An automated method for comparing motion artifacts in cine four‐dimensional computed tomography images

Cui, Guoqing; Jew, B.; Hong, Julian C.; Johnston, Eric; Loo, Billy W.; Maxim, Peter G.

doi:10.1120/jacmp.v13i6.3838

Cited by 17 publications

(21 citation statements)

References 19 publications

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“…We performed these acquisitions on a relatively large sample of thoracic patients, without the use of simulations or phantoms, (23,28,39) which may not truly represent breathing and anatomic changes present in actual patients. Artifacts were evaluated based on quantitative assessments of the images, relying on validation of the metric (29,40) and general agreement with visual observation. The metric does not correlate exactly to visual assessment, but provided a consistent method to evaluating scans in a relative fashion.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore there are at least three image choices per breathing phase per couch position; this allowed retention of breathing information with the potential for artifact reduction in available data. We then incorporated a previously described metric of 4D CT artifact evaluation (29) that is based on correlation coefficients across couch positions, termed CM. The absolute value of the sum of CM values across the scan extent was minimized using the shortest path Dijkstra's algorithm (30,31) .…”

Section: Methodsmentioning

confidence: 99%

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Evaluation of 4D CT acquisition methods designed to reduce artifacts

Castillo

et al. 2015

J Applied Clin Med Phys

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Four-dimensional computed tomography (4D CT) is used to account for respiratory motion in radiation treatment planning, but artifacts resulting from the acquisition and postprocessing limit its accuracy. We investigated the efficacy of three experimental 4D CT acquisition methods to reduce artifacts in a prospective institutional review board approved study. Eighteen thoracic patients scheduled to undergo radiation therapy received standard clinical 4D CT scans followed by each of the alternative 4D CT acquisitions: 1) data oversampling, 2) beam gating with breathing irregularities, and 3) rescanning the clinical acquisition acquired during irregular breathing. Relative values of a validated correlation-based artifact metric (CM) determined the best acquisition method per patient. Each 4D CT was processed by an extended phase sorting approach that optimizes the quantitative artifact metric (CM sorting). The clinical acquisitions were also postprocessed by phase sorting for artifact comparison of our current clinical implementation with the experimental methods. The oversampling acquisition achieved the lowest artifact presence among all acquisitions, achieving a 27% reduction from the current clinical 4D CT implementation (95% confidence interval = 34–20). The rescan method presented a significantly higher artifact presence from the clinical acquisition (37%; p < 0.002), the gating acquisition (26%; p < 0.005), and the oversampling acquisition (31%; p < 0.001), while the data lacked evidence of a significant difference between the clinical, gating, and oversampling methods. The oversampling acquisition reduced artifact presence from the current clinical 4D CT implementation to the largest degree and provided the simplest and most reproducible implementation. The rescan acquisition increased artifact presence significantly, compared to all acquisitions, and suffered from combination of data from independent scans over which large internal anatomic shifts occurred.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Evaluation of 4D CT acquisition methods designed to reduce artifacts

Castillo

et al. 2015

J Applied Clin Med Phys

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“…where NCC(I, I ) is the normalized cross correlation (NCC) value between two CT slices (I and I ), I[s, i] is the ith slice in the sth CT data segment, k is the number of slices per CT data segment (k = 8 for the scanner used here), a is the index of the CT data segment that contains the most superior slice of the lung, b is the index of the CT data segment that contains the most inferior slice of the lung, and N is the number of CT data segments that contain a lung. 30 The artifact scores of the phase-sorted and anatomic similarity-sorted 4D CT images were compared using the two-tailed paired t-test.…”

Section: B 4d Ct Imaging and Sorting: Phase-based And Anatomic Simmentioning

confidence: 99%

“…To quantitatively evaluate artifacts in the resulting 4D CT images, we employed the artifact score (AS) defined by 30 The artifact scores of the phase-sorted and anatomic similarity-sorted 4D CT images were compared using the two-tailed paired t-test.…”

Section: B 4d Ct Imaging and Sortingmentioning

confidence: 99%

4D CT lung ventilation images are affected by the 4D CT sorting method

et al. 2013

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Purpose: Four-dimensional (4D) computed tomography (CT) ventilation imaging is a novel promising technique for lung functional imaging. The current standard 4D CT technique using phase-based sorting frequently results in artifacts, which may deteriorate the accuracy of ventilation imaging. The purpose of this study was to quantify the variability of 4D CT ventilation imaging due to 4D CT sorting. Methods: 4D CT image sets from nine lung cancer patients were each sorted by the phase-based method and anatomic similarity-based method, designed to reduce artifacts, with corresponding ventilation images created for each method. Artifacts in the resulting 4D CT images were quantified with the artifact score which was defined based on the difference between the normalized cross correlation for CT slices within a CT data segment and that for CT slices bordering the interface between adjacent CT data segments. The ventilation variation was quantified using voxel-based Spearman rank correlation coefficients for all lung voxels, and Dice similarity coefficients (DSC) for the spatial overlap of low-functional lung volumes. Furthermore, the correlations with matching single-photon emission CT (SPECT) ventilation images (assumed ground truth) were evaluated for three patients to investigate which sorting method provides higher physiologic accuracy. Results: Anatomic similarity-based sorting reduced 4D CT artifacts compared to phase-based sorting (artifact score, 0.45 ± 0.14 vs 0.58 ± 0.24, p = 0.10 at peak-exhale; 0.63 ± 0.19 vs 0.71 ± 0.31, p = 0.25 at peak-inhale). The voxel-based correlation between the two ventilation images was 0.69 ± 0.26 on average, ranging from 0.03 to 0.85. The DSC was 0.71 ± 0.13 on average. Anatomic similarity-based sorting yielded significantly fewer lung voxels with paradoxical negative ventilation values than phase-based sorting (5.0 ± 2.6% vs 9.7 ± 8.4%, p = 0.05), and improved the correlation with SPECT ventilation regionally. Conclusions: The variability of 4D CT ventilation imaging due to 4D CT sorting was moderate overall and substantial in some cases, suggesting that 4D CT artifacts are an important source of variations in 4D CT ventilation imaging. Reduction of 4D CT artifacts provided more physiologically convincing and accurate ventilation estimates. Further studies are needed to confirm this result.

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“…NCCS and VAS evaluated artifacts at the 0% (peak-inhale), 30% (mid-exhale), 50% (peak-exhale) and 80% (mid-inhale) phases. A NCCbased metric has been demonstrated to replicate the findings of human observers [14]. The VAS was determined by an observer (medical physicist) through comparing five pairs of coronal CT slices displayed side by side and blinded to the breathing method.…”

Section: Quantificationmentioning

confidence: 99%