[1]彭新华,朱研佳,朱 磊,等.PET/CT正则化重建与非正则化重建对肿瘤病灶定量参数及图像质量的影响[J].医学信息,2022,35(23):96-100.[doi:10.3969/j.issn.1006-1959.2022.23.017]
　PENG Xin-hua,ZHU Yan-jia,ZHU Lei,et al.Effect of PET/CT Regularized Reconstruction and Non-regularized Reconstruction on the Quantitative Parameters and Image Quality of Tumor Lesions[J].Journal of Medical Information,2022,35(23):96-100.[doi:10.3969/j.issn.1006-1959.2022.23.017]
点击复制

PET/CT正则化重建与非正则化重建对肿瘤病灶定量参数及图像质量的影响()
分享到：

医学信息[ISSN:1006-1959/CN:61-1278/R]

卷:
35卷
期数:
2022年23期
页码:
96-100
栏目:
论著
出版日期:
2022-12-01

文章信息/Info

Title:
Effect of PET/CT Regularized Reconstruction and Non-regularized Reconstruction on the Quantitative Parameters and Image Quality of Tumor Lesions
文章编号:
1006-1959（2022）23-0096-05
作者:
彭新华; 朱研佳; 朱 磊; 等
（天津医科大学肿瘤医院分子影像与核医学诊疗科/国家恶性肿瘤临床医学研究中心/天津市“肿瘤防治”重点实验室/天津市恶性肿瘤临床医学研究中心，天津 300060）
Author(s):
PENG Xin-hua; ZHU Yan-jia; ZHU Lei; et al.
(Department of Molecular Imaging and Nuclear Medicine,Cancer Institute and Hospital,Tianjin Medical University/National Clinical Research Center for Cancer/Key Laboratory of Cancer Prevention and Therapy,Tianjin/Tianjin’s Clinical Research Center for Cancer,Tianjin 300060,China)
关键词:
正电子发射断层显像; 正则化重建算法; 图像质量
Keywords:
Positron emission tomography; Regularization reconstruction algorithm; Image quality
分类号:
R817.4；R445.6
DOI:
10.3969/j.issn.1006-1959.2022.23.017
文献标志码:
A
摘要:
目的 通过对正则化重建方式与非正则化重建方式的PET图像进行分析，比较不同重建方式对肿瘤病灶定量参数的差异以及图像质量方面的影响。方法 回顾性分析2021年10月-12月于天津医科大学肿瘤医院进行18F-FDG PET/CT检查并经病理诊断为恶性肿瘤的患者共43例，共计71个病灶。非正则化重建（non-Q.Clear）选择OSEM+TOF+SharpIR，设为non-Q.Clear组，子集数16，迭代次数3次；正则化重建（Q.Clear）选择OSEM+TOF+SharpIR+Q.Clear，设为Q.Clear组，子集数16，迭代次数3次，滤波5 mm，β值350。获取病灶定量参数并计算SBR、SNR，比较两组图像定量参数的差异和不同重建方式下PET图像质量的差异。结果 Q.Clear组病灶定量参数SUVmax、SUVmean、SULmax高于non-Q.Clear组，MTV低于non-Q.Clear组（P＜0.05）；Q.Clear组病灶SBR、SNR高于non-Q.Clear组（P＜0.05）；两组SUVpeak、TLG数值比较，差异无统计学意义（P＞0.05）；Dmax＜10 mm的病灶△SUVmax、△SUVmean、△SULmax、△SBR高于Dmax≥10 mm的病灶，△SUVpeak低于Dmax≥10 mm的病灶（P＜0.05）；Dmax≥10 mm的病灶和Dmax＜10 mm的病灶的△MTV、△TLG和△SNR比较，差异无统计学意义（P＞0.05）；BMI＞25 kg/m2的患者与BMI≤25 kg/m2的患者各病灶定量参数比较，差异无统计学意义（P＞0.05）。结论 正则化重建算法对肿瘤病灶定量参数相比于非正则化重建算法有一定差异，并且对小病灶的定量参数影响较大，应用正则化算法可提升图像质量。
Abstract:
Objective To compare the difference of quantitative parameters of tumor lesions and the effect of different reconstruction methods on image quality by analyzing the PET images of regularization reconstruction and non-regularization reconstruction.Methods A total of 43 patients who underwent 18F-FDG PET/CT examination and were pathologically diagnosed as malignant tumors in Tianjin Medical University Cancer Hospital from October to December 2021 were retrospectively analyzed, including 71 lesions. For non-regularized reconstruction (non-Q.Clear), use OSEM+TOF+SharpIR, the number of subsets was 16, and the number of iterations was 3，set to non-Q.Clear group; for regularized reconstruction (Q.Clear), use OSEM+TOF+SharpIR+Q.Clear, the number of subsets was 16, the number of iterations was 3, the filtering was 5mm, and the β value was 350，set to Q.Clear group. The differences of quantitative parameters between the two groups and the differences of PET image quality under different reconstruction methods were compared.Results The quantitative parameters of SUVmax, SUVmean and SULmax in the Q.Clear group were higher than those in the non-Q.Clear group, and the MTV was lower than that in the non-Q.Clear group (P<0.05). SBR and SNR in Q. Clear group were higher than those in non-Q. Clear group (P<0.05). There was no significant difference in SUVpeak and TLG between the two groups (P>0.05). The △SUVmax, △SUVmean, △SULmax and △SBR of lesions with Dmax<10 mm were higher than those of lesions with Dmax≥10 mm, and the △SUVpeak was lower than that of lesions with Dmax≥10 mm (P<0.05). There was no significant difference in △MTV, △TLG and △SNR between lesions with Dmax≥10 mm and lesions with Dmax<10 mm (P>0.05). There was no significant difference in quantitative parameters between patients with BMI>25 kg/m2 and patients with BMI≤25 kg/m2 (P>0.05).Conclusion Regularization reconstruction algorithm has some differences in quantitative parameters of tumor lesions compared with non-regularization reconstruction algorithm, and has a great influence on quantitative parameters of small lesions. Regularization algorithm can improve image quality.

参考文献/References:

[1]李艳,代永亮,张卫善,等.18F-FDG PET/CT在乳腺癌诊断和分期中的应用价值[J].实用放射学杂志,2019,35(9):1436-1439.[2]居热提·阿扎提,柴黎明,等.18F-脱氧葡萄糖PET/CT代谢参数对食管恶性肿瘤患者放化疗疗效的预测价值[J].医学信息,2020,33(5):80-84.[3]Yoshii T,Miwa K,Yamaguchi M,et al.Optimization of a Bayesian penalized likelihood algorithm （Q.Clear) for 18F-NaF bone PET/CT images acquired over shorter durations using a custom-designed phantom[J].EJNMMI Phys,2020,7(1):56.[4]Lindstr?觟m E,Lindsj?觟 L,Sundin A,et al.Evaluation of block-sequential regularized expectation maximization reconstruction of 68Ga-DOTATOC,18F-fluoride, and 11C-acetate whole-body examinations acquired on a digital time-of-flight PET/CT scanner[J].EJNMMI Phys,2020,7(1):40.[5]崔碧霄,卢洁,王曼,等.TOF-PET图像重建技术评价小肿瘤病灶的临床价值[J].医学影像学杂志,2016,26(7):1237-1239.[6]Lindstr?觟m E,Sundin A,Trampal C,et al.Evaluation of Penalized-Likelihood Estimation Reconstruction on a Digital Time-of-Flight PET/CT Scanner for 18F-FDG Whole-Body Examinations[J].J Nucl Med,2018,59(7):1152-1158.[7]Matti A,Lima GM,Pettinato C,et al.How Do the More Recent Reconstruction Algorithms Affect the Interpretation Criteria of PET/CT Images?[J].Nucl Med Mol Imaging,2019,53(3):216-222.[8]Liu Y,Gao MJ,Zhou J,et al.Changes of 18F-FDG-PET/CT quantitative parameters in tumor lesions by the Bayesian penalized-likelihood PET reconstruction algorithm and its influencing factors[J].BMC Med Imaging,2021,21(1):133.[9]王旭,许莎莎,王卓,等.18F-FDG PET/CT贝叶斯正则化似然重建算法对肿瘤定量参数的影响[J].中国医学影像技术,2021,37(11):1720-1724.[10]Teoh EJ,Mcgowan DR,Bradley KM,et al.Novel penalised likelihood reconstruction of PET in the assessment of histologically verified small pulmonary nodules[J].Eur Radiol,2016,26(2):576-584.[11]Tatsumi M,Soeda F,Kamiya T,et al.Effects of New Bayesian Penalized Likelihood Reconstruction Algorithm on Visualization and Quantification of Upper Abdominal Malignant Tumors in Clinical FDG PET/CT Examinations[J].Front Oncol,2021,11:707023.[12]Parvizi N,Franklin JM,Mcgowan DR,et al.Does a novel penalized likelihood reconstruction of 18F-FDG PET-CT improve signal-to-background in colorectal liver metastases?[J].Eur J Radiol,2015,84(10):1873-1878.[13]Wyrzykowski M,Siminiak N,Kaｚmierczak M,et al.Impact of the Q.Clear reconstruction algorithm on the interpretation of PET/CT images in patients with lymphoma[J].EJNMMI Res,2020,10(1):99.[14]Yamaguchi S,Wagatsuma K,Miwa K,et al.Bayesian penalized-likelihood reconstruction algorithm suppresses edge artifacts in PET reconstruction based on point-spread-function[J].Phys Med,2018,47:73-79.[15]Chilcott AK,Bradley KM,Mcgowan DR.Effect of a Bayesian Penalized Likelihood PET Reconstruction Compared With Ordered Subset Expectation Maximization on Clinical Image Quality Over a Wide Range of Patient Weights[J].AJR Am J Roentgenol,2018,210(1):153-157.[16]Te riet J,Rijnsdorp S,Roef MJ,et al.Evaluation of a Bayesian penalized likelihood reconstruction algorithm for low-count clinical?18F-FDG PET/CT[J].EJNMMI Phys,2019,6(1):32.[17]Sah BR,Stolzmann P,Delso G,et al.Clinical evaluation of a block sequential regularized expectation maximization reconstruction algorithm in 18F-FDG PET/CT studies[J].Nucl Med Commun,2017,38(1):57-66.[18]陈炜,耿建华,卢洪辉,等.正则化最大期望值重建算法中β值对PET图像质量和定量分析的影响[J].中国医学装备,2021,18(11):27-31.[19]Elin T,David M,Helen A,et al.Impact of acquisition time and penalizing factor in a block-sequential regularized expectation maximization reconstruction algorithm on a Si-photomultiplier-based PET-CT system for 18F-FDG[J].EJNMMI Res,2019,9(1):64[20]Texte E,Gouel P,Thureau S,et al.Impact of the Bayesian penalized likelihood algorithm (Q.Clear?) in comparison with the OSEM reconstruction on low contrast PET hypoxic images[J].EJNMMI Phys,2020,7(1):28.

更新日期/Last Update: 1900-01-01