A quality assurance framework for the fully automated and objective evaluation of image quality in cone‐beam computed tomography

Steiding, Christian; Kolditz, Daniel; Kalender, Willi A.

doi:10.1118/1.4863507

Cited by 36 publications

(60 citation statements)

References 70 publications

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“…They improve the precision and efficiency of the acceptance test, and could be a useful software tool for routine QA procedures. The automatic image analysis procedure for both MRI and cone beam can help finish the uniform acceptance and constancy testing 25, 26. Fully automatic image quality assurance for an MRI simulator system should be carefully considered in the future to minimize manual intervention.…”

Section: Discussionmentioning

confidence: 99%

Quantitative analysis of image quality for acceptance and commissioning of an MRI simulator with a semiautomatic method

Chen

Dai

2018

J Applied Clin Med Phys

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Magnetic Resonance Imaging (MRI) simulation differs from diagnostic MRI in purpose, technical requirements, and implementation. We propose a semiautomatic method for image acceptance and commissioning for the scanner, the radiofrequency (RF) coils, and pulse sequences for an MRI simulator. The ACR MRI accreditation large phantom was used for image quality analysis with seven parameters. Standard ACR sequences with a split head coil were adopted to examine the scanner's basic performance. The performance of simulation RF coils were measured and compared using the standard sequence with different clinical diagnostic coils. We used simulation sequences with simulation coils to test the quality of image and advanced performance of the scanner. Codes and procedures were developed for semiautomatic image quality analysis. When using standard ACR sequences with a split head coil, image quality passed all ACR recommended criteria. The image intensity uniformity with a simulation RF coil decreased about 34% compared with the eight‐channel diagnostic head coil, while the other six image quality parameters were acceptable. Those two image quality parameters could be improved to more than 85% by built‐in intensity calibration methods. In the simulation sequences test, the contrast resolution was sensitive to the FOV and matrix settings. The geometric distortion of simulation sequences such as T1‐weighted and T2‐weighted images was well‐controlled in the isocenter and 10 cm off‐center within a range of ±1% (2 mm). We developed a semiautomatic image quality analysis method for quantitative evaluation of images and commissioning of an MRI simulator. The baseline performances of simulation RF coils and pulse sequences have been established for routine QA.

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Section: Discussionmentioning

confidence: 99%

Quantitative analysis of image quality for acceptance and commissioning of an MRI simulator with a semiautomatic method

Chen

Dai

2018

J Applied Clin Med Phys

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“…A polyvinyl chloride (PVC) air test insert is embedded in the centre of phantom section 3 to determine all desired IQ parameters as stated by [21]. A modular CT IQ phantom of a previously proposed QA framework [19] (QRM GmbH, Möhrendorf, Germany) was used to measure conventional IQ metrics. Image noise, uniformity, contrast, and 3 D spatial resolution were assessed [22] by using phantom sections 1 -3 shown in • " Fig.…”

Section: Phantomsmentioning

confidence: 99%

“…The conventional IQ parameters were determined in accordance with the IEC standard based on a previously proposed QA framework [19]. The volume data analysis is summarized below.…”

Section: Conventional Image Quality Parametersmentioning

confidence: 99%

“…The standard deviation (σ) was computed for a circular ROI of 40 mm in diameter placed in the phantom centre to examine the amount of fluctuation in voxel values. The uniformity index (UI) [19] denotes the normalized percent difference between CT p, i (with i = 1, …, 4) of the peripheral ROI labelled by i and CT c and allows for quantifying uniformity in voxel values. Each of these circular ROIs had a diameter of 10 mm.…”

Section: Conventional Image Quality Parametersmentioning

confidence: 99%

“…Fig. 2 Design of the modular IEC-compliant phantom [19]. 3 D rendered sketch of the complete phantom consisting of five sections a and the optional extension ring b.…”

Section: Conventional Image Quality Parametersmentioning

confidence: 99%

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Comparison of Methods for Acceptance and Constancy Testing in Dental Cone-beam Computed Tomography

Steiding¹,

Kolditz²,

Kalender³

2014

Fortschr Röntgenstr

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Ziel: Das Ziel dieser Arbeit war die Anwendung, Validierung und der Vergleich zweier Konzepte zur Beurteilung der Bildqualität (BQ) in der dentalen digitalen Volumentomografie (DVT): 1. der 2013 neu eingeführte, deutsche Standard DIN 6868 -161 und 2. der fest etablierte Standard IEC 61 223 -3-5 von Röntgeneinrichtungen für klinische CT (nachfolgend als "DIN" und "IEC" bezeichnet). The aim of this work was to implement, validate, and compare two procedures for routine image quality (IQ) assurance in dental cone-beam computed tomography (CBCT): 1. the German standard DIN 6868 -161 introduced in 2013 and 2. the established standard IEC 61 223 -3-5 for clinical CT x-ray equipment referenced as "DIN" and "IEC" below. Materials and Methods: The approximated inplane modulation transfer function (MTF), the contrast-to-noise indicator (CNI), and the uniformity indicator (UI*) were determined in accordance with DIN. Image noise, the uniformity index (UI), the contrast-to-noise ratio (CNR), and the 3 D MTF were measured according to IEC 61 223 -3-5 using a previously proposed quality assurance (QA) framework. For this, a modular phantom was used. All experiments were performed on a clinical dental CBCT unit. The severity of image artefacts was measured at different z-positions. A dedicated computer program was implemented to allow for automated QA procedure. Results: The position and orientation of the phantoms were detected automatically in all of the measurements providing a reproducible placement of the evaluation regions and volumes. 50 % and 10 % in-plane MTF values of the approximated and the exact MTF calculation procedure were in agreement to within 5 %. With increasing axial distance from the isocentre, UI* and CNI dropped by 30 % and 19 %, respectively. Conventional IQ parameters showed higher sensitivity to image artefacts; i. e., UI and CNR were reduced by about 197 % and 37 %. Conclusion: The implemented automated QA routines are compatible with both the DIN and the IEC approach and offer reliable and quantitative tracking of imaging performance in dental CBCT for clinical practice. However, there is no equivaThis document was downloaded for personal use only. Unauthorized distribution is strictly prohibited.

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Technical evaluation of the cone‐beam computed tomography imaging performance of a novel, mobile, gantry‐based X‐ray system for brachytherapy

Karius

Karolczak

Strnad

et al. 2021

J Applied Clin Med Phys

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A novel, mobile cone-beam computed tomography (CBCT) system for image-guided adaptive brachytherapy was recently deployed at our hospital as worldwide first site. Prior to the device's clinical operation, a profound characterization of its imaging performance was conducted. This was essential to optimize both the imaging workflow and image quality for achieving the best possible clinical outcomes. We present the results of our investigations. Methods: The novel CBCT-system features a ring gantry with 121 cm clearance as well as a 43.2 × 43.2 cm 2 flat-panel detector, and is controlled via a tabletpersonal computer (PC). For evaluating its imaging performance, the geometric reproducibility as well as imaging fidelity, computed tomography (CT)-number accuracy, uniformity, contrast-noise-ratio (CNR), noise characteristics, and spatial resolution as fundamental image quality parameters were assessed.As dose metric the weighted cone-beam dose index (CBDI w ) was measured.Image quality was evaluated using standard quality assurance (QA) as well as anthropomorphic upper torso and breast phantoms. Both in-house and manufacturer protocols for abdomen, pelvis, and breast imaging were examined. Results: Using the in-house protocols, the QA phantom scans showed altogether a high image quality, with high CT-number accuracy (R 2 > 0.97) and uniformity (<12 Hounsfield Unit (HU) cupping), reasonable noise and imaging fidelity, and good CNR at bone-tissue transitions of up to 28:1. Spatial resolution was strongly limited by geometric instabilities of the device. The breast phantom scans fulfilled clinical requirements, whereas the abdomen and pelvis scans showed severe artifacts, particularly at air/bone-tissue transitions. Conclusion:With the novel CBCT-system, achieving a high image quality appears possible in principle. However, adaptations of the standard protocols, performance enhancements in image reconstruction referring to artifact reductions, as well as the extinction of geometric instabilities are imperative.

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A quality assurance framework for the fully automated and objective evaluation of image quality in cone‐beam computed tomography

Cited by 36 publications

References 70 publications

Quantitative analysis of image quality for acceptance and commissioning of an MRI simulator with a semiautomatic method

Quantitative analysis of image quality for acceptance and commissioning of an MRI simulator with a semiautomatic method

Comparison of Methods for Acceptance and Constancy Testing in Dental Cone-beam Computed Tomography

Technical evaluation of the cone‐beam computed tomography imaging performance of a novel, mobile, gantry‐based X‐ray system for brachytherapy

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