Dr. Umberto E. Villa Profile

Dr. Umberto E. Villa

at Univ of Texas at Austin

SPIE Involvement:

Author

Area of Expertise:

Computational imaging , Inverse problems , Photoacoustic tomography , Ultrasound tomography , Virtual imaging trials , Theoretical imaging science

Profile Summary

I define myself as a mathematical engineer and computational scientist. My work focuses on advancing our understanding of physical and biological phenomena, improving the design of engineering systems, and supporting informed decision-making under uncertainty by use of mathematical/statistical modeling and high-performance computing. An important component of my work is the systematic integration of mathematical models and data (e.g., experimental measures and images) using the Bayesian framework. Throughout my career, I have led the development of novel formulations and algorithms for the solution of both forward and inverse problems. I have achieved this by establishing a close working collaboration with some of the leading research experts in the fields of medical imaging, geophysics, engineering, computational sciences, and applied mathematics.

Publications (28)

Proceedings Article | 2 April 2024 Poster + Paper

Poster + Paper

Self-supervised learning based on StyleGAN for medical image classification on small labeled dataset

Zong Fan, Zhimin Wang, Chaojie Zhang, Muzaffer Özbey, Umberto Villa, Yao Hao, Zhongwei Zhang, Xiaowei Wang, Hua Li

Proceedings Volume 12926, 1292630 (2024) https://doi.org/10.1117/12.3006959

KEYWORDS: Education and training, Data modeling, Image classification, Medical imaging, Feature extraction, Performance modeling, Solid state lighting, Classification systems, Network architectures, Design

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Proceedings Article | 1 April 2024 Poster + Paper

Poster + Paper

Bayesian Cramér-Rao bound optimization of the illumination pattern in quantitative photoacoustic computed tomography

Evan Scope Crafts, Mark Anastasio, Umberto Villa

Proceedings Volume 12925, 129254A (2024) https://doi.org/10.1117/12.3005856

KEYWORDS: Photoacoustic tomography, Imaging systems, Absorption, Design, Inverse optics, Error analysis

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Proceedings Article | 1 April 2024 Presentation + Paper

Presentation + Paper

Learned measurement correction for simplified acoustic forward models in ultrasound computed tomography

Luke Lozenski, Hanchen Wang, Brendt Wohlberg, Umberto Villa, Youzuo Lin

Proceedings Volume 12932, 129320I (2024) https://doi.org/10.1117/12.3006948

KEYWORDS: Image restoration, Machine learning, Data corrections, Acoustics, Physics, Computed tomography, Ultrasound tomography

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Proceedings Article | 1 April 2024 Poster + Paper

Poster + Paper

A learning-based method for compensating 3D-2D model mismatch in ring-array ultrasound computed tomography

Fu Li, Umberto Villa, Mark Anastasio

Proceedings Volume 12932, 129321E (2024) https://doi.org/10.1117/12.3006968

KEYWORDS: 3D modeling, 3D image reconstruction, 3D image processing, Data modeling, Transducers, Wave propagation, Image restoration, Data acquisition

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Ultrasound computed tomography (USCT) is an emerging imaging technique that holds great promise for breast cancer diagnosis and screening. Full-waveform inversion (FWI)-based image reconstruction methods can produce high spatial resolution images that depict the acoustic properties of soft tissues. However, FWI is computationally demanding, especially when wave physics is modeled in three dimensions (3D). A common USCT design employs a circular ring-array comprised of elevation-focused ultrasonic transducers, where the array is translated orthogonally to achieve volumetric (3D) imaging. This design allows efficient two-dimensional (2D) slice-byslice reconstruction methods to estimate a 3D volume by stacking reconstructed cross-sectional images at each ring-array position. However, these 2D methods do not account for the 3D wave propagation physics and the focusing properties of the transducers and thus can result in out-of-plane scattering-based artifacts and inaccuracies. Previous work has investigated a learning-based method, in which a deep neural network was used to map the 3D ring-array USCT data to idealized 2D USCT measurements, from which acoustic properties can be estimated by use of 2D FWI. The presented work extends that previous study in two ways. First, sophisticated anatomically realistic 3D numerical breast phantoms are used to construct clinically relevant training and testing sets. Second, a high-fidelity 3D wave propagation forward imaging model incorporating elevation focusing effects is used to virtually image the phantoms. The results show promise in improving image accuracy compared to both 3D FWI and conventional 2D FWI, achieving both mitigation of out-of-plane scattering and 3D-2D model mismatches and significant reduction of computational cost compared to 3D FWI. Additionally, the use of multiple ring-array measurements from adjacent elevations are explored as concurrent neural network inputs, showing improved accuracy compared to only using single-ring data.

SPIE Journal Paper | 20 March 2024 Open Access

Open Access

Application of learned ideal observers for estimating task-based performance bounds for computed imaging systems

Kaiyan Li, Umberto Villa, Hua Li, Mark Anastasio

JMI, Vol. 11, Issue 02, 026002, (March 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.2.026002

KEYWORDS: Information operations, Signal detection, Image restoration, Binary data, Imaging systems, Education and training, Biomedical applications, Stochastic processes, Medical image reconstruction, Tomography

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PurposeThe performance of the ideal observer (IO) acting on imaging measurements has long been advocated as a figure-of-merit (FOM) to guide the optimization of imaging systems. For computed imaging systems, the performance of the IO acting on imaging measurements also sets an upper bound on task-performance that no image reconstruction method can transcend. As such, estimation of IO performance can provide valuable guidance when designing data-acquisition techniques by enabling the identification of designs that will not permit the reconstruction of diagnostically useful images for a specified task – no matter how advanced the reconstruction method is or plausible the reconstructed images appear. While such data space IO analyses are known conceptually, they have generally remained infeasible to widely implement. In this work, convolutional neural network (CNN) approximated IOs (CNN-IOs) are investigated for estimating the performance of data space IOs for the purpose of guiding hardware and data-acquisition designs and establishing task-based performance bounds for image reconstruction.ApproachNumerical studies that utilized a stylized breast X-ray computed tomography test bed are conducted to validate and demonstrate the approach. Signal-known-statistically and background-known-statistically (SKS/BKS) binary signal detection and discrimination tasks are addressed and the impact of the number of views and beam intensities on IO performance is investigated as a case study. The image space CNN-IO performance is also computed by use of images reconstructed by both U-Net and FBP reconstruction methods and compared to the corresponding data space CNN-IO performance to assess task-related information loss.ResultsFor all considered cases, task-performance bounds were established by use of the data space CNN-IO performance. A comparison of the data space and image space CNN-IO performances quantified the task-relevant information loss induced by the considered image reconstruction methods. Moreover, the U-Net reconstructed images possessed improved traditional metrics compared to those produced by the FBP method but resulted in lower image space CNN-IO performance. This demonstrates that traditional IQ measures can be misleading if task-performance is of ultimate interest.ConclusionThis work confirms that recent developments in learning-based IO approximation methods can enable the ranking of data-acquisition designs based on optimal task-performance with consideration of object variability. The work also demonstrates that such methods can enable estimation of task-based performance bounds for image reconstruction.

Proceedings Article | 12 March 2024 Presentation + Paper

Presentation + Paper

High-throughput photoacoustic tomography by integrated robotics and automation

Nathanael Marshall, Hans-Peter Brecht, Weylan Thompson, Dylan Lawrence, Vanessa Marshall, Samuel Toler, Stanislav Emelianov, Anthony Yu, Mark Anastasio, Umberto Villa, Joshua Maxwell, Sergey Ermilov

Proceedings Volume 12842, 128420I (2024) https://doi.org/10.1117/12.3005569

KEYWORDS: Imaging systems, Animals, Photoacoustic spectroscopy, Control systems, Spatial resolution, Animal model studies, Robotics, Acquisition tracking and pointing, Automation, Laser safety

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Proceedings Article | 12 March 2024 Presentation + Paper

Presentation + Paper

A learning-based image reconstruction method for skull-induced aberration compensation in transcranial photoacoustic computed tomography

Hsuan-Kai Huang, Joseph Kuo, Seonyeong Park, Umberto Villa, Lihong Wang, Mark Anastasio

Proceedings Volume 12842, 128420C (2024) https://doi.org/10.1117/12.3008569

KEYWORDS: Image restoration, Skull, Photoacoustic tomography, Elasticity, 3D modeling, Model based design, Acoustics, 3D image reconstruction

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Proceedings Article | 12 March 2024 Poster + Paper

Poster + Paper

Learning a semi-analytic reconstruction method for photoacoustic computed tomography with hemispherical measurement geometries

Panpan Chen, Seonyeong Park, Refik Mert Cam, Hsuan-Kai Huang, Umberto Villa, Mark Anastasio

Proceedings Volume 12842, 128420U (2024) https://doi.org/10.1117/12.3008778

KEYWORDS: Image restoration, Photoacoustic tomography, Tunable filters, Data modeling, Education and training, Data acquisition, Signal filtering, Linear filtering, Image filtering, 3D modeling

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Proceedings Article | 12 March 2024 Presentation + Paper

Presentation + Paper

Investigation of a learned image reconstruction method for three-dimensional quantitative photoacoustic tomography of the breast

Refik Mert Cam, Seonyeong Park, Umberto Villa, Mark Anastasio

Proceedings Volume 12842, 128420M (2024) https://doi.org/10.1117/12.3008904

KEYWORDS: Breast, Tumors, Photoacoustic tomography, Arteries, Veins, Computer simulations, Deep learning, Inverse imaging problems, Blood oxygen saturation

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SPIE Journal Paper | 19 January 2024 Open Access

Open Access

Spatiotemporal image reconstruction to enable high-frame-rate dynamic photoacoustic tomography with rotating-gantry volumetric imagers

Refik Mert Cam, Chao Wang, Weylan Thompson, Sergey A. Ermilov, Mark A. Anastasio, Umberto Villa

JBO, Vol. 29, Issue S1, S11516, (January 2024) https://doi.org/10.1117/12.10.1117/1.JBO.29.S1.S11516

KEYWORDS: Image restoration, Tomography, Photoacoustic tomography, Imaging systems, Transducers, Data acquisition, Biomedical optics, Matrices, Image processing, Video

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SignificanceDynamic photoacoustic computed tomography (PACT) is a valuable imaging technique for monitoring physiological processes. However, current dynamic PACT imaging techniques are often limited to two-dimensional spatial imaging. Although volumetric PACT imagers are commercially available, these systems typically employ a rotating measurement gantry in which the tomographic data are sequentially acquired as opposed to being acquired simultaneously at all views. Because the dynamic object varies during the data-acquisition process, the sequential data-acquisition process poses substantial challenges to image reconstruction associated with data incompleteness. The proposed image reconstruction method is highly significant in that it will address these challenges and enable volumetric dynamic PACT imaging with existing preclinical imagers.AimThe aim of this study is to develop a spatiotemporal image reconstruction (STIR) method for dynamic PACT that can be applied to commercially available volumetric PACT imagers that employ a sequential scanning strategy. The proposed reconstruction method aims to overcome the challenges caused by the limited number of tomographic measurements acquired per frame.ApproachA low-rank matrix estimation-based STIR (LRME-STIR) method is proposed to enable dynamic volumetric PACT. The LRME-STIR method leverages the spatiotemporal redundancies in the dynamic object to accurately reconstruct a four-dimensional (4D) spatiotemporal image.ResultsThe conducted numerical studies substantiate the LRME-STIR method’s efficacy in reconstructing 4D dynamic images from tomographic measurements acquired with a rotating measurement gantry. The experimental study demonstrates the method’s ability to faithfully recover the flow of a contrast agent with a frame rate of 10 frames per second, even when only a single tomographic measurement per frame is available.ConclusionsThe proposed LRME-STIR method offers a promising solution to the challenges faced by enabling 4D dynamic imaging using commercially available volumetric PACT imagers. By enabling accurate STIRs, this method has the potential to significantly advance preclinical research and facilitate the monitoring of critical physiological biomarkers.

SPIE Journal Paper | 20 June 2023 Open Access

Open Access

Stochastic three-dimensional numerical phantoms to enable computational studies in quantitative optoacoustic computed tomography of breast cancer

Seonyeong Park, Umberto Villa, Fu Li, Refik Mert Cam, Alexander A. Oraevsky, Mark A. Anastasio

JBO, Vol. 28, Issue 06, 066002, (June 2023) https://doi.org/10.1117/12.10.1117/1.JBO.28.6.066002

KEYWORDS: Breast, Tissues, Acoustics, Anatomy, Blood, Tissue optics, Blood vessels, Tumors, Stochastic processes, Imaging systems

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SignificanceWhen developing a new quantitative optoacoustic computed tomography (OAT) system for diagnostic imaging of breast cancer, objective assessments of various system designs through human trials are infeasible due to cost and ethical concerns. In prototype stages, however, different system designs can be cost-efficiently assessed via virtual imaging trials (VITs) employing ensembles of digital breast phantoms, i.e., numerical breast phantoms (NBPs), that convey clinically relevant variability in anatomy and optoacoustic tissue properties.AimThe aim is to develop a framework for generating ensembles of realistic three-dimensional (3D) anatomical, functional, optical, and acoustic NBPs and numerical lesion phantoms (NLPs) for use in VITs of OAT applications in the diagnostic imaging of breast cancer.ApproachThe generation of the anatomical NBPs was accomplished by extending existing NBPs developed by the U.S. Food and Drug Administration. As these were designed for use in mammography applications, substantial modifications were made to improve blood vasculature modeling for use in OAT. The NLPs were modeled to include viable tumor cells only or a combination of viable tumor cells, necrotic core, and peripheral angiogenesis region. Realistic optoacoustic tissue properties were stochastically assigned in the NBPs and NLPs.ResultsTo advance optoacoustic and optical imaging research, 84 datasets have been released; these consist of anatomical, functional, optical, and acoustic NBPs and the corresponding simulated multi-wavelength optical fluence, initial pressure, and OAT measurements. The generated NBPs were compared with clinical data with respect to the volume of breast blood vessels and spatially averaged effective optical attenuation. The usefulness of the proposed framework was demonstrated through a case study to investigate the impact of acoustic heterogeneity on OAT images of the breast.ConclusionsThe proposed framework will enhance the authenticity of virtual OAT studies and can be widely employed for the investigation and development of advanced image reconstruction and machine learning-based methods, as well as the objective evaluation and optimization of the OAT system designs.

Proceedings Article | 10 April 2023 Presentation + Paper

Presentation + Paper

3D full-waveform inversion in ultrasound computed tomography employing a ring-array

Fu Li, Umberto Villa, Nebojsa Duric, Mark Anastasio

Proceedings Volume 12470, 124700K (2023) https://doi.org/10.1117/12.2654406

KEYWORDS: Ultrasound tomography, Breast imaging, 3D image reconstruction, Data acquisition

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Proceedings Article | 10 April 2023 Presentation + Paper

Presentation + Paper

A deep-learning-based image reconstruction method for USCT that employs multimodality inputs

Gangwon Jeong, Fu Li, Umberto Villa, Mark Anastasio

Proceedings Volume 12470, 124700M (2023) https://doi.org/10.1117/12.2654564

KEYWORDS: Image restoration, Tomography, Education and training, Medical image reconstruction, Image quality, Ultrasound reflection tomography, Deep learning, Machine learning, Acoustics, Tissues

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Proceedings Article | 7 April 2023 Presentation + Paper

Presentation + Paper

Memory-efficient self-supervised learning of null space projection operators

Albert Zhai, Joseph Kuo, Mark Anastasio, Umberto Villa

Proceedings Volume 12463, 124631I (2023) https://doi.org/10.1117/12.2654260

KEYWORDS: Imaging systems, Matrices, Tomography, Singular value decomposition, Image processing, Radon transform, Mathematical optimization, Machine learning, Linear algebra, Medical image processing, Computational imaging, Medical imaging

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Proceedings Article | 7 April 2023 Presentation + Paper

Presentation + Paper

Data driven methods for ultrasound computed tomography

Luke Lozenski, Hanchen Wang, Brendt Wohlberg, Umberto Villa, Youzuo Lin

Proceedings Volume 12463, 124630Q (2023) https://doi.org/10.1117/12.2654442

KEYWORDS: Image restoration, Breast, Ultrasonography, Breast density, Machine learning, Computed tomography, Acoustics, Imaging systems

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Proceedings Article | 7 April 2023 Presentation

Presentation

Low-rank matrix estimation-based spatiotemporal image reconstruction from few tomographic measurements per frame for dynamic photoacoustic computed tomography

Refik Mert Cam, Chao Wang, Weylan Thompson, Sergey Ermilov, Mark Anastasio, Umberto Villa

Proceedings Volume 12463, 124630R (2023) https://doi.org/10.1117/12.2654199

KEYWORDS: Photoacoustic tomography, Tomography, Image restoration, Image analysis, Tissue optics, Biomedical optics, Tissues, Data acquisition, Biological research, Tumors

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Proceedings Article | 9 March 2023 Presentation

Presentation

Numerical investigation of impact of skin phototype on three-dimensional optoacoustic tomography of the breast

Seonyeong Park, Umberto Villa, Alexander Oraevsky, Mark Anastasio

Proceedings Volume PC12379, PC123790E (2023) https://doi.org/10.1117/12.2650740

KEYWORDS: Skin, Breast, Tissue optics, Tomography, Optoacoustics, Signal to noise ratio, Photoacoustic tomography, Interfaces, Image filtering, Breast imaging

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Proceedings Article | 9 March 2023 Presentation + Paper

Presentation + Paper

Dynamic image reconstruction to monitor tumor vascular perfusion in small animals using 3D photoacoustic computed-tomography imagers with rotating gantries

Refik Mert Cam, Chao Wang, Seonyeong Park, Weylan Thompson, Sergey Ermilov, Mark Anastasio, Umberto Villa

Proceedings Volume 12379, 123790F (2023) https://doi.org/10.1117/12.2647663

KEYWORDS: Photoacoustic tomography, Tumors, Imaging systems, Acoustics, Matrices, Tomography, 3D image reconstruction, Preclinical imaging, Iterative methods, Inverse imaging problems, Small animal whole body imaging

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Proceedings Article | 4 April 2022 Presentation + Paper

Presentation + Paper

Neural fields for dynamic imaging

Luke Lozenski, Mark Anastasio, Umberto Villa

Proceedings Volume 12031, 120310X (2022) https://doi.org/10.1117/12.2613148

KEYWORDS: Image restoration, Imaging systems, Sensors, Neural networks, Image quality, Photoacoustic tomography, Optimization (mathematics), Data acquisition

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Proceedings Article | 4 April 2022 Poster + Presentation + Paper

Poster + Presentation + Paper

A learned filtered backprojection method for use with half-time circular radon transform data

Refik Mert Cam, Umberto Villa, Mark Anastasio

Proceedings Volume 12031, 1203134 (2022) https://doi.org/10.1117/12.2612941

KEYWORDS: Tomography, Radon transform, Photoacoustic tomography, Data modeling, Acoustics, Linear filtering, Image filtering, Reconstruction algorithms

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Proceedings Article | 4 April 2022 Poster + Presentation + Paper

Poster + Presentation + Paper

Learned Hotelling observers for use with multi-modal data

Jason Granstedt, Fu Li, Umberto Villa, Mark Anastasio

Proceedings Volume 12035, 1203512 (2022) https://doi.org/10.1117/12.2613138

KEYWORDS: Imaging systems, Signal detection, Signal to noise ratio, Data modeling, Stochastic processes, Information operations, Image quality, Image processing, Computed tomography, Targeting Task Performance metric, Ultrasound tomography, Machine learning, Artificial neural networks

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Proceedings Article | 4 April 2022 Presentation + Paper

Presentation + Paper

Investigation of an elevation-focused transducer model for three-dimensional full-waveform inversion in ultrasound computed tomography

Fu Li, Umberto Villa, Nebojsa Duric, Mark Anastasio

Proceedings Volume 12038, 120380S (2022) https://doi.org/10.1117/12.2613207

KEYWORDS: Transducers, Acoustics, 3D image processing, Systems modeling, 3D image reconstruction, Wave propagation, Imaging systems, Ultrasound tomography

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SPIE Journal Paper | 16 March 2022 Open Access

Open Access

Normalization of optical fluence distribution for three-dimensional functional optoacoustic tomography of the breast

Seonyeong Park, Frank Brooks, Umberto Villa, Richard Su, Mark Anastasio, Alexander Oraevsky

JBO, Vol. 27, Issue 03, 036001, (March 2022) https://doi.org/10.1117/12.10.1117/1.JBO.27.3.036001

KEYWORDS: Breast, Tissue optics, 3D image processing, Signal attenuation, Optoacoustics, Blood, 3D image reconstruction, Blood vessels, Imaging systems, Breast imaging

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Proceedings Article | 6 March 2021 Presentation

Presentation

Three-dimensional quantitative functional optoacoustic tomography to estimate vascular blood oxygenation of the breast

Seonyeong Park, Umberto Villa, Frank Brooks, Richard Su, Alexander Oraevsky, Mark Anastasio

Proceedings Volume 11642, 116421N (2021) https://doi.org/10.1117/12.2585341

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The goal of quantitative optoacoustic tomography (qOAT) is to reconstruct a distribution of absolute chromophore concentrations and/or functional properties from measurements of the optically induced pressure (ultrasound signals) acquired at multiple excitation wavelengths. Estimating the distribution of hemoglobin, an endogenous OAT chromophore, is important because the oxygen saturation distribution of the blood vessels is a well-known indicator of aggressive growth of a cancerous tumor. In a number of studies, a spectral linear unmixing method has been applied to two-dimensional slices of tissue acquired with OAT at multiple wavelengths, leading to promising results at moderate penetration depths of ≤ 2 cm. In the three-dimensional (3D) OAT of the breast, such functional images cannot be accurately reconstructed via the spectral linear unmixing method due to unknown spatial distribution of the optical fluence in a relatively large size of the volume of interest (≥ 4 cm). Optical attenuation in biological tissue depends on the optical wavelength, and the optical fluence is exponentially attenuated with increasing imaging depth. Thus, the accuracy of the estimated distribution decreases with depth. To overcome this challenge, we investigated a spectral linear unmixing method with a simplified optical fluence normalization based on measurements of background absorbed optical energy in the breast. We compare estimates of blood oxygen saturations from two-wavelength clinical OAT breast images and demonstrate acceptable accuracy of ~10% while lack of compensation for the optical fluence distribution can lead to values outside the physiological range. We also quantitatively compare the accuracy of oxygen saturation estimates using numerical simulation of photon transport in realistic 3D OAT breast phantoms at dual wavelengths of 757 and 850 nm with inverse ratio of the optical absorption by deoxy- (Hb) and oxy-hemoglobin (HbO2) and three wavelengths of 757, 800, and 850 nm with inclusion of isosbestic point of the optical absorption in Hb/HbO₂.

Proceedings Article | 6 March 2021 Presentation

Presentation

Dynamic reconstruction of three-dimensional photoacoustic tomography from few projections

Chao Wang, Umberto Villa, Weylan Thompson, Seonyeong Park, Sergey Ermilov, Mark Anastasio

Proceedings Volume 11642, 116421A (2021) https://doi.org/10.1117/12.2585344

KEYWORDS: Acquisition tracking and pointing, Photoacoustic tomography, 3D metrology, Reconstruction algorithms, 3D image reconstruction, Tomography, Tissues, Tissue optics, Temporal resolution, Numerical simulations

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Proceedings Article | 15 February 2021 Poster + Presentation + Paper

Poster + Presentation + Paper

Learning a projection operator onto the null space of a linear imaging operator

Joseph Kuo, Jason Granstedt, Umberto Villa, Mark Anastasio

Proceedings Volume 11595, 115953X (2021) https://doi.org/10.1117/12.2582263

KEYWORDS: Space operations, Reconstruction algorithms, Imaging systems, Magnetic resonance imaging, Linear algebra, Image restoration, Image resolution, Artificial neural networks

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Proceedings Article | 15 February 2021 Presentation

Presentation

A framework for ultrasound computed tomography virtual imaging trials that employs anatomically realistic numerical breast phantoms

Fu Li, Umberto Villa, Seonyeong Park, Shenghua He, Mark Anastasio

Proceedings Volume 11602, 116020V (2021) https://doi.org/10.1117/12.2582260

KEYWORDS: Breast, Ultrasonography, Computed tomography, 3D modeling, Tissues, Reconstruction algorithms, Algorithm development, Acoustics, Wave propagation, Tumors

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Proceedings Article | 6 March 2020 Presentation

Presentation

Realistic three-dimensional optoacoustic tomography imaging trials using the VICTRE breast phantom of FDA (Conference Presentation)

Seonyeong Park, Umberto Villa, Richard Su, Alexander Oraevsky, Frank Brooks, Mark Anastasio

Proceedings Volume 11240, 112401H (2020) https://doi.org/10.1117/12.2552380

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Showing 5 of 28 publications

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