pediatric psychiatric patients.

Purpose: To reduce patient anxiety caused by the MRI scanner acoustic noise.

Material And Methods: We developed a simple and low-cost system for patient distraction using visual computer animations that were synchronized to the MRI scanner's acoustic noise during the MRI exam. The system was implemented on a 3T MRI system and tested in 28 pediatric patients with bipolar disorder.

Ongoing Secondary Degeneration of the Limbic System in Patients With Ischemic Stroke: A Longitudinal MRI Study.

Ongoing post-stroke structural degeneration and neuronal loss preceding neuropsychological symptoms such as cognitive decline and depression are poorly understood. Various substructures of the limbic system have been linked to cognitive impairment. In this longitudinal study, we investigated the post-stroke macro- and micro-structural integrity of the limbic system using structural and diffusion tensor magnetic resonance imaging.

Serial Cerebral Metabolic Changes in Patients With Ischemic Stroke Treated With Autologous Bone Marrow Derived Mononuclear Cells.

Cell-based therapy offers new opportunities for the development of novel treatments to promote tissue repair, functional restoration, and cerebral metabolic balance. N-acetylasperate (NAA), Choline (Cho), and Creatine (Cr) are three major metabolites seen on proton magnetic resonance spectroscopy (MRS) that play a vital role in balancing the biochemical processes and are suggested as markers of recovery. In this preliminary study, we serially monitored changes in these metabolites in ischemic stroke patients who were treated with autologous bone marrow-derived mononuclear cells (MNCs) using non-invasive MRS.

Automated image quality evaluation of structural brain MRI using an ensemble of deep learning networks.

Background: Deep learning (DL) is a promising methodology for automatic detection of abnormalities in brain MRI.

Purpose: To automatically evaluate the quality of multicenter structural brain MRI images using an ensemble DL model based on deep convolutional neural networks (DCNNs).

Study Type: Retrospective.

Cardiac work is related to creatine kinase energy supply in human heart failure: a cardiovascular magnetic resonance spectroscopy study.

Background: It has been hypothesized that the supply of chemical energy may be insufficient to fuel normal mechanical pump function in heart failure (HF). The creatine kinase (CK) reaction serves as the heart's primary energy reserve, and the supply of adenosine triphosphate (ATP flux) it provides is reduced in human HF. However, the relationship between the CK energy supply and the mechanical energy expended has never been quantified in the human heart.

Prospective study of the feasibility and dosimetric advantages of MRI-guided dose adaptation for human papillomavirus positive oropharyngeal cancer patients compared with standard IMRT.

Purpose: We aim to determine the feasibility and dosimetric benefits of a novel MRI-guided IMRT dose-adaption strategy for human papillomavirus positive (HPV+) oropharyngeal squamous cell carcinoma (OPC).

Materials/methods: Patients with locally advanced HPV+ OPC underwent pre-treatment and in-treatment MRIs every two weeks using RT immobilization setup. For each patient, two IMRT plans were created (i.

Platform for Automated Real-Time High Performance Analytics on Medical Image Data.

Biomedical data are quickly growing in volume and in variety, providing clinicians an opportunity for better clinical decision support. Here, we demonstrate a robust platform that uses software automation and high performance computing (HPC) resources to achieve real-time analytics of clinical data, specifically magnetic resonance imaging (MRI) data. We used the Agave application programming interface to facilitate communication, data transfer, and job control between an MRI scanner and an off-site HPC resource.

Interleaved susceptibility-weighted and FLAIR MRI for imaging lesion-penetrating veins in multiple sclerosis.

Purpose: To simultaneously image brain lesions and veins in multiple sclerosis.

Methods: An interleaved sequence was developed to simultaneously acquire 3D T2*-weighted (or susceptibility-weighted, SW) and fluid-attenuated inversion recovery (FLAIR) images on a 3.0T MRI system.