How to measure CT image quality: Variations in CT-numbers, uniformity and low contrast resolution for a CT quality assurance phantom

Quality control (QC) of CT scanners is important to evaluate image quality and radiation dose. Different QC phantoms for testing image quality parameters on CT are commercially available, and Catphan phantoms are widely used for this purpose. More data from measured image quality parameters on CT are necessary to assess test methods, tolerance levels, and test frequencies. The aim of this study was to evaluate the stability of essential image quality parameters for axial and helical scans on one CT scanner over time. A Catphan 600 phantom was scanned on a Philips Ingenuity CT scanner for 100 days over a period of 6 months. At each day of testing, one helical scan covering the entire phantom and four axial scans covering four different modules in the phantom were performed. All images were uploaded into Image Owl for automatic analysis of CT numbers, modular transfer function (MTF), low‐contrast resolution, noise, and uniformity. In general, the different image quality parameters for both scan techniques were stable over time compared to given tolerance levels. Average measured CT numbers differed between axial and helical scans, while MTF was almost identical for helical and axial scans. Axial scans had better low‐contrast resolution and less noise than helical scans. The uniformity was relatively similar for axial and helical scans. Most standard deviations of measured values were larger for helical scans compared to axial scans. Test results in this study were stable over time for both scan techniques, but further studies on different CT scanners are required to confirm that this also holds true for other systems.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

100 days with scans of the same Catphan phantom on the same CT scanner

Husby

Svendsen

Andersen

et al. 2017

Recommendations of megavoltage computed tomography settings for the implementation of adaptive radiotherapy on helical tomotherapy units

Velten

Boyd

Jeong

et al. 2020

Megavoltage computed tomography (MVCT) image quality metrics were evaluated on an Accuray Radixact unit to recommend scan settings for the implementation of a consistent adaptive radiotherapy program. Megavoltage computed tomography image quality was evaluated and compared to a kilovoltage CT (kVCT) simulator using a commercial cone beam computed tomography image quality phantom. Megavoltage computed tomographies were acquired on the Accuray Radixact using fine, normal, and coarse pitches, with all available reconstruction slice thicknesses, each of which were reconstructed using standard and iterative reconstruction (IR). Image quality metrics (IQM) were evaluated using DoseLab: automatically and manually calculated spatial resolution, subject contrast, and contrast‐to‐noise ratio (CNR). Scanning time was 15.6 s/cm for fine, 8.1 s/cm for normal, and 5.6 s/cm for coarse pitch. Automatically evaluated spatial resolutions ranged from 0.39, 0.41, to 0.42 lp/mm for standard reconstruction and from 0.24, 0.21, to 0.18 lp/mm for soft‐tissue IR, respectively, with general IR yielding values in between these. Spatial resolution for kVCT was measured to be at least 0.42 lp/mm. Contrast was consistent across MVCT settings with 8.1 ± 0.2%, while kVCT contrast was 10.27 ± 0.05%. CNR was calculated to be 3.3 ± 0.4 for standard reconstruction, 7.4 ± 0.4 for general IR, and 12.0 ± 1.9 for soft‐tissue IR. It was found that increasing reconstruction slice thickness for a given pitch does not improve IQMs. Based on the consistency of contrast metrics across pitch values and the only slightly reduced spatial resolution using normal compared to fine pitch, we recommend the use of normal pitch with 2 mm slice thickness to maximize image quality for ART while limiting scanning time. Only for sites for which improved CNR is required and reduced spatial resolution is acceptable, soft‐tissue IR is recommended.

Automated procedure for slice thickness verification of computed tomography images: Variations of slice thickness, position from iso‐center, and reconstruction filter

Lasiyah

Anam

Hidayanto

et al. 2021

Purpose: The purpose of this study is to automate the slice thickness verification on the AAPM CT performance phantom and validate it for variations of slice thickness, position from iso-center, and reconstruction filter.Methods: An automatic procedure for slice thickness verification on AAPM CT performance phantom was developed using MATLAB R2015b. The stair object image within the phantom was segmented, and the middle stair object was located. Its angle was determined using the Hough transformation, and the image was rotated accordingly. The profile through this object was obtained, and its full-width of half maximum (FWHM) was automatically measured. The FWHM indicated the slice thickness of the image. The automated procedure was applied with variations in three independent parameters, i.e., the slice thickness, the distance from the phantom to the iso-center, and the reconstruction filter. The automated results were compared to manual measurements made using electronic calipers.Results: The differences of the automated results from the nominal slice thicknesses were within 1.0 mm. The automated results are comparable to those from manual approach (i.e., the difference of both is within 12%). The automatic procedure accurately obtained slice thickness even when the phantom was moved from the iso-center position by up to 4 cm above and 4 cm below the iso-center. The automated results were similar (to within 0.1 mm) for various reconstruction filters. Conclusions:We successfully developed an automated procedure of slice thickness verification and confirmed that the automated procedure provided accurate results.It provided an easy and effective method of determining slice thickness.