Dynamic focal plane estimation for dental panoramic radiography

Abstract

Purpose: The digital panoramic radiography is widely used in dental clinics and provides the anatomical information of the intraoral structure along the predefined arc-shaped path. Since the intraoral structure varies depending on the patient, however, it is nearly impossible to design a common and static focal path or plane fitted to the dentition of all patients. In response, we introduce an imaging algorithm for digital panoramic radiography that can provide a focused panoramic radiographic image for all patients, by automatically estimating the best focal plane for each patient. Methods: The aim of this study is to improve the image quality of dental panoramic radiography based on a three-dimensional (3D) dynamic focal plane. The plane is newly introduced to represent the arbitrary 3D intraoral structure of each patient. The proposed algorithm consists of three steps: preprocessing, focal plane estimation, and image reconstruction. We first perform preprocessing to improve the accuracy of focal plane estimation. The 3D dynamic focal plane is then estimated by adjusting the position of the image plane so that object boundaries in the neighboring projection data are aligned or focused on the plane. Finally, a panoramic radiographic image is reconstructed using the estimated dynamic focal plane. Results: The proposed algorithm is evaluated using a numerical phantom dataset and four clinical human datasets. In order to examine the image quality improvement owing to the proposed algorithm, we generate panoramic radiographic images based on a conventional static focal plane and estimated 3D dynamic focal planes, respectively. Experimental results show that the image quality is dramatically improved for all datasets using the 3D dynamic focal planes that are estimated from the proposed algorithm. Conclusions: We propose an imaging algorithm for digital panoramic radiography that provides improved image quality by estimating dynamic focal planes fitted to each individual patient's intraoral structure.

Artificial Intelligence Model to Detect Real Contact Relationship between Mandibular Third Molars and Inferior Alveolar Nerve Based on Panoramic Radiographs

“…In radiographic examination, panoramic radiographs are most commonly used and aid dentists in determining the relationship between MM3s and the IAN canal because it can provide clinical dental image with short scan-time and low radiation dose [ 10 ]. However, panoramic radiographs have many shortcomings, such as anatomical noise, superimposition, and geometric distortion effect [ 11 ].…”

“…Therefore, it is necessary to predict the contact relationship between MM3s and the IAN with radiographic examination before tooth extraction, which contributes to preoperatively predicting surgical difficulty and the possibility of complications [8,9], thereby developing a more minimally invasive extraction strategy and reducing the risk of IAN injury. In radiographic examination, panoramic radiographs are most commonly used and aid dentists in determining the relationship between MM3s and the IAN canal because it can provide clinical dental image with short scan-time and low radiation dose [10]. However, panoramic radiographs have many shortcomings, such as anatomical noise, superimposition, and geometric distortion effect [11].…”

Dental panoramic imaging by use of CBCT projection data

“…The most representative method is to acquire multiple panoramic slice images from different layers at different depths and then acquire panoramic images by selecting focused patches from multiple images based on the sharpness metric [15]. Another method is to calculate a dynamic focal plane that represents the patient's dentition well from panoramic projection data and then acquire a panoramic image based on the calculated focal plane [20]. However, even with those correction methods, an out-of-focus panoramic image will be obtained if the patient's dentition is too far from the set focal plane.…”

“…Thereafter, panoramic projection data is extracted from the CBCT projection data along the de ned panoramic projection trajectory. Finally, the panoramic image is reconstructed using the extracted panoramic projection data [4,15,20]. Compared to the hardware approach, the proposed panoramic image reconstruction method has the advantage of obtaining a panoramic image of the focal plane optimized for the patient because the focal plane can be set free from the CBCT image.…”

Optimization of the Progressive Image Mosaicing Algorithm in Fine Art Image Fusion for Virtual Reality

“…The algorithm directly uses the camera to rotate around the vertical axis to shoot the sequence of images, complete the mosaic in the cylindrical coordinate system, and obtain the ideal mosaic result. Kim et al [18] extended the panoramic image mosaic algorithm to a wider range of fields, and proposed a dynamic panoramic image generation algorithm, which greatly enhanced the realism of the image. Han et al [19] proposed an image mosaic algorithm based on dynamic programming.…”