Using computational simulation, we can analyze cardiovascular disease in non-invasive and quantitative manners. More specifically, computational modeling and simulation technology has enabled us to analyze functional aspect such as blood flow, as well as anatomical aspect such as stenosis, from medical images without invasive measurements. Note that the simplest ways to perform blood flow simulation is to apply patient-specific coronary anatomy with other average-valued properties; in this case, however, such conditions cannot fully reflect accurate physiological properties of patients. To resolve this limitation, we present a new patient-specific coronary blood flow simulation method by myocardial volume partitioning considering artery/myocardium structural correspondence. We focus on that blood supply is closely related to the mass of each myocardial segment corresponding to the artery. Therefore, we applied this concept for setting-up simulation conditions in the way to consider many patient-specific features as possible from medical image: First, we segmented coronary arteries and myocardium separately from cardiac CT; then the myocardium is partitioned into multiple regions based on coronary vasculature. The myocardial mass and required blood mass for each artery are estimated by converting myocardial volume fraction. Finally, the required blood mass is used as boundary conditions for each artery outlet, with given average aortic blood flow rate and pressure. To show effectiveness of the proposed method, fractional flow reserve (FFR) by simulation using CT image has been compared with invasive FFR measurement of real patient data, and as a result, 77% of accuracy has been obtained.
Our aim in this study was to optimize and validate an adaptive denoising algorithm based on Block-Matching 3D, for
reducing image noise and improving assessment of left ventricular function from low-radiation dose coronary CTA. In
this paper, we describe the denoising algorithm and its validation, with low-radiation dose coronary CTA datasets from 7
consecutive patients. We validated the algorithm using a novel method, with the myocardial mass from the low-noise
cardiac phase as a reference standard, and objective measurement of image noise. After denoising, the myocardial mass
were not statistically different by comparison of individual datapoints by the students' t-test (130.9±31.3g in low-noise
70% phase vs 142.1±48.8g in the denoised 40% phase, p= 0.23). Image noise improved significantly between the 40%
phase and the denoised 40% phase by the students' t-test, both in the blood pool (p <0.0001) and myocardium (p
<0.0001). In conclusion, we optimized and validated an adaptive BM3D denoising algorithm for coronary CTA. This
new method reduces image noise and has the potential for improving assessment of left ventricular function from low-dose
KEYWORDS: Image segmentation, Heart, Magnetic resonance imaging, Computed tomography, Single photon emission computed tomography, 3D modeling, 3D image processing, Visualization, Expectation maximization algorithms, Medicine
Computer-aided segmentation of cardiac images obtained by various modalities plays an important role and is a prerequisite for a wide range of cardiac applications by facilitating the delineation of anatomical regions of interest. Numerous computerized methods have been developed to tackle this problem. Recent studies employ sophisticated techniques using available cues from cardiac anatomy such as geometry, visual appearance, and prior knowledge. In addition, new minimization and computational methods have been adopted with improved computational speed and robustness. We provide an overview of cardiac segmentation techniques, with a goal of providing useful advice and references. In addition, we describe important clinical applications, imaging modalities, and validation methods used for cardiac segmentation.