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DOE OSTI2016

Localization and spectral isolation of special nuclear material using stochastic image reconstruction

Here, in this work we present a technique for isolating the gamma-ray and neutron energy spectra from multiple radioactive sources localized in an image. Image reconstruction algorithms for radiation scatter cameras typically focus on improving image quality. However, with scatter cameras being developed for non-proliferation applications, there is a need for not only source localization but also source identification. This work outlines a modified stochastic origin ensembles algorithm that provides localized spectra for all pixels in the image. We demonstrated the technique by performing three experiments with a dual-particle imager that measured various gamma-ray and neutron sources simultaneously. We showed that we could isolate the peaks from 22 Na and 137 Cs and that the energy resolution is maintained in the isolated spectra. To evaluate the spectral isolation of neutrons, a 252 Cf source and a PuBe source were measured simultaneously and the reconstruction showed that the isolated PuBe spectrum had a higher average energy and a greater fraction of neutrons at higher energies than the 252 Cf. Finally, spectrum isolation was used for an experiment with weapons grade plutonium, 252 Cf, and AmBe. The resulting neutron and gamma-ray spectra showed the expected characteristics that could then be used to identify the sources.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2014

Tomopy: A Python toolbox to perform X-Ray data proessing and image reconstruction.

Tomopy is a Python toolbox to perform x-ray data processing, image reconstruction and data exchange tasks at synchrotron facilities. The dependencies of the software are currently as follows: -Python related python standard library (http://docs.python.org/2/library/) numpy (http://www.numpy.org/) scipy (http://scipy.org/) matplotlib (http://matplotlip.org/) sphinx (http://sphinx-doc.org) pil (http://www.pythonware.com/products/pil/) pyhdf (http://pysclint.sourceforge.net/pyhdf/) h5py (http://www.h5py.org) pywt (http://www.pybytes.com/pywavelets/) file.py (https://pyspec.svn.sourceforge.net/svnroot/pyspec/trunk/pyspec/ccd/files.py) -C/C++ related: gridec (anonymous?? C-code written back in 1997 that uses standard C library) fftw (http://www.fftw.org/) tomoRecon (multi-threaded C++ verion of gridrec. Author: Mark Rivers from APS. http://cars9.uchicago.edu/software/epics/tomoRecon.html) epics (http://www.aps.anl.gov/epics/)

Source record↗

DOE OSTI2014

Tomopy: A Python toolbox to perform X-Ray data proessing and image reconstruction.

Source record↗

DOE OSTI2020

Considerations for three-dimensional image reconstruction from experimental data in coherent diffractive imaging

The Datasets folder contains 3 datasets, corresponding to the datasets mentioned in the article with 670, 2691 and 586 images. The Runs folder has all the runs used. Besides the images itself it also contains tags for each image under `/entry_1/image_1/detector_and_photon_corrected/tags`. The `headings` attribute gives the name of each tag. The `Jonas Strict` tag corresponds to the 670 images, the `Redflamingo` tag to the 2691 images and the `Redflamingo_Ida_subset` tag to the 586 images. The final dataset with 260 images can be obtained from `Datasets/amoc6914-Redflamingo-manual-586.h5` and use the `/model/diameterNM` group to select the 260 images with size closest to 214 nm.

AMO↗

NASA NTRS1991

Error analysis of image reconstruction by a synthetic aperture interferometric radiometer

An error analysis is presented for the image reconstruction algorithm currently employed by the electronically steered thinned array radiometer aircraft instrument. This instrument samples components of the Fourier transform of the brightness temperature distribution at 1.4 GHz. Sources of error are identified, including systematic offsets and stochastic noise in the measurements, errors in the interference pattern antenna calibration, multipath scattering from the antenna support structure, mutual coupling between nearby array elements, and variations in the antenna patterns of the array elements. These errors are shown to produce artifacts in the image which appear as 'streaks' running along the instrument flight line. Possible hardware corrections are discussed. These include a reduction in the profile of the antenna support structure and a decoupling of adjacent and other nearby array elements using corrugated 'skirts'. Also discussed are several image enhancement techniques which detect and correct for some of the streaking. These techniques include a renormalization of the interference patterns based on the measured element patterns, a perturbation based interpretation of image contrast, and a covariance analysis of noise present in the image.

Ruf, Christopher S.↗

DOE OSTI2018

Muon Tracing and Image Reconstruction Algorithms for Cosmic Ray Muon Computed Tomography

Cosmic ray muon-computed tomography (μCT) is a new imaging modality with unique characteristics that could be particularly important for diverse applications including nuclear nonproliferation, spent nuclear fuel monitoring, cargo scanning, and volcano imaging. The strong scattering dependence of muons on atomic number Z in combination with high penetration range could offer a significant advantage over existing techniques when dense, shielded containers must be imaged. However, μCT reconstruction using conventional filtered back-projection is limited due to the overly simple assumptions that do not consider the curved path caused by multiple Coulomb scattering prompting the need for more sophisticated approaches to be developed. Here, we argue that the use of improved muon tracing and scattering angle projection algorithms as well as an algebraic reconstruction technique should produce muon tomographic images with improved quality–or require fewer muons to produce the same image quality–compared to the case where conventional methods are used. We report on the development and assessment of three novel muon tracing methods and two scattering angle projection methods for μCT. Simulated dry storage casks with single and partial missing fuel assemblies were used as numerical examples to assess and compare the proposed methods. The reconstructed images showed an expected improvement in image quality when compared with conventional techniques, even without muon momentum information, which should lead to improved detection capability, even for partial defects.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2014

SU-D-18A-02: Towards Real-Time On-Board Volumetric Image Reconstruction for Intrafraction Target Verification in Radiation Therapy

Purpose: To expedite on-board volumetric image reconstruction from limited-angle kV—MV projections for intrafraction verification. Methods: A limited-angle intrafraction verification (LIVE) system has recently been developed for real-time volumetric verification of moving targets, using limited-angle kV—MV projections. Currently, it is challenged by the intensive computational load of the prior-knowledge-based reconstruction method. To accelerate LIVE, we restructure the software pipeline to make it adaptable to model and algorithm parameter changes, while enabling efficient utilization of rapidly advancing, modern computer architectures. In particular, an innovative two-level parallelization scheme has been designed: At the macroscopic level, data and operations are adaptively partitioned, taking into account algorithmic parameters and the processing capacity or constraints of underlying hardware. The control and data flows of the pipeline are scheduled in such a way as to maximize operation concurrency and minimize total processing time. At the microscopic level, the partitioned functions act as independent modules, operating on data partitions in parallel. Each module is pre-parallelized and optimized for multi-core processors (CPUs) and graphics processing units (GPUs). Results: We present results from a parallel prototype, where most of the controls and module parallelization are carried out via Matlab and its Parallel Computing Toolbox. The reconstruction is 5 times faster on a data-set of twice the size, compared to recently reported results, without compromising on algorithmic optimization control. Conclusion: The prototype implementation and its results have served to assess the efficacy of our system concept. While a production implementation will yield much higher processing rates by approaching full-capacity utilization of CPUs and GPUs, some mutual constraints between algorithmic flow and architecture specifics remain. Based on a careful analysis of the prototype performance, it will be feasible to resolve such issues through appropriate algorithmic modifications or special-purpose hardware, thus enabling target verification in seconds with the LIVE system. This work was partially supported by a research grant from Varian Medical Systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2014

MO-G-17A-07: Improved Image Quality in Brain F-18 FDG PET Using Penalized-Likelihood Image Reconstruction Via a Generalized Preconditioned Alternating Projection Algorithm: The First Patient Results

Purpose: To investigate the performance of a new penalized-likelihood PET image reconstruction algorithm using the 1{sub 1}-norm total-variation (TV) sum of the 1st through 4th-order gradients as the penalty. Simulated and brain patient data sets were analyzed. Methods: This work represents an extension of the preconditioned alternating projection algorithm (PAPA) for emission-computed tomography. In this new generalized algorithm (GPAPA), the penalty term is expanded to allow multiple components, in this case the sum of the 1st to 4th order gradients, to reduce artificial piece-wise constant regions (“staircase” artifacts typical for TV) seen in PAPA images penalized with only the 1st order gradient. Simulated data were used to test for “staircase” artifacts and to optimize the penalty hyper-parameter in the root-mean-squared error (RMSE) sense. Patient FDG brain scans were acquired on a GE D690 PET/CT (370 MBq at 1-hour post-injection for 10 minutes) in time-of-flight mode and in all cases were reconstructed using resolution recovery projectors. GPAPA images were compared PAPA and RMSE-optimally filtered OSEM (fully converged) in simulations and to clinical OSEM reconstructions (3 iterations, 32 subsets) with 2.6 mm XYGaussian and standard 3-point axial smoothing post-filters. Results: The results from the simulated data show a significant reduction in the 'staircase' artifact for GPAPA compared to PAPA and lower RMSE (up to 35%) compared to optimally filtered OSEM. A simple power-law relationship between the RMSE-optimal hyper-parameters and the noise equivalent counts (NEC) per voxel is revealed. Qualitatively, the patient images appear much sharper and with less noise than standard clinical images. The convergence rate is similar to OSEM. Conclusions: GPAPA reconstructions using the 1{sub 1}-norm total-variation sum of the 1st through 4th-order gradients as the penalty show great promise for the improvement of image quality over that currently achieved with clinical OSEM reconstructions.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Adorym: a multi-platform generic X-ray image reconstruction framework based on automatic differentiation

We describe and demonstrate an optimization-based X-ray image reconstruction framework called Adorym. Our framework provides a generic forward model, allowing one code framework to be used for a wide range of imaging methods ranging from near-field holography to fly-scan ptychographic tomography. By using automatic differentiation for optimization, Adorym has the flexibility to refine experimental parameters including probe positions, multiple hologram alignment, and object tilts. It is written with strong support for parallel processing, allowing large datasets to be processed on high-performance computing systems. We demonstrate its use on several experimental datasets to show improved image quality through parameter refinement.

36 MATERIALS SCIENCE↗

NASA NTRS2002

The Pixon Method for Data Compression Image Classification, and Image Reconstruction

As initially proposed, this program had three goals: (1) continue to develop the highly successful Pixon method for image reconstruction and support other scientist in implementing this technique for their applications; (2) develop image compression techniques based on the Pixon method; and (3) develop artificial intelligence algorithms for image classification based on the Pixon approach for simplifying neural networks. Subsequent to proposal review the scope of the program was greatly reduced and it was decided to investigate the ability of the Pixon method to provide superior restorations of images compressed with standard image compression schemes, specifically JPEG-compressed images.

Puetter, Richard↗

DOE OSTI2014

SU-E-I-73: Clinical Evaluation of CT Image Reconstructed Using Interior Tomography

Purpose: Radiation dose reduction has been a long standing challenge in CT imaging of obese patients. Recent advances in interior tomography (reconstruction of an interior region of interest (ROI) from line integrals associated with only paths through the ROI) promise to achieve significant radiation dose reduction without compromising image quality. This study is to investigate the application of this technique in CT imaging through evaluating imaging quality reconstructed from patient data. Methods: Projection data were directly obtained from patients who had CT examinations in a Dual Source CT scanner (DSCT). Two detectors in a DSCT acquired projection data simultaneously. One detector provided projection data for full field of view (FOV, 50 cm) while another detectors provided truncated projection data for a FOV of 26 cm. Full FOV CT images were reconstructed using both filtered back projection and iterative algorithm; while interior tomography algorithm was implemented to reconstruct ROI images. For comparison reason, FBP was also used to reconstruct ROI images. Reconstructed CT images were evaluated by radiologists and compared with images from CT scanner. Results: The results show that the reconstructed ROI image was in excellent agreement with the truth inside the ROI, obtained from images from CT scanner, and the detailed features in the ROI were quantitatively accurate. Radiologists evaluation shows that CT images reconstructed with interior tomography met diagnosis requirements. Radiation dose may be reduced up to 50% using interior tomography, depending on patient size. Conclusion: This study shows that interior tomography can be readily employed in CT imaging for radiation dose reduction. It may be especially useful in imaging obese patients, whose subcutaneous tissue is less clinically relevant but may significantly increase radiation dose.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Development and Validation of Quantitative Model-Based Image Reconstruction for NDE of Reinforced Steel-Lined Concrete Structures: Kal-El FY 2020

This project studied the capabilities of Model-Based Image Reconstruction (MBIR) and Machine Learning (ML) algorithms in the imaging and estimation of rebar corrosion from ultrasound signals. The application was challenging due to the introduction of a steel liner between the sensor and the concrete containing the rebar. The study focused in a synthetic specimen with a 6.35 mm thick steel liner, and a 19.05 mm diameter rebar at a depth of 44.5 mm and with four levels of corrosion; 0%, 20%, 50%, and 100% corrosion level. The corrosion was applied uniformly around the rebar, generating a ring in the perimeter of the rebar with different acoustic density. In order to generate a realistic synthetic dataset, we added random texture to the concrete of the synthetic specimens. This texture will mimic the variations encountered in real concrete specimens.We adapted our MBIR algorithm for the application and enhanced the algorithm to integrate in the physical model known specimen features, such as the steel liner thickness. The new MBIR method was able to cancel out reverberation from the steel liner and properly image the rebar. In particular, corrosion for the 50% and 100% cases were easily visible and quantitative metrics showed a slight separation for the 0% and 20% levels. The quantitative results were in agreement with the qualitative assessment. We also developed a ML XGBoost model to estimate corrosion level from the ultrasound signals. By combining the ML method with MBIR, we can pinpoint in the ultrasound signals the section that corresponds to the echoes from the rebar. The extract signals are processed by the XGBoost model for prediction. From each system scan, we obtain 15 predictions. The median prediction value is used as the final prediction. The True Acceptance Rate for the method is over 99%.

36 MATERIALS SCIENCE↗

DOE OSTI2015

Polychromatic sparse image reconstruction and mass attenuation spectrum estimation via B-spline basis function expansion

We develop a sparse image reconstruction method for polychromatic computed tomography (CT) measurements under the blind scenario where the material of the inspected object and the incident energy spectrum are unknown. To obtain a parsimonious measurement model parameterization, we first rewrite the measurement equation using our mass-attenuation parameterization, which has the Laplace integral form. The unknown mass-attenuation spectrum is expanded into basis functions using a B-spline basis of order one. We develop a block coordinate-descent algorithm for constrained minimization of a penalized negative log-likelihood function, where constraints and penalty terms ensure nonnegativity of the spline coefficients and sparsity of the density map image in the wavelet domain. This algorithm alternates between a Nesterov’s proximal-gradient step for estimating the density map image and an active-set step for estimating the incident spectrum parameters. Numerical simulations demonstrate the performance of the proposed scheme.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS1983

Effects of photon noise on speckle image reconstruction with the Knox-Thompson algorithm

An analysis of the effects of photon noise on astronomical speckle image reconstruction using the Knox-Thompson algorithm is presented. It is shown that the quantities resulting from the speckle average arre biased, but that the biases are easily estimated and compensated. Calculations are also made of the convergence rate for the speckle average as a function of the source brightness. An illustration of the effects of photon noise on the image recovery process is included.

Nisenson, P.↗

DOE OSTI2014

WE-G-18A-01: JUNIOR INVESTIGATOR WINNER - Low-Dose C-Arm Cone-Beam CT with Model-Based Image Reconstruction for High-Quality Guidance of Neurosurgical Intervention

Purpose: To address the challenges of image quality, radiation dose, and reconstruction speed in intraoperative cone-beam CT (CBCT) for neurosurgery by combining model-based image reconstruction (MBIR) with accelerated algorithmic and computational methods. Methods: Preclinical studies involved a mobile C-arm for CBCT imaging of two anthropomorphic head phantoms that included simulated imaging targets (ventricles, soft-tissue structures/bleeds) and neurosurgical procedures (deep brain stimulation (DBS) electrode insertion) for assessment of image quality. The penalized likelihood (PL) framework was used for MBIR, incorporating a statistical model with image regularization via an edgepreserving penalty. To accelerate PL reconstruction, the ordered-subset, separable quadratic surrogates (OS-SQS) algorithm was modified to incorporate Nesterov's method and implemented on a multi-GPU system. A fair comparison of image quality between PL and conventional filtered backprojection (FBP) was performed by selecting reconstruction parameters that provided matched low-contrast spatial resolution. Results: CBCT images of the head phantoms demonstrated that PL reconstruction improved image quality (∼28% higher CNR) even at half the radiation dose (3.3 mGy) compared to FBP. A combination of Nesterov's method and fast projectors yielded a PL reconstruction run-time of 251 sec (cf., 5729 sec for OS-SQS, 13 sec for FBP). Insertion of a DBS electrode resulted in severe metal artifact streaks in FBP reconstructions, whereas PL was intrinsically robust against metal artifact. The combination of noise and artifact was reduced from 32.2 HU in FBP to 9.5 HU in PL, thereby providing better assessment of device placement and potential complications. Conclusion: The methods can be applied to intraoperative CBCT for guidance and verification of neurosurgical procedures (DBS electrode insertion, biopsy, tumor resection) and detection of complications (intracranial hemorrhage). Significant improvement in image quality, dose reduction, and reconstruction time of ∼4 min will enable practical deployment of low-dose C-arm CBCT within the operating room. AAPM Research Seed Funding (2013-2014); NIH Fellowship F32EB017571; Siemens Healthcare (XP Division)

60 APPLIED LIFE SCIENCES↗

NASA NTRS1983

Image reconstruction by parametric cubic convolution

Cubic convolution, which has been discussed by Rifman and McKinnon (1974), was originally developed for the reconstruction of Landsat digital images. In the present investigation, the reconstruction properties of the one-parameter family of cubic convolution interpolation functions are considered and thee image degradation associated with reasonable choices of this parameter is analyzed. With the aid of an analysis in the frequency domain it is demonstrated that in an image-independent sense there is an optimal value for this parameter. The optimal value is not the standard value commonly referenced in the literature. It is also demonstrated that in an image-dependent sense, cubic convolution can be adapted to any class of images characterized by a common energy spectrum.

Park, S. K.↗

DOE OSTI2020

Efficient Image Reconstruction Method for a Millimeter-wave Shoe Scanner

The Pacific Northwest National Laboratory (PNNL) has recently developed an active 3D microwave/millimeter-wave shoe scanner. This system is designed to detect threats concealed within the soles of common footwear. The system operates over the 10-40 GHz frequency range for excellent penetration of common materials. The system presents several challenges for efficient image reconstruction, including the thick dielectric window, multi-row linear arrays, and focusing close to the antenna elements. In this paper, an efficient back-projection reconstruction algorithm is presented that overcomes these challenges. Experimental imaging results are shown that demonstrate high-resolution imaging performance for this new scanner.

Sheen, David M.↗

DOE OSTI2020

Relevance Vector Machine Image Reconstruction Algorithm for Electrical Capacitance Tomography With Explicit Uncertainty Estimates

We present a Relevance Vector Machine (RVM) based algorithm for electrical capacitance tomography (ECT) applications that can concurrently provide image reconstruction results and uncertainty estimates about the reconstruction. To illustrate the RVM operation in ECT, we simulate typical ECT scenarios, making explicit the connection between the reconstructed pixel values and the corresponding uncertainty estimates in each case. We compare the RVM reconstruction performance with that of the Iterative Landweber Method (ILM) and the least absolute shrinkage and selection operator (LASSO) in all the considered scenarios. The results show that, in addition to the key advantage of providing uncertainty measures, RVM can achieve similar reconstruction results with either lower or similar computational complexity.

42 ENGINEERING↗