Application of texture analysis to DAT SPECT imaging: Relationship to clinical assessments.
Identifieur interne : 000292 ( PubMed/Corpus ); précédent : 000291; suivant : 000293Application of texture analysis to DAT SPECT imaging: Relationship to clinical assessments.
Auteurs : Arman Rahmim ; Yousef Salimpour ; Saurabh Jain ; Stephan A L. Blinder ; Ivan S. Klyuzhin ; Gwenn S. Smith ; Zoltan Mari ; Vesna SossiSource :
- NeuroImage. Clinical [ 2213-1582 ] ; 2016.
Abstract
Dopamine transporter (DAT) SPECT imaging is increasingly utilized for diagnostic purposes in suspected Parkinsonian syndromes. We performed a cross-sectional study to investigate whether assessment of texture in DAT SPECT radiotracer uptake enables enhanced correlations with severity of motor and cognitive symptoms in Parkinson's disease (PD), with the long-term goal of enabling clinical utility of DAT SPECT imaging, beyond standard diagnostic tasks, to tracking of progression in PD. Quantitative analysis in routine DAT SPECT imaging, if performed at all, has been restricted to assessment of mean regional uptake. We applied a framework wherein textural features were extracted from the images. Notably, the framework did not require registration to a common template, and worked in the subject-native space. Image analysis included registration of SPECT images onto corresponding MRI images, automatic region-of-interest (ROI) extraction on the MRI images, followed by computation of Haralick texture features. We analyzed 141 subjects from the Parkinson's Progressive Marker Initiative (PPMI) database, including 85 PD and 56 healthy controls (HC) (baseline scans with accompanying 3 T MRI images). We performed univariate and multivariate regression analyses between the quantitative metrics and different clinical measures, namely (i) the UPDRS (part III - motor) score, disease duration as measured from (ii) time of diagnosis (DD-diag.) and (iii) time of appearance of symptoms (DD-sympt.), as well as (iv) the Montreal Cognitive Assessment (MoCA) score. For conventional mean uptake analysis in the putamen, we showed significant correlations with clinical measures only when both HC and PD were included (Pearson correlation r = - 0.74, p-value < 0.001). However, this was not significant when applied to PD subjects only (r = - 0.19, p-value = 0.084), and no such correlations were observed in the caudate. By contrast, for the PD subjects, significant correlations were observed in the caudate when including texture metrics, with (i) UPDRS (p-values < 0.01), (ii) DD-diag. (p-values < 0.001), (iii) DD-sympt (p-values < 0.05), and (iv) MoCA (p-values < 0.01), while no correlations were observed for conventional analysis (p-values = 0.94, 0.34, 0.88 and 0.96, respectively). Our results demonstrated the ability to capture valuable information using advanced texture metrics from striatal DAT SPECT, enabling significant correlations of striatal DAT binding with clinical, motor and cognitive outcomes, and suggesting that textural features hold potential as biomarkers of PD severity and progression.
DOI: 10.1016/j.nicl.2016.02.012
PubMed: 27995072
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Blinder</name><affiliation><nlm:affiliation>Pacific Parkinson's Research Centre, University of British Columbia, Vancouver, Canada.</nlm:affiliation></affiliation></author><author><name sortKey="Klyuzhin, Ivan S" sort="Klyuzhin, Ivan S" uniqKey="Klyuzhin I" first="Ivan S" last="Klyuzhin">Ivan S. Klyuzhin</name><affiliation><nlm:affiliation>Department of Physics & Astronomy, University of British Columbia, Vancouver, Canada.</nlm:affiliation></affiliation></author><author><name sortKey="Smith, Gwenn S" sort="Smith, Gwenn S" uniqKey="Smith G" first="Gwenn S" last="Smith">Gwenn S. Smith</name><affiliation><nlm:affiliation>Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, United States.</nlm:affiliation></affiliation></author><author><name sortKey="Mari, Zoltan" sort="Mari, Zoltan" uniqKey="Mari Z" first="Zoltan" last="Mari">Zoltan Mari</name><affiliation><nlm:affiliation>Department of Neurology and Neurosurgery, Johns Hopkins University, Baltimore, MD, United States.</nlm:affiliation></affiliation></author><author><name sortKey="Sossi, Vesna" sort="Sossi, Vesna" uniqKey="Sossi V" first="Vesna" last="Sossi">Vesna Sossi</name><affiliation><nlm:affiliation>Department of Physics & Astronomy, University of British Columbia, Vancouver, Canada.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2016">2016</date><idno type="RBID">pubmed:27995072</idno><idno type="pmid">27995072</idno><idno type="doi">10.1016/j.nicl.2016.02.012</idno><idno type="wicri:Area/PubMed/Corpus">000292</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000292</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Application of texture analysis to DAT SPECT imaging: Relationship to clinical assessments.</title><author><name sortKey="Rahmim, Arman" sort="Rahmim, Arman" uniqKey="Rahmim A" first="Arman" last="Rahmim">Arman Rahmim</name><affiliation><nlm:affiliation>Department of Radiology, Johns Hopkins University, Baltimore, MD, United States; Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, United States.</nlm:affiliation></affiliation></author><author><name sortKey="Salimpour, Yousef" sort="Salimpour, Yousef" uniqKey="Salimpour Y" first="Yousef" last="Salimpour">Yousef Salimpour</name><affiliation><nlm:affiliation>Department of Neurology and Neurosurgery, Johns Hopkins University, Baltimore, MD, United States.</nlm:affiliation></affiliation></author><author><name sortKey="Jain, Saurabh" sort="Jain, Saurabh" uniqKey="Jain S" first="Saurabh" last="Jain">Saurabh Jain</name><affiliation><nlm:affiliation>Center for Imaging Science, Johns Hopkins University, Baltimore, MD, United States.</nlm:affiliation></affiliation></author><author><name sortKey="Blinder, Stephan A L" sort="Blinder, Stephan A L" uniqKey="Blinder S" first="Stephan A L" last="Blinder">Stephan A L. Blinder</name><affiliation><nlm:affiliation>Pacific Parkinson's Research Centre, University of British Columbia, Vancouver, Canada.</nlm:affiliation></affiliation></author><author><name sortKey="Klyuzhin, Ivan S" sort="Klyuzhin, Ivan S" uniqKey="Klyuzhin I" first="Ivan S" last="Klyuzhin">Ivan S. Klyuzhin</name><affiliation><nlm:affiliation>Department of Physics & Astronomy, University of British Columbia, Vancouver, Canada.</nlm:affiliation></affiliation></author><author><name sortKey="Smith, Gwenn S" sort="Smith, Gwenn S" uniqKey="Smith G" first="Gwenn S" last="Smith">Gwenn S. Smith</name><affiliation><nlm:affiliation>Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, United States.</nlm:affiliation></affiliation></author><author><name sortKey="Mari, Zoltan" sort="Mari, Zoltan" uniqKey="Mari Z" first="Zoltan" last="Mari">Zoltan Mari</name><affiliation><nlm:affiliation>Department of Neurology and Neurosurgery, Johns Hopkins University, Baltimore, MD, United States.</nlm:affiliation></affiliation></author><author><name sortKey="Sossi, Vesna" sort="Sossi, Vesna" uniqKey="Sossi V" first="Vesna" last="Sossi">Vesna Sossi</name><affiliation><nlm:affiliation>Department of Physics & Astronomy, University of British Columbia, Vancouver, Canada.</nlm:affiliation></affiliation></author></analytic><series><title level="j">NeuroImage. Clinical</title><idno type="eISSN">2213-1582</idno><imprint><date when="2016" type="published">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Dopamine transporter (DAT) SPECT imaging is increasingly utilized for diagnostic purposes in suspected Parkinsonian syndromes. We performed a cross-sectional study to investigate whether assessment of texture in DAT SPECT radiotracer uptake enables enhanced correlations with severity of motor and cognitive symptoms in Parkinson's disease (PD), with the long-term goal of enabling clinical utility of DAT SPECT imaging, beyond standard diagnostic tasks, to tracking of progression in PD. Quantitative analysis in routine DAT SPECT imaging, if performed at all, has been restricted to assessment of mean regional uptake. We applied a framework wherein textural features were extracted from the images. Notably, the framework did not require registration to a common template, and worked in the subject-native space. Image analysis included registration of SPECT images onto corresponding MRI images, automatic region-of-interest (ROI) extraction on the MRI images, followed by computation of Haralick texture features. We analyzed 141 subjects from the Parkinson's Progressive Marker Initiative (PPMI) database, including 85 PD and 56 healthy controls (HC) (baseline scans with accompanying 3 T MRI images). We performed univariate and multivariate regression analyses between the quantitative metrics and different clinical measures, namely (i) the UPDRS (part III - motor) score, disease duration as measured from (ii) time of diagnosis (DD-diag.) and (iii) time of appearance of symptoms (DD-sympt.), as well as (iv) the Montreal Cognitive Assessment (MoCA) score. For conventional mean uptake analysis in the putamen, we showed significant correlations with clinical measures only when both HC and PD were included (Pearson correlation r = - 0.74, p-value < 0.001). However, this was not significant when applied to PD subjects only (r = - 0.19, p-value = 0.084), and no such correlations were observed in the caudate. By contrast, for the PD subjects, significant correlations were observed in the caudate when including texture metrics, with (i) UPDRS (p-values < 0.01), (ii) DD-diag. (p-values < 0.001), (iii) DD-sympt (p-values < 0.05), and (iv) MoCA (p-values < 0.01), while no correlations were observed for conventional analysis (p-values = 0.94, 0.34, 0.88 and 0.96, respectively). Our results demonstrated the ability to capture valuable information using advanced texture metrics from striatal DAT SPECT, enabling significant correlations of striatal DAT binding with clinical, motor and cognitive outcomes, and suggesting that textural features hold potential as biomarkers of PD severity and progression.</div></front></TEI><pubmed><MedlineCitation Status="In-Process" Owner="NLM"><PMID Version="1">27995072</PMID><DateCreated><Year>2016</Year><Month>12</Month><Day>20</Day></DateCreated><DateRevised><Year>2017</Year><Month>02</Month><Day>20</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Electronic">2213-1582</ISSN><JournalIssue CitedMedium="Internet"><Volume>12</Volume><PubDate><Year>2016</Year></PubDate></JournalIssue><Title>NeuroImage. Clinical</Title><ISOAbbreviation>Neuroimage Clin</ISOAbbreviation></Journal><ArticleTitle>Application of texture analysis to DAT SPECT imaging: Relationship to clinical assessments.</ArticleTitle><Pagination><MedlinePgn>e1-e9</MedlinePgn></Pagination><Abstract><AbstractText>Dopamine transporter (DAT) SPECT imaging is increasingly utilized for diagnostic purposes in suspected Parkinsonian syndromes. We performed a cross-sectional study to investigate whether assessment of texture in DAT SPECT radiotracer uptake enables enhanced correlations with severity of motor and cognitive symptoms in Parkinson's disease (PD), with the long-term goal of enabling clinical utility of DAT SPECT imaging, beyond standard diagnostic tasks, to tracking of progression in PD. Quantitative analysis in routine DAT SPECT imaging, if performed at all, has been restricted to assessment of mean regional uptake. We applied a framework wherein textural features were extracted from the images. Notably, the framework did not require registration to a common template, and worked in the subject-native space. Image analysis included registration of SPECT images onto corresponding MRI images, automatic region-of-interest (ROI) extraction on the MRI images, followed by computation of Haralick texture features. We analyzed 141 subjects from the Parkinson's Progressive Marker Initiative (PPMI) database, including 85 PD and 56 healthy controls (HC) (baseline scans with accompanying 3 T MRI images). We performed univariate and multivariate regression analyses between the quantitative metrics and different clinical measures, namely (i) the UPDRS (part III - motor) score, disease duration as measured from (ii) time of diagnosis (DD-diag.) and (iii) time of appearance of symptoms (DD-sympt.), as well as (iv) the Montreal Cognitive Assessment (MoCA) score. For conventional mean uptake analysis in the putamen, we showed significant correlations with clinical measures only when both HC and PD were included (Pearson correlation r = - 0.74, p-value < 0.001). However, this was not significant when applied to PD subjects only (r = - 0.19, p-value = 0.084), and no such correlations were observed in the caudate. By contrast, for the PD subjects, significant correlations were observed in the caudate when including texture metrics, with (i) UPDRS (p-values < 0.01), (ii) DD-diag. (p-values < 0.001), (iii) DD-sympt (p-values < 0.05), and (iv) MoCA (p-values < 0.01), while no correlations were observed for conventional analysis (p-values = 0.94, 0.34, 0.88 and 0.96, respectively). Our results demonstrated the ability to capture valuable information using advanced texture metrics from striatal DAT SPECT, enabling significant correlations of striatal DAT binding with clinical, motor and cognitive outcomes, and suggesting that textural features hold potential as biomarkers of PD severity and progression.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Rahmim</LastName><ForeName>Arman</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Department of Radiology, Johns Hopkins University, Baltimore, MD, United States; Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, United States.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Salimpour</LastName><ForeName>Yousef</ForeName><Initials>Y</Initials><AffiliationInfo><Affiliation>Department of Neurology and Neurosurgery, Johns Hopkins University, Baltimore, MD, United States.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Jain</LastName><ForeName>Saurabh</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Center for Imaging Science, Johns Hopkins University, Baltimore, MD, United States.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Blinder</LastName><ForeName>Stephan A L</ForeName><Initials>SA</Initials><AffiliationInfo><Affiliation>Pacific Parkinson's Research Centre, University of British Columbia, Vancouver, Canada.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Klyuzhin</LastName><ForeName>Ivan S</ForeName><Initials>IS</Initials><AffiliationInfo><Affiliation>Department of Physics & Astronomy, University of British Columbia, Vancouver, Canada.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Smith</LastName><ForeName>Gwenn S</ForeName><Initials>GS</Initials><AffiliationInfo><Affiliation>Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, United States.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Mari</LastName><ForeName>Zoltan</ForeName><Initials>Z</Initials><AffiliationInfo><Affiliation>Department of Neurology and Neurosurgery, Johns Hopkins University, Baltimore, MD, United States.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Sossi</LastName><ForeName>Vesna</ForeName><Initials>V</Initials><AffiliationInfo><Affiliation>Department of Physics & Astronomy, University of British Columbia, Vancouver, Canada.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2016</Year><Month>02</Month><Day>23</Day></ArticleDate></Article><MedlineJournalInfo><Country>Netherlands</Country><MedlineTA>Neuroimage 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