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Three-dimensional (3D) information on blood flow and vessel morphology is important when assessing cerebrovascular disease and when monitoring interventions. Rotational angiography is nowadays routinely used to determine the geometry of the cerebral vasculature. To this end, contrast agent is injected into one of the supplying arteries and the x-ray system rotates around the head of the patient while it acquires a sequence of x-ray images. Besides information on the 3D geometry, this sequence also contains information on blood flow, as it is possible to observe how the contrast agent is transported by the blood. The main goal of this thesis is to exploit this information for the quantitative analysis of blood flow. I propose a model-based method, called flow map fitting, which determines the blood flow waveform and the mean volumetric flow rate in the large cerebral arteries. The method uses a model of contrast agent transport to determine the flow parameters from the spatio-temporal progression of the contrast agent concentration, represented by a flow map. Furthermore, it overcomes artefacts due to the rotation (overlapping vessels and foreshortened vessels at some projection angles) of the c-arm using a reliability map. For the flow quantification, small changes to the clinical protocol of rotational angiography are desirable. These, however, hamper the standard 3D reconstruction. Therefore, a new method for the 3D reconstruction of the vessel morphology which is tailored to this application is also presented. To the best of my knowledge, I have presented the first quantitative results for blood flow quantification from rotational angiography. Additionally, the model-based approach overcomes several problems which are known from flow quantification methods for planar angiography. The method was mainly validated on images from different phantom experiments. In most cases, the relative error was between 5% and 10% for the volumetric mean flow rate and between 10% and 15% for the blood flow waveform. Additionally, the applicability of the flow model was shown on clinical images from planar angiographic acquisitions. From this, I conclude that the method has the potential to give quantitative estimates of blood flow parameters during cerebrovascular interventions.

Breast cancer diagnosis currently requires biopsy samples to be analysed by a histopathologist a time consuming, highly specialised process. X-ray diffraction is a quantitative technique that can distinguish between healthy and diseased breast biopsy samples using the change in proportions of fat and fibrous tissue that occurs when cancer invades. A semi-automated breast biopsy analysis system based on X-ray diffraction could yield a faster patient diagnosis. Recording X-ray diffraction patterns is a challenging task needing low noise, large area, and wide dynamic range detectors. Scientific complementary metal oxide semiconductor (CMOS) Active Pixel Sensors will soon be able to meet all of these demands in a single device. Characterization of two novel Active Pixel Sensors that advance towards an ideal X-ray diffraction detector is presented. 'Vanilla' exhibits a low read noise of 55e r.m.s. and high quantum efficiency of up to 70% so was selected for the design and implementation of the first 'Active Pixel X-ray Diffraction' (APXRD) system. Following on from Vanilla, the 'Large Area Sensor' (LAS) covered an area of over 29cm2 and had a wide dynamic range of over 95dB. The first linear systems model of an Active Pixel Flat Panel Imager (scintillator coupled APS) was formulated in the design of the APXRD system, to select filters to narrow the spectral width of the X-ray beam and predict the recorded scatter intensity. Following system implementation, scatter signatures were recorded for numerous breast tissue equivalent samples. A multivariate analysis model calibrated with these was able to predict the percentage fat content of an 'unknown' sample to within 3% a very promising result. The width of the filtered polychromatic X-ray spectrum had only a minor influence on the APXRD scatter signatures indicating that the system preserves all relevant structural information.

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