Diagnosis of adhesions is currently made by clinicians based on medical history and symptoms and is often a diagnosis of exclusion. This is highly subjective with variable results. Invasive procedures are the only reliable method to diagnose adhesions (explorative laparoscopy/laparotomy) and are known to cause further adhesion formation. This is the justification for a more consistent method based on the non-invasive detection/confirmation of abdominal adhesions. This would be of significant clinical benefit. Abdominal adhesions have proven a challenge to detect non-invasively using conventional imaging techniques (CT, static MRI). Both ultrasound and cine-MR have been used to assess visceral sliding motion
19 anomalies with some success. Ultrasound has advantages (e.g. non-ionising, excellent temporal resolution) but also has drawbacks:
It has a limited depth of detection and is therefore primarily useful for detection of parietal wall adhesions. This issue becomes exacerbated when scanning obese patients and in the presence of intestinal gas.
Using conventional 2D ultrasound is suitable for indicating a suitable incision site, as the reviewed papers have shown, but is unlikely to be ideal to diagnose and map adhesions throughout the abdomen. To use the 2D ultrasound visceral slide technique as an adhesion screening procedure may prove too cumbersome and time consuming. However, despite mixed reports, overall, ultrasound appears to have value in detecting abdominal wall adhesions. In contrast, conventional, static MRI assigned to assess structural changes characteristic of adhesions/EPS has received little attention and is reported to be inadequate [29, 28]. However, cine-MRI to analyse movement and visceral slide has also been identified to have potential. Several groups have reported successful statistics for adhesion detection with cine-MRI, although a study by Zinther et al. (2010) resulted in a lower sensitivity [32, 20, 31].
Despite numerous efforts, a reliable technique for non-invasive detection of adhesions remains elusive. The application of cine-MRI for adhesion detection has shown some success but remains plagued by high inter-operator variability, time-consuming radiologist examination and a large amount of training required to become proficient [63]. The addition of a diagnostic aid for cine-MRI adhesion detection could therefore help improve reliability, particularly with less experienced or trainee radiologists.
Previous work completed at Sheffield has produced a technique for motion analysis of the abdominal contents aimed at the detection of gross abnormalities. A previous PhD has thoroughly tested ShIRT (the primary registration algorithm used throughout this current PhD) for its suitability for abdominal motion analysis in cine-MRI. ShIRT was selected as the registration algorithm and incorporated into AbsCAT – a program created to streamline the processing technique for movement analysis. Work published by the group at Sheffield has demonstrated cine-MRI’s potential application for EPS detection when aided by image processing [33].
20 Early diagnosis of EPS was the main focus of work prior to this PhD and remains an unmet challenge. The AbsCAT technique looked promising but a lack of clinical data limited subsequent application and provoked re-examination of the method for adhesion detection. The outcome was this PhD, which stands on the foundations laid by previous work but pursues the detection of subtler, more common adhesive pathologies rather than its extensive manifestation in conditions such as EPS.
With consideration to the background of the project, the hypothesis for this PhD is:
“The appropriate manipulation and analysis of image registration applied to cine-MRI can yield improved diagnostic signatures for detection of abdominal adhesions”
To address the hypothesis, the thesis is arranged into 6 further chapters:
Chapter 2: Discusses the background to image registration and places this theory in the context of adhesion detection in dynamic abdominal imaging.
Chapter 3: Communicates a new approach developed for adhesion detection by quantifying the amount of visceral slide around the perimeter of the abdominal cavity.
Chapter 4: Characterises and tests several features of the technique described in Chapter 3 via a series of experiments.
Chapter 5: Tests the technique on clinical data in a pilot study to determine its efficacy for adhesion detection.
Chapter 6: Provides an overarching discussion of the work presented throughout the thesis. Chapter 7: Summarises the thesis and confirms the trajectory for future work.
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Chapter 2
Image processing in abdominal cine-
MRI
The post-processing of medical images can extract or highlight information relevant to a particular diagnostic investigation for enhanced interpretation and diagnostic power [64, 65, 63]. There are two principal image processing techniques which are of relevance to the cine- MRI images in this project: image segmentation and image registration. Image segmentation is the process of separating different regions of an image from one another, usually identifying and isolating coherent anatomical structures [66]. Image registration is a tool used to achieve spatial alignment between corresponding features in two images [67]. Image registration can be used for a wide variety of applications in medical imaging [68, 69, 70, 71, 72]. In this project it is used to track the path of objects in the abdomen through a dynamic imaging sequence. This chapter introduces these image processing techniques to inform project direction and justify choices made. An introduction to image segmentation and registration are offered followed by a review of existing image processing techniques which have been applied to the abdomen. Fundamental components of image registration are presented in 1D, via a specific 1D registration implementation and then generalised for wider application in higher dimensions.
However, it is first important to gain an appreciation of the images which are to be processed. For reference, two typical abdominal sagittal MR frames from different patients are shown in Figure 2.1 to aid subsequent discussions. These images are two frames from a dynamic image set of, typically, 30 frames designed to capture motion.
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(a) (b)
Figure 2.1: Two typical sagittal MR slices from different patients: a) typical right paramedian slice, b) typical midline slice