Multisequence 3-T Image Synthesis from 64-mT Low-Field-Strength MRI Using Generative Adversarial Networks in Multiple Sclerosis.

Background Portable low-field-strength (64-mT) MRI scanners show promise for increasing access to neuroimaging for clinical and research purposes; however, these devices produce lower-quality images than conventional high-field-strength scanners. Purpose To develop and evaluate a deep learning architecture to generate high-field-strength quality brain images from low-field-strength inputs using paired data from patients with multiple sclerosis (MS) who underwent MRI at 64 mT and 3 T. Materials and Methods Adults with MS at two institutions were scanned using portable 64-mT and standard 3-T scanners, with T1-weighted, T2-weighted, and fluid-attenuated inversion recovery (FLAIR) acquisitions as part of an observational study (October 2020 to January 2022); a second validation group (January 2023 to January 2024) was also included.

Portable ultra-low-field MRI for progressive multifocal leukoencephalopathy: Case studies, sensitivity, and potential applications.

Background And Objective: Progressive multifocal leukoencephalopathy (PML) is a severe, disabling infection caused by JC virus reactivation. PML-related disability complicates the MRI monitoring needed to assess treatment interventions in clinical trial or compassionate use settings. Portable ultra-low-field MRI (pULF-MRI) offers a convenient approach when such frequent imaging is needed.

High-Field-Blinded Assessment of Portable Ultra-Low-Field Brain MRI for Multiple Sclerosis.

Background And Purpose: MRI is crucial for multiple sclerosis (MS), but the relative value of portable ultra-low field MRI (pULF-MRI), a technology that holds promise for extending access to MRI, is unknown. We assessed white matter lesion (WML) detection on pULF-MRI compared to high-field MRI (HF-MRI), focusing on blinded assessments, assessor self-training, and multiplanar acquisitions.

Methods: Fifty-five adults with MS underwent pULF-MRI following their HF-MRI.

Multi-contrast high-field quality image synthesis for portable low-field MRI using generative adversarial networks and paired data.

Introduction: Portable low-field strength (64mT) MRI scanners promise to increase access to neuroimaging for clinical and research purposes, however these devices produce lower quality images compared to high-field scanners. In this study, we developed and evaluated a deep learning architecture to generate high-field quality brain images from low-field inputs using a paired dataset of multiple sclerosis (MS) patients scanned at 64mT and 3T.

Methods: A total of 49 MS patients were scanned on portable 64mT and standard 3T scanners at Penn (n=25) or the National Institutes of Health (NIH, n=24) with T1-weighted, T2-weighted and FLAIR acquisitions.

Sensitivity of portable low-field magnetic resonance imaging for multiple sclerosis lesions.

Magnetic resonance imaging (MRI) is a fundamental tool in the diagnosis and management of neurological diseases such as multiple sclerosis (MS). New portable, low-field strength, MRI scanners could potentially lower financial and technical barriers to neuroimaging and reach underserved or disabled populations, but the sensitivity of these devices for MS lesions is unknown. We sought to determine if white matter lesions can be detected on a portable 64mT scanner, compare automated lesion segmentations and total lesion volume between paired 3T and 64mT scans, identify features that contribute to lesion detection accuracy, and explore super-resolution imaging at low-field.