Joint compensation of motion and partial volume effects by iterative deconvolution incorporating wavelet-based denoising in oncologic PET/CT imaging

Sahar Rezaei; Pardis Ghafarian; Abhinav K Jha; Arman Rahmim; Saeed Sarkar; Mohammad Reza Ay

doi:10.1016/j.ejmp.2019.10.031

Joint compensation of motion and partial volume effects by iterative deconvolution incorporating wavelet-based denoising in oncologic PET/CT imaging

Phys Med. 2019 Dec:68:52-60. doi: 10.1016/j.ejmp.2019.10.031. Epub 2019 Nov 18.

Authors

Sahar Rezaei¹, Pardis Ghafarian², Abhinav K Jha³, Arman Rahmim⁴, Saeed Sarkar⁵, Mohammad Reza Ay¹

Affiliations

¹ Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran; Research Center for Molecular and Cellular Imaging (RCMCI), Tehran University of Medical Sciences, Tehran, Iran.
² Chronic Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran; PET/CT and Cyclotron Center, Masih Daneshvari Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Electronic address: pardis.ghafarian@sbmu.ac.ir.
³ Department of Biomedical Engineering, Washington University in St. Louis, USA; Mallinckrodt Institute of Radiology, Washington University in St. Louis, USA.
⁴ Departments of Radiology and Physics, University of British Columbia, Vancouver, Canada; Department of Integrative Oncology, BC Cancer Research Center, Vancouver, Canada.
⁵ Research Center for Molecular and Cellular Imaging (RCMCI), Tehran University of Medical Sciences, Tehran, Iran.

PMID: 31743884
DOI: 10.1016/j.ejmp.2019.10.031

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.

Keywords: (18)F-FDG PET/CT; Combined compensation; Lung cancer; PSF; Partial volume effect; Quantification; Reconstruction algorithm; Respiratory motion; TOF.

MeSH terms

Algorithms
Carcinoma, Non-Small-Cell Lung / diagnostic imaging
Female
Humans
Image Processing, Computer-Assisted / methods*
Lung Neoplasms / diagnostic imaging
Male
Movement*
Positron Emission Tomography Computed Tomography*
Signal-To-Noise Ratio*
Wavelet Analysis*