Real-time data-driven rigid motion detection and correction for brain scan with listmode PET

Patient motion can cause image blurring effects and mismatch of attenuation maps during a brain scan. To detect and correct patient head movement during a PET scan, external devices are usually used. The goal of this study is to present a method that automatically detects and corrects rigid patient motion in real time, without any additional external device using only the listmode data. Within each time interval (1 second) the listmode data were first rebinned into 3D sinogram. First and second moment of the data (expectation and variance) was then calculated for each angle. The mathematical relationship of the expected values, covariance matrix of the three dimensions in the image space and the calculated first and second moment from the listmode data were then derived. The expected values of the three dimensions in the image spaces represent the shift information, and the eigenvector matrix of the covariance matrix represents the rotation information. The acquired motion information was then applied directly on the listmode data to reduce motion effects. Several brain scans were carried out with FDG tracer to evaluate the method. Conventional method using dynamic frames were also implemented to compare with the new method. The measured translation and rotation information over time using both new listmode based approach and conventional dynamic frame approach matched very well. The motion corrected images using the new method shows much reduced blurring effects. This result demonstrated the possibility of applying data-driven approach for detection and correction of patient head movement during PET scans without adding additional hardware and procedure, or additional post processing time.