Validation of a novel cardiac motion correction algorithm for x-ray computed tomography: From phantom experiments to initial clinical experience

Lee, Duhgoon; Choi, Jiyoung; Kim, Hye‐Sun; Cho, Minkook; Lee, Kyoung‐Yong

doi:10.1371/journal.pone.0239511

Cited by 5 publications

(7 citation statements)

References 19 publications

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“…However, validation of these methods has been limited especially when correcting complex motions. However, the development of advanced phantoms such as XCAT [26] have recently enabled improved evaluation [27]. In addition, recent work has leverage machine learning to improve motion estimation [28].…”

Section: B Motion Estimation Approachesmentioning

confidence: 99%

Neural Computed Tomography

Gupta¹,

Colvert²,

Contijoch³

2022

Preprint

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Motion during acquisition of a set of projections can lead to significant motion artifacts in computed tomography reconstructions despite fast acquisition of individual views. In cases such as cardiac imaging, motion may be unavoidable and evaluating motion may be of clinical interest. Reconstructing images with reduced motion artifacts has typically been achieved by developing systems with faster gantry rotation or using algorithms which measure and/or estimate the displacements. However, these approaches have had limited success due to both physical constraints as well as the challenge of estimating/measuring non-rigid, temporally varying, and patient-specific motions. We propose a novel reconstruction framework, NeuralCT, to generate time-resolved images free from motion artifacts. Our approaches utilizes a neural implicit approach and does not require estimation or modeling of the underlying motion. Instead, boundaries are represented using a signed distance metric and neural implicit framework. We utilize 'analysis-by-synthesis' to identify a solution consistent with the acquired sinogram as well as spatial and temporal consistency constraints. We illustrate the utility of NeuralCT in three progressively more complex scenarios: translation of a small circle, heartbeat-like change in an ellipse's diameter, and complex topological deformation. Without hyperparameter tuning or change to the architecture, NeuralCT provides high quality image reconstruction for all three motions, as compared to filtered backprojection using mean-square-error and Dice metrics. Code and supplemental movies are available at https://kunalmgupta.github.io/projects/NeuralCT.html.

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Section: B Motion Estimation Approachesmentioning

confidence: 99%

Neural Computed Tomography

Gupta¹,

Colvert²,

Contijoch³

2022

Preprint

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“…We are of the opinion that the motion estimation based on two reference images could be a better option over a pure image-based approach, since two complete or incomplete images could provide motion information and guide the search for the direction and amount of motion. The concept of making use of two conjugate partial angle reconstruction (PAR) images and its efficacy in cardiac motion correction were already reported in literature 10 – 15 . The proposed method serves as an extension of the original concept 10 , 11 to non-gating CT scans and deals with lung and thoracic motions.…”

Section: Introductionmentioning

confidence: 99%

“…Handling other cardiac motion correction algorithms would be beyond the scope of this paper. Some related works can be found in our latest study 15 .…”

Section: Introductionmentioning

confidence: 99%

“…We adopted an FBP-type reconstruction to provide a natural noise appearance that is favorable for radiologists. For more details, please refer to the previous works 8 , 9 , 15 .…”

Section: Introductionmentioning

confidence: 99%

“…reconstruction (PAR) images and its efficacy in cardiac motion correction were already reported in literature [10][11][12][13][14][15] . The proposed method serves as an extension of the original concept 10,11 to non-gating CT scans and deals with lung and thoracic motions.…”

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confidence: 99%

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Motion correction for routine X-ray lung CT imaging

Kim

Choi

Lee

et al. 2021

Sci Rep

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A novel motion correction algorithm for X-ray lung CT imaging has been developed recently. It was designed to perform for routine chest or thorax CT scans without gating, namely axial or helical scans with pitch around 1.0. The algorithm makes use of two conjugate partial angle reconstruction images for motion estimation via non-rigid registration which is followed by a motion compensated reconstruction. Differently from other conventional approaches, no segmentation is adopted in motion estimation. This makes motion estimation of various fine lung structures possible. The aim of this study is to explore the performance of the proposed method in correcting the lung motion artifacts which arise even under routine CT scans with breath-hold. The artifacts are known to mimic various lung diseases, so it is of great interest to address the problem. For that purpose, a moving phantom experiment and clinical study (seven cases) were conducted. We selected the entropy and positivity as figure of merits to compare the reconstructed images before and after the motion correction. Results of both phantom and clinical studies showed a statistically significant improvement by the proposed method, namely up to 53.6% (p < 0.05) and up to 35.5% (p < 0.05) improvement by means of the positivity measure, respectively. Images of the proposed method show significantly reduced motion artifacts of various lung structures such as lung parenchyma, pulmonary vessels, and airways which are prominent in FBP images. Results of two exemplary cases also showed great potential of the proposed method in correcting motion artifacts of the aorta which is known to mimic aortic dissection. Compared to other approaches, the proposed method provides an excellent performance and a fully automatic workflow. In addition, it has a great potential to handle motions in wide range of organs such as lung structures and the aorta. We expect that this would pave a way toward innovations in chest and thorax CT imaging.

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DiFiR‐CT: Distance field representation to resolve motion artifacts in computed tomography

et al. 2023

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Background: Motion during data acquisition leads to artifacts in computed tomography (CT) reconstructions. In cases such as cardiac imaging, not only is motion unavoidable, but evaluating the motion of the object is of clinical interest. Reducing motion artifacts has typically been achieved by developing systems with faster gantry rotation or via algorithms which measure and/or estimate the displacement. However, these approaches have had limited success due to both physical constraints as well as the challenge of estimating non-rigid, temporally varying, and patient-specific motion fields. Purpose: To develop a novel reconstruction method which generates timeresolved, artifact-free images without estimation or explicit modeling of the motion. Methods:We describe an analysis-by-synthesis approach which progressively regresses a solution consistent with the acquired sinogram. In our method, we focus on the movement of object boundaries. Not only are the boundaries the source of image artifacts, but object boundaries can simultaneously be used to represent both the object as well as its motion over time without the need for an explicit motion model. We represent the object boundaries via a signed distance function (SDF) which can be efficiently modeled using neural networks. As a result, optimization can be performed under spatial and temporal smoothness constraints without the need for explicit motion estimation. Results: We illustrate the utility of DiFiR-CT in three imaging scenarios with increasing motion complexity: translation of a small circle, heart-like change in an ellipse's diameter, and a complex topological deformation. Compared to filtered backprojection, DiFiR-CT provides high quality image reconstruction for all three motions without hyperparameter tuning or change to the architecture. We also evaluate DiFiR-CT's robustness to noise in the acquired sinogram and found its reconstruction to be accurate across a wide range of noise levels. Lastly, we demonstrate how the approach could be used for multi-intensity scenes and illustrate the importance of the initial segmentation providing a realistic initialization. Code and supplemental movies are available at https://kunalmgupta.github.io/projects/DiFiR-CT.html. Conclusions: Projection data can be used to accurately estimate a temporallyevolving scene without the need for explicit motion estimation using a neural implicit representation and analysis-by-synthesis approach.

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Validation of a novel cardiac motion correction algorithm for x-ray computed tomography: From phantom experiments to initial clinical experience

Cited by 5 publications

References 19 publications

Neural Computed Tomography

Neural Computed Tomography

Motion correction for routine X-ray lung CT imaging

DiFiR‐CT: Distance field representation to resolve motion artifacts in computed tomography

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