Generation of Dynamic Heart Model Based on 4D Echocardiographic Images

Chlebiej, Michał; Mikołajczak, P.; Nowiński, Krzysztof; Ścisło, Piotr; Bała, Piotr

doi:10.1007/11751649_43

Cited by 4 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The diffusion algorithm has been extended to the fourth dimensional block of data with the time taken as the fourth dimension. As it was presented in [3] such filtering drastically reduces the speckle noise and enhances the structure boundaries.…”

Section: Methodsmentioning

confidence: 92%

See 1 more Smart Citation

Development of heart motion reconstruction framework based on the 4D echocardiographic data

Chlebiej¹,

Nowiński²,

Ścisło³

et al. 2008

Annales UMCS, Informatica

Self Cite

View full text Add to dashboard Cite

Abnormalities in heart motion can eventually lead to life threatening cardiac injuries therefore measurements of dynamic heart functions are of great clinical importance. The images of moving spatial heart structures can be efficiently acquired using 4D echocardiography. Unfortunately, because of the low quality such images do not allow for precise measurements. To overcome this problem images need to be further processed and moving structures have to be extracted. In this work we present a method for estimating heart motion from the 3D echocardiographic image sequence. On the basis of this method we have developed an application that enables qualitative and quantitative (i.e. volume changes, stroke volume, ejection fraction and cardiac output parameters) description of the heart wall motion. We provide a set of tools for denoising images using the anisotropic diffusion algorithm extended to the fourth dimension and the time averaging method based on non-linear registration efficiently parameterized using the B-spline based Free Form Deformation. We have also developed a non-linear deformable segmentation algorithm for extraction of the inner ventricular surface. The motion of the left ventricle is reconstructed in our approach by recovering deformations *

show abstract

Section: Methodsmentioning

confidence: 92%

“…In this work we present a set of the heart wall visualization methods. These methods make an extensive use of the left ventricle motion description method [3] briefly presented in the next section.…”

Section: Introductionmentioning

confidence: 99%

Development of heart motion reconstruction framework based on the 4D echocardiographic data

Chlebiej¹,

Nowiński²,

Ścisło³

et al. 2008

Annales UMCS, Informatica

Self Cite

View full text Add to dashboard Cite

show abstract

“…As the well based technique of volumetric data alignment is data registration algorithm [8,9,10,11,12], we recover the transformation that aligns the reference volumetric dataset with that under examination using intensity based 3D volume registration. Such approach relies on an nonlinear transformation which allows to model local deformation of spatial objects.…”

Section: Mathematical Settingmentioning

confidence: 99%

Automated geometric features evaluation method for normal foot skeleton model

Borucki

Nowiński

Chlebiej

et al. 2011

Annales UMCS, Informatica

Self Cite

View full text Add to dashboard Cite

Normal foot model" is a geometric model of a healthy human foot. As the comparison of the processed feet requires a reference ideal healthy foot parameterization it was necessary to create such a model by defining skeleton geometric features and generating the feature set on a dataset population. Manual positioning of such number of landmarks is both a complex and time consuming task for a skilled radiologist, not to mention the total cost of such a procedure. Thus it was recommended to formulate an automated computer algorithm to perform this procedure with accuracy at a comparable level as the manual process. The following paper describes our approach based on automatic landmark positioning in a volumetric foot dataset. The proposed automated procedure is based on four main steps: manual landmark positioning on a reference dataset, registration of the reference dataset with the examined study, transformation of landmark positions from the reference dataset space into the examined dataset space, and calculation of the geometric features on the basis of landmarks positions. The results of our algorithm are presented and discussed in the context of pros and cons of the automated method itself as well as in the context of the generated normal foot model. *

show abstract