Lung cancer is a disease affecting millions of people every year and poses a serious threat to global public health. Accurate lung cancer staging is crucial to choose an appropriate treatment protocol and to determine prognosis, this requires the acquisition of contrast-enhanced diagnostic CT (d-CT) that is usually followed by a PET/CT scan. Information from both d-CT and PET scan is used by the clinician in the staging process; however, these images are not intrinsically aligned because they are acquired on different days and on different scanners. Establishing anatomical correspondence, i.e., aligning the d-CT and the PET images is an inherently difficult task due to the absence of a direct relationship between the intensities of the images. The CT acquired during the PET/CT scan is used for attenuation correction (AC-CT) and is implicitly aligned with the PET image as they are acquired at the same time using a hybrid scanner. Patients are required to maintain shallow breathing for both scans. In contrast to that, the d-CT image is acquired after the injection of a contrast agent, and patients are required to maximally inhale, for better view of the lungs. Differences in the AC-CT and d-CT image volumes are thus due to differences in breathhold positions and image contrast. Nonetheless, both images are from the same modality. In this thesis, we present a new approach that aligns the d-CT with the PET image through an indirect registration process that uses the AC-CT. The deformation field obtained after the registration of the AC-CT to d-CT is used to align the PET image to the d-CT. Conventional image registration techniques deform the entire image using homogeneous regularization without taking into consideration the physical properties of the various anatomical structures. This homogeneous regularization may lead to physiologically and physically implausible deformations. To register the d-CT and AC-CT images, we developed a 3D registration framework based on a fluid transformation model including three physically motivated properties: (i) sliding motion of the lungs against the pleura; (ii) preservation of rigid structures; and (iii) preservation of topology. The sliding motion is modeled using a direction dependent regularization that decouples the tangential and the normal components of the external force term. The rigid shape of the bones is preserved using a spatially varying filter for the deformations. Finally, the topology is maintained using the concept of log-unbiased deformations. To solve the multi-modal registration problem due to the contrast injected for the d-CT, but lack thereof in the AC-CT, we use local cross correlation (LCC) as the similarity measure. To illustrate and validate the proposed registration framework, different intra-patient CT datasets are used, including the NCAT phantom, EMPIRE10 and POPI datasets. Results show that our proposed registration framework provides improved alignment and physically motivated deformations when compared to the classic elastic and fluid registration techniques. The final goal of our work was to demonstrate the clinical utility of our new approach that aligns d-CT and PET/AC-CT images for fusion. We apply our method to ten real patients. Our results show that the PET images have much improved alignment with the d-CT images using our proposed registration technique. Our method was successful in providing a good overlap of the lungs, improved alignment of the tumours and a lower target registration error for landmarks in comparison to the classic fluid registration. The main contribution of this thesis is the development of a comprehensive registration framework that integrates important physical properties into a state-of-the-art transformation model with application to lung imaging in cancer.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:581337 |
| Date | January 2013 |
| Creators | Baluwala, Habib |
| Contributors | Schnabel, Julia; Saddi, Kinda; Brady, Michael |
| Publisher | University of Oxford |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://ora.ox.ac.uk/objects/uuid:797c00c0-1efa-43e2-8268-e3d09ced0e06 |
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