Automated multi-atlas segmentation of cardiac 4D flow MRI
| Autor: | Carl-Johan Carlhäll, Daniel Forsberg, Mariana Bustamante, Jan Engvall, Tino Ebbers, Vikas Gupta |
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| Rok vydání: | 2018 |
| Předmět: |
Male
Computer science heart cycle Multi-atlas segmentation Hemodynamic parameters 030218 nuclear medicine & medical imaging Ventricular Dysfunction Left 0302 clinical medicine motion Computer vision Segmentation nuclear magnetic resonance imaging Image segmentation Radiological and Ultrasound Technology medicine.diagnostic_test Cardiac cycle adult Left ventricular function Medicinsk bildbehandling article Heart Middle Aged cohort analysis error Computer Graphics and Computer-Aided Design female Female Computer Vision and Pattern Recognition Algorithms Blood Flow Velocity Cardiac segmentation anatomy Cardiac anatomy Semiautomatic methods Health Informatics Directional velocities volunteer Steady state free precessions 03 medical and health sciences Magnetic resonance imaging male Image Interpretation Computer-Assisted medicine Humans Multi atlas segmentation controlled study steady state Radiology Nuclear Medicine and imaging human Aged Automatic segmentations business.industry Segmentation methods 4D Flow MRI major clinical study human tissue thickness Visualization Medical Image Processing Flow (mathematics) Case-Control Studies heart left ventricle function Automatic segmentation Artificial intelligence business Magnetic Resonance Angiography 030217 neurology & neurosurgery |
| Zdroj: | Medical Image Analysis. 49:128-140 |
| ISSN: | 1361-8415 2014-6191 |
| Popis: | Four-dimensional (4D) flow magnetic resonance imaging (4D Flow MRI) enables acquisition of time-resolved three-directional velocity data in the entire heart and all major thoracic vessels. The segmentation of these tissues is typically performed using semi-automatic methods. Some of which primarily rely on the velocity data and result in a segmentation of the vessels only during the systolic phases. Other methods, mostly applied on the heart, rely on separately acquired balanced Steady State Free Precession (b-SSFP) MR images, after which the segmentations are superimposed on the 4D Flow MRI. While b-SSFP images typically cover the whole cardiac cycle and have good contrast, they suffer from a number of problems, such as large slice thickness, limited coverage of the cardiac anatomy, and being prone to displacement errors caused by respiratory motion. To address these limitations we propose a multi-atlas segmentation method, which relies only on 4D Flow MRI data, to automatically generate four-dimensional segmentations that include the entire thoracic cardiovascular system present in these datasets. The approach was evaluated on 4D Flow MR datasets from a cohort of 27 healthy volunteers and 83 patients with mildly impaired systolic left-ventricular function. Comparison of manual and automatic segmentations of the cardiac chambers at end-systolic and end-diastolic timeframes showed agreements comparable to those previously reported for automatic segmentation methods of b-SSFP MR images. Furthermore, automatic segmentation of the entire thoracic cardiovascular system improves visualization of 4D Flow MRI and facilitates computation of hemodynamic parameters. Funding details: 310612; Funding details: FP7, Seventh Framework Programme; Funding details: 621-2014-6191, VR, Vetenskapsrådet; Funding details: 223615; Funding details: 20140398; Funding text: This work was partially funded by the FP7-funded project DOPPLER-CIP [grant number 223615]; the European Union’s Seventh Framework Programme ( FP7/2007-2013 ) [grant number 310612 ]; the Swedish Research Council [grant number 621-2014-6191 ]; and the Swedish Heart and Lung Foundation [grant number 20140398 ]. |
| Databáze: | OpenAIRE |
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