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

Science Communicator Platform

Stay connected! Follow us on X network (Twitter):

Share By

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

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.

Related Docs

View other Related Docs

1. Deep Active Learning Model for Adaptive Pet Attenuation and Scatter Correction in Multi-Centric Studies, 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference Record# NSS/MIC 2021 and 28th International Symposium on Room-Temperature Semiconductor Detectors# RTSD 2022 (2021)

2. Deep Vision Transformers for Prognostic Modeling in Covid-19 Patients Using Large Multi-Institutional Chest Ct Dataset, 2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium# Medical Imaging Conference and Room Temperature Semiconductor Detector Conference (2022)

3. Artificial Intelligence-Based Analysis of Whole-Body Bone Scintigraphy: The Quest for the Optimal Deep Learning Algorithm and Comparison With Human Observer Performance, Zeitschrift fur Medizinische Physik (2023)

Experts (# of related papers)

View all Related Experts

Peyman Sheikhzadeh (7)

Mohammad Reza Ay (5)

Other Related Docs

4. Differential Privacy Preserved Federated Transfer Learning for Multi-Institutional 68Ga-Pet Image Artefact Detection and Disentanglement, European Journal of Nuclear Medicine and Molecular Imaging (2023)

5. Deep-Learning-Based Attenuation Correction For68 Ga-Dotatate Whole-Body Pet Imaging: A Dual-Center Clinical Study; [68Ga-Dotatate Tum Vucut Pet Goruntuleme Icin Derin Ogrenme Tabanli Atenuasyon Duzeltmesi: Cift Merkezli Bir Klinik Calisma], Molecular Imaging and Radionuclide Therapy (2024)

6. Coli-Net: Deep Learning-Assisted Fully Automated Covid-19 Lung and Infection Pneumonia Lesion Detection and Segmentation From Chest Computed Tomography Images, International Journal of Imaging Systems and Technology (2022)

7. A Ct-Free Deep-Learning-Based Attenuation and Scatter Correction for Copper-64 Pet in Different Time-Point Scans, Radiological Physics and Technology (2025)

8. Enhanced Direct Joint Attenuation and Scatter Correction of Whole-Body Pet Images Via Context-Aware Deep Networks, Zeitschrift fur Medizinische Physik (2024)

9. Deep-Jasc: Joint Attenuation and Scatter Correction in Whole-Body 18F-Fdg Pet Using a Deep Residual Network, European Journal of Nuclear Medicine and Molecular Imaging (2020)

10. Information Fusion for Fully Automated Segmentation of Head and Neck Tumors From Pet and Ct Images, Medical Physics (2024)

11. A Deep Neural Network to Recover Missing Data in Small Animal Pet Imaging: Comparison Between Sinogram-And Image-Domain Implementations, Proceedings - International Symposium on Biomedical Imaging (2021)

12. Deep Learning-Based Techniques for Estimating High-Quality Full-Dose Positron Emission Tomography Images From Low-Dose Scans: A Systematic Review, BMC Medical Imaging (2024)

13. Automated Deep Identification of Radiopharmaceutical Type and Body Region From Pet Images, Iranian Journal of Nuclear Medicine (2024)

14. Atb-Net: A Novel Attention-Based Convolutional Neural Network for Predicting Full-Dose From Low-Dose Pet Images, 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference Record# NSS/MIC 2021 and 28th International Symposium on Room-Temperature Semiconductor Detectors# RTSD 2022 (2021)

15. Pet Images Enhancement Using Deep Training of Reconstructed Images With Bayesian Penalized Likelihood Algorithm, Journal of Medical and Biological Engineering (2024)

16. Deep Learning-Based Automated Delineation of Head and Neck Malignant Lesions From Pet Images, 2020 IEEE Nuclear Science Symposium and Medical Imaging Conference# NSS/MIC 2020 (2020)

17. Deep Learning-Based Partial Volume Correction in Standard and Low-Dose Positron Emission Tomography-Computed Tomography Imaging, Quantitative Imaging in Medicine and Surgery (2024)

18. Lung Cancer Management: Revolutionizing Patient Outcomes Through Machine Learning and Artificial Intelligence, Cancer Reports (2025)

19. Fully Automated Gross Tumor Volume Delineation From Pet in Head and Neck Cancer Using Deep Learning Algorithms, Clinical Nuclear Medicine (2021)

20. Application of Artificial Intelligence in Oncologic Molecular Pet-Imaging: A Narrative Review on Beyond [18F]F-Fdg Tracers - Part I. Psma, Choline, and Dota Radiotracers, Seminars in Nuclear Medicine (2024)

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 -