Rigid Motion Compensation in Interventional C-arm CT Using Consistency Measure on Projection Data

Frysch, Robert; Rose, Georg

doi:10.1007/978-3-319-24553-9_37

Cited by 16 publications

(22 citation statements)

References 5 publications

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“…We use z-motion, because z-translations produce inconsistencies. In contrast, motions within the acquisition plane do not necessarily violate the consistency measure [3,4]. We show our results on three phantoms (cf.…”

Section: Experiments and Resultsmentioning

confidence: 51%

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Maximum Likelihood Estimation of Head Motion Using Epipolar Consistency

et al. 2019

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Open gantry C-arm systems that are placed within the interventional room enable 3-D imaging and guidance for stroke therapy without patient transfer. This can profit in drastically reduced time-totherapy, however, due to the interventional setting, the data acquisition is comparatively slow. Thus, involuntary patient motion needs to be estimated and compensated to achieve high image quality. Patient motion results in a misalignment of the geometry and the acquired image data. Consistency measures can be used to restore the correct mapping to compensate the motion. They describe constraints on an idealized imaging process which makes them also sensitive to beam hardening, scatter, truncation or overexposure. We propose a probabilistic approach based on the Student's t-distribution to model image artifacts that affect the consistency measure without sourcing from motion. arXiv:1812.05405v2 [physics.med-ph]

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Section: Experiments and Resultsmentioning

confidence: 51%

“…This gives rise to a strong need for motion compensation algorithms [2]. In recent years, consistency conditions have been shown to be promising in this context [3,4]. Besides the compensation of motion, consistency measures are heavily used in cone-beam CT, as they provide a mathematical model constraining the imaging process.…”

Section: Introductionmentioning

confidence: 99%

Maximum Likelihood Estimation of Head Motion Using Epipolar Consistency

et al. 2019

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“…This consistency measure has been successfully applied for the compensation of motion perpendicular to the trajectory plane (out-plane) [8]. However, motion within the trajectory plane (in-plane) is merely detectable [8]. This poses the consistency condition improper as a cost function in a general motion compensation framework, but makes it a perfect fit as a constraint for the proposed framework.…”

Section: Cone-beam Consistency Constraintmentioning

confidence: 99%

“…This compensated arXiv:1911.13162v3 [cs.LG] 4 Dec 2019 trajectory is denoted as the motion free trajectory. Multiple methods for rigid motion estimation in transmission imaging have been proposed, which can be clustered in three categories: (1) image-based autofocus [5,6], (2) registrationbased [7] and (3) consistency-based [8,9]. Within those categories, learning-based approaches have been presented that detect anatomical landmarks for registration [10,11] or assess the reconstruction quality to guide an image-based autofocus [12].…”

Section: Introductionmentioning

confidence: 99%

Deep Autofocus with Cone-Beam CT Consistency Constraint

et al. 2020

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High quality reconstruction with interventional C-arm conebeam computed tomography (CBCT) requires exact geometry information. If the geometry information is corrupted, e. g., by unexpected patient or system movement, the measured signal is misplaced in the backprojection operation. With prolonged acquisition times of interventional C-arm CBCT the likelihood of rigid patient motion increases. To adapt the backprojection operation accordingly, a motion estimation strategy is necessary. Recently, a novel learning-based approach was proposed, capable of compensating motions within the acquisition plane. We extend this method by a CBCT consistency constraint, which was proven to be efficient for motions perpendicular to the acquisition plane. By the synergistic combination of these two measures, in and out-plane motion is well detectable, achieving an average artifact suppression of 93 %. This outperforms the entropy-based state-of-the-art autofocus measure which achieves on average an artifact suppression of 54 %.

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“…We define this plane coincident with the x, y-plane. Rigid patient motion or geometry misalignment can be estimated and compensated for using EC [7,8,9]. This is achieved by finding a rigid transformation T i for each projection matrix P i , accounting for the patient motion at acquisition of projection i.…”

Section: Epipolar Consistency Conditionsmentioning

confidence: 99%

Double Your Views – Exploiting Symmetry in Transmission Imaging

Preuhs¹,

Maier²,

Manhart³

et al. 2018

Medical Image Computing and Computer Assisted Intervention – MICCAI 2018

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For a plane symmetric object we can find two views -mirrored at the plane of symmetry -that will yield the exact same image of that object. In consequence, having one image of a plane symmetric object and a calibrated camera, we can automatically have a second, virtual image of that object if the 3-D location of the symmetry plane is known. In this work, we show for the first time that the above concept naturally extends to transmission imaging and present an algorithm to estimate the 3-D symmetry plane from a set of projection domain images based on Grangeat's theorem. We then exploit symmetry to generate a virtual trajectory by mirroring views at the plane of symmetry. If the plane is not perpendicular to the acquired trajectory plane, the virtual and real trajectory will be oblique. The resulting X-shaped trajectory will be data-complete, allowing for the compensation of in-plane motion using epipolar consistency. We evaluate the proposed method on a synthetic symmetric phantom and, in a proof-of-concept study, apply it to a real scan of an anthropomorphic human head phantom.

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Rigid Motion Compensation in Interventional C-arm CT Using Consistency Measure on Projection Data

Cited by 16 publications

References 5 publications

Maximum Likelihood Estimation of Head Motion Using Epipolar Consistency

Maximum Likelihood Estimation of Head Motion Using Epipolar Consistency

Deep Autofocus with Cone-Beam CT Consistency Constraint

Double Your Views – Exploiting Symmetry in Transmission Imaging

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