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Enhanced Direct Joint Attenuation and Scatter Correction of Whole-Body Pet Images Via Context-Aware Deep Networks Publisher Pubmed



Izadi S1 ; Shiri I2, 3 ; F Uribe C4, 5, 6 ; Geramifar P7 ; Zaidi H2, 8, 9, 10 ; Rahmim A4, 5, 11 ; Hamarneh G1
Authors
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Authors Affiliations
  1. 1. Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, Canada
  2. 2. Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva 4, Geneva, CH-1211, Switzerland
  3. 3. Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
  4. 4. Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, Canada
  5. 5. Department of Radiology, University of British Columbia, Vancouver, Canada
  6. 6. Molecular Imaging and Therapy, BC Cancer, Vancouver, BC, Canada
  7. 7. Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran
  8. 8. Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
  9. 9. Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark
  10. 10. University Research and Innovation Center, Obuda University, Budapest, Hungary
  11. 11. Department of Physics and Astronomy, University of British Columbia, Vancouver, Canada

Source: Zeitschrift fur Medizinische Physik Published:2024


Abstract

In positron emission tomography (PET), attenuation and scatter corrections are necessary steps toward accurate quantitative reconstruction of the radiopharmaceutical distribution. Inspired by recent advances in deep learning, many algorithms based on convolutional neural networks have been proposed for automatic attenuation and scatter correction, enabling applications to CT-less or MR-less PET scanners to improve performance in the presence of CT-related artifacts. A known characteristic of PET imaging is to have varying tracer uptakes for various patients and/or anatomical regions. However, existing deep learning-based algorithms utilize a fixed model across different subjects and/or anatomical regions during inference, which could result in spurious outputs. In this work, we present a novel deep learning-based framework for the direct reconstruction of attenuation and scatter-corrected PET from non-attenuation-corrected images in the absence of structural information in the inference. To deal with inter-subject and intra-subject uptake variations in PET imaging, we propose a novel model to perform subject- and region-specific filtering through modulating the convolution kernels in accordance to the contextual coherency within the neighboring slices. This way, the context-aware convolution can guide the composition of intermediate features in favor of regressing input-conditioned and/or region-specific tracer uptakes. We also utilized a large cohort of 910 whole-body studies for training and evaluation purposes, which is more than one order of magnitude larger than previous works. In our experimental studies, qualitative assessments showed that our proposed CT-free method is capable of producing corrected PET images that accurately resemble ground truth images corrected with the aid of CT scans. For quantitative assessments, we evaluated our proposed method over 112 held-out subjects and achieved an absolute relative error of 14.30±3.88% and a relative error of -2.11%±2.73% in whole-body. © 2024 The Author(s)
7. 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)
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