Tetrahedral Volume Reconstruction in X-Ray Tomography using GPU Architecture

International audienceIn this paper, we propose the use of the graphics processor unit (GPU) to accelerate a ray-tracing method in the framework of X-ray tomographic image reconstruction. We first describe an innovative iterative reconstruction method we have developed based on a tetrahedral volume with conjugate gradient. We do not use voxels here but instead tetrahedrons to increase the quality of reconstruction and the reduction of data as thus we need less resolution of the volume to fit the object reconstructed. This is an important point to use the GPU. We present here the algorithms adapted to the GPU and the results obtained compared to CPU

Adapted sampling for 3D X-ray computed tomography

International audience—In this paper, we introduce a method to build an adapted mesh representation of a 3D object for X-Ray tomogra-phy reconstruction. Using this representation, we provide means to reduce the computational cost of reconstruction by way of iterative algorithms. The adapted sampling of the reconstruction space is directly obtained from the projection dataset and prior to any reconstruction. It is built following two stages : firstly, 2D structural information is extracted from the projection images and is secondly merged in 3D to obtain a 3D pointcloud sampling the interfaces of the object. A relevant mesh is then built from this cloud by way of tetrahedralization. Critical parameters selections have been automatized through a statistical framework, thus avoiding dependence on users expertise. Applying this approach on geometrical shapes and on a 3D Shepp-Logan phantom, we show the relevance of such a sampling-obtained in a few seconds-and the drastic decrease in cells number to be estimated during reconstruction when compared to the usual regular voxel lattice. A first iterative reconstruction of the Shepp-Logan using this kind of sampling shows the relevant advantages in terms of low dose or sparse acquisition sampling contexts. The method can also prove useful for other applications such as finite element method computations

Accurate geometry reconstruction of vascular structures using implicit splines

3-D visualization of blood vessel from standard medical datasets (e.g. CT or MRI) play an important role in many clinical situations, including the diagnosis of vessel stenosis, virtual angioscopy, vascular surgery planning and computer aided vascular surgery. However, unlike other human organs, the vasculature system is a very complex network of vessel, which makes it a very challenging task to perform its 3-D visualization. Conventional techniques of medical volume data visualization are in general not well-suited for the above-mentioned tasks. This problem can be solved by reconstructing vascular geometry. Although various methods have been proposed for reconstructing vascular structures, most of these approaches are model-based, and are usually too ideal to correctly represent the actual variation presented by the cross-sections of a vascular structure. In addition, the underlying shape is usually expressed as polygonal meshes or in parametric forms, which is very inconvenient for implementing ramification of branching. As a result, the reconstructed geometries are not suitable for computer aided diagnosis and computer guided minimally invasive vascular surgery. In this research, we develop a set of techniques associated with the geometry reconstruction of vasculatures, including segmentation, modelling, reconstruction, exploration and rendering of vascular structures. The reconstructed geometry can not only help to greatly enhance the visual quality of 3-D vascular structures, but also provide an actual geometric representation of vasculatures, which can provide various benefits. The key findings of this research are as follows: 1. A localized hybrid level-set method of segmentation has been developed to extract the vascular structures from 3-D medical datasets. 2. A skeleton-based implicit modelling technique has been proposed and applied to the reconstruction of vasculatures, which can achieve an accurate geometric reconstruction of the vascular structures as implicit surfaces in an analytical form. 3. An accelerating technique using modern GPU (Graphics Processing Unit) is devised and applied to rendering the implicitly represented vasculatures. 4. The implicitly modelled vasculature is investigated for the application of virtual angioscopy

Discrete ordinates CT organ dose simulator (DOCTORS)

Computed tomography (CT) has become pervasive in medical diagnostics as improved imaging techniques and processing algorithms provide higher quality information to doctors. However, the exponentially increasing usage of CT has raised concerns regarding long term low-dose radiological risks. Currently, the dose to patients is computed using Monte Carlo methods and experimental tests. In other areas of radiation transport, deterministic codes have been shown to be much faster than Monte Carlo codes. Currently, no deterministic methodology exists to automatically generate a spatially distributed dose profile from a CT voxel phantom. This work proposes a new code, Discrete Ordinate CT Organ Dose Simulator (DOCTORS) which utilizes a GPU accelerated raytracer and discrete ordinate solver to compute photon flux in the patient. The flux is then converted to dose. The DOCTORS code was benchmarked against MCNP6 and found to have good qualitative agreement using both a water phantom and a realistic patient phantom. DOCTORS was also found to be much faster than MCNP6; MCNP takes hours to compute flux profiles that take less than a minute using DOCTORS. A GPU algorithm was implemented that speeds up the DOCTORS code by a factor of up to nearly 40 for large problems. GPU acceleration was found to benefit smaller problems much less. Speedup was seen in both single precision and double precision problems --Abstract, page iii

Data-Driven Volumetric Image Generation from Surface Structures using a Patient-Specific Deep Leaning Model

The advent of computed tomography significantly improves patient health regarding diagnosis, prognosis, and treatment planning and verification. However, tomographic imaging escalates concomitant radiation doses to patients, inducing potential secondary cancer. We demonstrate the feasibility of a data-driven approach to synthesize volumetric images using patient surface images, which can be obtained from a zero-dose surface imaging system. This study includes 500 computed tomography (CT) image sets from 50 patients. Compared to the ground truth CT, the synthetic images result in the evaluation metric values of 26.9 Hounsfield units, 39.1dB, and 0.965 regarding the mean absolute error, peak signal-to-noise ratio, and structural similarity index measure. This approach provides a data integration solution that can potentially enable real-time imaging, which is free of radiation-induced risk and could be applied to image-guided medical procedures