Fumio Hashimoto, Ph.D.

Positron emission tomography (PET),

image reconstruction,

denoising,

deep learning

Post-Doctoral Associate, January 2025 - present

J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, FL, United States

[Gong Lab - website]

Visiting Collaborative Researcher, October 2022 - December 2024

Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology (QST), Chiba, Japan

[Yamaya Lab - website]

Researcher, April 2016 - December 2024

Central Research Laboratory, Hamamatsu Photonics K.K., Shizuoka, Japan

Ph.D., October 2022 - September 2024

Department of Medical Engineering, Graduate School of Science and Engineering, Chiba University, Chiba, Japan

“Improving PET Image Quality Using Deep Image Prior”

Advisor: Prof. Taiga Yamaya

M.S., April 2014 - March 2016

Graduate School of Health Sciences, Fujita Health University, Aichi, Japan

B.S., April 2010 - March 2014

School of Health Sciences, Fujita Health University, Aichi, Japan

Journal papers

1. F. Hashimoto, and K. Gong, “PET Image Reconstruction Using Deep Diffusion Image Prior,” arXiv preprint, arXiv:2507.15078, 2025. (Preprint)

2. T. Ishikawa, G. Akamatsu, H. Tashima, F. Nishikido, F. Hashimoto, R. Ota, H. Haneishi, S. I. Kwon, S. R. Cherry, and T. Yamaya, “Imaging simulation of a dual-panel PET geometry with ultrafast TOF detectors,” IEEE Transactions on Radiation and Plasma Medical Sciences, 2025. [arXiv version]

3. K. Miwa, T. Yamao, F. Hashimoto, N. Miyaji, Y. Kamitaka, M. Masubuchi, T. Murata, T. Yoshii, R. Kobayashi, S. Fukuda, N. Akiya, K. Wachi, and K. Wagatsuma, “Innovations in clinical PET image reconstruction: advances in Bayesian penalized likelihood algorithm and deep learning,” Annals of Nuclear Medicine, vol. 39, no. 9, pp. 875-898, 2025. (Invited review paper)

4. K. Hori, F. Hashimoto, K. Koyama, and T. Hashimoto, "Limited-angle SPECT image reconstruction using deep image prior," Physics in Medicine and Biology, vol. 70, no. 14, 145005, 2025. [arXiv version]

5. F. Hashimoto, K. Ote, Y. Onishi, H. Tashima, G. Akamatsu, Y. Iwao, M. Takahashi, and T. Yamaya, " Exploiting network optimization stability for enhanced PET image denoising using deep image prior," Physics in Medicine and Biology, vol. 70, no. 10, 105019, 2025. [arXiv version]

6. K. Ote, F. Hashimoto, Y. Onishi, and Y. Ouchi, “List-Mode PET Image Reconstruction Using Dykstra-Like Splitting,” IEEE Transactions on Radiation and Plasma Medical Sciences, vol. 9, no. 1, pp. 29-39, 2025. [arXiv version]

7. F. Hashimoto, Y. Onishi, K. Ote, H. Tashima, and T. Yamaya, “Two-step optimization for accelerating deep image prior-based PET image reconstruction,” Radiological Physics and Technology, vol. 17, no. 3, pp. 776-781, 2024.

8. F. Hashimoto, and K. Ote, “ReconU-Net: a direct PET image reconstruction using U-Net architecture with back projection-induced skip connection,” Physics in Medicine and Biology, vol. 69, no. 10, 105022, 2024. [arXiv version]

9. Y. Tomonari, Y. Onishi, F. Hashimoto, K. Ote, T. Okamoto, and H. Ohba, “Animal PET scanner with a large field of view is suitable for high-throughput scanning of rodents,” Annals of Nuclear Medicine, vol. 38, no. 7, pp. 544-552, 2024.

10. Y. Onishi*, F. Hashimoto, K. Ote, and R. Ota*, “Whole reconstruction-free system design for direct positron emission imaging from image generation to attenuation correction,” IEEE Transactions on Medical Imaging, vol. 43, no. 5, pp. 1654-1663, 2024. (* Equal contribution)

11. Y. Onishi, F. Hashimoto, K. Ote, K. Matsubara, and M. Ibaraki, “Self-Supervised Pre-Training for Deep Image Prior-Based Robust PET Image Denoising,” IEEE Transactions on Radiation and Plasma Medical Sciences, vol. 8, no. 4, pp. 348-356, 2024. [arXiv version]

12. A. Obana, K. Ote, Y. Gohto, H. Yamada, F. Hashimoto, S. Okazaki, and R. Asaoka, “Deep learning-based correction of cataract-induced influence on macular pigment optical density measurement by autofluorescence spectroscopy,” PLoS ONE, vol. 19, no. 2, e0298132, 2024.

13. F. Hashimoto, Y. Onishi, K. Ote, H. Tashima, A. J. Reader, and T. Yamaya, “Deep learning-based PET image denoising and reconstruction: a review,” Radiological Physics and Technology, vol. 17, no. 1, pp. 24-46, 2024. (Invited review paper)

14. F. Hashimoto, Y. Onishi, K. Ote, H. Tashima, and T. Yamaya, “Fully 3D implementation of the end-to-end deep image prior-based PET image reconstruction using block iterative algorithm,” Physics in Medicine and Biology, vol. 68, no. 15, 155009, 2023. [arXiv version]

15. K. Ote, F. Hashimoto, Y. Onishi, T. Isobe, and Y. Ouchi, “List-Mode PET Image Reconstruction Using Deep Image Prior,” IEEE Transactions on Medical Imaging, vol. 42, no. 6, pp. 1822-1834, 2023. [arXiv version]

16. F. Hashimoto, K. Ote, and Y. Onishi, “PET Image Reconstruction Incorporating Deep Image Prior and a Forward Projection Model,” IEEE Transactions on Radiation and Plasma Medical Sciences, vol. 6, no. 8, pp. 841-846, 2022. [arXiv version]

17. Y. Onishi, T. Isobe, M. Ito, F. Hashimoto, T. Omura, and E. Yoshikawa, “Performance evaluation of dedicated brain PET scanner with motion correction system,” Annals of Nuclear Medicine, vol. 36, no. 8, pp. 746-755, 2022.

18. Y. Onishi, F. Hashimoto, K. Ote, and R. Ota, “Unbiased TOF estimation using leading-edge discriminator and convolutional neural network trained by single-source-position waveforms,” Physics in Medicine and Biology, vol. 67, no. 7, 04NT01, 2022.

19. K. Ote, and F. Hashimoto, “Deep-learning-based fast TOF-PET image reconstruction using direction information,” Radiological Physics and Technology, vol. 15, no. 1, pp. 72-82, 2022. [preprint]

20. S. I. Kwon*, R. Ota*, E. Berg*, F. Hashimoto, K. Nakajima, I. Ogawa, Y. Tamagawa, T. Omura, T. Hasegawa, and S. R. Cherry, “Ultrafast timing enables reconstruction-free positron emission imaging,” Nature Photonics, vol. 15, no. 12, pp. 914-918, 2021. (* Equal contribution) [arXiv version]

21. Y. Onishi*, F. Hashimoto*, K. Ote, H. Ohba, R. Ota, E. Yoshikawa, and Y. Ouchi, “Anatomical-Guided Attention Enhances Unsupervised PET Image Denoising Performance,” Medical Image Analysis, vol. 74, 102226, 2021. (* Equal contribution) [arXiv version]

22. F. Hashimoto*, M. Ito*, K. Ote, T. Isobe, H. Okada, and Y. Ouchi, “Deep learning-based attenuation correction for brain PET with various radiotracers,” Annals of Nuclear Medicine, vol. 35, no. 6, pp. 691-701, 2021. (* Equal contribution)

23. A. Obana, K. Ote, F. Hashimoto, R. Asaoka, Y. Gohto, S. Okazaki, and H. Yamada, “Correction for the Influence of Cataract on Macular Pigment Measurement by Autofluorescence Technique Using Deep Learning,” Translational Vision Science and Technology, vol. 10, no. 2, 18, 2021.

24. F. Hashimoto, H. Ohba, K. Ote, A. Kakimoto, H. Tsukada, and Y. Ouchi, “4D deep image prior: dynamic PET image denoising using an unsupervised four-dimensional branch convolutional neural network,” Physics in Medicine and Biology, vol. 66, no. 1, 015006, 2021.

25. J. Fang, H. Ohba, F. Hashimoto, H. Tsukada, F. Chen, and H. Liu, “Imaging mitochondrial complex I activation during a vibrotactile stimulation: A PET study using [18F]BCPP-EF in the conscious monkey brain,” Journal of cerebral blood flow and metabolism, vol. 40, no. 12, pp. 2521-2532, 2020.

26. K. Ote, R. Ota, F. Hashimoto, and T. Hasegawa, “Direct Annihilation Position Classification Based on Deep Learning Using Paired Cherenkov Detectors: A Monte Carlo Study,” Applied Sciences, vol. 10, no. 22, 7957, 2020.

27. K. Ote, F. Hashimoto, A. Kakimoto, T. Isobe, T. Inubushi, et al., “Kinetics-Induced Block Matching and 5-D Transform Domain Filtering for Dynamic PET Image Denoising,” IEEE Transactions on Radiation and Plasma Medical Sciences, vol. 4, no. 6, pp. 720-728, 2020.

28. F. Hashimoto, H. Ohba, K. Ote, and A. Teramoto, “Unsupervised dynamic PET image denoising with anatomical information,” Medical Imaging and Information Sciences, vol. 37, no. 3, pp.58-61, 2020.

29. F. Hashimoto, K. Ote, T. Oida, A. Teramoto, and Y. Ouchi, “Compressed-sensing magnetic resonance image reconstruction using an iterative convolutional neural network approach,” Applied Sciences, vol. 10, no. 6, 1902, 2020.

30. F. Hashimoto, H. Ohba, K. Ote, A. Teramoto, and H. Tsukada, “Dynamic PET image denoising using deep convolutional neural networks without prior training datasets,” IEEE Access, vol. 7, pp. 96594-96603, 2019.

31. F. Hashimoto, A. Kakimoto, N. Ota, S. Ito, and S. Nishizawa, “Automated segmentation of 2D low-dose CT images of the psoas-major muscle using deep convolutional neural networks,” Radiological Physics and Technology, vol. 12, no. 2, pp. 210-215, 2019.

32. F. Hashimoto, K. Ote, R. Ota, and T. Hasegawa, “A feasibility study on 3D interaction position estimation using deep neural network in Cherenkov-based detector: a Monte Carlo simulation study,” Biomedical Physics and Engineering Express, vol. 5, no. 3, 035001, 2019.

33. F. Hashimoto, H. Ohba, K. Ote, and H. Tsukada, “Denoising of Dynamic Sinogram by Image Guided Filtering for Positron Emission Tomography,” IEEE Transactions on Radiation and Plasma Medical Sciences, vol. 2, no. 6, pp. 541-548, 2018.

34. M. Kanazawa, S. Nishiyama, F. Hashimoto, T. Kakiuchi, and H. Tsukada, “Evaluation of D-isomers of 4-borono-2-18F-fluoro-phenylalanine and O-11C-methyl-tyrosine as brain tumor imaging agents: a comparative PET study with their L-isomers in rat brain glioma,” EJNMMI Research, vol. 8, 47, 2018.

35. F. Hashimoto, A. Teramoto, Y. Asada, S. Suzuki, and H. Fujita, “Dose reduction technique in diagnostic X-ray computed tomography by use of 6-channel multileaf collimators,” Radiological Physics and Technology, vol. 10, no. 1, pp. 60-67, 2017.

International conferences

1. T. Ishikawa, G. Akamatsu, H. Tashima, F. Nishikido, F. Hashimoto, R. Ota, H. Haneishi, S.I. Kwon, S. R. Cherry, and T. Yamaya, "Simulation Study of a Panel-Type PET Geometry: Effect of TOF and Sensitivity on Image Quality," The 2024 IEEE Nuclear Science Symposium and Medical Imaging Conference, Nov. 2024.

2. F. Hashimoto, K. Ote, H. Tashima, G. Akamatsu, Y. Iwao, M. Takahashi, and T. Yamaya, "An Approach to Reliable PET Image Denoising Using Deep Image Prior," The 2024 IEEE Nuclear Science Symposium and Medical Imaging Conference, Oct. 2024.

3. F. Hashimoto, H. Tashima, K. Ote, T. Yamaya, "Synergistic PET-Compton Reconstruction for Whole Gamma Imaging of Positron Emitters," The 2024 IEEE Nuclear Science Symposium and Medical Imaging Conference, Oct. 2024.

4. K. Ote, F. Hashimoto, Y. Onishi, and Y. Ouchi, "List-Mode PET Image Reconstruction Using Dykstra-Like Splitting," The 2024 IEEE Nuclear Science Symposium and Medical Imaging Conference, Oct. 2024.

5. Y. Onishi, K. Ote, F. Hashimoto, and R. Ota, "Single Photon Response Deconvolution for Boosting an Understanding of BGO Emission," The 2024 IEEE Nuclear Science Symposium and Medical Imaging Conference, Oct. 2024.

6. T. Okamoto, F. Hashimoto, and H. Haneishi, "Sino2Sino: deep learning-based direct sparse-view CT reconstruction with only sinogram data training," The 2024 IEEE Nuclear Science Symposium and Medical Imaging Conference, Oct. 2024.

7. T. Yamaya, T. Ishikawa, G. Akamatsu, H. Tashima, F. Nishikido, M. Takahashi, F. Hashimoto, R. Ota, S. I. Kwon, and S. R. Cherry, "Dual-panel PET system to be enabled by 30-ps super-fast detector: simulation study," Society of Nuclear Medicine and Molecular Imaging (SNMMI) Annual Meeting 2024, June, 2024.

8. T. Yamaya, T. Ishikawa, G. Akamatsu, H. Tashima, F. Nishikido, M. Takahashi, C. Toramatsu, Y. Iwao, F. Hashimoto, R. Ota, S. I. Kwon, and S. R. Cherry, "Dual-panel geometry for PET-guided therapy to be enabled by super-fast detector: simulation study," The 10th Conference on PET, SPECT, and MR Multimodal Technologies, Total Body and Fast Timing in Medical Imaging, May, 2024. (Oral presentation)

9. F. Hashimoto, K. Ote, H. Tashima, G. Akamatsu, Y. Iwao, M. Takahashi, and T. Yamaya, "Uncertainty-based mixture of a deep image prior and an original reconstructed images in PET," The 3rd International Conference on Radiological Physics and Technology, April, 2024. (Oral presentation)

10. T. Yamaya, T. Ishikawa, G. Akamatsu, H. Tashima, F. Nishikido, M. Takahashi, F. Hashimoto, and R. Ota, "Dual-panel PET system to be enabled by 30-ps super-fast detector: a preliminary simulation study," The 3rd International Conference on Radiological Physics and Technology, April, 2024. (Oral presentation)

11. F. Hashimoto, Y. Onishi, K. Ote, H. Tashima, and T. Yamaya, "Accelerated Deep Image Prior-based PET Image Reconstruction Using Two-Step Optimization," The 2023 IEEE Nuclear Science Symposium and Medical Imaging Conference, Nov. 2023.

12. F. Hashimoto, K. Ote, and Y. Onishi, "ReconU-Net: Direct PET Image Reconstruction Using Back Projection-induced Skip Connection," The 2023 IEEE Nuclear Science Symposium and Medical Imaging Conference, Nov. 2023.

13. Y. Onishi, F. Hashimoto, K. Ote, and R. Ota, "Proposal of Morphological Imaging for direct Positron Emission Imaging," The 2023 IEEE Nuclear Science Symposium and Medical Imaging Conference, Nov. 2023. (Oral presentation)

14. F. Hashimoto, K. Ote, Y. Onishi, H. Tashima, and T. Yamaya, "End-to-end Unsupervised CNN-based PET Image Reconstruction with Relative Difference Penalty," The 2nd International Conference on Radiological Physics and Technology, April, 2023. (Oral presentation)

15. F. Hashimoto, K. Ote, Y. Onishi, H. Tashima, and T. Yamaya, "3D Implementation of the End-to-end Deep Image Prior-based PET Image Reconstruction," The 2022 IEEE Nuclear Science Symposium and Medical Imaging Conference, Nov. 2022. (Oral presentation)

16. Y. Onishi, F. Hashimoto, K. Ote, K. Matsubara, and M. Ibaraki, "Using Self-Supervised Pretraining Model for Unsupervised PET Image Denoising," The 2022 IEEE Nuclear Science Symposium and Medical Imaging Conference, Nov. 2022.

17. K. Ote, F. Hashimoto, Y. Onishi, and T. Isobe, "List-Mode PET Image Reconstruction Using Deep Image Prior," The 2022 IEEE Nuclear Science Symposium and Medical Imaging Conference, Nov. 2022.

18. R. Ota, S. I. Kwon, E. Berg, F. Hashimoto, K. Nakajima, I. Ogawa, Y. Tamagawa, T. Omura, T. Hasegawa, and S. R. Cherry, "Reconstruction-free imaging of positron-emitting radionuclides using ultra-fast detectors," The virtual 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference, Nov. 2021. (Oral presentation)

19. Y. Onishi, F. Hashimoto, K. Ote, H. Ohba, R. Ota, E. Yoshikawa, and Y. Ouchi, "Unsupervised PET Image Denoising Using Attention-Guided Anatomical Information," The virtual 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference, Nov. 2021. (Oral presentation)

20. K. Ote, and F. Hashimoto, "Deep Learning-based Fast TOF-PET Image Reconstruction Using Direction Information," The virtual 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference, Nov. 2021. (Oral presentation)

21. F. Hashimoto, H. Ohba, K. Ote, A. Kakimoto, H. Tsukada, A. Teramoto, and Y. Ouchi, "Dynamic PET Image Denoising Using 4-dimensional Deep Image Prior," The virtual 2020 IEEE Nuclear Science Symposium and Medical Imaging Conference, Nov. 2020.

22. K. Ote, R. Ota, F. Hashimoto, and T. Hasegawa, "Direct Annihilation Position Regression based on Deep Learning and Digital Offset using Pair of Cherenkov Detectors: Monte Carlo Study," The virtual 2020 IEEE Nuclear Science Symposium and Medical Imaging Conference, Nov. 2020.

23. A. Obana, K. Ote, F. Hashimoto, Y. Gohto, S. Okazaki, and H. Yamada, "Correction of the influence of cataract on macular pigment measurement by autofluorescence technique using deep learning," Association for Research in Vision and Ophthalmology (ARVO) 2020 Annual Meeting, June, 2020.

24. F. Hashimoto, H. Ohba, K. Ote, A. Kakimoto, H. Tsukada, A. Teramoto, and Y. Ouchi, “End-to-end Dynamic PET Image Denoising Using 4-dimensional Unsupervised Convolutional Neural Network,” The 18th Conference of Peace through Mind/Brain Science, Feb. 2020.

25. K. Ote, F. Hashimoto, A. Kakimoto, T. Isobe, T. Inubushi, A. Tokui, A. Saito, T. Omura, E. Yoshikawa, A. Teramoto, and Y. Ouchi, “Kinetics-Induced Block Matching and 5D Transform Domain Filtering for Dynamic PET Image Denoising,” The 18th Conference of Peace through Mind/Brain Science, Feb. 2020.

26. F. Hashimoto, M. Ito, K. Ote, T. Isobe, H. Okada, Y. Ouchi, "Emis2Trans: Attenuation Correction for Brain PET With Many Types of PET Ligands Using Convolutional Neural Networks," The 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference, Oct. 2019.

27. K. Ote, F. Hashimoto, A. Kakimoto, T. Isobe, A. Tokui, E. Yoshikawa, T. Omura, A. Teramoto, and Y. Ouchi, "Block Matching and 5D Filtering of Dynamic PET Images," The 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference, Oct. 2019.

28. K. Ote, R. Ota, F. Hashimoto, and T. Hasegawa, "Direct Annihilation Position Classification based on Deep Learning using Pair of Cherenkov Detectors: Monte Carlo Study," The 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference, Oct. 2019.

29. F. Hashimoto, K. Ote, and H. Tsukada, "Dynamic PET Image Denoising Using Deep Convolutional Neural Network Without Training Datasets," Society of Nuclear Medicine and Molecular Imaging (SNMMI) Annual Meeting 2019, June, 2019. (Oral presentation)

30. F. Hashimoto, K. Ote, R. Ota, R. Yamada, and T. Hasegawa, "Using deep learning to Estimate 3D Interaction Position in Cherenkov-based Detector: A Monte Carlo Simulation Study," The 2018 IEEE Nuclear Science Symposium and Medical Imaging Conference, Nov. 2018. (Oral presentation)

31. F. Hashimoto, H. Ohba, K. Ote, and H. Tsukada, "Dynamic PET Denoising by Image Guided Filtering: A Preliminary Study," The 17th Conference of Peace through Mind/Brain Science, Feb. 2018.

32. F. Hashimoto, A. Teramoto, Y. Asada, S. Suzuki, and H. Fujita, "Novel Concept for Dose Reduction -Region-setting CT: Is Multileaf Collimator Also Valuable for Diagnostic CT?," RSNA2015, Dec. 2015. (Oral presentation)

33. F. Hashimoto, A. Teramoto, S. Suzuki, and H. Fujita, “A preliminary study on the development of a region-setting CT system using multi-leaf active collimators,” The International Forum on Medical Imaging in Asia, Jan. 2015.

34. F. Hashimoto, C. Murata, A. Teramoto, S. Suzuki, and H. Fujita, “A basic study on region setting CT system: three-dimensional data collection and reconstruction using experimental system,” The 15th Asian Oceanian Congress of Radiology 2014, Sep. 2014.

35. A. Teramoto, T. Ohno, F. Hashimoto, C. Murata, K. Takahashi, and H. Fujita, "Basic study on the development of a high-resolution breast CT,” International Workshop on Breast Imaging, June. 2014.

36. C. Murata, F. Hashimoto, A. Teramoto, and H. Fujita,"Educational system of computed tomography using optical computed tomography,” Computer Assisted Radiology and Surgery 28th International Congress and Exhibition, June. 2014.

fumio.hashimo@ufl.edu;

fumio.hashimoto@ieee.org