Registration of prone and supine CT colonography images and its clinical application

Computed tomographic (CT) colonography is a technique for detecting bowel cancer and potentially precancerous polyps. CT imaging is performed on the cleansed and insufflated bowel in order to produce a virtual endoluminal representation similar to optical colonoscopy. Because fluids and stool can mimic pathology, images are acquired with the patient in both prone and supine positions. Radiologists then match endoluminal locations visually between the two acquisitions in order to determine whether pathology is real or not. This process is hindered by the fact that the colon can undergo considerable deformation between acquisitions. Robust and accurate automated registration between prone and supine data acquisitions is therefore pivotal for medical interpretation, but a challenging problem. The method proposed in this thesis reduces the complexity of the registration task of aligning the prone and supine CT colonography acquisitions. This is done by utilising cylindrical representations of the colonic surface which reflect the colon's specific anatomy. Automated alignment in the cylindrical domain is achieved by non-rigid image registration using surface curvatures, applicable even when cases exhibit local luminal collapses. It is furthermore shown that landmark matches for initialisation improve the registration's accuracy and robustness. Additional performance improvements are achieved by symmetric and inverse-consistent registration and iteratively deforming the surface in order to compensate for differences in distension and bowel preparation. Manually identified reference points in human data and fiducial markers in a porcine phantom are used to validate the registration accuracy. The potential clinical impact of the method has been evaluated using data that reflects clinical practise. Furthermore, correspondence between follow-up CT colonography acquisitions is established in order to facilitate the clinical need to investigate polyp growth over time. Accurate registration has the potential to both improve the diagnostic process and decrease the radiologist's interpretation time. Furthermore, its result could be integrated into algorithms for improved computer-aided detection of colonic polyps

Facilitating Colorectal Cancer Diagnosis with Computed Tomographic Colonography

Computed tomographic colonography (CTC) is a diagnostic technique involving helical volume acquisition of the cleansed, distended colorectum to detect colorectal cancer or potentially premalignant polyps. This Thesis summarises the evidence base, identifies areas in need of further research, quantifies sources of bias and presents novel techniques to facilitate colorectal cancer diagnosis using CTC. CTC literature is reviewed to justify the rationale for current implementation and to identify fruitful areas for research. This confirms excellent diagnostic performance can be attained providing CTC is interpreted by trained, experienced observers employing state-of-the-art implementation. The technique is superior to barium enema and consequently, it has been embraced by radiologists, clinicians and health policy-makers. Factors influencing generalisability of CTC research are investigated, firstly with a survey of European educational workshop participants which revealed limited CTC experience and training, followed by a systematic review exploring bias in research studies of diagnostic test accuracy which established that studies focussing on these aspects were lacking. Experiments to address these sources of bias are presented, using novel methodology: Conjoint analysis is used to ascertain patients‘ and clinicians’ attitudes to false-positive screening diagnoses, showing that both groups overwhelmingly value sensitivity over specificity. The results inform a weighted statistical analysis for CAD which is applied to the results of two previous studies showing the incremental benefit is significantly higher for novices than experienced readers. We have employed eye-tracking technology to establish the visual search patterns of observers reading CTC, demonstrated feasibility and developed metrics for analysis. We also describe development and validation of computer software to register prone and supine endoluminal surface locations demonstrating accurate matching of corresponding points when applied to a phantom and a generalisable, publically available, CTC database. Finally, areas in need of future development are suggested

Remote access computed tomography colonography

This thesis presents a novel framework for remote access Computed Tomography Colonography (CTC). The proposed framework consists of several integrated components: medical image data delivery, 2D image processing, 3D visualisation, and feedback provision. Medical image data sets are notoriously large and preserving the integrity of the patient data is essential. This makes real-time delivery and visualisation a key challenge. The main contribution of this work is the development of an efficient, lossless compression scheme to minimise the size of the data to be transmitted, thereby alleviating transmission time delays. The scheme utilises prior knowledge of anatomical information to divide the data into specific regions. An optimised compression method for each anatomical region is then applied. An evaluation of this compression technique shows that the proposed ‘divide and conquer’ approach significantly improves upon the level of compression achieved using more traditional global compression schemes. Another contribution of this work resides in the development of an improved volume rendering technique that provides real-time 3D visualisations of regions within CTC data sets. Unlike previous hardware acceleration methods which rely on dedicated devices, this approach employs a series of software acceleration techniques based on the characteristic properties of CTC data. A quantitative and qualitative evaluation indicates that the proposed method achieves real-time performance on a low-cost PC platform without sacrificing any image quality. Fast data delivery and real-time volume rendering represent the key features that are required for remote access CTC. These features are ultimately combined with other relevant CTC functionality to create a comprehensive, high-performance CTC framework, which makes remote access CTC feasible, even in the case of standard Web clients with low-speed data connections

Computer-aided detection of polyps in CT colonography

Master'sMASTER OF ENGINEERIN

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Towards a non-invasive diagnostic aid for abdominal adhesions using dynamic MRI and image processing

This work presents a strategy for detection of abdominal adhesions based on cine-MRI data, image processing and the production of a ‘sheargram’. Abdominal adhesions are a common complication of abdominal surgery and can cause serious morbidity. Diagnosis is difficult and often one of exclusion. A conclusive diagnosis typically requires laparoscopic explorative surgery, which itself may cause further adhesions. A non-invasive means of diagnosis is preferred and likely to aid patient management. Cine-MRI can capture the motion of the abdominal structures during respiration and has shown promise for adhesion detection. However, such images are difficult and time consuming to interpret. A previous PhD considered augmenting cine-MRI by quantifying movement for detection of gross adhesive pathology. This thesis presents a refined image processing approach aimed at detection of more subtle adhesions to the abdominal wall. In the absence of adhesive pathology, the abdominal contents (bowels, kidneys, liver) slide smoothly against the perimeter of the abdominal cavity – a process termed visceral slide. An adhesion is expected to produce a localised resistance that inhibits smooth visceral sliding. In this PhD, development of a 2D technique to quantify sliding around the perimeter of the abdominal cavity (with particular emphasis on the abdominal wall) sought to highlight regions of reduced sliding. Segmentation and image registration were employed to quantify movement and shear, the latter used as an analogue for sliding. The magnitude of shear over all frames in the dynamic MR image sequence was extracted and displayed as a colour plot over the MR image for anatomical context. This final output is termed a ‘sheargram’. Suitability of the technique for diagnosis was assessed through a series of experimental tests and correlation with clinical data. The latter involved a retrospective pilot study incorporating data from 52 patients scanned for suspected adhesions. A total of 141 slices were processed and reported. The validation experiments confirmed the technique had the attributes to accurately and reproducibly report sliding and demonstrated proof of concept for detection of adhered regions. The pilot study confirmed the sheargram matched expert clinical judgement in the vast majority of cases (>84%) and detected >93% of all adhesions. However, the investigation also highlighted limitations, principally structures moving out of the imaging plane creates a fundamental problem and requires a 3D imaging solution. In conclusion, the work has produced encouraging results and merits further development