Magnetic Resonance Imaging (MRI) Biomarkers for Therapeutic Response Prediction in Rectal Cancer

Prediction of chemoradiotherapy (CRT) response in rectal cancer would enable stratification of management whereby responders could undergo ‘watch-and-wait’ to avoid surgical morbidity, and non-responders could have early treatment intensification to improve therapeutic outcomes. Functional MRI can assess tumour function and heterogeneity, and may improve therapeutic response prediction. The aims of this PhD were to (i) prospectively evaluate multi-parametric MRI at 3.0 tesla in vivo combining diffusion weighted imaging (DWI) and dynamic contrast enhanced (DCE) MRI for prediction of CRT response and 2 year disease-free survival (DFS), and (ii) examine diffusion tensor imaging (DTI) MRI biomarkers of rectal cancer extent and heterogeneity at ultra-high field 11.7 tesla ex vivo in order to establish a pipeline for MRI biomarker discovery from ultra-high field to clinical field. Patients with locally advanced rectal cancer undergoing CRT followed by surgery underwent multi-parametric MRI before, during, and after CRT. A whole tumour voxelwise histogram analysis of apparent diffusion co-efficient (ADC) and Ktrans heterogeneity was performed and correlated with histopathology tumour regression grade. After CRT (before surgery) ADC 75th and 90th quantiles were significantly higher in responders than non-responders. Patients with higher Ktrans values after CRT or greater increase in Ktrans values from before to after CRT had a significantly higher risk of distant metastases, and lower 2 year DFS. Biobank tissue from patients with rectal cancer were examined at 11.7 tesla and DTI-MRI results correlated with histopathology. This work established a discovery framework for screening Biobank cancer tissue for novel MRI biomarkers of tumour extent and heterogeneity, and resulted in good preservation of tissue integrity and MRI-histopathology alignment. DTI-MRI derived fractional anisotropy (FA) was able to differentiate between tumour and desmoplasia, fibrous tissue, and muscularis propria, allowing for more accurate delineation of rectal cancer tumour extent and stromal heterogeneity ex vivo. In conclusion, DWI-MRI was predictive of CRT response, DCE-MRI was predictive of 2 year DFS, and DTI-MRI was able to more accurately define tumour extent and heterogeneity in rectal cancer. These findings could be useful for stratification of patients for individualised treatment based on accurate assessment of tumour extent and therapeutic response prediction

Semi-Automatic Segmentation of Normal Female Pelvic Floor Structures from Magnetic Resonance Images

Stress urinary incontinence (SUI) and pelvic organ prolapse (POP) are important health issues affecting millions of American women. Investigation of the cause of SUI and POP requires a better understand of the anatomy of female pelvic floor. In addition, pre-surgical planning and individualized treatment plans require development of patient-specific three-dimensional or virtual reality models. The biggest challenge in building those models is to segment pelvic floor structures from magnetic resonance images because of their complex shapes, which make manual segmentation labor-intensive and inaccurate. In this dissertation, a quick and reliable semi-automatic segmentation method based on a shape model is proposed. The model is built on statistical analysis of the shapes of structures in a training set. A local feature map of the target image is obtained by applying a filtering pipeline, including contrast enhancement, noise reduction, smoothing, and edge extraction. With the shape model and feature map, automatic segmentation is performed by matching the model to the border of the structure using an optimization technique called evolution strategy. Segmentation performance is evaluated by calculating a similarity coefficient between semi-automatic and manual segmentation results. Taguchi analysis is performed to investigate the significance of segmentation parameters and provide tuning trends for better performance. The proposed method was successfully tested on both two-dimensional and three-dimensional image segmentation using the levator ani and obturator muscles as examples. Although the method is designed for segmentation of female pelvic floor structures, it can also be applied to other structures or organs without large shape variatio

Semi-Automatic Segmentation of Normal Female Pelvic Floor Structures from Magnetic Resonance Images

Stress urinary incontinence (SUI) and pelvic organ prolapse (POP) are important health issues affecting millions of American women. Investigation of the cause of SUI and POP requires a better understand of the anatomy of female pelvic floor. In addition, pre-surgical planning and individualized treatment plans require development of patient-specific three-dimensional or virtual reality models. The biggest challenge in building those models is to segment pelvic floor structures from magnetic resonance images because of their complex shapes, which make manual segmentation labor-intensive and inaccurate. In this dissertation, a quick and reliable semi-automatic segmentation method based on a shape model is proposed. The model is built on statistical analysis of the shapes of structures in a training set. A local feature map of the target image is obtained by applying a filtering pipeline, including contrast enhancement, noise reduction, smoothing, and edge extraction. With the shape model and feature map, automatic segmentation is performed by matching the model to the border of the structure using an optimization technique called evolution strategy. Segmentation performance is evaluated by calculating a similarity coefficient between semi-automatic and manual segmentation results. Taguchi analysis is performed to investigate the significance of segmentation parameters and provide tuning trends for better performance. The proposed method was successfully tested on both two-dimensional and three-dimensional image segmentation using the levator ani and obturator muscles as examples. Although the method is designed for segmentation of female pelvic floor structures, it can also be applied to other structures or organs without large shape variatio

Semi-Automatic Segmentation of Normal Female Pelvic Floor Structures from Magnetic Resonance Images

Stress urinary incontinence (SUI) and pelvic organ prolapse (POP) are important health issues affecting millions of American women. Investigation of the cause of SUI and POP requires a better understand of the anatomy of female pelvic floor. In addition, pre-surgical planning and individualized treatment plans require development of patient-specific three-dimensional or virtual reality models. The biggest challenge in building those models is to segment pelvic floor structures from magnetic resonance images because of their complex shapes, which make manual segmentation labor-intensive and inaccurate. In this dissertation, a quick and reliable semi-automatic segmentation method based on a shape model is proposed. The model is built on statistical analysis of the shapes of structures in a training set. A local feature map of the target image is obtained by applying a filtering pipeline, including contrast enhancement, noise reduction, smoothing, and edge extraction. With the shape model and feature map, automatic segmentation is performed by matching the model to the border of the structure using an optimization technique called evolution strategy. Segmentation performance is evaluated by calculating a similarity coefficient between semi-automatic and manual segmentation results. Taguchi analysis is performed to investigate the significance of segmentation parameters and provide tuning trends for better performance. The proposed method was successfully tested on both two-dimensional and three-dimensional image segmentation using the levator ani and obturator muscles as examples. Although the method is designed for segmentation of female pelvic floor structures, it can also be applied to other structures or organs without large shape variatio

A novel diffusion tensor imaging-based computer-aided diagnostic system for early diagnosis of autism.

Autism spectrum disorders (ASDs) denote a significant growing public health concern. Currently, one in 68 children has been diagnosed with ASDs in the United States, and most children are diagnosed after the age of four, despite the fact that ASDs can be identified as early as age two. The ultimate goal of this thesis is to develop a computer-aided diagnosis (CAD) system for the accurate and early diagnosis of ASDs using diffusion tensor imaging (DTI). This CAD system consists of three main steps. First, the brain tissues are segmented based on three image descriptors: a visual appearance model that has the ability to model a large dimensional feature space, a shape model that is adapted during the segmentation process using first- and second-order visual appearance features, and a spatially invariant second-order homogeneity descriptor. Secondly, discriminatory features are extracted from the segmented brains. Cortex shape variability is assessed using shape construction methods, and white matter integrity is further examined through connectivity analysis. Finally, the diagnostic capabilities of these extracted features are investigated. The accuracy of the presented CAD system has been tested on 25 infants with a high risk of developing ASDs. The preliminary diagnostic results are promising in identifying autistic from control patients

Application of diffusion techniques to the segmentation of Mr 3D images for virtual colonoscopy

Master'sMASTER OF ENGINEERIN