中华放射医学与防护杂志

黎恩廷,郑万佳,连锦兴,等.逆向形变场的雅可比行列式在形变配准算法评估中的应用[J].中华放射医学与防护杂志,2024,44(2):133-139.Li Enting,Zheng Wanjia,Lian Jinxing,et al.Application of Jacobian determinant of reverse deformation field to evaluation of deformation registration algorithm[J].Chin J Radiol Med Prot,2024,44(2):133-139

逆向形变场的雅可比行列式在形变配准算法评估中的应用

Application of Jacobian determinant of reverse deformation field to evaluation of deformation registration algorithm

投稿时间：2023-05-28

DOI：10.3760/cma.j.cn112271-20230528-00164

中文关键词: 雅可比行列式形变配准算法评估肺癌鼻咽癌形变场

英文关键词:Jacobian determinant Deformation registration algorithm evaluation Lung cancer Nasopharyngeal carcinoma Displacement vector field

基金项目:广东省基础与应用基础研究基金(2021A1515220140);中山大学肿瘤防治中心"青年优创"计划(QNYCPY32);全国大学生创新训练项目(202213902102,S202213902029,S202113902030)广东省食管癌研究所科技计划项目(Q202221,Q202311);北京市希思科临床肿瘤学研究基金(Y-Young2023-0156)

作者	单位	E-mail
黎恩廷	中山大学附属第八医院放射科, 深圳 518033
郑万佳	中国人民解放军南部战区空军医院肿瘤科, 广州 510050
连锦兴	广州中医药大学第一附属医院放疗科, 广州 510000
朱伟婷	广州新华学院生物医学工程学院, 广州 510520
周酥	广州新华学院生物医学工程学院, 广州 510520
安雅琪	广州新华学院生物医学工程学院, 广州 510520
黄思娟	中山大学肿瘤防治中心华南肿瘤学国家重点实验室肿瘤医学协同创新中心广东省鼻咽癌诊治研究重点实验室, 广州 510060
杨鑫	中山大学肿瘤防治中心华南肿瘤学国家重点实验室肿瘤医学协同创新中心广东省鼻咽癌诊治研究重点实验室, 广州 510060	yangxin@sysucc.org.cn

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中文摘要:

目的提出量化评估不同形变图像配准(DIR)算法的新指标,增加形变配准向临床实践进一步发展的可能。方法首先基于正向形变场(DVF)的雅可比行列式(JD)提出雅可比行列式均值(JDM),然后引入逆向DVF的JD,提出DVF雅可比行列式误差(DJDE)。在肺癌和鼻咽癌图像上采用光流场形变配准算法(OF-DIR)和快速弹性正则化Demons形变配准算法(FD-DIR)进行配准。最后使用JDM和DJDE,以及雅可比行列式负值百分比(JDNP)、逆一致性误差(ICE)及归一化均方误差(NMSE)进行配准算法评估。对比不同评估指标在不同肿瘤图像以及不同算法上的差异,并采用受试者工作特征曲线(ROC)进行分析。结果在肺癌中,OF-DIR的JDM、NMSE、DJDE和ICE均优于FD-DIR,差异具有统计学意义(z = -2.24、-4.84;t = 4.01、6.54,P<0.05)。在鼻咽癌中,OF-DIR的DJDE、ICE和NMSE均优于FD-DIR,差异具有统计学意义(t = 4.46、-7.49,z = -2.22, P<0.05),JDM差异无统计学意义(P>0.05)。在肺癌及鼻咽癌中,OF-DIR的JDNP均差于FD-DIR,差异具有统计学意义(z = -4.29、-4.02,P<0.01)。此外,DJDE在ROC曲线上更具特异性及敏感性(AUC=0.77),针对不同部位肿瘤图像有不同表现结果。结论 JDM及DJDE是评估形变算法的有效指标,肺癌及鼻咽癌均适合使用OF-DIR,而FD-DIR在使用时需注意器官组织动度对配准效果的影响。

英文摘要:

Objective To effectively quantify and evaluate the quality of different deformation registration algorithms, in order to enhance the possibility of implementing deformation registration in clinical practice. Methods The Jacobian determinant mean (JDM) is proposed based on the Jacobian determinant (JD) of displacement vector field (DVF), and the Jacobian determinant error (DJDE) is introduced by incorporating the JD of the inverse DVF. The optical flow method (OF-DIR) and fast demons method with elastic regularization (FD-DIR) were tested on nasopharyngeal and lung cancer datasets. Finally, JDM and DJDE with the Jacobian determinant negative percentage (JDNP), inverse consistency error (ICE) and normalized mean square error (NMSE) were used to evaluate the registration algorithms and compare the differences evaluation indicators in different tumor images and different algorithms, and the receiver operating curve (ROC) was analyzed in evaluation. Results In lung cancer, OF-DIR outperformed FD-DIR in terms of JDM, NMSE,DJDE and ICE, and the difference was statistically significant(z = -2.24,-4.84, t = 4.01,6.54,P<0.05). In nasopharyngeal carcinoma, DJDE, ICE and NMSE of OF-DIR were superior to FD-DIR, and the difference was statistically significant (t = 4.46,-7.49, z = -2.22,P<0.05), but there was no significant difference in JDM (P>0.05). In lung cancer and nasopharyngeal carcinoma, JDNP of OF-DIR was worse than that of FD-DIR, and the difference was statistically significant (z = -4.29,-4.02,P<0.01). In addition, DJDE is more specific and sensitive on ROC curve (AUC=0.77), and has different performance result for tumor images at different sites. Conclusions The JDM and DJDE evaluation metrics proposed are effective for deformation registration algorithms. OF-DIR is suitable for both lung cancer and nasopharyngeal carcinoma, while the influence of organ motion on the registration effect should be considered when using FD-DIR.

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