Coronary artery motion estimation and compensation: A feasibility study

Iatrou, Maria; Pack, Jed D.; Bhagalia, Roshni; Bequé, D.; Seamans, John

doi:10.1109/nssmic.2010.5874307

Cited by 4 publications

(14 citation statements)

References 3 publications

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“…Because of the complexity of motion artifacts, it is difficult to quantify these motion artifact effects individually. Therefore, previous studies have proposed motion artifact metrics that evaluate the overall vessel image quality …”

Section: Methodologies and Materialsmentioning

confidence: 99%

“…Numerous improvements have been made to CCTA acquisition techniques to increase the temporal resolution and to reduce the effects of vessel motion, including retrospective and prospective ECG gating, dual‐source acquisition, and single‐beat wide‐cone beam imaging . Algorithms have also been introduced to reduce the effects of vessel motion after acquisition, including motion compensated reconstruction algorithms, motion correction algorithms, and automatic determination of the lowest‐motion phase for viewing . While these efforts have reduced the effects of motion, residual vessel motion artifacts such as blur, deformation, and shading may still be present.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of motion artifact metrics for coronary CT angiography

Gros²,

Szabó

et al. 2018

Medical Physics

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Section: Methodologies and Materialsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of motion artifact metrics for coronary CT angiography

Gros²,

Szabó

et al. 2018

Medical Physics

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“…One correction approach uses motion artifacts metrics (MAM) to quantify image quality and then optimizes the CT reconstruction based on a MAM gradient descent procedure . Another approach characterizes artery motion by a bidirectional label point matching method and then compensates the motion to a target phase during reconstruction …”

Section: Introductionmentioning

confidence: 99%

“…The automated motion artifact decision method could also improve workflow by enabling automatic application of motion correction only for datasets that need correction, while minimizing computation time for studies of adequate diagnostic quality. For example, previously proposed motion correction algorithms require multiple or iterative reconstructions that may be time consuming . The performance of the Motion IQ Decision method, based on the Motion Artifact Quantification algorithm, is evaluated in this work through an observer study.…”

Section: Introductionmentioning

confidence: 99%

Automated quantification and evaluation of motion artifact on coronary CT angiography images

Gros²,

Baginski

et al. 2018

Medical Physics

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Purpose This study developed and validated a Motion Artifact Quantification algorithm to automatically quantify the severity of motion artifacts on coronary computed tomography angiography (CCTA) images. The algorithm was then used to develop a Motion IQ Decision method to automatically identify whether a CCTA dataset is of sufficient diagnostic image quality or requires further correction. Method The developed Motion Artifact Quantification algorithm includes steps to identify the right coronary artery (RCA) regions of interest (ROIs), segment vessel and shading artifacts, and to calculate the motion artifact score (MAS) metric. The segmentation algorithms were verified against ground‐truth manual segmentations. The segmentation algorithms were also verified by comparing and analyzing the MAS calculated from ground‐truth segmentations and the algorithm‐generated segmentations. The Motion IQ Decision algorithm first identifies slices with unsatisfactory image quality using a MAS threshold. The algorithm then uses an artifact‐length threshold to determine whether the degraded vessel segment is large enough to cause the dataset to be nondiagnostic. An observer study on 30 clinical CCTA datasets was performed to obtain the ground‐truth decisions of whether the datasets were of sufficient image quality. A five‐fold cross‐validation was used to identify the thresholds and to evaluate the Motion IQ Decision algorithm. Results The automated segmentation algorithms in the Motion Artifact Quantification algorithm resulted in Dice coefficients of 0.84 for the segmented vessel regions and 0.75 for the segmented shading artifact regions. The MAS calculated using the automated algorithm was within 10% of the values obtained using ground‐truth segmentations. The MAS threshold and artifact‐length thresholds were determined by the ROC analysis to be 0.6 and 6.25 mm by all folds. The Motion IQ Decision algorithm demonstrated 100% sensitivity, 66.7% ± 27.9% specificity, and a total accuracy of 86.7% ± 12.5% for identifying datasets in which the RCA required correction. The Motion IQ Decision algorithm demonstrated 91.3% sensitivity, 71.4% specificity, and a total accuracy of 86.7% for identifying CCTA datasets that need correction for any of the three main vessels. Conclusion The Motion Artifact Quantification algorithm calculated accurate (<10% error) motion artifact scores using the automated segmentation methods. The developed algorithms demonstrated high sensitivity (91.3%) and specificity (71.4%) in identifying datasets of insufficient image quality. The developed algorithms for automatically quantifying motion artifact severity may be useful for comparing acquisition techniques, improving best‐phase selection algorithms, and evaluating motion compensation techniques.

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“…A good deal of research has been aimed at producing an algorithm that can meet this challenge. [15][16][17][18][19][20][21] Algorithms that use this estimate and compensate approach have many advantages: they do not rely on costly modifications to the scanner hardware, they make no assumptions about the nature of the cardiac motion, they can mitigate motion induced blur and artifacts from coronary arteries without compromising spatial resolution, and finally, they can be leveraged to further improve temporal resolution in suboptimal images acquired from CT systems featuring faster gantry speeds or two-tube two-detector geometries.…”

Section: Introductionmentioning

confidence: 99%

Nonrigid registration-based coronary artery motion correction for cardiac computed tomography

et al. 2012

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Results from the observer study show that the SWA method described here can dramatically reduce coronary artery motion and preserve real pathology, without affecting spatial resolution. In particular, the method successfully mitigated motion artifacts in 75% of all initially nondiagnostic coronary artery segments, and in over 45% of the cases this improvement was enough to make a previously nondiagnostic vessel segment clinically diagnostic.

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Coronary artery motion estimation and compensation: A feasibility study

Cited by 4 publications

References 3 publications

Evaluation of motion artifact metrics for coronary CT angiography

Evaluation of motion artifact metrics for coronary CT angiography

Automated quantification and evaluation of motion artifact on coronary CT angiography images

Nonrigid registration-based coronary artery motion correction for cardiac computed tomography

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