Deep Adaptive Transfer Learning for Site-Specific Pet Attenuation and Scatter Correction From Multi-National/Institutional Datasets

Deep Adaptive Transfer Learning for Site-Specific Pet Attenuation and Scatter Correction From Multi-National/Institutional Datasets Publisher

Shiri I¹ ; Salimi Y¹ ; Maghsudi M² ; Hajianfar G² ; Jafari E³ ; Samimi R⁴ ; Khateri M⁵ ; Sheikhzadeh P⁶ ; Geramifar P⁷ ; Dadgar H⁸ ; Rajabi AB² ; Assadi M³ ; Benard F^{9, 10} ; Uribe C^{9, 10} Show All Authors

Shiri I¹
Salimi Y¹
Maghsudi M²
Hajianfar G²
Jafari E³
Samimi R⁴
Khateri M⁵
Sheikhzadeh P⁶
Geramifar P⁷
Dadgar H⁸
Rajabi AB²
Assadi M³
Benard F^{9, 10}
Uribe C^{9, 10}
Rahmim A^{9, 10}
Zaidi H¹

Show Affiliations

1. Geneva University Hospital, Division of Nuclear Medicine and Molecular Imaging, Geneva, 1211, Switzerland
2. Iran University of Medical Sciences, Rajaie Cardiovascular Medical and Research Center, Tehran, Iran
3. Bushehr Medical University Hospital, Department of Molecular Imaging and Radionuclide Therapy, Bushehr, Iran
4. Shahid Beheshti University, Department of Medical Radiation Engineering, Tehran, Iran
5. Islamic Azad University, Science and Research Branch, Department of Medical Radiation Engineering, Iran
6. Tehran University of Medical Sciences, Imam Khomeini Hospital Complex, Department of Nuclear Medicine, Tehran, Iran
7. Tehran University of Medical Sciences, Research Center for Nuclear Medicine, Shariati Hospital, Tehran, Iran
8. Imam Reza International University, Cancer Research Center, Razavi Hospital, Mashhad, Iran
9. The Department of Radiology, University of British Columbia, Canada
10. Bc Cancer, Vancouver, Canada

Source: 2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium# Medical Imaging Conference and Room Temperature Semiconductor Detector Conference Published:2022

Abstract

Attenuation and scatter correction (ASC) must be performed for quantitative PET imaging. ASC is a challenging task in PET-only and PET/MRI systems. Different single-center or scanner-specific studies have been performed by using deep learning (DL) algorithms. However, the generalizability of these models is limited. In addition, providing large datasets for data-hungry DL models is a bottleneck. However, a universal model may not perform very well for each center because of high variability across different centers in scanners and image acquisition and reconstruction protocols. Therefore, we aimed to apply deep transfer learning for site-specific PET ASC utilizing a multi-center dataset. Altogether, 43068Ga-PSMA/DOTA PET/CT images from three countries (Switzerland, Iran, and Canada), including eight different centers, were enrolled in this study. In all centers, PET images were corrected by using CT images for ASC. In addition, we implemented a deep supervised U-Net network architecture, U2Net, as the core DL model. Different scenarios were investigated in this study, including (i) training and testing models for each center separately: center-based (CeBa); (ii) training and testing models using the entirety of the dataset: centralized (CeZe); i.e., entire data pooled together; and (iii) transfer learning (TrLe) where training was performed using pooled data to one server using entire dataset and then TrLe were performed for each center separately to build site-specific models for each center. In terms of absolute relative error (ARE%), CeBa, CeZe, and TrLe achieved 36 ± 13 (CI95%: 33 to 39), 31 ± 34 (CI95%: 24 to 38).Furthermore, using TrLe outperformed the centralized and center-based models in terms of accurate ASC image generation. © 2022 IEEE.

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Parham Geramifar (5)

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Style	Citing Format
MLA	Shiri I, et al.. "Deep Adaptive Transfer Learning for Site-Specific Pet Attenuation and Scatter Correction From Multi-National/Institutional Datasets." 2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium# Medical Imaging Conference and Room Temperature Semiconductor Detector Conference, vol. , no. , 2022, pp. -.
APA	Shiri I, Salimi Y, Maghsudi M, Hajianfar G, Jafari E, Samimi R, Khateri M, Sheikhzadeh P, Geramifar P, Dadgar H, Rajabi AB, Assadi M, Benard F, Uribe C, Rahmim A, Zaidi H (2022). Deep Adaptive Transfer Learning for Site-Specific Pet Attenuation and Scatter Correction From Multi-National/Institutional Datasets. 2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium# Medical Imaging Conference and Room Temperature Semiconductor Detector Conference, (), -.
Chicago	Shiri I, Salimi Y, Maghsudi M, Hajianfar G, Jafari E, Samimi R, Khateri M, et al.. "Deep Adaptive Transfer Learning for Site-Specific Pet Attenuation and Scatter Correction From Multi-National/Institutional Datasets." 2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium# Medical Imaging Conference and Room Temperature Semiconductor Detector Conference , no. (2022): -.
Harvard	Shiri I et al. (2022) 'Deep Adaptive Transfer Learning for Site-Specific Pet Attenuation and Scatter Correction From Multi-National/Institutional Datasets', 2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium# Medical Imaging Conference and Room Temperature Semiconductor Detector Conference, (), pp. -.
Vancouver	Shiri I, Salimi Y, Maghsudi M, Hajianfar G, Jafari E, Samimi R, et al.. Deep Adaptive Transfer Learning for Site-Specific Pet Attenuation and Scatter Correction From Multi-National/Institutional Datasets. 2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium# Medical Imaging Conference and Room Temperature Semiconductor Detector Conference. 2022;():-.
BibTex	@article{ author = {Shiri I and Salimi Y and Maghsudi M and Hajianfar G and Jafari E and Samimi R and Khateri M and Sheikhzadeh P and Geramifar P and Dadgar H and Rajabi AB and Assadi M and Benard F and Uribe C and Rahmim A and Zaidi H}, title = {Deep Adaptive Transfer Learning for Site-Specific Pet Attenuation and Scatter Correction From Multi-National/Institutional Datasets}, journal = {2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium# Medical Imaging Conference and Room Temperature Semiconductor Detector Conference}, volume = {}, number = {}, pages = {-}, year = {2022} }
RIS	TY - JOUR AU - Shiri I AU - Salimi Y AU - Maghsudi M AU - Hajianfar G AU - Jafari E AU - Samimi R AU - Khateri M AU - Sheikhzadeh P AU - Geramifar P AU - Dadgar H AU - Rajabi AB AU - Assadi M AU - Benard F AU - Uribe C AU - Rahmim A AU - Zaidi H TI - Deep Adaptive Transfer Learning for Site-Specific Pet Attenuation and Scatter Correction From Multi-National/Institutional Datasets JO - 2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium# Medical Imaging Conference and Room Temperature Semiconductor Detector Conference VL - IS - SP - EP - PY - 2022 ER -

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