| 1. |
- Ehsan Saffari, Seyed, et al.
(författare)
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Regression models for analyzing radiological visual grading studies - an empirical comparison
- 2015
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Ingår i: BMC Medical Imaging. - : BioMed Central. - 1471-2342. ; 15
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Tidskriftsartikel (refereegranskat)abstract
- Background: For optimizing and evaluating image quality in medical imaging, one can use visual grading experiments, where observers rate some aspect of image quality on an ordinal scale. To analyze the grading data, several regression methods are available, and this study aimed at empirically comparing such techniques, in particular when including random effects in the models, which is appropriate for observers and patients. Methods: Data were taken from a previous study where 6 observers graded or ranked in 40 patients the image quality of four imaging protocols, differing in radiation dose and image reconstruction method. The models tested included linear regression, the proportional odds model for ordinal logistic regression, the partial proportional odds model, the stereotype logistic regression model and rank-order logistic regression (for ranking data). In the first two models, random effects as well as fixed effects could be included; in the remaining three, only fixed effects. Results: In general, the goodness of fit (AIC and McFadden's Pseudo R-2) showed small differences between the models with fixed effects only. For the mixed-effects models, higher AIC and lower Pseudo R-2 was obtained, which may be related to the different number of parameters in these models. The estimated potential for dose reduction by new image reconstruction methods varied only slightly between models. Conclusions: The authors suggest that the most suitable approach may be to use ordinal logistic regression, which can handle ordinal data and random effects appropriately.
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| 2. |
- Smedby, Örjan, et al.
(författare)
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Quantifying effects of post-processing with visual grading regression
- 2012
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Ingår i: Medical Imaging 2012: Image Perception, Observer Performance, and Technology Assessment. - : SPIE - International Society for Optical Engineering. - 9780819489678 ; , s. Art. no. 83181N-
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Konferensbidrag (refereegranskat)abstract
- For optimization and evaluation of image quality, one can use visual grading experiments, where observers rate some aspect of image quality on an ordinal scale. To take into account the ordinal character of the data, ordinal logistic regression is used in the statistical analysis, an approach known as visual grading regression (VGR). In the VGR model one may include factors such as imaging parameters and post-processing procedures, in addition to patient and observer identity. In a single-image study, 9 radiologists graded 24 cardiac CTA images acquired with ECG-modulated tube current using standard settings (310 mAs), reduced dose (62 mAs) and reduced dose after post-processing. Image quality was assessed using visual grading with five criteria, each with a five-level ordinal scale from 1 (best) to 5 (worst). The VGR model included one term estimating the dose effect (log of mAs setting) and one term estimating the effect of postprocessing. The model predicted that 115 mAs would be required to reach an 80% probability of a score of 1 or 2 for visually sharp reproduction of the heart without the post-processing filter. With the post-processing filter, the corresponding figure would be 86 mAs. Thus, applying the post-processing corresponded to a dose reduction of 25%. For other criteria, the dose-reduction was estimated to 16-26%. Using VGR, it is thus possible to quantify the potential for dose-reduction of post-processing filters.
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| 3. |
- Smedby, Örjan, et al.
(författare)
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Quantifying the potential for dose reduction with visual grading regression
- 2013
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Ingår i: British Journal of Radiology. - : British Institute of Radiology. - 0007-1285 .- 1748-880X. ; 86:1021
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Tidskriftsartikel (refereegranskat)abstract
- Objectives To propose a method to study the effect of exposure settings on image quality and to estimate the potential for dose reduction when introducing dose-reducing measures.Methods Using the framework of visual grading regression (VGR), a log(mAs) term is included in the ordinal logistic regression equation, so that the effect of reducing the dose can be quantitatively related to the effect of adding post-processing. In the ordinal logistic regression, patient and observer identity are treated as random effects using generalised linear latent and mixed models. The potential dose reduction is then estimated from the regression coefficients. The method was applied in a single-image study of coronary CT angiography (CTA) to evaluate two-dimensional (2D) adaptive filters, and in an image-pair study of abdominal CT to evaluate 2D and three-dimensional (3D) adaptive filters.Results For five image quality criteria in coronary CTA, dose reductions of 16–26% were predicted when adding 2D filtering. Using five image quality criteria for abdominal CT, it was estimated that 2D filtering permits doses were reduced by 32–41%, and 3D filtering by 42–51%.Conclusions VGR including a log(mAs) term can be used for predictions of potential dose reduction that may be useful for guiding researchers in designing subsequent studies evaluating diagnostic value. With appropriate statistical analysis, it is possible to obtain direct numerical estimates of the dose-reducing potential of novel acquisition, reconstruction or post-processing techniques.
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| 4. |
- Smedby, Örjan, et al.
(författare)
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Visual grading regression : analysing data from visual grading experiments with regression models
- 2010
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Ingår i: British Journal of Radiology. - : British Institute of Radiology. - 0007-1285 .- 1748-880X. ; 83:993, s. 767-775
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Tidskriftsartikel (refereegranskat)abstract
- For visual grading experiments, which are an easy and increasingly popular way of studying image quality, hitherto used data analysis methods are often inadequate. Visual grading analysis makes assumptions that are not statistically appropriate for ordinal data, and visual grading characteristic curves are difficult to apply in more complex experimental designs. The approach proposed in this paper, visual grading regression (VGR), consists of an established statistical technique, ordinal logistic regression, applied to data from single-image and image-pair experiments with visual grading scores selected on an ordinal scale. The approach is applicable for situations in which, for example, the effects of the choice of imaging equipment and post-processing method are to be studied simultaneously, while controlling for potentially confounding variables such as patient and observer identity. The analysis can be performed with standard statistical software packages using straightforward coding of the data. We conclude that the proposed statistical technique is useful in a wide range of visual grading studies.
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| 5. |
- Smedby, Örjan, et al.
(författare)
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Visual grading regression with random effects
- 2012
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Ingår i: MEDICAL IMAGING 2012: IMAGE PERCEPTION, OBSERVER PERFORMANCE, AND TECHNOLOGY ASSESSMENT. - : SPIE - International Society for Optical Engineering. - 9780819489678 ; , s. Art. no. 831805-
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Konferensbidrag (refereegranskat)abstract
- To analyze visual grading experiments, ordinal logistic regression (here called visual grading regression, VGR) may be used in the statistical analysis. In addition to types of imaging or post-processing, the VGR model may include factors such as patient and observer identity, which should be treated as random effects. Standard software does not allow random factors in ordinal logistic regression, but using Generalized Linear Latent And Mixed Models (GLLAMM) this is possible. In a single-image study, 9 radiologists graded 24 cardiac Computed Tomography Angiography (CTA) images with reduced dose without and after post-processing with a 2D adaptive filter, using five image quality criteria. First, standard ordinal logistic regression was carried out, treating filtering, patient and observer identity as fixed effects. The same analysis was then repeated with GLLAMM, treating filtering as a fixed effect and patient and observer identity as random effects. With both approaches, a significant effect (pless than0.01) of the filtering was found for all five criteria. No dramatic differences in parameter estimates or significance levels were found between the two approaches. It is concluded that random effects can be appropriately handled in VGR using GLLAMM, but no major differences in the results were found in a preliminary evaluation.
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