Fast voxel line update for time-space image reconstruction

Recent applications of model-based iterative reconstruction(MBIR) algorithm to time-space Computed Tomography (CT) have shown that MBIR can greatly improve image quality by increasing resolution as well as reducing noise and some artifacts. Among the various iterative methods that have been studied for MBIR, iterative coordinate descent(ICD) has been found to have relatively low overall computational requirements due to its fast convergence. However, high computational cost and long reconstruction times remain as a barrier to the use of MBIR in practical applications. This disadvantage is especially prominent in time-space reconstruction because of the large volume of data. This thesis presents a new data structure, called VL-Buffer , for time-space reconstruction that significantly improves the cache locality while retaining good parallel performance. Experimental results show an average speedup of 40% using VL-Buffer

SurfelMeshing: Online Surfel-Based Mesh Reconstruction

We address the problem of mesh reconstruction from live RGB-D video, assuming a calibrated camera and poses provided externally (e.g., by a SLAM system). In contrast to most existing approaches, we do not fuse depth measurements in a volume but in a dense surfel cloud. We asynchronously (re)triangulate the smoothed surfels to reconstruct a surface mesh. This novel approach enables to maintain a dense surface representation of the scene during SLAM which can quickly adapt to loop closures. This is possible by deforming the surfel cloud and asynchronously remeshing the surface where necessary. The surfel-based representation also naturally supports strongly varying scan resolution. In particular, it reconstructs colors at the input camera's resolution. Moreover, in contrast to many volumetric approaches, ours can reconstruct thin objects since objects do not need to enclose a volume. We demonstrate our approach in a number of experiments, showing that it produces reconstructions that are competitive with the state-of-the-art, and we discuss its advantages and limitations. The algorithm (excluding loop closure functionality) is available as open source at https://github.com/puzzlepaint/surfelmeshing .Comment: Version accepted to IEEE Transactions on Pattern Analysis and Machine Intelligenc

GPU-based Iterative Cone Beam CT Reconstruction Using Tight Frame Regularization

X-ray imaging dose from serial cone-beam CT (CBCT) scans raises a clinical concern in most image guided radiation therapy procedures. It is the goal of this paper to develop a fast GPU-based algorithm to reconstruct high quality CBCT images from undersampled and noisy projection data so as to lower the imaging dose. For this purpose, we have developed an iterative tight frame (TF) based CBCT reconstruction algorithm. A condition that a real CBCT image has a sparse representation under a TF basis is imposed in the iteration process as regularization to the solution. To speed up the computation, a multi-grid method is employed. Our GPU implementation has achieved high computational efficiency and a CBCT image of resolution 512\times512\times70 can be reconstructed in ~5 min. We have tested our algorithm on a digital NCAT phantom and a physical Catphan phantom. It is found that our TF-based algorithm is able to reconstrct CBCT in the context of undersampling and low mAs levels. We have also quantitatively analyzed the reconstructed CBCT image quality in terms of modulation-transfer-function and contrast-to-noise ratio under various scanning conditions. The results confirm the high CBCT image quality obtained from our TF algorithm. Moreover, our algorithm has also been validated in a real clinical context using a head-and-neck patient case. Comparisons of the developed TF algorithm and the current state-of-the-art TV algorithm have also been made in various cases studied in terms of reconstructed image quality and computation efficiency.Comment: 24 pages, 8 figures, accepted by Phys. Med. Bio

Fast tomographic inspection of cylindrical objects

This paper presents a method for improved analysis of objects with an axial symmetry using X-ray Computed Tomography (CT). Cylindrical coordinates about an axis fixed to the object form the most natural base to check certain characteristics of objects that contain such symmetry, as often occurs with industrial parts. The sampling grid corresponds with the object, allowing for down-sampling hence reducing the reconstruction time. This is necessary for in-line applications and fast quality inspection. With algebraic reconstruction it permits the use of a pre-computed initial volume perfectly suited to fit a series of scans where same-type objects can have different positions and orientations, as often encountered in an industrial setting. Weighted back-projection can also be included when some regions are more likely subject to change, to improve stability. Building on a Cartesian grid reconstruction code, the feasibility of reusing the existing ray-tracers is checked against other researches in the same field.Comment: 13 pages, 13 figures. submitted to Journal Of Nondestructive Evaluation (https://www.springer.com/journal/10921

SkiMap: An Efficient Mapping Framework for Robot Navigation

We present a novel mapping framework for robot navigation which features a multi-level querying system capable to obtain rapidly representations as diverse as a 3D voxel grid, a 2.5D height map and a 2D occupancy grid. These are inherently embedded into a memory and time efficient core data structure organized as a Tree of SkipLists. Compared to the well-known Octree representation, our approach exhibits a better time efficiency, thanks to its simple and highly parallelizable computational structure, and a similar memory footprint when mapping large workspaces. Peculiarly within the realm of mapping for robot navigation, our framework supports realtime erosion and re-integration of measurements upon reception of optimized poses from the sensor tracker, so as to improve continuously the accuracy of the map.Comment: Accepted by International Conference on Robotics and Automation (ICRA) 2017. This is the submitted version. The final published version may be slightly differen

High resolution in-vivo MR-STAT using a matrix-free and parallelized reconstruction algorithm

MR-STAT is a recently proposed framework that allows the reconstruction of multiple quantitative parameter maps from a single short scan by performing spatial localisation and parameter estimation on the time domain data simultaneously, without relying on the FFT. To do this at high-resolution, specialized algorithms are required to solve the underlying large-scale non-linear optimisation problem. We propose a matrix-free and parallelized inexact Gauss-Newton based reconstruction algorithm for this purpose. The proposed algorithm is implemented on a high performance computing cluster and is demonstrated to be able to generate high-resolution ($1mm \times 1mm$ in-plane resolution) quantitative parameter maps in simulation, phantom and in-vivo brain experiments. Reconstructed $T_1$ and $T_2$ values for the gel phantoms are in agreement with results from gold standard measurements and for the in-vivo experiments the quantitative values show good agreement with literature values. In all experiments short pulse sequences with robust Cartesian sampling are used for which conventional MR Fingerprinting reconstructions are shown to fail.Comment: Accepted by NMR in Biomedicine on 2019-12-0