Benchmarking deep learning-based reconstruction methods for photoacoustic computed tomography with clinically relevant synthetic datasets

Panpan Chen, Refik Mert Cam

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DOI: 10.1117/1.jbo.32.s1.s14903
Published: 2026-10-14
Container: Journal of Biomedical Optics
Publisher: SPIE-Intl Soc Optical Eng
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1117/1.jbo.32.s1.s14903,
  title = {Benchmarking deep learning-based reconstruction methods for photoacoustic computed tomography with clinically relevant synthetic datasets},
  author = {Panpan Chen and Refik Mert Cam},
  year = {2026},
  journal = {Journal of Biomedical Optics},
  doi = {10.1117/1.jbo.32.s1.s14903},
  url = {https://doi.org/10.1117/1.jbo.32.s1.s14903}
}

RIS

TY  - JOUR
TI  - Benchmarking deep learning-based reconstruction methods for photoacoustic computed tomography with clinically relevant synthetic datasets
AU  - Panpan Chen
AU  - Refik Mert Cam
PY  - 2026
JO  - Journal of Biomedical Optics
DO  - 10.1117/1.jbo.32.s1.s14903
UR  - https://doi.org/10.1117/1.jbo.32.s1.s14903
ER  -

APA

Chen, P., & Cam, R. M. (2026). Benchmarking deep learning-based reconstruction methods for photoacoustic computed tomography with clinically relevant synthetic datasets. Journal of Biomedical Optics. https://doi.org/10.1117/1.jbo.32.s1.s14903

Source records

crossref · retrieved 2026-11-05T15:10:09.395Z