Deep-Learning Based Contrast Boosting Improves Lesion Visualization and Image Quality: A Multi-Center Multi-Reader Study on Clinical Performance with Standard Contrast Enhanced MRI of Brain Tumors

Background

Gadolinium-based Contrast Agents (GBCAs) are used in brain MRI exams to improve the visualization of pathology and improve the delineation of lesions. Higher doses of GBCAs can improve lesion sensitivity but involve substantial deviation from standard-of-care procedures and may have safety implications, particularly in the 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.

Methods

A multi-center retrospective database of contrast-enhanced brain MRI images (obtained from April 2017 to December 2023) 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. Quantitative performance of CB images in comparison to SC images was compared using contrast-to-noise ratio (CNR), lesion-to-brain ratio (LBR) and contrast enhancement percentage (CEP). Three board-certified radiologists 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 artefacts, and changes in vessel conspicuity. The presence, cause, and severity of any false lesions was recorded. CB results were compared to SC using Wilcoxon signed rank test for statistical significance.

Results

Brain MRI images from 110 patients (47 ± 22 years; 52 Females, 47 Males, 11 N/A) were evaluated. CB images had superior quantitative performance than SC images in terms of CNR (+634%), LBR (+70%) and CEP (+150%). In the qualitative assessment CB images showed better lesion visualization (3.73 vs 3.16) and had better image quality (3.55 vs 3.07). Readers were able to rule out all false lesions on CB by using SC for comparison.

Conclusions

Deep learning based contrast boosting improves lesion visualization and image quality without increasing contrast dosage.

Key Results

• In a retrospective study of 110 patients, deep-learning based contrast boosted (CB) images showed better lesion visualization than standard post-contrast (SC) brain MRI images (3.73 vs 3.16; mean reader scores [4-point Likert scale])

• CB images had better overall image quality than SC images (3.55 vs 3.07)

• Contrast-to-noise ratio, Lesion-to-brain Ratio and Contrast Enhancement Percentage for CB images were significantly higher than SC images (+729%, +88% and +165%; p < 0.001)

Summary Statement

Deep-learning based contrast boosting achieves better lesion visualization and overall image quality and provides more contrast information, without increasing the contrast dosage in contrast-enhanced brain MR protocols.