Creation of an anthropomorphic CT head phantom for verification of image segmentation
Many methods are available to segment structural MR images of the brain into different tissue types. These have generally been developed for research purposes but there is some clinical use in the diagnosis of neurodegenerative diseases such as dementia. The potential exists for CT segmentation to be used in place of MRI segmentation, but this will require a method to verify the accuracy of CT processing, particularly if algorithms developed for MR are used, as MR has notably greater tissue contrast.
To investigate these issues we have created a 3D printed brain with realistic HU values based on tissue maps segmented directly from an individual T1 MRI scan of a normal subject. Several T1 MRI scans of normal subjects from the ADNI database were segmented using SPM12 and used to create STL files of different tissues for 3D printing. The attenuation properties of several material blends were investigated, and 3 suitable formulations were used to print an object expected to have realistic geometry and attenuation properties. A skull was simulated by coating the object with plaster of Paris impregnated bandages. Using 2 CT scanners, the realism of the phantom was assessed by measurement of HU values, SPM12 segmentation and comparison with the source data used to create the phantom.
Realistic relative HU values were measured although a subtraction of 60 was required to obtain equivalence with the expected values (grey matter 32.9 – 35.8 phantom, 29.9 – 34.2 literature). Segmentation of images acquired at different kVps/mAs showed excellent agreement with the source data (Dice Similarity Coefficient 0.79 for grey matter). The performance of 2 scanners with 2 segmentation methods were compared, with the scanners found to have similar performance and with 1 segmentation method clearly superior to the other.
The ability to use 3D printing to create a realistic (in terms of geometry and attenuation properties) head phantom has been demonstrated and used in an initial assessment of CT segmentation accuracy using freely available software developed for MRI.