IMAGE PROCESSING, SEGMENTATION AND MACHINE LEARNING MODELS TO CLASSIFY AND DELINEATE TUMOR VOLUMES TO SUPPORT MEDICAL DECISION

Techniques for processing and analysing images and medical data have become the main’s translational applications and researches in clinical and pre-clinical environments. The advantages of these techniques are the improvement of diagnosis accuracy and the assessment of treatment response by means of quantitative biomarkers in an efficient way. In the era of the personalized medicine, an early and efficacy prediction of therapy response in patients is still a critical issue. In radiation therapy planning, Magnetic Resonance Imaging (MRI) provides high quality detailed images and excellent soft-tissue contrast, while Computerized Tomography (CT) images provides attenuation maps and very good hard-tissue contrast. In this context, Positron Emission Tomography (PET) is a non-invasive imaging technique which has the advantage, over morphological imaging techniques, of providing functional information about the patient’s disease. In the last few years, several criteria to assess therapy response in oncological patients have been proposed, ranging from anatomical to functional assessments. Changes in tumour size are not necessarily correlated with changes in tumour viability and outcome. In addition, morphological changes resulting from therapy occur slower than functional changes. Inclusion of PET images in radiotherapy protocols is desirable because it is predictive of treatment response and provides crucial information to accurately target the oncological lesion and to escalate the radiation dose without increasing normal tissue injury. For this reason, PET may be used for improving the Planning Treatment Volume (PTV). Nevertheless, due to the nature of PET images (low spatial resolution, high noise and weak boundary), metabolic image processing is a critical task. The aim of this Ph.D thesis is to develope smart methodologies applied to the medical imaging field to analyse different kind of problematic related to medical images and data analysis, working closely to radiologist physicians. Various issues in clinical environment have been addressed and a certain amount of improvements has been produced in various fields, such as organs and tissues segmentation and classification to delineate tumors volume using meshing learning techniques to support medical decision. In particular, the following topics have been object of this study: • Technique for Crohn’s Disease Classification using Kernel Support Vector Machine Based; • Automatic Multi-Seed Detection For MR Breast Image Segmentation; • Tissue Classification in PET Oncological Studies; • KSVM-Based System for the Definition, Validation and Identification of the Incisinal Hernia Reccurence Risk Factors; • A smart and operator independent system to delineate tumours in Positron Emission Tomography scans; 3 • Active Contour Algorithm with Discriminant Analysis for Delineating Tumors in Positron Emission Tomography; • K-Nearest Neighbor driving Active Contours to Delineate Biological Tumor Volumes; • Tissue Classification to Support Local Active Delineation of Brain Tumors; • A fully automatic system of Positron Emission Tomography Study segmentation. This work has been developed in collaboration with the medical staff and colleagues at the: • Dipartimento di Biopatologia e Biotecnologie Mediche e Forensi (DIBIMED), University of Palermo • Cannizzaro Hospital of Catania • Istituto di Bioimmagini e Fisiologia Molecolare (IBFM) Centro Nazionale delle Ricerche (CNR) of Cefalù • School of Electrical and Computer Engineering at Georgia Institute of Technology The proposed contributions have produced scientific publications in indexed computer science and medical journals and conferences. They are very useful in terms of PET and MRI image segmentation and may be used daily as a Medical Decision Support Systems to enhance the current methodology performed by healthcare operators in radiotherapy treatments. The future developments of this research concern the integration of data acquired by image analysis with the managing and processing of big data coming from a wide kind of heterogeneous sources

Quantitative imaging for targeted radionuclide therapy dosimetry : technical review

Targeted radionuclide therapy (TRT) is a promising technique for cancer therapy. However, in order to deliver the required dose to the tumor, minimize potential toxicity in normal organs, as well as monitor therapeutic effects, it is important to assess the individualized internal dosimetry based on patient-specific data. Advanced imaging techniques, especially radionuclide imaging, can be used to determine the spatial distribution of administered tracers for calculating the organ-absorbed dose. While planar scintigraphy is still the mainstream imaging method, SPECT, PET and bremsstrahlung imaging have promising properties to improve accuracy in quantification. This article reviews the basic principles of TRT and discusses the latest development in radionuclide imaging techniques for different theranostic agents, with emphasis on their potential to improve personalized TRT dosimetry

CT Scanning

Since its introduction in 1972, X-ray computed tomography (CT) has evolved into an essential diagnostic imaging tool for a continually increasing variety of clinical applications. The goal of this book was not simply to summarize currently available CT imaging techniques but also to provide clinical perspectives, advances in hybrid technologies, new applications other than medicine and an outlook on future developments. Major experts in this growing field contributed to this book, which is geared to radiologists, orthopedic surgeons, engineers, and clinical and basic researchers. We believe that CT scanning is an effective and essential tools in treatment planning, basic understanding of physiology, and and tackling the ever-increasing challenge of diagnosis in our society

Virtual clinical trials in medical imaging: a review

The accelerating complexity and variety of medical imaging devices and methods have outpaced the ability to evaluate and optimize their design and clinical use. This is a significant and increasing challenge for both scientific investigations and clinical applications. Evaluations would ideally be done using clinical imaging trials. These experiments, however, are often not practical due to ethical limitations, expense, time requirements, or lack of ground truth. Virtual clinical trials (VCTs) (also known as in silico imaging trials or virtual imaging trials) offer an alternative means to efficiently evaluate medical imaging technologies virtually. They do so by simulating the patients, imaging systems, and interpreters. The field of VCTs has been constantly advanced over the past decades in multiple areas. We summarize the major developments and current status of the field of VCTs in medical imaging. We review the core components of a VCT: computational phantoms, simulators of different imaging modalities, and interpretation models. We also highlight some of the applications of VCTs across various imaging modalities

Analysis of Subchondral Bone and Microvessels Using a Novel Vascular Perfusion Contrast Agent and Optimized Dual-Energy Computed Tomography

Osteoarthritis (OA), is a chronic debilitating disease that affects millions of individuals and is characterized by the degeneration of joint subchondral bone and cartilage. These tissue degenerations manifest as joint pain, limited range of joint motion, and overall diminished quality of life. Currently, the exact mechanism(s) and cause(s) by which OA initiates and progresses remain unknown. The multi-factorial complex nature of OA (i.e. age, diabetes, obesity, and prior injuries have all been shown to play a role in OA) contributes to the current lack of a cure or effective long-term treatment for OA. One re-emerging and interesting hypothesis revolves around the delicate homeostatic microvascular environment around the cartilage – an avascular tissue. The absence of blood vessels within cartilage stresses the importance of nutrient and oxygen delivery from the neighbouring synovium and subchondral bone. Currently, the effects of changes in the subchondral bone microvessel density on cartilage health remain unknown due to the difficulties in simultaneously studying dense bone and the associated small microvessels. Computed tomography (CT) is widely used in the diagnosis of OA, as the use of x-rays provide detailed images of the bone degeneration associated with OA. However, the study of microvessels using CT has been exceptionally difficult due to their small (\u3c 10 µm) size, lack of contrast from neighbouring soft tissues, and proximity to dense bone. The purpose of this thesis was to develop a novel dual-energy micro-computed tomography (DECT) compatible vascular perfusion contrast agent and the associated instrumentation to optimize DECT on pre-clinical, cone-beam micro-CT scanners. The combination of these two techniques would facilitate the simultaneous visualization and quantification of subchondral bone and microvessels within the bone underlining the cartilage (i.e. distal femoral epiphysis and proximal tibial epiphysis) of rats that have undergone an OA-induced surgery. Results gained from this study will further provide information into the role that microvessels may play in OA