SLIC robust (SLICR) processing for fast, robust CT myocardial blood flow quantification

Hao Wu; Brendan L. Eck; Jacob Levi; Anas Fares; Yuemeng Li; Di Wen; Hiram G. Bezerra; David L. Wilson

doi:10.1117/12.2293829

12 March 2018 SLIC robust (SLICR) processing for fast, robust CT myocardial blood flow quantification

Hao Wu, Brendan L. Eck, Jacob Levi, Anas Fares, Yuemeng Li, Di Wen, Hiram G. Bezerra, David L. Wilson

Author Affiliations +

Proceedings Volume 10578, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging; 105781U (2018) https://doi.org/10.1117/12.2293829
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

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 ~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.

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Hao Wu, Brendan L. Eck, Jacob Levi, Anas Fares, Yuemeng Li, Di Wen, Hiram G. Bezerra, and David L. Wilson "SLIC robust (SLICR) processing for fast, robust CT myocardial blood flow quantification ", Proc. SPIE 10578, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging, 105781U (12 March 2018); https://doi.org/10.1117/12.2293829

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