Volumetric reconstruction in the microCAT tomography system

A new system for x-ray cone-beam micro-tomography has been developed to screen mice for internal phenotypic abnormalities at the Oak Ridge National Laboratory Mammalian Genetics Facility. Currently this system uses an image reconstruction algorithm that is based on two-dimensional (fan-beam) reconstruction techniques. The disparity between the actual scanner geometry and that assumed for reconstruction purposes introduces artifacts into the reconstruction volume that become increasingly worse the further their axial distance from the midplane. In order to reconcile this disparity and reduce axial distortion artifacts, a volumetric reconstruction algorithm based on cone beam geometry was implemented. The volumetric algorithm is derived and its heuristic implementation is explained within the constraints of the system, which limit the arclength of the scanning trajectory. Reconstructions using the volumetric algorithm are analyzed and compared to reconstructions from the current method. We show that our implementation produces images of equivalent quality in the midplane, and a marked decrease in axial distortion elsewhere Volume reconstruction times are shown to be comparable to those currently achieved. The theoretical foundations are given for future work to optimize the implementation through parallelization and by overcoming the data sufficiency problem

Fast reconstruction of 3D volumes from 2D CT projection data with GPUs

cited By 0International audienceMeso-F.E. modelling of 3D textile composites is a powerful tool, which can help determine mechanical properties and permeability of the reinforcements or composites. The quality of the meso F.E. analyses depends on the quality of the initial model. A direct method based on X-ray tomography imaging is introduced to determine finite element models based on the real geometry of 3D composite reinforcements. The method is particularly suitable regarding 3D textile reinforcements for which internal geometries are numerous and complex. An analysis of the image's texture is performed. A hyperelastic model developed for fibre bundles is used for the simulation of the deformation of the 3D reinforcement. © EDP Sciences, 2016

Quasi-Exact Helical Cone Beam Reconstruction for Micro CT

A cone beam micro-CT system is set up to collect truncated helical cone beam data. This system includes a micro-focal X-ray source, a precision computer-controlled X-Y-Z-theta stage, and an image-intensifier coupled to a large format CCD detector. The helical scanning mode is implemented by rotating and translating the stage while keeping X-ray source and detector stationary. A chunk of bone and a mouse leg are scanned and quasi-exact reconstruction is performed using the approach proposed in J. Hu et al. (2001). This approach introduced the original idea of accessory paths with upper and lower virtual detectors having infinite axial extent. It has a filtered backprojection structure which is desirable in practice and possesses the advantages of being simple to implement and computationally efficient compared to other quasi-exact helical cone beam algorithms for the long object problem

Analysis of 3D Cone-Beam CT Image Reconstruction Performance on a FPGA

Efficient and accurate tomographic image reconstruction has been an intensive topic of research due to the increasing everyday usage in areas such as radiology, biology, and materials science. Computed tomography (CT) scans are used to analyze internal structures through capture of x-ray images. Cone-beam CT scans project a cone-shaped x-ray to capture 2D image data from a single focal point, rotating around the object. CT scans are prone to multiple artifacts, including motion blur, streaks, and pixel irregularities, therefore must be run through image reconstruction software to reduce visual artifacts. The most common algorithm used is the Feldkamp, Davis, and Kress (FDK) backprojection algorithm. The algorithm is computationally intensive due to the O(n4) backprojection step, running slowly with large CT data files on CPUs, but exceptionally well on GPUs due to the parallel nature of the algorithm. This thesis will analyze the performance of 3D cone-beam CT image reconstruction implemented in OpenCL on a FPGA embedded into a Power System

PYRO-NN: Python Reconstruction Operators in Neural Networks

Purpose: Recently, several attempts were conducted to transfer deep learning to medical image reconstruction. An increasingly number of publications follow the concept of embedding the CT reconstruction as a known operator into a neural network. However, most of the approaches presented lack an efficient CT reconstruction framework fully integrated into deep learning environments. As a result, many approaches are forced to use workarounds for mathematically unambiguously solvable problems. Methods: PYRO-NN is a generalized framework to embed known operators into the prevalent deep learning framework Tensorflow. The current status includes state-of-the-art parallel-, fan- and cone-beam projectors and back-projectors accelerated with CUDA provided as Tensorflow layers. On top, the framework provides a high level Python API to conduct FBP and iterative reconstruction experiments with data from real CT systems. Results: The framework provides all necessary algorithms and tools to design end-to-end neural network pipelines with integrated CT reconstruction algorithms. The high level Python API allows a simple use of the layers as known from Tensorflow. To demonstrate the capabilities of the layers, the framework comes with three baseline experiments showing a cone-beam short scan FDK reconstruction, a CT reconstruction filter learning setup, and a TV regularized iterative reconstruction. All algorithms and tools are referenced to a scientific publication and are compared to existing non deep learning reconstruction frameworks. The framework is available as open-source software at \url{https://github.com/csyben/PYRO-NN}. Conclusions: PYRO-NN comes with the prevalent deep learning framework Tensorflow and allows to setup end-to-end trainable neural networks in the medical image reconstruction context. We believe that the framework will be a step towards reproducible researchComment: V1: Submitted to Medical Physics, 11 pages, 7 figure

Algebraic and analytic reconstruction methods for dynamic tomography.

In this work, we discuss algebraic and analytic approaches for dynamic tomography. We present a framework of dynamic tomography for both algebraic and analytic approaches. We finally present numerical experiments