Evaluation of an AI-powered tool in improving lesion visualization on standard-dose contrast-enhanced brain MRI: a retrospective, multicenter study.
Authors
Affiliations (2)
Affiliations (2)
- From the Radiology Department, the Third Affiliated Hospital of Wenzhou Medical University, Rui'an City, Wenzhou City, Zhejiang Province, 325200, China; Radiology Department, Ningbo No.2 Hospital, Wenzhou Medical University, Ningbo City, Zhejiang Province, 315010, China; and Radiology Department, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, 325000, China.
- From the Radiology Department, the Third Affiliated Hospital of Wenzhou Medical University, Rui'an City, Wenzhou City, Zhejiang Province, 325200, China; Radiology Department, Ningbo No.2 Hospital, Wenzhou Medical University, Ningbo City, Zhejiang Province, 315010, China; and Radiology Department, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, 325000, China. [email protected].
Abstract
Artificial intelligence (AI) algorithms have been used to synthesize standard-dose images from low-dose images in brain MRI, but have less been evaluated to boost standard-dose contrast to approximate higher-dose effect. This study aims to evaluate the performance of a deep learning-based post-processing tool (AiMIFY) in enhancing contrast and improving lesion visualization on standard-dose contrast-enhanced brain MRI. In this retrospective, multicenter, multireader study, 86 adult patients who underwent standard-dose contrast-enhanced brain MRI were included. Pre-contrast and post-contrast three-dimensional T1-weighted images were processed using AiMIFY to generate contrast-boosting images. Three independent neuroradiologists performed blinded assessments. Quantitative metrics, including contrast-to-noise ratio (CNR), lesion-to-brain ratio (LBR), and contrast enhancement percentage (CEP), were measured. Subjective image quality (border delineation, internal morphology, and contrast enhancement) were evaluated using a Likert scale. The overall diagnostic preference was recorded. Comparisons between standard post-contrast and AiMIFY-processed images were performed using the Wilcoxon signed-rank test. AiMIFY-processed images demonstrated significantly higher CNR, LBR, and CEP compared with standard-dose post-contrast images across all readers (all <i>P</i> < 0.001), with mean increases of 495.16%, 58.94%, and 160.55%, respectively. Subjective assessments showed significant improvements in border delineation, internal morphology, and contrast enhancement (all <i>P</i> < 0.05). Subgroup analysis of small lesions (< 10 mm) revealed consistently higher subjective scores for AiMIFY-processed images (all <i>P</i> < 0.05). AiMIFY-processed images were preferred in the majority of cases (84.9%, 29.1%, and 68.6% across readers; all <i>P</i> < 0.001). Deep learning-based enhancement using AiMIFY significantly improves lesion conspicuity on standard-dose contrast-enhanced brain MRI, with notable benefits for small lesions. This approach may represent an adjuvant to standard-dose MRI to enhance diagnostic confidence while avoiding increased contrast agent exposure.