Abstract
Dynamic positron emission tomography (PET) image reconstruction is challenging due to the low-count statistics of individual frames. This study proposes a novel reconstruction framework aiming to enhance the quantitative accuracy of individual dynamic frames via the introduction of priors based on multiscale superpixel clusters. The clusters are derived from pre-reconstruction composite images using superpixel clustering followed by fuzzy c-means (FCM) clustering. A multiscale aggregation is exploited during the superpixel clustering to generate multiscale superpixel clusters. Then, maximum a posteriori (MAP) PET reconstruction with different-scale clusters is separately applied to individual frame and fused to generate the final result. Using realistic simulated dynamic brain PET data, the quantitative performance of the proposed method is investigated and compared with the maximum-likelihood expectation-maximization (MLEM), Bowsher method, and kernelized expectation-maximization (the kernel method). The proposed method achieves substantial improvements in both visual and quantitative accuracy (in terms of the signal-to-noise ratio and contrast versus noise performances). The method is also tested with a 60 min 18F-FDG rat study performed with an Inveon small animal PET scanner. The proposed method is shown to outperform the other methods via improvements in visual and quantitative accuracy (in terms of noise versus the mean intensity of the region of interest).
Highlights
Positron emission tomography (PET), a functional imaging modality widely used in oncology, cardiology, and neurology, can measure radiotracer distribution in vivo [1]–[3]
The clusters are derived from pre-reconstruction composite images using superpixel clustering followed by fuzzy c-means (FCM) clustering
We propose a two-step clustering: superpixel clustering followed by FCM clustering
Summary
Positron emission tomography (PET), a functional imaging modality widely used in oncology, cardiology, and neurology, can measure radiotracer distribution in vivo [1]–[3]. Accurate tracer kinetic modeling requires very short time frames, which result in low statistic counts and high noise in each frame [6]–[8]. The tomography image reconstruction is inherently an ill-posed problem, which is further accentuated with lowcount projection data from short individual time frames. Conventional analytical reconstruction, such as filtered back projection (FBP) [9], [10], often results in noisy images. Statistical image reconstruction, such as the maximum-likelihood expectation-maximization (MLEM) algorithm [11], [12], can exploit the statistical property of the detected data and produce improved reconstructed images compared with FBP. Direct MLEM estimates of PET images often exhibit high variances at low counts with increasing iterations
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
