Diagnosis of Regional Cerebral Blood Flow Abnormalities Using Spect: Agreement between Individualized Statistical Parametric Maps and Visual Inspection by Nuclear Medicine Physicians with Different Levels of Expertise in Nuclear Neurology

Visual analysis is widely used to interpret regional cerebral blood flow (rCBF) SPECT images in clinical practice despite its limitations. Automated methods are employed to investigate between-group rCBF differences in research studies but have rarely been explored in individual analyses. To compare...

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Bibliographic Details
Published in:Clinics (São Paulo, Brazil) Brazil), 2009, Vol.64 (12), p.1145-1153
Main Authors: Rocha, Euclides Timóteo da, Buchpiguel, Carlos Alberto, Nitrini, Ricardo, Tazima, Sergio, Peres, Stela Verzinhase, Filho, Geraldo Busatto
Format: Article
Language:English
Subjects:
Adult
Aged
Aged, 80 and over
Brain - blood supply
Brain - diagnostic imaging
Brain Diseases - diagnostic imaging
Brain Diseases - physiopathology
Brain Mapping - methods
Brain SPECT
Case-Control Studies
Cerebral blood flow
Cerebrovascular Circulation
Clinical Science
Dementia
ECD
Educational Status
Female
Humans
Male
MEDICINE, GENERAL & INTERNAL
Middle Aged
Nuclear Medicine - standards
Observer Variation
Pattern Recognition, Automated - methods
Reproducibility of Results
Statistical parametric mapping
Tomography, Emission-Computed, Single-Photon
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Summary:Visual analysis is widely used to interpret regional cerebral blood flow (rCBF) SPECT images in clinical practice despite its limitations. Automated methods are employed to investigate between-group rCBF differences in research studies but have rarely been explored in individual analyses. To compare visual inspection by nuclear physicians with the automated statistical parametric mapping program using a SPECT dataset of patients with neurological disorders and normal control images. Using statistical parametric mapping, 14 SPECT images from patients with various neurological disorders were compared individually with a databank of 32 normal images using a statistical threshold of p
ISSN:1807-5932
1980-5322
1980-5322
DOI:10.1590/S1807-59322009001200003