用于动态F正电子发射断层显像的Q.Clear重建优化

Optimization of Q.Clear reconstruction for dynamic F PET imaging.

作者信息

Lysvik Elisabeth Kirkeby, Mikalsen Lars Tore Gyland, Rootwelt-Revheim Mona-Elisabeth, Emblem Kyrre Eeg, Hjørnevik Trine

机构信息

Department of Physics and Computational Radiology, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Building 20, Gaustad Sykehus, Sognsvannveien 21, 0372, Oslo, Norway.

Institute of Clinical Medicine, University of Oslo, Oslo, Norway.

出版信息

EJNMMI Phys. 2023 Oct 20;10(1):65. doi: 10.1186/s40658-023-00584-1.

BACKGROUND

Q.Clear, a Bayesian penalized likelihood reconstruction algorithm, has shown high potential in improving quantitation accuracy in PET systems. The Q.Clear algorithm controls noise during the iterative reconstruction through a β penalization factor. This study aimed to determine the optimal β-factor for accurate quantitation of dynamic PET scans.

METHODS

A Flangeless Esser PET Phantom with eight hollow spheres (4-25 mm) was scanned on a GE Discovery MI PET/CT system. Data were reconstructed into five sets of variable acquisition times using Q.Clear with 18 different β-factors ranging from 100 to 3500. The recovery coefficient (RC), coefficient of variation (CV) and root-mean-square error (RMSE) were evaluated for the phantom data. Two male patients with recurrent glioblastoma were scanned on the same scanner using F-PSMA-1007. Using an irreversible two-tissue compartment model, the area under curve (AUC) and the net influx rate K were calculated to assess the impact of different β-factors on the pharmacokinetic analysis of clinical PET brain data.

RESULTS

In general, RC and CV decreased with increasing β-factor in the phantom data. For small spheres (< 10 mm), and in particular for short acquisition times, low β-factors resulted in high variability and an overestimation of measured activity. Increasing the β-factor improves the variability, however at a cost of underestimating the measured activity. For the clinical data, AUC decreased and K increased with increased β-factor; a change in β-factor from 300 to 1000 resulted in a 25.5% increase in the K.

CONCLUSION

In a complex dynamic dataset with variable acquisition times, the optimal β-factor provides a balance between accuracy and precision. Based on our results, we suggest a β-factor of 300-500 for quantitation of small structures with dynamic PET imaging, while large structures may benefit from higher β-factors.

TRIAL REGISTRATION

Clinicaltrials.gov, NCT03951142. Registered 5 October 2019, https://clinicaltrials.gov/ct2/show/NCT03951142 . EudraCT no 2018-003229-27. Registered 26 February 2019, https://www.clinicaltrialsregister.eu/ctr-search/trial/2018-003229-27/NO .

背景

Q.Clear是一种贝叶斯惩罚似然重建算法，在提高PET系统定量准确性方面显示出巨大潜力。Q.Clear算法通过一个β惩罚因子在迭代重建过程中控制噪声。本研究旨在确定用于动态PET扫描准确定量的最佳β因子。

方法

在GE Discovery MI PET/CT系统上扫描带有八个空心球体（4 - 25毫米）的无框埃塞尔PET体模。使用Q.Clear将数据重建为五组不同采集时间的数据，β因子有18种，范围从100到3500。对体模数据评估恢复系数（RC）、变异系数（CV）和均方根误差（RMSE）。两名复发性胶质母细胞瘤男性患者在同一台扫描仪上使用F - PSMA - 1007进行扫描。使用不可逆双组织房室模型计算曲线下面积（AUC）和净流入率K，以评估不同β因子对临床PET脑数据药代动力学分析的影响。