EVALUATION OF THE QUANTITATIVE ACCURACY OF A COMMERCIALLY-AVAILABLE POSITRON EMISSION MAMMOGRAPHY SCANNER

Objective: The PEM Flex Solo II (Naviscan, Inc., San Diego, CA) is currently the only commercially-available positron emission mammography (PEM) scanner. This scanner does not apply corrections for count rate effects, attenuation or scatter during image reconstruction, potentially affecting the quantitative accuracy of images. This work measures the overall quantitative accuracy of the PEM Flex system, and determines the contributions of error due to count rate effects, attenuation and scatter. Materials and Methods: Gelatin phantoms were designed to simulate breasts of different sizes (4 – 12 cm thick) with varying uniform background activity concentration (0.007 – 0.5 μCi/cc), cysts and lesions (2:1, 5:1, 10:1 lesion-to-background ratios). The overall error was calculated from ROI measurements in the phantoms with a clinically relevant background activity concentration (0.065 μCi/cc). The error due to count rate effects was determined by comparing the overall error at multiple background activity concentrations to the error at 0.007 μCi/cc. A point source and cold gelatin phantoms were used to assess the errors due to attenuation and scatter. The maximum pixel values in gelatin and in air were compared to determine the effect of attenuation. Scatter was evaluated by comparing the sum of all pixel values in gelatin and in air. Results: The overall error in the background was found to be negative in phantoms of all thicknesses, with the exception of the 4-cm thick phantoms (0%±7%), and it increased with thickness (-34%±6% for the 12-cm phantoms). All lesions exhibited large negative error (-22% for the 2:1 lesions in the 4-cm phantom) which increased with thickness and with lesion-to-background ratio (-85% for the 10:1 lesions in the 12-cm phantoms). The error due to count rate in phantoms with 0.065 μCi/cc background was negative (-23%±6% for 4-cm thickness) and decreased with thickness (-7%±7% for 12 cm). Attenuation was a substantial source of negative error and increased with thickness (-51%±10% to -77% ±4% in 4 to 12 cm phantoms, respectively). Scatter contributed a relatively constant amount of positive error (+23%±11%) for all thicknesses. Conclusion: Applying corrections for count rate, attenuation and scatter will be essential for the PEM Flex Solo II to be able to produce quantitatively accurate images.

Improved cerebellar tissue classification on magnetic resonance images of brain.

PURPOSE: To develop and implement a method for improved cerebellar tissue classification on the MRI of brain by automatically isolating the cerebellum prior to segmentation. MATERIALS AND METHODS: Dual fast spin echo (FSE) and fluid attenuation inversion recovery (FLAIR) images were acquired on 18 normal volunteers on a 3 T Philips scanner. The cerebellum was isolated from the rest of the brain using a symmetric inverse consistent nonlinear registration of individual brain with the parcellated template. The cerebellum was then separated by masking the anatomical image with individual FLAIR images. Tissues in both the cerebellum and rest of the brain were separately classified using hidden Markov random field (HMRF), a parametric method, and then combined to obtain tissue classification of the whole brain. The proposed method for tissue classification on real MR brain images was evaluated subjectively by two experts. The segmentation results on Brainweb images with varying noise and intensity nonuniformity levels were quantitatively compared with the ground truth by computing the Dice similarity indices. RESULTS: The proposed method significantly improved the cerebellar tissue classification on all normal volunteers included in this study without compromising the classification in remaining part of the brain. The average similarity indices for gray matter (GM) and white matter (WM) in the cerebellum are 89.81 (+/-2.34) and 93.04 (+/-2.41), demonstrating excellent performance of the proposed methodology. CONCLUSION: The proposed method significantly improved tissue classification in the cerebellum. The GM was overestimated when segmentation was performed on the whole brain as a single object.