X-ray CT Reconstruction Research Articles

Influence of coarse aggregates’ morphological characteristics on the pore structures of skeleton in porous asphalt mixture

Coarse aggregates play a significant role in configuring the pore structures in the porous asphalt mixture (PAM). This study aims to reveal the influence of coarse aggregates’ morphological characteristics on the pore features in the coarse aggregate skeleton (CAS) of PAM. To achieve this goal, X-ray CT and three-dimensional (3D) visualization reconstruction technology were utilized to acquire the 3D morphology of different kinds of coarse aggregates. Based on the morphology of real aggregate particles, a 3D simulation model of CAS was developed, and virtual scanning imaging technology was used to obtain the pore features in the CAS. The sensitive influence of coarse aggregate morphologies on the pore features was evaluated by GA-BP neural network. Results show that air void in the skeleton is most sensitive to the composite flat-elongated index and composite sphericity of 13.2mm aggregates, and composite sphericity of 9.5mm aggregates. The composite sphericity of 13.2mm aggregates, composite flat-elongated index and composite texture index of 9.5mm aggregates, composite flat-elongated index, composite angularity index, and composite texture index of 4.75mm aggregates are the high-sensitivity influencing factors to the fractal characteristic of pore structures.

MCR toolkit: A GPU-based toolkit for multi-channel reconstruction of preclinical and clinical x-ray CT data.

The advancement of x-ray CT into the domains of photon counting spectral imaging and dynamic cardiac and perfusion imaging has created many new challenges and opportunities for clinicians and researchers. To address challenges such as dose constraints and scanning times while capitalizing on opportunities such as multi-contrast imaging and low-dose coronary angiography, these multi-channel imaging applications require a new generation of CT reconstruction tools. These new tools should exploit the relationships between imaging channels during reconstruction to set new image quality standards while serving as a platform for direct translation between the preclinical and clinical domains. We outline and demonstrate a new Multi-Channel Reconstruction (MCR) Toolkit for GPU-based analytical and iterative reconstruction of preclinical and clinical multi-energy and dynamic x-ray CT data. To promote open science, open-source distribution of the Toolkit will coincide with the release of this publication (GPL v3; gitlab.oit.duke.edu/dpc18/mcr-toolkit-public). The MCR Toolkit source code is implemented in C/C++ and NVIDIA's CUDA GPU programming interface, with scripting support from MATLAB and Python. The Toolkit implements matched, separable footprint CT reconstruction operators for projection and backprojection in two geometries: planar, cone-beam CT (CBCT) and 3rd generation, cylindrical multi-detector row CT (MDCT). Analytical reconstruction is performed using filtered backprojection (FBP) for circular CBCT, weighted FBP (WFBP) for helical CBCT, and cone-parallel projection rebinning followed by WFBP for MDCT. Arbitrary combinations of energy and temporal channels are iteratively reconstructed under a generalized multi-channel signal model for joint reconstruction. We solve this generalized model algebraically using the split Bregman optimization method and the BiCGSTAB(l) linear solver interchangeably for both CBCT and MDCT data. Rank-sparse kernel regression (RSKR) and patch-based singular value thresholding (pSVT) are used to regularize the energy and time dimensions, respectively. Under a Gaussian noise model, regularization parameters are estimated automatically from the input data, dramatically reducing algorithm complexity for end users. Multi-GPU parallelization of the reconstruction operators is supported to manage reconstruction times. Denoising with RSKR and pSVT and post-reconstruction material decomposition are illustrated with preclinical and clinical cardiac photon-counting (PC)CT data. A digital MOBY mouse phantom with cardiac motion is used to illustrate single energy (SE), multi-energy (ME), time resolved (TR), and combined multi-energy and time-resolved (METR) helical, CBCT reconstruction. A fixed set of projection data is used across all reconstruction cases to demonstrate the Toolkit's robustness to increasing data dimensionality. Identical reconstruction code is applied to in vivo cardiac PCCT data acquired in a mouse model of atherosclerosis (METR). Clinical cardiac CT reconstruction is illustrated using the XCAT phantom and the DukeSim CT simulator, while dual-source, dual-energy CT reconstruction is illustrated for data acquired with a Siemens Flash scanner. Benchmarking results with NVIDIA RTX 8000 GPU hardware demonstrate 61%-99% efficiency in scaling computation from one to four GPUs for these reconstruction problems. The MCR Toolkit provides a robust solution for temporal and spectral x-ray CT reconstruction problems and was built from the ground up to facilitate translation of CT research and development between preclinical and clinical applications.

Critique of “MemXCT: Memory-Centric X-Ray CT Reconstruction With Massive Parallelization” by SCC Team From University of California San Diego

In this paper, we describe our efforts to reproduce the results reported in the SC19 paper by Hidayetoglu et al., titledMemXCT: Memory-Centric X-ray CT Reconstruction withMassive Parallelization. MemXCTs single-device performance, parallelized via OpenMP and MPI using AMD Zen2 CPU cores (second-generation EPYC processor) and NVIDIA V100 GPU devices. Both compute families are available as Microsoft Azurecloud HPC offerings. With these resources, we were are able to reproduce most of the results and exceed the performance of larger inputs on an AMD EPYC HBv2 cluster. We are also able to reproduce the trends of Strong Scaling (SS) for Optimized CPU and GPU versions. Differences in performance of the CPU version were observed due to differences in the underlying hardware, input size, and number of available nodes.

MemXCT: Design, Optimization, Scaling, and Reproducibility of X-Ray Tomography Imaging

This work extends our previous research entitled &#x201C;MemXCT: Memory-centric X-ray CT Reconstruction with Massive Parallelization&#x201D; that was originally published at SC19 conference (Hidayeto&#x011F;lu <i>et al.</i>, 2019) with reproducibility of the computational imaging performance. X-ray computed tomography (XCT) is regularly used at synchrotron light sources to study the internal morphology of materials at high resolution. However, experimental constraints, such as radiation sensitivity, can result in noisy or undersampled measurements. Further, depending on the resolution, sample size and data acquisition rates, the resulting noisy dataset can be in the order of terabytes. Advanced iterative reconstruction techniques can produce high-quality images from noisy measurements, but their computational requirements have made their use an exception rather than the rule. We propose a novel memory-centric approach that avoids redundant computations at the expense of additional memory complexity. We develop a memory-centric iterative reconstruction system, MemXCT, that uses an optimized SpMV implementation with two-level pseudo-Hilbert ordering and multi-stage input buffering. We evaluate MemXCT on various supercomputer architectures involving KNL and GPU. MemXCT can reconstruct a large (11K&#x00D7;11K) mouse brain tomogram in 10 seconds using 4096 KNL nodes (256K cores). The results presented in our original article at the SC19 were based on large-scale supercomputing resources. The MemXCT application was selected for the Student Cluster Competition (SCC) Reproducibility Challenge and evaluated on a variety of cloud computing resources by universities around the world in the SC20 conference. We summarize the results of the top-ranked SCC Reproducibility Challenge teams and identify the most pertinent measures for ensuring the reproducibility of our experiments in this article.

Critique of “MemXCT: Memory-Centric X-Ray CT Reconstruction With Massive Parallelization” by SCC Team From Georgia Tech

For the 2020 Student Cluster Competition, we reproduced results from “MemXCT: Memory-Centric X-ray CT Reconstruction with Massive Parallelization” (Hidayetoğlu <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> ). Reproducibility is of critical importance to the scientific community, not just to verify correctness of results but also to see how easily others can understand and work with the given methods. MemXCT is an approach for image reconstruction in X-ray ptychography, which has a broad range of applications in materials science. MemXCT is not the only X-ray tomography algorithm, though; as opposed to compute-centric algorithms, it is designed to scale better by optimizing for memory bandwidth and memory latency. MemXCT also applies several key optimizations in order to ease memory pressure. In this article, we test the performance and strong scaling of MemXCT on 1 to 256 AMD CPU cores (1-4 nodes) and 1-16 Nvidia V100 GPUs (1-4 nodes). We confirm the impact of MemXCT’s optimizations. Still, we find that the performance of some important loops in the MemXCT kernel is much lower on the AMD processors (with AVX2) of our CPU nodes compared to the Intel CPUs (with AVX-512) used in the original article. We also confirm MemXCT performance on Tesla V100 GPUs, as reported in the article.

Critique of “MemXCT: Memory-Centric X-Ray CT Reconstruction With Massive Parallelization” by SCC Team From ETH Zürich

Critique of “MemXCT: Memory-Centric X-Ray CT Reconstruction With Massive Parallelization” by SCC Team From ETH Zürich

Critique of “MemXCT: Memory-Centric X-Ray CT Reconstruction With Massive Parallelization” by SCC Team From Peking University

Hidayetolu <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> (2019) proposed a novel memory-centric computation system, MemXCT. As a challenge at SC20, we reproduce the computational efficiency of MemXCT on our Azure cloud cluster. Our experiments evaluate the overall performance and the strong scalability with real datasets and verify part of the conclusions in the original article.

Critique of “MemXCT: Memory-Centric X-Ray CT Reconstruction With Massive Parallelization” by SCC Team From the University of Texas at Austin

This report describes The University of Texas Student Cluster Competition team's effort to reproduce the results of "MemXCT: memory-centric X-ray CT reconstruction with massive parallelization" \cite{memxct}. The paper details a new memory-centric approach that reconstructs X-ray computed tomography (XCT) from noisy raw data. In our reproduction experiments, we utilized Microsoft Azure's CycleCloud tool to provision, orchestrate, and manage our computing cluster in the cloud. In particular, we scheduled and benchmarked reconstruction workloads using Azure's CPU-based HC44rs and GPU-based NC12s v2 virtual machine (VM) types to evaluate the scalability properties of the reconstruction approach and the performance differences between architectures. The HC44rs VMs contained 44 Intel Xeon Platinum cores, while the NC12s v2 VM was equipped with two NVIDIA P100 GPUs. We used a recent version of Intel's compiler stack with the MKL library for our CPU code along with CUDA 11.1 on GPUs. Overall, our results confirm the findings of the original paper, demonstrating similar acceleration on GPUs and scalability properties on CPUs.

Critique of ”MemXCT: Memory-Centric X-Ray CT Reconstruction With Massive Parallelization” by SCC Team From Nanyang Technological University

In this technical report, we focus on reproducing the results reported in the paper MemXCT: Memory-Centric X-ray CT Reconstruction with Massive Parallelization. MemXCT is a scalable approach to X-ray Computed Tomography reconstruction which removes redundant computation. We reproduced the single CPU/GPU performance as well as strong scaling experiments. We set up our congurations on Microsoft Azure CycleCloud and have two clusters. One cluster has 4 nodes with 60 CPUs on each node and the other cluster has 4 nodes with 4 NVIDIA V100 GPUs on each node. Both clusters come with InniBand. The original author conducted his experiments on Theta and Blue Waters supercomputers. We were able to reproduce part of the results in the original paper, however, failed to produce similar performance on other experiments. This report was submitted as part of the reproducibility challenge in SC20 Student Cluster Competition.

Critique of “MemXCT: Memory-Centric X-Ray CT Reconstruction With Massive Parallelization” by SCC Team From Clemson University

This paper reproduces the results of the Supercomputing 2019 paper, MemXCT: Memory-Centric X-ray CT Reconstruction with Massive Parallelization by Hidayeto˘ glu et al. as part of the Supercomputing 2020 Student Cluster Competition Reproducibility Challenge. We reproduce the single CPU-GPU performance experiments and the strong scaling experiments and compare the results to the original paper. Although we are not able to use the exact HPC system from the original paper, we use similar hardware and configurations to recreate the original environment in Microsoft Azure Cloud services. We were not able to demonstrate exact performance characteristics from the original paper due to hardware limitations in our environment, but we are able to reproduce similar performance trends for both single-node and scaling experiments. Digital artifacts from these experiments are available at: 10.5281/zenodo.5598108.

Critique of “MemXCT: Memory-Centric X-Ray CT Reconstruction With Massive Parallelization” by SCC Team From Tsinghua University

Critique of “MemXCT: Memory-Centric X-Ray CT Reconstruction With Massive Parallelization” by SCC Team From Tsinghua University

Compressive Spectral X-Ray CT Reconstruction via Deep Learning

Compressive spectral X-ray imaging (CSXI) uses coded illumination projections to reconstruct tomographic images at multiple energy bins. Different K-edge materials are used to modulate the spectrum of the X-ray source at various angles, thereby capturing the projections containing spectral attenuation information. It is a cost-effective and low-dose sensing approach; however, the image reconstruction is a nonlinear and ill-posed problem. Current methods of solving the inverse problem are computationally expensive and require extensive iterations. This paper proposes a deep learning model consisting of a set of convolutional neural networks to reconstruct the CSXI spectral images, which correspond to inpainting the subsampled sinograms, recovering the monoenergetic sinograms, and removing the artifacts from a fast but low-quality analytical reconstruction. Numerical experiments show that the proposed method significantly improves the quality of reconstructed image compared with that attained by the state-of-the-art reconstruction methods. Moreover, it significantly reduces the time-required for CSXI reconstruction.

High Resolution X-Ray CT Reconstruction of Additively Manufactured Metal Parts using Generative Adversarial Network-based Domain Adaptation in AI-CT

High Resolution X-Ray CT Reconstruction of Additively Manufactured Metal Parts using Generative Adversarial Network-based Domain Adaptation in AI-CT

Effectiveness of extended Carlson approach in treatment of lateral femoral condylar Hoffa fractures

To investigate the effectiveness of the extended Carlson approach in the treatment of lateral femoral condylar Hoffa fractures. The clinical data of 17 patients with lateral femoral condyle Hoffa fractures between September 2012 and January 2019 were retrospectively analyzed. There were 10 males and 7 females, with a mean age of 43 years (range, 32-68 years). Fractures were caused by traffic accident in 9 cases, by falling from height in 6 cases, and by the other mechanism in 2 cases. According to the Letenneur's classification, there were 8 cases of typeⅠ, 6 cases of type Ⅱ, and 3 cases of type Ⅲ. The mean time from injury to operation was 7 days (range, 3-32 days). All patients were treated with extended Carlson approach. Patients with Letenneur types Ⅰ and Ⅲ were fixed by a posterior antigliding plate combined with headless compression screws from anteroposterior direction, and patients with Letenneur typeⅡ were fixed by headless compression screws from anteroposterior direction. The anteroposterior and lateral X-ray films, CT and three-dimensional reconstruction of the knee joint were taken after operation to assess fracture healing and position of the internal fixators. The knee function was evaluated according to Letenneur's functional assessment system. All patients were followed up 13-28 months (mean, 15 months). All the incisions healed by first intention, and no complication such as fracture redisplacement, fracture nonunion, internal fixator fracture, and common peroneal nerve injury occurred. The mean time of fracture healing was 18 weeks (range, 16-32 weeks). At last follow-up, according to Letenneur's functional assessment system, the knee function was excellent in 12 cases and good in 5 cases, with an excellent and good rate of 100%. The extended Carlson approach for the treatment of lateral femoral condylar Hoffa fractures has the advantages of clear exposure, easy reduction and fixation, high fracture healing rate, few complications, and good recovery of knee joint function.

First application of the GPU-based software framework TIGRE for proton CT image reconstruction.

In proton therapy, the knowledge of the proton stopping power, i.e. the energy deposition per unit length within human tissue, is essential for accurate treatment planning. One suitable method to directly measure the stopping power is proton computed tomography (pCT). Due to the proton interaction mechanisms in matter, pCT image reconstruction faces some challenges: the unique path of each proton has to be considered separately in the reconstruction process adding complexity to the reconstruction problem. This study shows that the GPU-based open-source software toolkit TIGRE, which was initially intended for X-ray CT reconstruction, can be applied to the pCT image reconstruction problem using a straight line approach for the proton path. This simplified approach allows for reconstructions within seconds. To validate the applicability of TIGRE to pCT, several Monte Carlo simulations modeling a pCT setup with two Catphan® modules as phantoms were performed. Ordered-Subset Simultaneous Algebraic Reconstruction Technique (OS-SART) and Adaptive-Steepest-Descent Projection Onto Convex Sets (ASD-POCS) were used for image reconstruction. Since the accuracy of the approach is limited by the straight line approximation of the proton path, requirements for further improvement of TIGRE for pCT are addressed.

Multi-Slice Fusion for Sparse-View and Limited-Angle 4D CT Reconstruction

Inverse problems spanning four or more dimensions such as space, time andother independent parameters have become increasingly important. State-of-the-art 4D reconstruction methods use model based iterative reconstruction (MBIR), but depend critically on the quality of the prior modeling. Recently, plug-and-play (PnP) methods have been shown to be an effective way to incorporate advanced prior models using state-of-the-art denoising algorithms. However, state-of-the-art denoisers such as BM4D and deep convolutional neural networks (CNNs) are primarily available for 2D or 3D images and extending them to higher dimensions is difficult due to algorithmic complexity and the increased difficulty of effective training. In this paper, we present multi-slice fusion , a novel algorithm for 4D reconstruction, based on the fusion of multiple low-dimensional denoisers. Our approach uses multi-agent consensus equilibrium (MACE), an extension of plug-and-play, as a framework for integrating the multiple lower-dimensional models. We apply our method to 4D cone-beam X-ray CT reconstruction for non destructive evaluation (NDE) of samples that are dynamically moving during acquisition. We implement multi-slice fusion on distributed, heterogeneous clusters in order to reconstruct large 4D volumes in reasonable time and demonstrate the inherent parallelizable nature of the algorithm. We present simulated and real experimental results on sparse-view and limited-angle CT data to demonstrate that multi-slice fusion can substantially improve the quality of reconstructions relative to traditional methods, while also being practical to implement and train.

X-RAY CT Reconstruction by using Spatially Non-Homogeneous ICD Optimization

Recent applications of conventional iterative coordinate descent (ICD) algorithms to multislice helical CT reconstructions have shown that conventional ICD can greatly improve image quality by increasing resolution as well as reducing noise and some artifacts. However, high computational cost and long reconstruction times remain as a barrier to the use of conventional algorithm in the practical applications. Among the various iterative methods that have been studied for conventional, ICD has been found to have relatively low overall computational requirements due to its fast convergence. This paper presents a fast model-based iterative reconstruction algorithm using spatially nonhomogeneous ICD (NH-ICD) optimization. The NH-ICD algorithm speeds up convergence by focusing computation where it is most needed. The NH-ICD algorithm has a mechanism that adaptively selects voxels for update. First, a voxel selection criterion VSC determines the voxels in greatest need of update. Then a voxel selection algorithm VSA selects the order of successive voxel updates based upon the need for repeated updates of some locations, while retaining characteristics for global convergence. In order to speed up each voxel update, we also propose a fast 3-D optimization algorithm that uses a quadratic substitute function to upper bound the local 3-D objective function, so that a closed form solution can be obtained rather than using a computationally expensive line search algorithm. The experimental results show that the proposed method accelerates the reconstructions by roughly a factor of three on average for typical 3-D multislice geometries.

X-Ray CT Reconstruction of Additively Manufactured Parts using 2.5D Deep Learning MBIR

In this paper, we present a deep learning algorithm to rapidly obtain high quality CT reconstructions for AM parts. In particular, we propose to use CAD models of the parts that are to be manufactured, introduce typical defects and simulate XCT measurements. These simulated measurements were processed using FBP (computationally simple but result in noisy images) and the MBIR technique. We then train a 2.5D deep convolutional neural network [4], deemed 2.5D Deep Learning MBIR (2.5D DL-MBIR), on these pairs of noisy and high-quality 3D volumes to learn a fast, non-linear mapping function. The 2.5D DL-MBIR reconstructs a 3D volume in a 2.5D scheme where each slice is reconstructed from multiple inputs slices of the FBP input. Given this trained system, we can take a small set of measurements on an actual part, process it using a combination of FBP followed by 2.5D DL-MBIR. Both steps can be rapidly performed using GPUs, resulting in a real-time algorithm that achieves the high-quality of MBIR as fast as standard techniques. Intuitively, since CAD models are typically available for parts to be manufactured, this provides a strong constraint "prior" which can be leveraged to improve the reconstruction.

Preliminary X-ray CT investigation to link Hounsfield unit measurements with the International System of Units (SI)

PurposeThis paper lays the groundwork for linking Hounsfield unit measurements to the International System of Units (SI), ultimately enabling traceable measurements across X-ray CT (XCT) machines. We do this by characterizing a material basis that may be used in XCT reconstruction giving linear combinations of concentrations of chemical elements (in the SI units of mol/m3) which may be observed at each voxel. By implication, linear combinations not in the set are not observable.Methods and materialsWe formulated a model for our material basis with a set of measurements of elemental powders at four tube voltages, 80 kV, 100 kV, 120 kV, and 140 kV, on a medical XCT. The samples included 30 small plastic bottles of powders containing various compounds spanning the atomic numbers up to 20, and a bottle of water and one of air. Using the chemical formulas and measured masses, we formed a matrix giving the number of Hounsfield units per (mole per cubic meter) at each tube voltage for each of 13 chemical elements. We defined a corresponding matrix in units we call molar Hounsfield unit (HU) potency, the difference in HU values that an added mole per cubic meter in a given voxel would add to the measured HU value. We built a matrix of molar potencies for each chemical element and tube voltage and performed a singular value decomposition (SVD) on these to formulate our material basis. We determined that the dimension of this basis is two. We then compared measurements in this material space with theoretical measurements, combining XCOM cross section data with the tungsten anode spectral model using interpolating cubic splines (TASMICS), a one-parameter filter, and a simple detector model, creating a matrix similar to our experimental matrix for the first 20 chemical elements. Finally, we compared the model predictions to Hounsfield unit measurements on three XCT calibration phantoms taken from the literature.ResultsWe predict the experimental HU potency values derived from our scans of chemical elements with our theoretical model built from XCOM data. The singular values and singular vectors of the model and powder measurements are in substantial agreement. Application of the Bayesian Information Criterion (BIC) shows that exactly two singular values and singular vectors describe the results over four tube voltages. We give a good account of the HU values from the literature, measured for the calibration phantoms at several tube voltages for several commercial instruments, compared with our theoretical model without introducing additional parameters.ConclusionsWe have developed a two-dimensional material basis that specifies the degree to which individual elements in compounds effect the HU values in XCT images of samples with elements up to atomic number Z = 20. We show that two dimensions is sufficient given the contrast and noise in our experiment. The linear combination of concentrations of elements that can be observed using a medical XCT have been characterized, providing a material basis for use in dual-energy reconstruction. This approach provides groundwork for improved reconstruction and for the link of Hounsfield units to the SI.

Bayesian 3D X-ray Computed Tomography with a Hierarchical Prior Model for Sparsity in Haar Transform Domain.

In this paper, a hierarchical prior model based on the Haar transformation and an appropriate Bayesian computational method for X-ray CT reconstruction are presented. Given the piece-wise continuous property of the object, a multilevel Haar transformation is used to associate a sparse representation for the object. The sparse structure is enforced via a generalized Student-t distribution (), expressed as the marginal of a normal-inverse Gamma distribution. The proposed model and corresponding algorithm are designed to adapt to specific 3D data sizes and to be used in both medical and industrial Non-Destructive Testing (NDT) applications. In the proposed Bayesian method, a hierarchical structured prior model is proposed, and the parameters are iteratively estimated. The initialization of the iterative algorithm uses the parameters of the prior distributions. A novel strategy for the initialization is presented and proven experimentally. We compare the proposed method with two state-of-the-art approaches, showing that our method has better reconstruction performance when fewer projections are considered and when projections are acquired from limited angles.