Retrospective analysis of artifacts in four-dimensional CT images of 50 abdominal and thoracic radiotherapy patients

Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA 94305-5847, USA.
International journal of radiation oncology, biology, physics (Impact Factor: 4.18). 10/2008; 72(4):1250-8. DOI: 10.1016/j.ijrobp.2008.06.1937
Source: PubMed

ABSTRACT To quantify the type, frequency, and magnitude of artifacts in four-dimensional (4D) CT images acquired using a multislice cine method.
Fifty consecutive patients who underwent 4D-CT scanning and radiotherapy for thoracic or abdominal cancers were included in this study. All the 4D-CT scans were performed on the GE multislice PET/CT scanner with the Varian Real-time Position Management system in cine mode. The GE Advantage 4D software was used to create 4D-CT data sets. The artifacts were then visually and quantitatively analyzed. We performed statistical analyses to evaluate the relationships between patient- or breathing-pattern-related parameters and the occurrence as well as magnitude of artifacts.
It was found that 45 of 50 patients (90%) had at least one artifact (other than blurring) with a mean magnitude of 11.6 mm (range, 4.4-56.0 mm) in the diaphragm or heart. We also observed at least one artifact in 6 of 20 lung or mediastinal tumors (30%). Statistical analysis revealed that there were significant differences between several breathing-pattern-related parameters, including abdominal displacement (p < 0.01), for the subgroups of patients with and without artifacts. The magnitude of an artifact was found to be significantly but weakly correlated with the abdominal displacement difference between two adjacent couch positions (R = 0.34, p < 0.01).
This study has identified that the frequency and magnitude of artifacts in 4D-CT is alarmingly high. Significant improvement is needed in 4D-CT imaging.

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Available from: Ulrich W Langner, Jul 31, 2015
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    • "For example, Yamamoto et al. found that 45 of 50 patients had at least one artifact, with mean magnitude of 11.6 mm (range: 4.4–56.0 mm) [6]. "
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    01/2014; 2014:485067. DOI:10.1155/2014/485067
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    • "In patients, however, GTV quantification is difficult since the actual tumor shape and volume cannot be directly measured as the ground truth [16]. As an alternative, frequency and amplitude of motion artifacts appearing in 4DCT can be manually evaluated by scrolling through all 4DCT images [16]. This is a tedious and time consuming process with an outcome difficult to compare among patients. "
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    ABSTRACT: 4-D-computed tomography (4DCT) provides not only a new dimension of patient-specific information for radiation therapy planning and treatment, but also a challenging scale of data volume to process and analyze. Manual analysis using existing 3-D tools is unable to keep up with vastly increased 4-D data volume, automated processing and analysis are thus needed to process 4DCT data effectively and efficiently. In this paper, we applied ideas and algorithms from image/signal processing, computer vision, and machine learning to 4DCT lung data so that lungs can be reliably segmented in a fully automated manner, lung features can be visualized and measured on the fly via user interactions, and data quality classifications can be computed in a robust manner. Comparisons of our results with an established treatment planning system and calculation by experts demonstrated negligible discrepancies (within ±2%) for volume assessment but one to two orders of magnitude performance enhancement. An empirical Fourier-analysis-based quality measure-delivered performances closely emulating human experts. Three machine learners are inspected to justify the viability of machine learning techniques used to robustly identify data quality of 4DCT images in the scalable manner. The resultant system provides a toolkit that speeds up 4-D tasks in the clinic and facilitates clinical research to improve current clinical practice.
    01/2014; Accepted. DOI:10.1109/JTEHM.2014.2381213
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    • "However, amplitude binning may undersample images in the bins where large motion is present within short time leading to interpolation artefacts in the affected bins. Regardless of binning method, the risk of artefacts will remain present and lead to uncertainties in delineation[20] [21] [22] which should be accounted for when 4DCT data are used for treatment planning. "
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    ABSTRACT: BACKGROUND: With 4DCT the risk of introducing positional systematic errors in lung cancer radiotherapy can be minimised. A common approach is to plan on the phase bin of the 4DCT best representing the tumour's time-weighted mean position also called the midventilation scan. However breathing irregularities can introduce uncertainties and potentially misrepresent both the tumour trajectory and the determination of the midventilation phase. In this study we evaluated the robustness of the midventilation approach in the presence of irregular breathing patterns. METHODS: A LEGO Mindstorms(®) phantom with compact balls simulating lung tumours was constructed. The breathing curves loaded in the phantom were either acquired from a human volunteer or constructed with various magnitudes (ranging from 12 to 29 mm) as well as various irregularities of motion pattern. Repeated 4DCT scans were performed while tumour trajectories were recorded with two motion tracking systems. RESULTS: The time-weighted mean tumour position is accurately represented in 4DCT scans, even for irregular breathing patterns: the position presentation in the midventilation scan was always within in one standard deviation of the global position presentation (3 mm and 2 mm for regular and irregular breathing patterns, respectively). The displacement representation tended to be underestimated in 4DCT scans. CONCLUSION: The midventilation approach is robust even in the presence of breathing irregularity. The representation of the tumour trajectory in 4DCT scans is affected by breathing irregularity and the extent of tumour motion can be underestimated, which will affect the calculation of patient-individualised margins based on the 4DCT scan.
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