Validation of an automated morphological MRI-based 123I-FP-CIT SPECT evaluation method

Gabor Perlaki, Sarolta Szekeres, Gergely Orsi, Laszlo Papp, Balazs Suha, Szilvia Anett Nagy, Tamas Doczi, Jozsef Janszky, Katalin Zambo, Norbert Kovacs

Research output: Article

3 Citations (Scopus)

Abstract

Introduction: Dopamine transporter imaging with 123I-FP-CIT single photon emission computed tomography (SPECT) is helpful for the differential diagnosis between Parkinsonian syndrome (PS) and essential tremor (ET). Although visual assessment and time-consuming manual evaluation techniques are readily available, a fully objective and automated dopamine transporter quantification technique is always preferable, at least in research and follow-up investigations. Our aim was to develop a novel automated magnetic resonance imaging (MRI)-based evaluation technique of dopamine transporter SPECT images and to compare its diagnostic accuracy with those of the gold-standard visual grading and manual dopamine transporter binding quantification methods. Methods: 123I-FP-CIT SPECT and MRI sessions were conducted in 33 patients with PS (15 men; mean age: 60.3 ± 9.7 years) and 15 patients with ET (8 men; mean age: 54.7 ± 16.3 years). Striatal dopamine transporter binding was visually classified by 2 independent experts as normal or abnormal grade I, II and III. Caudal and putaminal specific uptake ratios were calculated by both automated MRI-based and manual evaluation techniques. Results: We found almost perfect agreement (κ = 0.829) between the visual scores by the 2 observers. The automated method showed strong correlation with the visual and manual evaluation techniques and its diagnostic accuracy (sensitivity = 97.0%; specificity = 93.3%) was also comparable to these methods. The automatically determined uptake parameters showed negative correlation with the clinical severity of parkinsonism. Based on ordinal regression modelling, the automated MRI-based method could reliably determine the visual grading scores. Conclusion: The novel MRI-based evaluation of 123I-FP-CIT SPECT images is useful for the differentiation of PS from ET.

Original languageEnglish
JournalParkinsonism and Related Disorders
DOIs
Publication statusAccepted/In press - márc. 9 2016

Fingerprint

Dopamine Plasma Membrane Transport Proteins
Single-Photon Emission-Computed Tomography
Parkinsonian Disorders
Essential Tremor
Magnetic Resonance Imaging
Corpus Striatum
Gold
Differential Diagnosis
2-carbomethoxy-8-(3-fluoropropyl)-3-(4-iodophenyl)tropane
Research

ASJC Scopus subject areas

  • Geriatrics and Gerontology
  • Clinical Neurology
  • Neurology

Cite this

Validation of an automated morphological MRI-based 123I-FP-CIT SPECT evaluation method. / Perlaki, Gabor; Szekeres, Sarolta; Orsi, Gergely; Papp, Laszlo; Suha, Balazs; Nagy, Szilvia Anett; Doczi, Tamas; Janszky, Jozsef; Zambo, Katalin; Kovacs, Norbert.

In: Parkinsonism and Related Disorders, 09.03.2016.

Research output: Article

Perlaki, Gabor ; Szekeres, Sarolta ; Orsi, Gergely ; Papp, Laszlo ; Suha, Balazs ; Nagy, Szilvia Anett ; Doczi, Tamas ; Janszky, Jozsef ; Zambo, Katalin ; Kovacs, Norbert. / Validation of an automated morphological MRI-based 123I-FP-CIT SPECT evaluation method. In: Parkinsonism and Related Disorders. 2016.
@article{10f6c2cae2d540019841468d08765ee2,
title = "Validation of an automated morphological MRI-based 123I-FP-CIT SPECT evaluation method",
abstract = "Introduction: Dopamine transporter imaging with 123I-FP-CIT single photon emission computed tomography (SPECT) is helpful for the differential diagnosis between Parkinsonian syndrome (PS) and essential tremor (ET). Although visual assessment and time-consuming manual evaluation techniques are readily available, a fully objective and automated dopamine transporter quantification technique is always preferable, at least in research and follow-up investigations. Our aim was to develop a novel automated magnetic resonance imaging (MRI)-based evaluation technique of dopamine transporter SPECT images and to compare its diagnostic accuracy with those of the gold-standard visual grading and manual dopamine transporter binding quantification methods. Methods: 123I-FP-CIT SPECT and MRI sessions were conducted in 33 patients with PS (15 men; mean age: 60.3 ± 9.7 years) and 15 patients with ET (8 men; mean age: 54.7 ± 16.3 years). Striatal dopamine transporter binding was visually classified by 2 independent experts as normal or abnormal grade I, II and III. Caudal and putaminal specific uptake ratios were calculated by both automated MRI-based and manual evaluation techniques. Results: We found almost perfect agreement (κ = 0.829) between the visual scores by the 2 observers. The automated method showed strong correlation with the visual and manual evaluation techniques and its diagnostic accuracy (sensitivity = 97.0{\%}; specificity = 93.3{\%}) was also comparable to these methods. The automatically determined uptake parameters showed negative correlation with the clinical severity of parkinsonism. Based on ordinal regression modelling, the automated MRI-based method could reliably determine the visual grading scores. Conclusion: The novel MRI-based evaluation of 123I-FP-CIT SPECT images is useful for the differentiation of PS from ET.",
keywords = "I-FP-CIT SPECT, DATSCAN, Dopamine transporter quantification, MRI, Parkinson's disease",
author = "Gabor Perlaki and Sarolta Szekeres and Gergely Orsi and Laszlo Papp and Balazs Suha and Nagy, {Szilvia Anett} and Tamas Doczi and Jozsef Janszky and Katalin Zambo and Norbert Kovacs",
year = "2016",
month = "3",
day = "9",
doi = "10.1016/j.parkreldis.2016.06.001",
language = "English",
journal = "Parkinsonism and Related Disorders",
issn = "1353-8020",
publisher = "Elsevier BV",

}

TY - JOUR

T1 - Validation of an automated morphological MRI-based 123I-FP-CIT SPECT evaluation method

AU - Perlaki, Gabor

AU - Szekeres, Sarolta

AU - Orsi, Gergely

AU - Papp, Laszlo

AU - Suha, Balazs

AU - Nagy, Szilvia Anett

AU - Doczi, Tamas

AU - Janszky, Jozsef

AU - Zambo, Katalin

AU - Kovacs, Norbert

PY - 2016/3/9

Y1 - 2016/3/9

N2 - Introduction: Dopamine transporter imaging with 123I-FP-CIT single photon emission computed tomography (SPECT) is helpful for the differential diagnosis between Parkinsonian syndrome (PS) and essential tremor (ET). Although visual assessment and time-consuming manual evaluation techniques are readily available, a fully objective and automated dopamine transporter quantification technique is always preferable, at least in research and follow-up investigations. Our aim was to develop a novel automated magnetic resonance imaging (MRI)-based evaluation technique of dopamine transporter SPECT images and to compare its diagnostic accuracy with those of the gold-standard visual grading and manual dopamine transporter binding quantification methods. Methods: 123I-FP-CIT SPECT and MRI sessions were conducted in 33 patients with PS (15 men; mean age: 60.3 ± 9.7 years) and 15 patients with ET (8 men; mean age: 54.7 ± 16.3 years). Striatal dopamine transporter binding was visually classified by 2 independent experts as normal or abnormal grade I, II and III. Caudal and putaminal specific uptake ratios were calculated by both automated MRI-based and manual evaluation techniques. Results: We found almost perfect agreement (κ = 0.829) between the visual scores by the 2 observers. The automated method showed strong correlation with the visual and manual evaluation techniques and its diagnostic accuracy (sensitivity = 97.0%; specificity = 93.3%) was also comparable to these methods. The automatically determined uptake parameters showed negative correlation with the clinical severity of parkinsonism. Based on ordinal regression modelling, the automated MRI-based method could reliably determine the visual grading scores. Conclusion: The novel MRI-based evaluation of 123I-FP-CIT SPECT images is useful for the differentiation of PS from ET.

AB - Introduction: Dopamine transporter imaging with 123I-FP-CIT single photon emission computed tomography (SPECT) is helpful for the differential diagnosis between Parkinsonian syndrome (PS) and essential tremor (ET). Although visual assessment and time-consuming manual evaluation techniques are readily available, a fully objective and automated dopamine transporter quantification technique is always preferable, at least in research and follow-up investigations. Our aim was to develop a novel automated magnetic resonance imaging (MRI)-based evaluation technique of dopamine transporter SPECT images and to compare its diagnostic accuracy with those of the gold-standard visual grading and manual dopamine transporter binding quantification methods. Methods: 123I-FP-CIT SPECT and MRI sessions were conducted in 33 patients with PS (15 men; mean age: 60.3 ± 9.7 years) and 15 patients with ET (8 men; mean age: 54.7 ± 16.3 years). Striatal dopamine transporter binding was visually classified by 2 independent experts as normal or abnormal grade I, II and III. Caudal and putaminal specific uptake ratios were calculated by both automated MRI-based and manual evaluation techniques. Results: We found almost perfect agreement (κ = 0.829) between the visual scores by the 2 observers. The automated method showed strong correlation with the visual and manual evaluation techniques and its diagnostic accuracy (sensitivity = 97.0%; specificity = 93.3%) was also comparable to these methods. The automatically determined uptake parameters showed negative correlation with the clinical severity of parkinsonism. Based on ordinal regression modelling, the automated MRI-based method could reliably determine the visual grading scores. Conclusion: The novel MRI-based evaluation of 123I-FP-CIT SPECT images is useful for the differentiation of PS from ET.

KW - I-FP-CIT SPECT

KW - DATSCAN

KW - Dopamine transporter quantification

KW - MRI

KW - Parkinson's disease

UR - http://www.scopus.com/inward/record.url?scp=84973468791&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84973468791&partnerID=8YFLogxK

U2 - 10.1016/j.parkreldis.2016.06.001

DO - 10.1016/j.parkreldis.2016.06.001

M3 - Article

C2 - 27290659

AN - SCOPUS:84973468791

JO - Parkinsonism and Related Disorders

JF - Parkinsonism and Related Disorders

SN - 1353-8020

ER -