Observational studyAJNR. American journal of neuroradiology2025
Enhancing Lesion Detection in Inflammatory Myelopathies: A Deep Learning-Reconstructed Double Inversion Recovery MRI Approach.
Observational study in AJNR. American journal of neuroradiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- The effect of head coil configuration and channel count on the quality of double inversion recovery (DIR) MRI images.BMC medical imaging · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
background and purposeThe imaging of inflammatory myelopathies has advanced significantly across time, with MRI techniques playing a pivotal role in enhancing lesion detection. However, the impact of deep learning (DL)-based reconstruction on 3D double inversion recovery (DIR) imaging for inflammatory myelopathies remains unassessed. This study aimed to compare the acquisition time, image quality, diagnostic confidence, and lesion detection rates among sagittal T2WI, standard DIR, and DL-reconstructed DIR in patients with inflammatory myelopathies. MATERIALS AND
methodsIn this observational study, patients diagnosed with inflammatory myelopathies were recruited between June 2023 and March 2024. Each patient underwent sagittal conventional TSE sequences and standard 3D DIR (T2WI and standard 3D DIR were used as references for comparison), followed by an undersampled accelerated double inversion recovery deep learning (DIR
resultsA total of 149 participants were evaluated (mean age, 40.6 [SD, 16.8] years; 71 women). The median acquisition time for DIR
conclusionsDIR
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.