Image Quality Assessment for Rigid Motion Compensation
This addresses image quality degradation in stroke diagnostic imaging, enabling faster endovascular therapy, though it is an incremental improvement over existing motion compensation techniques.
The paper tackles rigid patient motion artifacts in C-arm cone-beam CT for stroke imaging by developing a neural network-guided autofocus method to estimate motion trajectories, achieving superior results compared to an entropy-based benchmark method.
Diagnostic stroke imaging with C-arm cone-beam computed tomography (CBCT) enables reduction of time-to-therapy for endovascular procedures. However, the prolonged acquisition time compared to helical CT increases the likelihood of rigid patient motion. Rigid motion corrupts the geometry alignment assumed during reconstruction, resulting in image blurring or streaking artifacts. To reestablish the geometry, we estimate the motion trajectory by an autofocus method guided by a neural network, which was trained to regress the reprojection error, based on the image information of a reconstructed slice. The network was trained with CBCT scans from 19 patients and evaluated using an additional test patient. It adapts well to unseen motion amplitudes and achieves superior results in a motion estimation benchmark compared to the commonly used entropy-based method.