In order to diagnose heart disease such as myocardial infarction, 2D strain through the speckle tracking echocardiography (STE) or the tagged MRI is often used. However out-of-plane strain measurement using STE or tagged MRI is inaccurate. Therefore, strain for whole organ which are analyzed by simulation of 3D cardiac model can be applied in clinical diagnosis. To simulate cardiac contraction in a cycle, cardiac physical properties should be reflected in cardiac model. The myocardial wall in left ventricle is represented as a transversely orthotropic hyperelastic material, with the fiber orientation varying sequentially from the epicardial surface, through about 0° at the midwall, to the endocardial surface. A time-varying elastance model is simulated to contract myocardial fiber, and physiological intraventricular systolic pressure curves are employed for the cardiac dynamics simulation in a cycle. And an exact description of the cardiac motion should be acquired in order that essential boundary conditions for cardiac simulation are obtained effectively. Real time cardiac motion can be acquired by using echocardiography and exact cardiac geometrical 3D model can be reconstructed using 3D CT data. In this research, image fusion technology from CT and echocardiography is employed in order to consider patient-specific left ventricle movement. Finally, longitudinal strain from speckle tracking echocardiography which is known to fit actual left ventricle deformation relatively well is used to verify these results.
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.
Minimally/Non-invasive surgery has become increasingly widespread because of its therapeutic benefits such as less
pain, less scarring, and shorter hospital stay. However, it is very difficult to eliminate the target cancer cells selectively
without damaging nearby normal tissues and vessels since the tumors inside organs cannot be visually tracked in realtime with the existing imaging devices while organs are deformed by respiration and surgical instruments. Note that realtime 2D US imaging is widely used for monitoring the minimally invasive surgery such as Radiofrequency ablation; however, it is difficult to detect target tumors except high-echogenic regions because of its noisy and limited field of view. To handle these difficulties, we present a novel framework for estimating organ motion and deformed shape during respiration from the available features of 2D US images, by means of inverse kinematics utilizing 3D CT volumes at the inhale and exhale phases. First, we generate surface meshes of the target organ and tumor as well as centerlines of vessels at the two extreme phases considering surface correspondence. Then, the corresponding tetrahedron meshes are generated by coupling the internal components for volumetric modeling. Finally, a deformed organ mesh at an arbitrary phase is generated from the 2D US feature points for estimating the organ deformation and tumor position. To show effectiveness of the proposed method, the CT scans from real patient has been tested for estimating the motion and deformation of the liver. The experimental result shows that the average errors are less than 3mm in terms of tumor position as well as the whole surface shape.
The Shack-Hartmann wavefront sensor is composed of a lenslet array generating the spot images from which local slope
is calculated and overall wavefront is measured. Generally the principle of wavefront reconstruction is that the spot
centroid of each lenslet array is calculated from pixel intensity values in its subaperture and then overall wavefront is
reconstructed by local slope of wavefront obtained by deviations from reference positions. Hence the spot image of each
lenslet array has to remain in its subaperture for exact measurement of wavefront. However the spot of each lenslet array
deviates from its subaperture area when wavefront with large local slopes enters the Shack-Hartmann sensor.
In this research, we propose the spot image searching method that finds area of each measured spot image flexibly and
determine the centroid of each spot in its area. Also the algorithms that match these centroids to their reference points
unequivocally even if some of them are situated off the allocated subaperture are proposed. Finally we verify the
proposed algorithm with the test of a defocus measurement through experimental setup for the Shack-Hartmann
wavefront sensor. It has been shown that the proposed algorithm can expand the dynamic range without additional