Molecular Imaging

The present book gives an exceptional overview of molecular imaging. Practical approach represents the red thread through the whole book, covering at the same time detailed background information that goes very deep into molecular as well as cellular level. Ideas how molecular imaging will develop in the near future present a special delicacy. This should be of special interest as the contributors are members of leading research groups from all over the world

Learning cell representations in temporal and 3D contexts

Cell morphology and its changes under different circumstances is one of the primary ways by which we can understand biology. Computational tools for characterization and analysis, therefore, play a critical role in advancing studies involving cell morphology. In this thesis, I explored the use of representation learning and self-supervised methods to analyze nuclear texture in fluorescence imaging across different contexts and scales. To analyze the cell cycle using 2D temporal imaging data, as well as DNA damage in 3D imaging data, I employed a simple model based on the VAE-GAN architecture. Through the VAE-GAN model, I constructed manifolds in which the latent representations of the data can be grouped and clustered based on textural similarities without the need for exhaustive training annotations. I used these representations, as well as manually engineered features, to perform various analyses both at the single cell and tissue levels. The application on the cell cycle data revealed that common tasks such as cell cycle staging and cell cycle time estimation can be done even with minimal fluorescence information and user annotation. On the other hand, the texture classes derived to characterize DNA damage in 3D histology images unveiled differences between control and treated tissue regions. Lastly, by aggregating cell-level information to characterize local cell neighborhoods, interactions between DNA-damaged cells and immune cells can be quantified and some tissue microstructures can be identified. The results presented in this thesis demonstrated the utility of the representations learned through my approach in supporting biological inquiries involving temporal and 3D spatial data. The quantitative measurements computed using the presented methods have the potential to aid not only similar experiments on the cell cycle and DNA damage but also in exploratory studies in 3D histology

Development and Modeling of a Polymer Construct for Perfusion Imaging and Tissue Engineering.

The physical and computational modeling of distributed fluid flow to vascular beds remains a challenging issue. The computational resources required, and the complexity of capillary networks makes modeling infeasible. The resolution limits of manufacturing techniques make physical models difficult to fabricate and manipulate under experimental conditions. As such, an in vitro polymer construct was developed with structural properties of small arteries and the bulk flow characteristics of capillary beds. Rapid prototyping and scaffolding techniques were used to fabricate vascular trees amendable to scaffold compartments. Several scaffold architectures were evaluated to achieve target fluid flow characteristics for implementation in a dynamic contrast-enhanced computed tomography (DCE-CT) imaging phantom and endothelial cell bioreactor, respectively. Experimental flow measurements were compared to measurements from computational simulations. In addition, the flow-induced shear stress across the construct was modeled to identify the optimal settings within the bioreactor. In addition, the cytocompatibility of the polymer construct was optimized. Vascular trees were reliably fabricated to achieve arteriole-like flow. Rapid prototyped polycaprolactone (PCL) scaffolds produced distinct differential flow ranges, marked by a decrease in flow rate across the network. The construct served as a viable dynamic flow phantom capable of generating signals typical of organs imaged with DCE-CT. Furthermore, simulations of the construct as a bioreactor provided guidance on the boundary conditions required for stimulatory shear stress within the scaffolds. Under static conditions, endothelial cells were cultured on PCL scaffolds modified with extra-cellular matrix mimicking biological and chemical agents. All surface modifications exhibited similar cell proliferation and function. However, the Arg-Gly-Asp (RGD) surface-modified constructs exhibited an optimal spatial distribution for future endothelial cell bioreactor investigations. This work demonstrates a method for modeling and physically simulating a bifurcating vascular tree adjoined to scaffold compartments with tunable flow, for application to perfusion imaging and in vitro tissue engineering (tissue and tumors).PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/107136/1/auresa_1.pd

2013 IMSAloquium, Student Investigation Showcase

This year, we are proudly celebrating the twenty-fifth anniversary of IMSA’s Student Inquiry and Research (SIR) Program. Our first IMSAloquium, then called Presentation Day, was held in 1989 with only ten presentations; this year we are nearing two hundred.https://digitalcommons.imsa.edu/archives_sir/1005/thumbnail.jp

Addressing the false positive MRI phenotype in prostate cancer diagnosis and management

Multiparametric magnetic resonance imaging (mpMRI) is set to dominate the diagnosis and active surveillance of prostate cancer. However, false positive MRIs confound clinical decision-making and prompt unwarranted biopsies that carry morbidity risks. This is a significant issue: NICE currently recommends pre-biopsy MRI in men with suspected prostate cancer and, as 80,000 patients undergo biopsy every year in England and Wales, between 12,600 to 17,300 are expected to be biopsy-negative. Furthermore, MRI in active surveillance (AS) is strongly recommended by NICE for risk stratification at baseline and for the detection of oncological progression. However, MRI-based AS is new and it is still unknown when observed dynamic MRI changes reflect true transition to clinically significant disease. Recognising this on imaging is important for optimising clinical decisions and reducing the overall number of biopsies during AS. In this thesis it will be shown that MRI lesions seen in biopsy-naïve individuals with clinically significant cancer are larger, more conspicuous and more diffusion-restricted compared to phenotypes seen in men without significant disease. Furthermore, in men with indeterminate MRI phenotypes, PSA density and index lesion ADC predict the presence of significant cancer through a logistic regression model (mean cross-validated AUC: 0.77 [95% CI: 0.67–0.87]) and could help men avoid unnecessary biopsies. It is also shown that false positive MRI phenotypes in such men arise in prostatic regions with increased overall cellularity and expanded epithelium, while assuming either focal or diffuse patterns. In addition, it is demonstrated that MRI-based AS can be safely used to monitor men with insignificant disease, as approximately 84.7% (95% CI: 82.0–87.6) and 71.8% (95% CI: 68.2–75.6) of patients remain on AS at 3 and 5 years (with those with MRI-visible disease at baseline exiting earlier). Finally, it will be shown that progressing MRI lesions during imaging-based AS have two distinct histological phenotypes: one characterised by increased overall cellularity and expansion of epithelial areas (typically seen with transition to higher grade cancer) and another by moderate, standalone stromal hyperplasia seen in cases of pathological stability, not ideally requiring biopsy. This finding could lead to the development of radiological metrics that distinguish the two progression types and spare men from unnecessary biopsies in AS contexts

Novel approaches to MRI of glioma

Gliomas are extremely heterogeneous, both morphologically and biologically, which contributes to a very poor prognosis. Current imaging of glioma is insufficient for a thorough diagnosis, therapy assessment and prognosis prediction. Moreover, refined and more sophisticated imaging technique could help in furthering our knowledge of gliomas. In order to facilitate proliferation, cancer cells undergo a change in structure and an increase in metabolism that results in distortion and disruption of tissue architecture. Gliomas are characterised by an increase in cells of variable sizes, as well as changes in the tissue microstructure. Diffusion-Weighted Imaging (DWI) and the apparent diffusion coefficient (ADC), have been extensively studied as potential imaging biomarkers for cellularity and tissue architecture. However, several studies have shown partial overlap in the measured values between tumour subtypes. Moreover, ADC is influenced by several factors and does not provide detailed information on the tissue microstructure. The Vascular, Extracellular and Restricted Diffusion for Cytometry in Tumours (VERDICT) is a novel diffusion model that infers tissue microstructure compartment from conventional DWI measurements. This model derives metrics for the intracellular, intravascular and extracellular– extravascular spaces providing a more detailed interpretation of the tissue microstructure. To date, VERDICT has been applied to xenograft models of colorectal cancer, patient studies of prostate cancer and recently its feasibility in glioma has been shown. In this PhD I have applied a shortened version of the VERDICT method to image intratumoral and intertumoral heterogeneity in glioma. The results have also been validated with histology as part of a prospective study. Gliomas also exhibit a significant increase in mitotic activity within the tumour. The increased number of mitosis alters cell density which, in turn, affects the total concentration of tissue sodium as the concentration of tissue sodium is approximately ten-fold higher in the extracellular compared to the intracellular space. In addition, there is a decrease in Na+/K+-ATPase activity in tumours due to ATP depletion, which contributes to disturb sodium homeostasis. Non-invasive detection of 23Na with MRI has the potential to quantify sodium concentration and therefore could be an imaging probe of cell morphology and membrane function within the tumour microenvironment, as well as a method of probing tissue heterogeneity. During my PhD, a novel 23Na-MRI technique has been used to evaluate sodium distribution within glioma and in the surrounding tissue. Metabolic reprogramming is one of the major driving forces for determining glioma growth and invasion. Therefore, the non-invasive characterization of metabolic intratumoral, peritumoral and intertumoral heterogeneity in vivo could help to better stratify patients and to develop novel therapeutic strategies targeting cancer-specific metabolic pathways. 13C magnetic resonance imaging (MRI) using dynamic nuclear polarization (DNP) is a novel technique that allows non-invasive assessment of the metabolism of hyperpolarized (HP) 13C-labelled molecules in vivo, such as the exchange of [1-13C]pyruvate to [1-13C]lactate in tumours (Warburg effect). Part of my PhD has focused on developing and translating HP [1-13C]pyruvate MRI to explore metabolic reprogramming in glioma and the surrounding microenvironment. The overall aim of my PhD has been to develop novel approaches to imaging glioma with MRI to probe both the architectural and metabolic changes of Glioma. The preliminary evidence suggests that these tools can more deeply phenotype tumours than conventional imaging approaches. Although the main focus of this work has been gliomas, the techniques developed and presented here may be applied to study other pathological conditions within the brain, which raises the possibility of other potential clinical applications for this work

Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis