Computer Aided Diagnosis systems for MR cancer detection

The research activity conducted during my PhD aims to develop two different Computer Aided Diagnosis (CAD) systems for breast and prostate cancer diagnosis using Magnetic Resonance Imaging. During the first part of this thesis I will illustrate a fully automatic CAD system for breast cancer detection and diagnosis with Dynamic Contrast Enhanced MRI (DCE-MRI) developed by our group. The main goal of a CAD system is lesions detection and characterization. The processing pipeline includes automatic segmentation of the breast and axillary regions, registration of unenhanced and contrast-enhanced frames, lesion detection and classification according to kinetic and morphological criteria. During my PhD I, firstly, studied the physiological phenomena correlated to breast tumors growth and diagnosis, then I elaborated and created C++ algorithms for: 1. breasts segmentation, where the breasts and axillary regions are automatically identified in order to reduce the computational burden and preventing false positives (FP) due to enhancing structures (such as the heart and extra-breast vessels) which are not of clinical interest. 2. lesion detection, in which suspicious areas showing contrast enhancement are automatically segmented and FPs are identified and discarded. These step are innovative as they are fully automatic, thus they do not suffer of inter- and intra-operator variability, and because of the normalization process, based on the mammary arteries segmentation, that makes the system able to deal with images coming from different centers, thus having different acquisition parameters. The second part of my thesis will concern the development of a CAD system for prostate cancer. The importance of this project is associated to the recent interest in adapting focal methods of tissue ablation, such as cryotherapy and Focused Ultrasound guided by MR (MRgFUS), to cure or control localized prostate cancer. Focal treatments rely on imaging to locate tumor, to determine the staging of disease, to detect recurrences and to guide the treatment. The aim of this part of my PhD was to create a multispectral computer aided diagnosis system able to: a) detect the tumor in order to guide real-time biopsy, b) characterize the malignancy of the lesion and c) guide the local treatment, by adopting a new multispectral approach. In this project I, actively, elaborated and developed C++ algorithm to register different datasets and to monitor the focal treatment using Diffusion Weighted-MRI (DWI) self-made acquisitions.The registration between T2-w, DCE-MRI and DWI images are applied in order to correct for patients movements and DWI distortions. Results obtained within 19 patients showed a Dice’s overlap coefficient higher than 0.7, considered optimal in literature. Monitoring the focal therapy was the aim of the last part of this project, that I actively developed during a visiting period in the Radiological Science Laboratory of the Stanford University (Kim Butts Pauly Research Lab). The main goal was to characterize the role of the DWI during MRgFUS. DWI, in fact, is very sensitive to cell death and tissue damage and information can be used to evaluate the treatment without relocating the patient and the applicators and without involving the administration of contrast agent. In this study, I wanted to assess the use of DWI images to estimate prostate tissue damage during HIFU ablation, by measuring diffusion coefficients of canine prostate pre and post ablation, using multiple b-factors ranging up to 3500 s/mm2 . This study demonstrated a bi-exponential decay of the signal increasing the b-values suggesting the presence of two different type of diffusion, called fast and slow.