AI Insight
PRISM is an automated software pipeline that converts multi-phase CT scans into color-coded digital subtraction angiography volumes for improved kidney tumor visualization. Through systematic testing of 200 registration combinations across five patients, the researchers identified optimal processing parameters that balance image quality with computational efficiency, achieving full processing in approximately 4 minutes per phase. The system uses deep learning for image interpolation, automated kidney segmentation, and a three-step deformable registration process to align different contrast phases and highlight areas of tissue enhancement.
Why it matters
This open-source tool could standardize and improve the assessment of renal cell carcinoma by replacing subjective visual comparison with quantitative, automated enhancement mapping. The optimized parameters make high-quality CT angiography processing accessible to clinical settings with limited computational resources, potentially improving diagnostic accuracy and consistency.
Understand the Science
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Multi-phase contrast-enhanced computed tomography (CT) is the gold standard for renal cell carcinoma (RCC) characterization, yet clinical interpretation relies on subjective visual comparison across phases. We present PRISM (Phase-Resolved Isotropic Subtraction Mapping), an open-source automated pipeline that transforms multi-phase CT acquisitions into registered digital subtraction angiography (DSA) volumes with color-coded enhancement maps. PRISM integrates six sequential processing stages: (1) DICOM loading with automated contrast-phase classification, (2) deep learning-based isotropic interpolation via RIFE, (3) automated kidney segmentation using TotalSegmentator v2, (4) enhancement-based tissue detection, (5) three-step deformable registration (rigid, affine, B-spline) using SimpleITK, and (6) dual-channel digital subtraction visualization. We present a systematic parameter optimization study comprising 200 registrations across five patients and five experiments. Key findings: We identify an efficient registration configuration combining a 40 mm B-spline grid (within 6% of the 30 mm quality optimum at 36% lower computational cost), 5% metric sampling (equivalent quality to 25% at 3.2x speedup), and a single-level multi-resolution pyramid (avoiding the 5.4x overhead of a 4,2 pyramid with no quality benefit); we show that registration quality is effectively independent of interpolation target spacing from 0.5-3.0 mm, enabling a coarse-register/fine-apply strategy that computes the full transform at 3.0 mm (approximately 4 minutes per phase) and applies it to 0.5 mm volumes for high-resolution visualization. We also determine that a 40 HU subtraction noise threshold optimally balances signal-to-noise ratio (2.00) against sensitivity (14.2% enhancing volume retained), with higher thresholds (60-80 HU) favoring specificity and lower thresholds (20 HU) favoring sensitivity.