OBJECTIVE: Magnetic resonance imaging-guided radiotherapy (MRIgRT) uses 2D+t cine-MRI to track intra-fractional motion with high spatial and temporal resolution. Growing usage and acquisition rates require efficient storage and transfer, especially for large-scale cine-MRI datasets. Approach. Lossless and lossy compression algorithms for cine-MRI are systematically evaluated, focusing on efficiency and preservation of downstream utility. Using the TrackRAD2025 dataset, entropy-, image-, and video-based codecs were tested in three scenarios: lossless at 16-bit, lossless after quantization to 8-bit or 10-bit, and lossy at 8-bit. The peak signal-to-noise ratio (PSNR) and mean absolute error (MAE) per normalized pixel were used as standard image quality metrics to assess compression artifacts. Additionally, the impact on machine learning performance metrics such as the Dice similarity coefficient (DSC) of artificial intelligence-based target tracking models was used to determine thresholds for negligible degradation. Main results. JPEG XL achieved the highest lossless compression ratios (median 4.4 at 16-bit; 9.9 at 8-bit) with real-time speeds, outperforming the zlib/DEFLATE baseline (2.1 at 16-bit; 4.0 at 8-bit). Quantization reduced file sizes and enabled alternative compression algorithms with small quality loss (median PSNR 59.8 dB for 8-bit and 72.5 dB for 10-bit). For target tracking, compression at PSNR > 45 dB maintained segmentation quality (|DSC| 0.005). Significance. The identified compression methods allow for efficient compression of 2D+t cine-MRI data, either losslessly, suitable for archival and quality assurance, or lossily, under well-characterized quality thresholds that preserve target tracking performance, at much higher rates.
Tom Julius Blöcker (2026) studied this question.