Fully automated image quality evaluation on patient CT: Multi-vendor and multi-reconstruction study

Chun, Minsoo; Choi, Jin Hwa; Kim, Sihwan; Ahn, Chulkyun; Kim, Jong Hyo

doi:10.1371/journal.pone.0271724

Cited by 8 publications

(6 citation statements)

References 82 publications

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“…Recently, Chun et al [44] presented a novel image quality evaluation method that allows a fully automated assessment of three image quality metrics (noise level, structure sharpness, and alteration of structure) on patient CT images. They applied this method to the contrast-enhanced liver CT images from four different CT scanners reconstructed with filtered back projection (FBP), vendor-specific iterative reconstruction (IR), and a vendoragnostic deep learning model (DLM).…”

Section: Discussionmentioning

confidence: 99%

Optimization of the “Perth CT” Protocol for Preoperative Planning and Postoperative Evaluation in Total Knee Arthroplasty

Stojadinović,

Mašulović,

Kadija

et al. 2024

Medicina

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Background and Objectives: Total knee arthroplasty (TKA) has become the treatment of choice for advanced osteoarthritis. The aim of this paper was to show the possibilities of optimizing the Perth CT protocol, which is highly effective for preoperative planning and postoperative assessment of alignment. Materials and Methods: The cross-sectional study comprised 16 patients for preoperative planning or postoperative evaluation of TKA. All patients were examined with the standard and optimized Perth CT protocol using advance techniques, including automatic exposure control (AEC), iterative image reconstruction (IR), as well as a single-energy projection-based metal artifact reduction algorithm for eliminating prosthesis artifacts. The effective radiation dose (E) was determined based on the dose report. Imaging quality is determined according to subjective and objective (values of signal to noise ratio (SdNR) and figure of merit (FOM)) criteria. Results: The effective radiation dose with the optimized protocol was significantly lower compared to the standard protocol (p < 0.001), while in patients with the knee prosthesis, E increased significantly less with the optimized protocol compared to the standard protocol. No significant difference was observed in the subjective evaluation of image quality between protocols (p > 0.05). Analyzing the objective criteria for image quality optimized protocols resulted in lower SdNR values and higher FOM values. No significant difference of image quality was determined using the SdNR and FOM as per the specified protocols and parts of extremities, and for the presence of prothesis. Conclusions: Retrospecting the ALARA (‘As Low As Reasonably Achievable’) principles, it is possible to optimize the Perth CT protocol by reducing the kV and mAs values and by changing the collimation and increasing the pitch factor. Advanced IR techniques were used in both protocols, and AEC was used in the optimized protocol. The effective dose of radiation can be reduced five times, and the image quality will be satisfactory.

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Section: Discussionmentioning

confidence: 99%

Optimization of the “Perth CT” Protocol for Preoperative Planning and Postoperative Evaluation in Total Knee Arthroplasty

Stojadinović,

Mašulović,

Kadija

et al. 2024

Medicina

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“…In comparison with every single patient audit procedure, as the overscan occurred in internal and external datasets at a frequency of 22.4% and 32%, it was estimated that the developed algorithm could reduce the workload of overscan range check by 68% and 77.6% for each dataset. We also expect that combinations of our algorithm with radiation dose and image quality monitoring could establish the fully automated and integrated CT quality system for every patient [ 53 – 57 ]. By reducing human and time resources with full automation, it is available to equip high throughput and objective quality monitoring platform for the entire CT scans.…”

Section: Discussionmentioning

confidence: 99%

Development of deep learning-assisted overscan decision algorithm in low-dose chest CT: Application to lung cancer screening in Korean National CT accreditation program

et al. 2022

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We propose a deep learning-assisted overscan decision algorithm in chest low-dose computed tomography (LDCT) applicable to the lung cancer screening. The algorithm reflects the radiologists’ subjective evaluation criteria according to the Korea institute for accreditation of medical imaging (KIAMI) guidelines, where it judges whether a scan range is beyond landmarks’ criterion. The algorithm consists of three stages: deep learning-based landmark segmentation, rule-based logical operations, and overscan determination. A total of 210 cases from a single institution (internal data) and 50 cases from 47 institutions (external data) were utilized for performance evaluation. Area under the receiver operating characteristic (AUROC), accuracy, sensitivity, specificity, and Cohen’s kappa were used as evaluation metrics. Fisher’s exact test was performed to present statistical significance for the overscan detectability, and univariate logistic regression analyses were performed for validation. Furthermore, an excessive effective dose was estimated by employing the amount of overscan and the absorbed dose to effective dose conversion factor. The algorithm presented AUROC values of 0.976 (95% confidence interval [CI]: 0.925–0.987) and 0.997 (95% CI: 0.800–0.999) for internal and external dataset, respectively. All metrics showed average performance scores greater than 90% in each evaluation dataset. The AI-assisted overscan decision and the radiologist’s manual evaluation showed a statistically significance showing a p-value less than 0.001 in Fisher’s exact test. In the logistic regression analysis, demographics (age and sex), data source, CT vendor, and slice thickness showed no statistical significance on the algorithm (each p-value > 0.05). Furthermore, the estimated excessive effective doses were 0.02 ± 0.01 mSv and 0.03 ± 0.05 mSv for each dataset, not a concern within slight deviations from an acceptable scan range. We hope that our proposed overscan decision algorithm enables the retrospective scan range monitoring in LDCT for lung cancer screening program, and follows an as low as reasonably achievable (ALARA) principle.

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“…Moreover, since the implementation of more complex and iterative image reconstruction algorithms, the use of traditional image quality metrics such as SNR and CNR for objective image assessment in PCCT remains limited due to the nonlinearity of iterative reconstruction methods [48]. This is also present for different model-based and deep learning reconstructions [49] offered by manufacturers. Further development of new, robust methods will be necessary in the future.…”

Section: Data Storage and Postprocessingmentioning

confidence: 99%

Photon-Counting Computed Tomography – Basic Principles, Potenzial Benefits, and Initial Clinical Experience

Stein

Rau

Russe

et al. 2023

Rofo

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Background Photon-counting computed tomography (PCCT) is a promising new technology with the potential to fundamentally change today’s workflows in the daily routine and to provide new quantitative imaging information to improve clinical decision-making and patient management. Method The content of this review is based on an unrestricted literature search on PubMed and Google Scholar using the search terms “Photon-Counting CT”, “Photon-Counting detector”, “spectral CT”, “Computed Tomography” as well as on the authors’ experience. Results The fundamental difference with respect to the currently established energy-integrating CT detectors is that PCCT allows counting of every single photon at the detector level. Based on the identified literature, PCCT phantom measurements and initial clinical studies have demonstrated that the new technology allows improved spatial resolution, reduced image noise, and new possibilities for advanced quantitative image postprocessing. Conclusion For clinical practice, the potential benefits include fewer beam hardening artifacts, radiation dose reduction, and the use of new contrast agents. In this review, we will discuss basic technical principles and potential clinical benefits and demonstrate first clinical use cases. Key Points: Citation Format

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Fully automated image quality evaluation on patient CT: Multi-vendor and multi-reconstruction study

Cited by 8 publications

References 82 publications

Optimization of the “Perth CT” Protocol for Preoperative Planning and Postoperative Evaluation in Total Knee Arthroplasty

Optimization of the “Perth CT” Protocol for Preoperative Planning and Postoperative Evaluation in Total Knee Arthroplasty

Development of deep learning-assisted overscan decision algorithm in low-dose chest CT: Application to lung cancer screening in Korean National CT accreditation program

Photon-Counting Computed Tomography – Basic Principles, Potenzial Benefits, and Initial Clinical Experience

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