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Visualizing Radiologic Data

¹ Department of Diagnostic Radiology and Nuclear Medicine, Rm. 2MR21, University of Western Ontario, London Health Sciences Center-University Campus, 339 Windermere Rd., London, Ontario N6A 5A5, Canada.

View larger version (16K): [in a new window]   Fig. 1A. —Example of presentation with high data-ink ratio. (Reprinted from [3]) Multivariable graphs show control MR imaging-determined parotid gland size () for male (A) and female (B) patients. Each patient data point represents parotid gland size, age, and patient condition. Parotid gland size increased in patients with hyperlipidemia () but not Sjögren's syndrome (). Mean values ± two standard deviations are plotted (containing 95% of data) to provide visualization of spread of control data versus patient values.

View larger version (18K): [in a new window]   Fig. 1B. —Example of presentation with high data-ink ratio. (Reprinted from [3]) Multivariable graphs show control MR imaging-determined parotid gland size () for male (A) and female (B) patients. Each patient data point represents parotid gland size, age, and patient condition. Parotid gland size increased in patients with hyperlipidemia () but not Sjögren's syndrome (). Mean values ± two standard deviations are plotted (containing 95% of data) to provide visualization of spread of control data versus patient values.

View larger version (28K): [in a new window]   Fig. 2. —Example of graph with high data-ink ratio that portrays related data in one presentation. Multivariable graph depicts attenuation versus time for several tissues after contrast injection. Conspicuity (•) and attenuation of liver (), tumor ([UNK]), aorta (), and portal vein () are plotted. Phases of hepatic enhancement are also illustrated. (Reprinted from [4])

View larger version (27K): [in a new window]   Fig. 3. —Example of figure that successfully illustrates temporal relationship between two dependent variables. Graph shows plotting relationship between two different but related phenomena using two different y axes: displacement (on left) and velocity (on right) for mean through-plane motion of prosthetic valve. This figure has high data-ink ratio, especially with error bars included. Choice for x-axis position is compromised, leading to some obscuring of data values and of x-axis tick labels. (Reprinted with permission from [5])

View larger version (24K): [in a new window]   Fig. 4. —Box-and-whiskers plots. Graph shows contrast-to-noise ratio in electron-beam CT coronary angiography for different coronary vessel segments. Bottom and top edges of box are 25th and 75th percentiles, horizontal line represents the median, and error bars delimit extent of 10th and 90th percentiles. No statistical differences were observed, and this type of plot effectively portrays this data variability. LM = left main coronary artery, LAD = left anterior descending coronary artery, LCX = left circumflex coronary artery, RCA = right coronary artery, p = proximal segment, m = middle segment, d = distal segment. (Reprinted from [6])

View larger version (30K): [in a new window]   Fig. 5. —Example of poor data-ink ratio for receiver operating characteristic curve. Graph shows only 10 data points, which are obscured by tremendous amount of nondata ink, including background grid, tick marks, and line of unity. DAFL = differential air-fluid level. (Reprinted from [7])

View larger version (23K): [in a new window]   Fig. 6A. —Example of data that could have been handled in table format. (Reprinted with permission from [8]) Graphs show findings for reticular (A), small nodular (B), and ground-glass (C) abnormalities in four display formats. Appropriate receiver operating characteristic curves are used, but curves are not significantly different for any abnormalities. Repetition is unproductive. In each graph, it is difficult to discern individual curves and their identification.

View larger version (24K): [in a new window]   Fig. 6B. —Example of data that could have been handled in table format. (Reprinted with permission from [8]) Graphs show findings for reticular (A), small nodular (B), and ground-glass (C) abnormalities in four display formats. Appropriate receiver operating characteristic curves are used, but curves are not significantly different for any abnormalities. Repetition is unproductive. In each graph, it is difficult to discern individual curves and their identification.

View larger version (22K): [in a new window]   Fig. 6C. —Example of data that could have been handled in table format. (Reprinted with permission from [8]) Graphs show findings for reticular (A), small nodular (B), and ground-glass (C) abnormalities in four display formats. Appropriate receiver operating characteristic curves are used, but curves are not significantly different for any abnormalities. Repetition is unproductive. In each graph, it is difficult to discern individual curves and their identification.

View larger version (21K): [in a new window]   Fig. 7. —Figure in which data-ink ratio and optical vibrations (moiré effect) are poor. Graph shows complex theoretic analysis of optimal treatment strategy using two-way sensitivity analysis. PTA = percutaneous transluminal angioplasty, SS = selective stent placement, CI = confidence interval. (Reprinted with permission from [9])

View larger version (45K): [in a new window]   Fig. 8. —Example of figure that could have been simplified. Bar chart shows average detectability of lung abnormalities divided into severity for two groups of radiologists and four display methods. The presentation has two principal problems: moiré vibrations (optical noise) and redundancy, with the two groups of radiologists repeated for each degree of abnormality. Reprinted with permission from [8])

View larger version (11K): [in a new window]   Fig. 9. —Example of another way data in Figure 8 might have been presented. Plot uses much less data ink without losing portrayal of any raw data. Different symbols are used to represent each radiologist.

View larger version (66K): [in a new window]   Fig. 10. —Example of bar charts dominated by moiré patterns. Illustration of all raw data for many areas from receiver operating characteristic analyses hides fact that multiple comparisons would require additional statistical tests. There is little value in occupying so much visual real estate for not much significant data. Reprinted with permission from [8])

View larger version (37K): [in a new window]   Fig. 11A. —Examples of moiré patterns associated with bars filled with opposing hash lines (each representing a different observer) and effect of including nondata ink (grids). Values for p are not indicated. (Reprinted with permission from [10]) Bar charts show findings in lung-equivalent (A), heart-equivalent (B), and sub-diaphragm-equivalent (C) regions.

View larger version (33K): [in a new window]   Fig. 11B. —Examples of moiré patterns associated with bars filled with opposing hash lines (each representing a different observer) and effect of including nondata ink (grids). Values for p are not indicated. (Reprinted with permission from [10]) Bar charts show findings in lung-equivalent (A), heart-equivalent (B), and sub-diaphragm-equivalent (C) regions.

View larger version (31K): [in a new window]   Fig. 11C. —Examples of moiré patterns associated with bars filled with opposing hash lines (each representing a different observer) and effect of including nondata ink (grids). Values for p are not indicated. (Reprinted with permission from [10]) Bar charts show findings in lung-equivalent (A), heart-equivalent (B), and sub-diaphragm-equivalent (C) regions.

View larger version (81K): [in a new window]   Fig. 12A. —Example of figure that provides value-filled expression of improvement in diagnostic accuracy and leaves variability visible. (Reprinted with permission from [11]) Bar charts show diagnostic accuracy without (A) and with (B) computer-aided diagnosis (CAD). Bars have muted moiré effect and charts have more pleasing overall appearance compared with those of Figures 8, 10, and 11A, 11B, 11C. Panel B shows that using CAD resulted in increase in diagnostic accuracy for all groups of radiologists.

View larger version (65K): [in a new window]   Fig. 12B. —Example of figure that provides value-filled expression of improvement in diagnostic accuracy and leaves variability visible. (Reprinted with permission from [11]) Bar charts show diagnostic accuracy without (A) and with (B) computer-aided diagnosis (CAD). Bars have muted moiré effect and charts have more pleasing overall appearance compared with those of Figures 8, 10, and 11A, 11B, 11C. Panel B shows that using CAD resulted in increase in diagnostic accuracy for all groups of radiologists.

View larger version (101K): [in a new window]   Fig. 13. —Example of visually effective use of filled versus open bars for comparing distribution of number of cases per channels visualized. Use of three-dimensional bars gives graphic variation but adds no value to depiction of data. Figure also has nondata ink in background. (Reprinted with permission from [12])

View larger version (17K): [in a new window]   Fig. 14. —Example of complicated scatterplot. Figure depicts large amount of information for variety of FDG parameters for 10 patients. It is difficult to follow specific values for individual patients and to discern mean percentage differences (•). Error bars are confusing. (Reprinted with permission from [13])

View larger version (61K): [in a new window]   Fig. 15A. —Example of interesting use of data ink to show proportions for two variables and 20 observers, with change in display parameter. No difference exists in discrimination between modalities; therefore, much ink is used to show no differences. (Reprinted with permission from [8]) Bar charts show differences in observer interpretation of nonzooming (A) and twofold zooming (B) soft-copy displays.

View larger version (62K): [in a new window]   Fig. 15B. —Example of interesting use of data ink to show proportions for two variables and 20 observers, with change in display parameter. No difference exists in discrimination between modalities; therefore, much ink is used to show no differences. (Reprinted with permission from [8]) Bar charts show differences in observer interpretation of nonzooming (A) and twofold zooming (B) soft-copy displays.

View larger version (19K): [in a new window]   Fig. 16A. —Example of Kaplan-Meier survival graphs. (Reprinted with permission from [14]) Graphs illustrate proportion of individuals who remain without stroke divided by degree of stenosis of less than 50% (A) and greater than 50% (B). Each group is further divided by nonhypoechoic and hypoechoic findings. Although patients with nonhypoechoic findings in B have higher occurrence of strokes than those of both groups in A, difference in y-axis range in B makes proportions appear nearly identical.

View larger version (21K): [in a new window]   Fig. 16B. —Example of Kaplan-Meier survival graphs. (Reprinted with permission from [14]) Graphs illustrate proportion of individuals who remain without stroke divided by degree of stenosis of less than 50% (A) and greater than 50% (B). Each group is further divided by nonhypoechoic and hypoechoic findings. Although patients with nonhypoechoic findings in B have higher occurrence of strokes than those of both groups in A, difference in y-axis range in B makes proportions appear nearly identical.

View larger version (18K): [in a new window]   Fig. 17A. —Three scatterplots showing attenuation of early-enhanced CT images of adenomas and nonadenomas at different times after injection of contrast material. No statistical differences were indicated. (Reprinted with permission from [15]) Scatterplots show data at different time intervals: 30, 60, and 90 sec (A); 180 sec only (B); and 30 min only (C). Because y-axis scales are changed for each part, this presentation visually suggests that discrimination between groups is noted at 30 min. Parts B and C should have also been plotted with attenuation versus all times of observation to reduce redundancy and nondata ink.

View larger version (15K): [in a new window]   Fig. 17B. —Three scatterplots showing attenuation of early-enhanced CT images of adenomas and nonadenomas at different times after injection of contrast material. No statistical differences were indicated. (Reprinted with permission from [15]) Scatterplots show data at different time intervals: 30, 60, and 90 sec (A); 180 sec only (B); and 30 min only (C). Because y-axis scales are changed for each part, this presentation visually suggests that discrimination between groups is noted at 30 min. Parts B and C should have also been plotted with attenuation versus all times of observation to reduce redundancy and nondata ink.

View larger version (13K): [in a new window]   Fig. 17C. —Three scatterplots showing attenuation of early-enhanced CT images of adenomas and nonadenomas at different times after injection of contrast material. No statistical differences were indicated. (Reprinted with permission from [15]) Scatterplots show data at different time intervals: 30, 60, and 90 sec (A); 180 sec only (B); and 30 min only (C). Because y-axis scales are changed for each part, this presentation visually suggests that discrimination between groups is noted at 30 min. Parts B and C should have also been plotted with attenuation versus all times of observation to reduce redundancy and nondata ink.

View larger version (49K): [in a new window]   Fig. 18A. —Example of complicated three-dimensional bar graphs that are difficult to understand. Moiré effects are present also. (Reprinted with permission from [16]) Graphs illustrate complex relationships between four measures and clinical out-come for three groups of patients: neonates (A), children (B) infants (C). Graphs appear to hold substantial amount of information, but close examination reveals that each bar represents few individuals and findings are visually overstated. This combination of moiré effects and complex data presentation makes data difficult to apprehend.

View larger version (92K): [in a new window]   Fig. 18B. —Example of complicated three-dimensional bar graphs that are difficult to understand. Moiré effects are present also. (Reprinted with permission from [16]) Graphs illustrate complex relationships between four measures and clinical out-come for three groups of patients: neonates (A), children (B) infants (C). Graphs appear to hold substantial amount of information, but close examination reveals that each bar represents few individuals and findings are visually overstated. This combination of moiré effects and complex data presentation makes data difficult to apprehend.

View larger version (66K): [in a new window]   Fig. 18C. —Example of complicated three-dimensional bar graphs that are difficult to understand. Moiré effects are present also. (Reprinted with permission from [16]) Graphs illustrate complex relationships between four measures and clinical out-come for three groups of patients: neonates (A), children (B) infants (C). Graphs appear to hold substantial amount of information, but close examination reveals that each bar represents few individuals and findings are visually overstated. This combination of moiré effects and complex data presentation makes data difficult to apprehend.

View larger version (60K): [in a new window]   Fig. 19A. —Example of material that could have been presented in text or table format because no significant differences were found and data content is minimal. (Reprinted with permission from [17]) Three-dimensional graphs show grade-scoring changes for subgroups sulcai (A), ventricular (B), and white matter (C) grades and ages. No error bars are shown, and numbers of subjects in each subgroup are not given. CHS = cardiovascular health study, NF = nonblack female, BF = black female, NM = nonblack male, BM = black male.

View larger version (56K): [in a new window]   Fig. 19B. —Example of material that could have been presented in text or table format because no significant differences were found and data content is minimal. (Reprinted with permission from [17]) Three-dimensional graphs show grade-scoring changes for subgroups sulcai (A), ventricular (B), and white matter (C) grades and ages. No error bars are shown, and numbers of subjects in each subgroup are not given. CHS = cardiovascular health study, NF = nonblack female, BF = black female, NM = nonblack male, BM = black male.

View larger version (55K): [in a new window]   Fig. 19C. —Example of material that could have been presented in text or table format because no significant differences were found and data content is minimal. (Reprinted with permission from [17]) Three-dimensional graphs show grade-scoring changes for subgroups sulcai (A), ventricular (B), and white matter (C) grades and ages. No error bars are shown, and numbers of subjects in each subgroup are not given. CHS = cardiovascular health study, NF = nonblack female, BF = black female, NM = nonblack male, BM = black male.

View larger version (19K): [in a new window]   Fig. 20. —Unusual figure that inserts graph of completely different phenomenon within main (enclosing) graph. Although it is sometimes useful to have different plots using different axes in one figure, this combination is both confusing and potentially misleading. Minimum acceptable figure would have identical time axis, perhaps with release point at which time equals zero. (Reprinted with permission from [18])

View larger version (25K): [in a new window]   Fig. 21A. —Examples of graphs in which changes in values for individual patients are almost impossible to follow. A large amount of data ink was used. (Reprinted with permission from [19]) Graphs illustrate changes before and after angioplasty in two vascular phenomena, ankle-brachial pressure (A) and peak velocity (B). Discerning mean values (thick dashed lines) is difficult. Limits for abnormal values (thin dashed lines) are useful. Y-axis scaling for part B is different below and above axis break, emphasizing lower values. No indication of reliability or statistical tests for measurements are provided, even for individual cases, so we cannot judge whether differences are significant.

View larger version (23K): [in a new window]   Fig. 21B. —Examples of graphs in which changes in values for individual patients are almost impossible to follow. A large amount of data ink was used. (Reprinted with permission from [19]) Graphs illustrate changes before and after angioplasty in two vascular phenomena, ankle-brachial pressure (A) and peak velocity (B). Discerning mean values (thick dashed lines) is difficult. Limits for abnormal values (thin dashed lines) are useful. Y-axis scaling for part B is different below and above axis break, emphasizing lower values. No indication of reliability or statistical tests for measurements are provided, even for individual cases, so we cannot judge whether differences are significant.

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