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

Jang, Seokhwan; Kim, Seungeon; Kim, Mina; Beom, Jong

doi:10.1002/mp.12705

Cited by 13 publications

(11 citation statements)

References 46 publications

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“…Sun et al devised an iterative motion estimation and compensation scheme for helical CT [14]. Jang et al proposed a motion estimation and compensation method based on filtered back projection for CBCT [15]. Chen et al presented a motion artifact correction method based on local linear embedding for CBCT [16].…”

Section: Introductionmentioning

confidence: 99%

A Rigid Motion Artifact Reduction Method for CT Based on Blind Deconvolution

Zhang

2019

Algorithms

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In computed tomography (CT), artifacts due to patient rigid motion often significantly degrade image quality. This paper suggests a method based on iterative blind deconvolution to eliminate motion artifacts. The proposed method alternately reconstructs the image and reduces motion artifacts in an iterative scheme until the difference measure between two successive iterations is smaller than a threshold. In this iterative process, Richardson–Lucy (RL) deconvolution with spatially adaptive total variation (SATV) regularization is inserted into the iterative process of the ordered subsets expectation maximization (OSEM) reconstruction algorithm. The proposed method is evaluated on a numerical phantom, a head phantom, and patient scan. The reconstructed images indicate that the proposed method can reduce motion artifacts and provide high-quality images. Quantitative evaluations also show the proposed method yielded an appreciable improvement on all metrics, reducing root-mean-square error (RMSE) by about 30% and increasing Pearson correlation coefficient (CC) and mean structural similarity (MSSIM) by about 15% and 20%, respectively, compared to the RL-OSEM method. Furthermore, the proposed method only needs measured raw data and no additional measurements are needed. Compared with the previous work, it can be applied to any scanning mode and can realize six degrees of freedom motion artifact reduction, so the artifact reduction effect is better in clinical experiments.

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Section: Introductionmentioning

confidence: 99%

A Rigid Motion Artifact Reduction Method for CT Based on Blind Deconvolution

Zhang

2019

Algorithms

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“…Apart from cone-beam artifacts, there are many other types of artifacts in dental CT images such as metal artifacts [ 24 , 25 , 26 ], motion artifacts [ 27 , 28 , 29 ], and limited-view-induced streak artifacts [ 30 , 31 , 32 ]. These artifacts can also induce errors in the 3D models.…”

Section: Discussionmentioning

confidence: 99%

Cone-Beam Angle Dependency of 3D Models Computed from Cone-Beam CT Images

Cho

Hegazy²,

Lee

2022

Sensors

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Cone-beam dental CT can provide high-precision 3D images of the teeth and surrounding bones. From the 3D CT images, 3D models, also called digital impressions, can be computed for CAD/CAM-based fabrication of dental restorations or orthodontic devices. However, the cone-beam angle-dependent artifacts, mostly caused by the incompleteness of the projection data acquired in the circular cone-beam scan geometry, can induce significant errors in the 3D models. Using a micro-CT, we acquired CT projection data of plaster cast models at several different cone-beam angles, and we investigated the dependency of the model errors on the cone-beam angle in comparison with the reference models obtained from the optical scanning of the plaster models. For the 3D CT image reconstruction, we used the conventional Feldkamp algorithm and the combined half-scan image reconstruction algorithm to investigate the dependency of the model errors on the image reconstruction algorithm. We analyzed the mean of positive deviations and the mean of negative deviations of the surface points on the CT-image-derived 3D models from the reference model, and we compared them between the two image reconstruction algorithms. It has been found that the model error increases as the cone-beam angle increases in both algorithms. However, the model errors are smaller in the combined half-scan image reconstruction when the cone-beam angle is as large as 10 degrees.

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“…The practicality of this approach was limited until recently by the extensive computation required to perform motion-corrected ML-EM reconstruction, which made the method too slow for clinical use. However, recent efforts to use analytical reconstruction algorithms such as FBP and FDK are bringing reconstruction times closer to clinically acceptable timeframes (Bruder et al 2016, Jang et al 2018, Nuyts and Fulton 2020. With further acceleration motion correction could become a clinically feasible option in head CT in the future.…”

Section: Discussionmentioning

confidence: 99%

“…The use of iterative reconstruction algorithms in conjunction with a virtual source/detector trajectory to obtain motion-corrected images can result in reconstruction times orders of magnitude longer than conventional analytical reconstruction algorithms used in clinical CT. Recently, the analytical reconstruction algorithms FBP and FDK have been successfully applied to a virtual source/detector trajectory to provide motion-corrected helical CT images in much shorter times (Bruder et al 2016, Jang et al 2018, Nuyts and Fulton 2020. These methods have the potential to be easily integrated into clinical imaging protocols as they can be applied retrospectively to raw CT datasets with no a priori knowledge of the motion.…”

Section: Ctmentioning

confidence: 99%

Motion estimation and correction in SPECT, PET and CT

Kyme¹,

Fulton²

2021

Phys. Med. Biol.

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Patient motion impacts single photon emission computed tomography (SPECT), positron emission tomography (PET) and x-ray computed tomography (CT) by giving rise to projection data inconsistencies that can manifest as reconstruction artifacts, thereby degrading image quality and compromising accurate image interpretation and quantification. Methods to estimate and correct for patient motion in SPECT, PET and CT have attracted considerable research effort over several decades. The aims of this effort have been two-fold: to estimate relevant motion fields characterizing the various forms of voluntary and involuntary motion; and to apply these motion fields within a modified reconstruction framework to obtain motion-corrected images. The aims of this review are to outline the motion problem in medical imaging and to critically review published methods for estimating and correcting for the relevant motion fields in clinical and preclinical SPECT, PET and CT. Despite many similarities in how motion is handled between these modalities, utility and applications vary based on differences in temporal and spatial resolution. Technical feasibility has been demonstrated in each modality for both rigid and non-rigid motion but clinical feasibility remains an important target. There is considerable scope for further developments in motion estimation and correction, and particularly in data-driven methods that will aid clinical utility. State-of-the-art deep learning methods may have a unique role to play in this context.

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

Cited by 13 publications

References 46 publications

A Rigid Motion Artifact Reduction Method for CT Based on Blind Deconvolution

A Rigid Motion Artifact Reduction Method for CT Based on Blind Deconvolution

Cone-Beam Angle Dependency of 3D Models Computed from Cone-Beam CT Images

Motion estimation and correction in SPECT, PET and CT

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