liu.seSearch for publications in DiVA
Change search
ReferencesLink to record
Permanent link

Direct link
Automatic Alignment of 2D Cine Morphological Images Using 4D Flow MRI Data
Linköping University, Department of Electrical Engineering, Computer Vision.
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Cardiovascular diseases are among the most common causes of death worldwide. One of the recently developed flow analysis technique called 4D flow magnetic resonance imaging (MRI) allows an early detection of such diseases. Due to the limited resolution and contrast between blood pool and myocardium of 4D flow images, cine MR images are often used for cardiac segmentation. The delineated structures are then transferred to the 4D Flow images for cardiovascular flow analysis. Cine MR images are however acquired with multiple breath-holds, which can be challenging for some people, especially, when a cardiovascular disease is present. Consequently, unexpected breathing motion by a patient may lead to misalignments between the acquired cine MR images.

The goal of the thesis is to test the feasibility of an automatic image registration method to correct the misalignment caused by respiratory motion in morphological 2D cine MR images by using the 4D Flow MR as the reference image. As a registration method relies on a set of optimal parameters to provide desired results, a comprehensive investigation was performed to find such parameters. Different combinations of registration parameters settings were applied on 20 datasets from both healthy volunteers and patients. The best combinations, selected on the basis of normalized cross-correlation, were evaluated using the clinical gold-standard by employing widely used geometric measures of spatial correspondence. The accuracy of the best parameters from geometric evaluation was finally validated by using simulated misalignments.

Using a registration method consisting of only translation improved the results for both datasets from healthy volunteers and patients and the simulated misalignment data. For the datasets from healthy volunteers and patients, the registration improved the results from 0.7074 ± 0.1644 to 0.7551 ± 0.0737 in Dice index and from 1.8818 ± 0.9269 to 1.5953 ± 0.5192 for point-to-curve error. These values are a mean value for all the 20 datasets.

The results from geometric evaluation on the data from both healthy volunteers and patients show that the developed correction method is able to improve the alignment of the cine MR images. This allows a reliable segmentation of 4D flow MR images for cardiac flow assessment.

Place, publisher, year, edition, pages
2016. , 63 p.
National Category
Medical Image Processing
URN: urn:nbn:se:liu:diva-131470ISRN: LiTH-ISY-EX–16/4992–SEOAI: diva2:972664
Subject / course
Computer Vision Laboratory
Available from: 2016-09-23 Created: 2016-09-21 Last updated: 2016-09-23Bibliographically approved

Open Access in DiVA

fulltext(2373 kB)13 downloads
File information
File name FULLTEXT01.pdfFile size 2373 kBChecksum SHA-512
Type fulltextMimetype application/pdf

By organisation
Computer Vision
Medical Image Processing

Search outside of DiVA

GoogleGoogle Scholar
Total: 13 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 67 hits
ReferencesLink to record
Permanent link

Direct link