It appears you don't have support to open PDFs in this web browser. To view this file, Open with your PDF reader
Abstract
Medical image registration is an important technology that can be used to align patient images from different treatment time points to a single reference frame. This process of establishing correspondences between images is important in both the clinic and research to make meaningful comparisons across scans and better understand changes that may have occurred over time. Traditional algorithms and those used in general practice assume a one-to-one correspondence between features in the images to be registered. However, this assumption is clearly violated when the images are missing correspondences, which often occurs when dealing with patient data due to treatment effects or disease progression. Standard registration methods, therefore, will likely cause misalignment of actual corresponding features, especially near regions with missing data, which are usually the locations we are most interested in aligning.
The purpose of this dissertation is to develop an automated image registration algorithm to deal with the missing correspondence problem. Our key idea is to incorporate the estimation of a label map segmenting the valid and missing correspondence voxels during the registration. We pose the registration goal as a parameter estimation problem in a maximum a posteriori framework and jointly solve for the transformation parameters and label map using the expectation-maximization (EM) algorithm. In each iteration of the algorithm, the E-step computes the probability of label assignment for valid and missing correspondences given the current transformation, while the M-step updates the registration parameters using the current label map probabilities. Under our mathematical formulation, we incorporate four models: image similarity, which defines how well the image intensities match given the registration parameters; an image intensity prior given a label map estimate; a prior on the registration parameters to constrain how an image can be deformed; and a prior on the label map segmentation.
The algorithmic framework we have developed is general and can be adapted to many missing correspondence problems by appropriate implementation of the four models. Here, we have designed implementations tailored to handle different missing correspondence situations in T1-weighted magnetic resonance images of the brain, using preoperative and postresection brain images from epilepsy patients and scans from brain tumor patients as our examples. We tested various implementations of our method against other automated intensity-based image registration algorithms and demonstrated improved alignment on both synthetic and patient data. Finally, we presented an application of our registration and labeling estimation algorithm for aiding in tracking brain metastases in a patient over time.
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer