Cardiorespiratory motion‐compensated micro‐CT image reconstruction using an artifact model‐based motion estimation

Brehm, Marcus; Sawall, Stefan; Maier, Joscha; Sauppe, Sebastian; Kachelrieß, Marc

doi:10.1118/1.4916083

Cited by 31 publications

(38 citation statements)

References 32 publications

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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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Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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“…Various software approaches have been explored for the correction of different motions in the human body. Cardiac or respiratory motion correction algorithms have been introduced, in which the motion is considered nonrigid . Several algorithms have also been investigated for rigid motion correction of extremities.…”

Section: Introductionmentioning

confidence: 99%

“…Cardiac or respiratory motion correction algorithms have been introduced, in which the motion is considered nonrigid. [9][10][11][12][13][14] Several algorithms have also been investigated for rigid motion correction of extremities. Among them, some adopt several fiducial markers to find the motion without using any external tracking devices.…”

Section: Introductionmentioning

confidence: 99%

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

Jang

Kim

et al. 2017

Medical Physics

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We propose a framework for head motion correction in an axial CT scan, which consists of motion estimation and compensation steps. Two image-based ME algorithms for rigid motion tracking are developed according to the degree of head motion. The estimated motion information is then used for MC image reconstruction. Both motion estimation and compensation algorithms are based on computationally efficient filtered backprojection. Excellent performance of the proposed framework is illustrated by means of simulations using a numerical phantom and experiments using a physical phantom.

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“…In contrast with cardiac motion, respiratory motion is low frequency such that only very few recurrences are observed during a standard acquisition. Respiratory phase gating or binning strategies followed by cyclic registration have proved robust in image‐guided radiation therapy . Unfortunately, in the context of rotational angiography respiratory phase binning is impossible as it would result in a highly ill‐posed problem, impeding the application of methods proposed by Sonke et al., Li et al., and Brehm et al .…”

Section: Introductionmentioning

confidence: 99%

“…Respiratory phase gating or binning strategies followed by cyclic registration have proved robust in image-guided radiation therapy. [11][12][13][14][15] Unfortunately, in the context of rotational angiography respiratory phase binning is impossible as it would result in a highly ill-posed problem, impeding the application of methods proposed by Sonke et al, 11 Li et al, 12 and Brehm et al 13,14 A prominent solution is to avoid the problem as a whole by requiring patients to hold their breath throughout the acquisition. If breath-hold is impossible or imperfect, motion estimation techniques must be applied to mitigate the corruption due to respiration and, finally, enable 3D reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Consistency‐based respiratory motion estimation in rotational angiography

et al. 2017

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Purpose: Rotational coronary angiography enables 3D reconstruction but suffers from intra-scan cardiac and respiratory motion. While gating handles cardiac motion, respiratory motion requires compensation. State-of-the-art algorithms rely on 3D-2D registration that depends on initial reconstructions of sufficient quality. We propose a compensation method that is applied directly in projection domain. It overcomes the need for reconstruction and thus complements the state-of-the-art. Methods: Virtual single-frame background subtraction based on vessel segmentation and spectral deconvolution yields non-truncated images of the contrasted lumen. This allows motion compensation based on data consistency conditions. We compensate craniocaudal shifts by optimizing epipolar consistency to (a) devise an image-based surrogate for cardiac motion and (b) compensate for respiratory motion. We validate our approach in two numerical phantom studies and three clinical cases. Results: Correlation of the image-based surrogate for cardiac motion with the ECG-based ground truth was excellent yielding a Pearson correlation of 0.93 AE 0.04. Considering motion compensation, the target error measure decreased by 98% and 69%, respectively, for the phantom experiments while for the clinical cases the same figure of merit improved by 46 AE 21%. Conclusions: The proposed method is entirely image-based and accurately estimates craniocaudal shifts due to respiration and cardiac contraction. Future work will investigate experimental trajectories and possibilities for simplification of the single-frame subtraction pipeline.

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Cardiorespiratory motion‐compensated micro‐CT image reconstruction using an artifact model‐based motion estimation

Cited by 31 publications

References 32 publications

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

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

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

Consistency‐based respiratory motion estimation in rotational angiography

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