Spa-RQ: an Image Analysis Tool to Visualise and Quantify Spatial Phenotypes Applied to Non-Small Cell Lung Cancer

To facilitate analysis of spatial tissue phenotypes, we created an open-source tool package named 'Spa-RQ' for 'Spatial tissue analysis: image Registration & Quantification'. Spa-RQ contains software for image registration (Spa-R) and quantitative analysis of DAB staining overlap (Spa-Q). It provides an easy-to-implement workflow for serial sectioning and staining as an alternative to multiplexed techniques. To demonstrate Spa-RQ's applicability, we analysed the spatial aspects of oncogenic KRAS-related signalling activities in non-small cell lung cancer (NSCLC). Using Spa-R in conjunction with ImageJ/Fiji, we first performed annotation-guided tumour-by-tumour phenotyping using multiple signalling markers. This analysis showed histopathology-selective activation of PI3K/AKT and MAPK signalling in Kras mutant murine tumours, as well as high p38MAPK stress signalling in p53 null murine NSCLC. Subsequently, Spa-RQ was applied to measure the co-activation of MAPK, AKT, and their mutual effector mTOR pathway in individual tumours. Both murine and clinical NSCLC samples could be stratified into 'MAPK/mTOR', 'AKT/mTOR', and 'Null' signature subclasses, suggesting mutually exclusive MAPK and AKT signalling activities. Spa-RQ thus provides a robust and easy to use tool that can be employed to identify spatially-distributed tissue phenotypes

Optimization of tau-PET neuroimaging signal preprocessing: application on alzheimer disease

En este proyecto se estudian las distintas metodologías de pre-procesado de imágenes tau-PET que cuantifican la cantidad y distribución de proteína tau dentro del cerebro. Se estudian los métodos de registro para agregarlas a imágenes de RM que se usan en la praxis usual dentro del campo de la neuroimagen así como técnicas innovadores planteadas dentro del grupo de investigación de la Unidad de Memoria del Hospital Sant Pau. Finalmente, se proponen modelos de regresión lineal para estudiar las capacidades clasificativas y predictivas de la medida de SUVr para el diagnostico de posibles pacientes.In this project, some of the most used pre-processing techniques for tau-PET imaging are studied. This imaging technique provides information on the quantity as well as topographical distribution of tau protein throughout the brain structure. Furthermore, we characterize the state-of-the-art registration process configurations, as well as novel techniques developed inside the Sant Pau Hospital Memory Unit, in order to align tau-PET images to MRI scans. Finally, we propose different logistic regression models in order to assess the classification and prediction capabilities of the SUVr measure for the diagnosis of potential patients

Image registration and visualization of in situ gene expression images.

In the age of high-throughput molecular biology techniques, scientists have incorporated the methodology of in-situ hybridization to map spatial patterns of gene expression. In order to compare expression patterns within a common tissue structure, these images need to be registered or organized into a common coordinate system for alignment to a reference or atlas images. We use three different image registration methodologies (manual; correlation based; mutual information based) to determine the common coordinate system for the reference and in-situ hybridization images. All three methodologies are incorporated into a Matlab tool to visualize the results in a user friendly way and save them for future work. Our results suggest that the user-defined landmark method is best when considering images from different modalities; automated landmark detection is best when the images are expected to have a high degree of consistency; and the mutual information methodology is useful when the images are from the same modality

Deformable Image Registration for Hyperspectral Images

Image registration is one of the basic image processing operations in remote sensing. A hyperspectral image has two spatial dimensions and one spectral dimension. There are many hyperspectral sensors used in remote sensing. Traditional intensity-based registration methods may fail for hyperspectral images because of the different spectral sensitivities for different sensors. In addition, not all spectral bands are required to achieve accurate registration. This thesis develops a modification of the large deformation diffeomorphic metric mappings (LDDMM) algorithm in order to deal with the challenges when applied to hyperspectral images. The transformation generated by our method that deforms one image to match the other is differentiable, isomorphic and invertible. We also propose a mutual information based band selection algorithm to reduce the data redundancy of the hyperspectral images. The approach is applied to two hyperspectral images from OMEGA instrument, with a better matching result than original LDDMM method with respect to mutual information

Quantification of tumour heterogenity in MRI

Cancer is the leading cause of death that touches us all, either directly or indirectly. It is estimated that the number of newly diagnosed cases in the Netherlands will increase to 123,000 by the year 2020. General Dutch statistics are similar to those in the UK, i.e. over the last ten years, the age-standardised incidence rate1 has stabilised at around 355 females and 415 males per 100,000. Figure 1 shows the cancer incidence per gender. In the UK, the rise in lifetime risk of cancer is more than one in three and depends on many factors, including age, lifestyle and genetic makeup

Computer image registration techniques applied to nuclear medicine images

Modern medicine has been using imaging as a fundamental tool in a wide range of applications. Consequently, the interest in automated registration of images from either the same or different modalities has increased. In this chapter, computer techniques of image registration are reviewed, and cover both their classification and the main steps involved. Moreover, the more common geometrical transforms, optimization and interpolation algorithms are described and discussed. The clinical applications examined emphases nuclear medicine