Epipolar Consistency in Transmission Imaging

Aichert, André; Berger, Martin; Wang, Jian; Maaß, Nicole; Doerfler, Arnd; Hornegger, Joachim; Maier, Andreas

doi:10.1109/tmi.2015.2426417

Cited by 58 publications

(73 citation statements)

References 16 publications

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“…Consistency‐based processing requires a series of non‐truncated images

D_{i}

, i = 1, …, M acquired in a known geometry that is defined by its projection matrices

{boldP}_{i} \in R^{3 \times 4}

. The epipolar geometry describes the geometric relation between two views

i_{1}

and

i_{2}

, where

i_{1 / 2} \in false{1, \dots, M false}

and

i_{1} \neq i_{2}

.…”

Section: Methodsmentioning

confidence: 99%

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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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“…Consistency‐based processing requires a series of non‐truncated images

D_{i}

, i = 1, …, M acquired in a known geometry that is defined by its projection matrices

{boldP}_{i} \in R^{3 \times 4}

. The epipolar geometry describes the geometric relation between two views

i_{1}

and

i_{2}

, where

i_{1 / 2} \in false{1, \dots, M false}

and

i_{1} \neq i_{2}

.…”

Section: Methodsmentioning

confidence: 99%

“…To omit the need for initial reconstructions, we build on a recently published motion estimation technique that is applied in projection domain and optimizes for consistency among all images. The method exploits redundancies in transmission imaging to derive data consistency conditions (DCC) from the epipolar geometry …”

Section: Introductionmentioning

confidence: 99%

Consistency‐based respiratory motion estimation in rotational angiography

et al. 2017

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“…They can be exploited, for example, in truncation and motion correction. Recently, the Epipolar Consistency Conditions (ECC) [1] have been derived from the epipolar geometry between two arbitrary flat-panel projections. The fact that they make no assumption on trajectory enables us to apply the ECC to patient tracking in fluoroscopy for interventional radiology.…”

mentioning

confidence: 99%

“…Let ρ I (l) = ρ I (α,t) denote the Radon transform of the projection image I at line l of angle α to the image u-axis and distance t to the origin. The Epipolar Consistency Conditions statewhere the derivative d dt in direction of line normals accounts for non-parallel ray geometries [1].In this work, we assume to have a fluoroscopic video of a patient and a set of reference projections of known projection geometry. Patient movement results in inconsistencies of the fluoroscopic image I 0 with respect to the reference projections.…”

mentioning

confidence: 99%

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Epipolar Consistency in Fluoroscopy for Image-Based Tracking

Aichert

Wang

Schaffert

et al. 2015

Procedings of the British Machine Vision Conference 2015

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Geometry and physics of absorption imaging impose certain constraints on X-ray projections. Various consistency conditions have been derived in the Computed Tomography (CT) community under the assumption of specific scanner trajectories. They can be exploited, for example, in truncation and motion correction. Recently, the Epipolar Consistency Conditions (ECC) [1] have been derived from the epipolar geometry between two arbitrary flat-panel projections. The fact that they make no assumption on trajectory enables us to apply the ECC to patient tracking in fluoroscopy for interventional radiology. For the first time, we perform 3D tracking of an object solely based on the consistency between 2D X-ray shots. The core idea is that before an intervention under fluoroscopy, between two and five reference images of the region of interest are acquired from different angles. We suggest, that an optimization of the ECC of an unseen image with respect to a few reference images enables us determine a rigid 3D pose of an object or patient.The ECC are based on the observation, that certain lines in X-ray images contain redundant information. Intuitively, integrating over a line in a projection image is roughly the same as integrating over the corresponding plane E of absorption coefficients through the object. The plane E is the plane through the source position which intersects the detector in the respective line. For the projection matrices P 0 , P 1 ∈ R 3×4 , the epipolar lines l 0 , l 1 ∈ P 2 are special lines in the two images, whose corresponding epipolar plane E is the same [2]:There are two redundant ways to compute the plane integral over E from either l 0 or l 1 , respectively. Epipolar Consistency can thus be quantified by taking several epipolar planes and measuring the difference between these redundant line integrals. Let ρ I (l) = ρ I (α,t) denote the Radon transform of the projection image I at line l of angle α to the image u-axis and distance t to the origin. The Epipolar Consistency Conditions statewhere the derivative d dt in direction of line normals accounts for non-parallel ray geometries [1].In this work, we assume to have a fluoroscopic video of a patient and a set of reference projections of known projection geometry. Patient movement results in inconsistencies of the fluoroscopic image I 0 with respect to the reference projections. Our goal is to estimate patient motion by minimizing these inconsistencies. A 6-DOF rigid motion is represented by the parameter vector ϕ . For each reference image I i we definite a metric over a set of epipolar planes E = {E 1 , . . . ,which should be minimized over patient motion ϕ . There are several assumptions involved in the ECC. Among those, first, an absorption-only model for X-ray physics is assumed when computing the "ray-sums", which neglects scatter and other non-linear effects of intensity. Second, the method is based on line-integrals in the projection images. Information is therefore obtained only in an orthogonal direction to these lines. It is essential,...

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Technical Note: Intrinsic raw data‐based CT misalignment correction without redundant data

et al. 2018

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Purpose: CT image reconstruction requires accurate knowledge of the used geometry or image quality might be degraded by misalignment artifacts. To overcome this issue, an intrinsic method, that is, a method not requiring a dedicated calibration phantom, to perform a raw data-based misalignment correction for CT is proposed herein that does not require redundant data and hence is applicable to measurements with less than 180 plus fan-angle of data. Methods: The forward projection of a volume reconstructed from a misaligned geometry resembles the acquired raw data if no redundant data are used, that is, if less than 180 plus fan-angle are used for image reconstruction. Hence, geometric parameters cannot be deduced from such data by an optimization of the geometry-dependent raw data fidelity. We propose to use a nonlinear transform applied to the reconstructed volume to introduce inconsistencies in the raw data that can be employed to estimate geometric parameters using less than 180 plus fan-angle of data. The proposed method is evaluated using simulations of the FORBILD head phantom and using actual measurements of a contrast-enhanced scan of a mouse acquired using a micro-CT. Results: Noisy simulations and actual measurements demonstrate that the proposed method is capable of correcting for artifacts arising from a misaligned geometry without redundant data while ensuring raw data fidelity. Conclusions: The proposed method extends intrinsic raw data-based misalignment correction methods to an angular range of 180 or less and is thus applicable to systems with a limited scan range.

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Epipolar Consistency in Transmission Imaging

Cited by 58 publications

References 16 publications

Consistency‐based respiratory motion estimation in rotational angiography

Consistency‐based respiratory motion estimation in rotational angiography

Epipolar Consistency in Fluoroscopy for Image-Based Tracking

Technical Note: Intrinsic raw data‐based CT misalignment correction without redundant data

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