Mr. Refik Mert Cam Profile

Refik Mert Cam

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Publications (7)

Proceedings Article | 12 March 2024 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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Proceedings Article | 12 March 2024 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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SPIE Journal Paper | 19 January 2024 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.

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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 | 7 April 2023 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 + 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 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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