Range Imaging for Motion Compensation in C-Arm Cone-Beam CT of Knees under Weight-Bearing Conditions

Abstract

C-arm cone-beam computed tomography (CBCT) has been used recently to acquire images of the human knee joint under weight-bearing conditions to assess knee joint health under load. However, involuntary patient motion during image acquisition leads to severe motion artifacts in the subsequent reconstructions. The state-of-the-art uses fiducial markers placed on the patient's knee to compensate for the induced motion artifacts. The placement of markers is time consuming, tedious, and requires user experience, to guarantee reliable motion estimates. To overcome these drawbacks, we recently investigated whether range imaging would allow to track, estimate, and compensate for patient motion using a range camera. We argue that the dense surface information observed by the camera could reveal more information than only a few surface points of the marker-based method. However, the integration of range-imaging with CBCT involves flexibility, such as where to position the camera and what algorithm to align the data with. In this work, three dimensional rigid body motion is estimated for synthetic data acquired with two different range camera trajectories: a static position on the ground and a dynamic position on the C-arm. Motion estimation is evaluated using two different types of point cloud registration algorithms: a pair wise Iterative Closest Point algorithm as well as a probabilistic group wise method. We compare the reconstruction results and the estimated motion signals with the ground truth and the current reference standard, a marker-based approach. To this end, we qualitatively and quantitatively assess image quality. The latter is evaluated using the Structural Similarity (SSIM). We achieved results comparable to the marker-based approach, which highlights the potential of both point set registration methods, for accurately recovering patient motion. The SSIM improved from 0.94 to 0.99 and 0.97 using the static and the dynamic camera trajectory, respectively. Accurate recovery of patient motion resulted in remarkable reduction in motion artifacts in the CBCT reconstructions, which is promising for future work with real data.

A C-arm photon counting CT prototype with volumetric coverage using multi-sweep step-and-shoot acquisitions

“…Taking neuro interventions as an example, the reliable differentiation between gray matter, white matter, fresh blood, and hyperattenuating sign of middle cerebral arteries requires a low-contrast head CT to consistently resolve sub-10 HU contrast differences (Gonzalez et al 2011), which can be provided by PCD-CT in the interventional suite. For anatomical regions where significant patient motion is unavoidable such as the abdomen, our future work will explore the use of motion control and motion artifact reduction methods such as respiratory gating, cushions or holding devices, fiducial markers, or range imaging (Bier et al 2018). Additionally, helical-like C-arm CT scan protocols (Cho et al 2008) that use continuous table translation rather than stop-and-start table translation while using the same back-and-forth C-arm short-scan gantry sweeps used in this work may offer a similar z-coverage with potentially lower scan times than step-and-shoot protocols.…”

Validation of watershed-based segmentation of the cartilage surface from sequential CT arthrography scans

“…This ability is particularly useful for techniques such as weight bearing CT imaging of the knee (9,10), which aim to detect small morphological changes in soft tissues such as articular cartilage compression over time. New developments in motion correction (11)(12)(13)(14)(15)(16)(17) have enabled imaging of weight bearing subjects with higher image quality. Weight bearing CT has been used previously to detect joint space width (18,19), knee alignment (18,20), and features of osteoarthritis (21) in bones, but examples of measurement of soft tissue morphology in the knee are limited due to difficulties in segmenting soft tissues from CT images.…”

CBCT of a Moving Sample From X-Rays and Multiple Videos

“…Motion artefact correction can be performed in postprocessing, and has been widely covered by relying on either specific anatomical models [6] or more generic approaches such as optical-flow [7]. Conceptually closer to our purpose, this issue has also been tackled by combining (RGB)D images to X-rays for motion tracking using either RGBD SLAM [8] or probabilistic ICP [9]. These methods only succeed on relatively minor, local motion artefacts.…”