A hybrid deconvolution approach for estimation of in vivo non-displaceable binding for brain PET targets without a reference region
Estimation of a PET tracer's non-displaceable distribution volume (VND) is required for quantification of specific binding to its target of interest. VND is generally assumed to be comparable brain-wide and is determined either from a reference region devoid of the target, often not available f...
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| Published in | PloS one Vol. 12; no. 5; p. e0176636 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Public Library of Science
01.05.2017
Public Library of Science (PLoS) |
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| Online Access | Get full text |
| ISSN | 1932-6203 1932-6203 |
| DOI | 10.1371/journal.pone.0176636 |
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| Abstract | Estimation of a PET tracer's non-displaceable distribution volume (VND) is required for quantification of specific binding to its target of interest. VND is generally assumed to be comparable brain-wide and is determined either from a reference region devoid of the target, often not available for many tracers and targets, or by imaging each subject before and after blocking the target with another molecule that has high affinity for the target, which is cumbersome and involves additional radiation exposure. Here we propose, and validate for the tracers [11C]DASB and [11C]CUMI-101, a new data-driven hybrid deconvolution approach (HYDECA) that determines VND at the individual level without requiring either a reference region or a blocking study.
HYDECA requires the tracer metabolite-corrected concentration curve in blood plasma and uses a singular value decomposition to estimate the impulse response function across several brain regions from measured time activity curves. HYDECA decomposes each region's impulse response function into the sum of a parametric non-displaceable component, which is a function of VND, assumed common across regions, and a nonparametric specific component. These two components differentially contribute to each impulse response function. Different regions show different contributions of the two components, and HYDECA examines data across regions to find a suitable common VND. HYDECA implementation requires determination of two tuning parameters, and we propose two strategies for objectively selecting these parameters for a given tracer: using data from blocking studies, and realistic simulations of the tracer. Using available test-retest data, we compare HYDECA estimates of VND and binding potentials to those obtained based on VND estimated using a purported reference region.
For [11C]DASB and [11C]CUMI-101, we find that regardless of the strategy used to optimize the tuning parameters, HYDECA provides considerably less biased estimates of VND than those obtained, as is commonly done, using a non-ideal reference region. HYDECA test-retest reproducibility is comparable to that obtained using a VND determined from a non-ideal reference region, when considering the binding potentials BPP and BPND.
HYDECA can provide subject-specific estimates of VND without requiring a blocking study for tracers and targets for which a valid reference region does not exist. |
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| AbstractList | Estimation of a PET tracer's non-displaceable distribution volume (V.sub.ND) is required for quantification of specific binding to its target of interest. V.sub.ND is generally assumed to be comparable brain-wide and is determined either from a reference region devoid of the target, often not available for many tracers and targets, or by imaging each subject before and after blocking the target with another molecule that has high affinity for the target, which is cumbersome and involves additional radiation exposure. Here we propose, and validate for the tracers [.sup.11 C]DASB and [.sup.11 C]CUMI-101, a new data-driven hybrid deconvolution approach (HYDECA) that determines V.sub.ND at the individual level without requiring either a reference region or a blocking study. HYDECA requires the tracer metabolite-corrected concentration curve in blood plasma and uses a singular value decomposition to estimate the impulse response function across several brain regions from measured time activity curves. HYDECA decomposes each region's impulse response function into the sum of a parametric non-displaceable component, which is a function of V.sub.ND, assumed common across regions, and a nonparametric specific component. These two components differentially contribute to each impulse response function. Different regions show different contributions of the two components, and HYDECA examines data across regions to find a suitable common V.sub.ND . HYDECA implementation requires determination of two tuning parameters, and we propose two strategies for objectively selecting these parameters for a given tracer: using data from blocking studies, and realistic simulations of the tracer. Using available test-retest data, we compare HYDECA estimates of V.sub.ND and binding potentials to those obtained based on V.sub.ND estimated using a purported reference region. For [.sup.11 C]DASB and [.sup.11 C]CUMI-101, we find that regardless of the strategy used to optimize the tuning parameters, HYDECA provides considerably less biased estimates of V.sub.ND than those obtained, as is commonly done, using a non-ideal reference region. HYDECA test-retest reproducibility is comparable to that obtained using a V.sub.ND determined from a non-ideal reference region, when considering the binding potentials BP.sub.P and BP.sub.ND. HYDECA can provide subject-specific estimates of V.sub.ND without requiring a blocking study for tracers and targets for which a valid reference region does not exist. Background and aim Estimation of a PET tracer’s non-displaceable distribution volume (V ND ) is required for quantification of specific binding to its target of interest. V ND is generally assumed to be comparable brain-wide and is determined either from a reference region devoid of the target, often not available for many tracers and targets, or by imaging each subject before and after blocking the target with another molecule that has high affinity for the target, which is cumbersome and involves additional radiation exposure. Here we propose, and validate for the tracers [ 11 C]DASB and [ 11 C]CUMI-101, a new data-driven hybrid deconvolution approach (HYDECA) that determines V ND at the individual level without requiring either a reference region or a blocking study. Methods HYDECA requires the tracer metabolite-corrected concentration curve in blood plasma and uses a singular value decomposition to estimate the impulse response function across several brain regions from measured time activity curves. HYDECA decomposes each region’s impulse response function into the sum of a parametric non-displaceable component, which is a function of V ND , assumed common across regions, and a nonparametric specific component. These two components differentially contribute to each impulse response function. Different regions show different contributions of the two components, and HYDECA examines data across regions to find a suitable common V ND . HYDECA implementation requires determination of two tuning parameters, and we propose two strategies for objectively selecting these parameters for a given tracer: using data from blocking studies, and realistic simulations of the tracer. Using available test-retest data, we compare HYDECA estimates of V ND and binding potentials to those obtained based on V ND estimated using a purported reference region. Results For [ 11 C]DASB and [ 11 C]CUMI-101, we find that regardless of the strategy used to optimize the tuning parameters, HYDECA provides considerably less biased estimates of V ND than those obtained, as is commonly done, using a non-ideal reference region. HYDECA test-retest reproducibility is comparable to that obtained using a V ND determined from a non-ideal reference region, when considering the binding potentials BP P and BP ND . Conclusions HYDECA can provide subject-specific estimates of V ND without requiring a blocking study for tracers and targets for which a valid reference region does not exist. Background and aim Estimation of a PET tracer’s non-displaceable distribution volume (VND) is required for quantification of specific binding to its target of interest. VND is generally assumed to be comparable brain-wide and is determined either from a reference region devoid of the target, often not available for many tracers and targets, or by imaging each subject before and after blocking the target with another molecule that has high affinity for the target, which is cumbersome and involves additional radiation exposure. Here we propose, and validate for the tracers [11C]DASB and [11C]CUMI-101, a new data-driven hybrid deconvolution approach (HYDECA) that determines VND at the individual level without requiring either a reference region or a blocking study. Methods HYDECA requires the tracer metabolite-corrected concentration curve in blood plasma and uses a singular value decomposition to estimate the impulse response function across several brain regions from measured time activity curves. HYDECA decomposes each region’s impulse response function into the sum of a parametric non-displaceable component, which is a function of VND, assumed common across regions, and a nonparametric specific component. These two components differentially contribute to each impulse response function. Different regions show different contributions of the two components, and HYDECA examines data across regions to find a suitable common VND. HYDECA implementation requires determination of two tuning parameters, and we propose two strategies for objectively selecting these parameters for a given tracer: using data from blocking studies, and realistic simulations of the tracer. Using available test-retest data, we compare HYDECA estimates of VND and binding potentials to those obtained based on VND estimated using a purported reference region. Results For [11C]DASB and [11C]CUMI-101, we find that regardless of the strategy used to optimize the tuning parameters, HYDECA provides considerably less biased estimates of VND than those obtained, as is commonly done, using a non-ideal reference region. HYDECA test-retest reproducibility is comparable to that obtained using a VND determined from a non-ideal reference region, when considering the binding potentials BPP and BPND. Conclusions HYDECA can provide subject-specific estimates of VND without requiring a blocking study for tracers and targets for which a valid reference region does not exist. Background and aim Estimation of a PET tracer's non-displaceable distribution volume (V.sub.ND) is required for quantification of specific binding to its target of interest. V.sub.ND is generally assumed to be comparable brain-wide and is determined either from a reference region devoid of the target, often not available for many tracers and targets, or by imaging each subject before and after blocking the target with another molecule that has high affinity for the target, which is cumbersome and involves additional radiation exposure. Here we propose, and validate for the tracers [.sup.11 C]DASB and [.sup.11 C]CUMI-101, a new data-driven hybrid deconvolution approach (HYDECA) that determines V.sub.ND at the individual level without requiring either a reference region or a blocking study. Methods HYDECA requires the tracer metabolite-corrected concentration curve in blood plasma and uses a singular value decomposition to estimate the impulse response function across several brain regions from measured time activity curves. HYDECA decomposes each region's impulse response function into the sum of a parametric non-displaceable component, which is a function of V.sub.ND, assumed common across regions, and a nonparametric specific component. These two components differentially contribute to each impulse response function. Different regions show different contributions of the two components, and HYDECA examines data across regions to find a suitable common V.sub.ND . HYDECA implementation requires determination of two tuning parameters, and we propose two strategies for objectively selecting these parameters for a given tracer: using data from blocking studies, and realistic simulations of the tracer. Using available test-retest data, we compare HYDECA estimates of V.sub.ND and binding potentials to those obtained based on V.sub.ND estimated using a purported reference region. Results For [.sup.11 C]DASB and [.sup.11 C]CUMI-101, we find that regardless of the strategy used to optimize the tuning parameters, HYDECA provides considerably less biased estimates of V.sub.ND than those obtained, as is commonly done, using a non-ideal reference region. HYDECA test-retest reproducibility is comparable to that obtained using a V.sub.ND determined from a non-ideal reference region, when considering the binding potentials BP.sub.P and BP.sub.ND. Conclusions HYDECA can provide subject-specific estimates of V.sub.ND without requiring a blocking study for tracers and targets for which a valid reference region does not exist. Estimation of a PET tracer's non-displaceable distribution volume (VND) is required for quantification of specific binding to its target of interest. VND is generally assumed to be comparable brain-wide and is determined either from a reference region devoid of the target, often not available for many tracers and targets, or by imaging each subject before and after blocking the target with another molecule that has high affinity for the target, which is cumbersome and involves additional radiation exposure. Here we propose, and validate for the tracers [11C]DASB and [11C]CUMI-101, a new data-driven hybrid deconvolution approach (HYDECA) that determines VND at the individual level without requiring either a reference region or a blocking study.BACKGROUND AND AIMEstimation of a PET tracer's non-displaceable distribution volume (VND) is required for quantification of specific binding to its target of interest. VND is generally assumed to be comparable brain-wide and is determined either from a reference region devoid of the target, often not available for many tracers and targets, or by imaging each subject before and after blocking the target with another molecule that has high affinity for the target, which is cumbersome and involves additional radiation exposure. Here we propose, and validate for the tracers [11C]DASB and [11C]CUMI-101, a new data-driven hybrid deconvolution approach (HYDECA) that determines VND at the individual level without requiring either a reference region or a blocking study.HYDECA requires the tracer metabolite-corrected concentration curve in blood plasma and uses a singular value decomposition to estimate the impulse response function across several brain regions from measured time activity curves. HYDECA decomposes each region's impulse response function into the sum of a parametric non-displaceable component, which is a function of VND, assumed common across regions, and a nonparametric specific component. These two components differentially contribute to each impulse response function. Different regions show different contributions of the two components, and HYDECA examines data across regions to find a suitable common VND. HYDECA implementation requires determination of two tuning parameters, and we propose two strategies for objectively selecting these parameters for a given tracer: using data from blocking studies, and realistic simulations of the tracer. Using available test-retest data, we compare HYDECA estimates of VND and binding potentials to those obtained based on VND estimated using a purported reference region.METHODSHYDECA requires the tracer metabolite-corrected concentration curve in blood plasma and uses a singular value decomposition to estimate the impulse response function across several brain regions from measured time activity curves. HYDECA decomposes each region's impulse response function into the sum of a parametric non-displaceable component, which is a function of VND, assumed common across regions, and a nonparametric specific component. These two components differentially contribute to each impulse response function. Different regions show different contributions of the two components, and HYDECA examines data across regions to find a suitable common VND. HYDECA implementation requires determination of two tuning parameters, and we propose two strategies for objectively selecting these parameters for a given tracer: using data from blocking studies, and realistic simulations of the tracer. Using available test-retest data, we compare HYDECA estimates of VND and binding potentials to those obtained based on VND estimated using a purported reference region.For [11C]DASB and [11C]CUMI-101, we find that regardless of the strategy used to optimize the tuning parameters, HYDECA provides considerably less biased estimates of VND than those obtained, as is commonly done, using a non-ideal reference region. HYDECA test-retest reproducibility is comparable to that obtained using a VND determined from a non-ideal reference region, when considering the binding potentials BPP and BPND.RESULTSFor [11C]DASB and [11C]CUMI-101, we find that regardless of the strategy used to optimize the tuning parameters, HYDECA provides considerably less biased estimates of VND than those obtained, as is commonly done, using a non-ideal reference region. HYDECA test-retest reproducibility is comparable to that obtained using a VND determined from a non-ideal reference region, when considering the binding potentials BPP and BPND.HYDECA can provide subject-specific estimates of VND without requiring a blocking study for tracers and targets for which a valid reference region does not exist.CONCLUSIONSHYDECA can provide subject-specific estimates of VND without requiring a blocking study for tracers and targets for which a valid reference region does not exist. Estimation of a PET tracer's non-displaceable distribution volume (VND) is required for quantification of specific binding to its target of interest. VND is generally assumed to be comparable brain-wide and is determined either from a reference region devoid of the target, often not available for many tracers and targets, or by imaging each subject before and after blocking the target with another molecule that has high affinity for the target, which is cumbersome and involves additional radiation exposure. Here we propose, and validate for the tracers [11C]DASB and [11C]CUMI-101, a new data-driven hybrid deconvolution approach (HYDECA) that determines VND at the individual level without requiring either a reference region or a blocking study. HYDECA requires the tracer metabolite-corrected concentration curve in blood plasma and uses a singular value decomposition to estimate the impulse response function across several brain regions from measured time activity curves. HYDECA decomposes each region's impulse response function into the sum of a parametric non-displaceable component, which is a function of VND, assumed common across regions, and a nonparametric specific component. These two components differentially contribute to each impulse response function. Different regions show different contributions of the two components, and HYDECA examines data across regions to find a suitable common VND. HYDECA implementation requires determination of two tuning parameters, and we propose two strategies for objectively selecting these parameters for a given tracer: using data from blocking studies, and realistic simulations of the tracer. Using available test-retest data, we compare HYDECA estimates of VND and binding potentials to those obtained based on VND estimated using a purported reference region. For [11C]DASB and [11C]CUMI-101, we find that regardless of the strategy used to optimize the tuning parameters, HYDECA provides considerably less biased estimates of VND than those obtained, as is commonly done, using a non-ideal reference region. HYDECA test-retest reproducibility is comparable to that obtained using a VND determined from a non-ideal reference region, when considering the binding potentials BPP and BPND. HYDECA can provide subject-specific estimates of VND without requiring a blocking study for tracers and targets for which a valid reference region does not exist. |
| Audience | Academic |
| Author | Zanderigo, Francesca Mann, J. John Ogden, R. Todd |
| AuthorAffiliation | 2 Department of Psychiatry, Columbia University, New York, New York, United States of America 1 Molecular Imaging and Neuropathology Division, New York State Psychiatric Institute, New York, New York, United States of America University of Manchester, UNITED KINGDOM 3 Department of Radiology, Columbia University, New York, New York, United States of America 4 Department of Biostatistics, Columbia University, Mailman School of Public Health, New York, New York, United States of America |
| AuthorAffiliation_xml | – name: 1 Molecular Imaging and Neuropathology Division, New York State Psychiatric Institute, New York, New York, United States of America – name: University of Manchester, UNITED KINGDOM – name: 4 Department of Biostatistics, Columbia University, Mailman School of Public Health, New York, New York, United States of America – name: 2 Department of Psychiatry, Columbia University, New York, New York, United States of America – name: 3 Department of Radiology, Columbia University, New York, New York, United States of America |
| Author_xml | – sequence: 1 givenname: Francesca orcidid: 0000-0001-6510-0676 surname: Zanderigo fullname: Zanderigo, Francesca – sequence: 2 givenname: J. John surname: Mann fullname: Mann, J. John – sequence: 3 givenname: R. Todd surname: Ogden fullname: Ogden, R. Todd |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28459878$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1016_j_neuroimage_2019_05_055 crossref_primary_10_1088_1361_6560_abd4f7 crossref_primary_10_1038_s41380_022_01578_8 crossref_primary_10_1002_syn_22042 crossref_primary_10_1016_j_jad_2019_07_035 |
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| Copyright | COPYRIGHT 2017 Public Library of Science 2017 Zanderigo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2017 Zanderigo et al 2017 Zanderigo et al |
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| DOI | 10.1371/journal.pone.0176636 |
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| DocumentTitleAlternate | Estimating PET binding without a reference region |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 ObjectType-Undefined-3 Conceptualization: FZ JJM RTO.Data curation: FZ.Formal analysis: FZ RTO.Funding acquisition: JJM.Methodology: FZ RTO.Project administration: FZ JJM RTO.Resources: JJM.Software: FZ.Supervision: RTO.Validation: FZ JJM RTO.Visualization: FZ.Writing – original draft: FZ JJM RTO.Writing – review & editing: FZ JJM RTO. Competing Interests: Drs. Zanderigo and Ogden declare no conflict of interest. Dr. Mann receives royalties for commercial use of the Columbia-Suicide Severity Rating Scale from the Research Foundation for Mental Hygiene Inc. This does not alter our adherence to PLOS ONE policies on sharing data and materials. |
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| Snippet | Estimation of a PET tracer's non-displaceable distribution volume (VND) is required for quantification of specific binding to its target of interest. VND is... Background and aim Estimation of a PET tracer's non-displaceable distribution volume (V.sub.ND) is required for quantification of specific binding to its... Estimation of a PET tracer's non-displaceable distribution volume (V.sub.ND) is required for quantification of specific binding to its target of interest.... Background and aim Estimation of a PET tracer’s non-displaceable distribution volume (VND) is required for quantification of specific binding to its target of... BACKGROUND AND AIM:Estimation of a PET tracer's non-displaceable distribution volume (VND) is required for quantification of specific binding to its target of... Background and aim Estimation of a PET tracer’s non-displaceable distribution volume (V ND ) is required for quantification of specific binding to its target... |
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| SubjectTerms | Algorithms Animals Benzylamines - pharmacokinetics Binding Biology and Life Sciences Blood levels Blood plasma Brain Brain - diagnostic imaging Brain - metabolism Brain research Carbon Radioisotopes - pharmacokinetics Computer and Information Sciences Computer Simulation Datasets as Topic Deconvolution Displacement Estimates Humans Impulse response In vivo methods and tests Kinetics Medicine and Health Sciences Metabolites Methods Models, Neurological Neuroimaging Nuclear medicine Papio Parameter estimation Positron emission tomography Positron-Emission Tomography - methods Radiation Radiation effects Radiopharmaceuticals - pharmacokinetics Reproducibility Reproducibility of Results Singular value decomposition Statistics, Nonparametric Target recognition Tomography Tracers Tuning |
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| Title | A hybrid deconvolution approach for estimation of in vivo non-displaceable binding for brain PET targets without a reference region |
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