Development of multiparametric MRI models for prostate cancer detection based on improved correlative pathology

University of Minnesota Ph.D. dissertation. June 2014. Major: Biophysical Sciences and Medical Physics. Advisor: Gregory J. Metzger. 1 computer file (PDF); xix, 116 pages.Prostate cancer (PCa) is a prevalent disease which affects 1 in 6 men in the United States and has overtaken lung cancer as the leading cause of cancer related deaths in American men and number two worldwide. Among several diagnostic imaging tests that are available for detection of PCa in the market today, Magnetic Resonance Imaging (MRI) occupies a unique position in the detection of PCa due to its excellent soft tissue contrast and its ability to generate tissue property dependent multi-parametric data. While MRI has become an increasingly valuable tool in the management of men with PCa, its use to identify aggressive disease and characterize extent have yet to be developed. Multi-parametric MRI (MP-MRI) studies have been shown to increase sensitivity and specificity towards PCa detection compared to any single MRI dataset. The ability to develop and evaluate MP-MRI to prospectively detect disease, assess aggressiveness and delineate extent, first requires the retrospective validation against post-surgical pathology sections. Despite the large effort made by many groups in this area of research, the correlation of in vivo MP-MRI with pathology is still a challenge and to date is insufficient to develop highly accurate models of disease. To address this problem this thesis showcases (1) a novel registration approach called LATIS (Local Affine Transformation assisted by Internal Structures) for co-registering post prostatectomy pseudo-whole mount (PWM) pathological sections with in vivo MRI images and (2) MP-MRI based predictive model for disease detection using a composite biomarker score based on a unique database of pathology co-registered MR data sets. Also showcased in this thesis is a study where r1 and r2* relaxivities of a common paramagnetic contrast agent were measured in blood and saline at both 3T and 7T. This is important information to have when attempting to perform DCE-MRI studies as part of a MP-MRI protocol at ultra-high magnetic field strengths

A fast robust geometric fitting method for parabolic curves.

Fitting discrete data obtained by image acquisition devices to a curve is a common task in many fields of science and engineering. In particular, the parabola is some of the most employed shape features in electrical engineering and telecommunication applications. Standard curve fitting techniques to solve this problem involve the minimization of squared errors. However, most of these procedures are sensitive to noise. Here, we propose an algorithm based on the minimization of absolute errors accompanied by a normalization of the directrix vector that leads to an improved stability of the method. This way, our proposal is substantially resilient to noisy samples in the input dataset. Experimental results demonstrate the good performance of the algorithm in terms of speed and accuracy when compared to previous approaches, both for synthetic and real data.This work is partially supported by the Ministry of Economy and Competitiveness of Spain [grant number TIN2014-53465-R], project name Video surveillance by active search of anomalous events. It is also partially supported by the Autonomous Government of Andalusia (Spain) [grant number TIC-6213], project name Development of Self-Organizing Neural Networks for Information Technologies; and [grant number TIC-657], project name Self-organizing systems and robust estimators for video surveillance. All of them include funds from the European Regional Development Fund (ERDF). The authors thankfully acknowledge the computer resources, technical expertise and assistance provided by the SCBI (Supercomputing and Bioinformatics) center of the University of Málaga. They have also been supported by the Biomedic Research Institute of Málaga (IBIMA). They also gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X GPU. Karl Thurnhofer-Hemsi is funded by a Ph.D. scholarship from the Spanish Ministry of Education, Culture and Sport under the FPU program [grant number FPU15/06512]

Development and application of quantitative image analysis for preclinical MRI research

The aim of this thesis is to develop quantitative analysis methods to validate MRI and improve the detection of tumour infiltration. The major components include a description of the development the quantitative methods to better validate imaging biomarkers and detect of infiltration of tumour cells into normal tissue, which were then applied to a mouse model of glioblastoma invasion. To do this, a new histology model, called Stacked In-plane Histology (SIH), was developed to allow a quantitative analysis of MRI. Validating imaging biomarkers for glioblastoma infiltration Cancer can be defined as a disease in which a group of abnormal cells grow uncontrollably, often with fatal outcomes. According to (Cancer research UK, 2019), there are more than 363,000 new cancer cases in the UK every year, an increase from the 990 cases reported daily in 2014-2016, with only half of all patients recovering. Glioblastoma (GB) is the most frequent and malignant form of primary brain tumours with a very poor prognosis. Even with the development of modern diagnostic strategies and new therapies, the five-year survival rate is just 5%, with the median survival time only 14 months. Unfortunately, glioblastoma can affect patients at any age, including young children, but has a peak occurrence between the ages of 65 and 75 years. The standard treatment for GB consists of surgical resection, followed by radiotherapy and chemotherapy. However, the infiltration of GB cells into healthy adjacent brain tissue is a major obstacle for successful treatment, making complete removal of a tumour by surgery a difficult task, with the potential for tumour recurrence. Magnetic Resonance Imaging (MRI) is a non-invasive, multipurpose imaging tool used for the diagnosis and monitoring of cancerous tumours. It can provide morphological, physiological, and metabolic information about the tumour. Currently, MRI is the standard diagnostic tool for GB before the pathological examination of tissue from surgical resection or biopsy specimens. The standard MRI sequences used for diagnosis of GB include T2-Weighted (T2W), T1-Weighted (T1W), Fluid-Attenuated Inversion Recovery (FLAIR), and Contrast Enhance T1 gadolinium (CE-T1) scans. These conventional scans are used to localize the tumour, in addition to associated oedema and necrosis. Although these scans can identify the bulk of the tumour, it is known that they do not detect regions infiltrated by GB cells. The MRI signal depends upon many physical parameters including water content, local structure, tumbling rates, diffusion, and hypoxia (Dominietto, 2014). There has been considerable interest in identifying whether such biologically indirect image contrasts can be used as non-invasive imaging biomarkers, either for normal biological functions, pathogenic processes or pharmacological responses to therapeutic interventions (Atkinson et al., 2001). In fact, when new MRI methods are proposed as imaging biomarkers of particular diseases, it is crucial that they are validated against histopathology. In humans, such validation is limited to a biopsy, which is the gold standard of diagnosis for most types of cancer. Some types of biopsies can take an image-guided approach using MRI, Computed Tomography (CT) or Ultrasound (US). However, a biopsy may miss the most malignant region of the tumour and is difficult to repeat. Biomarker validation can be performed in preclinical disease models, where the animal can be terminated immediately after imaging for histological analysis. Here, in principle, co-registration of the biomarker images with the histopathology would allow for direct validation. However, in practice, most preclinical validation studies have been limited to using simple visual comparisons to assess the correlation between the imaging biomarker and underlying histopathology. First objective (Chapter 5): Histopathology is the gold standard for assessing non-invasive imaging biomarkers, with most validation approaches involving a qualitative visual inspection. To allow a more quantitative analysis, previous studies have attempted to co-register MRI with histology. However, these studies have focused on developing better algorithms to deal with the distortions common in histology sections. By contrast, we have taken an approach to improve the quality of the histological processing and analysis, for example, by taking into account the imaging slice orientation and thickness. Multiple histology sections were cut in the MR imaging plane to produce a Stacked In-plane Histology (SIH) map. This approach, which is applied to the next two objectives, creates a histopathology map which can be used as the gold standard to quantitatively validate imaging biomarkers. Second objective (Chapter 6): Glioblastoma is the most malignant form of primary brain tumour and recurrence following treatment is common. Non-invasive MR imaging is an important component of brain tumour diagnosis and treatment planning. Unfortunately, clinic MRI (T1W, T2W, CE-T1, and FLAIR) fails to detect regions of glioblastoma cell infiltration beyond the solid tumour region identified by contrast enhanced T1 scans. However, advanced MRI techniques such as Arterial Spin Labelling (ASL) could provide us with extra information (perfusion) which may allow better detection of infiltration. In order to assess whether local perfusion perturbation could provide a useful biomarker for glioblastoma cell infiltration, we quantitatively analysed the correlation between perfusion MRI (ASL) and stacked in-plane histology. This work used a mouse model of glioblastoma that mimics the infiltrative behaviour found in human patients. The results demonstrate the ability of perfusion imaging to probe regions of low tumour cell infiltration, while confirming the sensitivity limitations of clinic imaging modalities. Third objective (Chapter 7): It is widely hypothesised that Multiparametric MRI (mpMRI), can extract more information than is obtained from the constituent individual MR images, by reconstructing a single map that contains complementary information. Using the MRI and histology dataset from objective 2, we used a multi-regression algorithm to reconstruct a single map which was highly correlated (r>0.6) with histology. The results are promising, showing that mpMRI can better predict the whole tumour region, including the region of tumour cell infiltration

Magnetic Resonance Imaging haemodynamic modelling in chronic liver disease: development, validation and translation

Though haemodynamic changes underpin the pathophysiology of chronic liver disease, there are currently no robust non-invasive methods available for their assessment. I propose ‘caval subtraction’ phase contrast MRI (PCMRI) a novel method to measure total liver blood flow (TLBF) and hepatic arterial (HA) flow using PCMRI measurements of caval and portal venous (PV) flow. I validate this method at 9.4T and 3.0T to demonstrate: agreement between preclinical PCMRI and invasive transit-time ultrasound (TTUS) and fluorescent microsphere measurements of flow parameters; good consistency between clinical caval subtraction PCMRI and independent direct PCMRI measurements; encouraging correlations between PCMRI and invasive ICG clearance in patients; and good seven-day reproducibility of PCMRI derived haemodynamic parameters in normal volunteers. Using dynamic contrast enhanced (DCE) MRI on a 3.0T system, I demonstrate improved seven-day reproducibility using dual input single compartment pharmacokinetic modelling with a novel method for obtaining physiological vascular input function delays, correction of arterial input functions using PCMRI aortic flow and use of PCMRI estimations of TLBF to correct DCE MRI quantification. I also implement arterial spin labelling (ASL) at 9.4T and demonstrate a tendency for ASL to underestimate PCMRI hepatic parenchymal perfusion. Using bile-duct ligated (BDL) rats to study cirrhosis, I demonstrate that these have reduced TLBF and HA fraction at baseline, impaired HA regulation and buffer response, cirrhotic cardiomyopathy, and a failure to match hepatic circulatory demands with increased liver:body mass ratio. Acute-on-chronic liver failure (simulated using endotoxaemia) demonstrates reductions in TLBF, HA flow, absence of normal sepsis-induced hepatic hyperaemia and blunted cardiac systolic response. Studies in cirrhotic patients demonstrate increased TLBF and HA flow in higher risk portal hypertensive patients; that HA flow, HA fraction and cardiac output are important correlative parameters with hepatic venous pressure gradient and that caval subtraction PCMRI has potential in evaluating treatments for portal hypertension

Robust computational intelligence techniques for visual information processing

The third part is exclusively dedicated to the super-resolution of Magnetic Resonance Images. In one of these works, an algorithm based on the random shifting technique is developed. Besides, we studied noise removal and resolution enhancement simultaneously. To end, the cost function of deep networks has been modified by different combinations of norms in order to improve their training. Finally, the general conclusions of the research are presented and discussed, as well as the possible future research lines that are able to make use of the results obtained in this Ph.D. thesis.This Ph.D. thesis is about image processing by computational intelligence techniques. Firstly, a general overview of this book is carried out, where the motivation, the hypothesis, the objectives, and the methodology employed are described. The use and analysis of different mathematical norms will be our goal. After that, state of the art focused on the applications of the image processing proposals is presented. In addition, the fundamentals of the image modalities, with particular attention to magnetic resonance, and the learning techniques used in this research, mainly based on neural networks, are summarized. To end up, the mathematical framework on which this work is based on, ₚ-norms, is defined. Three different parts associated with image processing techniques follow. The first non-introductory part of this book collects the developments which are about image segmentation. Two of them are applications for video surveillance tasks and try to model the background of a scenario using a specific camera. The other work is centered on the medical field, where the goal of segmenting diabetic wounds of a very heterogeneous dataset is addressed. The second part is focused on the optimization and implementation of new models for curve and surface fitting in two and three dimensions, respectively. The first work presents a parabola fitting algorithm based on the measurement of the distances of the interior and exterior points to the focus and the directrix. The second work changes to an ellipse shape, and it ensembles the information of multiple fitting methods. Last, the ellipsoid problem is addressed in a similar way to the parabola

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity