Prediction of total cerebral tissue volumes in normal appearing brain from sub-sampled segmentation volumes

The need for anatomical coverage and multi-spectral information must be balanced against examination and processing time to ensure high-quality, feasible imaging protocols for clinical research of cerebral development in normal-appearing brains. The focus of this study was to create and assess models to estimate total cerebral volumes of gray matter, white matter, and cerebrospinal fluid (CSF) from anatomically defined sub-samples of full clinical examinations. Pediatric patients (18F, 11M; aged 1.7 to 18.7, median 5.2 years) underwent a clinical imaging protocol consisting of 3 mm contiguous T1-, T2-, PD-, and FLAIR-weighted images after obtaining informed consent. Magnetic resonance imaging (MRI) sets were registered, RF-corrected, and then analyzed with a hybrid neural network segmentation and classification algorithm to identify normal brain parenchyma. The correlation between the image subsets and the total cerebral volumes of gray matter, white matter and CSF were examined through linear regression analyses. Five sub-sampled sets were defined and assessed in each patient to produce estimation models which were all significantly correlated (p < 0.001) with the total cerebral volumes of gray matter, white matter, and CSF. Volumes were estimated from as little as a single representative slice requiring minimal processing time, 27 min, but with an average estimation error of approximately 6%. Larger sub-samples of approximately three-quarters of the full cerebral volume required much more processing time, 2 h and 4 min, but produced estimates with an average error less than 2%. This study demonstrated that investigators can choose the amount of cerebrum sampled to optimize the acquisition and processing time against the degree of accuracy needed in the total cerebral volume estimates.