Dataset Information

Change in Image Quality According to the 3D Locations of a CBCT Phantom.

ABSTRACT: A patient's position changes in every CBCT scan despite patient alignment protocols. However, there have been studies to determine image quality differences when an object is located at the center of the field of view (FOV). To evaluate changes in the image quality of the CBCT scan according to different object positions, the image quality indexes of the Alphard 3030 (Alphard Roentgen Ind., Ltd., Kyoto, Japan) and the Rayscan Symphony (RAY Ind., Ltd., Suwon, Korea) were measured using the Quart DVT_AP phantom at the center of the FOV and 6 peripheral positions under four types of exposure conditions. Anterior, posterior, right, left, upper, and lower positions 1 cm offset from the center of the FOV were used for the peripheral positions. We evaluated and compared the voxel size, homogeneity, contrast to noise ratio (CNR), and the 10% point of the modulation transfer function (MTF10%) of the center and periphery. Because the voxel size, which is determined by the Nyquist frequency, was within tolerance, other image quality indexes were not influenced by the voxel size. For the CNR, homogeneity, and MTF10%, there were peripheral positions which showed considerable differences with statistical significance. The average difference between the center and periphery was up to 31.27% (CNR), 70.49% (homogeneity), and 13.64% (MTF10%). Homogeneity was under tolerance at some of the peripheral locations. Because the CNR, homogeneity, and MTF10% were significantly affected by positional changes of the phantom, an object's position can influence the interpretation of follow up CBCT images. Therefore, efforts to locate the object in the same position are important.

SUBMITTER: Hwang JJ

PROVIDER: S-EPMC4836729 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Change in Image Quality According to the 3D Locations of a CBCT Phantom.

Hwang Jae Joon JJ Park Hyok H Jeong Ho-Gul HG Han Sang-Sun SS

PloS one 20160419 4

A patient's position changes in every CBCT scan despite patient alignment protocols. However, there have been studies to determine image quality differences when an object is located at the center of the field of view (FOV). To evaluate changes in the image quality of the CBCT scan according to different object positions, the image quality indexes of the Alphard 3030 (Alphard Roentgen Ind., Ltd., Kyoto, Japan) and the Rayscan Symphony (RAY Ind., Ltd., Suwon, Korea) were measured using the Quart ...[more]

PMID: 27093639

Similar Datasets

Project description:PurposeRoutine quality assurance (QA) of cone-beam computed tomography (CBCT) scans used for image-guided radiotherapy is prescribed by the American Association of Physicists in Medicine Task Group (TG)-142 report. For CBCT image quality, TG-142 recommends using clinically established baseline values as QA tolerances. This work examined how image quality parameters vary both across machines of the same model and across different CBCT techniques. Additionally, this work investigated how image quality values are affected by imager recalibration and repeated exposures during routine QA.MethodsCone-beam computed tomography scans of the Catphan 604 phantom were taken on four TrueBeam® and one Edge™ linear accelerator using four manufacturer-provided techniques. TG-142 image quality parameters were calculated for each CBCT scan using SunCHECK Machine™. The variability of each parameter with machine and technique was evaluated using a two-way ANOVA test on a dataset consisting of 200 CBCT scans. The impact of imager calibration on image quality parameters was examined for a subset of three machines using an unpaired Student's t-test. The effect of artifacts appearing on CBCTs taken in rapid succession was characterized and an approach to reduce their appearance was evaluated. Additionally, a set of baselines and tolerances for all image quality metrics was presented.ResultsAll imaging parameters except geometric distortion varied with technique (P < 0.05) and all imaging parameters except slice thickness varied with machine (P < 0.05). Imager calibration can change the expected value of all imaging parameters, though it does not consistently do so. While changes are statistically significant, they may not be clinically significant. Finally, rapid acquisition of CBCT scans can introduce image artifacts that degrade CBCT uniformity.ConclusionsThis work characterized the variability of acquired CBCT data across machines and CBCT techniques along with the impact of imager calibration and rapid CBCT acquisition on image quality.

Project description:PurposeRadiomics utilizes a large number of image-derived features for quantifying tumor characteristics that can in turn be correlated with response and prognosis. Unfortunately, extraction and analysis of such image-based features is subject to measurement variability and bias. The challenge for radiomics is particularly acute in Positron Emission Tomography (PET) where limited resolution, a high noise component related to the limited stochastic nature of the raw data, and the wide variety of reconstruction options confound quantitative feature metrics. Extracted feature quality is also affected by tumor segmentation methods used to define regions over which to calculate features, making it challenging to produce consistent radiomics analysis results across multiple institutions that use different segmentation algorithms in their PET image analysis. Understanding each element contributing to these inconsistencies in quantitative image feature and metric generation is paramount for ultimate utilization of these methods in multi-institutional trials and clinical oncology decision making.MethodsTo assess segmentation quality and consistency at the multi-institutional level, we conducted a study of seven institutional members of the National Cancer Institute Quantitative Imaging Network. For the study, members were asked to segment a common set of phantom PET scans acquired over a range of imaging conditions as well as a second set of head and neck cancer (HNC) PET scans. Segmentations were generated at each institution using their preferred approach. In addition, participants were asked to repeat segmentations with a time interval between initial and repeat segmentation. This procedure resulted in overall 806 phantom insert and 641 lesion segmentations. Subsequently, the volume was computed from the segmentations and compared to the corresponding reference volume by means of statistical analysis.ResultsOn the two test sets (phantom and HNC PET scans), the performance of the seven segmentation approaches was as follows. On the phantom test set, the mean relative volume errors ranged from 29.9 to 87.8% of the ground truth reference volumes, and the repeat difference for each institution ranged between -36.4 to 39.9%. On the HNC test set, the mean relative volume error ranged between -50.5 to 701.5%, and the repeat difference for each institution ranged between -37.7 to 31.5%. In addition, performance measures per phantom insert/lesion size categories are given in the paper. On phantom data, regression analysis resulted in coefficient of variation (CV) components of 42.5% for scanners, 26.8% for institutional approaches, 21.1% for repeated segmentations, 14.3% for relative contrasts, 5.3% for count statistics (acquisition times), and 0.0% for repeated scans. Analysis showed that the CV components for approaches and repeated segmentations were significantly larger on the HNC test set with increases by 112.7% and 102.4%, respectively.ConclusionAnalysis results underline the importance of PET scanner reconstruction harmonization and imaging protocol standardization for quantification of lesion volumes. In addition, to enable a distributed multi-site analysis of FDG PET images, harmonization of analysis approaches and operator training in combination with highly automated segmentation methods seems to be advisable. Future work will focus on quantifying the impact of segmentation variation on radiomics system performance.

Project description:The image quality (IQ) of imaging systems must be sufficiently high for image-guided radiation therapy (IGRT). Hence, users should implement a quality assurance program to maintain IQ. In our routine IQ tests of the kV cone-beam CT system (Elekta XVI), image noise was quantified by noise standard deviation (NSD), which was the standard deviation of CT numbers measured in a small area in an image of an IQ test phantom (Catphan), and the high spatial resolution (HSR) was evaluated by the number of line-pairs (LPN) visually recognizable in the image. We also measured the image uniformity, the low contrast resolution, and the distances of two points for geometrical accuracy. For this study, we did an additional evaluation of the XVI data for 12 monthly IQ tests by using noise power spectrum (NPS) for noise, modulation transfer function (MTF) for HSR, and CT number-to-density relationship. NPS was obtained by applying Fourier analysis in a small area on the uniformity test section of Catphan. The MTF analysis was performed by applying the Droege-Morin (D-M) method to the line-pair bar regions in the phantom. The CT number-to-density relationship was obtained for insert materials in the low-contrast test section of the phantom. All the quantities showed a noticeable change over the one-year period. Especially the noise level improved significantly after a repair of the imager. NPS was more sensitive to the IQ change than NSD. MTF could provide more quantitative and objective evaluation of HSR. The CT number was very different from the expected CT number, but the CT number-to-density curves were constant within 5% except for two months. Since the D-M method is easy to implement, we recommend using MTF instead of LPN even for routine QA. The IQ of the imaging systems was constantly changing; hence, IQ tests should be periodically performed. Additionally, we found the importance of IQ tests after every service work, including detector calibration as well as preventive maintenance.

Project description:PurposeInvestigate the impact of acquisition time and reconstruction parameters on single-photon emission computed tomography/computed tomography (SPECT/CT) image quality with the ultimate aim of finding the shortest possible acquisition time for clinical whole-body SPECT/CT (WB-SPECT/CT) while maintaining image quality METHODS: The National Electrical Manufacturers Association (NEMA) image quality measurements were performed on a SPECT/CT imaging system using a NEMA International Electrotechnical Commission (IEC) phantom with spherical inserts of varying diameter (10-37 mm), filled with 99m Tc in activity sphere-to-background concentration ratio of 8.5:1. A gated acquisition was acquired and binned data were summed to simulate acquisitions of 15, 8, and 3 s per projection angle. Images were reconstructed on a Hermes (HERMES Medical Solutions AB, Stockholm, Sweden) workstation using eight subsets and between 4 and 24 iterations of the three-dimensional (3D) ordered subset expectation maximization (OSEM) algorithm. Reconstructed images were post-smoothed with 3D Gaussian filter ranging from 0 to 12 mm full-width at half maximum (FWHM). Contrast recovery, background variability, and contrast-to-noise ratio were evaluated RESULTS: As expected, the spheres were more clearly defined as acquisition time and count statistics improved. The optimal iteration number and Gaussian filter were determined from the contrast recovery convergence and level of noise. Convergence of contrast recovery was observed at eight iterations while 12 iterations yielded stabilized values at all acquisition times. In addition, it was observed that applying 3D Gaussian filter of 8-12 mm FWHM suppressed the noise and mitigated Gibbs artifacts. Background variability was larger for small spheres than larger spheres and the noise decreased when acquisition time became longer. A contrast-to-noise ratio >5 was reached for the two smallest spheres of 10 and 13 mm at acquisition times of 8 s CONCLUSION: Optimized reconstruction parameters preserved image quality with reduce acquisition time in present study. This study suggests an optimal protocol for clinical 99m Tc SPECT/CT can be reached at 8 s per projection angle, with data reconstructed using 12 iterations and eight subset of the 3D OSEM algorithm and 8 mm Gaussian post-filter.

Dataset Information

Change in Image Quality According to the 3D Locations of a CBCT Phantom.

Publications

Change in Image Quality According to the 3D Locations of a CBCT Phantom.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets