Perfusion CT imaging is commonly used for the rapid assessment of patients presenting with symptoms of acute stroke. Maps of perfusion parameters such as cerebral blood volume (CBV), cerebral blood flow (CBF), and mean transit time (MTT) derived from the scan data provide crucial information for stroke diagnosis and treatment decisions. Most vendors implement singular value decomposition (SVD)-based methods on their scanners to calculate these parameters. However, SVD-based method is known to have issues of improperly handling the imperfect scan. For example, increasing the acquisition interval or decreasing the scan duration may introduce a bias in the estimated perfusion parameters. In this work, we propose a Bayesian inference algorithm, which can tolerate the imperfect scan conditions better than conventional method and is able to derive the uncertainty of a given perfusion parameter. We apply the variational technique to the inference problem, which becomes an expectation-maximization problem. The probability distribution (with Gaussian mean and variance) of each estimated parameter can be obtained. We perform evaluations in simulation studies both with full and incomplete data. The proposed method can obtain much less bias in estimation than the conventional method, and additionally providing the degree of the uncertainty in measurement.
Amyloid-beta imaging with 11C-PiB shows a robust increase in cortical binding in Alzheimer’s Disease (AD). Standardized uptake value ratio (SUVR) and distribution volume ratio (DVR) were shown to be effective quantitative measures in discriminating healthy and AD subjects. However, these measures typically require long wait/scan time and optimal selection of the reference region. The aim of this study is to propose a novel measure, which has superior diagnostic performance than conventional measures and has a less complicate scan workflow. We used 11C-PiB datasets that is publicly available from OASIS project. A total number of 50 dynamic scans are included in this study, of which 25 are from AD and 25 are from heathy control (HC) subjects. For all subjects, we first quantified standardized uptake value ratio (SUVR, 50-60 mins) and distribution volume ratio (DVR, T*=30 min) in frontal cortex region. Reference region was selected as whole cerebellum. We then defined a new measure, Peak-Clearance-Rate (PCR) which calculated the uptake decay rate in early stage of the scan (from the peak uptake time to 20 min). The new measure, SUVR and DVR all can differentiate the HC and AD subjects (p<0.05). ROC analysis was performed and indicated that PCR has better performance (AUC=0.912) than DVR (AUC=0.846) and SUVR (AUC=0.843). Cohen’s size for PCR is 2.53, while the ones for SUVR and DVR are 1.79 and 1.75. The proposed measure is an alternate measure of diagnosis in AD with 11C-PiB imaging. Additionally, it only requires first 20-min scan and does not require the procedure of delineation of reference tissue region. Further investigation will focus on staging, specifically in differentiating mild cognitive impairment (MCI) subjects from aged healthy and AD subjects.
Purpose: Image quality of cardiac PET is degraded by cardiac, respiratory, and bulk motion. The purpose of this work is to use PET list-mode data to detect and correct for bulk motion, which is unpredictable and must therefore be tracked at all times. Methods: We propose a data-driven approach that can detect and compensate bulk motion in cardiac PET imaging. Events in a motion-contaminated scan are binned into static (without intra-frame motion) and moving (with intra-frame motion) frames based on the variance of the center positions of line-of-responses calculated in each 1-second time window. Each moving frame is further divided into subframes, within which no motion is assumed. Data in each static and sub-moving-frame are then back-projected to the image space. The resulting images are used to estimate motion transformation from all static and sub-moving frames to a selected static reference frame. Finally, the data in all the frames are jointly reconstructed by incorporating motion estimation in the system matrix. We have applied our method to three human cardiac PET studies. Results: Visual assessment indicated the greatly improved image quality of the motion-corrected image over non-motion-corrected image. Also, motion correction yielded higher myocardium to blood pool concentration ratios than non-motion correction. Conclusion: The proposed bulk motion correction method improves the image quality of cardiac PET and can potentially be applied to other PET imaging applications such as brain PET.