Quantitative image quality metrics enable resource-efficient quality control of clinically applied AI-based reconstructions in MRI

Objective AI-based MRI reconstruction techniques improve efficiency by reducing acquisition times whilst maintaining or improving image quality. Recent recommendations from professional bodies suggest centres should perform quality assessments on AI tools. However, monitoring long-term performance p...

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Veröffentlicht in:Magma (New York, N.Y.) Jg. 38; H. 3; S. 547 - 560
Hauptverfasser: White, Owen A., Shur, Joshua, Castagnoli, Francesca, Charles-Edwards, Geoff, Whitcher, Brandon, Collins, David J., Cashmore, Matthew T. D., Hall, Matt G., Thomas, Spencer A., Thompson, Andrew, Harrison, Ciara A., Hopkinson, Georgina, Koh, Dow-Mu, Winfield, Jessica M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cham Springer International Publishing 01.07.2025
Springer Nature B.V
Schlagworte:
Adult
Aged
Algorithms
Artificial Intelligence
Basic Science - Reconstruction algorithms and artificial intelligence
Biomedical Engineering and Bioengineering
Clinical medicine
Colorectal cancer
Computer Appl. in Life Sciences
Control charts
Datasets
Female
Health Informatics
Humans
Image acquisition
Image Interpretation, Computer-Assisted - methods
Image Processing, Computer-Assisted - methods
Image quality
Image reconstruction
Imaging
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Male
Medical imaging
Medicine
Medicine & Public Health
Middle Aged
Patients
Performance evaluation
Quality assessment
Quality Control
Radiology
Rectum
Rectum - diagnostic imaging
Reproducibility of Results
Research Article
Retrospective Studies
Signal to noise ratio
Solid State Physics
Standard of care
Germany
Quality assurance, Healthcare
Magnetic resonance imaging
Artificial intelligence
Image reconstruction
Quality control
ISSN:1352-8661, 0968-5243, 1352-8661
Online-Zugang:Volltext
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Zusammenfassung:Objective AI-based MRI reconstruction techniques improve efficiency by reducing acquisition times whilst maintaining or improving image quality. Recent recommendations from professional bodies suggest centres should perform quality assessments on AI tools. However, monitoring long-term performance presents challenges, due to model drift or system updates. Radiologist-based assessments are resource-intensive and may be subjective, highlighting the need for efficient quality control (QC) measures. This study explores using image quality metrics (IQMs) to assess AI-based reconstructions. Materials and methods 58 patients undergoing standard-of-care rectal MRI were imaged using AI-based and conventional T2-weighted sequences. Paired and unpaired IQMs were calculated. Sensitivity of IQMs to detect retrospective perturbations in AI-based reconstructions was assessed using control charts, and statistical comparisons between the four MR systems in the evaluation were performed. Two radiologists evaluated the image quality of the perturbed images, giving an indication of their clinical relevance. Results Paired IQMs demonstrated sensitivity to changes in AI-reconstruction settings, identifying deviations outside ± 2 standard deviations of the reference dataset. Unpaired metrics showed less sensitivity. Paired IQMs showed no difference in performance between 1.5 T and 3 T systems ( p  > 0.99), whilst minor but significant ( p  < 0.0379) differences were noted for unpaired IQMs. Discussion IQMs are effective for QC of AI-based MR reconstructions, offering resource-efficient alternatives to repeated radiologist evaluations. Future work should expand this to other imaging applications and assess additional measures.
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ISSN:1352-8661
0968-5243
1352-8661
DOI:10.1007/s10334-025-01253-3