Effect of beam quality and readout direction in the edge profile on the modulation transfer function of photostimulable phosphor systems via the edge method

Relationship between image information content and observer performance in digital intraoral radiography

OBJECTIVES

This study conducted a receiver operating characteristic (ROC) analysis by applying improved cluster signal-to-noise (CSN) analysis to digital intraoral radiographs and develop an observer-free method of analyzing image quality related to the observer performance in the detection task.

METHODS

Two aluminum step phantoms with a thickness interval of 1.0 mm were used for this study. One phantom had holes of increasing depth (from 0.05 to 0.35 mm) and the other had no holes. Phantom images were obtained under various exposure dose and image capture modes using a dental X-ray unit, a photostimulable phosphor imaging plate, and scanner system. These phantom images were analyzed using the FindFoci plugin in ImageJ software. Subsequently, true positive rates (TPRs) and false positive rates (FPRs) were calculated by analyzing phantom images with and without holes. We constructed ROC curves by plotting the TPRs against the FPRs and calculated the area under the ROC curve (AUC). Using the same phantom images with holes, eight observers assessed the number of detectable holes. Correlations between observer detection performance and AUC values were evaluated.

RESULTS

AUC values increased as the exposure dose increased and showed different tendencies depending on the image capture mode. The AUC values showed a high correlation with observer detection performance (r = 0.76).

CONCLUSIONS

AUC values obtained from CSN analysis reflect image quality and replace the observer detection performance test of image quality.

Evaluation of resolution characteristics of digital intraoral radiographic images using a task transfer function

OBJECTIVES

This study aimed to quantitatively examine the effect of digital image processing of digital intraoral radiographic images on the resolution characteristics of the output image using a task transfer function (TTF).

METHODS

A photostimulable phosphor system with three types of image processing filters, including periodontal, endodontic, and dentine-to-enamel junction filters, was used. Each filter can be used in conjunction with the sharpness filter (+ S). Images were obtained from the original phantom, which combined aluminum disk and plate. The TTF, which indicates the resolution characteristics, was calculated. A one-dimensional profile curve was also measured, and the fluctuation in the pixel value was evaluated in detail. The results were compared to investigate the effects of digital image processing on digital intraoral radiographic images.

RESULTS

The TTF values were specific to each filter. The change in the TTF strongly reflected the characteristics of the one-dimensional profile curve. The TTF was compared with a one-dimensional profile curve and was able to quantitatively express the resolution characteristics of all directions in the image.

CONCLUSIONS

We attempted to evaluate the resolution characteristics of digital intraoral radiographic images with image processing filters using the TTF. The effect of each image processing filter and the + S filter on the resolution can be simply expressed using the TTF. Our results show that the TTF is useful for characterizing the resolution characteristics of image processing filters for image quality.

Integrating Image Analysis and Dental Radiography for Periodontal and Peri-Implant Diagnosis

The recent change in classification of periodontal and peri-implant diseases includes objective evaluation of intra-oral radiographs and quantification of bone loss for disease staging and grading. Assessment of the progression of periodontal disease requires deduction of bone loss longitudinally, and its interpretation as (1) a percentage in relation to tooth root and (2) as a function of the patient's age. Similarly, bone loss around dental implants, after accounting for initial remodeling, is central for determining diagnosis, severity, and progression of peri-implantitis. Bone gain secondary to periodontal regeneration can be measured using standardized dental radiographs and compared to baseline morphology to determine treatment success. Computational image analysis, including machine learning (ML), has the potential to develop and automate quantitative measures of tooth, implant, bone volumes, and predict disease progression. The developed algorithms need to be standardized while considering pre-analytic, analytic, and post-analytic factors for successful translation to clinic. This review will introduce image analysis and machine learning in the context of dental radiography, and expand on the potential for integration of image analysis for assisted diagnosis of periodontitis and peri-implantitis.