A Systematic Benchmark of Intraoperative Ultrasound-to-MR Synthesis for Brain Tumour Surgery
Researchers have systematically benchmarked six different AI architectures for synthesizing MRI-like images from intraoperative ultrasound data. The study evaluated 48 experiments across various inference regimes and target modalities using the ReMIND dataset. Critically, perceptual quality metrics like LPIPS correlated more closely with downstream surgical utility, such as tumour segmentation, than traditional fidelity metrics like SSIM. AI
IMPACT Establishes best practices for evaluating medical imaging synthesis models, prioritizing downstream task performance over simple fidelity metrics.