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: 4.26). 10/2008; 72(4):1250-8. DOI: 10.1016/j.ijrobp.2008.06.1937
Source: PubMed


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.

Download full-text


Available from: Ulrich W Langner,
22 Reads
  • Source
    • "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]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Cycle-to-cycle variations in respiratory motion can cause significant geometric and dosimetric errors in the administration of lung cancer radiation therapy. A common limitation of the current strategies for motion management is that they assume a constant, reproducible respiratory cycle. In this work, we investigate the feasibility of using rapid MRI for providing long-term imaging of the thorax in order to better capture cycle-to-cycle variations. Two nonsmall-cell lung cancer patients were imaged (free-breathing, no extrinsic contrast, and 1.5 T scanner). A balanced steady-state-free-precession (b-SSFP) sequence was used to acquire cine-2D and cine-3D (4D) images. In the case of Patient 1 (right midlobe lesion, ~40 mm diameter), tumor motion was well correlated with diaphragmatic motion. In the case of Patient 2, (left upper-lobe lesion, ~60 mm diameter), tumor motion was poorly correlated with diaphragmatic motion. Furthermore, the motion of the tumor centroid was poorly correlated with the motion of individual points on the tumor boundary, indicating significant rotation and/or deformation. These studies indicate that image quality and acquisition speed of cine-2D MRI were adequate for motion monitoring. However, significant improvements are required to achieve comparable speeds for truly 4D MRI. Despite several challenges, rapid MRI offers a feasible and attractive tool for noninvasive, long-term motion monitoring.
    01/2014; 2014(9):485067. DOI:10.1155/2014/485067
  • Source
    • "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. "
    [Show abstract] [Hide abstract]
    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.
    IEEE Journal of Translational Engineering in Health and Medicine 01/2014; Accepted. DOI:10.1109/JTEHM.2014.2381213
  • Source
    • "The reliability of motion results in this work was dependent on two factors, regularity in patient breathing and consistency in GTV delineation. Data from both phantoms and clinical practice demonstrated that respiratory regularity was of most importance to reduce motion artifacts during 4DCT scan [22, 23]. In our research, all patients underwent breathing training and those with breathing variability >5% were eliminated from the study. "
    [Show abstract] [Hide abstract]
    ABSTRACT: This study presented the analysis of free-breathing lung tumor motion characteristics using GE 4DCT and Varian RPM systems. Tumor respiratory movement was found to be associated with GTV size, the superior-inferior tumor location in the lung, and the attachment degree to rigid structure (e.g., chest wall, vertebrae, or mediastinum), with tumor location being the most important factor among the other two. Improved outcomes in survival and local control of 43 lung cancer patients were also reported. Consideration of respiration-induced motion based on 4DCT for lung cancer yields individualized margin and more accurate and safe target coverage and thus can potentially improve treatment outcome.
    06/2013; 2013:872739. DOI:10.1155/2013/872739
Show more