PT - JOURNAL ARTICLE AU - Cyrus Raji AU - Lindsay Quandt TI - Going Against the Norm: A Novel Alternative to a Normative Dataset for Clinical Brain SPECT DP - 2020 May 01 TA - Journal of Nuclear Medicine PG - 76--76 VI - 61 IP - supplement 1 4099 - http://jnm.snmjournals.org/content/61/supplement_1/76.short 4100 - http://jnm.snmjournals.org/content/61/supplement_1/76.full SO - J Nucl Med2020 May 01; 61 AB - 76Introduction: Quantitative analysis of clinical SPECT brain perfusion imaging is dependent on comparison to normal control datasets that are difficult to produce and are not readily accessible for clinical use. Consequently, the number of persons comprising commercially owned SPECT brain datasets are rather small, ranging from 35 to 90 participants per dataset. This study investigated an alternative approach to traditional normative databases by creating a population template that combines SPECT brain scans from a large number of clinical patients instead of small number of healthy individuals. The diagnoses in this population template included: traumatic brain injury, toxic exposure, neuropsychiatric disorders, and more. The result was a large heterogeneous dataset with no single defining perfusion pattern. We hypothesized that this dataset would prove equivalent to a smaller control dataset and therefore provide a viable substitute for use in identifying and quantifying brain perfusion abnormalities in SPECT scans. Methods: A total of 2,330 patient SPECT brain scans were acquired and processed according to protocols set forth by the American College of Radiology and the supervising imaging clinic, CereScan (IntegReview IRB Certificate CHDB112019). Of these, 2,068 met our inclusion criteria. The cohort contained 879 females and 1,189 males with ages ranging from 4 to 83 years (mean = 37.22 years). The most common diagnostic impressions assigned to these patients were traumatic brain injury (n = 1,768), anxiety disorder (n = 1,046), and mood disorder (n = 1,018). Each scan was reconstructed and attenuation corrected in Segami Oasis software. Registration to the Montreal Neurological Institute standard single subject template was completed in CereMetrix software using an affine transformation with 12 degrees of freedom and image intensities were normalized to the average intensity value of the whole brain. The registered scans were then averaged to form a single image. This “population template” image was imported into quantitative SPECT brain analysis software, MIMneuro, and mathematically compared against their normal control dataset of 90 individuals through region and cluster analysis tools. Cognitively impaired patient scans (n=10) were evaluated against both the population template and the control dataset in MIMneuro and region-based metrics computed in both softwares were analyzed to assess agreement. Results: MIMneuro classifies any regions or voxels with an average z-score value outside +/- 1.65 as abnormal. No regions of the population template fell outside +/- 1 z-score, and 84% were within +/- 0.5 z-score. Voxel analysis determined that each image contained 3-4 clusters that met minimum volume and z-score parameters, but their statistical significance rendered these findings immaterial (p>0.93). Additionally, the intraclass correlation coefficient (ICC) computed on region-based metrics derived from the scans of cognitively impaired individuals revealed good to excellent agreement between the two softwares. Average z-scores for frontal, temporal, parietal, and limbic areas achieved ICCs that ranged between 0.76 and 0.94. Conclusions: These results validate that the population template closely aligns with the smaller control dataset contained within MIMneuro. More work is needed to ensure the template maintains equivalency when segmented by age and/or sex and that established perfusion patterns for brain injury and disease appear when compared to it. However, despite inclusion of abnormal scans, the heterogeneity and volume of the dataset render the template as an alternative to control datasets for the purposes of quantifying perfusion abnormalities in brain SPECT.