Chi-hua Tung

Algorithm enabled TOF-PET imaging with reduced scan time

Time-of-flight (TOF) positron emission tomography (PET) has gained remarkable development recently due to the advances in scintillator, silicon photomultipliers (SiPM), and fast electronics. However, current clinical reconstruction algorithms in TOF-PET are still based on ordered-subset-expectation-maximization (OSEM) and its variants, which may face challenges in non-conventional imaging applications, such as fast imaging within short scan time. In this work, we propose an image-TV constrained optimization problem, and tailor a primal- dual algorithm for solving the problem and reconstructing images. We collect list-mode data of a Jaszczak phantom with a prototype digital TOF-PET scanner. We focus on investigating image reconstruction from data collected within reduced scan time, and thus of lower count levels. Results of the study indicate that our proposed algorithm can 1) yield image reconstruction with suppressed noise, extended axial volume coverage, and improved spatial resolution over that obtained in conventional reconstructions, and 2) yield reconstructions with potential clinical utility from data collected within shorter scan time.

Preliminary study of TV-constrained-likelihood-maximization image reconstruction from list-mode TOF-PET data

Time-of-flight (TOF) positron emission tomography (PET) has the potential to yield images with improved quality by comparison to conventional PET. Numerous iterative algorithms have been investigated for image reconstruction in TOF-PET. In this work, we investigate optimization based image reconstruction from list-mode TOF-PET data collected with a digital PET system under clinical evaluation. Specifically, the reconstruction problem is formulated as an image-TV-constrained data-likelihood-maximization program. We then tailor a primal-dual algorithm to reconstruct the image through solving the problem. In an attempt to evaluate the reconstruction design, we have performed investigation on reconstructions from list-mode TOF-PET data in real-data study in which data were collected from the standard IEC phantom by use of the digital PET system. Results reveal that the proposed algorithm yields image reconstruction with improved image quality in terms of visualization and standard quantitative metrics, than does the conventional reconstruction algorithm under the study conditions.

Initial experience in primal-dual optimization reconstruction from sparse-PET patient data

There exists interest in designing a PET system with reduced detectors due to cost concerns, while not significantly compromising the PET utility. Recently developed optimization-based algorithms, which have demonstrated the potential clinical utility in image reconstruction from sparse CT data, may be used for enabling such design of innovative PET systems. In this work, we investigate a PET configuration with reduced number of detectors, and carry out preliminary studies from patient data collected by use of such sparse-PET configuration. We consider an optimization problem combining Kullback-Leibler (KL) data fidelity with an image TV constraint, and solve it by using a primal-dual optimization algorithm developed by Chambolle and Pock. Results show that advanced algorithms may enable the design of innovative PET configurations with reduced number of detectors, while yielding potential practical PET utilities.

SU‐F‐I‐57: Evaluate and Optimize PET Acquisition Overlap in 18F‐FDG Oncology Wholebody PET/CT: Can We Scan PET Faster?

PURPOSE The longer patient has to remain on the table during PET imaging, the higher the likelihood of motion artifacts due to patient discomfort. This study was to investigate and optimize PET acquisition overlap in 18F-FDG oncology wholebody PET/CT to speed up PET acquisition and improve patient comfort. METHODS Wholebody 18F-FDG PET/CT of phantoms, 8 pre-clinical patients (beagles) and 5 clinical oncology patients were performed in 90s/bed on a time-of-flight Gemini TF 64 system. Imaging of phantoms and beagles was acquired with reduced PET overlaps (40%, 33%, 27%, 20%, 13% and no overlap) in addition to the system default (53%). In human studies, 1 or 2 reduced overlaps from the listed options were used to acquire PET/CT sweeps right after the default standard of care imaging. Image quality was blindly reviewed using visual scoring criteria and quantitative SUV assessment. NEMA PET sensitivity was performed under different overlaps. RESULTS All PET exams demonstrated no significant impact on the visual grades for overlaps >20%. Blinded reviews assigned the best visual scores to PET using overlaps 53%-27%. Reducing overlap to 27% for oncology patients (12-bed) saved an average of ∼40% acquisition time (11min) compared to using the default overlap (18min). No significant SUV variances were found when reducing overlap to half of default for cerebellum, lung, heart, aorta, liver, fat, muscle, bone marrow, thighs and target lesions (p>0.05), except expected variability in urinary system. CONCLUSION This study demonstrated by combined phantom, pre-clinical and clinical PET/CT scans that PET acquisition overlap in axial of todays systems can be reduced and optimized. It showed that a reduction of PET acquisition overlap to 27% (half of system default) can be implemented to reduce table time by ∼40% to improve patient comfort and minimize potential motion artifacts, without prominently degrading image quality or compromising PET quantification.

Validation of GATE Monte Carlo simulations of the Philips GEMINI TF and TruFlight Select PET/CT scanners based on NEMA NU2 standards

The objective of this study is to validate the in-house GATE simulations of the Philips GEMINI TF and TruFlight Select PET scanners and evaluate their accuracy for further research and optimization of current and future PET products. GATE results are compared to experimental data obtained according to the National Electrical Manufacturers Association (NEMA) NU2-2007 standards. A detailed implementation of the geometrical and functional models of the scanners and the NEMA phantoms was conducted, allowing the evaluation of the simulated absolute sensitivity, spatial resolution, count rates and the image quality of both systems. All Monte Carlo data production was performed according to the NEMA protocols. Simulated data were converted into the Philips list-mode format and analyzed using the same software tools as in the quality control step of the production line. Good agreement was found between the simulated results and the measured data from both scanners. This validation study represents an important step towards the use of these tools as an aid for the optimization of the current acquisition protocols and the validation of reconstruction and data correction techniques.