Validation of an automated morphological MRI-based 123I-FP-CIT SPECT evaluation method. (August 2016)

• Record Type: Journal Article
• Authors: Perlaki, Gabor; Szekeres, Sarolta; Orsi, Gergely; Papp, Laszlo; Suha, Balazs; Nagy, Szilvia Anett; Doczi, Tamas; Janszky, Jozsef; Zambo, Katalin; Kovacs, Norbert
• Is Part Of: Parkinsonism & related disorders. Volume 29(2016)
• Page Start: 24
• Page End: 29
• Publication Date: 2016-08
• Subjects: 123I-FP-CIT SPECT -- Parkinson's disease -- MRI -- DATSCAN -- Dopamine transporter quantification; Parkinson's disease -- Periodicals; Movement disorders -- Periodicals; Movement Disorders -- Periodicals; Nerve Degeneration -- Periodicals; Nervous System Diseases -- Periodicals; Parkinson Disease -- Periodicals; Tremor -- Periodicals; Parkinson, Maladie de -- Périodiques; Parkinson's disease; 616.833
• DOI: 10.1016/j.parkreldis.2016.06.001
• Languages: English
• ISSNs: 1353-8020

Abstract

Abstract: Introduction: Dopamine transporter imaging with 123 I-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: 123 I-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 123 I-FP-CIT SPECT images is useful for the differentiation of PS from ET. Highlights: Objective DAT quantification is useful for the differentiation of PS from ET. At moment the visual evaluation of SPECT images is the gold-standard. The accuracy of visual evaluation is highly dependent on the specialists' experience. Our MRI-based DAT quantification has good diagnostic accuracy. MRI-based evaluation methods may be automatized to improve accuracy.