[P4–260]: INVESTIGATION OF A TASK FOR EARLY DETECTION OF COGNITIVE DECLINE IN ELDERLY PEOPLE USING FUNCTIONAL NEAR‐INFRARED SPECTROSCOPY (FNIRS) FOR DIAGNOSIS OF ALZHEIMER'S DISEASE

require manual intervention. As such, these methods are susceptible to intra/inter-segmentor variability and require trained resource for quality control (QC). Herewe present a fully automated pipeline for estimation of whole-brain atrophy with subsequent automatic QC testing, minimising sources of potential variability and need for visual inspection. Methods: We employed a subset (n1⁄4372) of the ADNI1 cohort with 1.5T T1 images and KMN-BSI values available (Fox Lab, ADNI) at Baseline, 12 month and a 24 month follow-up as validation data set. Baseline images were brain extracted with PinCram (Heckemann;PLOS,2015) and N4 Bias field corrected (Tustison;IEEE,2010). Longitudinal images were registered to the baseline image, bias corrected and intensity normalised to the baseline intensity profile. Segmentation was performed on baseline images using a whole-brain multi-atlas segmentation approach (WB LEAP; Wolz, NeuroImage, 2010; Ledig,Proc ISBI,2012). From this, we obtain subject probabilistic maps of cerebral tissue types used to initialise a probabilistic temporospatial segmentation using expectation maximisation based optimisation (Ledig,IEEE,2014). To ensure data quality is maintained, a visual inspection of the input baseline tissue segmentations is required only. QC of image registrations and longitudinal segmentations is performed automatically through tolerance testing on the spatial cross-correlation between registered images, annualised atrophy and normalised volumetric change. Resultant atrophy estimations were compared to KMNBSI values for validation. Results: The proposed automatic QC reported a 90% pass rate for longitudinal segmentations; upon inspection, all automatic QC pass grades were found to be correct. Segmentations failing automatic QC were visually inspected, 8 subjects failed due to poor registrations. Good agreement was observed between atrophy estimated with KNM-BSI and the proposed method; significant correlation was observed in atrophy estimations at month 12 (r1⁄40.62,p<<0.01) and month 24 (r1⁄40.73,p<<0.01). Reported effect and sample sizes were not significantly different between methods for any clinical group or follow-up. Conclusions: We have proposed a fully automatic method for longitudinal assessment of whole-brain atrophy with semi-automatic QC, reducing potential segmentor/rater variance and reporting atrophy consistent with the BSI method.