Charles A. Bouman

Neutron time-of-flight imaging can provide a unique contrast mechanism of crystalline properties. Recently, computed tomography scans using time-of-flight instruments have been used to study structural and spectral characteristics of samples in 3D. To address the challenge of long measurement times associated with hyperspectral neutron computed tomography, the Oak Ridge National Laboratory neutron imaging team has recently demonstrated an autonomous system which can significantly reduce the measurement time by enabling high quality reconstructions from a sparse set of measurements. Some of the core components of such systems are the novel tomographic reconstruction algorithms including those based on artificial intelligence methods. In this work, a new training method is proposed to improve the performance of the artificially intelligent CT reconstruction algorithms. This training method can improve the quality of the reconstruction from very sparse time-of-flight scans. Our method helps hyperspectral tomography systems to obtain high-quality reconstructions with sparse scanning, which can potentially enable hyperspectral neutron computed tomography with reasonable acquisition times and particularly impact research projects which need to scan multiple similar sample beamlines such as the newly constructed VENUS beamline at the Spallation Neutron Source.

Ptychography is an imaging technique that enables nanometer-scale reconstruction of complex transmittance images by scanning objects with overlapping X-ray illumination patterns. However, the illumination function is typically unknown and only partially coherent, which presents challenges for reconstruction. In this paper, we introduce Blind Multi-Mode Projected Multi-Agent Consensus Equilibrium (BM-PMACE) for blind ptychographic reconstruction. BM-PMACE jointly estimates both the complex transmittance image and the multi-modal probe functions associated with a partially coherent probe source. Importantly, BM-PMACE maintains a location-specific probe state that captures spatially varying probe aberrations. Our method also incorporates a dynamic strategy for integrating additional probe modes. Our experiments on synthetic and measured data demonstrate that BM-PMACE outperforms existing approaches in reconstruction quality and convergence rate.

Standard computed tomography (CT) reconstruction algorithms such as filtered back projection (FBP) and Feldkamp-Davis-Kress (FDK) require many views for producing high-quality reconstructions, which can slow image acquisition and increase cost in non-destructive evaluation (NDE) applications. Over the past 20 years, a variety of methods have been developed for computing high-quality CT reconstructions from sparse views. However, the problem of how to select the best views for CT reconstruction remains open. In this paper, we present a novel view covariance loss (VCL) function that measures the joint information of a set of views by approximating the normalized mean squared error (NMSE) of the reconstruction. We present fast algorithms for computing the VCL along with an algorithm for selecting a subset of views that approximately minimizes its value. Our experiments on simulated and measured data indicate that for a fixed number of views our proposed view covariance loss selection (VCLS) algorithm results in reconstructions with lower NRMSE, fewer artifacts, and greater accuracy than current alternative approaches.

Deep neural networks (DNNs) are commonly used to denoise and sharpen X-ray computed tomography (CT) images with the goal of reducing patient X-ray dosage while maintaining reconstruction quality. However, naive application of DNN-based methods can result in image texture that is undesirable in clinical applications. Alternatively, generative adversarial network (GAN)-based methods can produce appropriate texture, but naive application of GANs can introduce inaccurate or even unreal image detail. In this paper, we propose a texture matching generative adversarial network (TMGAN) that enhances CT images while generating an image texture that can be matched to a target texture. We use parallel generators to separate anatomical features from the generated texture, which allows the GAN to be trained to match the desired texture without directly affecting the underlying CT image. We demonstrate that TMGAN generates enhanced image quality while also producing image texture that is desirable for clinical application.

Hyperspectralneutron computed tomography is a tomographic imaging technique in which thousands of wavelength-specific neutron radiographs are measured for each tomographic view. In conventional hyperspectral reconstruction, data from each neutron wavelength bin are reconstructed separately, which is extremely time-consuming. These reconstructions often suffer from poor quality due to low signal-to-noise ratios. Consequently, material decomposition based on these reconstructions tends to produce inaccurate estimates of the material spectra and erroneous volumetric material separation. In this paper, we present two novel algorithms for processing hyperspectral neutron data: fast hyperspectral reconstruction and fast material decomposition. Both algorithms rely on a subspace decomposition procedure that transforms hyperspectral views into low-dimensional projection views within an intermediate subspace, where tomographic reconstruction is performed. The use of subspace decomposition dramatically reduces reconstruction time while reducing both noise and reconstruction artifacts. We apply our algorithms to both simulated and measured neutron data and demonstrate that they reduce computation and improve the quality of the results relative to conventional methods.

Coherent lidar uses a chirped laser pulse for 3D imaging of distant targets. However, existing coherent lidar image reconstruction methods do not account for the system's aperture, resulting in sub-optimal resolution. Moreover, these methods use majorization-minimization for computational efficiency, but do so without a theoretical treatment of convergence. In this paper, we present Coherent Lidar Aperture Modeled Plug-and-Play (CLAMP) for multi-look coherent lidar image reconstruction. CLAMP uses multi-agent consensus equilibrium (a form of PnP) to combine a neural network denoiser with an accurate surrogate forward model of coherent lidar. Additionally, CLAMP introduces a computationally efficient FFT-based method to account for the system's aperture to improve resolution of reconstructed images. Furthermore, we formalize the use of majorization-minimization in consensus optimization problems and prove convergence to the exact consensus equilibrium solution. Finally, we apply CLAMP to synthetic and measured data to demonstrate its effectiveness in producing high-resolution, speckle-free, 3D imagery

Industrial cone-beam X-ray computed tomography (CT) produces 3D reconstructions of objects using projection measurements taken at multiple predetermined rotation angles around a single axis. Achieving high-quality reconstructions with traditional analytic reconstruction algorithms typically requires a large number of projections, which can be both time-consuming and costly. State-of-the-art reconstruction algorithms, such as model-based iterative reconstruction (MBIR), have made it possible to achieve high-quality reconstructions using significantly fewer projections. However, the process of acquiring these sparse projections often fails to account for the specific geometry of the scanned object. In this paper, we propose an algorithm to optimize the scanning process by using geometric information about the object to be scanned. Our approach strategically selects the most informative views by assessing their alignment with the object’s long edges while ensuring that the selected projections maintain sufficient diversity. To make the algorithm practical for large 3D volumes, we developed a two-stage method. The first stage uses a low-resolution version of the voxelized CAD model to obtain edge information and combines it with view diversity information for the optimization in the initial iterations, while the second stage relies on a single low-resolution reconstruction based on measurements from the first stage to gather edge and view diversity information during the later iterations. During the view selection process, all reconstructions are performed using MBIRJAX, a novel software library that enables fast, high-quality cone-beam CT reconstructions. Through simulations and measured datasets, we demonstrate that our algorithm produces higher quality reconstructions, particularly in preserving sharp edges, while requiring fewer measurements compared to the traditional method.

Ptychography is an imaging technique that enables nanometer-scale reconstruction of complex transmittance images by scanning objects with overlapping illumination patterns. However, the illumination function is typically unknown, which presents challenges for reconstruction, especially when using partially coherent light sources. In this paper, we introduce Blind Multi-Mode Projected Multi-Agent Consensus Equilibrium (BM-PMACE) for blind ptychographic reconstruction. We extend the PMACE framework for distributed inverse problems to jointly estimate the complex transmittance image and multiple, unknown, partially coherent probe functions. Importantly, our method maintains local probe estimates to exploit complementary information at multiple probe locations. Our method also incorporates a dynamic strategy for integrating additional probe modes. Through experimental simulations and validations using both synthetic and measured data, we demonstrate that BM-PMACE outperforms existing approaches in reconstruction quality and convergence rate.

Industrial cone-beam X-ray computed tomography (CT) scans of additively manufactured components produce a 3D reconstruction from projection measurements acquired at multiple predetermined rotation angles of the component about a single axis. Typically, a large number of projections are required to achieve a high-quality reconstruction, a process that can span several hours or days depending on the part size, material composition, and desired resolution. This paper introduces a novel real-time system designed to optimize the scanning process by intelligently selecting the best next angle based on the object's geometry and computer-aided design (CAD) model. This selection process strategically balances the need for measurements aligned with the part's long edges against the need for maintaining a diverse set of overall measurements. Through simulations, we demonstrate that our algorithm significantly reduces the number of projections needed to achieve high-quality reconstructions compared to traditional methods.

An approach is described for spectrally parallel hyperspectral mid-infrared imaging with spatial resolution dictated by fluorescence imaging. Quantum cascade laser (QCL)-based dual-comb mid-infrared spectroscopy enables acquisition of infrared spectra at high speed (<1 millisecond) through generation of optical beat patterns and radio-frequency detection. The high-speed nature of the spectral acquisition is shown to support spectral mapping in microscopy measurements. Direct detection of the transmitted infrared beam yields high signal-to-noise spectral information, but the long infrared wavelength imposes low diffraction-limited spatial resolution. Use of fluorescence detected photothermal infrared (F-PTIR) imaging provides high spatial resolution tied directly to the integrated IR absorption. Computational imaging using a multi-agent consensus equilibrium (MACE) approach combines the high spatial resolution of F-PTIR and the high spectral information of dual-comb infrared transmission in a single optimized equilibrium hyperspectral data cube