Motion corrected LV quantification based on 3D modelling for improved functional assessment in cardiac MRI
Cine MRI is a clinical reference standard for the quantitative assessment of cardiac function, but reproducibility is confounded by motion artefacts. We explore the feasibility of a motion corrected 3D left ventricle (LV) quantification method, incorporating multislice image registration into the 3D...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2015
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| Subjects: | |
| Online Access: | http://eprints.um.edu.my/15667/1/Motion_corrected_LV_quantification_based_on_3D_modelling_for_improved_functional_assessment.pdf http://eprints.um.edu.my/15667/ http://www.ncbi.nlm.nih.gov/pubmed/25768708 |
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| Summary: | Cine MRI is a clinical reference standard for the quantitative assessment of cardiac function, but reproducibility is confounded by motion artefacts. We explore the feasibility of a motion corrected 3D left ventricle (LV) quantification method, incorporating multislice image registration into the 3D model reconstruction, to improve reproducibility of 3D LV functional quantification. Multi-breath-hold short-axis and radial long-axis images were acquired from 10 patients and 10 healthy subjects. The proposed framework reduced misalignment between slices to subpixel accuracy (2.88 to 1.21 mm), and improved interstudy reproducibility for 5 important clinical functional measures, i.e. end-diastolic volume, end-systolic volume, ejection fraction, myocardial mass and 3D-sphericity index, as reflected in a reduction in the sample size required to detect statistically significant cardiac changes: a reduction of 21-66. Our investigation on the optimum registration parameters, including both cardiac time frames and number of long-axis (LA) slices, suggested that a single time frame is adequate for motion correction whereas integrating more LA slices can improve registration and model reconstruction accuracy for improved functional quantification especially on datasets with severe motion artefacts. |
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