Moradi Hamed, Vashistha Rajat, Ghosh Soumen, O'Brien Kieran, Hammond Amanda, Rominger Axel, Sari Hasan, Shi Kuangyu, Vegh Viktor, Reutens David
Centre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia.
ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia.
EJNMMI Res. 2024 Apr 1;14(1):33. doi: 10.1186/s13550-024-01100-x.
Accurate measurement of the arterial input function (AIF) is crucial for parametric PET studies, but the AIF is commonly derived from invasive arterial blood sampling. It is possible to use an image-derived input function (IDIF) obtained by imaging a large blood pool, but IDIF measurement in PET brain studies performed on standard field of view scanners is challenging due to lack of a large blood pool in the field-of-view. Here we describe a novel automated approach to estimate the AIF from brain images.
Total body F-FDG PET data from 12 subjects were split into a model adjustment group (n = 6) and a validation group (n = 6). We developed an AIF estimation framework using wavelet-based methods and unsupervised machine learning to distinguish arterial and venous activity curves, compared to the IDIF from the descending aorta. All of the automatically extracted AIFs in the validation group had similar shape to the IDIF derived from the descending aorta IDIF. The average area under the curve error and normalised root mean square error across validation data were - 1.59 ± 2.93% and 0.17 ± 0.07.
Our automated AIF framework accurately estimates the AIF from brain images. It reduces operator-dependence, and could facilitate the clinical adoption of parametric PET.
准确测量动脉输入函数（AIF）对于参数化PET研究至关重要，但AIF通常源自侵入性动脉血采样。可以使用通过对大血池成像获得的图像衍生输入函数（IDIF），但由于在标准视野扫描仪上进行的PET脑研究中缺乏视野内的大血池，因此IDIF测量具有挑战性。在此，我们描述了一种从脑图像估计AIF的新型自动化方法。
将12名受试者的全身F-FDG PET数据分为模型调整组（n = 6）和验证组（n = 6）。我们使用基于小波的方法和无监督机器学习开发了一个AIF估计框架，以区分动脉和静脉活动曲线，并与降主动脉的IDIF进行比较。验证组中所有自动提取的AIF与从降主动脉IDIF得出的IDIF具有相似的形状。验证数据的曲线下面积误差平均值和归一化均方根误差分别为-1.59±2.93%和0.17±0.07。
我们的自动化AIF框架可从脑图像准确估计AIF。它减少了对操作员的依赖，并可能促进参数化PET在临床上的应用。
