Positron Emission tomography (PET) is a major molecular imaging tool in oncology, with applications ranging from diagnosis and staging to patient management. Despite the broad use of PET in the clinical environment, there is no quantitative PET imaging method available for routine clinical practice. The currently used static scan can provide a semi-quantitative measurement, standardized uptake value (SUV), for a whole body scan. However, it completely ignores the dynamic nature of radiopharmaceutical kinetics. The popular semi-quantitative dual time point method can approximate the kinetic differences at two time points by comparing activities but usually requires an extended waiting time for the second scan. The multiple time point method can calculate the net influx rate but still requires long scan duration and makes a whole body scan infeasible. The challenge of a quantitative whole body dynamic PET scan lies in how to estimate the quantitative functional values, such as net flux rate, using data from a short acquisition period, and how to accelerate the computation to make it practical in a clinical setting. We address this challenge by developing and optimizing a novel data analysis method and implementing it using a high performance computing tool. We take advantage of the linearity of Patlak graphic analysis to model the tracer activity in each voxel as a linear combination of the blood input function and its integral, weighted by the Patlak parameters including net influx rate. In addition, we derive a simplified model of the blood input function, based on the same assumptions used to derive Patlak parameters from the kinetic compartment model. We then estimate the Patlak parameters and the parameters in the blood input function in a penalized maximum likelihood estimation framework using the list mode data and its associated inhomogeneous Poisson statistical model. We also theoretically analyze the performance of our Patlak estimator in terms of noise, resolution and signal-to-noise ratio (SNR), and use the results to guide us in optimizing the scan duration and any movement of imaging bed to achieve the best SNR. The advanced estimation algorithm, along with an accurate imaging system model, can robustly compute the net influx rate using the list mode data in a short acquisition without a measured blood input function, and make whole body dynamic scans practical. Our algorithms will be implemented on an Nvidia Tesla GPU (graphics processing unit) based workstation, a new computing tool that provides computational power previously available only on a mini super computer. We will further accelerate our algorithms using a combination of efficient representation of the list mode data and the system matrix. We will evaluate the performance of the proposed method and compare it with SUV, the dual time point method, and the traditional Patlak method, using simulated and clinical data. We will use a range of performance metrics, including region of interest (ROI) bias, ROI variance, lesion detectability, and computer and human observers. This project will eventually provide a quantitative dynamic whole body PET imaging protocol that can potentially improve the sensitivity and specificity of PET imaging in oncology.
Positron Emission Tomography (PET) has been widely used in cancer diagnosis, staging, treatment planning, management and evaluation. However, its potential is not yet fully realized, in part because we are not able to take full advantage of the dynamic information that can be collected by the PET scanner. In this project we will develop a new approach to the acquisition and analysis of PET data that will allow us for the first time to scan the whole body of the patient and produce quantitative estimates of PET tracer uptake from dynamically acquired data. These measures may be more sensitive indicators of the presence and metabolic activity of tumors, so that their use would lead to improved detection, staging and monitoring of primary and metastatic tumors.
|Wu, Dufan; Kim, Kyungsang; El Fakhri, Georges et al. (2017) Iterative Low-Dose CT Reconstruction With Priors Trained by Artificial Neural Network. IEEE Trans Med Imaging 36:2479-2486|
|Kim, Kyungsang; El Fakhri, Georges; Li, Quanzheng (2017) Low-dose CT reconstruction using spatially encoded nonlocal penalty. Med Phys 44:e376-e390|
|Yang, Jiarui; Hu, Chenhui; Guo, Ning et al. (2017) Partial volume correction for PET quantification and its impact on brain network in Alzheimer's disease. Sci Rep 7:13035|
|Li, Quanzheng; Li, Hao; Kim, Kyungsang et al. (2017) Joint estimation of activity image and attenuation sinogram using time-of-flight positron emission tomography data consistency condition filtering. J Med Imaging (Bellingham) 4:023502|
|Sepulcre, Jorge; Sabuncu, Mert R; Li, Quanzheng et al. (2017) Tau and amyloid ? proteins distinctively associate to functional network changes in the aging brain. Alzheimers Dement 13:1261-1269|
|Hu, Chenhui; Hua, Xue; Ying, Jun et al. (2016) Localizing Sources of Brain Disease Progression with Network Diffusion Model. IEEE J Sel Top Signal Process 10:1214-1225|
|Ying, Jun; Dutta, Joyita; Guo, Ning et al. (2016) Classification of Exacerbation Frequency in the COPDGene Cohort Using Deep Learning with Deep Belief Networks. IEEE J Biomed Health Inform :|
|Lorsakul, Auranuch; Fakhri, Georges El; Worstell, William et al. (2016) Numerical observer for atherosclerotic plaque classification in spectral computed tomography. J Med Imaging (Bellingham) 3:035501|
|Sitek, Arkadiusz; Li, Quanzheng; El Fakhri, Georges et al. (2016) Validation of Bayesian analysis of compartmental kinetic models in medical imaging. Phys Med 32:1252-1258|
|Hu, Chenhui; Sepulcre, Jorge; Johnson, Keith A et al. (2016) Matched signal detection on graphs: Theory and application to brain imaging data classification. Neuroimage 125:587-600|
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