WE‐AB‐BRA‐08: Correction of Patient Motion in C‐Arm Cone‐Beam CT Using 3D‐2D Registration

Abstract

Purpose: Intraoperative C‐arm cone‐beam CT (CBCT) is subject to artifacts arising from patient motion during the fairly long (∼5–20 s) scan times. We present a fiducial free method to mitigate motion artifacts using 3D‐2D image registration that simultaneously corrects residual errors in geometric calibration. Methods: A 3D‐2D registration process was used to register each projection to DRRs computed from the 3D image by maximizing gradient orientation (GO) using the CMA‐ES optimizer. The resulting rigid 6 DOF transforms were applied to the system projection matrices, and a 3D image was reconstructed via model‐based image reconstruction (MBIR, which accommodates the resulting noncircular orbit). Experiments were conducted using a Zeego robotic C‐arm (20 s, 200°, 496 projections) to image a head phantom undergoing various types of motion: 1) 5° lateral motion; 2) 15° lateral motion; and 3) 5° lateral motion with 10 mm periodic inferior‐superior motion. Images were reconstructed using a penalized likelihood (PL) objective function, and structural similarity (SSIM) was measured for axial slices of the reconstructed images. A motion‐free image was acquired using the same protocol for comparison. Results: There was significant improvement (p < 0.001) in the SSIM of the motion‐corrected (MC) images compared to uncorrected images. The SSIM in MC‐PL images was >0.99, indicating near identity to the motion‐free reference. The point spread function (PSF) measured from a wire in the phantom was restored to that of the reference in each case. Conclusion: The 3D‐2D registration method provides a robust framework for mitigation of motion artifacts and is expected to hold for applications in the head, pelvis, and extremities with reasonably constrained operative setup. Further improvement can be achieved by incorporating multiple rigid components and non‐rigid deformation within the framework. The method is highly parallelizable and could in principle be run with every acquisition. Research supported by National Institutes of Health Grant No. R01‐EB‐017226 and academic‐industry partnership with Siemens Healthcare (AX Division, Forcheim, Germany).

An ex vivo study of automated motion artefact correction and the impact on cone beam CT image quality and interpretability

“…3 This approach has shown good results for thoracic and abdominal areas 18 and has also been mentioned for imaging of the head and neck region. 19 This is, however, the first study to qualitatively assess the impact of automated motion artefact correction on CBCT image quality and interpretability for common dentomaxillofacial tasks. Various approaches to correct CBCT data sets for motion have been suggested.…”

“…Various approaches to correct CBCT data sets for motion have been suggested. 19 An important step refers to the accuracy of the method used to detect and quantify (i.e. track) patient movement.…”

“…One possibility is to rely solely on the raw image projections generated during an examination, tracking patient movement based on optical flow measurements. 6,19 This method, however, might not be accurate enough to detect small-distance movements which take place perpendicular to the CBCT detector. 6 Further, as optical flow measurements are directly related to brightness variation in the images, it may be affected by the innate voxel value variation present in CBCT data sets, leading to inaccuracy in patient movement tracking.…”

“…[20][21][22][23][24] The use of movement tracking sensors and fiducial markers placed on the patient is another possibility. 19 A recent study suggested to use accelerometers and gyroscopes to track patient head movements. 11 Such systems, however, might have their precision affected by their innate inaccuracy, which induces errors in the measured angular and spatial positions.…”

Motion compensation in extremity cone-beam CT using a penalized image sharpness criterion

“…Fiducial-free motion compensation using 2D/3D registration that relied only on the data available in the motion-contaminated scan has been investigated, showing degraded performance compared to the fiducial-based approach (Unberath et al 2015). Better results, approaching or even surpassing the fiducial-based algorithm, were obtained using a 2D/3D registration with a motion-free prior CT or CBCT (Berger et al 2016, Ouadah et al 2016a). However, such prior information is not always available.…”