scholarly journals Automatic Assessment of Cardiac Perfusion MRI

2004 ◽  
pp. 1060-1061 ◽  
Author(s):  
Hildur Ólafsdóttir ◽  
Mikkel B. Stegmann ◽  
Henrik B. W. Larsson
Keyword(s):  
Perfusion Mri ◽  
Automatic Assessment ◽  
Cardiac Perfusion

Level sets and shape models for segmentation of cardiac perfusion MRI

2006 ◽  
Author(s):  
Lucas Lorenzo ◽  
Robert S. MacLeod ◽  
Ross T. Whitaker ◽  
Ganesh Adluru ◽  
Edward V. R. DiBella
Keyword(s):  
Level Sets ◽  
Perfusion Mri ◽  
Cardiac Perfusion ◽  
Shape Models

scholarly journals Is Qualitative Cardiac Perfusion MRI “Good Enough”?

2018 ◽  
Vol 11 (5) ◽  
pp. 719-721 ◽  
Author(s):  
Leon Axel
Keyword(s):  
Perfusion Mri ◽  
Cardiac Perfusion

scholarly journals Clinical evaluation of inline motion correction for cardiac perfusion MRI

2011 ◽  
Vol 13 (S1) ◽  
Author(s):  
Aya Kino ◽  
Christopher Glielmi ◽  
Mauricio S Galizia ◽  
Andrada R Popescu ◽  
Hui Xue ◽  
...  
Keyword(s):  
Clinical Evaluation ◽  
Motion Correction ◽  
Perfusion Mri ◽  
Cardiac Perfusion

Cardiac Perfusion MRI

2018 ◽  
pp. 471-485
Author(s):  
Amedeo Chiribiri
Keyword(s):  
Perfusion Mri ◽  
Cardiac Perfusion

Model-Based Image Reconstruction for Dynamic Cardiac Perfusion MRI from Sparse Data

2006 ◽  
Author(s):  
Suyash P. Awate ◽  
Edward V. R. DiBella ◽  
Tolga Tasdizen ◽  
Ross T. Whitaker
Keyword(s):  
Image Reconstruction ◽  
Sparse Data ◽  
Perfusion Mri ◽  
Cardiac Perfusion ◽  
Model Based

Multislice first-pass cardiac perfusion MRI: Validation in a model of myocardial infarction

2002 ◽  
Vol 47 (3) ◽  
pp. 482-491 ◽  
Author(s):  
Frederick H. Epstein ◽  
James F. London ◽  
Dana C. Peters ◽  
Lino M. Goncalves ◽  
Kwabena Agyeman ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Perfusion Mri ◽  
Cardiac Perfusion ◽  
First Pass

scholarly journals Image fusion of coronary CT angiography and cardiac perfusion MRI: a pilot study

2010 ◽  
Vol 20 (5) ◽  
pp. 1174-1179 ◽  
Author(s):  
Paul Stolzmann ◽  
Hatem Alkadhi ◽  
Hans Scheffel ◽  
Anja Hennemuth ◽  
Caroline Kuehnel ◽  
...  
Keyword(s):  
Pilot Study ◽  
Image Fusion ◽  
Ct Angiography ◽  
Coronary Ct Angiography ◽  
Perfusion Mri ◽  
Cardiac Perfusion ◽  
Coronary Ct

scholarly journals Factors Affecting the Segmentation of the Heart Ventricles in Short Axis Cardiac Perfusion MRI Images

2016 ◽  
Vol 15 (11) ◽  
pp. 7218-7226 ◽  
Author(s):  
Doaa Mousa ◽  
Nourhan Zayed ◽  
Inas A. Yassine
Keyword(s):  
Frequency Domain ◽  
Level Set ◽  
Perfusion Mri ◽  
Initial Contour ◽  
Short Axis ◽  
Contour Extraction ◽  
Cardiac Perfusion ◽  
Segmentation Accuracy ◽  
Left And Right ◽  
Initialization Strategy

Cardiovascular diseases (CVDs) cause 31% of the death rate globally. Automatic accurate segmentation is needed for CVDs early detection. In this paper, we study the effect of the registration and initialization of the level set segmentation on the performance of extracting the heart ventricles for the short axis cardiac perfusion MRI images, as a result, we propose a modified workflow to automatically segment the ventricles by mitigating the levelset initial contour extraction in order to improve the segmentation results accuracy. In the registration experiments, the translational transformation was studied based on both the spatial and frequency domain. The frequency domain based registration is mainly established based on the phase correlation methodology. As for the segmentation experiments, the level set initialization was done through extracting the ventricles’ real shape from each slice. Though, the final contour of any frame will be used as the initial contour for the next frame. The second initialization strategy was based on defining the initial contour for each frame using the polar representation of the image. Two short axis view datasets of cardiac magnetic resonance (CMR) perfusion imaging were used in testing the proposed methods. Dice coefficient, sensitivity, specificity and Hausdorff distance have been used to evaluate and validate the segmentation results. The system workflow consists of five main modules: preprocessing, localization, initial contour extraction, registration, and segmentation. The segmentation accuracy for left and right ventricles improved from 72% to 77% and from 70% to 81% using the spatial domain based registration algorithm. The polar-based initialization strategy improves the segmentation accuracy from 77% to 81% and from 81% to 82% for the left and right ventricles respectively.

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