Dedicated breast-imaging scanners using radiotracers, e.g., PET scanners (positron emission tomography) have been proposed and evaluated since the late 1980s [1]. These systems trade a reduction in the size of the imaging field of view for improved resolution, and potentially also a lower cost, higher sensitivity, and a smaller form factor. The higher resolution can improve both detection and quantitation of concentrations of radiotracer, although for the latter to be true, tomographic imaging is mandatory. Several commercial dedicated breast PEM (positron emission mammography), PET scanners, and gamma camera systems have been developed and marketed [2].

Advanced manufacturing creates increasingly complex objects with material compositions that are often difficult to characterize by a single modality. Our collaborating domain scientists are going beyond traditional methods by employing both X-ray and neutron computed tomography to obtain complementary representations expected to better resolve material boundaries. However, the use of two modalities creates its own challenges for visualization, requiring either complex adjustments of bimodal transfer functions or the need for multiple views. Together with experts in nondestructive evaluation, we designed a novel interactive bimodal visualization approach to create a combined view of the co-registered X-ray and neutron acquisitions of industrial objects. Using an automatic topological segmentation of the bivariate histogram of X-ray and neutron values as a starting point, the system provides a simple yet effective interface to easily create, explore, and adjust a bimodal visualization. We propose a widget with simple brushing interactions that enables the user to quickly correct the segmented histogram results. Our semiautomated system enables domain experts to intuitively explore large bimodal datasets without the need for either advanced segmentation algorithms or knowledge of visualization techniques. We demonstrate our approach using synthetic examples, industrial phantom objects created to stress bimodal scanning techniques, and real-world objects, and we discuss expert feedback.

Computed Tomography (CT) reconstruction of objects with cylindrical symmetry can be performed with a single projection. When the measured rays are parallel, and the axis of symmetry is perpendicular to the optical axis, the data can be modeled with the so-called Abel Transform. The Abel Transform has been extensively studied and many methods exist for accurate reconstruction. However, most CT geometries are cone-beam rather than parallel-beam. Using Abel methods for reconstruction in these cases can lead to distortions and reconstruction artifacts. Here, we develop analytic and model-based iterative reconstruction (MBIR) methods to reconstruct symmetric objects with an arbitrary axis of symmetry from a cone-beam geometry. The MBIR methods demonstrate superior results relative to the analytic inversion methods by mitigating artifacts and reducing noise while retaining fine image features. We demonstrate the efficacy of our methods using simulated and experimentally-acquired x-ray and neutron projections.

Computed Tomography (CT) is an essential non-destructive three dimensional imaging modality used in medicine, security screening, and inspection of manufactured components. Typical CT data acquisition entails the collection of a thousand or more projections through the object under investigation through a range of angles covering one hundred eighty degrees or more. It may be desirable or required that the number of projections angles be reduced by one or two orders of magnitude for reasons such as acquisition time or dose. Unless specialized reconstruction algorithms are applied, reconstructing with fewer views will result in streak artifacts and failure to resolve object boundaries at certain orientations. These artifacts may substantially diminish the usefulness of the reconstructed CT volumes. Here we develop constrained and regularized numerical optimization methods to reconstruct CT volumes from 4-28 projections. These methods entail utilization of novel data fidelity and convex and non-convex regularization terms. In addition, the methods outlined here are usually carried out by a sequence of two or three numerical optimization methods in sequence. The efficacy of our methods is demonstrated on four measured and three simulated few-view CT data sets. We show that these methods outperform other state of the art few-view numerical optimization methods.

Data-driven deep learning has been successfully applied to various computed tomographic reconstruction problems. The deep inference models may outperform existing analytical and iterative algorithms, especially in ill-posed CT reconstruction. However, those methods often predict images that do not agree with the measured projection data. This paper presents an accurate differentiable forward and back projection software library to ensure the consistency between the predicted images and the original measurements. The software library efficiently supports various projection geometry types while minimizing the GPU memory footprint requirement, which facilitates seamless integration with existing deep learning training and inference pipelines. The proposed software is available as open source: https://github.com/LLNL/LEAP.

In this work we perform ultrasound measurements on an impedance graded impactor made by tape casting magnesium, copper, and tungsten. We also destructively extract small representative samples from the part for complementary characterization with x-ray computed tomography. Combining the two data sets enables direct assignment of some of the measured ultrasound features to specific material characteristics identified by x-ray tomography. Our results demonstrate how ultrasound inspection, informed by x-ray computed tomography, can be used to identify sub-millimeter material amalgamations and spatial heterogeneities in this graded material.

Megavolt x-ray computed tomography (CT) is a powerful tool for three-dimensional characterization. However, its utility is limited for large objects composed of high-atomic number (Z) materials, where x rays fail to penetrate. Information from fast neutron CT (FNCT) can complement x-ray CT reconstructions since fast neutrons can more readily penetrate high-Z objects. Here, we demonstrate a method for combining FNCT and x-ray CT data to create a single reconstruction, more accurate than could be achieved with either x rays or fast neutrons alone. The algorithm was tested on an exemplar comprising multiple concentric, nested cylinders of different materials. Simulated and empirical x-ray CT data were acquired for the exemplar using a 9 MV bremsstrahlung spectrum. Additional simulated and empirical FNCT data were acquired using an accelerator based fast neutron source. The FNCT data were used to synthesize x-ray CT data and augment the x-ray CT data missing due to lack of penetration. This approach mitigates artifacts that would otherwise negatively affect the accuracy and resolution of a single-modality reconstructed volume.

For material identification, characterization, and quantification, it is useful to estimate system-independent material properties that do not depend on the detailed specifications of the X-ray computed tomography (CT) system such as spectral response. System independent rho-e and Z-e (SIRZ) refers to a suite of methods for estimating the system independent material properties of electron density, rho-e, and effective atomic number, Z-e, of an object scanned using dual-energy X-ray CT (DECT). The current state-of-the-art approach, SIRZ-2, makes certain approximations that lead to inaccurate estimates for large atomic numbered (Z-e) materials. In this paper, we present an extension, SIRZ-3, which iteratively reconstructs the unknown rho-e and Z-e while avoiding the limiting approximations made by SIRZ-2. Unlike SIRZ-2, this allows SIRZ-3 to accurately reconstruct rho-e and Z-e even at large values of Z-e. SIRZ-3 relies on the use of a new non-linear differentiable forward measurement model that expresses the DECT measurement data as a direct analytical function of rho-e and Z-e. Leveraging this new forward model, we use an iterative optimization algorithm to reconstruct (or solve for) rho-e and Z-e directly from the DECT data. Compared to SIRZ-2, we show that the magnitude of performance improvement using SIRZ-3 increases with increasing values for Z-e.

Advanced manufacturing techniques, such as additive manufacturing, enable the design of increasingly complex components for a wide range of industrial applications. However, this complexity makes qualification of the parts, determining whether a part is within some margin of error from the initial design, difficult. To inspect and qualify complex internal geometries that are not accessible with an external probe, parts are typically scanned with computed tomography (CT), and manually compared to the computer-aided design (CAD) model using visual inspections. Matching the CAD model to the 3D reconstructed object is challenging in a traditional desktop environment due to the lack of depth perception and 3D interaction. An additional challenge comes from the geometric complexity of CAD meshes and large-scale CT scans. We present a virtual reality (VR) system for manual qualification, providing a novel defect visualization method. First, we describe a semiautomatic CAD-to-Scan Registration approach in VR using a finite element mesh. Second, we introduce the Defect Box, which enables full-resolution inspection for massive scans and CAD-CT comparison of local defect regions. Finally, our system includes intuitive 3D Metrology methods that enable natural interactions for the measurement of features and defects in VR. We demonstrate our approach on both real and synthetic data and discuss feedback from four expert users in nondestructive qualification.

A novel dual-energy fast neutron imaging technique is presented using short-pulse laser-driven neutron sources to leverage their inherent adaptive spectral control to enable 3D volume segmentation and reconstruction. Laser-accelerated ion beams incident onto secondary targets create directional, broadband, MeV-class neutrons. Synthetic radiographs are produced of multi-material objects using ion and neutron spectra derived from analytic and numerical models. It is demonstrated that neutron images generated from small changes to the neutron spectra, controlled by altering the initial laser conditions, are sufficient to isolate materials with differing attenuation coefficients. This is first demonstrated using a simplistic combinatorial isolation method and then by employing more advanced reconstruction algorithms to reduce artifacts and generate a segmentation volume of the constituent materials.

X-ray Computed Tomography (CT) is essential for nondestructive inspection of many manufactured components but is susceptible to various forms of artifacts and noise. In particular, multi-detector row cone-beam CT systems can suffer from so called "cone-beam" artifacts and partial volume effects, particularly on planar edges at the periphery of the field of view in the cone angle dimension. Uni-directional ultrasonic testing methods generally have extremely accurate in-plane depth resolution but poorer lateral resolution due to physical and geometric constraints. This paper presents a novel technique called Computed Tomographic Fusion (CT-F) which uses three-dimensional Fourier filtering to combine x-ray cone-beam CT reconstructions and ultrasound data in the frequency space. The result is a single image with improved accuracy and quality. CT-F mitigates artifacts while allowing for rapid, accurate characterization of large three-dimensional CT volumes. Using simulations and experiments, we demonstrate artifact reduction and edge contrast improvement in volumetric reconstructions.

A portable fast-neutron imaging system is being developed to provide complementary information to field X-ray imaging. Applications include inspection of vehicles and infrastructure for corrosion, measurement of material levels in containers, and inspection of munitions and suspicious packages. While fast-neuron imaging generally provides lower imaging resolution compared to X-rays, fast-neutron interaction cross-sections have a weak dependence on material Z. This enables imaging of low-Z materials inside high-Z materials. Here, we discuss the limitations and current improvements in fast-neuron imaging. Limitations in portable fast-neutron imaging systems include low D-T neutron generator output, low light production in ZnS(Cu) imaging scintillators, low resolution due to scintillator thickness and D-T spot size, and digital-panel darknoise that varies in time and position and that can be 100× larger than the neutron signal. We have made improvements in these areas through development of a segmented high light yield scintillator, panel noise mitigation techniques, and testing of new high-output, small spot size D-T neutron generators. The segmented high light yield fast-neutron scintillator demonstrated 5× increase in light compared to ZnS(Cu). An additional 2× improvement in signal-to-noise was demonstrated with panel-noise mitigation techniques. Our MCNP calculations also show good agreement with neutron imaging results We have demonstrated improvements in fast-neutron imaging through development of a segmented high light yield neutron scintillator, mitigation of digital panel noise, and preliminary testing with new high-output, small spot size D-T neutron generators. We have also demonstrated good results modeling fast-neutron images and scatter effects using MCNP.

X-ray computed tomography (CT) systems can produce high resolution images, in which small (sub-millimeter) features can be detected. This requires the X-rays to sufficiently penetrate the object and interact strongly enough to produce measurable attenuation. Low atomic number (low Z), low density objects shielded by high atomic number (high Z) materials result in X-ray reconstructions that lack sufficient contrast to differentiate interior features from noise and reconstruction artifacts. Fast neutron CT offers complementary information to X-rays with superior penetration through high Z shielding and with less severe beam hardening artifacts. However, spatial resolution in X-ray imaging systems is generally superior to that of fast neutron imagers. Here, we quantitatively compare these two complementary modalities to demonstrate the ability to observe small feature locations within two multi-material objects. Quantitative measures include calculation of image gradient at material edges, contrast-to-noise ratio, and F1 score.

Phase unwrapping is an integral part of multiple imaging techniques, and as a result, a wide range of algorithms have been created to unwrap phases. One such algorithm is the minimum Lp-norm phase unwrapping algorithm. This algorithm transforms the phase unwrapping problem into a minimization problem of a certain functional, which it solves with an iterative method. However, the problem is usually not convex, and when there are many sharp edges in the data to be unwrapped, the algorithm often produces a local minimum with new discontinuities in originally smooth areas. To prioritize solutions which minimize the functional better in smooth areas, we use weights to deprioritize data lying along edges in the ground-truth image. This requires a method to find ground-truth edges using the wrapped image, which we describe. When using the modified algorithm, we generally obtain improved results on images with multiple edges (both lower errors and more correct edge placement).

This paper introduces the capabilities and availability of a customizable scientific software package called Livermore Tomography Tools (LTT) built for computed tomography (CT) research. It was initially developed to process x-ray and neutron CT data accurately and rapidly from raw detector counts to reconstructed volumes with the flexibility to handle many special cases. Our goals were to provide quantitatively accurate results reported in physical units (e.g., mm−1 or cm−1) while exploiting all available computational advantages to maximize speed and conserve memory. Written in C/C++ with support for multiple CPUs and GPUs, LTT runs on many computing platforms (Linux/Unix, Windows, and Mac; laptops to supercomputers). As a result, LTT can: process data acquired from various custom-built and commercially available CT scanners, model and simulate x-ray and neutron interactions to encourage algorithm prototyping, and allow for rapid insertion of the latest algorithms. We describe LTT's software architecture, user interfaces, and its 88 algorithms (as of this writing) for pre-processing, reconstruction, post-processing, and simulation that support many scanner geometries (parallel-, fan-, cone-beam, and custom). Several applications are presented that illustrate LTT's accuracy, speed, and flexibility relative to other solutions.