Robust PET Motion Correction Using Non-local Spatio-temporal Priors
Robust PET Motion Correction Using Non-local Spatio-temporal Priors
复制标题
使用非局部时空先验的鲁棒 PET 运动校正
DOI：
10.1007/978-3-319-24571-3_77
复制
发表时间：
2015
影响因子：
3.5
通讯作者：
S. Wollenweber
中科院分区：
文献类型：
--
作者：
S. Thiruvenkadam;K. Shriram;R. Manjeshwar;S. Wollenweber
呼吸运动对正电子发射断层扫描/计算机断层扫描（PET/CT）采集提出了重大挑战，可能导致标准摄取值（SUV）定量不准确。由于运动幅度大、固有噪声以及需要保留肿瘤等明确特征，门控PET图像的非刚性配准（NRR）极具挑战性。在这项工作中，我们在分组式非刚性配准中使用非局部时空约束，以获得一个稳定的框架，该框架能够处理少量的PET门控数据，并应对PET数据的上述挑战。此外，我们提出了用于测量非刚性配准所引入的对齐情况和伪影的指标，这一点很少被提及。我们的结果在20例临床PET病例上与相关研究进行了定量比较。
Respiratory motion presents significant challenges for PET/ CT acquisitions, potentially leading to inaccurate SUV quantitation. Non Rigid Registration [NRR] of gated PET images is quite challenging due to large motion, intrinsic noise, and the need to preserve definitive features like tumors. In this work, we use non-local spatio-temporal constraints within group-wise NRR to get a stable framework which can work with few number of PET gates, and handle the above challenges of PET data. Additionally, we propose metrics for measuring alignment and artifacts introduced by NRR which is rarely addressed. Our results are quantitatively compared to related works, on 20 clinical PET cases.