ArticleBMC medical imaging2024
Comprehensive assessment of imaging quality of artificial intelligence-assisted compressed sensing-based MR images in routine clinical settings.
Article in BMC medical imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- AI-Assisted Compressed Sensing for Optimized Inner Ear Imaging: a Multi-Rater MRI Evaluation of CSAI T2-DRIVE.Clinical neuroradiology · 2026Article
- Improving small brain metastasis detection with composite and augmented TQuantitative imaging in medicine and surgery · 2026Article
- Bridging modalities with AI: a review of AI advances in multimodal biomedical imaging.Communications engineering · 2026Review
- AI-assisted compressed sensing and gadoxetic acid-enhanced MRI for evaluating colorectal liver metastases in complex hepatic backgrounds: a prospective 5T MRI study.Quantitative imaging in medicine and surgery · 2026Article
- Accelerating brain T2-weighted imaging using artificial intelligence-assisted compressed sensing combined with deep learning-based reconstruction: a feasibility study at 5.0T MRI.BMC medical imaging · 2025Article
- AI-Driven Advances in Low-Dose Imaging and Enhancement-A Review.Diagnostics (Basel, Switzerland) · 2025Review
- AI-Assisted Compressed Sensing Enables Faster Brain MRI for the Elderly: Image Quality and Diagnostic Equivalence with Conventional Imaging.International journal of general medicine · 2025Article
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Authors and funding
7 authors.
Funding
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Abstract
backgroundConventional MR acceleration techniques, such as compressed sensing, parallel imaging, and half Fourier often face limitations, including noise amplification, reduced signal-to-noise ratio (SNR) and increased susceptibility to artifacts, which can compromise image quality, especially in high-speed acquisitions. Artificial intelligence (AI)-assisted compressed sensing (ACS) has emerged as a novel approach that combines the conventional techniques with advanced AI algorithms. The objective of this study was to examine the imaging quality of the ACS approach by qualitative and quantitative analysis for brain, spine, kidney, liver, and knee MR imaging, as well as compare the performance of this method with conventional (non-ACS) MR imaging.
methodsThis study included 50 subjects. Three radiologists independently assessed the quality of MR images based on artefacts, image sharpness, overall image quality and diagnostic efficacy. SNR, contrast-to-noise ratio (CNR), edge content (EC), enhancement measure (EME), scanning time were used for quantitative evaluation. The Cohen's kappa correlation coefficient (k) was employed to measure radiologists' inter-observer agreement, and the Mann Whitney U-test used for comparison between non-ACS and ACS.
resultsThe qualitative analysis of three radiologists demonstrated that ACS images showed superior clinical information than non-ACS images with a mean k of ~ 0.70. The images acquired with ACS approach showed statistically higher values (p < 0.05) for SNR, CNR, EC, and EME compared to the non-ACS images. Furthermore, the study's findings indicated that ACS-enabled images reduced scan time by more than 50% while maintaining high imaging quality.
conclusionIntegrating ACS technology into routine clinical settings has the potential to speed up image acquisition, improve image quality, and enhance diagnostic procedures and patient throughput.
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