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Joint Compensation of Motion and Partial Volume Effects by Iterative Deconvolution Incorporating Wavelet-Based Denoising in Oncologic Pet/Ct Imaging Publisher Pubmed



Rezaei S1, 2 ; Ghafarian P3, 4 ; Jha AK5, 6 ; Rahmim A7, 8 ; Sarkar S2 ; Ay MR1, 2
Authors
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Authors Affiliations
  1. 1. Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran
  2. 2. Research Center for Molecular and Cellular Imaging (RCMCI), Tehran University of Medical Sciences, Tehran, Iran
  3. 3. Chronic Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran
  4. 4. PET/CT and Cyclotron Center, Masih Daneshvari Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  5. 5. Department of Biomedical Engineering, Washington University in St. Louis, United States
  6. 6. Mallinckrodt Institute of Radiology, Washington University in St. Louis, United States
  7. 7. Departments of Radiology and Physics, University of British Columbia, Vancouver, Canada
  8. 8. Department of Integrative Oncology, BC Cancer Research Center, Vancouver, Canada

Source: Physica Medica Published:2019


Abstract

Objectives: We aim to develop and rigorously evaluate an image-based deconvolution method to jointly compensate respiratory motion and partial volume effects (PVEs) for quantitative oncologic PET imaging, including studying the impact of various reconstruction algorithms on quantification performance. Procedures: An image-based deconvolution method that incorporated wavelet-based denoising within the Lucy-Richardson algorithm was implemented and assessed. The method was evaluated using phantom studies with signal-to-background ratios (SBR) of 4 and 8, and clinical data of 10 patients with 42 lung lesions ≤30 mm in diameter. In each study, PET images were reconstructed using four different algorithms: OSEM-basic, PSF, TOF, and TOFPSF. The performance was quantified using contrast recovery (CR), coefficient of variation (COV) and contrast-to-noise-ratio (CNR) metrics. Further, in each study, variabilities arising due to the four different reconstruction algorithms were assessed. Results: In phantom studies, incorporation of wavelet-based denoising improved COV in all cases. Processing images using proposed method yielded significantly higher CR and CNR particularly in small spheres, for all reconstruction algorithms and all SBRs (P < 0.05). In patient studies, processing images using the proposed method yielded significantly higher CR and CNR (P < 0.05). The choice of the reconstruction algorithm impacted quantification performance for changes in motion amplitude, tumor size and SBRs. Conclusions: Our results provide strong evidence that the proposed joint-compensation method can yield improved PET quantification. The choice of the reconstruction algorithm led to changes in quantitative accuracy, emphasizing the need to carefully select the right combination of reconstruction-image-based compensation methods. © 2019
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