Image Based Biomarkers from Magnetic Resonance Modalities: Blending Multiple Modalities, Dimensions and Scales.

The successful analysis and processing of medical imaging data is a multidisciplinary work that requires the application and combination of knowledge from diverse fields, such as medical engineering, medicine, computer science and pattern classification. Imaging biomarkers are biologic features detectable by imaging modalities and their use offer the prospect of more efficient clinical studies and improvement in both diagnosis and therapy assessment. The use of Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) and its application to the diagnosis and therapy has been extensively validated, nevertheless the issue of an appropriate or optimal processing of data that helps to extract relevant biomarkers to highlight the difference between heterogeneous tissue still remains. Together with DCE-MRI, the data extracted from Diffusion MRI (DWI-MR and DTI-MR) represents a promising and complementary tool. This project initially proposes the exploration of diverse techniques and methodologies for the characterization of tissue, following an analysis and classification of voxel-level time-intensity curves from DCE-MRI data mainly through the exploration of dissimilarity based representations and models. We will explore metrics and representations to correlate the multidimensional data acquired through diverse imaging modalities, a work which starts with the appropriate elastic registration methodology between DCE-MRI and DWI- MR on the breast and its corresponding validation. It has been shown that the combination of multi-modal MRI images improve the discrimination of diseased tissue. However the fusion of dissimilar imaging data for classification and segmentation purposes is not a trivial task, there is an inherent difference in information domains, dimensionality and scales. This work also proposes a multi-view consensus clustering methodology for the integration of multi-modal MR images into a unified segmentation of tumoral lesions for heterogeneity assessment. Using a variety of metrics and distance functions this multi-view imaging approach calculates multiple vectorial dissimilarity-spaces for each one of the MRI modalities and makes use of the concepts behind cluster ensembles to combine a set of base unsupervised segmentations into an unified partition of the voxel-based data. The methodology is specially designed for combining DCE-MRI and DTI-MR, for which a manifold learning step is implemented in order to account for the geometric constrains of the high dimensional diffusion information.The successful analysis and processing of medical imaging data is a multidisciplinary work that requires the application and combination of knowledge from diverse fields, such as medical engineering, medicine, computer science and pattern classification. Imaging biomarkers are biologic features detectable by imaging modalities and their use offer the prospect of more efficient clinical studies and improvement in both diagnosis and therapy assessment. The use of Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) and its application to the diagnosis and therapy has been extensively validated, nevertheless the issue of an appropriate or optimal processing of data that helps to extract relevant biomarkers to highlight the difference between heterogeneous tissue still remains. Together with DCE-MRI, the data extracted from Diffusion MRI (DWI-MR and DTI-MR) represents a promising and complementary tool. This project initially proposes the exploration of diverse techniques and methodologies for the characterization of tissue, following an analysis and classification of voxel-level time-intensity curves from DCE-MRI data mainly through the exploration of dissimilarity based representations and models. We will explore metrics and representations to correlate the multidimensional data acquired through diverse imaging modalities, a work which starts with the appropriate elastic registration methodology between DCE-MRI and DWI- MR on the breast and its corresponding validation. It has been shown that the combination of multi-modal MRI images improve the discrimination of diseased tissue. However the fusion of dissimilar imaging data for classification and segmentation purposes is not a trivial task, there is an inherent difference in information domains, dimensionality and scales. This work also proposes a multi-view consensus clustering methodology for the integration of multi-modal MR images into a unified segmentation of tumoral lesions for heterogeneity assessment. Using a variety of metrics and distance functions this multi-view imaging approach calculates multiple vectorial dissimilarity-spaces for each one of the MRI modalities and makes use of the concepts behind cluster ensembles to combine a set of base unsupervised segmentations into an unified partition of the voxel-based data. The methodology is specially designed for combining DCE-MRI and DTI-MR, for which a manifold learning step is implemented in order to account for the geometric constrains of the high dimensional diffusion information

Functional and structural MRI image analysis for brain glial tumors treatment

Cotutela con il Dipartimento di Biotecnologie e Scienze della Vita, Universiità degli Studi dell'Insubria.openThis Ph.D Thesis is the outcome of a close collaboration between the Center for Research in Image Analysis and Medical Informatics (CRAIIM) of the Insubria University and the Operative Unit of Neurosurgery, Neuroradiology and Health Physics of the University Hospital ”Circolo Fondazione Macchi”, Varese. The project aim is to investigate new methodologies by means of whose, develop an integrated framework able to enhance the use of Magnetic Resonance Images, in order to support clinical experts in the treatment of patients with brain Glial tumor. Both the most common uses of MRI technology for non-invasive brain inspection were analyzed. From the Functional point of view, the goal has been to provide tools for an objective reliable and non-presumptive assessment of the brain’s areas locations, to preserve them as much as possible at surgery. From the Structural point of view, methodologies for fully automatic brain segmentation and recognition of the tumoral areas, for evaluating the tumor volume, the spatial distribution and to be able to infer correlation with other clinical data or trace growth trend, have been studied. Each of the proposed methods has been thoroughly assessed both qualitatively and quantitatively. All the Medical Imaging and Pattern Recognition algorithmic solutions studied for this Ph.D. Thesis have been integrated in GliCInE: Glioma Computerized Inspection Environment, which is a MATLAB prototype of an integrated analysis environment that offers, in addition to all the functionality specifically described in this Thesis, a set of tools needed to manage Functional and Structural Magnetic Resonance Volumes and ancillary data related to the acquisition and the patient.openInformaticaPedoia, ValentinaPedoia, Valentin

Quantification of tumor heterogeneity using PET/MRI and machine learning

Despite a broad understanding that solid tumors exhibit significant tissue heterogeneity, clinical trials have not seen a remarkable development in techniques that aid in characterizing cancer. Needle biopsies often represent only a partial view of the tumor profile, lacking the ability to comprehensively reflect spatiotemporal phenotypic changes. Recent multimodal multiparametric imaging techniques could provide further valuable insights if the complementary imaging information is sufficiently analyzed. Therefore, in this work I developed and applied machine learning methods on multiparametric positron emission tomography (PET) and magnetic resonance imaging (MRI) datasets, acquired using mice bearing subcutaneous tumors, to obtain a precise spatio-temporal characterization of intratumor heterogeneity

Functional and structural MRI image analysis for brain glial tumors treatment

This Ph.D Thesis is the outcome of a close collaboration between the Center for Research in Image Analysis and Medical Informatics (CRAIIM) of the Insubria University and the Operative Unit of Neurosurgery, Neuroradiology and Health Physics of the University Hospital ”Circolo Fondazione Macchi”, Varese. The project aim is to investigate new methodologies by means of whose, develop an integrated framework able to enhance the use of Magnetic Resonance Images, in order to support clinical experts in the treatment of patients with brain Glial tumor. Both the most common uses of MRI technology for non-invasive brain inspection were analyzed. From the Functional point of view, the goal has been to provide tools for an objective reliable and non-presumptive assessment of the brain’s areas locations, to preserve them as much as possible at surgery. From the Structural point of view, methodologies for fully automatic brain segmentation and recognition of the tumoral areas, for evaluating the tumor volume, the spatial distribution and to be able to infer correlation with other clinical data or trace growth trend, have been studied. Each of the proposed methods has been thoroughly assessed both qualitatively and quantitatively. All the Medical Imaging and Pattern Recognition algorithmic solutions studied for this Ph.D. Thesis have been integrated in GliCInE: Glioma Computerized Inspection Environment, which is a MATLAB prototype of an integrated analysis environment that offers, in addition to all the functionality specifically described in this Thesis, a set of tools needed to manage Functional and Structural Magnetic Resonance Volumes and ancillary data related to the acquisition and the patient

Computational methods to predict and enhance decision-making with biomedical data.

The proposed research applies machine learning techniques to healthcare applications. The core ideas were using intelligent techniques to find automatic methods to analyze healthcare applications. Different classification and feature extraction techniques on various clinical datasets are applied. The datasets include: brain MR images, breathing curves from vessels around tumor cells during in time, breathing curves extracted from patients with successful or rejected lung transplants, and lung cancer patients diagnosed in US from in 2004-2009 extracted from SEER database. The novel idea on brain MR images segmentation is to develop a multi-scale technique to segment blood vessel tissues from similar tissues in the brain. By analyzing the vascularization of the cancer tissue during time and the behavior of vessels (arteries and veins provided in time), a new feature extraction technique developed and classification techniques was used to rank the vascularization of each tumor type. Lung transplantation is a critical surgery for which predicting the acceptance or rejection of the transplant would be very important. A review of classification techniques on the SEER database was developed to analyze the survival rates of lung cancer patients, and the best feature vector that can be used to predict the most similar patients are analyzed

Complexity Reduction in Image-Based Breast Cancer Care

The diversity of malignancies of the breast requires personalized diagnostic and therapeutic decision making in a complex situation. This thesis contributes in three clinical areas: (1) For clinical diagnostic image evaluation, computer-aided detection and diagnosis of mass and non-mass lesions in breast MRI is developed. 4D texture features characterize mass lesions. For non-mass lesions, a combined detection/characterisation method utilizes the bilateral symmetry of the breast s contrast agent uptake. (2) To improve clinical workflows, a breast MRI reading paradigm is proposed, exemplified by a breast MRI reading workstation prototype. Instead of mouse and keyboard, it is operated using multi-touch gestures. The concept is extended to mammography screening, introducing efficient navigation aids. (3) Contributions to finite element modeling of breast tissue deformations tackle two clinical problems: surgery planning and the prediction of the breast deformation in a MRI biopsy device

Microstructural imaging of the human spinal cord with advanced diffusion MRI

The aim of this PhD thesis is to advance the state-of-the-art of spinal cord magnetic resonance imaging (MRI) in multiple sclerosis (MS), a demyelinating, inflammatory and neurodegenerative disease of the central nervous system. Neurite orientation dispersion and density imaging (NODDI) is a recent diffusion-weighted (DW) MRI technique that provides indices of density and orientation dispersion of neuronal processes. These could be new useful biomarkers for the spinal cord, since they could better characterise overall, widespread MS pathology than conventional metrics. In this thesis, we test innovative clinically feasible acquisitions as well as signal analysis methods to study the potential of NODDI for the spinal cord. We also design and run computer simulations that corroborate our in vivo findings. Furthermore, we compare NODDI metrics to quantitative histological features, with the aim of validating their specificity. The thesis is divided in two parts. In the first part, in vivo experiments are described. Specific objectives are: i) to demonstrate the feasibility of performing NODDI in the spinal cord and in clinical settings; ii) to study the possibility of extracting with new approaches such as NODDI more specific microstructural information from standard DW acquisitions; iii) to assess how features typical of spinal cord microstructure, such as presence of large axons, influence NODDI metrics. In the second part of the thesis, ex vivo experiments are discussed. Their objective is the validation of the specificity of NODDI metrics via comparison to quantitative histology in post mortem spinal cord tissue. The experiments required the implementation of high-field DW scans as well as histological procedures and complex analysis pipelines. The results of this thesis contribute to current scientific knowledge. They prove that NODDI offers new opportunities to study how neurodegenerative diseases such as MS alter neural tissue complexity. We showed for the first time that NODDI can be performed in the spinal cord in vivo and in clinical scans. We also demonstrated that NODDI analysis of standard DW data is challenging, and quantified how the presence of large axons in the spinal cord influences NODDI metrics. Lastly, our ex vivo data highlight that unlike routine DW MRI methods, NODDI can detect reliably pathological variations of neurite orientation dispersion. NODDI is also sensitive to the density of axons and dendrites, but can not fully resolve axonal loss and demyelination in MS. We believe that the technique is a key element of a more general multi-modal MRI approach, which is necessary to obtain a complete description of complex diseases such as MS

Chemical Exchange Saturation Transfer Imaging Of Endogenous Metabolites For Monitoring Oxidative Phosphorylation And Glycolysis In Vivo

Oxidative phosphorylation (OXPHOS) and glycolysis are two cellular metabolic pathways that play a crucial role in the functions of biological systems. Currently, magnetic resonance spectroscopy (MRS) (13C, 31P, and 1H) and positron emission tomography (PET) methods are used to investigate changes in these pathways that result from metabolic dysfunction. However, MRS methods are limited by low resolution and long acquisition times. While 18F-fluoro-2-deoxy-D-glucose (18F-FDG) PET is a widely used clinical modality, it requires the use of radioactive ligands. Thus, there is an unmet need for techniques to image these metabolic processes noninvasively, and with higher resolution in vivo. In this dissertation, we exploited the chemical exchange saturation (CEST) phenomenon to develop and optimize endogenous CEST magnetic resonance imaging (MRI) methods to measure OXPHOS and glycolysis, and demonstrated application of those techniques to study impaired metabolism in vivo. These CEST methods offer several orders of magnitude higher sensitivity compared to traditional spectroscopic techniques. Recently developed CEST imaging of free creatine (CrCEST) was targeted as a means of measuring OXPHOS. We optimized and validated this technique in healthy human skeletal muscle, showing that CrCEST imaging in dynamic exercise studies provides a measure of the mitochondrial rate of OXPHOS. CrCEST imaging was then implemented in a cohort of subjects affected by genetic disorders of the mitochondria. The results of these studies demonstrate that CrCEST has the capability to distinguish between healthy and impaired OXPHOS in muscle. In some diseases with altered metabolism, like cancer, aerobic glycolysis dominates, leading to increased lactate production. Existing methods for imaging lactate in vivo involve expensive, radiolabeled tracers. In this work, we demonstrated the feasibility of imaging lactate with CEST (“LATEST”) in phantoms with physiological concentrations. Then, we validated the method dynamically in vivo by measuring lactate production and clearance in intensely exercised human skeletal muscle, which utilizes anaerobic glycolysis. Finally, we infused rats bearing lymphoma tumors with non-labeled pyruvate and demonstrated the ability of LATEST MRI to image tumors and measure dynamic lactate changes over time. Together, these studies demonstrate that metabolic processes can be monitored in vivo using CEST MRI, with potential for widespread clinical applications