Name
Title | ||
|---|---|---|
SLIC robust (SLICR) processing for fast, robust CT myocardial blood flow quantification. |
Abstract | ||
|---|---|---|
There are several computational methods for estimating myocardial blood flow (MBF) using CT myocardial perfusion imaging (CT-MPI). Previous work has shown that model-based deconvolution methods are more accurate and precise than model-independent methods such as singular value decomposition and max-upslope. However, iterative optimization is computationally expensive and models are sensitive to image noise, thus limiting the utility of low x-ray dose acquisitions. We propose a new processing method, SLICR, which segments the myocardium into super-voxels using a modified simple linear iterative clustering (SLIC) algorithm and quantifies MBF via a robust physiologic model (RPM). We compared SLICR against voxel-wise SVD and voxel-wise model-based deconvolution methods (RPM, single-compartment and Johnson-Wilson). We used image data from a digital CT-MPI phantom to evaluate robustness of processing methods to noise at reduced x-ray dose. We validate SLICR in a porcine model with and without partial occlusion of the LAD coronary artery with known pressure-wire fractional flow reserve. SLICR was similar to 50 times faster than voxel-wise RPM and other model-based methods while retaining sufficient resolution to show all clinically interesting features (e.g., a flow deficit in the endocardial wall). SLICR showed much better precision and accuracy than the other methods. For example, at simulated MBF=100 mL/min/100g and 100 mAs exposure (50% of nominal dose) in the digital simulator, MBF estimates were 101 +/- 12 mL/min/100g, 160 +/- 54 mL/min/100g, and 122 +/- 99 mL/min/100g for SLICR, SVD, and Johnson-Wilson, respectively. SLICR even gave excellent results (103 +/- 23 ml/min/100g) at 50 mAs, corresponding to 25% nominal dose. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1117/12.2293829 | Proceedings of SPIE |
Keywords | DocType | Volume |
Dynamic CT perfusion,myocardial blood flow,super-voxel,dose reduction | Conference | 10578 |
ISSN | Citations | PageRank |
0277-786X | 0 | 0.34 |
References | Authors | |
4 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hao Wu | 1 | 0 | 0.34 |
| Brendan L. Eck | 2 | 1 | 2.37 |
| Jacob Levi | 3 | 0 | 2.37 |
| Anas Fares | 4 | 0 | 1.01 |
| Yuemeng Li | 5 | 0 | 0.68 |
| Di Wen | 6 | 0 | 0.68 |
| Hiram G. Bezerra | 7 | 10 | 6.64 |
| David L. Wilson | 8 | 174 | 36.04 |