Deep Learning-Based Denoising for Interactive Realistic Rendering of Biomedical Volumes

Monte Carlo Path Tracing (MCPT) provides highly realistic visualization of biomedical volumes, but its computational cost limits real-time interaction. The Advanced Realistic Rendering Technique (AR²T) adapts MCPT to enable interactive exploration through coarse images generated at low sample counts. This study explores the application of deep learning models for denoising in the early iterations of the AR²T to enable higher-quality interaction with biomedical data. We evaluate five deep learning architectures, both pre-trained and trained from scratch, in terms of denoising performance. A comprehensive evaluation framework, combining metrics such as PSNR and SSIM for image fidelity and tPSNR and LDR-FLIP for temporal and perceptual consistency, highlights that models trained from scratch on domain-specific data outperform pre-trained models. Our findings challenge the conventional reliance on large, diverse datasets and emphasize the importance of domain-specific training for biomedical imaging. Furthermore, subjective clinical assessments through expert evaluations underscore the significance of aligning objective metrics with clinical relevance, highlighting the potential of the proposed approach for improving interactive visualization for analysis of bones, joints, and vessels in clinical and research environments.