ArticleSkeletal radiology2023
Rapid lumbar MRI protocol using 3D imaging and deep learning reconstruction.
Article in Skeletal radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic utility of 3D MRI sequences in the assessment of central, recess and foraminal stenoses of the spine: a systematic review.Skeletal radiology · 2024Pooled it
- Mitigating radiation in robotic-assisted sacropelvic screw placement using 3D magnetic resonance imaging.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- MRI evaluation of the lumbar spine: a survey-based assessment of protocols and practice patterns used by musculoskeletal radiologists in the United States.Skeletal radiology · 2026Article
- Response to the letter: "deep learning-driven knee MRI acceleration: evidence, enhancements, and the path to universal adoption".La Radiologia medica · 2025Article
- Evaluation of Image Quality and Scan Time Efficiency in Accelerated 3D T1-Weighted Pediatric Brain MRI Using Deep Learning-Based Reconstruction.Korean journal of radiology · 2025Article
- Faster Acquisition and Improved Image Quality of T2-Weighted Dixon Breast MRI at 3T Using Deep Learning: A Prospective Study.Korean journal of radiology · 2025Article
- Axial T1-weighted imaging of the lumbar spine: a redundancy or an asset?Skeletal radiology · 2024Article
- Diagnostic evaluation of deep learning accelerated lumbar spine MRI.The neuroradiology journal · 2024Article
- Optimized 3D brachial plexus MR neurography using deep learning reconstruction.Skeletal radiology · 2024Article
- Application of a deep learning algorithm for three-dimensional T1-weighted gradient-echo imaging of gadoxetic acid-enhanced MRI in patients at a high risk of hepatocellular carcinoma.Abdominal radiology (New York) · 2024Article
- Quantitative double echo steady state T2 mapping of upper extremity peripheral nerves and muscles.Frontiers in neurology · 2024Article
- How AI May Transform Musculoskeletal Imaging.Radiology · 2024Review
- LAVA HyperSense and deep-learning reconstruction for near-isotropic (3D) enhanced magnetic resonance enterography in patients with Crohn's disease: utility in noise reduction and image quality improvement.Diagnostic and interventional radiology (Ankara, Turkey) · 2023Article
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
background and purposeThree-dimensional (3D) imaging of the spine, augmented with AI-enabled image enhancement and denoising, has the potential to reduce imaging times without compromising image quality or diagnostic performance. This work evaluates the time savings afforded by a novel, rapid lumbar spine MRI protocol as well as image quality and diagnostic differences stemming from the use of an AI-enhanced 3D T2 sequence combined with a single Dixon acquisition. MATERIALS AND
methodsThirty-five subjects underwent MRI using standard 2D lumbar imaging in addition to a "rapid protocol" consisting of 3D imaging, enhanced and denoised using a prototype DL reconstruction algorithm as well as a two-point Dixon sequence. Images were graded by subspecialized radiologists and imaging times were collected. Comparison was made between 2D sagittal T1 and Dixon fat images for neural foraminal stenosis, intraosseous lesions, and fracture detection.
resultsThis study demonstrated a 54% reduction in total acquisition time of a 3D AI-enhanced imaging lumbar spine MRI rapid protocol combined with a sagittal 2D Dixon sequence, compared to a 2D standard-of-care protocol. The rapid protocol also demonstrated strong agreement with the standard-of-care protocol with respect to osseous lesions (κ = 0.88), fracture detection (κ = 0.96), and neural foraminal stenosis (ICC > 0.9 at all levels).
conclusion3D imaging of the lumbar spine with AI-enhanced DL reconstruction and Dixon imaging demonstrated a significant reduction in imaging time with similar performance for common diagnostic metrics. Although previously limited by long postprocessing times, this technique has the potential to enhance patient throughput in busy radiology practices while providing similar or improved image quality.
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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.