Increasing Efficiency of Intracranial Cavity Determination from High-Resolution 3 Tesla(T) MRI Scans of the Brain (P06.121)

OBJECTIVE: To find optimal resolution and orientation for manual correction of automated Intracranial cavity (ICC) segmentation from 3T-MRI scans of the brain. BACKGROUND: Accurate ICC segmentation is a critical step in image analysis, e.g. cerebral atrophy measurement in Multiple Sclerosis (MS). The ongoing switch from 1.5T to 3T scanners substantially increases the data-volume per scan and hence effort required for manual correction. Manual editing is imperative for robust morphometry, but effort is rapidly becoming prohibitive. DESIGN/METHODS: 3T brain MRI scans were obtained at 1mm isotropic resolution (30 subjects, 10 each from MS, normal aging, gait impairment). ICCs were segmented using automated freely available softwares, and best outputs were chosen for manual editing. Subsampled (2-10-fold) output versions were generated for testing, then restored to original resolution with custom algorithm combining iterative Gaussian smoothing and upsampling. Accuracy was assessed by comparing manual edits of subsampled versions with manual gold-standards created at original resolution. Optimal reduction orientation and ratios were determined by measuring accuracy loss through subsampling the gold-standard, assessed as Dice overlap coefficient, sensitivity, specificity and positive predictive value (PPV). RESULTS: Segmentation results could be safely reduced from 1mm to 3mm axial for manual editing without significant accuracy loss [(mean± SD) sensitivity= 0.9895±0.0060, specificity=0.9978±0.0005, PPV=0.9874±0.0029, dice=0.9885±0.0023]. Automated 2mm-10mm subsampling showed no significant accuracy loss for up to 5mm-subsampling, with best-accuracy at 3mm-subsampling. Accuracy declined for subsampling beyond the 5mm-level. Axial orientation proved most reliable. CONCLUSIONS: Even after threefold subsampling , the same level of robustness in ICC segmentation can be achieved as with 1mm isotropic brain images. This is pertinent for large centers and high volumes of scans, where reducing manual editing time is critical. Furthermore these studies set the stage for further investigations regarding the feasibility of performing high throughput routine processing of 3T brain images to obtain quantitative data in diseases such as MS. Supported by: In part by National Institute on Aging grant# AG022092. Disclosure: Dr. Arora has nothing to disclose. Dr. Moscufo has nothing to disclose. Dr. Neema has nothing to disclose. Dr. Weiner has received personal compensation for activities with Biogen Idec, Novartis, Serono, Inc., Teva Neuroscience, GlaxoSmithKline, Inc., Nasvax, Xenoport and Genzyme Corporation as a consultant, speaker and/or participant on an advisory board. Dr. Weiner has received research support from Merck Serono. Dr. Guttmann has nothing to disclose. Dr. Bakshi has received personal compensation for activities with Biogen Idec, Novartis, Questcor, and Teva Neuroscience. Dr. Bakshi has received personal compensation in an editorial capacity for Neurotherapeutics. Dr. Bakshi has received research support from Biogen Idec, EMD Serono, and Teva Neuroscience. Dr. Meier has nothing to disclose.