Method for robotic motion compensation during PET imaging of mobile subjects (2311.17861v1)

Abstract: Studies of the human brain during natural activities, such as locomotion, would benefit from the ability to image deep brain structures during these activities. While Positron Emission Tomography (PET) can image these structures, the bulk and weight of current scanners are not compatible with the desire for a wearable device. This has motivated the design of a robotic system to support a PET imaging system around the subject's head and to move the system to accommodate natural motion. We report here the design and experimental evaluation of a prototype robotic system that senses motion of a subject's head, using parallel string encoders connected between the robot-supported imaging ring and a helmet worn by the subject. This measurement is used to robotically move the imaging ring (coarse motion correction) and to compensate for residual motion during image reconstruction (fine motion correction). Minimization of latency and measurement error are the key design goals, respectively, for coarse and fine motion correction. The system is evaluated using recorded human head motions during locomotion, with a mock imaging system consisting of lasers and cameras, and is shown to provide an overall system latency of about 80 ms, which is sufficient for coarse motion correction and collision avoidance, as well as a measurement accuracy of about 0.5 mm for fine motion correction.

• The paper presents a robotic solution that compensates for head movement during PET imaging with both coarse mechanical and fine digital corrections.
• The system uses string encoders to accurately measure head position, achieving approximately 0.5mm precision for motion tracking.
• Experimental results show an overall system latency of about 80ms for coarse correction, enabling dynamic neuroimaging studies.

Introduction

Positron Emission Tomography (PET) is a valuable imaging technique used in medical research and diagnosis. PET captures images of the body's internal function by detecting radioactive tracers, making it a powerful tool for studying organ activity. Specifically, imaging the human brain during dynamic activities like walking can provide insights into the functioning of deep brain structures. However, a significant challenge in conducting such studies is that current PET scanners are too cumbersome to be worn by a person during natural activities.

System Design

Researchers have developed a prototype robotic system designed to support a PET imaging system around a subject's head, compensating for the subject's movement. This system uses string encoders attached to both a robotic arm and a helmet worn by the subject. These encoders provide accurate measurements of the head's position and movement, allowing the robot to adjust the position of the PET imaging ring accordingly. The goal is for the robot to perform two types of motion corrections: coarse correction by physically moving the PET imaging ring, and fine correction by adjusting image reconstruction based on residual motion.

Experimental Evaluation

The researchers conducted experiments using mock-ups of the PET imaging system and emulated human motion using a separate robotic system. They were able to record and emulate typical human head motions and then test the robotic compensation system's performance against these movements. Preliminary results showed that the robotic system could maintain a close proximity to the moving head, with an overall system latency of approximately 80ms for coarse motion correction. The string encoders exhibited a static measurement accuracy of about 0.5mm, which is within the desired precision for fine motion correction during image reconstruction.

Conclusion and Future Work

The findings suggest that the robotic system is capable of compensating for human head motion to a degree that is applicable for real-time neuroimaging studies. This opens up opportunities for research into brain activity in scenarios that were not previously possible with stationary PET scanners. In the future, improvements are sought in measurement accuracy and the development of predictive control strategies. Additionally, scaling the system to support actual PET imaging devices will be necessary, alongside rigorous risk assessment and safety design due to the close proximity of the robotic system to human subjects.