Zhong Hualiang, Ren Lei, Lu Yonggang, Liu Yu
Department of Radiation Oncology, Medical College of Wisconsin Milwaukee, WI, USA.
Department of Radiation Oncology, University of Maryland Baltimore, MD, USA.
Phys Imaging Radiat Oncol. 2023 Mar 16;26:100430. doi: 10.1016/j.phro.2023.100430. eCollection 2023 Apr.
Free breathing (FB) positron emission tomography (PET) images are routinely used in radiotherapy for lung cancer patients. Respiration-induced artifacts in these images compromise treatment response assessment and obstruct clinical implementation of dose painting and PET-guided radiotherapy. The purpose of this study is to develop a blurry image decomposition (BID) method to correct motion-induced image-reconstruction errors in FB-PETs.
Assuming a blurry PET is represented as an average of multi-phase PETs. A four-dimensional computed-tomography image is deformably registered from the end-inhalation (EI) phase to other phases. With the registration-derived deformation maps, PETs at other phases can be deformed from a PET at the EI phase. To reconstruct the EI-PET, the difference between the blurry PET and the average of the deformed EI-PETs is minimized using a maximum-likelihood expectation-maximization algorithm. The developed method was evaluated with computational and physical phantoms as well as PET/CT images acquired from three patients.
The BID method increased the signal-to-noise ratio from 1.88 ± 1.05 to 10.5 ± 3.3 and universal-quality index from 0.72 ± 0.11 to 1.0 for the computational phantoms, and reduced the motion-induced error from 69.9% to 10.9% in the maximum of activity concentration and from 317.5% to 8.7% in the full width at half maximum of the physical PET-phantom. The BID-based corrections increased the maximum standardized-uptake values by 17.7 ± 15.4% and reduced tumor volumes by 12.5 ± 10.4% on average for the three patients.
The proposed image-decomposition method reduces respiration-induced errors in PET images and holds potential to improve the quality of radiotherapy for thoracic and abdominal cancer patients.
自由呼吸（FB）正电子发射断层扫描（PET）图像常用于肺癌患者的放射治疗。这些图像中由呼吸引起的伪影会影响治疗反应评估，并阻碍剂量勾画和PET引导放射治疗的临床应用。本研究的目的是开发一种模糊图像分解（BID）方法，以纠正FB-PET中运动引起的图像重建误差。
假设模糊的PET表示为多期PET的平均值。将四维计算机断层扫描图像从吸气末（EI）期到其他期进行可变形配准。利用配准得到的变形图，其他期的PET可以从EI期的PET变形而来。为了重建EI-PET，使用最大似然期望最大化算法使模糊PET与变形后的EI-PET平均值之间的差异最小化。使用计算体模和物理体模以及从三名患者获取的PET/CT图像对所开发的方法进行评估。
对于计算体模，BID方法将信噪比从1.88±1.05提高到10.5±3.3，通用质量指数从0.72±0.11提高到1.0；对于物理PET体模，在最大活性浓度方面，将运动引起的误差从69.9%降低到10.9%，在半高宽方面从317.5%降低到8.7%。基于BID的校正使三名患者的最大标准化摄取值平均提高了17.7±15.4%，肿瘤体积平均减小了12.5±10.4%。
所提出的图像分解方法可减少PET图像中由呼吸引起的误差，并具有提高胸腹部癌症患者放射治疗质量的潜力。
