Deep-Learning Based Contrast Boosting: A Multi-Center Multi-Reader Study on Clinical Performance With Standard Contrast Enhanced Brain MRI.
TL;DR
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.
Abstract
Background
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.
Purpose
To evaluate the clinical performance of an FDA-cleared deep-learning (DL)-based contrast boosting algorithm in routine clinical brain MRI exams. STUDY TYPE Retrospective. POPULATION One hundred ten patients (47 ± 22 years; 52 Females, 47 Males, 11 N/A) with clinical contrast-enhanced brain MR studies. FIELD STRENGTH AND SEQUENCES T1 weighted pre-contrast and post-contrast brain MRI sequences at 0.3 T, 1.5 T, and 3 T. ASSESSMENT 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. STATISTICAL TESTS Wilcoxon signed rank test. A p value < 0.05 was considered significant.
Results
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). DATA
Conclusion
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. EVIDENCE LEVEL 4. TECHNICAL EFFICACY Stage 3.