Vashistha Rajat, Moradi Hamed, Hammond Amanda, O'Brien Kieran, Rominger Axel, Sari Hasan, Shi Kuangyu, Vegh Viktor, Reutens David
Centre for Advanced Imaging, University of Queensland, Brisbane, Australia.
ARC Training Centre for Innovation in Biomedical Imaging Technology, University of Queensland, Brisbane, Australia.
EJNMMI Res. 2025 May 23;15(1):58. doi: 10.1186/s13550-025-01253-3.
Quantifying biological parameters of interest through dynamic positron emission tomography (PET) requires an arterial input function (AIF) conventionally obtained from arterial blood samples. The AIF can also be non-invasively estimated from blood pools in PET images, often identified using co-registered MRI images. Deploying methods without blood sampling or the use of MRI generally requires total body PET systems with a long axial field-of-view (LAFOV) that includes a large cardiovascular blood pool. However, the number of such systems in clinical use is currently much smaller than that of short axial field-of-view (SAFOV) scanners. We propose a data-driven approach for AIF estimation for SAFOV PET scanners, which is non-invasive and does not require MRI or blood sampling using brain PET scans. The proposed method was validated using dynamic F-fluorodeoxyglucose [F]FDG total body PET data from 10 subjects. A variational inference-based machine learning approach was employed to correct for peak activity. The prior was estimated using a probabilistic vascular MRI atlas, registered to each subject's PET image to identify cerebral arteries in the brain.
The estimated AIF using brain PET images (IDIF-Brain) was compared to that obtained using data from the descending aorta of the heart (IDIF-DA). Kinetic rate constants (K, k, k) and net radiotracer influx (K) for both cases were computed and compared. Qualitatively, the shape of IDIF-Brain matched that of IDIF-DA, capturing information on both the peak and tail of the AIF. The area under the curve (AUC) of IDIF-Brain and IDIF-DA were similar, with an average relative error of 9%. The mean Pearson correlations between kinetic parameters (K, k, k) estimated with IDIF-DA and IDIF-Brain for each voxel were between 0.92 and 0.99 in all subjects, and for K, it was above 0.97.
This study introduces a new approach for AIF estimation in dynamic PET using brain PET images, a probabilistic vascular atlas, and machine learning techniques. The findings demonstrate the feasibility of non-invasive and subject-specific AIF estimation for SAFOV scanners.
通过动态正电子发射断层扫描（PET）对感兴趣的生物学参数进行量化需要传统上从动脉血样本中获取的动脉输入函数（AIF）。AIF也可以从PET图像中的血池进行无创估计，通常使用配准的MRI图像来识别血池。部署无需采血或使用MRI的方法通常需要具有长轴向视野（LAFOV）的全身PET系统，该系统包括一个大的心血管血池。然而，目前临床使用的此类系统数量远少于短轴向视野（SAFOV）扫描仪。我们提出了一种用于SAFOV PET扫描仪的AIF估计的数据驱动方法，该方法是非侵入性的，并且不需要使用脑部PET扫描进行MRI或采血。使用来自10名受试者的动态F-氟脱氧葡萄糖[F]FDG全身PET数据对所提出的方法进行了验证。采用基于变分推理的机器学习方法来校正峰值活性。使用概率性血管MRI图谱估计先验，将其配准到每个受试者的PET图像以识别大脑中的脑动脉。
将使用脑部PET图像估计的AIF（IDIF-Brain）与使用心脏降主动脉数据获得的AIF（IDIF-DA）进行比较。计算并比较了两种情况下的动力学速率常数（K、k、k）和净放射性示踪剂流入量（K）。定性地说，IDIF-Brain的形状与IDIF-DA的形状匹配，捕获了AIF峰值和尾部的信息。IDIF-Brain和IDIF-DA的曲线下面积（AUC）相似，平均相对误差为9%。在所有受试者中，每个体素使用IDIF-DA和IDIF-Brain估计的动力学参数（K、k、k）之间的平均皮尔逊相关性在0.92至0.99之间，对于K，相关性高于0.97。
本研究介绍了一种使用脑部PET图像、概率性血管图谱和机器学习技术在动态PET中进行AIF估计的新方法。研究结果证明了对SAFOV扫描仪进行无创且针对个体的AIF估计的可行性。
