المفاهيم الأساسية
A reinforcement learning approach is proposed to automatically generate an ultrasound scanning path that fully covers and reconstructs single or multiple target volumes beneath the rib cage, while minimizing acoustic attenuation and shadow effects.
الملخص
The content presents an approach for autonomous path planning of robotic ultrasound (US) imaging to fully cover and reconstruct regions of interest (e.g., liver tumors) that are occluded by the rib cage.
Key highlights:
Ultrasound imaging is challenging for thoracic applications due to acoustic shadows cast by the rib cage. An intercostal scanning path is necessary to properly visualize the target anatomy.
The authors propose a reinforcement learning (RL) framework to plan the scanning trajectory, using a virtual environment constructed from CT templates with randomly initialized tumors.
The state representation integrates 3D anatomical information (rib cage, scanning target, and US imaging plane) to cast the partially observable problem into a fully observable Markov decision process.
Task-specific reward functions are designed to encourage full coverage of the target volume, minimize acoustic attenuation, and avoid US shadows caused by bones.
Experiments on unseen patient data demonstrate the effectiveness of the proposed RL-based approach in planning non-shadowed US scanning trajectories in areas with limited acoustic access.
The trained RL model can handle single or multiple scanning targets, with success rates up to 95%, 92%, and 81% for small, medium, and large targets, respectively.
الإحصائيات
The average percentage of non-shadow volume during the scanning process is 95.6% and 95.2% for small and medium-sized targets, respectively.
The average distance between the probe and the scanning target is 36.6% and 36.8% of the cylinder radius for small and medium-sized targets, respectively.
اقتباسات
"To fully cover and reconstruct the region of interest in US for diagnosis, an intercostal scanning path is necessary."
"Structured anatomical information of the human skeleton is crucial for planning these intercostal paths."
"Task-specific state representation and reward functions are introduced to ensure the convergence of the training process while minimizing the effects of acoustic attenuation and shadows during scanning."