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Attention-Based Deep Neural Network for Partial Volume Correction in Brain 18F-Fdg Pet Imaging Publisher Pubmed



Azimi M1 ; Kamaliasl A1 ; Ay MR2, 3 ; Zeraatkar N4 ; Hosseini MS1 ; Sanaat A5 ; Arabi H5
Authors
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Authors Affiliations
  1. 1. Department of Medical Radiation Engineering, Shahid Beheshti University, Tehran, Iran
  2. 2. Research Center for Molecular and Cellular Imaging (RCMCI), Advanced Medical Technologies and Equipment Institute (AMTEI), Tehran University of Medical Sciences (TUMS), Tehran, Iran
  3. 3. Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran
  4. 4. Siemens Medical Solutions USA, Inc., Knoxville, TN, United States
  5. 5. Division of Nuclear Medicine & Molecular Imaging, Geneva University Hospital, Geneva, CH-1211, Switzerland

Source: Physica Medica Published:2024


Abstract

Purpose: This work set out to propose an attention-based deep neural network to predict partial volume corrected images from PET data not utilizing anatomical information. Methods: An attention-based convolutional neural network (ATB-Net) is developed to predict PVE-corrected images in brain PET imaging by concentrating on anatomical areas of the brain. The performance of the deep neural network for performing PVC without using anatomical images was evaluated for two PVC methods, including iterative Yang (IY) and reblurred Van-Cittert (RVC) approaches. The RVC and IY PVC approaches were applied to PET images to generate the reference images. The training of the U-Net network for the partial volume correction was trained twice, once without using the attention module and once with the attention module concentrating on the anatomical brain regions. Results: Regarding the peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and root mean square error (RMSE) metrics, the proposed ATB-Net outperformed the standard U-Net model (without attention compartment). For the RVC technique, the ATB-Net performed just marginally better than the U-Net; however, for the IY method, which is a region-wise method, the attention-based approach resulted in a substantial improvement. The mean absolute relative SUV difference and mean absolute relative bias improved by 38.02 % and 91.60 % for the RVC method and 77.47 % and 79.68 % for the IY method when using the ATB-Net model, respectively. Conclusions: Our results propose that without using anatomical data, the attention-based DL model could perform PVC on PET images, which could be employed for PVC in PET imaging. © 2024 Associazione Italiana di Fisica Medica e Sanitaria
2. A Novel Attention-Based Convolutional Neural Network for Joint Denoising and Partial Volume Correction of 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)
3. 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)
4. Investigation of Noise Reduction in Low-Dose Spect Myocardial Perfusion Images With a Generative Adversarial Network, 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)
5. Deep Learning-Based Low-Dose Cardiac Gated Spect: Implementation in Projection Space Vs. Image Space, 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)
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