This paper presents a method for the detection, representation and visualisation of the cerebral vascular tree and its application to magnetic resonance angiography (MRA) images. The detection method is an iterative tracking of the vessel centreline with subvoxel accuracy and precise orientation estimation. This tracking algorithm deals with forks. Centrelines of the vessels are modelled by second-order B-spline. This method is used to obtain a high-level description of the whole vascular network. Applications to real angiographic data are presented. An MRA sequence has been designed, and a global segmentation of the whole vascular tree is realised in three steps. Applications of this work are accurate 3D representation of the vessel centreline and of the vascular tree, and visualisation. The tracking process is also successfully applied to 3D contrast enhanced MR digital subtracted angiography (3D-CE-MRA) of the inferior member vessels. In addition, detection of artery stenosis for routine clinical use is possible due to the high precision of the tracking algorithm.

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Back-Projection (BP) is a costly computational step in tomography image reconstruction such as Positron Emission Tomography (PET). To reduce the computation time, this paper presents a Pipelined, Pre-fetch and Parallelized Architecture for PET BP (3PA-PET).The key feature of this architecture is its original memory access strategy, masking the high latency of the external memory. Indeed, the pattern of the memory references to the data acquired hinder the processing unit.The memory access bottleneck is overcome by an efficient use of the intrinsic temporal and spatial locality of the BP algorithm. A loop reordering allows an efficient use of general purpose processor's caches, for software implementation, as well as the 3D Predictive and Adaptive Cache (3D-AP Cache), when considering hardware implementations. Parallel hardware pipelines are also efficient thanks to a hierarchical 3D-AP Cache: each pipeline performs a memory reference in about one clock cycle to reach a computational throughput close to 100%.The 3PA-PET architecture is prototyped on a System on Programmable Chip (SoPC) to validate the system and to measure its expected performances. Time performances are compared with a desktop PC, a workstation and a GPU (Graphic Processor Unit).

We present a method to correct intensity variations and voxel shifts caused by non-linear gradient fields in Magnetic Resonance Images. The principal sources of distortion are briefly exposed, as well as the methods of correction currently in use. The implication of the gradient fields non-linearities on the signal equations are described in a detailed way for the case of 2D and 3D Fourier imagery. A model of these nonlinearities, derived from the geometry of the gradient coils, is proposed and then applied in post-processing to correct any images regardless of the acquisition sequence. Initial position errors, as large as 4 mm (i.e. 4 voxels of 1x1x1.4 mm 3 ) before correction, are reduced to less than the voxel sizes after correction.