Head movement poses a significant challenge in brain positron emission tomography (PET) imaging, resulting in image artifacts and tracer uptake quantification inaccuracies. Effective head motion estimation and correction are crucial for precise quantitative image analysis and accurate diagnosis of neurological disorders. Hardware-based motion tracking (HMT) has limited applicability in real-world clinical practice. To overcome this limitation, we propose a deep-learning head motion correction approach with cross-attention (DL-HMC++) to predict rigid head motion from one-second 3D PET raw data. DL-HMC++ is trained in a supervised manner by leveraging existing dynamic PET scans with gold-standard motion measurements from external HMT. We evaluate DL-HMC++ on two PET scanners (HRRT and mCT) and four radiotracers (18F-FDG, 18F-FPEB, 11C-UCB-J, and 11C-LSN3172176) to demonstrate the effectiveness and generalization of the approach in large cohort PET studies. Quantitative and qualitative results demonstrate that DL-HMC++ consistently outperforms state-of-the-art data-driven motion estimation methods, producing motion-free images with clear delineation of brain structures and reduced motion artifacts that are indistinguishable from gold-standard HMT. Brain region of interest standard uptake value analysis exhibits average difference ratios between DL-HMC++ and gold-standard HMT to be 1.2 plus-minus 0.5% for HRRT and 0.5 plus-minus 0.2% for mCT. DL-HMC++ demonstrates the potential for data-driven PET head motion correction to remove the burden of HMT, making motion correction accessible to clinical populations beyond research settings. The code is available at https://github.com/maxxxxxxcai/DL-HMC-TMI.

翻译：头部运动对脑部正电子发射断层扫描（PET）成像构成重大挑战，导致图像伪影和示踪剂摄取定量不准确。有效的头部运动估计与校对于精确的定量图像分析和神经系统疾病的准确诊断至关重要。基于硬件的运动追踪（HMT）在实际临床应用中适用性有限。为克服此限制，我们提出一种融合交叉注意力机制的深度学习头部运动校正方法（DL-HMC++），用于从一秒三维PET原始数据预测刚性头部运动。DL-HMC++通过利用现有动态PET扫描数据及来自外部HMT的金标准运动测量值进行监督式训练。我们在两种PET扫描仪（HRRT和mCT）和四种放射性示踪剂（18F-FDG、18F-FPEB、11C-UCB-J和11C-LSN3172176）上评估DL-HMC++，以证明该方法在大规模队列PET研究中的有效性和泛化能力。定量与定性结果表明，DL-HMC++始终优于最先进的数据驱动运动估计方法，生成的无运动图像能清晰勾勒脑部结构轮廓，其减少运动伪影的效果与金标准HMT难以区分。脑部感兴趣区标准摄取值分析显示，DL-HMC++与金标准HMT的平均差异比在HRRT上为1.2±0.5%，在mCT上为0.5±0.2%。DL-HMC++证明了数据驱动PET头部运动校正技术具备替代HMT负担的潜力，使运动校正能够应用于研究场景之外的临床人群。代码发布于https://github.com/maxxxxxxcai/DL-HMC-TMI。