ArticleMedical image analysis2022
Artifact- and content-specific quality assessment for MRI with image rulers.
Article in Medical image analysis, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- A Study of Why We Need to Reassess Full Reference Image Quality Assessment with Medical Images.Journal of imaging informatics in medicine · 2025Review
- Effect of respiration-induced motion on a three-dimensional magnetic resonance imaging-based adaptive radiotherapy workflow in a 1.5T magnetic resonance linear accelerator.Quantitative imaging in medicine and surgery · 2025Article
- CMMIQA: a prompt-driven cross-modality multi-organ medical image quality assessment model.Quantitative imaging in medicine and surgery · 2025Article
- Self-supervised identification and elimination of harmful datasets in distributed machine learning for medical image analysis.NPJ digital medicine · 2025Article
- Inline automatic quality control of 2D phase-contrast flow MRI for subject-specific scan time adaptation.Magnetic resonance in medicine · 2024Article
- Task-based assessment for neural networks: evaluating undersampled MRI reconstructions based on human observer signal detection.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Deep Learning Approaches for Automatic Quality Assurance of Magnetic Resonance Images Using ACR Phantom.BMC medical imaging · 2023Article
- Quantifying MR head motion in the Rhineland Study - A robust method for population cohorts.NeuroImage · 2023Article
- Automated MRI Field of View Prescription from Region of Interest Prediction by Intra-Stack Attention Neural Network.Bioengineering (Basel, Switzerland) · 2023Article
- End to End Multitask Joint Learning Model for Osteoporosis Classification in CT Images.Computational intelligence and neuroscience · 2023Article
- Classification of Diabetic Retinopathy Severity in Fundus Images Using the Vision Transformer and Residual Attention.Computational intelligence and neuroscience · 2023Article
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Authors and funding
5 authors.
Funding
Abstract
In clinical practice MR images are often first seen by radiologists long after the scan. If image quality is inadequate either patients have to return for an additional scan, or a suboptimal interpretation is rendered. An automatic image quality assessment (IQA) would enable real-time remediation. Existing IQA works for MRI give only a general quality score, agnostic to the cause of and solution to low-quality scans. Furthermore, radiologists' image quality requirements vary with the scan type and diagnostic task. Therefore, the same score may have different implications for different scans. We propose a framework with multi-task CNN model trained with calibrated labels and inferenced with image rulers. Labels calibrated by human inputs follow a well-defined and efficient labeling task. Image rulers address varying quality standards and provide a concrete way of interpreting raw scores from the CNN. The model supports assessments of two of the most common artifacts in MRI: noise and motion. It achieves accuracies of around 90%, 6% better than the best previous method examined, and 3% better than human experts on noise assessment. Our experiments show that label calibration, image rulers, and multi-task training improve the model's performance and generalizability.
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