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 is presented.
Abstract
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.
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PURPOSE
To evaluate the image quality of super-resolution deep learning reconstruction (SR-DLR) for 3-dimensional (3D) T1-weighted gradient-echo (GRE) imaging in contrast-enhanced MRI, compared with conventional reconstruction (Conv.) and standard deep learning reconstruction (DLR).
MATERIALS AND METHODS
This retrosp...
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