Evaluation of cone-beam computed tomography diagnostic image quality using cluster signal-to-noise analysis.

OBJECTIVES: (1) We sought to assess correlation among four representative parameters from a cluster signal-to-noise curve (true-positive rate [TPR] corresponding to background noise, accuracy corresponding to background noise, maximum TPR, and maximum accuracy) and the diagnostic accuracy of the identification of the mandibular canal using data from observers in a previous study, under the same exposure conditions. (2) We sought to clarify the relationship between the hole depths of a phantom and diagnostic accuracy. METHODS: CBCT images of a Teflon plate phantom with holes of decreasing depths from 0.7 to 0.1 mm were analyzed using the FindFoci plugin of ImageJ. Subsequently, we constructed cluster signal-to-noise curves by plotting TPRs against false-positive rates. The four parameters were assessed by comparing with the diagnostic accuracy calculated from the observers. To analyze image contrast ranges related to detection of mandibular canals, we determined five ranges of hole depths, to represent different contrast ranges-0.1-0.7, 0.1-0.5, 0.2-0.6, 0.2-0.7 and 0.3-0.7 mm-and compared them with observers' diagnostic accuracy. RESULTS: Among the four representative parameters, accuracy corresponding to background noise had the highest correlation with the observers' diagnostic accuracy. Hole depths of 0.3-0.7 and 0.1-0.7 mm had the highest correlation with observers' diagnostic accuracy in mandibles with distinct and indistinct mandibular canals, respectively. CONCLUSIONS: The accuracy corresponding to background noise obtained from the cluster signal-to-noise curve can be used to evaluate the effects of exposure conditions on diagnostic accuracy.

Determination of optimum exposure parameters for dentoalveolar structures of the jaws using the CB MercuRay system with cluster signal-to-noise analysis.

OBJECTIVE: To determine the optimum cone beam computed tomography exposure parameters for specific diagnostic tasks. METHODS: A Teflon phantom attached to a half-mandible in a large container was scanned in dental (D), implant (I), and panoramic (P) modes. An identical phantom in a small container was scanned in D mode. Both were scanned at 60, 80, 100, and 120 kV. We evaluated the image quality of five anatomical structures [dentinoenamel junction (1), lamina dura and periodontal ligament space (2), trabecular pattern (3), cortex-spongy bone junction (4), and pulp chamber and root canal (5)] and analyzed the diagnostic image quality with cluster signal-to-noise analysis. We then evaluated correlations between the two image qualities and calculated the threshold of acceptable diagnostic image quality. Optimum exposure parameters were determined from images with acceptable diagnostic image quality. RESULTS: For the small container, the optimum exposure parameters were D mode, 80 kV for (1), (3), and (4) and D mode, 100 kV for (5). For the large container, they were D mode, 120 kV for (1), (3), and (5) and D mode, 100 kV for (4). I mode, 120 kV reached the acceptable level for (4). No images reached the acceptable level for (2). CONCLUSIONS: No optimum exposure parameters were identified for the evaluation of the lamina dura and periodontal ligament space. D mode was sufficient for the other structures; however, the tube voltage required for each structure differed. Smaller patients required lower tube voltage. I mode, 120 kV may be used for larger lesions.

Prediction of detectability of the mandibular canal by quantitative image quality evaluation using cone beam CT.

OBJECTIVES: To compare the results of a new quantitative image quality evaluation method that requires no observers with the results of receiver operating characteristic (ROC) analysis in detecting the mandibular canal (MC) in cone beam CT (CBCT) images. METHODS: A Teflon (polytetrafluoroethylene) plate phantom with holes of different depths was scanned with two CBCT systems. One CBCT system was equipped with an image intensifier (Experiment 1), and the other was equipped with a flat panel detector (Experiment 2). Holes that were above the threshold gray value (ΔG), calculated using just-noticeable difference (JND), were extracted. The number of extracted holes was used as the index of the image quality, and was compared with the Az values calculated by ROC analysis to detect the MC. RESULTS: The number of extracted holes reflected the influence of different scanning conditions, and showed a strong correlation with the Az values calculated by ROC analysis. Indices of the number of extracted holes corresponding to high Az values for detecting the MC were obtained in both experiments. CONCLUSIONS: Our image quality evaluation method applying JND to images of a standardized phantom is a quantitative method that could be useful for evaluating the detectability of the MC in CBCT images.

Effects of exposure parameters and slice thickness on detecting clear and unclear mandibular canals using cone beam CT.

OBJECTIVES: The purpose of this study was to clarify the effects of exposure parameters and image-processing methods when using CBCT to detect clear and unclear mandibular canals (MCs). METHODS: 24 dry half mandibles were divided into 2 groups with clear and unclear MCs based on a previous CBCT study. Mandibles were scanned using a CBCT system with varying exposure parameters (tube voltages 60 kV, 70 kV and 90 kV; and tube currents 2 mA, 5 mA, 10 mA and 15 mA) to obtain a total of 144 scans. The images were processed with different slice thicknesses using ImageJ software (National Institutes of Health, Bethesda, MD). Five radiologists evaluated the cross-sectional images of the first molar region to detect the MCs. The diagnostic accuracy of varying exposure parameters and image-processing conditions was compared with the area under the curve (Az) in receiver-operating characteristic analysis. RESULTS: The Az values for clear MCs were higher than those for unclear MCs (p < 0.0001). With increasing exposure voltages and currents, Az values increased, but no significant differences were found with high voltages and currents in clear MCs (p = 1.0000 and p = 0.9340). The Az values of serial images were higher than those of overlaid images (p < 0.0001), and those for thicker slices were higher than those for thinner slices (p < 0.0001). CONCLUSIONS: Our findings indicate that detection of unclear MCs requires either higher exposure parameters or processing of the images with thicker slices. To detect clear MCs, lower exposure parameters can be used.

A new method to evaluate image quality of CBCT images quantitatively without observers.

OBJECTIVES: To develop an observer-free method for quantitatively evaluating the image quality of CBCT images by applying just-noticeable difference (JND). METHODS: We used two test objects: (1) a Teflon (polytetrafluoroethylene) plate phantom attached to a dry human mandible; and (2) a block phantom consisting of a Teflon step phantom and an aluminium step phantom. These phantoms had holes with different depths. They were immersed in water and scanned with a CB MercuRay (Hitachi Medical Corporation, Tokyo, Japan) at tube voltages of 120 kV, 100 kV, 80 kV and 60 kV. Superimposed images of the phantoms with holes were used for evaluation. The number of detectable holes was used as an index of image quality. In detecting holes quantitatively, the threshold grey value (ΔG), which differentiated holes from the background, was calculated using a specific threshold (the JND), and we extracted the holes with grey values above ΔG. The indices obtained by this quantitative method (the extracted hole values) were compared with the observer evaluations (the observed hole values). In addition, the contrast-to-noise ratio (CNR) of the shallowest detectable holes and the deepest undetectable holes were measured to evaluate the contribution of CNR to detectability. RESULTS: The results of this evaluation method corresponded almost exactly with the evaluations made by observers. The extracted hole values reflected the influence of different tube voltages. All extracted holes had an area with a CNR of ≥1.5. CONCLUSIONS: This quantitative method of evaluating CBCT image quality may be more useful and less time-consuming than evaluation by observation.