Parallélisation sur GPU d'un algorithme de reconstruction 3D bayésien en tomographie X.

An important number of image reconstruction algorithms are implemented in the literature on X-ray CT (Computed Tomography) data. The main family of methods are analytical, mainly filtered backprojection which are implemented generally in medical imaging for their fast reconstruction time. The limits of these methods appear when the number of projections is small, and/or not equidistributed around the object. In this specific context, iterative algebraic methods are implemented. A great number of them are mainly based on least square criterion. We propose a regularized version of iterative algorithms to improve results. The main problem that appears when using iterative algebraic methods is the computation time and especially for projection and backprojection steps. We propose to implement some steps of the iterations on GPU hardware. We present an original method based on a Bayesian statistical method for 3D tomographic reconstructions. The main interest is to apply it in a context of non-consistent data sets, for example with a small number of projections. We show a good quality of results and a significant speed up of the calculation with GPU implementation

Model based learning for accelerated, limited-view 3D photoacoustic tomography

Recent advances in deep learning for tomographic reconstructions have shown great potential to create accurate and high quality images with a considerable speed-up. In this work we present a deep neural network that is specifically designed to provide high resolution 3D images from restricted photoacoustic measurements. The network is designed to represent an iterative scheme and incorporates gradient information of the data fit to compensate for limited view artefacts. Due to the high complexity of the photoacoustic forward operator, we separate training and computation of the gradient information. A suitable prior for the desired image structures is learned as part of the training. The resulting network is trained and tested on a set of segmented vessels from lung CT scans and then applied to in-vivo photoacoustic measurement data

Iterative CT reconstruction using shearlet-based regularization

In computerized tomography, it is important to reduce the image noise without increasing the acquisition dose. Extensive research has been done into total variation minimization for image denoising and sparse-view reconstruction. However, TV minimization methods show superior denoising performance for simple images (with little texture), but result in texture information loss when applied to more complex images. Since in medical imaging, we are often confronted with textured images, it might not be beneficial to use TV. Our objective is to find a regularization term outperforming TV for sparse-view reconstruction and image denoising in general. A recent efficient solver was developed for convex problems, based on a split-Bregman approach, able to incorporate regularization terms different from TV. In this work, a proof-of-concept study demonstrates the usage of the discrete shearlet transform as a sparsifying transform within this solver for CT reconstructions. In particular, the regularization term is the 1-norm of the shearlet coefficients. We compared our newly developed shearlet approach to traditional TV on both sparse-view and on low-count simulated and measured preclinical data. Shearlet-based regularization does not outperform TV-based regularization for all datasets. Reconstructed images exhibit small aliasing artifacts in sparse-view reconstruction problems, but show no staircasing effect. This results in a slightly higher resolution than with TV-based regularization