ArticleMultiple sclerosis (Houndmills, Basingstoke, England)2022
Artificial double inversion recovery images for (juxta)cortical lesion visualization in multiple sclerosis.
Article in Multiple sclerosis (Houndmills, Basingstoke, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 26 citations in OpenAlex.
- Comparison of iron and myelin changes in U‑fiber regions of relapsing-remitting multiple sclerosis and neuromyelitis optica spectrum disorders.Journal of neurology · 2026Article
- Quantifying cortical lesions in multiple sclerosis MRI datasets using multi-contrast post-processing and deep learning.Communications medicine · 2026Article
- The effect of head coil configuration and channel count on the quality of double inversion recovery (DIR) MRI images.BMC medical imaging · 2026Article
- Evaluation of cortical pathology in primary-progressive multiple sclerosis: a post hoc analysis of the SPRINT-MS trial.BMJ neurology open · 2026Article
- Superiority of 3D-DIR over 3D-FLAIR in the Detection of Cortical Lesions and Correlation with Disability in Multiple Sclerosis: A Multicenter Study.Diagnostics (Basel, Switzerland) · 2025Article
- Longitudinal changes in cortical cell body and neurite density in people with multiple sclerosis.Neuroscience · 2025Article
- Enhancing Lesion Detection in Inflammatory Myelopathies: A Deep Learning-Reconstructed Double Inversion Recovery MRI Approach.AJNR. American journal of neuroradiology · 2025Observational
- Machine learning for refining interpretation of magnetic resonance imaging scans in the management of multiple sclerosis: a narrative review.Royal Society open science · 2025Review
- Artificial Intelligence and Multiple Sclerosis.Current neurology and neuroscience reports · 2024Review
- Multicenter Evaluation of AI-generated DIR and PSIR for Cortical and Juxtacortical Multiple Sclerosis Lesion Detection.Radiology · 2023Article
- Periventricular and juxtacortical characterization of UManitoba-JHU functionally defined human white matter atlas networks.Frontiers in human neuroscience · 2023Article
- Deep Learning for Automated Elective Lymph Node Level Segmentation for Head and Neck Cancer Radiotherapy.Cancers · 2022Article
- Artificial double inversion recovery images can substitute conventionally acquired images: an MRI-histology study.Scientific reports · 2022Article
- Uncertainty-Aware and Lesion-Specific Image Synthesis in Multiple Sclerosis Magnetic Resonance Imaging: A Multicentric Validation Study.Frontiers in neuroscience · 2022Article
- Sex bias in multiple sclerosis and neuromyelitis optica spectrum disorders: How it influences clinical course, MRI parameters and prognosis.Frontiers in immunology · 2022Review
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCortical lesions are highly inconspicuous on magnetic resonance imaging (MRI). Double inversion recovery (DIR) has a higher sensitivity than conventional clinical sequences (i.e. T1, T2, FLAIR) but is difficult to acquire, leading to overseen cortical lesions in clinical care and clinical trials.
objectiveTo evaluate the usability of artificially generated DIR (aDIR) images for cortical lesion detection compared to conventionally acquired DIR (cDIR).
methodsThe dataset consisted of 3D-T1 and 2D-proton density (PD) T2 images of 73 patients (49RR, 20SP, 4PP) at 1.5 T. Using a 4:1 train:test-ratio, a fully convolutional neural network was trained to predict 3D-aDIR from 3D-T1 and 2D-PD/T2 images. Randomized blind scoring of the test set was used to determine detection reliability, precision and recall.
resultsA total of 626 vs 696 cortical lesions were detected on 15 aDIR vs cDIR images (intraclass correlation coefficient (ICC) = 0.92). Compared to cDIR, precision and recall were 0.84 ± 0.06 and 0.76 ± 0.09, respectively. The frontal and temporal lobes showed the largest differences in discernibility.
conclusionCortical lesions can be detected with good reliability on artificial DIR. The technique has potential to broaden the availability of DIR in clinical care and provides the opportunity of ex post facto implementation of cortical lesions imaging in existing clinical trial data.
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