Mu Chen

High quality image reconstruction for short frame in dynamic PET

Low count PET studies usually suffer from high image noise and quantitative bias in reconstructed image. This is particularly significant for short frames (temporal-ROI, or TROI) in dynamic PET studies, which in some cases may be as short as 30 seconds or less. In this paper, we proposed a method to improve the quality of the reconstructed short frame by utilizing information from a longer acquisition time that contains the short frame. The data of the longer acquisition excluding the short frame is first sorted to perform a reconstruction. The reconstructed image is then forward projected to obtain its contribution in the data space. A second reconstruction is then executed with data from the entire long acquisition (including the short frame) and the previous contribution in the data space to estimate the activity for the short frame. Results show that the image quality and CRC-noise performance are both improved with our proposed method than the standard reconstruction with counts from the target frame only, as well as the complementary reconstruction method published earlier.

Ultra efficientand robust estimation of the attenuation map in PET imaging

Time-of-Flight (TOF) PET data determines the attenuation map up to a constant. MLAA (Maximum Likelihood Activity and Attenuation Estimation) was proposed for this purpose. However, in real systems the estimated attenuation map usually results in bias and artifacts, due to various factors such as non-uniform timing resolution, detector timing drift, and biased scatter estimation. Moreover, MLAA has much higher computational cost than conventional PET reconstruction. Improving the practical performance of MLAA is important. We proposed an efficient and robust emission-attenuation joint estimation framework, based on the condition that regions with almost uniform attenuation coefficients are segmented. Following the derivation, the update of attenuation map only requires a few weighted additions in sinogram space, which reduces overall computational cost significantly compared with conventional MLAA, as the latter demands at least two backward projections in each iteration. Furthermore, as the parameter space for the attenuation map is reduced and that the math model encourages an averaging effect in reach region, the bias and artifacts due to various factors above can be reduced. We used clinical TOF PET data to evaluate the performance of our proposed method. In each iteration, the computation time for updating the attenuation map was less than 20% of that for conventional MLAA, leading to significant improvement in overall computation efficiency (twice as fast as conventional MLAA). More importantly, the method resulted in high quantitative accuracy. In population study, SUV computed with the estimated attenuation map and the CT based attenuation map had maximum relative error less than 5.7% in multiple VOIs including the spine, liver, kidney, and heart. Our method can be used as a robust and efficient solution to estimate the attenuation map for quantitative PET image reconstruction, based on a single assumption that the attenuation coefficients are similar in each segmented regions. The numerical error caused by treating attenuation coefficients as uniform within each segmented region is acceptable.

Joint direct dynamic analysis in dual-tracer PET imaging

Dual-tracer PET imaging may improve overall lesion detectability due to different tracer kinetics. However, separating two tracers in the mixed acquired data is difficult because of unknown individual activity change over time. We proposed an effective and robust method to separate two tracers dynamically, by introducing joint Patlak and Logan analysis in dual-tracer PET imaging. Several patients underwent dual-tracer brain PET/CT scans. The entire scan time was 100 min. 13N ammonia was injected at t=0 min and 18F-FDG was at t=20min. The dual blood input functions were separated and estimated non-invasively from static frame reconstructions, assisted with an exponential model to fit the blood input function after certain elapse of time. Direct Logan analysis was performed for data 0∼20min to generate Logan parametric images for 13N. For the 20∼100 min data, direct Patlak estimation from raw data was performed to generate Patlak parametric images for FDG, with a modified iterative algorithm including the contribution of 13N activities. Results showed that the proposed method is robust and applicable to dual-tracer PET imaging in separation of two tracers. In addition, dynamic images were generated to assist lesion detection. Specifically, in simulation the estimated Logan slope and the Patlak slope yielded relative error 3% for all rods in the NEMA-like phantom. In the application on clinical data, the estimated Logan parametric images and Patlak parametric images revealed distinguished contrasts. The Patlak parametric images estimated with our method also resulted in significantly less noise than conventional image based method, and at the same time preserved the quantitative accuracy, with <5% difference from the ones estimated with the conventional image based estimation method. The proposed Logan and Patlak joint estimation method can be used in dual tracer imaging to separate the two tracers dynamically and torobustly obtain parametric images with higher SNR than conventional methods. It may also be used to improve lesion detectability.