About

Charles A. Bouman is the Weldon Professor of Electrical and Computer Engineering and Biomedical Engineering at Purdue University. His research interests include positron emission tomography (PET), transmission tomography (CT), optical diffusion tomography, magnetic resonance imaging (MRI), and functional imaging modalities. He specializes in statistical methods for inverse problems, multiscale image modeling and processing, and multigrid algorithms. Bouman has contributed to the development of advanced imaging techniques and computational methods in biomedical engineering, leveraging his expertise in electrical and computer engineering to enhance medical imaging technologies.

Research topics

• Artificial Intelligence
• Computer Science
• Machine Learning
• Radiology
• Computer vision
• Algorithm
• Medicine
• Mathematics
• Medical physics
• Optics
• Mathematical optimization
• Physics

Selected publications

• Texture Matching GAN for CT Image Enhancement

  Journal of Mathematical Imaging and Vision · 2025-07-22

  articleOpen accessSenior author

  Abstract 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.

  PublisherOA PDFDOI

• CLAMP: Majorized Plug-and-Play for Coherent 3D Lidar Imaging

  IEEE Transactions on Computational Imaging · 2025-01-01

  articleSenior author

  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.

  PublisherDOI

• Tomographic Sparse View Selection Using the View Covariance Loss

  IEEE Transactions on Pattern Analysis and Machine Intelligence · 2025-01-01 · 1 citations

  articleSenior author

  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.

  PublisherDOI

• An Adaptive View Selection Algorithm for Large-scale Cone-Beam CT Reconstruction

  e-Journal of Nondestructive Testing · 2025-01-30 · 1 citations

  articleOpen accessSenior author

  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.

  PublisherOA PDFDOI

• Fast Hyperspectral Neutron Tomography

  IEEE Transactions on Computational Imaging · 2025-01-01

  articleSenior author

  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.

  PublisherDOI

• Boiling flow parameter estimation from boundary layer data

  2025-09-18

  article

  Atmospheric turbulence and aero-optic effects cause phase aberrations in propagating light waves, thereby reducing effectiveness in transmitting and receiving coherent light from an aircraft. Existing optical sensors can measure the resulting phase aberrations, but the physical experiments required to induce these aberrations are expensive and time-intensive. Simulation methods could provide a less expensive alternative. For example, an existing simulation algorithm called boiling flow, which generalizes the Taylor frozen-flow method, can generate synthetic phase aberration data (i.e., phase screens) induced by atmospheric turbulence. However, boiling flow depends on physical parameters, such as the Fried coherence length <i>r</i>₀, which are not well-defined for aero-optic effects. In this paper, we introduce a method to estimate the parameters of boiling flow from measured aero-optic phase aberration data. Our algorithm estimates these parameters to fit the spatial and temporal statistics of the measured data. This method is computationally efficient and our experiments show that the temporal power spectral density of the slopes of the synthetic phase screens reasonably matches that of the measured phase aberrations from two turbulent boundary layer data sets, with errors between 8-9%. However, the Kolmogorov spatial structure function of the phase screens does not match that of the measured phase aberrations, with errors above 28%. This suggests that, while the parameters of boiling flow can reasonably fit the temporal statistics of highly convective data, they cannot fit the complex spatial statistics of aero-optic phase aberrations.

  PublisherDOI

• Tomographic wavefront sensing simulations for wind tunnel measurements

  2025-09-18

  article

  Aero-optical turbulence distorts laser beams for airborne systems due to the high-speed airflow around the aircraft. The complexity of the high-speed airflow around the aircraft makes predicting and mitigating these distortions challenging. Experimentalists often use wind tunnels to collect data for aero-optical turbulence, but most wind tunnel imaging techniques are limited in their ability to capture 3D turbulence dynamics. Many standard noninvasive measurements of aero-optical turbulence, such as optical path difference, yield only 2D data and fail capture the underlying 3D nature of the turbulence. Wind tunnel seeding provides a way to capture 3D information, but it is invasive and can change the flow dynamics. While computational fluid dynamics can model 3D turbulence, it frequently fails to align with experimental test outcomes. In this paper, we propose a tomographic model-based iterative reconstruction technique that uses multiple noninvasive optical path difference measurements to reconstruct the 3D volume of wavefront aberrations in a wind tunnel. We validated our approach by reconstructing simulated volumes of atmospheric turbulence. Our simulated results indicate that our technique can accurately distinguish up to 4 layers of turbulence using a small range of viewing angles.

  PublisherDOI

• Unrolled Video Super-Resolution Network with Autoregressive Prior for the Case of Known Motion

  2025-09-01

  report
  PublisherDOI

• MONSTR: Model-Oriented Neutron Strain Tomographic Reconstruction

  2025-08-18

  articleSenior author

  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.

  PublisherDOI

• XCal: model-based approach to X-ray CT spectral calibration

  Optics Express · 2025-06-27

  articleOpen accessSenior authorCorresponding

  Transmission X-ray computed tomography (CT) is widely used to quantitatively reconstruct 3D objects composed of multiple materials. However, accurate CT reconstruction requires the system to be calibrated to account for the effective X-ray spectrum. Unfortunately, measurement of the effective spectrum is ill-posed, and existing calibration methods require that the system be recalibrated when the system parameters are changed. In this paper, we propose XCal, a multi-energy model-based spectral calibration approach for X-ray CT. The XCal approach models the effective spectrum using a separable physics-based model of the CT system. The model parameters are then estimated by fitting calibration data with known objects at multiple energies. An important advantage of XCal is that it allows the user to change scanner settings, such as the source voltage or X-ray filters, without the need for recalibration. Evaluations on simulated and measured datasets demonstrate that XCal significantly improves the accuracy of the estimated spectrum as compared to existing calibration methods.

  PublisherDOI

Recent grants

• Parametric Optical Diffusion Tomography

  NSF · $450k · 2004–2009

• CIF: Medium: Multi-Agent Consensus Equilibrium: Modular Methods for Integrating Disparate Sources of Expertise

  NSF · $1.2M · 2018–2024

Frequent coauthors

• Alex Acero

  Apple (Israel)

  1854 shared

• Björn Ottersten

  University of Luxembourg

  1843 shared

• J Apostolopoulos

  University of Genoa

  1843 shared

• Mari Ostendorf

  1817 shared

• Alex C. Kot

  1817 shared

• Anna Scaglione

  Cornell University

  1816 shared

• W.C. Karl

  Boston University

  1430 shared

• Sergios Theodoridis

  National and Kapodistrian University of Athens

  1424 shared

Education

• Ph.D., Electrical Engineering

  University of California, San Diego

  1986

• M.S., Electrical Engineering

  University of California, San Diego

  1982

• B.S., Electrical Engineering

  University of California, San Diego

  1980