Assessing the effect of tissue structural changes during cardiac deformation using magnetic resonance imaging techniques

Thesis

Global assessment of the myocardial tissue wall motion from cine MRI (Magnetic Resonance Imaging) is widely used in clinical practice, however, the estimation is done, in the majority of cases, visually rather than quantitatively. This doctoral thesis proposes steps forward in the local assessment of the left ventricular (LV) function in order to provide a better estimation of the damage post acute myocardial infarction (MI). Accurate post acute MI tissue damage estimation from cine MR tracking might help clinicians to perform, better, more personalised, revascu larisation treatments. This thesis makes three technical contributions.

The first technical contribution focuses on LV scar segmentation from Late Gadolium Enhancement (LGE) using supervoxels. LGE represents the gold standard for assessing the viability of the heart muscle. However, considerable user input is needed in order to correctly segment the scar. A global intensity threshold is used, and a further, remote, area of "healthy myocardium" also needs to be segmented. Furthermore, an additional number of corrections are required to remove false positive areas of enhancement. We found that by using a strong regulariser such as a modified version of the Single Linear Iterative Clustering (SLIC) supervoxels, we can eliminate the false positive enhancement areas. Furthermore, there is no need for a remote "healthy myocardium" area to be contoured.

The second technical contribution is to provide a 4D map of supervoxels (voxels clustered together based on local similarity) computed using similarity in strain curves. Our study found that while LGE performs extremely well in highlighting the area where scarring occurs, at the same time it underestimates the area of affected myocardium when compared with strain analysis. Here we investigated various registration methods, based on optical flow deformable registration, in order to evaluate the accuracy of computing the displacement curves, and subsequently validated the registration method against manually placed landmarks in the LV. The third technical contribution is a modification to the maskSLIC algorithm to generate six cluster centers in each short axis, basal and mid-ventricular slice, equally distributed at 60◦ from each other in order to mimic the AHA 16 segments model used in clinical practice.

Considering clinical perspectives centered approach, these technical advances will contribute to the field of regional assessment of the cardiac function, therefore potentially improving patient outcome.

Authors: Popescu, I

• Alternative title: Cardiac tissue wall motion abnormality assessment using CMR
• DOI: 10.5287/ora-yx8enzoo2
• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: maskSLIC; patient specific; wall motion assessment; late gadolinium assessment; tissue parcellation; LGE; SLIC supervoxels; cardiac MR; medical image registration
• Subjects: image registration; cardiac MRI; medical image analysis