Mr. Fu Li Profile

Fu Li

Grad Student at Univ of Illinois

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Publications (7)

Proceedings Article | 1 April 2024 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 June 2023 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.

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Proceedings Article | 10 April 2023 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 | 10 April 2023 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 | 4 April 2022 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

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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Proceedings Article | 15 February 2021 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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