Integrated Image Enhancement and Radiomic Analysis for Ultra-High-Field 7T Time-of-Flight MR Angiography

Ultra-high-field 7 T time-of-flight MR angiography (TOF-MRA) provides detailed visualization of intracranial vasculature, but quantitative analysis is sensitive to intensity inhomogeneity and non-standardized post-processing. We present TOFR-VET (Time-of-Flight Radiomics and Vessel Enhancement Toolkit), a unified MATLAB workflow that integrates vessel enhancement, objective image quality evaluation (PSNR, SSIM, entropy, contrast improvement index (CII), and brightness preservation factor (BPF)), and radiomic feature extraction within a single as a proposed methodological framework for consistent analysis. Using 7 T TOF-MRA datasets (n = 120 subjects), we comparatively evaluated histogram-based and adaptive enhancement approaches and quantified their impact on image quality behavior and radiomic feature sensitivity. Adaptive Gamma Correction with Weighting Distribution (AGCWD), as one of the methods tested, resulted in higher fidelity-to-baseline metrics than standard histogram equalization (HE), demonstrated by improved PSNR metrics (23.54 dB versus 15.82 dB; increase of 7.72 dB) along with observed complementary trends in SSIM and BPF. Brightness-preserving methods maintained BPF values close to unity. Because enhancement methods intentionally reshape intensity distributions, PSNR/SSIM are interpreted here as structural deviation indicators relative to the original image rather than absolute measures of diagnostic quality. Quantitative analysis of 120 subjects using one-way ANOVA revealed that while Laplacian of Gaussian (LoG) filtering provides the highest contrast improvement for visual vessel continuity (CII = 1.65 ± 0.22, p < 0.001), Adaptive Gamma Correction (AGCWD) is superior for radiomic pipelines, maintaining an Intraclass Correlation Coefficient (ICC) of 0.94. These findings provide a specific decision-making framework for standardizing 7T TOF-MRA preprocessing based on the desired diagnostic or quantitative objective.