Residual strain, a tensor quantity, is a critical material property that impacts the overall performance of metal parts. Neutron Bragg edge strain tomography is a technique for imaging residual strain that works by making conventional hyperspectral computed tomography measurements, extracting the average projected strain at each detector pixel, and processing the resulting strain sinogram using a reconstruction algorithm. However, the reconstruction is severely ill-posed as the underlying inverse problem involves inferring a tensor at each voxel from scalar sinogram data.In this paper, we introduce the model-oriented neutron strain tomographic reconstruction (MONSTR) algorithm that reconstructs the 2D residual strain tensor from the neutron Bragg edge strain measurements. MONSTR is based on using the multi-agent consensus equilibrium framework for the tensor tomographic reconstruction. Specifically, we formulate the reconstruction as a consensus solution of a collection of agents representing detector physics, the tomographic reconstruction process, and physics-based constraints from continuum mechanics. Using simulated data, we demonstrate high-quality reconstruction of the strain tensor even when using very few measurements.

Neutron computed tomography (nCT) is a 3D char-acterization technique used to image the internal morphology or chemical composition of samples in biology and materials sciences. A typical workflow involves placing the sample in the path of a neutron beam, acquiring projection data at a predefined set of orientations, and processing the resulting data using an analytic reconstruction algorithm. Typical nCT scans require hours to days to complete and are then processed using conventional filtered back-projection (FBP), which performs poorly with sparse views or noisy data. Hence, the main methods in order to reduce overall acquisition time are the use of an improved sampling strategy combined with the use of advanced reconstruction methods such as model-based iterative reconstruction (MBIR). In this paper, we propose an adaptive orientation selection method in which an MBIR reconstruction on previously-acquired measurements is used to define an objective function on orientations that balances a data-fitting term promoting edge alignment and a regularization term promoting orientation diversity. Using simulated and experimental data, we demonstrate that our method produces high-quality reconstructions using significantly fewer total measurements than the conventional approach.

Non-destructive characterization of multi-layered structures that can be accessed from only a single side is important for applications such as well-bore integrity inspection. Existing methods related to Synthetic Aperture Focusing Technique (SAFT) rapidly produce acceptable results but with significant artifacts. Recently, ultrasound model-based iterative reconstruction (UMBIR) approaches have shown significant improvements over SAFT. However, even these methods produce ringing artifacts due to the high fractional-bandwidth of the excitation signal.In this paper, we propose a ringing artifact reduction method for ultrasound image reconstruction that uses a multi-agent consensus equilibrium (RARE-MACE) framework. Our approach integrates a physics-based forward model that accounts for the propagation of a collimated ultrasonic beam in multi-layered media, a spatially varying image prior, and a denoiser designed to suppress the ringing artifacts that are characteristic of reconstructions from high-fractional bandwidth ultrasound sensor data. We test our method on simulated and experimental measurements and show substantial improvements in image quality compared to SAFT and UMBIR.

Hyperspectral neutron tomography is an effective method for analyzing crystalline material samples with complex compositions in a non-destructive manner. Since the counts in the hyperspectral neutron radiographs directly depend on the neutron cross-sections, materials may exhibit contrasting neutron responses across wavelengths. Therefore, it is possible to extract the unique signatures associated with each material and use them to separate the crystalline phases simultaneously.We introduce an autonomous material decomposition (AMD) algorithm to automatically characterize and localize polycrystalline structures using Bragg edges with contrasting neutron responses from hyperspectral data. The algorithm estimates the linear attenuation coefficient spectra from the measured radiographs and then uses these spectra to perform polycrystalline material decomposition and reconstructs 3D material volumes to localize materials in the spatial domain. Our results demonstrate that the method can accurately estimate both the linear attenuation coefficient spectra and associated reconstructions on both simulated and experimental neutron data.

X-ray computed tomography (XCT) is an important tool for high-resolution non-destructive characterization of additively-manufactured metal components. XCT reconstructions of metal components may have beam hardening artifacts such as cupping and streaking which makes reliable detection of flaws and defects challenging. Furthermore, traditional workflows based on using analytic reconstruction algorithms require a large number of projections for accurate characterization - leading to longer measurement times and hindering the adoption of XCT for in-line inspections. In this paper, we introduce a new workflow based on the use of two neural networks to obtain high-quality accelerated reconstructions from sparse-view XCT scans of single material metal parts. The first network, implemented using fully-connected layers, helps reduce the impact of BH in the projection data without the need of any calibration or knowledge of the component material. The second network, a convolutional neural network, maps a low-quality analytic 3D reconstruction to a high-quality reconstruction. Using experimental data, we demonstrate that our method robustly generalizes across several alloys, and for a range of sparsity levels without any need for retraining the networks thereby enabling accurate and fast industrial XCT inspections.

Collimated beam ultrasound systems are a novel technology for imaging inside multi-layered structures such as geothermal wells. Such systems include a transmitter and multiple receivers to capture reflected signals. Common algorithms for ultrasound reconstruction use delay-and-sum (DAS) approaches; these have low computational complexity but produce inaccurate images in the presence of complex structures and specialized geometries such as collimated beams.In this paper, we propose a multi-layer, ultrasonic, model-based iterative reconstruction algorithm designed for collimated beam systems. We introduce a physics-based forward model to accurately ac-count for the propagation of a collimated ultrasonic beam in multi-layer media and describe an efficient implementation using binary search. We model direct arrival signals, detector noise, and a spatially varying image prior, then cast the reconstruction as a maximum a posteriori estimation problem. Using simulated and experimental data we obtain significantly fewer artifacts relative to DAS while running in near real time using commodity compute resources.

High-resolution X-ray computed tomography (XCT) is an important technique for the inspection of additively manufactured (AM) parts. While XCT is typically used off-line to inspect a subset of manufactured parts, significantly accelerating measurement speed while retaining accuracy would enable use of XCT for in-line inspection to rapidly identify defects in each part as it is manufactured. Here, we propose a deep learning (DL) based approach that uses computer aided design (CAD) models of the AM parts and physics-based information to rapidly produce high-quality reconstructions from sparse XCT measurements without high quality ground truth data. Our approach uses a generative adversarial neural network (GAN) to produced realistic training data from the CAD-based simulations and a deep neural network that is trained using data from the first stage to produce accurate 3D reconstructions. Using experimental XCT data of metal parts, we demonstrate enhanced defect detection capabilities while dramatically reducing the scan time.

Wavelength-resolved (WR) neutron transmission tomography is an emerging technique to characterize engineering materials. While tomographic reconstruction for amorphous samples is straightforward, it is challenging to reconstruct samples with single-crystal domains because the attenuation of the sample varies as a function of its orientation with respect to the incident beam due to Bragg scattering. In this paper, we present an algorithm that can reconstruct samples with single-crystal domains from WR neutron tomographic measurements. In particular, we use a model-based iterative reconstruction (MBIR) technique that reconstructs the volume by identifying and leaving out the regions of the measurement that are affected by Bragg scatter. We combine the output of the MBIR method with an algorithm that matches the reconstruction to the identified Bragg scatter to reconstruct a feature that corresponds to the local crystallography of the sample being measured. Using simulated data, we demonstrate how our algorithm can reconstruct materials with single-crystal domains, thereby adding a powerful new capability for WR neutron imaging instruments.

There has been a growing interest in the use of data-driven regularizers to solve inverse problems associated with computational imaging systems. The convolutional sparse representation model has recently gained attention, driven by the development of fast algorithms for solving the dictionary learning and sparse coding problems for sufficiently large images and data sets. Nevertheless, this model has seen very limited application to tomographic reconstruction problems. In this paper, we present a model-based tomographic reconstruction algorithm using a learnt convolutional dictionary as a regularizer. The key contribution is the use of a data-dependent weighting scheme for the l1regularization to construct an effective denoising method that is integrated into the inversion using the Plug-and-Play reconstruction framework. Using simulated data sets we demonstrate that our approach can improve performance over traditional regularizers based on a Markov random field model and a patch-based sparse representation model for sparse and limited-view tomographic data sets.

Synchrotron-based soft X-ray ptychography has enabled the reconstruction of both the phase and attenuation projections of samples relevant to the physical and biological sciences. The phase projection images typically have higher fidelity and hence are used for tomographic reconstruction. In practice, three-dimensional tomographic reconstruction can be challenging because the measurements may have outliers, a fluctuating background and may be restricted to a limited angular range of sample rotations. Thus, conventional reconstruction algorithms such as filtered back projection can result in reconstructions with strong artifacts. In this paper, we present a robust model-based iterative reconstruction algorithm for X-ray ptychography-based phase tomography. Our method casts the reconstruction as a regularized inverse problem, involving a novel data fitting term that accounts for noise, the fluctuating background as well as outliers, combined with an image model term that enforces regularity on the volume to be reconstructed. We use a majorization-minimization strategy to find a minimum of the formulated cost function. Reconstructions on a simulated as well as a real dataset show that it is possible to acquire high-quality phase reconstructions compared to the typically used filtered-back projection algorithm as well as conventional regularized inversion approaches.

Tomographic reconstruction, which involves computing a 3-D volume from its 2-D projections, is an important problem in imaging with wide-ranging applications, including medical scanners, electron microscopy, nondestructive testing, and transportation security. Model-based iterative reconstruction (MBIR) is a popular approach to 3-D reconstruction that has demonstrated the state-of-the-art reconstruction quality on several applications, and has been deployed in commercial healthcare systems. However, software implementations of MBIR on commodity general-purpose processors demonstrate poor performance due to its high compute and data requirements and cache unfriendly data access patterns. In this paper, we develop an efficient MBIR accelerator (EMBIRA) that achieves significant performance and energy improvement over software implementations. EMBIRA utilizes arrays of three types of specialized processing elements that match MBIR's computation patterns, and is further operated as a two-level nested pipeline to fully exploit the parallelism present in the algorithm. Another important source from which EMBIRA derives its efficiency is by constraining the sequence in which voxels1 in the 3-D volume are reconstructed. This enables better data reuse within the accelerator, thereby significantly reducing the number of off-chip memory accesses. To demonstrate the benefits of EMBIRA, we implemented a prototype on an Altera DE5 field-programmable gate array (FPGA) platform that includes an Altera Stratix V GX FPGA and DDR3 memory. Our implementation of EMBIRA, operating at 165 MHz, achieved 51.8× (5.8×) improvement in performance, and 355× (199×) improvement in energy, compared with optimized sequential (multithreaded) software implementations on a 48-core 2.3-GHz AMD Opteron-based server.

X-ray tomography is increasingly being used for 4D spatio-temporal imaging of material samples at micron and finer scales. However, the temporal resolution of widely used 4D reconstruction methods is severely limited by the need to acquire a very large number of views for each reconstructed 3D volume. In this paper, we present a time interlaced model-based iterative reconstruction (TIMBIR) method which can significantly improve the temporal resolution of reconstructions. TIMBIR is a synergistic combination of two innovations. The first innovation, interlaced view sampling, is a novel approach to data acquisition which distributes the view angles more evenly in time. The second innovation is a 4D model based iterative reconstruction algorithm (MBIR) which can produce time resolved volumetric reconstructions of the sample from the interlaced views. Reconstructions of simulated data indicate that TIMBIR can improve the temporal resolution by an order of magnitude relative to existing approaches.

Many samples imaged in structural biology and material science contain several similar particles at random locations and orientations. Model-based iterative reconstruction (MBIR) methods can in principle be used to exploit such redundancies in images through log prior probabilities that accurately account for non-local similarity between the particles. However, determining such a log prior term can be challenging. Several denoising algorithms like non-local means (NLM) successfully capture such non-local redundancies, but the problem is two-fold: NLM is not explicitly formulated as a cost function, and neither can it capture similarity between randomly oriented particles. In this paper, we propose a rotationally-invariant nonlocal means (RINLM) algorithm, and describe a method to implement RINLM as a prior model using a novel framework that we call plug-and-play priors. We introduce the idea of patch pre-rotation to make RINLM computationally tractable. Finally, we showcase image denoising and 2D tomography results, using the proposed RINLM algorithm, as we highlight high reconstruction quality, image sharpness, and artifact suppression.

Synchrotron based X-ray tomography is widely used for three dimensional imaging of materials at the micron scale. Tomographic data collected from a synchrotron is often affected by non-idealities in the measurement system and sudden “blinding” of detector pixels during the acquisition. Typically, reconstructions are done using analytical reconstruction techniques combined with pre/post-processing steps to correct for the non-idealities, resulting in loss of detail while still producing noisy reconstructions with some artifacts. In this paper, we present a model-based iterative reconstruction (MBIR) algorithm for synchrotron X-ray tomography that can automatically handle the non-idealities as a part of the reconstruction. First, we develop a forward model that accounts for the non-idealities in the measurement system and for the occurrence of outliers in the measurement. Next, we combine the forward model with a prior model of the object to formulate the MBIR cost function and propose an algorithm to minimize the cost. Results on a real data set show that the MBIR reconstructions are superior to the analytical reconstructions effectively suppressing noise as well as other artifacts.

High angle annular dark field (HAADF)-scanning transmission electron microscope (STEM) data is increasingly being used in the physical sciences to research materials in 3D because it reduces the effects of Bragg diffraction seen in bright field TEM data. Typically, tomographic reconstructions are performed by directly applying either filtered back projection (FBP) or the simultaneous iterative reconstruction technique (SIRT) to the data. Since HAADF-STEM tomography is a limited angle tomography modality with low signal to noise ratio, these methods can result in significant artifacts in the reconstructed volume. In this paper, we develop a model based iterative reconstruction algorithm for HAADF-STEM tomography. We combine a model for image formation in HAADF-STEM tomography along with a prior model to formulate the tomographic reconstruction as a maximum a posteriori probability (MAP) estimation problem. Our formulation also accounts for certain missing measurements by treating them as nuisance parameters in the MAP estimation framework. We adapt the iterative coordinate descent algorithm to develop an efficient method to minimize the corresponding MAP cost function. Reconstructions of simulated as well as experimental data sets show results that are superior to FBP and SIRT reconstructions, significantly suppressing artifacts and enhancing contrast.