Development of matching digital and physical brain phantoms for testing quantitative amyloid PET neuroimaging (2016)

1799 Objectives PET amyloid imaging has potential applications in assessing response to promising therapies for Alzheimer’s disease. However, the anatomically complex distribution of amyloid in both normal and diseased brain is not accurately simulated by currently available phantoms. In addition, there are no reference standards for testing software tools that analyze uptake of amyloid tracer in PET images. This motivated our development of matching digital and physical brain phantoms designed to test quantitative approaches to PET amyloid imaging. This work was performed to support the development of the Amyloid Imaging Profile by the RSNA Quantitative Imaging Biomarkers Alliance (QIBA) for promoting quantitative amyloid PET neuroimaging. Methods This was a two-part project. We first developed a PET Brain Digital Reference Object (DRO) with CSF, gray, and white matter anatomies. From a database of high resolution T1 MRI brain images, an experienced neuroradiologist identified a typical brain for elderly subjects with dementia. Gray matter, white matter, and CSF regions were segmented from the MRI images using the manual skull-stripping and the NeuroQuant brain segmentation software package. Amyloid tracer distributions for the DRO image were set by assigning SUV values of [white matter : grey matter : CSF] of [1.67 : 1 : 0]. We added plausible levels of blurring and noise to mimic PET image characteristics and saved the final images in DICOM format. These images were then evaluated by four experts in PET amyloid imaging using different software viewing and analysis packages. In the second part, the same image templates and segmentation used to define the DRO were used to define the structure of a physical brain phantom. The templates were edited for this purpose, including reclassification of pixels, correction of topology, upsampling, and creation of smooth boundaries necessary for final segmentation and translation to machining vectors. The resulting vector maps were used to guide a CO2 laser in cutting thin-layer sections of porous plastic of the brainregion shapes. The sheets were 1.57mm thick, and manufactured from hydrophilic polyethylene with a 50-90um pore size. Solid plastic sheets were used for regions with no activity uptake (CSF). This configuration lead to a phantom with features on the scale of 1-2 mm and uptake ratios for [white matter : grey matter : CSF] of [2.3 : 1 : 0]. The phantom with filled with aqueous F-18 (under pressure to enable uniform filling) and imaged on a PET and a PET/CT scanner. Results The PET Amyloid DRO was evaluated by four experts using different software packages. All agreed that the DRO provides a realistic appearance of a PET brain image of amyloid binding radiotracer, as well as ground truth information for evaluation of image analysis software. Suggestions for improvement were noted, and there were also differences noted in the behavior with respect to the software packages. The physical phantom provided realistic PET images and there was a clear correspondence with the DRO images. Challenges with uniform filling were also noted. Conclusions Our matching digital and physical PET amyloid phantoms have reasonable anthropomorphic and tracer uptake fidelity. The overall process of segmentation through brain structure to vector drawing to machining to assembly to filling to imaging was successfully achieved. The use of these matched phantoms should enhance the accuracy of quantitative amyloid PET neuroimaging.