Dynamic PET simulator via tomographic emission projection for kinetic modeling and parametric image studies
Purpose: To develop and evaluate a fast and simple tool called dpetstep (Dynamic PET Simulator of Tracers via Emission Projection), for dynamic PET simulations as an alternative to Monte Carlo (MC), useful for educational purposes and evaluation of the effects of the clinical environment, postproces...
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| Veröffentlicht in: | Medical physics (Lancaster) 2016-06, Vol.43 (6), p.3104-3116 |
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| creator | Häggström, Ida Beattie, Bradley J. Schmidtlein, C. Ross |
| description | Purpose:
To develop and evaluate a fast and simple tool called dpetstep (Dynamic PET Simulator of Tracers via Emission Projection), for dynamic PET simulations as an alternative to Monte Carlo (MC), useful for educational purposes and evaluation of the effects of the clinical environment, postprocessing choices, etc., on dynamic and parametric images.
Methods:
The tool was developed in matlab using both new and previously reported modules of petstep (PET Simulator of Tracers via Emission Projection). Time activity curves are generated for each voxel of the input parametric image, whereby effects of imaging system blurring, counting noise, scatters, randoms, and attenuation are simulated for each frame. Each frame is then reconstructed into images according to the user specified method, settings, and corrections. Reconstructed images were compared to MC data, and simple Gaussian noised time activity curves (GAUSS).
Results:
dpetstep was 8000 times faster than MC. Dynamic images from dpetstep had a root mean square error that was within 4% on average of that of MC images, whereas the GAUSS images were within 11%. The average bias in dpetstep and MC images was the same, while GAUSS differed by 3% points. Noise profiles in dpetstep images conformed well to MC images, confirmed visually by scatter plot histograms, and statistically by tumor region of interest histogram comparisons that showed no significant differences (p < 0.01). Compared to GAUSS, dpetstep images and noise properties agreed better with MC.
Conclusions:
The authors have developed a fast and easy one-stop solution for simulations of dynamic PET and parametric images, and demonstrated that it generates both images and subsequent parametric images with very similar noise properties to those of MC images, in a fraction of the time. They believe dpetstep to be very useful for generating fast, simple, and realistic results, however since it uses simple scatter and random models it may not be suitable for studies investigating these phenomena. dpetstep can be downloaded free of cost from https://github.com/CRossSchmidtlein/dPETSTEP. |
| doi_str_mv | 10.1118/1.4950883 |
| format | Article |
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To develop and evaluate a fast and simple tool called dpetstep (Dynamic PET Simulator of Tracers via Emission Projection), for dynamic PET simulations as an alternative to Monte Carlo (MC), useful for educational purposes and evaluation of the effects of the clinical environment, postprocessing choices, etc., on dynamic and parametric images.
Methods:
The tool was developed in matlab using both new and previously reported modules of petstep (PET Simulator of Tracers via Emission Projection). Time activity curves are generated for each voxel of the input parametric image, whereby effects of imaging system blurring, counting noise, scatters, randoms, and attenuation are simulated for each frame. Each frame is then reconstructed into images according to the user specified method, settings, and corrections. Reconstructed images were compared to MC data, and simple Gaussian noised time activity curves (GAUSS).
Results:
dpetstep was 8000 times faster than MC. Dynamic images from dpetstep had a root mean square error that was within 4% on average of that of MC images, whereas the GAUSS images were within 11%. The average bias in dpetstep and MC images was the same, while GAUSS differed by 3% points. Noise profiles in dpetstep images conformed well to MC images, confirmed visually by scatter plot histograms, and statistically by tumor region of interest histogram comparisons that showed no significant differences (p < 0.01). Compared to GAUSS, dpetstep images and noise properties agreed better with MC.
Conclusions:
The authors have developed a fast and easy one-stop solution for simulations of dynamic PET and parametric images, and demonstrated that it generates both images and subsequent parametric images with very similar noise properties to those of MC images, in a fraction of the time. They believe dpetstep to be very useful for generating fast, simple, and realistic results, however since it uses simple scatter and random models it may not be suitable for studies investigating these phenomena. dpetstep can be downloaded free of cost from https://github.com/CRossSchmidtlein/dPETSTEP.</description><identifier>ISSN: 0094-2405</identifier><identifier>EISSN: 2473-4209</identifier><identifier>DOI: 10.1118/1.4950883</identifier><identifier>PMID: 27277057</identifier><identifier>CODEN: MPHYA6</identifier><language>eng</language><publisher>United States: American Association of Physicists in Medicine</publisher><subject>60 APPLIED LIFE SCIENCES ; Biological material, e.g. blood, urine; Haemocytometers ; Biomedical modeling ; BIOMEDICAL RADIOGRAPHY ; Cameras ; Cancer ; compartment modeling ; Computed tomography ; DIAGNOSTIC IMAGING (IONIZING AND NON-IONIZING) ; Digital computing or data processing equipment or methods, specially adapted for specific applications ; dynamic PET ; Gaussian noise ; Image data processing or generation, in general ; Image enhancement or restoration, e.g. from bit‐mapped to bit‐mapped creating a similar image ; image reconstruction ; image restoration ; MATHEMATICAL SOLUTIONS ; Measuring half‐life of a radioactive substance ; Medical image noise ; medical image processing ; Medical image reconstruction ; Monte Carlo ; MONTE CARLO METHOD ; Monte Carlo methods ; NOISE ; parametric imaging ; PETSTEP ; POSITRON COMPUTED TOMOGRAPHY ; positron emission tomography ; Positron emission tomography (PET) ; radiation physics ; RADIATION PROTECTION AND DOSIMETRY ; radiofysik ; Reconstruction ; Scintigraphy ; SIMULATION ; SIMULATORS ; tumours</subject><ispartof>Medical physics (Lancaster), 2016-06, Vol.43 (6), p.3104-3116</ispartof><rights>American Association of Physicists in Medicine</rights><rights>2016 American Association of Physicists in Medicine</rights><rights>2016 American Association of Physicists in Medicine. 2016 American Association of Physicists in Medicine</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c5493-413eb20d384073109f6446c69f5cca812a595f723c9d6bd13f81722651cf36ee3</citedby><cites>FETCH-LOGICAL-c5493-413eb20d384073109f6446c69f5cca812a595f723c9d6bd13f81722651cf36ee3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1118%2F1.4950883$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1118%2F1.4950883$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>230,314,780,784,885,1417,27924,27925,45574,45575</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/27277057$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://www.osti.gov/biblio/22685113$$D View this record in Osti.gov$$Hfree_for_read</backlink><backlink>$$Uhttps://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-121069$$DView record from Swedish Publication Index$$Hfree_for_read</backlink></links><search><creatorcontrib>Häggström, Ida</creatorcontrib><creatorcontrib>Beattie, Bradley J.</creatorcontrib><creatorcontrib>Schmidtlein, C. Ross</creatorcontrib><title>Dynamic PET simulator via tomographic emission projection for kinetic modeling and parametric image studies</title><title>Medical physics (Lancaster)</title><addtitle>Med Phys</addtitle><description>Purpose:
To develop and evaluate a fast and simple tool called dpetstep (Dynamic PET Simulator of Tracers via Emission Projection), for dynamic PET simulations as an alternative to Monte Carlo (MC), useful for educational purposes and evaluation of the effects of the clinical environment, postprocessing choices, etc., on dynamic and parametric images.
Methods:
The tool was developed in matlab using both new and previously reported modules of petstep (PET Simulator of Tracers via Emission Projection). Time activity curves are generated for each voxel of the input parametric image, whereby effects of imaging system blurring, counting noise, scatters, randoms, and attenuation are simulated for each frame. Each frame is then reconstructed into images according to the user specified method, settings, and corrections. Reconstructed images were compared to MC data, and simple Gaussian noised time activity curves (GAUSS).
Results:
dpetstep was 8000 times faster than MC. Dynamic images from dpetstep had a root mean square error that was within 4% on average of that of MC images, whereas the GAUSS images were within 11%. The average bias in dpetstep and MC images was the same, while GAUSS differed by 3% points. Noise profiles in dpetstep images conformed well to MC images, confirmed visually by scatter plot histograms, and statistically by tumor region of interest histogram comparisons that showed no significant differences (p < 0.01). Compared to GAUSS, dpetstep images and noise properties agreed better with MC.
Conclusions:
The authors have developed a fast and easy one-stop solution for simulations of dynamic PET and parametric images, and demonstrated that it generates both images and subsequent parametric images with very similar noise properties to those of MC images, in a fraction of the time. They believe dpetstep to be very useful for generating fast, simple, and realistic results, however since it uses simple scatter and random models it may not be suitable for studies investigating these phenomena. dpetstep can be downloaded free of cost from https://github.com/CRossSchmidtlein/dPETSTEP.</description><subject>60 APPLIED LIFE SCIENCES</subject><subject>Biological material, e.g. blood, urine; Haemocytometers</subject><subject>Biomedical modeling</subject><subject>BIOMEDICAL RADIOGRAPHY</subject><subject>Cameras</subject><subject>Cancer</subject><subject>compartment modeling</subject><subject>Computed tomography</subject><subject>DIAGNOSTIC IMAGING (IONIZING AND NON-IONIZING)</subject><subject>Digital computing or data processing equipment or methods, specially adapted for specific applications</subject><subject>dynamic PET</subject><subject>Gaussian noise</subject><subject>Image data processing or generation, in general</subject><subject>Image enhancement or restoration, e.g. from bit‐mapped to bit‐mapped creating a similar image</subject><subject>image reconstruction</subject><subject>image restoration</subject><subject>MATHEMATICAL SOLUTIONS</subject><subject>Measuring half‐life of a radioactive substance</subject><subject>Medical image noise</subject><subject>medical image processing</subject><subject>Medical image reconstruction</subject><subject>Monte Carlo</subject><subject>MONTE CARLO METHOD</subject><subject>Monte Carlo methods</subject><subject>NOISE</subject><subject>parametric imaging</subject><subject>PETSTEP</subject><subject>POSITRON COMPUTED TOMOGRAPHY</subject><subject>positron emission tomography</subject><subject>Positron emission tomography (PET)</subject><subject>radiation physics</subject><subject>RADIATION PROTECTION AND DOSIMETRY</subject><subject>radiofysik</subject><subject>Reconstruction</subject><subject>Scintigraphy</subject><subject>SIMULATION</subject><subject>SIMULATORS</subject><subject>tumours</subject><issn>0094-2405</issn><issn>2473-4209</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><recordid>eNp9kctu1DAYhS0EokNhwQugSGwAKcXX2NkgVW25SEV0UdhaHsfJuE3sYDtTzdvjaMLQCsHKlv_Px8fnAPASwROEkHiPTmjNoBDkEVhhyklJMawfgxWENS0xhewIPIvxBkJYEQafgiPMMeeQ8RW4Pd85NVhdXF1cF9EOU6-SD8XWqiL5wXdBjZs8NYON0XpXjMHfGJ3mbZu5W-tMyvPBN6a3riuUa4pRBTWYFPK5HVRnipimxpr4HDxpVR_Ni2U9Bt8_XlyffS4vv336cnZ6WWpG62weEbPGsCGCQk4QrNuK0kpXdcu0VgJhxWrWckx03VTrBpFWII5xxZBuSWUMOQblXjfemXFayzFkG2EnvbLy3P44lT50chomiTCCVZ35D3s-w4NptHEpqP7BtYcTZzey81tJhaBIkCzwei_gY7IyapuM3mjvXE5KZmeCITRTb5Zngv85mZhkDlWbvlfO-ClKxGsmOMN4dvR2j-rgYwymPZhBUM6VSySXyjP76r77A_m74z9x3Nne7P6tJL9eLYLvlvjyR9Rc9eHO1od7_Ni0_4P_tvoLp7PRXw</recordid><startdate>201606</startdate><enddate>201606</enddate><creator>Häggström, Ida</creator><creator>Beattie, Bradley J.</creator><creator>Schmidtlein, C. Ross</creator><general>American Association of Physicists in Medicine</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>OTOTI</scope><scope>5PM</scope><scope>ADTPV</scope><scope>AOWAS</scope><scope>D93</scope></search><sort><creationdate>201606</creationdate><title>Dynamic PET simulator via tomographic emission projection for kinetic modeling and parametric image studies</title><author>Häggström, Ida ; Beattie, Bradley J. ; Schmidtlein, C. Ross</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5493-413eb20d384073109f6446c69f5cca812a595f723c9d6bd13f81722651cf36ee3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>60 APPLIED LIFE SCIENCES</topic><topic>Biological material, e.g. blood, urine; Haemocytometers</topic><topic>Biomedical modeling</topic><topic>BIOMEDICAL RADIOGRAPHY</topic><topic>Cameras</topic><topic>Cancer</topic><topic>compartment modeling</topic><topic>Computed tomography</topic><topic>DIAGNOSTIC IMAGING (IONIZING AND NON-IONIZING)</topic><topic>Digital computing or data processing equipment or methods, specially adapted for specific applications</topic><topic>dynamic PET</topic><topic>Gaussian noise</topic><topic>Image data processing or generation, in general</topic><topic>Image enhancement or restoration, e.g. from bit‐mapped to bit‐mapped creating a similar image</topic><topic>image reconstruction</topic><topic>image restoration</topic><topic>MATHEMATICAL SOLUTIONS</topic><topic>Measuring half‐life of a radioactive substance</topic><topic>Medical image noise</topic><topic>medical image processing</topic><topic>Medical image reconstruction</topic><topic>Monte Carlo</topic><topic>MONTE CARLO METHOD</topic><topic>Monte Carlo methods</topic><topic>NOISE</topic><topic>parametric imaging</topic><topic>PETSTEP</topic><topic>POSITRON COMPUTED TOMOGRAPHY</topic><topic>positron emission tomography</topic><topic>Positron emission tomography (PET)</topic><topic>radiation physics</topic><topic>RADIATION PROTECTION AND DOSIMETRY</topic><topic>radiofysik</topic><topic>Reconstruction</topic><topic>Scintigraphy</topic><topic>SIMULATION</topic><topic>SIMULATORS</topic><topic>tumours</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Häggström, Ida</creatorcontrib><creatorcontrib>Beattie, Bradley J.</creatorcontrib><creatorcontrib>Schmidtlein, C. Ross</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>OSTI.GOV</collection><collection>PubMed Central (Full Participant titles)</collection><collection>SwePub</collection><collection>SwePub Articles</collection><collection>SWEPUB Umeå universitet</collection><jtitle>Medical physics (Lancaster)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Häggström, Ida</au><au>Beattie, Bradley J.</au><au>Schmidtlein, C. Ross</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Dynamic PET simulator via tomographic emission projection for kinetic modeling and parametric image studies</atitle><jtitle>Medical physics (Lancaster)</jtitle><addtitle>Med Phys</addtitle><date>2016-06</date><risdate>2016</risdate><volume>43</volume><issue>6</issue><spage>3104</spage><epage>3116</epage><pages>3104-3116</pages><issn>0094-2405</issn><eissn>2473-4209</eissn><coden>MPHYA6</coden><abstract>Purpose:
To develop and evaluate a fast and simple tool called dpetstep (Dynamic PET Simulator of Tracers via Emission Projection), for dynamic PET simulations as an alternative to Monte Carlo (MC), useful for educational purposes and evaluation of the effects of the clinical environment, postprocessing choices, etc., on dynamic and parametric images.
Methods:
The tool was developed in matlab using both new and previously reported modules of petstep (PET Simulator of Tracers via Emission Projection). Time activity curves are generated for each voxel of the input parametric image, whereby effects of imaging system blurring, counting noise, scatters, randoms, and attenuation are simulated for each frame. Each frame is then reconstructed into images according to the user specified method, settings, and corrections. Reconstructed images were compared to MC data, and simple Gaussian noised time activity curves (GAUSS).
Results:
dpetstep was 8000 times faster than MC. Dynamic images from dpetstep had a root mean square error that was within 4% on average of that of MC images, whereas the GAUSS images were within 11%. The average bias in dpetstep and MC images was the same, while GAUSS differed by 3% points. Noise profiles in dpetstep images conformed well to MC images, confirmed visually by scatter plot histograms, and statistically by tumor region of interest histogram comparisons that showed no significant differences (p < 0.01). Compared to GAUSS, dpetstep images and noise properties agreed better with MC.
Conclusions:
The authors have developed a fast and easy one-stop solution for simulations of dynamic PET and parametric images, and demonstrated that it generates both images and subsequent parametric images with very similar noise properties to those of MC images, in a fraction of the time. They believe dpetstep to be very useful for generating fast, simple, and realistic results, however since it uses simple scatter and random models it may not be suitable for studies investigating these phenomena. dpetstep can be downloaded free of cost from https://github.com/CRossSchmidtlein/dPETSTEP.</abstract><cop>United States</cop><pub>American Association of Physicists in Medicine</pub><pmid>27277057</pmid><doi>10.1118/1.4950883</doi><tpages>13</tpages><oa>free_for_read</oa></addata></record> |
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| source | Wiley Journals; Alma/SFX Local Collection |
| subjects | 60 APPLIED LIFE SCIENCES Biological material, e.g. blood, urine Haemocytometers Biomedical modeling BIOMEDICAL RADIOGRAPHY Cameras Cancer compartment modeling Computed tomography DIAGNOSTIC IMAGING (IONIZING AND NON-IONIZING) Digital computing or data processing equipment or methods, specially adapted for specific applications dynamic PET Gaussian noise Image data processing or generation, in general Image enhancement or restoration, e.g. from bit‐mapped to bit‐mapped creating a similar image image reconstruction image restoration MATHEMATICAL SOLUTIONS Measuring half‐life of a radioactive substance Medical image noise medical image processing Medical image reconstruction Monte Carlo MONTE CARLO METHOD Monte Carlo methods NOISE parametric imaging PETSTEP POSITRON COMPUTED TOMOGRAPHY positron emission tomography Positron emission tomography (PET) radiation physics RADIATION PROTECTION AND DOSIMETRY radiofysik Reconstruction Scintigraphy SIMULATION SIMULATORS tumours |
| title | Dynamic PET simulator via tomographic emission projection for kinetic modeling and parametric image studies |
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