Joint compensation of motion and partial volume effects by iterative deconvolution incorporating wavelet-based denoising in oncologic PET/CT imaging
•The developed method to jointly compensate yielded improved PET quantification.•Incorporation of wavelet-based denoising improved coefficient of variance.•Higher contrast recovery and contrast-to-noise ratio were seen in compensated images. We aim to develop and rigorously evaluate an image-based d...
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| Veröffentlicht in: | Physica medica 2019-12, Vol.68, p.52-60 |
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| creator | Rezaei, Sahar Ghafarian, Pardis Jha, Abhinav K. Rahmim, Arman Sarkar, Saeed Ay, Mohammad Reza |
| description | •The developed method to jointly compensate yielded improved PET quantification.•Incorporation of wavelet-based denoising improved coefficient of variance.•Higher contrast recovery and contrast-to-noise ratio were seen in compensated images.
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.
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.
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 |
| doi_str_mv | 10.1016/j.ejmp.2019.10.031 |
| format | Article |
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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.
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.
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.
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.</description><identifier>ISSN: 1120-1797</identifier><identifier>EISSN: 1724-191X</identifier><identifier>DOI: 10.1016/j.ejmp.2019.10.031</identifier><identifier>PMID: 31743884</identifier><language>eng</language><publisher>Italy: Elsevier Ltd</publisher><subject>18F-FDG PET/CT ; Algorithms ; Carcinoma, Non-Small-Cell Lung - diagnostic imaging ; Combined compensation ; Female ; Humans ; Image Processing, Computer-Assisted - methods ; Lung cancer ; Lung Neoplasms - diagnostic imaging ; Male ; Movement ; Partial volume effect ; Positron Emission Tomography Computed Tomography ; PSF ; Quantification ; Reconstruction algorithm ; Respiratory motion ; Signal-To-Noise Ratio ; TOF ; Wavelet Analysis</subject><ispartof>Physica medica, 2019-12, Vol.68, p.52-60</ispartof><rights>2019</rights><rights>Copyright © 2019. Published by Elsevier Ltd.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c356t-e400900547920b7c01cf93c43ffc9322d2ecd0f86e8d4b1f2553cc5ebb6f289d3</citedby><cites>FETCH-LOGICAL-c356t-e400900547920b7c01cf93c43ffc9322d2ecd0f86e8d4b1f2553cc5ebb6f289d3</cites><orcidid>0000-0002-9980-2403</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S1120179719304818$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31743884$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Rezaei, Sahar</creatorcontrib><creatorcontrib>Ghafarian, Pardis</creatorcontrib><creatorcontrib>Jha, Abhinav K.</creatorcontrib><creatorcontrib>Rahmim, Arman</creatorcontrib><creatorcontrib>Sarkar, Saeed</creatorcontrib><creatorcontrib>Ay, Mohammad Reza</creatorcontrib><title>Joint compensation of motion and partial volume effects by iterative deconvolution incorporating wavelet-based denoising in oncologic PET/CT imaging</title><title>Physica medica</title><addtitle>Phys Med</addtitle><description>•The developed method to jointly compensate yielded improved PET quantification.•Incorporation of wavelet-based denoising improved coefficient of variance.•Higher contrast recovery and contrast-to-noise ratio were seen in compensated images.
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.
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.
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.
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.</description><subject>18F-FDG PET/CT</subject><subject>Algorithms</subject><subject>Carcinoma, Non-Small-Cell Lung - diagnostic imaging</subject><subject>Combined compensation</subject><subject>Female</subject><subject>Humans</subject><subject>Image Processing, Computer-Assisted - methods</subject><subject>Lung cancer</subject><subject>Lung Neoplasms - diagnostic imaging</subject><subject>Male</subject><subject>Movement</subject><subject>Partial volume effect</subject><subject>Positron Emission Tomography Computed Tomography</subject><subject>PSF</subject><subject>Quantification</subject><subject>Reconstruction algorithm</subject><subject>Respiratory motion</subject><subject>Signal-To-Noise Ratio</subject><subject>TOF</subject><subject>Wavelet Analysis</subject><issn>1120-1797</issn><issn>1724-191X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kctuFDEQRS0EIg_4ARbISzY98aOfEhs0GkiiSLAYJHaWu1weedRtN3bPRPmPfDDuTMgyK5eqzq1y1SXkE2crznh9tV_hfpxWgvEuJ1ZM8jfknDeiLHjH_7zNMRes4E3XnJGLlPaMSSGq6j05k7wpZduW5-TxNjg_UwjjhD7p2QVPg6VjeIq0N3TScXZ6oMcwHEakaC3CnGj_QN2MMSuOSA1C8AvwpHIeQpzCUvM7eq-POOBc9DqhyaQPLi15lwdlcAg7B_TXZnu13lI36l2ufSDvrB4Sfnx-L8nv75vt-rq4-_njZv3trgBZ1XOBJWMdY1XZdIL1DTAOtpNQSmuhy6sagWCYbWtsTdlzm3eXABX2fW1F2xl5Sb6c-k4x_D1gmtXoEuAwaI_hkJSQvC4la-smo-KEQgwpRbRqivm38UFxphY31F4tbqjFjSWX3ciiz8_9D_2I5kXy__wZ-HoCMG95dBhVAoce0LiYr6xMcK_1_wfldZ8x</recordid><startdate>201912</startdate><enddate>201912</enddate><creator>Rezaei, Sahar</creator><creator>Ghafarian, Pardis</creator><creator>Jha, Abhinav K.</creator><creator>Rahmim, Arman</creator><creator>Sarkar, Saeed</creator><creator>Ay, Mohammad Reza</creator><general>Elsevier Ltd</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-9980-2403</orcidid></search><sort><creationdate>201912</creationdate><title>Joint compensation of motion and partial volume effects by iterative deconvolution incorporating wavelet-based denoising in oncologic PET/CT imaging</title><author>Rezaei, Sahar ; Ghafarian, Pardis ; Jha, Abhinav K. ; Rahmim, Arman ; Sarkar, Saeed ; Ay, Mohammad Reza</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c356t-e400900547920b7c01cf93c43ffc9322d2ecd0f86e8d4b1f2553cc5ebb6f289d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>18F-FDG PET/CT</topic><topic>Algorithms</topic><topic>Carcinoma, Non-Small-Cell Lung - diagnostic imaging</topic><topic>Combined compensation</topic><topic>Female</topic><topic>Humans</topic><topic>Image Processing, Computer-Assisted - methods</topic><topic>Lung cancer</topic><topic>Lung Neoplasms - diagnostic imaging</topic><topic>Male</topic><topic>Movement</topic><topic>Partial volume effect</topic><topic>Positron Emission Tomography Computed Tomography</topic><topic>PSF</topic><topic>Quantification</topic><topic>Reconstruction algorithm</topic><topic>Respiratory motion</topic><topic>Signal-To-Noise Ratio</topic><topic>TOF</topic><topic>Wavelet Analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Rezaei, Sahar</creatorcontrib><creatorcontrib>Ghafarian, Pardis</creatorcontrib><creatorcontrib>Jha, Abhinav K.</creatorcontrib><creatorcontrib>Rahmim, Arman</creatorcontrib><creatorcontrib>Sarkar, Saeed</creatorcontrib><creatorcontrib>Ay, Mohammad Reza</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Physica medica</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Rezaei, Sahar</au><au>Ghafarian, Pardis</au><au>Jha, Abhinav K.</au><au>Rahmim, Arman</au><au>Sarkar, Saeed</au><au>Ay, Mohammad Reza</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Joint compensation of motion and partial volume effects by iterative deconvolution incorporating wavelet-based denoising in oncologic PET/CT imaging</atitle><jtitle>Physica medica</jtitle><addtitle>Phys Med</addtitle><date>2019-12</date><risdate>2019</risdate><volume>68</volume><spage>52</spage><epage>60</epage><pages>52-60</pages><issn>1120-1797</issn><eissn>1724-191X</eissn><abstract>•The developed method to jointly compensate yielded improved PET quantification.•Incorporation of wavelet-based denoising improved coefficient of variance.•Higher contrast recovery and contrast-to-noise ratio were seen in compensated images.
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.
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.
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.
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.</abstract><cop>Italy</cop><pub>Elsevier Ltd</pub><pmid>31743884</pmid><doi>10.1016/j.ejmp.2019.10.031</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0002-9980-2403</orcidid></addata></record> |
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| source | MEDLINE; Elsevier ScienceDirect Journals |
| subjects | 18F-FDG PET/CT Algorithms Carcinoma, Non-Small-Cell Lung - diagnostic imaging Combined compensation Female Humans Image Processing, Computer-Assisted - methods Lung cancer Lung Neoplasms - diagnostic imaging Male Movement Partial volume effect Positron Emission Tomography Computed Tomography PSF Quantification Reconstruction algorithm Respiratory motion Signal-To-Noise Ratio TOF Wavelet Analysis |
| title | Joint compensation of motion and partial volume effects by iterative deconvolution incorporating wavelet-based denoising in oncologic PET/CT imaging |
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