Local Tomography and the Motion Estimation Problem

Katsevich, Alexander; Silver, Michael; Zamyatin, Alexander A.

doi:10.1137/100796728

Cited by 34 publications

(45 citation statements)

References 28 publications

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“…This estimation problem is studied in [16] for linear scaling. An iterative estimation method based on edge entropy is proposed in [9].…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of dynamic objects with affine deformations in computerized tomography

Hahn

2013

Journal of Inverse and Ill-Posed Problems

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The data acquisition in computerized tomography takes a certain amount of time since the x-ray source has to be rotated around the specimen. An object that changes during the scanning causes inconsistent data sets. To avoid the motion artefacts in reconstructions, the algorithm has to take the dynamic behavior of the specimen into account. In this context, some a priori information about the movement is required. A reconstruction method is proposed that compensates for the motion with a special focus on affine deformations. It also permits the combination of reconstruction and image analysis tools to extract features of the object without motion artefacts. The algorithm is validated with a numerical example from medical imaging.

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“…This estimation problem is studied in [16] for linear scaling. An iterative estimation method based on edge entropy is proposed in [9].…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of dynamic objects with affine deformations in computerized tomography

Hahn

2013

Journal of Inverse and Ill-Posed Problems

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“…23 The motion field parameters are iteratively adjusted by means of a gradient descent procedure, such that the MAM of the motion compensated reconstruction is optimized in the segmented regions, i.e., the motion artifacts are minimized. In contrast to previous work, 21,22 only analytical methods are used in the definition of the motion model, MAM, and reconstruction algorithm. This allows for an efficient handling of the MAM minimization problem despite the large number of motion parameters.…”

Section: Iia Proposed Approachmentioning

confidence: 99%

“…Furthermore, our algorithm differs from the approach presented in Ref. 22 because our algorithm involves a presegmentation of the coronary arteries, is not based on local tomography and does not involve thresholding operations.…”

Section: Iia Proposed Approachmentioning

confidence: 99%

Improving best‐phase image quality in cardiac CT by motion correction with MAM optimization

et al. 2013

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“…image entropy, total variation or a gradient-based metric). Autofocus techniques have been previously employed in CT and CBCT for correction of geometric misalignment (Wicklein et al 2013, Kyriakou et al 2008, Kingston et al 2011) and for motion compensation in cardiac (Brehm et al 2015, Wicklein et al 2015, Katsevich et al 2011, Hahn et al 2016) and head imaging (Wicklein et al 2013). Early application to extremities CBCT was reported in (Sisniega et al 2016).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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Cone-beam CT (CBCT) for musculoskeletal imaging would benefit from a method to reduce the effects of involuntary patient motion. In particular, the continuing improvement in spatial resolution of CBCT may enable tasks such as quantitative assessment of bone microarchitecture (0.1 mm – 0.2 mm detail size), where even subtle, sub-mm motion blur might be detrimental. We propose a purely image based motion compensation method that requires no fiducials, tracking hardware or prior images. A statistical optimization algorithm (CMA-ES) is used to estimate a motion trajectory that optimizes an objective function consisting of an image sharpness criterion augmented by a regularization term that encourages smooth motion trajectories. The objective function is evaluated using a volume of interest (VOI, e.g. a single bone and surrounding area) where the motion can be assumed to be rigid. More complex motions can be addressed by using multiple VOIs. Gradient variance was found to be a suitable sharpness metric for this application. The performance of the compensation algorithm was evaluated in simulated and experimental CBCT data, and in a clinical dataset. Motion-induced artifacts and blurring were significantly reduced across a broad range of motion amplitudes, from 0.5 mm to 10 mm. Structure Similarity Index (SSIM) against a static volume was used in the simulation studies to quantify the performance of the motion compensation. In studies with translational motion, the SSIM improved from 0.86 before compensation to 0.97 after compensation for 0.5 mm motion, from 0.8 to 0.94 for 2 mm motion and from 0.52 to 0.87 for 10 mm motion (~70% increase). Similar reduction of artifacts was observed in a benchtop experiment with controlled translational motion of an anthropomorphic hand phantom, where SSIM (against a reconstruction of a static phantom) improved from 0.3 to 0.8 for 10 mm motion. Application to a clinical dataset of a lower extremity showed dramatic reduction of streaks and improvement in delineation of tissue boundaries and trabecular structures throughout the whole volume. The proposed method will support new applications of extremity CBCT in areas where patient motion may not be sufficiently managed by immobilization, such as imaging under load and quantitative assessment of subchondral bone architecture.

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Local Tomography and the Motion Estimation Problem

Cited by 34 publications

References 28 publications

Reconstruction of dynamic objects with affine deformations in computerized tomography

Reconstruction of dynamic objects with affine deformations in computerized tomography

Improving best‐phase image quality in cardiac CT by motion correction with MAM optimization

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

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