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Overall patient risk including radiation risk and clinical risk as a function of dose. Dashed lines represent the optimum target. The units of both axes are arbitrary. Two examples of individual imaging procedures, each represented with three corresponding risk value datapoints, demonstrate different degrees of accuracy in meeting the optimisation target.
Source publication
As in any medical intervention, there is either a known or an anticipated benefit to the patient from undergoing a medical imaging procedure. This benefit is generally significant as demonstrated by the manner in which medical imaging has transformed clinical medicine. At the same time, when it comes to imaging that deploys ionizing radiation, ther...
Contexts in source publication
Context 1
... figure 1, increasing dose (in terms of a relevant metric being optimised) in a given imaging examination increases the radiation risk to the patient. In this illustration, the radiation risk is assumed to follow the linear-no-threshold model, but any alternative model can also be considered here. The increase in dose has a corresponding associated influence on clinical risk: as the dose increases, the resultant image quality increases, which in turn improves the information content available to the clinician reducing the likelihood of sub- optimal diagnosis. The shape of this dependency follows an inversion of the asymptotic relationship universally observed between radiation dose and image quality [41]. The two risk models follow reversing trends such that the total risk to the patient exhibits a minimum 'valley' of lowest risk. The valley essentially provides a target for the objective of optim- isation of overall risk. It should be noted that in the vertical axis, the 1.0 value of relative clinical risk at a zero radiation dose level does not imply fatal clinical endpoint (of death) but rather an indication dependent measure of disease or diagnosis related detriment. This may be further normalised to scale with the radiation risk endpoint (of cancer death) but such nor- malisation requires inclusion of other clinical factors and possibly estimates related to an extended set of indications and pathological prevalence. The above characterization of optimisation is consistent with the ALARA principle, which has been used to represent the principle of optimisation efforts. However, it provides a more granular and targeted definition. By framing the optimisation as a balancing act between two similar quantities, one risk against another, the process involves a comparison of 'apples and apples.' This is a much more comprehensible task than balancing risk and benefit, factors that follow different scales and correlations that rarely, if ever, demonstrate an optimum 'peak or valley' that optimisation processes generally seek. In this framing of optimisation, optimisation further becomes a process by which a patient is positioned to experience the lowest combined radiation risk and clinical risk and thus highest level of overall welfare, which is the very purpose of performing a medical ...
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... should be noted that the concept illustrated in figure 1 is broadly applicable to most imaging modalities using ionising radiation, except those that use a detector technology with a limited, narrow dynamic range, i.e. screen-film. In film radiography, the film speed and the narrow range of recordable exposures limit the choices for optimisation for a given screen- film system. While the ability to optimise imaging more flexibly is a possibility for digital imaging technologies, this possibility has not been fully taken advantage of in most clinical applications ...
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... through characterisation and minimisation of total risk can be applied towards an individual or a population. In doing so, the concepts of accuracy and precision come into play. The accuracy (or bias) of optimisation pertains to the ability to perform an imaging procedure at the lowest point of the risk−dose continuum. In figure 1, the two example cases, shown by datapoints represented at two different dose levels, exhibit different degrees of accuracy in terms of their proximity to the optimum, minimum risk target. Each patient is represented by three associated data points for radiation, clinical, and total risk respectively. In a population of cases, the bias is the difference between the optimum target and the median of the population distribution. The precision is likewise the deviation of the total risk values across the population. Figure 2 demonstrates the application of the accuracy and precision of optimisation across a ...
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... heterogeneity of this landscape leads to sub-optimal and variable image quality and dose such that the above stated goal of the procedure, 'safely obtaining useful information relevant to a target indication of interest for accurate and precise management of patient care,' is not achieved. Thus, sub-optimal imaging carries the risk of not achieving the very purpose for which it was performed. Optimisation should provide an assurance that the goal of the imaging procedure is achieved. This includes consideration of the risk associated with the application of the ionising radiation used in the process-the so-called radiation risk. But most importantly, the likelihood of not delivering the very purpose of imaging, i.e. delivering the desired benefit, should be recog- nised as a risk-to which we refer, in this report, as clinical risk. Comprehensive optimisation combines these two risks-radiological and clinical-as a unified total risk estimate (or index) within an indication-informed process. Other sources of risk, such as the use of contrast medium, can be added to this framework, though that is beyond the scope of the present paper. Figure 1 offers a schematic illustration of this mental viewing of 'optimisation'. The figure also unveils the physical meaning of optimisation. Optimisation means to make a choice (to opt) between two designs (procedures, dosages, options), which are represented by the two asymptotes shown in the figure. The optimal choice turns out to be the tradeoff between the two, the optimum balance that in the figure is represented by the minimum of the upper curve. Incidentally, design in nature and in the human realm is often informed by a balance between two competing options; the design emerges from the competition between extremes. This phenomenon is ubiquitous, in both biological and non-biological systems, and is formulated in the constructal law in physics [36]. As such, similar graphic constructions of optimal design in figure 1 appear in the constructal design of speed and frequency in animal locomotion [37], turbulent eddy size, snowflake design, cooling channels for electronics, Bénard convection, jet engine size for aircraft, and others ...
Context 5
... heterogeneity of this landscape leads to sub-optimal and variable image quality and dose such that the above stated goal of the procedure, 'safely obtaining useful information relevant to a target indication of interest for accurate and precise management of patient care,' is not achieved. Thus, sub-optimal imaging carries the risk of not achieving the very purpose for which it was performed. Optimisation should provide an assurance that the goal of the imaging procedure is achieved. This includes consideration of the risk associated with the application of the ionising radiation used in the process-the so-called radiation risk. But most importantly, the likelihood of not delivering the very purpose of imaging, i.e. delivering the desired benefit, should be recog- nised as a risk-to which we refer, in this report, as clinical risk. Comprehensive optimisation combines these two risks-radiological and clinical-as a unified total risk estimate (or index) within an indication-informed process. Other sources of risk, such as the use of contrast medium, can be added to this framework, though that is beyond the scope of the present paper. Figure 1 offers a schematic illustration of this mental viewing of 'optimisation'. The figure also unveils the physical meaning of optimisation. Optimisation means to make a choice (to opt) between two designs (procedures, dosages, options), which are represented by the two asymptotes shown in the figure. The optimal choice turns out to be the tradeoff between the two, the optimum balance that in the figure is represented by the minimum of the upper curve. Incidentally, design in nature and in the human realm is often informed by a balance between two competing options; the design emerges from the competition between extremes. This phenomenon is ubiquitous, in both biological and non-biological systems, and is formulated in the constructal law in physics [36]. As such, similar graphic constructions of optimal design in figure 1 appear in the constructal design of speed and frequency in animal locomotion [37], turbulent eddy size, snowflake design, cooling channels for electronics, Bénard convection, jet engine size for aircraft, and others ...
Citations
... In addition, to maximize the clinical utility of CT imaging, an understanding and characterization of image quality parameters are imperative. 3 In clinical practice, the quality of CT images is contingent upon multiple parameters, including spatial resolution 4 and noise power spectrum. 5 Spatial resolution serves as a critical metric for assessing the discernibility of adjacent structures within an image. ...
Purpose
This study aimed to develop and validate a method for characterizing the spatial resolution of clinical chest computed tomography (CT) sequence images.
Methods
An algorithm for characterizing spatial resolution based on clinical chest CT sequence images was developed in Matlab (2021b). The algorithm was validated using CT sequence images from a custom‐made chest automatic tube current modulation (ATCM) phantom and clinically reconstructed chest CT sequence images. A region of interest (ROI) was automatically established at the edges of CT image subject to calculate the edge spread function (ESF). The ESF curves from consecutive CT images within the same sequence were fitted into a curve, and the line spread function (LSF) was derived through differentiation. A Fourier transformation of the LSF curve was conducted to obtain the modulation transfer function (MTF). The method's effectiveness was verified by comparing the 50% MTF and 10% MTF values with those calculated using IndoQCT (22a) software. The method was also applied to clinical CT images to calculate MTF values for various reconstructions, confirming its sensitivity by determining spatial resolution of clinically reconstructed images.
Results
Validation experiments based on the phantom CT sequence images demonstrated that the MTF values calculated using the proposed method had an average difference of within ± 5% compared to the results obtained with IndoQCT. Validation experiments with clinical CT sequence images indicated that the method effectively reflects differences and variations in spatial resolution of images under different reconstruction kernels, with the MTF values for B10f‐B50f and D10f‐D50f exhibiting a consistent increase.
Conclusion
A method for measuring spatial resolution using clinical chest CT sequence images was developed. This method provides a direct means of spatial resolution characterization for clinical CT datasets and a more accurate representation of CT imaging quality, effectively reflects variations across different reconstruction convolution kernels, demonstrating its sensitivity.
... Klinik risk, görüntü kalitesinin yeterliliği ile belirlenir ve bir hastalığın ya da lezyonun güvenli bir şekilde tespit edilip edilemeyeceğini gösterir. Gelecekte, radyasyon riskin yanı sıra klinik risk de mortalite gibi klinik sonuçlarla ilişkilendirilebilir [4,17]. ...
... Therefore, the proper practice of radiology should take into consideration the simultaneous and quantitative assessment of both radiation risk and benefit of a procedure. However, this has been a major challenge as the risk and benefit rarely use comparable units 3 . The need for optimization is particularly relevant in Computed Tomography (CT) as the leading source of radiation exposure per capita in the US 4 . ...
Background
Risk-versus-benefit optimization required a quantitative comparison of the two. The latter, directly related to effective diagnosis, can be associated to clinical risk. While many strategies have been developed to ascertain radiation risk, there has been a paucity of studies assessing clinical risk, thus limiting the optimization reach to achieve a minimum total risk to patients undergoing imaging examinations. In this study, we developed a mathematical framework for an imaging procedure total risk index considering both radiation and clinical risks based on specific tasks and investigated diseases.
Methods
The proposed model characterized total risk as the sum of radiation and clinical risks defined as functions of radiation burden, disease prevalence, false-positive rate, expected life-expectancy loss for misdiagnosis, and radiologist interpretative performance (i.e., AUC). The proposed total risk model was applied to a population of one million cases simulating a liver cancer scenario.
Results
For all demographics, the clinical risk outweighs radiation risk by at least 400%. The optimization application indicates that optimizing typical abdominal CT exams should involve a radiation dose increase in over 90% of the cases, with the highest risk optimization potential in Asian population (24% total risk reduction; 306% increase) and lowest in Hispanic population (5% total risk reduction; 89% increase).
Conclusions
Framing risk-to-benefit assessment as a risk-versus-risk question, calculating both clinical and radiation risk using comparable units, allows a quantitative optimization of total risks in CT. The results highlight the dominance of clinical risk at typical CT examination dose levels, and that exaggerated dose reductions can even harm patients.
... Although patient doses for typical abdominal and abdomino-pelvic imaging have decreased over time [4,5], techniques such as multiphasic CT and volumetric imaging have resulted in an increase in the collective dose, particularly for some patient groups [4][5][6] and in particular larger patients [7][8][9][10][11][12]. Optimisation of abdomino-pelvic CT protocols is therefore an ongoing concern. ...
... In diagnostic radiological imaging, it is important to optimize the balance between the radiation dose to which patients are exposed and the specific image quality required for diagnosis and operate under conditions supported by objective data [1][2][3][4][5][6]. It is desirable to use quantitative image quality metrics that correlate with the subjective perceptions of the doctors that observe medical images and perform diagnostic decisions [7][8][9]. ...
Optimization tasks in diagnostic radiological imaging require objective quantitative metrics that correlate with the subjective perception of observers. However, although one such metric, the structural similarity index (SSIM), is popular, it has limitations across various aspects in its application to medical images. In this study, we introduce a novel image quality evaluation approach based on keypoints and their associated unique image feature values, focusing on developing a framework to address the need for robustness and interpretability that are lacking in conventional methodologies. The proposed index quantifies and visualizes the distance between feature vectors associated with keypoints, which varies depending on changes in the image quality. This metric was validated on images with varying noise levels and resolution characteristics, and its applicability and effectiveness were examined by evaluating images subjected to various affine transformations. In the verification of X-ray computed tomography imaging using a head phantom, the distances between feature descriptors for each keypoint increased as the image quality degraded, exhibiting a strong correlation with the changes in the SSIM. Notably, the proposed index outperformed conventional full-reference metrics in terms of robustness to various transformations which are without changes in the image quality. Overall, the results suggested that image analysis performed using the proposed framework could effectively visualize the corresponding feature points, potentially harnessing lost feature information owing to changes in the image quality. These findings demonstrate the feasibility of applying the novel index to analyze changes in the image quality. This method may overcome limitations inherent in conventional evaluation methodologies and contribute to medical image analysis in the broader domain.
... Assessing IQ and patient exposure is crucial to ensure that a dose reduction still provides adequate IQ for an accurate diagnosis [13,14]. To our knowledge, there has been no previous analysis that jointly examines IQ and DRLs in the context of CTPA, specifically to ensure appropriate patient dose reduction. ...
... Therefore, any claim about effective dose values and trends, should be always followed by the adopted calculation method description in order to avoid comparisons between distributions coming from different type of calculations. These findings are consistent with a 2018 recommendation from the International Atomic Energy Agency that highlighted how all dose quantities can relate to radiation dose falling into a relevance hierarchy 18 . In particular, E DLP relies on CT device output that is converted to a risk surrogate simply by the application of a body region conversion factor. ...
An updated extension of effective dose was recently introduced, namely relative effective dose ( E r ), incorporating age and sex factors. In this study we extended E r application to a population of about 9000 patients who underwent multiple CT imaging exams, and we compared it with other commonly used radiation protection metrics in terms of their correlation with radiation risk. Using Monte Carlo methods, E r , dose-length-product based effective dose ( E DLP ), organ-dose based effective dose ( E OD ), and organ-dose based risk index ( RI ) were calculated for each patient. Each metric’s dependency to RI was assessed in terms of its sensitivity and specificity. E r showed the best sensitivity, specificity, and agreement with RI (R ² = 0.97); while E DLP yielded the lowest specificity and, along with E OD , the lowest sensitivity. Compared to other metrics, E r provided a closer representation of patient and group risk also incorporating age and sex factors within the established framework of effective dose.
... Over the past two decades, use of computed tomography (CT) has increased and one of its important consequences is increasing of radiation exposure for nearly 50% of the total collective dose (person-Sieverts) to the population. 1 Although CT greatly has enhanced diagnostic abilities, its ionising radiation dose is 100-500 times more than conventional radiography. 2 This becomes concerning when we take into account the annual increase in the number of CT examinations for both children and adults. ...
Introduction
Concerns regarding the adverse consequences of radiation have increased due to the expanded application of computed tomography (CT) in medical practice. Certain studies have indicated that the radiation dosage depends on the anatomical region, the imaging technique employed and patient‐specific variables. The aim of this study is to present fitting models for the estimation of age‐specific dose estimates (ASDE), in the same direction of size‐specific dose estimates, and effective doses based on patient age, gender and the type of CT examination used in paediatric head, chest and abdomen–pelvis imaging.
Methods
A total of 583 paediatric patients were included in the study. Radiometric data were gathered from DICOM files. The patients were categorised into five distinct groups (under 15 years of age), and the effective dose, organ dose and ASDE were computed for the CT examinations involving the head, chest and abdomen–pelvis. Finally, the best fitting models were presented for estimation of ASDE and effective doses based on patient age, gender and the type of examination.
Results
The ASDE in head, chest, and abdomen–pelvis CT examinations increases with increasing age. As age increases, the effective dose in head and abdomen–pelvis CT scans decreased. However, for chest scans, the effective dose initially showed a decreasing trend until the first year of life; after that, it increases in correlation with age.
Conclusions
Based on the presented fitting model for the ASDE, these CT scan quantities depend on factors such as patient age and the type of CT examination. For the effective dose, the gender was also included in the fitting model. By utilising the information about the scan type, region and age, it becomes feasible to estimate the ASDE and effective dose using the models provided in this study.
... In this context, continuing education and training of all stakeholders (radiological medical practitioners, medical physicists, medical radiation technologists and other health professionals) is paramount. More specifically, users' familiarisation with technical aspects of modern CT systems that affect patient doses is of great importance in order to exploit the technological advances of modern scanners [21,22]. EEAE will communicate the findings of this study and the need for further action to the professional associations of the stakeholders in the country and promote the implementation of the necessary optimisation strategies. ...
... Concerns about radiation dose burden have accelerated the technical development of optimization methods to enable CT scans with the most beneficial balance between image quality (IQ) and radiation dose [3,4]. Maintaining adequate IQ is an absolute requirement to secure reliable diagnostic information and provide correct care decisions targeted for the effective care of each individual patient [5]. ...
... However, the ability of any artificial phantom model to represent the characteristics of a human being is limited. More specifically, these models do not provide a comprehensive surrogate for patients with anatomical and pathological variability, gender and age representations, tissue textures and compositions, and physiological motion [5,6], although elaborate virtual clinical trials are striving toward this level of detail [11]. ...
