Learning approach to analyze tumour heterogeneity in DCE-MRI data during anti-cancer treatment

The paper proposes a learning approach to support medical researchers in the context of in-vivo cancer imaging, and specifically in the analysis of Dynamic Contrast-Enhanced MRI (DCE-MRI) data. Tumour heterogeneity is characterized by identifying regions with different vascular perfusion. The overall aim is to measure volume differences of such regions for two experimental groups: the treated group, to which an anticancer therapy is administered, and a control group. The proposed approach is based on a three-steps procedure: (i) robust features extraction from raw time-intensity curves, (ii) sample-regions identification manually traced by medical researchers on a small portion of input data, and (iii) overall segmentation by training a Support Vector Machine (SVM) to classify the MRI voxels according to the previously identified cancer areas. In this way a non-invasive method for the analysis of the treatment efficacy is obtained as shown by the promising results reported in our experiments. © 2009 Springer Berlin Heidelberg

Learning Approach to Analyze Tumour Heterogeneity in DCE-MRI Data During Anti-cancer Treatment

The paper proposes a learning approach to support, medical researchers in the context of in-vivo cancer imaging, and specifically in the analysis of Dynamic Contrast-Enhanced MRI (DCE-MRI) data. Tumour heterogeneity is characterized by identifying regions with different vascular perfusion. The overall aim is to measure volume differences of such regions for two experimental groups: the treated group, to which all anticancer therapy is administered, and a control group. The proposed approach is based oil a three-steps procedure: (i) robust features extraction from raw time-intensity curves, (ii) sample-regions identification manually traced by medical researchers oil a small portion of input, data, and (iii) overall segmentation by training a Support Vector Machine (SVM) to classify the MRI voxels according to the previously identified cancer areas. In this way a non-invasive method for the analysis of the treatment efficacy is obtained as shown by the promising results reported in our experiments

Cluster analysis of the signal curves in perfusion DCE-MRI datasets

Pathological studies show that tumors consist of different sub-regions with more homogeneous vascular properties during their growth. In addition, destroying tumor's blood supply is the target of most cancer therapies. Finding the sub-regions in the tissue of interest with similar perfusion patterns provides us with valuable information about tissue structure and angiogenesis. This information on cancer therapy, for example, can be used in monitoring the response of the cancer treatment to the drug. Cluster analysis of perfusion curves assays to find sub-regions with a similar perfusion pattern. The present work focuses on the cluster analysis of perfusion curves, measured by dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). The study, besides searching for the proper clustering method, follows two other major topics, the choice of an appropriate similarity measure, and determining the number of clusters. These three subjects are connected to each other in such a way that success in one direction will help solving the other problems. This work introduces a new similarity measure, parallelism measure (PM), for comparing the parallelism in the washout phase of the signal curves. Most of the previous works used the Euclidean distance as the measure of dissimilarity. However, the Euclidean distance does not take the patterns of the signal curves into account and therefore for comparing the signal curves is not sufficient. To combine the advantages of both measures a two-steps clustering is developed. The two-steps clustering uses two different similarity measures, the introduced PM measure and Euclidean distance in two consecutive steps. The results of two-steps clustering are compared with the results of other clustering methods. The two-steps clustering besides good performance has some other advantages. The granularity and the number of clusters are controlled by thresholds defined by considering the noise in signal curves. The method is easy to implement and is robust against noise. The focus of the work is mainly the cluster analysis of breast tumors in DCE-MRI datasets. The possibility to adopt the method for liver datasets is studied as well

Advanced image-processing techniques in magnetic resonance imaging for the investigation of brain pathologies and tumour angiogenesis

L'imaging a risonanza magnetica (MRI) \ue8 sempre pi\uf9 utilizzato in ambiente medico per la sua abilit\ue0 di produrre in modo non invasivo immagini di alt\ue0 qualit\ue0 dell'interno del corpo umano. Sin dalla sua introduzione nei primi anni 70, techiche di acquisizione via via pi\uf9 complesse sono state proposte, portando l'MRI ad essere utilizzata su uno spettro di applicazioni sempre pi\uf9 ampio. Le tecniche pi\uf9 innovative, tra cui la risonanza magnetica funzionale e di diffusione, richiedono tecniche di analisi ed algoritmi di elaborazione molto complessi per estrarre informazioni utili dai dati acquisiti. Lo scopo di questa tesi \ue8 stato quello di sviluppare e ottimizzare tecniche avanzate di elaborazione per applicarle all'analisi di dati di risonanza magnetica sia in ambiente preclinico che clinico. Durante il corso di dottorato sono stato coinvolto attivamente in diversi progetti di ricerca, ed ogni volta mi sono trovato ad affrontare problematiche diverse. In questa tesi, tuttavia, saranno riportati i risultati ottenuti nei tre progetti pi\uf9 interessanti a cui ho preso parte. Tali progetti avevano come obiettivo (i) l'implementazione di un protocollo sperimentale innovativo per imaging funzionale in animali da laboratorio, (ii) lo sviluppo di nuovi metodi per l'analisi di dati di Dynamic Contrast Enhanced MRI in modelli sperimentali di tumore e (iii) l'analisi di dati di diffusione in pazienti affetti da ischemia cerebrale. Particolare enfasi sar\ue0 posta sugli aspetti tecnici che riguardano gli algoritmi ed i metodi di elaborazione utilizzati nel processo di analisi.Magnetic resonance imaging (MRI) is increasingly being used in medical settings because of its ability to produce, non-invasively, high quality images of the inside of the human body. Since its introduction in early 70\u2019s, more and more complex acquisition techniques have been proposed, raising MRI to be exploited in a wide spectrum of applications. Innovative MRI modalities, such as diffusion and functional imaging, require complex analysis techniques and advanced algorithms in order to extract useful information from the acquired data. The aim of the present work has been to develop and optimize state-of-the-art techniques to be applied in the analysis of MRI data both in experimental and clinical settings. During my doctoral program I have been actively involved in several research projects, each time facing many different issues. In this dissertation, however, I will report the results obtained in three most appealing projects I partecipated to. These projects were devoted (i) to the implementation of an innovative experimental protocol for functional MRI in laboratory animals, (ii) to the development of new methods for the analysis of Dynamic Contrast Enhanced MRI data in experimental tumour models and (iii) to the analysis of diffusion MRI data in stroke patients. Particular emphasis will be given to the technical aspects regarding the algorithms and processing methods used in the analysis of data