ArticleAJNR. American journal of neuroradiology2019
A Deep Learning-Based Approach to Reduce Rescan and Recall Rates in Clinical MRI Examinations.
Article in AJNR. American journal of neuroradiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
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- Multi-contrast generation and quantitative MRI using a transformer-based framework with RF excitation embeddings.Communications biology · 2025Article
- Revolutionizing emergency care: an overview of the transformative role of artificial intelligence in diagnosis, triage, and patient management.International journal of emergency medicine · 2025Review
- Res-MoCoDiff: residual-guided diffusion models for motion artifact correction in brain MRI.Physics in medicine and biology · 2025Article
- Emergency radiology: roadmap for radiology departments.Japanese journal of radiology · 2025Review
- Systematic Review and Meta-analysis of AI-driven MRI Motion Artifact Detection and Correction.ArXiv · 2025Article
- Res-MoCoDiff: Residual-guided diffusion models for motion artifact correction in brain MRI.ArXiv · 2025Article
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- Usefulness of T2-Weighted Images with Deep-Learning-Based Reconstruction in Nasal Cartilage.Diagnostics (Basel, Switzerland) · 2023Article
- Can we predict motion artifacts in clinical MRI before the scan completes?Proceedings of the International Society for Magnetic Resonance in Medicine ... Scientific Meeting and Exhibition. International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition · 2023Article
- Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders-A Scoping Review.Sensors (Basel, Switzerland) · 2023Article
- Motion artifact reduction for magnetic resonance imaging with deep learning and k-space analysis.PloS one · 2023Article
- Neuroimaging in the Era of Artificial Intelligence: Current Applications.Federal practitioner : for the health care professionals of the VA, DoD, and PHS · 2022Review
- MR Imaging in the 21st Century: Technical Innovation over the First Two Decades.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2022Review
- Overview of Noninterpretive Artificial Intelligence Models for Safety, Quality, Workflow, and Education Applications in Radiology Practice.Radiology. Artificial intelligence · 2022Review
- Lung Nodule Malignancy Prediction in Sequential CT Scans: Summary of ISBI 2018 Challenge.IEEE transactions on medical imaging · 2021Article
- Opportunities for Understanding MS Mechanisms and Progression With MRI Using Large-Scale Data Sharing and Artificial Intelligence.Neurology · 2021Review
- Emerging deep learning techniques using magnetic resonance imaging data applied in multiple sclerosis and clinical isolated syndrome patients (Review).Experimental and therapeutic medicine · 2021Review
- Diverse Applications of Artificial Intelligence in Neuroradiology.Neuroimaging clinics of North America · 2020Review
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Authors and funding
13 authors.
Funding
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Abstract
background and purposeMR imaging rescans and recalls can create large hospital revenue loss. The purpose of this study was to develop a fast, automated method for assessing rescan need in motion-corrupted brain series. MATERIALS AND
methodsA deep learning-based approach was developed, outputting a probability for a series to be clinically useful. Comparison of this per-series probability with a threshold, which can depend on scan indication and reading radiologist, determines whether a series needs to be rescanned. The deep learning classification performance was compared with that of 4 technologists and 5 radiologists in 49 test series with low and moderate motion artifacts. These series were assumed to be scanned for 2 scan indications: screening for multiple sclerosis and stroke.
resultsThe image-quality rating was found to be scan indication- and reading radiologist-dependent. Of the 49 test datasets, technologists created a mean ratio of rescans/recalls of (4.7 ± 5.1)/(9.5 ± 6.8) for MS and (8.6 ± 7.7)/(1.6 ± 1.9) for stroke. With thresholds adapted for scan indication and reading radiologist, deep learning created a rescan/recall ratio of (7.3 ± 2.2)/(3.2 ± 2.5) for MS, and (3.6 ± 1.5)/(2.8 ± 1.6) for stroke. Due to the large variability in the technologists' assessments, it was only the decrease in the recall rate for MS, for which the deep learning algorithm was trained, that was statistically significant (
conclusionsFast, automated deep learning-based image-quality rating can decrease rescan and recall rates, while rendering them technologist-independent. It was estimated that decreasing rescans and recalls from the technologists' values to the values of deep learning could save hospitals $24,000/scanner/year.
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