SLICR super-voxel algorithm for fast, robust quantification of myocardial blood flow by dynamic computed tomography myocardial perfusion imaging.

Autor:	Wu H; Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States., Eck BL; Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States., Levi J; Case Western Reserve University, Department of Physics, Cleveland, Ohio, United States., Fares A; University Hospitals Cleveland Medical Center, Harrington Heart and Vascular Institute, Cardiovascular Imaging Core Laboratory, Cleveland, Ohio, United States., Li Y; Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States., Wen D; Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States., Bezerra HG; University Hospitals Cleveland Medical Center, Harrington Heart and Vascular Institute, Cardiovascular Imaging Core Laboratory, Cleveland, Ohio, United States., Muzic RF Jr; Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States.; Case Western Reserve University, Department of Radiology, Cleveland, Ohio, United States., Wilson DL; Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States.; Case Western Reserve University, Department of Radiology, Cleveland, Ohio, United States.
Jazyk:	angličtina
Zdroj:	Journal of medical imaging (Bellingham, Wash.) [J Med Imaging (Bellingham)] 2019 Oct; Vol. 6 (4), pp. 046001. Date of Electronic Publication: 2019 Nov 06.
DOI:	10.1117/1.JMI.6.4.046001
Abstrakt:	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 ∼ 50 -fold faster than voxel-wise RPM and other model-based methods while retaining sufficient resolution to show clinically relevant features, such as a transmural perfusion gradient. SLICR showed markedly improved accuracy and precision, as compared with other methods. At a simulated MBF of 100 mL/min-100 g and a tube current-time product of 100 mAs (50% of nominal), the MBF estimates were 101 ± 12 , 94 ± 56 , and 54 ± 24    mL / min - 100    g for SLICR, the voxel-wise Johnson-Wilson model, and a singular value decomposition-model independent method with STBF, respectively. SLICR estimated MBF precisely and accurately ( 103 ± 23    mL / min - 100    g ) at 25% nominal dose, while other methods resulted in larger errors. With the porcine model, the SLICR results were consistent with the induced ischemia. SLICR simultaneously accelerated and improved the quality of quantitative perfusion processing without compromising clinically relevant distributions of perfusion characteristics. (© 2019 Society of Photo-Optical Instrumentation Engineers (SPIE).)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu