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Registration of 3D Ultrasound Volumes with Applications in Neurosurgery and Prostate Radiotherapy


Registration of 3D Ultrasound Volumes with Applications in Neurosurgery and Prostate Radiotherapy

Zhou, Hang (2016) Registration of 3D Ultrasound Volumes with Applications in Neurosurgery and Prostate Radiotherapy. Masters thesis, Concordia University.

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Brain tissue deforms significantly after opening the dura and during tumor resection, invalidating pre-operative imaging data. Ultrasound is a popular imaging modality for providing the neurosurgeon with real-time updated images of brain tissue. Interpretation of post-resection ultrasound images is difficult due to large brain shift and tissue resection. Furthermore, several factors degrade the quality of post-resection ultrasound images such as strong reflection of waves at the interface of saline water and brain tissue in resection cavities, air bubbles and the application of blood-clotting agents around the edges of resection. Image registration allows comparison of post-resection ultrasound images with higher quality pre-resection images, assists in interpretation of post-resection images and may help identify residual tumor, and as such, is of significant clinical importance. Prostate motion is known to reduce the precision of prostate radiotherapy. This motion can be categorized into intrafraction and interfraction. Interfraction motion introduces large systematic errors into the treatment and is the largest contributor to prostate planning treatment volume (PTV) margins. Conventional solutions to interfraction motion all have respective drawbacks. Clarity Autoscan system provides continuous ultrasound imaging of the prostate for interfraction motion correction, however it is time-consuming and can have large interobserver errors. The intension of accurately targeting the prostate and reducing the side effects in treatment requests a faster and more accurate registration framework for interfraction motion correction.

In this thesis, we first propose a registration framework called Nonrigid Symmetric Registration (NSR) for accurate alignment of pre- and post-resection volumetric ultrasound images in near real-time. An outlier detection algorithm is proposed and utilized in this framework to identify non-corresponding regions (outliers) and therefore improve the robustness and accuracy of registration. We use an Efficient Second-order Minimization (ESM) method for fast and robust optimization. A symmetric and inverse-consistent method is exploited to generate realistic deformation fields. The results show that NSR significantly improves the quality of alignment between pre- and post-resection ultrasound images. Then based on this framework, we develop a rigid registration framework called Prostate Registration Framework (PRF) for alignment of the prosate region in simulation and treatment volumes. PRF is trained using 2 3D transperineal ultrasound (TPUS) images of an ultrasound prostate phantom and 20 3D TPUS images from 11 patients receiving Clarity Autoscan. Algorithm performance is evaluated using further 21 TPUS images from a total of 8 patients by comparison of the PRF with manual matching of landmarks and Clarity-based estimation of interfraction motion performed by three observers. The results show that PRF outputs more accurate alignment of the prosate region in simulation and treatment volumes than Clarity, and further, provides the reposition of the prostate in treatment images efficiently and accurately.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering
Item Type:Thesis (Masters)
Authors:Zhou, Hang
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Electrical and Computer Engineering
Date:12 December 2016
Thesis Supervisor(s):Hassan, Rivaz
Keywords:Efficient second-order minimization (ESM), Image registration, Neurosurgery, Outlier detection, Ultrasound imaging, Prostate radiotherapy, Rigid registration, Prostate motion.
ID Code:982049
Deposited By: HANG ZHOU
Deposited On:09 Jun 2017 14:22
Last Modified:18 Jan 2018 17:54


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