ArticleJournal of medical imaging (Bellingham, Wash.)2019
SLICR super-voxel algorithm for fast, robust quantification of myocardial blood flow by dynamic computed tomography myocardial perfusion imaging.
Article in Journal of medical imaging (Bellingham, Wash.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Cardiac CT Perfusion Imaging of Pericoronary Adipose Tissue (PCAT) Highlighting Potential Confounds in CTA Analysis.Journal of clinical medicine · 2025Article
- Quantitative cardiac CT perfusion: physiologically-inspired model and identifying microvascular disease from discordant CTA CAD-RADS.Frontiers in cardiovascular medicine · 2025Article
- SLIC-Occ: functional segmentation of occupancy images improves precision of ECEJNMMI physics · 2023Article
- Comparison of automated beam hardening correction (ABHC) algorithms for myocardial perfusion imaging using computed tomography.Medical physics · 2021Article
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Authors and funding
9 authors.
Funding
Abstract
We created and evaluated a processing method for dynamic computed tomography myocardial perfusion imaging (CT-MPI) of myocardial blood flow (MBF), which combines a modified simple linear iterative clustering algorithm (SLIC) with robust perfusion quantification, hence the name SLICR. SLICR adaptively segments the myocardium into nonuniform super-voxels with similar perfusion time attenuation curves (TACs). Within each super-voxel, an α-trimmed-median TAC was computed to robustly represent the super-voxel and a robust physiological model (RPM) was implemented to semi-analytically estimate MBF. SLICR processing was compared with another voxel-wise MBF preprocessing approach, which included a spatiotemporal bilateral filter (STBF) for noise reduction prior to perfusion quantification. Image data from a digital CT-MPI phantom and a porcine ischemia model were evaluated. SLICR was
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