Displacement‐based binning of time‐dependent computed tomography image data sets

Fitzpatrick, Mathew J.; Starkschall, George; Antolak, John A.; Fu, Jun; Shukla, H; Keall, P.; Klahr, Paul; Mohan, Radhe

doi:10.1118/1.2044427

Cited by 53 publications

(57 citation statements)

References 18 publications

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“…We have employed a commercially available sorting method (i.e., phase-based sorting), which is a standard in clinical practice, and a published method designed to reduce artifacts (i.e., anatomic similarity-based sorting). Investigation of other sorting methods such as displacement-based sorting 21,22,24 and internal anatomic feature-based sorting 27 could be a subject of future work.…”

Section: Discussionmentioning

confidence: 99%

“…In addition to improved sorting [21][22][23][24][25][26][27][28] as studied here, there have been several other strategies to improve 4D CT in the literature, including postprocessing, [43][44][45] respiratory training, [46][47][48] respiration-synchronized acquisition, 49-51 and a wide-area detector multidetector CT (MDCT). 52 These approaches have been demonstrated to improve 4D CT image quality, however, each approach has different disadvantages as described below.…”

Section: Discussionmentioning

confidence: 99%

“…19)], while there have been several improved sorting methods that reduce artifacts. [21][22][23][24][25][26][27][28] This study is the first to quantify the ventilation variation arising from different 4D CT sorting methods. In addition, we also investigated which provides higher physiologic accuracy by comparing with SPECT ventilation (assumed ground truth).…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2013

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“…The time stamps of the reconstructed CT images and the measured respiratory signal of the patient are retrospectively matched. The reconstructed images are sorted either by the phase (2) or by the displacement, (3) which are then stacked to create a three‐dimensional (3D) image of the patient for each image bin. A 4D image set is then reconstructed by viewing the 3D images in sequence for each image bin.…”

Section: Introductionmentioning

confidence: 99%

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

Cui

Jew

Hong

et al. 2012

J Applied Clin Med Phys

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The aim of this study is to develop an automated method to objectively compare motion artifacts in two four‐dimensional computed tomography (4D CT) image sets, and identify the one that would appear to human observers with fewer or smaller artifacts. Our proposed method is based on the difference of the normalized correlation coefficients between edge slices at couch transitions, which we hypothesize may be a suitable metric to identify motion artifacts. We evaluated our method using ten pairs of 4D CT image sets that showed subtle differences in artifacts between images in a pair, which were identifiable by human observers. One set of 4D CT images was sorted using breathing traces in which our clinically implemented 4D CT sorting software miscalculated the respiratory phase, which expectedly led to artifacts in the images. The other set of images consisted of the same images; however, these were sorted using the same breathing traces but with corrected phases. Next we calculated the normalized correlation coefficients between edge slices at all couch transitions for all respiratory phases in both image sets to evaluate for motion artifacts. For nine image set pairs, our method identified the 4D CT sets sorted using the breathing traces with the corrected respiratory phase to result in images with fewer or smaller artifacts, whereas for one image pair, no difference was noted. Two observers independently assessed the accuracy of our method. Both observers identified 9 image sets that were sorted using the breathing traces with corrected respiratory phase as having fewer or smaller artifacts. In summary, using the 4D CT data of ten pairs of 4D CT image sets, we have demonstrated proof of principle that our method is able to replicate the results of two human observers in identifying the image set with fewer or smaller artifacts.PACS number: 87.57.cp; 87.57.N‐

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“…21 Approaches to 4D-CT gating include retrospective gating 17,20,24,26,28,29 and prospective gating 30 and commonly applied acquisition methods include phase- 4,24,26,28,31 and amplitude-based sorting. 29,[32][33][34] However, all the current 4D-CT acquisition and reconstruction methods require multiple cycles of patient respiration, which frequently lead to spatial artifacts. A recent study showed these artifacts occurred with an alarmingly high frequency and spatial magnitude.…”

Section: Introductionmentioning

confidence: 99%

Characterization and identification of spatial artifacts during 4D‐CT imaging

et al. 2011

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Purpose: The purpose of this work is twofold: First, to characterize the artifacts occurring in helical 4D-CT imaging; second, to propose a method that can automatically identify the artifacts in 4D-CT images. The authors have designed a process that can automatically identify the artifacts in 4D-CT images, which may be invaluable in quantifying the quality of 4D-CT images and reducing the artifacts from the reconstructed images on a large dataset. Methods: Given two adjacent stacks obtained from the same respiration phase, the authors determine if there are artifacts between them. The proposed method uses a "bridge" stack strategy to connect the two stacks. Using normalized cross correlation convolution ͑NCCC͒, the two stacks are mapped to the bridge stack and the best matching positions can be located. Using this position information, the authors can then determine if there are artifacts between the two stacks. By combining the matching positions with NCCC values, the performance can be improved. Results: To validate the method, three expert observers independently labeled over 600 stacks on five patients. The results confirmed that high performance was obtained using the proposed method. The average sensitivity was about 0.87 and the average specificity was 0.82. The proposed method also outperformed the method of using respiratory signal ͑sensitivity increased from 0.50 to 0.87 and specificity increased from 0.70 to 0.82͒. Conclusions: This study shows that the spatial artifacts during 4D-CT imaging are characterized and can be located automatically by the proposed method. The method is relatively simple but effective. It provides a way to evaluate the artifacts more objectively and accurately. The reported approach has promising potential for automatically identifying the types and frequency of artifacts on large scale 4D-CT image dataset.

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Displacement‐based binning of time‐dependent computed tomography image data sets

Cited by 53 publications

References 18 publications

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

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

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

Characterization and identification of spatial artifacts during 4D‐CT imaging

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