Development of an algorithm to automatically compress a CT image to visually lossless threshold.

BACKGROUND: To develop an algorithm to predict the visually lossless thresholds (VLTs) of CT images solely using the original images by exploiting the image features and DICOM header information for JPEG2000 compression and to evaluate the algorithm in comparison with pre-existing image fidelity metrics. METHODS: Five radiologists independently determined the VLT for 206 body CT images for JPEG2000 compression using QUEST procedure. The images were divided into training (n = 103) and testing (n = 103) sets. Using the training set, a multiple linear regression (MLR) model was constructed regarding the image features and DICOM header information as independent variables and regarding the VLTs determined with median value of the radiologists' responses (VLTrad) as dependent variable, after determining an optimal subset of independent variables by backward stepwise selection in a cross-validation scheme. The performance was evaluated on the testing set by measuring absolute differences and intra-class correlation (ICC) coefficient between the VLTrad and the VLTs predicted by the model (VLTmodel). The performance of the model was also compared two metrics, peak signal-to-noise ratio (PSNR) and high-dynamic range visual difference predictor (HDRVDP). The time for computing VLTs between MLR model, PSNR, and HDRVDP were compared using the repeated ANOVA with a post-hoc analysis. P < 0.05 was considered to indicate a statistically significant difference. RESULTS: The means of absolute differences with the VLTrad were 0.58 (95% CI, 0.48, 0.67), 0.73 (0.61, 0.85), and 0.68 (0.58, 0.79), for the MLR model, PSNR, and HDRVDP, respectively, showing significant difference between them (p < 0.01). The ICC coefficients of MLR model, PSNR, and HDRVDP were 0.88 (95% CI, 0.81, 0.95), 0.85 (0.79, 0.91), and 0.84 (0.77, 0.91). The computing times for calculating VLT per image were 1.5 ± 0.1 s, 3.9 ± 0.3 s, and 68.2 ± 1.4 s, for MLR metric, PSNR, and HDRVDP, respectively. CONCLUSIONS: The proposed MLR model directly predicting the VLT of a given CT image showed competitive performance to those of image fidelity metrics with less computational expenses. The model would be promising to be used for adaptive compression of CT images.

Use of image features in predicting visually lossless thresholds of JPEG2000 compressed body CT images: initial trial.

PURPOSE: To test the image features that may be useful in predicting the visually lossless thresholds (VLTs) of body computed tomographic (CT) images for Joint Photographic Experts Group 2000 (JPEG2000) compression. MATERIALS AND METHODS: The institutional review board approved this study, with a waiver of informed patient consent. One hundred body CT studies obtained in different patients by using five scanning protocols were obtained, and 100 images, each of which was selected from each of the 100 studies, were collected. Five radiologists independently determined the VLT of each image for JPEG2000 compression by using the QUEST algorithm. The 100 images were randomly divided into two data sets-the training set (50 images) and the testing set (50 images)-and the division was repeated 200 times. For each of the 200 divisions, a multiple linear regression model was constructed on a training set and tested on a testing set regarding each of five image features-standard deviation of image intensity, image entropy, relative percentage of low-frequency (LF) energy, variation in high-frequency (HF) energy, and visual complexity-as independent variables and considering the VLTs determined with the median value of the radiologists' responses as a dependent variable. The root mean square residual and intraclass correlation coefficient (ICC) for the 200 divisions between the VLTs predicted by the models and those determined by radiologists were compared between the models by using repeated-measures analysis of variance with post-hoc comparisons. RESULTS: Mean root-mean-square residuals for multiple linear regression models constructed with variation in HF energy (1.20 ± 0.10 [standard deviation]) and visual complexity (1.09 ± 0.07) were significantly lower than those for standard deviation of image intensity (1.65 ± 0.13), image entropy (1.63 ± 0.14), and relative percentage of LF energy (1.58 ± 0.12) (P < .01). ICCs for variation in HF energy (0.64 ± 0.05) and visual complexity (0.71 ± 0.04) were significantly higher than those for standard deviation of image intensity (0.04 ± 0.02), image entropy (0.05 ± 0.02), and relative percentage of LF energy (0.20 ± 0.04) (P < .01). CONCLUSION: Among the five tested image features, variation in HF energy and visual complexity were the most promising in predicting the VLTs of body CT images for JPEG2000 compression.

JPEG2000 compression of CT images used for measuring coronary artery calcification score: assessment of optimal compression threshold.

OBJECTIVE: The purpose of our study was to assess the acceptable compression threshold for JPEG2000 compression of CT images used for measuring coronary artery calcification scores (CACS) in terms of variability. MATERIALS AND METHODS: In a retrospective review, 80 patients who had undergone CT for determination of the CACS were compiled in four subsets (20 scans each) according to CACS: 0, subset A; > 0 to ≥ 100, subset B; > 100 to ≤ 400, subset C; and > 400, subset D. Each scan was compressed using eight compression ratios (CRs). We measured the CACS on all 720 CT scans (80 original and 640 compressed scans). For each compressed scan, the variability in CACS was evaluated by comparing with the CACS of the corresponding original CT scan. RESULTS: For each subset and each CR, we determined whether the upper limit of the one-sided 95% CI of the variability in CACS exceeded 5%. The variability in CACS tended to increase as the CR increased and tended to decrease in the order of increasing CACSs at each CR (i.e., subset B > subset C > subset D). With 5% as the limit of variability, acceptable compression CRs were between 20:1 and 25:1 for subset B; between 40:1 and 60:1 for subset C; and > 100:1 for subset D. CONCLUSION: A level of 20:1 could be a potentially acceptable threshold for JPEG2000 compression of CT images used for measuring CACS, with 5% of the variability in CACS as the acceptable limit of variability.

JPEG2000 2D and 3D reversible compressions of thin-section chest CT images: improving compressibility by increasing data redundancy outside the body region.

PURPOSE: To propose a preprocessing technique that increases the compressibility in reversible compressions of thin-section chest computed tomographic (CT) images and to measure the increase in compression ratio (CR) in Joint Photographic Experts Group (JPEG) 2000 two-dimensional (2D) and three-dimensional (3D) compressions. MATERIALS AND METHODS: This study had institutional review board approval, with waiver of informed patient consent. A preprocessing technique that automatically segments pixels outside the body region and replaces their values with a constant value to maximize data redundancy was developed. One hundred CT studies (50 standard-radiation dose and 50 low-radiation dose studies) were preprocessed by using the technique and then reversibly compressed by using the JPEG2000 2D and 3D compression methods. The CRs (defined as the original data size divided by the compressed data size) with and those without use of the preprocessing technique were compared by using paired t tests. The percentage increase in the CR was measured. RESULTS: The CR increased significantly (without vs with preprocessing) in JPEG2000 2D (mean CR, 2.40 vs 3.80) and 3D (mean CR, 2.61 vs 3.99) compressions for the standard-dose studies and in JPEG2000 2D (mean CR, 2.38 vs 3.36) and 3D (mean CR, 2.54 vs 3.55) compressions for the low-dose studies (P < .001 for all). The mean percentage increases in CR with preprocessing were 58.2% (95% confidence interval [CI]: 53.1%, 63.4%) and 52.4% (95% CI: 47.5%, 57.2%) in JPEG2000 2D and 3D compressions, respectively, for the standard-dose studies and 41.1% (95% CI: 38.8%, 43.4%) and 39.4% (95% CI: 37.4%, 41.7%) in JPEG2000 2D and 3D compressions, respectively, for the low-dose studies. CONCLUSION: The described preprocessing technique considerably increases CRs for reversible compressions of thin-section chest CT studies.

Does the amount of tagged stool and fluid significantly affect the radiation exposure in low-dose CT colonography performed with an automatic exposure control?

OBJECTIVE: To determine whether the amount of tagged stool and fluid significantly affects the radiation exposure in low-dose screening CT colonography performed with an automatic tube-current modulation technique. METHODS: The study included 311 patients. The tagging agent was barium (n = 271) or iodine (n = 40). Correlation was measured between mean volume CT dose index (CTDI (vol)) and the estimated x-ray attenuation of the tagged stool and fluid (ATT). Multiple linear regression analyses were performed to determine the effect of ATT on CTDI (vol ) and the effect of ATT on image noise while adjusting for other variables including abdominal circumference. RESULTS: CTDI (vol) varied from 0.88 to 2.54 mGy. There was no significant correlation between CTDI (vol) and ATT (p = 0.61). ATT did not significantly affect CTDI (vol) (p = 0.93), while abdominal circumference was the only factor significantly affecting CTDI (vol) (p < 0.001). Image noise ranged from 59.5 to 64.1 HU. The p value for the regression model explaining the noise was 0.38. CONCLUSION: The amount of stool and fluid tagging does not significantly affect radiation exposure.

Comparison of three image comparison methods for the visual assessment of the image fidelity of compressed computed tomography images.

PURPOSE: This study aimed to comparatively evaluate three different image comparison methods: alternate display without an intervening blank image (AWOB), alternate display with an intervening blank image (AWB), and side-by-side display (SSD), in terms of the perceptual sensitivity to image differences between Joint Photographic Experts Group 2000 (JPEG2000) compressed body CT images and their originals. METHODS: A total of 50 body CT images obtained with five different scan protocols (5-mm-thick abdomen, 0.67-mm-thick abdomen, 5-mm-thick lung, 0.67-mm-thick lung, and 5-mm-thick low-dose lung) were compressed to one of five compression ratios (reversible, 6:1, 8:1, 10:1, and 15:1) using JPEG2000 algorithm. The fidelity of the compressed images was visually assessed on a four-grade scale independently by five radiologists using each of the three image comparison methods of AWOB, AWB, and SSD. The fidelity grading results for the 40 irreversibly compressed images were compared between the three image comparison methods using the Friedman tests with post hoc Tukey tests. The number of image pairs with no perceptible difference was compared using the exact tests for paired proportions. The time required for the fidelity assessment for all of the 50 compressed images was also compared using the Friedman tests with post hoc Tukey tests. RESULTS: For the 40 irreversibly compressed images, the fidelity grade was significantly lower for AWOB than for AWB or SSD (p < 0.01 for all readers); however, there was no significant difference between AWB and SSD (p-value range, 0.06-0.92). The percentage of image pairs with no perceptible difference tended to be smaller for AWOB than for AWB (p < 0.01 for all readers) or SSD (p < 0.01 for readers 1-3, p = 0.04 for reader 4, and p = 0.23 for reader 5). However, there was no significant difference between AWB and SSD (p-value range, 0.12- >0.99). For all of the 50 compressed images, the fidelity grading time significantly increased in the order of AWOB, SSD, and AWB. CONCLUSIONS: In assessing the image fidelity of JPEG2000 compressed body CT images, AWOB yields lower fidelity grade and requires less fidelity grading time than AWB or SSD, indicating that AWOB is most sensitive to image differences among of them.

Introduction of heat map to fidelity assessment of compressed CT images.

PURPOSE: This study aimed to introduce heat map, a graphical data presentation method widely used in gene expression experiments, to the presentation and interpretation of image fidelity assessment data of compressed computed tomography (CT) images. METHODS: The authors used actual assessment data that consisted of five radiologists' responses to 720 computed tomography images compressed using both Joint Photographic Experts Group 2000 (JPEG2000) 2D and JPEG2000 3D compressions. They additionally created data of two artificial radiologists, which were generated by partly modifying the data from two human radiologists. RESULTS: For each compression, the entire data set, including the variations among radiologists and among images, could be compacted into a small color-coded grid matrix of the heat map. A difference heat map depicted the advantage of 3D compression over 2D compression. Dendrograms showing hierarchical agglomerative clustering results were added to the heat maps to illustrate the similarities in the data patterns among radiologists and among images. The dendrograms were used to identify two artificial radiologists as outliers, whose data were created by partly modifying the responses of two human radiologists. CONCLUSIONS: The heat map can illustrate a quick visual extract of the overall data as well as the entirety of large complex data in a compact space while visualizing the variations among observers and among images. The heat map with the dendrograms can be used to identify outliers or to classify observers and images based on the degree of similarity in the response patterns.

Predicting the fidelity of JPEG2000 compressed CT images using DICOM header information.

PURPOSE: To propose multiple logistic regression (MLR) and artificial neural network (ANN) models constructed using digital imaging and communications in medicine (DICOM) header information in predicting the fidelity of Joint Photographic Experts Group (JPEG) 2000 compressed abdomen computed tomography (CT) images. METHODS: Our institutional review board approved this study and waived informed patient consent. Using a JPEG2000 algorithm, 360 abdomen CT images were compressed reversibly (n = 48, as negative control) or irreversibly (n = 312) to one of different compression ratios (CRs) ranging from 4:1 to 10:1. Five radiologists independently determined whether the original and compressed images were distinguishable or indistinguishable. The 312 irreversibly compressed images were divided randomly into training (n = 156) and testing (n = 156) sets. The MLR and ANN models were constructed regarding the DICOM header information as independent variables and the pooled radiologists' responses as dependent variable. As independent variables, we selected the CR (DICOM tag number: 0028, 2112), effective tube current-time product (0018, 9332), section thickness (0018, 0050), and field of view (0018, 0090) among the DICOM tags. Using the training set, an optimal subset of independent variables was determined by backward stepwise selection in a four-fold cross-validation scheme. The MLR and ANN models were constructed with the determined independent variables using the training set. The models were then evaluated on the testing set by using receiver-operating-characteristic (ROC) analysis regarding the radiologists' pooled responses as the reference standard and by measuring Spearman rank correlation between the model prediction and the number of radiologists who rated the two images as distinguishable. RESULTS: The CR and section thickness were determined as the optimal independent variables. The areas under the ROC curve for the MLR and ANN predictions were 0.91 (95% CI; 0.86, 0.95) and 0.92 (0.87, 0.96), respectively. The correlation coefficients of the MLR and ANN predictions with the number of radiologists who responded as distinguishable were 0.76 (0.69, 0.82, p < 0.001) and 0.78 (0.71, 0.83, p < 0.001), respectively. CONCLUSIONS: The MLR and ANN models constructed using the DICOM header information offer promise in predicting the fidelity of JPEG2000 compressed abdomen CT images.

Advantage in image fidelity and additional computing time of JPEG2000 3D in comparison to JPEG2000 in compressing abdomen CT image datasets of different section thicknesses.

PURPOSE: This study aimed to assess the advantage of the Joint Photographic Experts Group 2000 (JPEG2000) 3D (part 2) over JPEG2000 in compressing abdomen computed tomography (CT) image data sets of different section thicknesses (STs). METHODS: Twenty CT scans were reconstructed with six STs (0.67, 1, 2, 3, 4, and 5 mm) and were then compressed to seven compression ratios (CRs) (reversible, 6:1, 8:1, 10:1, 12:1, 14:1, and 16:1) using JPEG2000 and JPEG2000 3D algorithms. Computing (encoding and decoding) times were measured. The image fidelity of the compressed images was quantitatively measured with two computerized image fidelity metrics, peak signal-to-noise ratio (PSNR) and multiscale structural similarity (MS-SSIM). For 120 selected case-relevant images (20 patients x one image per patient x 6 STs), five radiologists independently compared original and compressed images and assessed the fidelity of the compressed images on a four-grade scale. Wilcoxon signed-rank tests and Friedman tests with post hoc Dunn tests were used for the comparisons between the two compressions and among the six STs, respectively RESULTS: For each combination of the ST and irreversible CR, JPEG2000 3D showed higher image fidelity than JPEG2000 in terms of PSNR (p < 0.0001), MS-SSIM (p < 0.0001), and five radiologists' grading (p-values ranged from <0.0001 to 0.003). At each CR, the advantage of JPEG2000 3D in image fidelity, measured as the differences in the two computerized image fidelity metrics (PSNR and MS-SSIM), significantly increased as the ST increased from 0.67 to 2 mm, and then slowly decreased as the ST increased from 2 to 5 mm. Similar trends were observed in visual analyses of 120 selected images by five radiologists. At each CR, the 3D-to-2D encoding-time ratio significantly decreased (p < 0.001) as the ST increased from 0.67 to 2 mm, and then slowly increased (p < 0.001) as the ST increased from 2 to 5 mm. The 3D-to-2D decoding-time ratio at each CR did not show a notable biphasic trend across the ST. CONCLUSIONS: In compressing abdomen CT image data sets of different STs, the advantage of JPEG2000 3D over JPEG2000 increases as the ST increases from 0.67 to 2 mm, and then slowly decreases as the ST increases from 2 to 5 mm. The practical advantage of JPEG2000 3D is limited for a submillimeter ST due to its greater computing time with only a marginal improvement in image fidelity.

Detection of the normal appendix with low-dose unenhanced CT: use of the sliding slab averaging technique.

PURPOSE: To determine the frequency of normal appendix visualization at low-dose (LD) unenhanced computed tomography (CT) performed with a 16- or 64-detector row scanner when images are reviewed by using the sliding slab averaging technique. MATERIALS AND METHODS: The institutional review board approved the study and waived the informed consent requirement. A total of 259 patients, 37 (14.3%) of whom had previously undergone appendectomy, underwent LD unenhanced CT (mean effective dose, 1.7 mSv) performed with a 16- or 64-detector row scanner to assess urinary colic. Three readers used the sliding slab averaging technique to retrospectively review the thin-section (0.67- or 2.00-mm section thickness) images and grade the appendix as absent, unsurely or partly visualized, or clearly and entirely visualized. Interobserver agreement was measured with weighted kappa statistics. McNemar tests were used to compare sensitivity between the readers. Logistic regression analysis was performed to assess the effects of body mass index, patient sex, and type of CT scanner on appendiceal visualization. RESULTS: The kappa statistics for each reader pair were as follows: 0.97 for agreement between readers 1 and 2, 0.93 for agreement between readers 2 and 3, and 0.92 for agreement between readers 1 and 3. Each reader clearly identified the entire appendix in 213 (96.0%), 209 (94.1%), and 205 (92.3%) of the 222 patients without a history of appendectomy. When unsurely or partly visualized appendices were included, the frequencies increased to 99.1% (n = 220), 98.7% (n = 219), and 97.3% (n = 216), respectively, for readers 1, 2, and 3. These frequencies rarely differed between the readers. (P values ranged from .021 to greater than .99.) The three readers consistently reported that the appendix was not visualized in the 37 patients who had undergone appendectomy. None of the tested variables significantly affected appendix visualization. CONCLUSION: Most normal appendices are visualized on thin-section LD unenhanced CT images reviewed with the sliding slab averaging technique.

JPEG2000 3D compression vs. 2D compression: an assessment of artifact amount and computing time in compressing thin-section abdomen CT images.

To assess the advantages of the Joint Photographic Experts Group (JPEG)2000 3D (part 2) over JPEG2000 in compressing thin-section abdomen CT data sets, 60 thin-section (0.67 mm) scans from 35 males and 25 females, ranging from 23 to 95 years of age (mean, 58 years), were compressed reversibly (as a negative control) and irreversibly to 4:1, 6:1, 8:1, 10:1, and 12:1 using JPEG2000 3D and JPEG2000 algorithms. Encoding and decoding times and peak signal-to-noise ratios (PSNRs) were measured. For 60 (one image per scan) representative sections containing abnormalities, three radiologists independently compared original and compressed images and graded compression artifacts as 0 (none, indistinguishable), 1 (barely perceptible), 2 (subtle), or 3 (significant). According to pooled radiologists' responses, the range of visually lossless threshold (VLT, the highest compression ratio at which a compressed image is indistinguishable from its original) was determined as one of <4:1, 4:1-6:1, 6:1-8:1, 8:1-10:1, 10:1-12:1, and >12:1. Wilcoxon signed rank tests and exact tests for paired proportions were used for the comparisons between the two compressions. At each irreversible compression ratio, compared to JPEG2000, JPEG2000 3D required two- or threefold greater computing times (p < 0.001) and introduced less artifacts in terms of PSNR (p <0.001) and the grade (p < 0.02 at 6:1 or higher) and the presence of perceived artifacts (p <0.008, at 6:1 for all readers and at 8:1 for two readers). According to PSNR and readers' responses, 6:1 and 8:1 JPEG2000 3D compressions showed more artifacts than 4:1 and 6:1 JPEG2000 compressions, respectively, and 10:1 and 12:1 JPEG2000 3D compressions showed similar artifacts to those of 8:1 and 10:1 JPEG2000 compressions, respectively. The determined VLT range was higher for JPEG2000 3D than for JPEG2000 (p < 0.001): the 3D compression showed the VLT ranges of 4:1-6:1, 6:1-8:1, and 8:1-10:1 for 24 (40%), 30 (50%), and 6 (10%) of the 60 original images, respectively, while the 2D compression showed the VLT ranges of <4:1, 4:1-6:1, and 6:1-8:1 for 1 (1.7%), 40 (66.7%), and 19 (31.6%) images, respectively. Compared to JPEG2000, JPEG2000 3D increased the VLT range in 23 of the 60 original images by one (n=22) or two ranges (n=1), while the remaining 37 images had the same VLT range between the two compressions. In conclusion, compared to JPEG2000 compression, JPEG2000 3D compression yields less artifacts in compressing thin-section abdomen CT images but requires significantly greater computing times. For the tested data set compressed to the range from 4:1 to 12:1, JPEG2000 3D could increase compression level reasonably (by 2 or less in terms of compression ratio) compared to JPEG2000 for the same amount of artifacts.

Objective index of image fidelity for JPEG2000 compressed body CT images.

Compression ratio (CR) has been the de facto standard index of compression level for medical images. The aim of the study is to evaluate the CR, peak signal-to-noise ratio (PSNR), and a perceptual quality metric (high-dynamic range visual difference predictor HDR-VDP) as objective indices of image fidelity for Joint Photographic Experts Group (JPEG) 2000 compressed body computed tomography (CT) images, from the viewpoint of visually lossless compression approach. A total of 250 body CT images obtained with five different scan protocols (5-mm-thick abdomen, 0.67-mm-thick abdomen, 5-mm-thick lung, 0.67-mm-thick lung, and 5-mm-thick low-dose lung) were compressed to one of five CRs (reversible, 6:1, 8:1, 10:1, and 15:1). The PSNR and HDR-VDP values were calculated for the 250 pairs of the original and compressed images. By alternately displaying an original and its compressed image on the same monitor, five radiologists independently determined if the pair was distinguishable or indistinguishable. The kappa statistic for the interobserver agreement among the five radiologists' responses was 0.70. According to the radiologists' responses, the number of distinguishable image pairs tended to significantly differ among the five scan protocols at 6:1-10:1 compressions (Fisher-Freeman-Halton exact tests). Spearman's correlation coefficients between each of the CR, PSNR, and HDR-VDP and the number of radiologists who responded as distinguishable were 0.72, -0.77, and 0.85, respectively. Using the radiologists' pooled responses as the reference standards, the areas under the receiver-operating-characteristic curves for the CR, PSNR, and HDR-VDP were 0.87, 0.93, and 0.97, respectively, showing significant differences between the CR and PSNR (p = 0.04), or HDR-VDP (p < 0.001), and between the PSNR and HDR-VDP (p < 0.001). In conclusion, the CR is less suitable than the PSNR or HDR-VDP as an objective index of image fidelity for JPEG2000 compressed body CT images. The HDR-VDP is more promising than the PSNR as such an index.

Prediction of perceptible artifacts in JPEG 2000-compressed chest CT images using mathematical and perceptual quality metrics.

OBJECTIVE: The objective of our study was to determine whether peak signal-to-noise ratio (PSNR) and a perceptual quality metric (High-Dynamic Range Visual Difference Predictor [HDR-VDP]) can predict the presence of perceptible artifacts in Joint Photographic Experts Group (JPEG) 2000-compressed chest CT images. MATERIALS AND METHODS: One hundred chest CT images were compressed to 5:1, 8:1, 10:1, and 15:1. Five radiologists determined if the original and compressed images were identical (negative response) or different (positive response). The correlation between the results for each metric and the number of readers with positive responses was evaluated using Spearman's rank correlation test. Using the pooled readers' responses as the reference standard, we performed receiver operating characteristic (ROC) analysis to determine the cutoff values balancing sensitivity and specificity and yielding 100% sensitivity in each metric. These cutoff values were then used to estimate the visually lossless thresholds for the compressions for the 100 original images, and the accuracy of the estimates of two metrics was compared (McNemar test). RESULTS: The correlation coefficients were -0.918 and 0.925 for PSNR and the HDR-VDP, respectively. The areas under the ROC curves for the two metrics were 0.983 and 0.984, respectively (p = 0.11). The PSNR and HDR-VDP accurately predicted the visually lossless threshold for 69% and 72% of the 100 images (p = 0.68), respectively, at the cutoff values balancing sensitivity and specificity and for 43% and 47% (p = 0.22), respectively, at the cutoff values reaching 100% sensitivity. CONCLUSION: Both metrics are promising in predicting the perceptible compression artifacts and therefore can potentially be used to estimate the visually lossless threshold.

Artifacts in slab average-intensity-projection images reformatted from JPEG 2000 compressed thin-section abdominal CT data sets.

OBJECTIVE: The objective of our study was to assess the effects of compressing source thin-section abdominal CT images on final transverse average-intensity-projection (AIP) images. MATERIALS AND METHODS: At reversible, 4:1, 6:1, 8:1, 10:1, and 15:1 Joint Photographic Experts Group (JPEG) 2000 compressions, we compared the artifacts in 20 matching compressed thin sections (0.67 mm), compressed thick sections (5 mm), and AIP images (5 mm) reformatted from the compressed thin sections. The artifacts were quantitatively measured with peak signal-to-noise ratio (PSNR) and a perceptual quality metric (High Dynamic Range Visual Difference Predictor [HDR-VDP]). By comparing the compressed and original images, three radiologists independently graded the artifacts as 0 (none, indistinguishable), 1 (barely perceptible), 2 (subtle), or 3 (significant). Friedman tests and exact tests for paired proportions were used. RESULTS: At irreversible compressions, the artifacts tended to increase in the order of AIP, thick-section, and thin-section images in terms of PSNR (p < 0.0001), HDR-VDP (p < 0.0001), and the readers' grading (p < 0.01 at 6:1 or higher compressions). At 6:1 and 8:1, distinguishable pairs (grades 1-3) tended to increase in the order of AIP, thick-section, and thin-section images. Visually lossless threshold for the compression varied between images but decreased in the order of AIP, thick-section, and thin-section images (p < 0.0001). CONCLUSION: Compression artifacts in thin sections are significantly attenuated in AIP images. On the premise that thin sections are typically reviewed using an AIP technique, it is justifiable to compress them to a compression level currently accepted for thick sections.

Regional difference in compression artifacts in low-dose chest CT images: effects of mathematical and perceptual factors.

OBJECTIVE: The objective of our study was to investigate the difference of perceptible artifacts between the lungs and the chest wall and mediastinum in Joint Photographic Experts Group (JPEG) 2000-compressed low-dose chest CT images and to show that a perceptual image quality metric-the High-Dynamic Range Visual Difference Predictor (HDR-VDP)-can reproduce this regional difference. MATERIALS AND METHODS: Twenty images were compressed reversibly and irreversibly to 6:1-30:1. To analyze the two regions separately (lungs; and chest wall and mediastinum), the compressed pixels outside each tested region were replaced with the original pixels. By comparing the compressed and original images, three radiologists independently rated the compression artifacts as grade 0, none, indistinguishable; 1, barely perceptible; 2, subtle; or 3, significant. At each compression level, the two regions were compared for the readers' responses, peak signal-to-noise ratio (PSNR), and HDR-VDP results. Wilcoxon's signed rank tests and exact tests for paired proportions were used with a p value threshold of 0.05. RESULTS: Artifacts were rated as lower grades in the lungs than in the chest wall and mediastinum, showing statistical significances at 10:1-20:1 for reader 1, 8:1-15:1 for reader 2, and 8:1-20:1 for reader 3. Grade 0 was more frequent in the lungs, showing statistical significances at 10:1 for reader 1 and at 8:1-10:1 for readers 2 and 3. The results of PSNR indicated greater artifacts in the lungs (p < 0.001), whereas HDR-VDP results indicated fewer artifacts in the lungs (p < 0.001). CONCLUSION: Although compression artifacts are mathematically greater in the lungs than in the chest wall and mediastinum, radiologists' artifact perceptions are the opposite, which can be successfully reproduced by HDR-VDP.

Differences in compression artifacts on thin- and thick-section lung CT images.

OBJECTIVE: The purpose of our study was to show the difference of Joint Photographic Experts Group (JPEG) 2000 compression artifacts in the lung between thin- and thick-section CT images. MATERIALS AND METHODS: Thirty-five thin-section (1 mm) and 35 corresponding thick-section (5 mm) images were compressed to reversible and irreversible 4:1, 6:1, 8:1, 10:1, and 15:1. In each compressed image, pixels outside the lung were replaced with those from the original image. By comparing the compressed and original images, three radiologists independently rated the compression artifacts using grades of 0 (none, the two images were indistinguishable), 1 (image differences were barely perceptible), 2 (image differences were subtle), or 3 (image differences were significant). At each compression level, thin and thick sections were compared for peak signal-to-noise ratio (PSNR) using paired t tests and for the readers' responses using Wilcoxon's signed rank tests and exact tests for paired proportions. RESULTS: Thin sections had smaller PSNR (p < 0.0001). Thin sections had higher grades of artifacts than thick sections, showing significant differences at compression levels of 10:1 (mean score, 0.8 vs 0.4, 0.9 vs 0.1, 0.3 vs 0.0; p < 0.009 for the three readers) and 15:1 (1.9 vs 1.0, 1.9 vs 1.1, 1.5 vs 0.6; p < 0.0001). The percentages of distinguishable pairs (grades 1-3) were greater for thin sections than for thick sections, showing a statistically significant difference at 10:1 for two readers (31% vs 3% and 74% vs 37%; p < 0.006). CONCLUSION: The lung shows more compression artifacts on thin sections than on thick sections. Section thickness should be taken into consideration when adjusting the compression level for lung CT images.

Prediction of perceptible artifacts in JPEG2000 compressed abdomen CT images using a perceptual image quality metric.

RATIONALE AND OBJECTIVES: To test a perceptual quality metric (high-dynamic range visual difference predictor, HDR-VDP) in predicting perceptible artifacts in Joint Photographic Experts Group 2000 compressed thin- and thick-section abdomen computed tomography images. MATERIALS AND METHODS: A total of 120 thin (0.67 mm) and corresponding thick (5 mm) sections were compressed to ratios from 4:1 to 15:1. Peak signal-to-noise ratio (PSNR), HDR-VDP results (paired t-tests), and five radiologists' pooled responses for the presence of artifacts (exact tests for paired proportions) were compared between the thin and thick sections. For three subsets of 120 thin- (subset A), 120 thick- (subset B), and 60 thin- and 60 thick-section compressed images (subset C), receiver operating curve analysis was performed to compare PSNR and HDR-VDP in predicting the radiologists' responses. Using the cutoff values where the sum of sensitivity and specificity was the maximum in subset C, visually lossless thresholds (VLTs) were estimated for the 240 original images and the estimation accuracy was compared (McNemar test). RESULTS: Thin sections showed more artifacts in terms of PSNR, HDR-VDP, and radiologists' responses (p < .0001). HDR-VDP outperformed PSNR for subset C (area under the curve: 0.97 versus 0.93, p = 0.03), whereas they did not differ significantly for subset A or B. Using the cutoff values, PSNR and HDR-VDP predicted the VLT accurately for 124 (51.7%) and 183 (76.3%) images, respectively (p < .0001). CONCLUSIONS: HDR-VDP can predict the perceptible compression artifacts, and therefore can be potentially used to estimate the VLT for such compressions.

Summation or axial slab average intensity projection of abdominal thin-section CT datasets: can they substitute for the primary reconstruction from raw projection data?

We hypothesized that that the summation or axial slab average intensity projection (AIP) techniques can substitute for the primary reconstruction (PR) from a raw projection data for abdominal applications. To compare with PR datasets (5-mm thick, 20% overlap) in 150 abdominal studies, corresponding summation and AIP datasets were calculated from 2-mm thick images (50% overlap). The root-mean-square error between PR and summation images was significantly greater than that between PR and AIP images (9.55 [median] vs. 7.12, p < 0.0001, Wilcoxon signed-ranks test). Four radiologists independently compared 2,000 test images (PR [as control], summation, or AIP) and their corresponding PR images to prove that the identicalness of summation or AIP images to PR images was not 1% less than the assessed identicalness of PR images to themselves (Wald-type test for clustered matched-pair data in a non-inferiority design). For each reader, both summation and AIP images were not inferior to PR images in terms of being rated identical to PR (p < 0.05). Although summation and AIP techniques produce images that differ from PR images, these differences are not easily perceived by radiologists. Thus, the summation or AIP techniques can substitute for PR for the primary interpretation of abdominal CT.

On-the-fly generation of multiplanar reformation images independent of CT scanner type.

We propose a system that automatically generates multiplanar reformation (MPR) images on-the-fly, which is independent of computed tomography (CT) scanner type. Triggered by digital imaging communication in medicine (DICOM) Storage Commitment or in a time threshold manner, this system generates MPR images from received thin-section CT data sets with predefined reformation parameters and then sends MPR images to DICOM stations. Users can specify the reformation parameters and the destination of the resulting MPR images for each CT study description. A pilot system was tested for 3 months. From thin-section data sets received from two 16- and one 64-detector-row CT scanners, this system generated MPR images and sent them to the picture archiving and communication system (PACS) without failure or any additional human operation. For 143 test thin-section CT studies (172-4,761 images in each study), the time to store reformatted images (axial and coronal with 5-mm thicknesses and 4-mm intervals) in PACS after the completion of the CT scan ranged from 92 to 1,772 s (mean +/- SD, 555.1 +/- 509.4).

Definition of compression ratio: difference between two commercial JPEG2000 program libraries.

The objective was to demonstrate the difference in the definition of compression ratio between two popular commercial JPEG 2000 program libraries. An institutional review board approved this study and waived informed consent. Using each of two JPEG 2000 libraries (libraries A and B), 20 abdomen computed tomography images with 12-bit depth (from scanner 1) and 20 images with 16-bit depth (from scanner 2) were compressed to three different nominal compression ratios: 10:1, 15:1, and 20:1. Achieved compression ratios (the original image file size to the compressed size) were compared with the nominal compression ratios using one-sample t-test tests. At each nominal compression level, the achieved compression ratios for scanner 1 images compressed using library A were approximately 1.33-fold greater than the nominal compression ratio (p < 0.0001), while the achieved compression ratios for the remaining three scanner-library combinations (scanner 1-library B, scanner 2-library A, and scanner 2-library B) were approximately the same as the nominal compression ratio (p-value range, 0.22-0.93). The definition of compression ratio is different between commercial JPEG 2000 program libraries. The definition should be standardized to facilitate the adoption and communication of an acceptable compression level.