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Enhancing Lesion Detection in Inflammatory Myelopathies: A Deep Learning-Reconstructed Double Inversion Recovery MRI
Qiang Fang1, Qing Yang1, Bao Wang1
1From the Department of Radiology (Q.F., Q.Y., B. Wang, J.H.), Qilu Hospital of Shandong University, Jinan, Shandong Province, China.
Background And Purpose:
The 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 Methods:
In 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 (DIRDL) examination. Three neuroradiologists evaluated the images using a 4-point Likert scale (from 1 to 4) for overall image quality, perceived SNR, sharpness, artifacts, and diagnostic confidence. The acquisition times and lesion detection rates were also compared among the acquisition protocols.
Results:
A total of 149 participants were evaluated (mean age, 40.6 [SD, 16.8] years; 71 women). The median acquisition time for DIRDL was significantly lower than for standard DIR (298 seconds [interquartile range, 288-301 seconds] versus 151 seconds [interquartile range, 148-155 seconds]; P < .001), showing a 49% time reduction. DIRDL images scored higher in overall quality, perceived SNR, and artifact noise reduction (all P < .001). There were no significant differences in sharpness (P = .07) or diagnostic confidence (P = .06) between the standard DIR and DIRDL protocols. Additionally, DIRDL detected 37% more lesions compared with T2WI (300 versus 219; P < .001).
Conclusions:
DIRDL significantly reduces acquisition time and improves image quality compared with standard DIR, without compromising diagnostic confidence. Additionally, DIRDL enhances lesion detection in patients with inflammatory myelopathies, making it a valuable tool in clinical practice. These findings underscore the potential for incorporating DIRDL into future imaging guidelines.
Insights
Deep learning reconstruction for 3D double inversion recovery (DIR) imaging significantly reduces scan time and improves image quality in inflammatory myelopathies. This advanced DIR technique enhances lesion detection without compromising diagnostic confidence, offering a valuable clinical tool.
Area of Science:
- Radiology
- Medical Imaging
- Artificial Intelligence in Medicine
Background:
- Magnetic resonance imaging (MRI) is crucial for detecting lesions in inflammatory myelopathies.
- Deep learning (DL) reconstruction's impact on 3D double inversion recovery (DIR) imaging for these conditions is not well-understood.
Purpose of the Study:
- To compare deep learning-reconstructed DIR (DIRDL) with standard DIR and conventional T2WI for inflammatory myelopathies.
- The study evaluated acquisition time, image quality, diagnostic confidence, and lesion detection.
Main Methods:
- An observational study included 149 patients with inflammatory myelopathies.
- Images acquired included sagittal T2WI, standard 3D DIR, and accelerated DIRDL.
- Neuroradiologists assessed image quality, SNR, artifacts, and diagnostic confidence using a Likert scale.
Main Results:
- DIRDL reduced acquisition time by 49% compared to standard DIR (151s vs 298s).
- DIRDL showed superior overall image quality, perceived SNR, and reduced artifacts (P < .001).
- DIRDL detected 37% more lesions than T2WI (300 vs 219; P < .001), with no significant difference in diagnostic confidence.
Conclusions:
- Deep learning reconstruction for 3D DIR significantly shortens scan times and enhances image quality in inflammatory myelopathies.
- DIRDL improves lesion detection compared to T2WI, proving valuable for clinical practice.
- DIRDL shows potential for integration into future imaging guidelines for inflammatory myelopathies.
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