Machine Learning-Based Noninvasive Quantification of Single-Imaging Session Dual-Tracer 18F-FDG and 68Ga-DOTATATE Dynamic PET-CT in Oncology

核医学 正电子发射断层摄影术 示踪剂 神经内分泌肿瘤 生长抑素受体 PET-CT 计算机科学 医学 生长抑素 物理 内科学 核物理学
作者
Wenxiang Ding,Jiangyuan Yu,Chaojie Zheng,Peng Fu,Qiu Huang,Dagan Feng,Zhi Yang,Richard L. Wahl,Yun Zhou
出处
期刊:IEEE Transactions on Medical Imaging [Institute of Electrical and Electronics Engineers]
卷期号:41 (2): 347-359 被引量:18
标识
DOI:10.1109/tmi.2021.3112783
摘要

68Ga-DOTATATE PET-CT is routinely used for imaging neuroendocrine tumor (NET) somatostatin receptor subtype 2 (SSTR2) density in patients, and is complementary to FDG PET-CT for improving the accuracy of NET detection, characterization, grading, staging, and predicting/monitoring NET responses to treatment. Performing sequential 18F-FDG and 68Ga-DOTATATE PET scans would require 2 or more days and can delay patient care. To align temporal and spatial measurements of 18F-FDG and 68Ga-DOTATATE PET, and to reduce scan time and CT radiation exposure to patients, we propose a single-imaging session dual-tracer dynamic PET acquisition protocol in the study. A recurrent extreme gradient boosting (rXGBoost) machine learning algorithm was proposed to separate the mixed 18F-FDG and 68Ga-DOTATATE time activity curves (TACs) for the region of interest (ROI) based quantification with tracer kinetic modeling. A conventional parallel multi-tracer compartment modeling method was also implemented for reference. Single-scan dual-tracer dynamic PET was simulated from 12 NET patient studies with 18F-FDG and 68Ga-DOTATATE 45-min dynamic PET scans separately obtained within 2 days. Our experimental results suggested an 18F-FDG injection first followed by 68Ga-DOTATATE with a minimum 5 min delayed injection protocol for the separation of mixed 18F-FDG and 68Ga-DOTATATE TACs using rXGBoost algorithm followed by tracer kinetic modeling is highly feasible.
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