A method for characterizing and matching <scp>CT</scp> image quality across <scp>CT</scp> scanners from different manufacturers

Winslow, J; Zhang, Yakun; Samei, Ehsan

doi:10.1002/mp.12554

Cited by 19 publications

(23 citation statements)

References 24 publications

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“…and Winslow et al . compared kernels on Siemens and GE 37 , 38 . Both found that the GE standard kernel was the closest match to the B31f or B31s kernel on Siemens, which agrees with our controlled protocol.…”

Section: Discussionmentioning

confidence: 99%

Comprehensive Investigation on Controlling for CT Imaging Variabilities in Radiomics Studies

Ger

Zhou

Melinda

et al. 2018

Sci Rep

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Radiomics has shown promise in improving models for predicting patient outcomes. However, to maximize the information gain of the radiomics features, especially in larger patient cohorts, the variability in radiomics features owing to differences between scanners and scanning protocols must be accounted for. To this aim, the imaging variability of radiomics feature values was evaluated on 100 computed tomography scanners at 35 clinics by imaging a radiomics phantom using a controlled protocol and the commonly used chest and head protocols of the local clinic. We used a linear mixed-effects model to determine the degree to which the manufacturer and individual scanners contribute to the overall variability. Using a controlled protocol reduced the overall variability by 57% and 52% compared to the local chest and head protocols respectively. The controlled protocol also reduced the relative contribution of the manufacturer to the total variability. For almost all variabilities (manufacturer, scanner, and residual with different preprocesssing), the controlled protocol scans had a significantly smaller variability than the local protocol scans did. For most radiomics features, the imaging variability was small relative to the inter-patient feature variability in non–small cell lung cancer and head and neck squamous cell carcinoma patient cohorts. From this study, we conclude that using controlled scans can reduce the variability in radiomics features, and our results demonstrate the importance of using controlled protocols in prospective radiomics studies.

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Section: Discussionmentioning

confidence: 99%

Comprehensive Investigation on Controlling for CT Imaging Variabilities in Radiomics Studies

Ger

Zhou

Melinda

et al. 2018

Sci Rep

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“…Kernels could be matched across scanners from different manufacturers to achieve similar noise texture based on noise power spectrum (NPS) analysis similar to that described in this report . This matching can be expanded to include resolution (TTF) and noise magnitude as well, while at the same time meeting constraints in the dose level of the examination . This method relies on the f A and f 50 metrics of noise and resolution that are described in Sections 2.B and 2.C of this summary paper, respectively.…”

Section: Clinical Utility and Future Extensionsmentioning

confidence: 99%

Performance evaluation of computed tomography systems: Summary of AAPM Task Group 233

et al. 2019

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Background: The rapid development and complexity of new x-ray computed tomography (CT) technologies and the need for evidence-based optimization of image quality with respect to radiation and contrast media dose call for an updated approach towards CT performance evaluation. Aims: This report offers updated testing guidelines for testing CT systems with an enhanced focus on the operational performance including iterative reconstructions and automatic exposure control (AEC) techniques. Materials and Methods: The report was developed based on a comprehensive review of best methods and practices in the scientific literature. The detailed methods include the assessment of 1) CT noise (magnitude, texture, nonuniformity, inhomogeneity), 2) resolution (task transfer function under varying conditions and its scalar reflections), 3) task-based performance (detectability, estimability), and 4) AEC performance (spatial, noise, and mA concordance of attenuation and exposure modulation). The methods include varying reconstruction and tube current modulation conditions, standardized testing protocols, and standardized quantities and metrology to facilitate tracking, benchmarking, and quantitative comparisons. Results: The methods, implemented in cited publications, are robust to provide a representative reflection of CT system performance as used operationally in a clinical facility. The methods include recommendations for phantoms and phantom image analysis. Discussion: In line with the current professional trajectory of the field toward quantitation and operational engagement, the stated methods offer quantitation that is more predictive of clinical performance than specification-based approaches. They can pave the way to approach performance testing of new CT systems not only in terms of acceptance testing (i.e., verifying a device meets predefined specifications), but also system commissioning (i.e., determining how the system can be used most effectively in clinical practice). Conclusion: We offer a set of common testing procedures that can be utilized towards the optimal clinical utilization of CT imaging devices, benchmarking across varying systems and times, and a basis to develop future performance-based criteria for CT imaging.

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“…Phantom studies can help match image appearance and thus identify comparable imaging settings across different vendors’ scanners, scanning techniques and parameters, etc. ( 68 , 69 ). For example, by analyzing noise power spectrum (NPS), a group of medical physicists conducted a study using the ACR CT phantom to quantitatively compare noise texture between two CT systems, GE and Siemens ( 68 ).…”

Section: Image Acquisitionmentioning

confidence: 99%

Understanding Sources of Variation to Improve the Reproducibility of Radiomics

Zhao

2021

Front. Oncol.

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Radiomics is the method of choice for investigating the association between cancer imaging phenotype, cancer genotype and clinical outcome prediction in the era of precision medicine. The fast dispersal of this new methodology has benefited from the existing advances of the core technologies involved in radiomics workflow: image acquisition, tumor segmentation, feature extraction and machine learning. However, despite the rapidly increasing body of publications, there is no real clinical use of a developed radiomics signature so far. Reasons are multifaceted. One of the major challenges is the lack of reproducibility and generalizability of the reported radiomics signatures (features and models). Sources of variation exist in each step of the workflow; some are controllable or can be controlled to certain degrees, while others are uncontrollable or even unknown. Insufficient transparency in reporting radiomics studies further prevents translation of the developed radiomics signatures from the bench to the bedside. This review article first addresses sources of variation, which is illustrated using demonstrative examples. Then, it reviews a number of published studies and progresses made to date in the investigation and improvement of feature reproducibility and model performance. Lastly, it discusses potential strategies and practical considerations to reduce feature variability and improve the quality of radiomics study. This review focuses on CT image acquisition, tumor segmentation, quantitative feature extraction, and the disease of lung cancer.

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A method for characterizing and matching CT image quality across CT scanners from different manufacturers

Abstract: Matching image appearance in terms of resolution, noise magnitude, and noise texture provides a quantitative and reproducible strategy to improve consistency in image quality among different CT scanners and reconstructions.

Cited by 19 publications

References 24 publications

Comprehensive Investigation on Controlling for CT Imaging Variabilities in Radiomics Studies

Comprehensive Investigation on Controlling for CT Imaging Variabilities in Radiomics Studies

Performance evaluation of computed tomography systems: Summary of AAPM Task Group 233

Understanding Sources of Variation to Improve the Reproducibility of Radiomics

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