Diagnostic imaging evaluating image quality using visual grading characteristic (VGC) analysis
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1 Vet Res Commun (2010) 34: DOI /s SHORT COMMUNICATION Diagnostic imaging evaluating image quality using visual grading characteristic (VGC) analysis Eberhard Ludewig & Andreas Richter & Mairi Frame Accepted: 26 April 2010 / Published online: 12 May 2010 # Springer Science+Business Media B.V Abstract Radiologists are regularly faced with the task of comparing image quality obtained using different imaging systems or settings. Visual grading techniques can be used to evaluate the quality of images by grading the clarity of reproduction of anatomical or pathological structures. The methods, which include visual grading analysis (VGA) and the image criteria (IC) study, are characterised by their attractive simplicity and reliability. Non-parametric rank-invariant statistical methods are suitable techniques for statistical analysis of VGA-data. Båth and Månsson (2007) introduced such a method and termed it visual grading characteristics (VGC) analysis. This paper gives an overview of the principle together with an example of its use in veterinary radiology. The aim of this review article is to encourage veterinary researchers to apply this method which has proven valuable in the human field. Basically, the method can also be applied for the analysis of other categories of images (e.g. histological sections, cytological smears) in cases where the task is to evaluate features subjectively on the basis of a score, allowing some degree of freedom of decision. Furthermore, the aim of the investigation is not necessarily restricted to quality aspects. Other questions such as the effects of treatment options on the appearance of certain structures can be compared as well. Keywords Statistics. Data analysis. Visual grading characteristics analysis. Radiology E. Ludewig (*) Department of Small Animal Medicine, Faculty of Veterinary Medicine, University of Leipzig, An den Tierkliniken 23, Leipzig, Germany ludewig@vetmed.uni-leipzig.de A. Richter Large Animal Clinic for Theriogenology and Ambulatory Services, Faculty of Veterinary Medicine, University of Leipzig, An den Tierkliniken 29, Leipzig, Germany M. Frame Royal (Dick) School of Veterinary Studies, Division of Veterinary Clinical Sciences, The University of Edinburgh, Hospital for Small Animals, Easter Bush Veterinary Centre, Roslin, Midlothian, GB-Scotland EH25 9RG, UK
2 474 Vet Res Commun (2010) 34: Introduction A number of established techniques exist to assess the quality of images, including physical methods, 1 psychophysical methods, 2 and techniques whereby an observer assesses the visibility of certain features within clinical images. Of these observer performance methods, which include preference studies, receiver operating characteristic (ROC) analysis, image criteria (IC) study, and visual grading analysis (VGA), the last two have been demonstrated to be useful for several reasons: (1) The observer s rating takes account of the contribution of all technical components of the imaging chain in reproducing image structures (i.e. image recording, postprocessing, display quality on film or a monitor) and the experience of the reader. This is one reason why the practical validity is assumed to be high. (2) The assessment of image quality is based on the visualisation of clinically relevant structures which can be selected and defined using established standards if available. (3) The studies are relatively easy to perform, in comparison with ROC studies. In ROC studies simulated lesions are used to mimic pathology. It takes some effort to create phantoms which reflect clinical conditions adequately. (4) The studies require only moderate time input by participating observers; hence the barriers to undertaking them should be low. (5) Visual grading characteristic (VGC) analysis is a valid new method of analysing data obtained from either VGA or IC-studies. The purpose of this review is to demonstrate the basic methodology of VGC analysis with a view to encouraging veterinary radiologists to utilize this technique. Visual grading analysis (VGA) There are two ways to perform VGA: & as relative grading & as absolute grading In relative VGA-studies one or several reference images are needed. The observer compares the display quality of the target structure of the test image with the corresponding landmark of the reference image. A scale with 3, 5, or 7 points is used to categorize the observer decision (e.g. a 5-step scale comparing visibility would appear thus: +2 = much better, +1 = slightly better, 0 = equal, 1 = slightly worse, 2 much worse). In absolute VGA-studies the observer states his opinion on the visibility of a certain feature without a reference image, using an absolute scale, typically consisting of 4 to 7 points (Table 1). VGA-studies can be used to evaluate anatomical or pathological structures and physical quality parameters. The majority of human studies focus on the evaluation of anatomical structures. The underlying theory is that the ability to detect pathology correlates well with accurate anatomical presentation. In contrast with pathological structures, anatomical landmarks have a more uniform appearance and therefore are more easily to describe. In 1 e.g. by calculating the detective quantum efficiency (DQE), the modulation transfer function (MTF), or the signal-to-noise ratio 2 e.g. contrast-detail diagrams
3 Vet Res Commun (2010) 34: Table 1 Examples for rating scales (A) Image Criteria (IC) - Study 1 - clear confident that the criterion is fulfilled 2 - somewhat confident that the criterion is fulfilled 3 - indecisive whether the criterion is fulfilled or not 4 - somewhat confident that the criterion is not fulfilled 5 - clear confident that the criterion is not fulfilled (B) relative Visual Grading Analysis (VGA) - Study 1 - test image clear superior to reference image 2 - test image somewhat superior to reference image 3 - test image equal to reference image 4 - test image somewhat inferior to reference image 5 - test image clear inferior to reference image (C) absolute Visual Grading Analysis (VGA) - Study 1 - excellent image quality: no limitations for clinical use 2 - good image quality: minimal limitations for clinical use 3 - sufficient image quality: moderate limitations for clinical use but no substantial loss of information 4 - restricted image quality: relevant limitations for clinical use, clear loss of information 5 - poor image quality: image not usable, loss of information, image must be repeated human studies the assessment of anatomical structures is often based on quality standards which have been established for a variety of examinations (CEC Report Eur ; CEC Report Eur ; CEC Report Eur ). Since the criteria for the presentation of target structures investigated in human studies follow identical standards, results obtained from different studies are comparable to a certain degree. Despite the lack of such quality standards in veterinary radiology, generally accepted principles of display characteristics could be used as basics for VGA-studies. The results of a VGA study can be summarised in a VGA-score (VGAS): P S C O;I VGAS ¼ N i N o S C for observer (O) and images (I) Ni total number of images No total number of observers With commercially available software, differences in the ratings can be tested using the analysis of variance (ANOVA) in combination with a test for multiple comparisons in order to reduce the risk of random significance. This method of data analysis has met with criticism since the statistical analysis of VGA-score is based upon the assumption that the scale steps characterize a discrete quantitative variable (Båth and Månsson 2007; Geijer et al. 2001). However they belong to an ordered qualitative variable and therefore it is inappropriate to transform it into numerical values. For this reason the mathematical and statistical validity of VGA-score is questionable. Strengths and weaknesses of the VGA methodology are discussed in detail in the literature (Månsson 2000; Båth and Månsson 2007).
4 476 Vet Res Commun (2010) 34: Image criteria (IC) study In an IC-study the observer is asked to state his opinion about the fulfilment of a certain quality criteria (in a yes-or-no decision). An IC-score is calculated as the ratio of the number of fulfilled criteria to the overall number of criteria assessed (Båth and Månsson 2007). In a modified version the extent of fulfilment of the criteria is also stated. In this extended version a multi-step categorization is available for further analysis (Table 1). As with VGA, the criteria for image quality assessment can be based on established standards of the required level of reproduction of important image structures (Månsson 2000; Båth and Månsson 2007). Visual grading characteristic (VGC) analysis Procedure As mentioned above the analysis of rating data from ordinal scales (e.g. from IC- or VGAstudies) requires non-parametric rank-invariant statistical methods (Sonn and Svensson 1997). Bath and Mansson (2007) introduced a method, which fulfils this requirement. The method, termed visual grading characteristics (VGC) analysis, treats the scale steps as ordinal with no assumptions of the distribution of the data being made. It handles visual grading data in similar fashion to ROC-data (Metz 1986). VGC-analysis can be applied to data from an expanded IC-study or a VGA-study (relative or absolute). VGC-analysis on the basis of IC-data can be considered as a repeated scoring, where the observer is able to adapt the degree of confidence of his decision concerning the fulfilment of pre-defined criteria. A VGC-analysis with data from a VGAstudy follows quite similar principles: the observer uses multiple scale steps to state his opinion about the image quality. Despite the differences in the meaning (IC-studies characterize the observers confidence about the fulfilment of a specific image quality criterion; VGA characterize the observers opinion about the reproduction of a certain structure) the statistical fundamentals of resulting data are the same. There are methodical similarities to ROC-studies. In ROCstudies the observer uses scale steps to state his confidence of each positive ( signal, disease ) or negative ( noise, normality ) decision (Metz 1986; van Erkel and Pattynama 1998). VGC-analysis is performed stepwise. In a first step a frequency table (2 n frequency table, n = number of categories) summarizes the results for the two modalities (A, B) separately. In a second step the VGC data points are calculated in a table. These points represent the coordinates of the VGC-curve. As in a ROC curve the origin of a VGC-curve per definition is 0. The following data points are arranged according to the cumulative (relative) frequencies of the corresponding categories. The last point includes all decisions and therefore is 1. The theoretical background will be clarified by an example below. Suitable ROC software 3,4,5 can be used for plotting the VGC-curve and for calculating the Area under the curve (AUC VGC ). The AUC VGC can be taken as a measure of the 3 ROCKFIT, Kurt Rossmann Laboratories for Radiologic Image Research, University of Chicago, 5841 South Maryland Avenue, Chicago, IL 60637, USA 4 SigmaStat, Systat Software Inc., 1735, Technology Drive, Ste 430, San Jose, CA 95110, USA 5 SPSS 14.0, SPSS Inc. Headquarters, 233 S. Wacker Drive, 11th floor, Chicago, IL 60606, USA
5 Vet Res Commun (2010) 34: difference in the image quality between two modalities or settings. Standard statistical procedures are used for calculating confidence intervals and p-values and for statistical testing. A curve equal or close to the diagonal, equivalent to an AUC VGC of around 0.5, indicates equality between the modalities or conditions investigated. Practical application To illustrate the practical application of the techniques described above, we use the example of a fictitious study comparing the display properties of two modalities (e.g. different digital detectors) in order to identify the superior system. The method can also be adopted for other purposes, e.g. to investigate the influence of exposure levels or of differences in postprocessing in a given digital system etc. In latero-lateral canine thoracic radiographs obtained by each system, the reproducibility of a number of target structures, for example the trachea, is assessed. The practical application of this feature mainly depends on the degree of visualisation of the boundaries of the trachea and the stem bronchi, so by definition a visually sharp delineation of these structures from the adjacent mediastinum and lung is required. Depending on the type of study, the categories of 5-step rating scales are specifically defined. In order to facilitate interpretation and to improve interobserver agreement it is important to translate the numerical scale-step into well-defined verbal description of the signification of each category (Table 1). For further illustration, data from a fictitious absolute VGA-study are used. A blinded observer evaluated the target structure in 100 radiographs from each of the two detector systems (A and B) using the categories from Table 1c. The results are summarized in Table 2. The visual grading characteristics (VGC) data points (pairs of VGA scores) obtained from the data presented in Table 2 are shown in Table 3. The calculated AUC VGC is 0.69 with a standard error of The 95% confidence interval (0.62, 0.77) is above 0.5 (Fig. 1). So it can be stated, the modality B offers a statistically superior image quality in comparison to modality A (with regard to the specific criterion and observer). Pros and cons The strengths of the VGC-analysis can be summarized as follows: (1) IC- or VGA-studies provide the data for VGC-analysis. Therefore the method offers high validity since it is based on assessment of clinically relevant structures. Although Table 2 Results of a fictitious absolute VGA-study Grade Number of Decisions Modality A Modality B Total
6 478 Vet Res Commun (2010) 34: Table 3 Data points of the VGC-study (calculated from results of Table 2) VGA A VGA B (Modality A) (Modality B) established quality standards as used in human radiology are not yet available in the veterinary field, it should be relatively straightforward to define relevant quality criteria for veterinary examinations. (2) The evaluation includes a measure of the observer s self reported confidence on a decision, which is analogous to the daily clinical situation. (3) In comparison to ROC-studies the expenditure of work to perform IC- or VGA-studies is significantly less. Preparation of the reading session in particular is comparably less laborious. This is a further argument to encourage veterinary radiologist to adopt the methodology. (4) The statistical methodology of VGC-analysis is appropriate when using a nonparametric rank-invariant approach to analyse data from an ordinal scale. (5) Major aspects of criticism relate to the same arguments given for the analysis of uncertain ROC-data (Båth and Månsson 2007; van Erkel and Pattynama 1998). So difficulties exist in the interpretation of curves crossing the diagonal. Further, if in ICstudies a large proportion of the decision has a low threshold and therefore represents 1 VGA B AUC VGC = 0.69 standard error: ± % confidence interval: p-value: < VGA A Fig. 1 VGC-curve obtained from the data presented in Tables 2 and 3. The boxes are the coordinates corresponding to the observer s interpretation of the scale steps of the rating scale. The Area under the curve (AUG VGC ) is statistically different from 0.5. Modality B offers a statistically significant superior image quality
7 Vet Res Commun (2010) 34: uncertain decision question arise about their meaning, because the underlying reasons such as poor image quality, observer influence etc. cannot be identified. In detail these aspects are discussed in the literature (Båth and Månsson 2007). In conclusion, VGC-analysis is a valid option for the quantitative assessment of image quality in veterinary radiology. The method also can be applied to other types of images (e.g. histological sections, cytological smears) or other objects of inquiry. References Båth, M., Månsson, L.G., Visual grading characteristics (VGC) analysis: a non-parametric rankinvariant statistical method for image quality analysis. The British Journal of Radiology 80, CEC (Commission of the European Community), European guidelines on quality criteria for diagnostic radiographic images. Report EUR EN. Luxembourg: Office for official publications of the European Communities. CEC (Commission of the European Community), European guidelines on quality criteria for diagnostic radiographic images in paediatrics. Report EUR EN. Luxembourg: Office for official publications of the European Communities. EuropeanGuidelinesEur16261.pdf. CEC (Commission of the European Community), European guidelines on quality criteria for computed tomography. Report EUR EN. Luxembourg: Office for official publications of the European Communities. Criteria_Computed_Tomography_Eur_16252.pdf. Published May Geijer, H., Verdonck, B., Beckman, K.W., Andersson, T., Persliden, J., Digital radiography of scoliosis with a scanning method: initial evaluation. Radiology 218, Månsson, L.G., Methods for the evaluation of image quality: A review. Radiation Protection Dosimetry 90, Metz, C.E., ROC methodology in radiologic imaging. Investigative Radiology 21, Sonn, U., Svensson, E., Measures of individual and group changes in ordered categorical data: application to the ADL staircase. Scandinavian Journal of Rehabilitation Medicine 29, van Erkel, A.R., Pattynama, P.M., Receiver operating characteristic (ROC) analysis: basic principles and applications in radiology. European Journal of Radiology 27, 88 94
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