In this study, multi-view synthetic radiographs of patients generated from stationary intraoral tomosynthesis (sIOT) images were compared with standard bitewing (SBW) radiographs in terms of proximal tooth overlap and image quality. Patient sIOT images from a previous study were used to create seven synthetic radiographs with different viewing angles through specialized code. The proximal tooth overlap, contrast, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) were calculated for representative patient data sets. The synthetic radiographs had a minimum overlap of 0%, median overlap of 0%, maximum overlap of 14%, and interquartile range of 0%. The SBW radiographs had a minimum overlap of 13%, median overlap of 23.5%, maximum overlap of 49%, and interquartile range of 14.5%. The ratio of mean contrast, mean CNR, and mean SNR between the synthetic radiographs and the SBW radiographs were 3.81, 2.63, and 0.821 respectively. The synthetic radiographs had decreased proximal overlap and increased contrast and CNR compared to SBW radiographs. These results suggest that synthetic radiographs can reduce proximal overlap and improve contrast compared to SBW radiographs.
Microfractures (cracks) are the third most common cause of tooth loss in industrialized countries. If they are not detected early, they continue to progress until the tooth is lost. Cone beam computed tomography (CBCT) has been used to detect microfractures, but has had very limited success. We propose an algorithm to detect cracked teeth that pairs high resolution (hr) CBCT scans with advanced image analysis and machine learning. First, microfractures were simulated in extracted human teeth (n=22). hr-CBCT and microCT scans of the fractured and control teeth (n=14) were obtained. Wavelet pyramid construction was used to generate a phase image of the Fourier transformed scan which were fed to a U-Net deep learning architecture that localizes the orientation and extent of the crack which yields slice-wise probability maps that indicate the presence of microfractures. We then examine the ratio of high-probability voxels to total tooth volume to determine the likelihood of cracks per tooth. In microCT and hr-CBCT scans, fractured teeth have higher numbers of such voxels compared to control teeth. The proposed analytical framework provides a novel way to quantify the structural breakdown of teeth, that was not possible before. Future work will expand our machine learning framework to 3D volumes, improve our feature extraction in hr-CBCT and clinically validate this model. Early detection of microfractures will lead to more appropriate treatment and longer tooth retention.
Studies show that cracked teeth are the third most common cause for tooth loss in industrialized countries. If detected early and accurately, patients can retain their teeth for a longer time. Most cracks are not detected early because of the discontinuous symptoms and lack of good diagnostic tools. Currently used imaging modalities like Cone Beam Computed Tomography (CBCT) and intraoral radiography often have low sensitivity and do not show cracks clearly. This paper introduces a novel method that can detect, quantify, and localize cracks automatically in high resolution CBCT (hr-CBCT) scans of teeth using steerable wavelets and learning methods. These initial results were created using hr-CBCT scans of a set of healthy teeth and of teeth with simulated longitudinal cracks. The cracks were simulated using multiple orientations. The crack detection was trained on the most significant wavelet coefficients at each scale using a bagged classifier of Support Vector Machines. Our results show high discriminative specificity and sensitivity of this method. The framework aims to be automatic, reproducible, and open-source. Future work will focus on the clinical validation of the proposed techniques on different types of cracks ex-vivo. We believe that this work will ultimately lead to improved tracking and detection of cracks allowing for longer lasting healthy teeth.
Stationary intraoral tomosynthesis (sIOT) is an experimental imaging approach using a fixed array of carbon nanotubeenabled x-ray sources to produce a series of projections from which three-dimensional information can be reconstructed and displayed. Customized to the dental workspace, the first-generation sIOT tube is compact, easy-to-operate, and designed to interface with standard digital intraoral detectors. The purpose of this work was to explore the utility of the sIOT device across a range of dental pathologies and thereby identify limitations potentially amenable to correction through post-acquisition processing. Phantoms, extracted human teeth, and cadaveric specimens containing caries, fractures, and dilacerated roots, often associated with amalgam restorations, were imaged using tube settings that match the kVp and mA used in conventional clinical 2D intraoral imaging. An iterative reconstruction approach generated a stack of image slices through which the reader scrolls to appreciate depth relationships. Initial experience demonstrated an improved ability to visualize occlusal caries, interproximal caries, crown and root fractures, and root dilacerations when compared to 2D imaging. However, artifacts around amalgam restorations and metal implants proved problematic, leading to the incorporation of an artifact reduction step in the post-acquisition processing chain. These findings support the continued study of sIOT as a viable limited-angle tomography tool for dental applications and provide a foundation for the ongoing development of image processing steps to maximize the diagnostic utility of the displayed images.
Despite recent advances in dental radiography, the diagnostic accuracies for some of the most common dental diseases have not improved significantly, and in some cases remain low. Intraoral x-ray is the most commonly used x-ray diagnostic tool in dental clinics. It however suffers from the typical limitations of a 2D imaging modality including structure overlap. Cone-beam computed tomography (CBCT) uses high radiation dose and suffers from image artifacts and relatively low resolution. The purpose of this study is to investigate the feasibility of developing a stationary intraoral tomosynthesis (s-IOT) using spatially distributed carbon nanotube (CNT) x-ray array technology, and to evaluate its diagnostic accuracy compared to conventional 2D intraoral x-ray. A bench-top s-IOT device was constructed using a linear CNT based X-ray source array and a digital intraoral detector. Image reconstruction was performed using an iterative reconstruction algorithm. Studies were performed to optimize the imaging configuration. For evaluation of s-IOT’s diagnostic accuracy, images of a dental quality assurance phantom, and extracted human tooth specimens were acquired. Results show s-IOT increases the diagnostic sensitivity for caries compared to intraoral x-ray at a comparable dose level.
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