Estimation of myocardial deformation using correlation image velocimetry View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-04-05

AUTHORS

Athira Jacob, Ganapathy Krishnamurthi, Manikandan Mathur

ABSTRACT

BACKGROUND: Tagged Magnetic Resonance (tMR) imaging is a powerful technique for determining cardiovascular abnormalities. One of the reasons for tMR not being used in routine clinical practice is the lack of easy-to-use tools for image analysis and strain mapping. In this paper, we introduce a novel interdisciplinary method based on correlation image velocimetry (CIV) to estimate cardiac deformation and strain maps from tMR images. METHODS: CIV, a cross-correlation based pattern matching algorithm, analyses a pair of images to obtain the displacement field at sub-pixel accuracy with any desired spatial resolution. This first time application of CIV to tMR image analysis is implemented using an existing open source Matlab-based software called UVMAT. The method, which requires two main input parameters namely correlation box size (C B ) and search box size (S B ), is first validated using a synthetic grid image with grid sizes representative of typical tMR images. Phantom and patient images obtained from a Medical Imaging grand challenge dataset ( http://stacom.cardiacatlas.org/motion-tracking-challenge/ ) were then analysed to obtain cardiac displacement fields and strain maps. The results were then compared with estimates from Harmonic Phase analysis (HARP) technique. RESULTS: For a known displacement field imposed on both the synthetic grid image and the phantom image, CIV is accurate for 3-pixel and larger displacements on a 512 × 512 image with (C B ,S B )=(25,55) pixels. Further validation of our method is achieved by showing that our estimated landmark positions on patient images fall within the inter-observer variability in the ground truth. The effectiveness of our approach to analyse patient images is then established by calculating dense displacement fields throughout a cardiac cycle, and were found to be physiologically consistent. Circumferential strains were estimated at the apical, mid and basal slices of the heart, and were shown to compare favorably with those of HARP over the entire cardiac cycle, except in a few (∼4) of the segments in the 17-segment AHA model. The radial strains, however, are underestimated by our method in most segments when compared with HARP. CONCLUSIONS: In summary, we have demonstrated the capability of CIV to accurately and efficiently quantify cardiac deformation from tMR images. Furthermore, physiologically consistent displacement fields and circumferential strain curves in most regions of the heart indicate that our approach, upon automating some pre-processing steps and testing in clinical trials, can potentially be implemented in a clinical setting. More... »

PAGES

25

References to SciGraph publications

  • 2017-01-24. Segmentation and Tracking of Myocardial Boundaries Using Dynamic Programming in STATISTICAL ATLASES AND COMPUTATIONAL MODELS OF THE HEART. IMAGING AND MODELLING CHALLENGES
  • 2000-12. Advanced optimization of correlation imaging velocimetry algorithms in EXPERIMENTS IN FLUIDS
  • 2009-11-16. Functional measurements based on feature tracking of cine magnetic resonance images identify left ventricular segments with myocardial scar in CARDIOVASCULAR ULTRASOUND
  • 2012. An Incompressible Log-Domain Demons Algorithm for Tracking Heart Tissue in STATISTICAL ATLASES AND COMPUTATIONAL MODELS OF THE HEART. IMAGING AND MODELLING CHALLENGES
  • 2014-04-26. Subclinical Myocardial Disease in Heart Failure Detected by CMR in CURRENT CARDIOVASCULAR IMAGING REPORTS
  • 2009-01-07. Prevalence and inter-relationship of different Doppler measures of dyssynchrony in patients with heart failure and prolonged QRS: a report from CARE-HF in CARDIOVASCULAR ULTRASOUND
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    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/28381245


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