迁移模糊聚类在医学PET/MRI快速衰减校正中的应用

J4 ›› 2016, Vol. 38 ›› Issue (04): 775-784.

迁移模糊聚类在医学PET/MRI快速衰减校正中的应用

孙寿伟1,钱鹏江1,胡凌志2,苏冠豪3,Raymond F. Muzic,Jr3

(1.江南大学数字媒体学院，江苏无锡214122；
2.北美飞利浦公司，俄亥俄州克利夫兰，44143；3.凯斯西储大学，俄亥俄州克利夫兰 44106)

收稿日期:2015-04-14 修回日期:2015-07-10 出版日期:2016-04-25 发布日期:2016-04-25
基金资助:
国家自然科学基金(61202311)；江苏省自然科学基金(BK201221834)；江苏省产学研前瞻性研究项目(BY201301502)

A transfer fuzzy clustering based fast PET/MRI AC method

SUN Shouwei1,QIAN Pengjiang1,HU Lingzhi2,SU Guanhao3,Raymond F. Muzic,Jr3

(1.School of Digital Media，Jiangnan University，Wuxi 214122，China；
2.Philips Electronics North America,Cleveland,OH 44143,USA；
3.Case Western Reserve University,Cleveland,OH 44106,USA)

Received:2015-04-14 Revised:2015-07-10 Online:2016-04-25 Published:2016-04-25

摘要/Abstract

摘要：

为了避免PET/CT对病人造成大剂量的X辐射伤害和更好地对PET/MRI混合成像系统进行信号衰减校正。在组织分割方法的指导下，利用迁移模糊聚类算法将对人体无伤害的磁共振成像(MRI) 划分成诸如空气、液体、软组织、骨头等不同组织成分，然后赋予不同组织不同的线性衰减系数，以此来实现配准的PET成像的衰减校正工作。本方法具有三大好处：（1) 迁移模糊聚类算法可以利用历史高级知识来辅助当前病人MRI组织分割任务，从而保证了临床有效性和鲁棒性，降低了环境噪声、数据缺失及个体解剖结构差异等因素对算法的不良影响；（2)本算法内嵌的基于迁移学习的简单抽样策略，在保证算法鲁棒性的同时，极大地缩短了聚类划分的整体时间，适用于医学MRI大数据快速聚类分割的场合，因而有效地增强了算法的实用性；（3)本算法涉及的历史MRI知识，都是通过历史MRI源数据高度总结得到，非历史MRI源数据，这有效地保护了病人隐私，符合医学诊断的基本要求。通过在真实数据集上的实验表明了上述优点。

关键词: PET/CT, X辐射, PET/MRI, 迁移模糊聚类, 衰减校正, 历史知识

Abstract:

In order to avoid Xradiation harm that PET/CT does to patients, and to achieve better PET/MRI attenuation correction, we divide MRI into different tissues,such as air liquid, soft tissues and bones under the guidance of tissue segmentation method by using fuzzy clustering algorithm. Then different organizations are given different linear attenuation coefficients so as to achieve better PET attenuation correction. The proposed method has three advantages: 1) benefiting from the guidance of historical knowledge, it tends to be effective in the situations when the data is insufficient or distorted by much noise; 2) the simple sampling strategy based on transfer learning greatly shortens the overall time of clustering, and at the same time ensures the robustness of the algorithm, thus suitable for medical MRI fast clustering; 3) as the historical MRI knowledge does not expose the raw data of the source domain, this algorithm is capable of protecting privacy of the source domain, and meets the basic requirements of medical diagnosis. Experimental studies on realworld datasets demonstrate these merits of our work.

Key words: PET/CT;Xray radiation;PET/MRI;transfer learning based fuzzy clustering;attenuation coefficient;historical data

孙寿伟1,钱鹏江1,胡凌志2,苏冠豪3,Raymond F. Muzic,Jr3. 迁移模糊聚类在医学PET/MRI快速衰减校正中的应用[J]. J4, 2016, 38(04): 775-784.

SUN Shouwei1,QIAN Pengjiang1,HU Lingzhi2,SU Guanhao3,Raymond F. Muzic,Jr3. A transfer fuzzy clustering based fast PET/MRI AC method [J]. J4, 2016, 38(04): 775-784.