Deep-Learning Based Contrast Boosting: A Multi-Center Multi-Reader Study on Clinical Performance With Standard Contrast Enhanced Brain MRI.
Authors
Affiliations (6)
Affiliations (6)
- Subtle Medical Inc., Menlo Park, California, USA.
- Global R&D, Bracco Imaging SpA, Colleretto Giacosa, Italy.
- Department of Radiology, University of California San Diego, La Jolla, California, USA.
- Department of Radiology, University of Washington Medicine, Seattle, Washington, USA.
- Global Medical & Regulatory Affairs, Bracco Imaging Deutschland GmbH, Konstanz, Germany.
- Department of Radiology, Stanford University, Palo Alto, California, USA.
Abstract
Gadolinium-based contrast agents are used in brain MRI to improve the visualization of disorders and improve the delineation of lesions. Higher doses of GBCAs can improve lesion sensitivity but may have safety implications, particularly in light of recent findings on gadolinium retention and deposition. To evaluate the clinical performance of an FDA-cleared deep-learning (DL)-based contrast boosting algorithm in routine clinical brain MRI exams. Retrospective. One hundred ten patients (47 ± 22 years; 52 Females, 47 Males, 11 N/A) with clinical contrast-enhanced brain MR studies. T1 weighted pre-contrast and post-contrast brain MRI sequences at 0.3 T, 1.5 T, and 3 T. A multi-center database of contrast-enhanced brain MR images was used to evaluate a DL-based contrast boosting algorithm. Pre-contrast and standard post-contrast (SC) images were processed with the algorithm to obtain contrast boosted (CB) images. CB images were compared to SC images in terms of contrast-to-noise ratio (CNR), lesion-to-brain ratio (LBR), and contrast enhancement percentage (CEP). Three board-certified radiologists with 9, 15, and 18 years of experience reviewed CB and SC images side-by-side for qualitative evaluation and rated them on a 4-point Likert scale for lesion contrast enhancement, border delineation, internal morphology, overall image quality, presence of artifacts, and changes in vessel conspicuity. The presence, cause, and severity of any false lesions was recorded. Wilcoxon signed rank test. A p value < 0.05 was considered significant. CB images had significantly superior quantitative performance than SC images in terms of CNR (729.17% ± 1576.92%), LBR (87.91% ± 72.12%), and CEP (165.81% ± 42%). In the qualitative assessment, CB images showed significantly better lesion visualization (3.73 vs. 3.16) and had significantly better image quality (3.55 vs. 3.07). In this multi-center, multi-reader study, deep learning-based contrast boosting demonstrates robust improvements in lesion visualization and image quality without increasing contrast dosage. 4. Stage 3.