A rigid motion correction method for helical computed tomography (CT)

Abstract

We propose a method to compensate for six degree-of-freedom rigid motion in helical CT of the head. The method is demonstrated in simulations and in helical scans performed on a 16-slice CT scanner. Scans of a Hoffman brain phantom were acquired while an optical motion tracking system recorded the motion of the bed and the phantom. Motion correction was performed by restoring projection consistency using data from the motion tracking system, and reconstructing with an iterative fully 3D algorithm. Motion correction accuracy was evaluated by comparing reconstructed images with a stationary reference scan. We also investigated the effects on accuracy of tracker sampling rate, measurement jitter, interpolation of tracker measurements, and the synchronization of motion data and CT projections. After optimization of these aspects, motion corrected images corresponded remarkably closely to images of the stationary phantom with correlation and similarity coefficients both above 0.9. We performed a simulation study using volunteer head motion and found similarly that our method is capable of compensating effectively for realistic human head movements. To the best of our knowledge, this is the first practical demonstration of generalized rigid motion correction in helical CT. Its clinical value, which we have yet to explore, may be significant. For example it could reduce the necessity for repeat scans and resource-intensive anesthetic and sedation procedures in patient groups prone to motion, such as young children. It is not only applicable to dedicated CT imaging, but also to hybrid PET/CT and SPECT/CT, where it could also ensure an accurate CT image for lesion localization and attenuation correction of the functional image data.

Head motion correction based on filtered backprojection for x‐ray CT imaging

“…CT acquisition of a rigidly moving object in a circular scan geometry can be considered equivalent to that of a static object in a scan geometry virtually transformed according to the object motion . We hence rebin the cone‐beam projection data according to the virtually transformed scan geometry.…”

“…On the other hand, there have been attempts to increase the gantry rotation speed so as to reduce motion artifacts, but blurring may still occur in the reconstructed image due to head motion regardless of how fast the rotation speed is in practice. A motion compensation approach via motion estimation by using an external optical motion tracking device is also proposed . This, however, requires an accurate synchronization between the scanner and the tracking device and the system complexity thereby increases.…”

Data sustained misalignment correction in microscopic cone beam CT via optimization under the Grangeat Epipolar consistency condition

“…The second category of misalignment correction is usually optimization based with no use of alignment phantom . Andò proposed to spatially register a geometric frame attached to the biological sample during data acquisition, but this approach should not be counted as a data sustained approach since the spatially registered frame is tantamount to a phantom .…”

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

“…While successful compensation has been achieved with fiducial-based methods in head CBCT (Jacobson and Stayman 2008, Kim et al 2015) and extremity CBCT (Choi et al 2014), the possible disadvantages of this technique include disruption of the imaging workflow and inaccuracies stemming from the assumption that the motion of the surface fiducials reflects the deformation of the internal structures. 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).…”