Accuracy of linear measurements using three imaging modalities: two lateral cephalograms and one 3D model from CBCT data

SummaryBackground: The aim of this study was to evaluate the accuracy of linear measurements on three imaging modalities: lateral cephalograms from a cephalometric machine with a 3 m source-to-mid-sagittal-plane distance (SMD), from a machine with 1.5 m SMD and 3D models from cone-beam computed tomography (CBCT) data. Methods: Twenty-one dry human skulls were used. Lateral cephalograms were taken, using two cephalometric devices: one with a 3 m SMD and one with a 1.5 m SMD. CBCT scans were taken by 3D Accuitomo® 170, and 3D surface models were created in Maxilim® software. Thirteen linear measurements were completed twice by two observers with a 4 week interval. Direct physical measurements by a digital calliper were defined as the gold standard. Statistical analysis was performed. Results: Nasion-Point A was significantly different from the gold standard in all methods. More statistically significant differences were found on the measurements of the 3 m SMD cephalograms in comparison to the other methods. Intra- and inter-observer agreement based on 3D measurements was slightly better than others. Limitations: Dry human skulls without soft tissues were used. Therefore, the results have to be interpreted with caution, as they do not fully represent clinical conditions. Conclusions: 3D measurements resulted in a better observer agreement. The accuracy of the measurements based on CBCT and 1.5 m SMD cephalogram was better than a 3 m SMD cephalogram. These findings demonstrated the linear measurements accuracy and reliability of 3D measurements based on CBCT data when compared to 2D techniques. Future studies should focus on the implementation of 3D cephalometry in clinical practic

Scan Integration as a Labeling Problem

Integration is a crucial step in the reconstruction of complete 3D surface model from multiple scans. Ever-present registration errors and scanning noise make integration a nontrivial problem. In this paper, we propose a novel method for multi-view scan integration where we solve it as a labelling problem. Unlike previous methods, which have been based on various merging schemes, our labelling-based method is essentially a selection strategy. The overall surface model is composed of surface patches from selected input scans. We formulate the labelling via a higher-order Markov Random Field (MRF) which assigns a label representing an index of some input scan to every point in a base surface. Using a higherorder MRF allows us to more effectively capture spatial relations between 3D points. We employ belief propagation to infer this labelling and experimentally demonstrate that this integration approach provides significantly improved integration via both qualitative and quantitative comparisons

Sub-pixel Registration In Computational Imaging And Applications To Enhancement Of Maxillofacial Ct Data

In computational imaging, data acquired by sampling the same scene or object at different times or from different orientations result in images in different coordinate systems. Registration is a crucial step in order to be able to compare, integrate and fuse the data obtained from different measurements. Tomography is the method of imaging a single plane or slice of an object. A Computed Tomography (CT) scan, also known as a CAT scan (Computed Axial Tomography scan), is a Helical Tomography, which traditionally produces a 2D image of the structures in a thin section of the body. It uses X-ray, which is ionizing radiation. Although the actual dose is typically low, repeated scans should be limited. In dentistry, implant dentistry in specific, there is a need for 3D visualization of internal anatomy. The internal visualization is mainly based on CT scanning technologies. The most important technological advancement which dramatically enhanced the clinician\u27s ability to diagnose, treat, and plan dental implants has been the CT scan. Advanced 3D modeling and visualization techniques permit highly refined and accurate assessment of the CT scan data. However, in addition to imperfections of the instrument and the imaging process, it is not uncommon to encounter other unwanted artifacts in the form of bright regions, flares and erroneous pixels due to dental bridges, metal braces, etc. Currently, removing and cleaning up the data from acquisition backscattering imperfections and unwanted artifacts is performed manually, which is as good as the experience level of the technician. On the other hand the process is error prone, since the editing process needs to be performed image by image. We address some of these issues by proposing novel registration methods and using stonecast models of patient\u27s dental imprint as reference ground truth data. Stone-cast models were originally used by dentists to make complete or partial dentures. The CT scan of such stone-cast models can be used to automatically guide the cleaning of patients\u27 CT scans from defects or unwanted artifacts, and also as an automatic segmentation system for the outliers of the CT scan data without use of stone-cast models. Segmented data is subsequently used to clean the data from artifacts using a new proposed 3D inpainting approach

Transcending Grids: Point Clouds and Surface Representations Powering Neurological Processing

In healthcare, accurately classifying medical images is vital, but conventional methods often hinge on medical data with a consistent grid structure, which may restrict their overall performance. Recent medical research has been focused on tweaking the architectures to attain better performance without giving due consideration to the representation of data. In this paper, we present a novel approach for transforming grid based data into its higher dimensional representations, leveraging unstructured point cloud data structures. We first generate a sparse point cloud from an image by integrating pixel color information as spatial coordinates. Next, we construct a hypersurface composed of points based on the image dimensions, with each smooth section within this hypersurface symbolizing a specific pixel location. Polygonal face construction is achieved using an adjacency tensor. Finally, a dense point cloud is generated by densely sampling the constructed hypersurface, with a focus on regions of higher detail. The effectiveness of our approach is demonstrated on a publicly accessible brain tumor dataset, achieving significant improvements over existing classification techniques. This methodology allows the extraction of intricate details from the original image, opening up new possibilities for advanced image analysis and processing tasks

Imagining 5G: Public sensemaking through advertising in China and the US

This study initiates a line of research on how the fifth generation of wireless infrastructure (“5G”) is being imagined through media portrayals—in this case through advertising. At the time of this writing, 5G is not yet widely available, however the media is saturated with narratives about how it will revolutionize everyday life. Drawing from the social imaginaries and media infrastructures traditions, this textual analysis examines the social shaping of 5G through advertisements from leading telecoms in leading markets, including China and the United States. Findings reveal an overarching trend with ads from both societies imagining 5G in futuristic and utopian ways, suggesting new possibilities for people, objects, and places to be connected through smart homes, vehicles, factories, and cities—not just through smart phones. The findings also reveal distinctions in how 5G is envisioned at the societal level. For example, ads from China imagine 5G as a source of national pride that will elevate its global standing, while the US telecoms have a more inward focus on domestic competition. The discussion offers interpretations of these and other findings, along with directions for future research

IGRT/ART phantom with programmable independent rib cage and tumor motion

Abstract PURPOSE: This paper describes the design and experimental evaluation of the Methods and Advanced Equipment for Simulation and Treatment in Radiation Oncology (MAESTRO) thorax phantom, a new anthropomorphic moving ribcage combined with a 3D tumor positioning system to move target inserts within static lungs. METHODS: The new rib cage design is described and its motion is evaluated using Vicon Nexus, a commercial 3D motion tracking system. CT studies at inhale and exhale position are used to study the effect of rib motion and tissue equivalence. RESULTS: The 3D target positioning system and the rib cage have millimetre accuracy. Each axis of motion can reproduce given trajectories from files or individually programmed sinusoidal motion in terms of amplitude, period, and phase shift. The maximum rib motion ranges from 7 to 20 mm SI and from 0.3 to 3.7 mm AP with LR motion less than 1 mm. The repeatability between cycles is within 0.16 mm root mean square error. The agreement between CT electron and mass density for skin, ribcage, spine hard and inner bone as well as cartilage is within 3%. CONCLUSIONS: The MAESTRO phantom is a useful research tool that produces programmable 3D rib motions which can be synchronized with 3D internal target motion. The easily accessible static lungs enable the use of a wide range of inserts or can be filled with lung tissue equivalent and deformed using the target motion system.status: publishe

Artifacts due to dental and maxillofacial restoration materials in cone beam computed tomography images

Over the last 20 years, three-dimensional X-ray imaging, cone beam computed tomography (CBCT), has become an important method when making the diagnoses in the dental and maxillofacial area. There has been rapid development in CBCT devices, and the image quality has improved considerably during the last two decades. Despite the many improvements in CBCT image quality, artifacts induced by dental and maxillofacial restoration materials are still a problem, especially when diagnosing the dental area. CBCT manufacturers produce artifact reduction algorithms, which are intended to decrease or remove the artifacts in the image. However, the results of the studies on artifact reduction algorithms vary and there is no final consensus, as yet, on their efficacy. The studies of the present thesis focus on the arti-facts induced by different dental restoration materials in CBCT images. Another aim was to compare how the different materials interfere with the radiologic diagnosis. The materials investigated were titanium, zirconia, composite, and fiber reinforced composite (FRC). The results showed that composites with ra-dio-opacifying BaAlSiO2 20% (weight%) or more caused artifacts in the CBCT images. Composites with BaAlSiO2 68% (weight%) or more caused artifacts with similar intensity as titanium. Titanium orbital floor implant caused artifacts in the CBCT images, whereas nonmetallic fiber reinforced composite (FRC) orbital floor implant did not cause hampering artifacts in the CBCT images. The diagnosis of apical perio-dontitis can be complicated in 70% of the CBCT images of paranasal sinuses because of the artifacts induced by dental and endodontic restorations. In the CBCT images, zirconia dental implants caused in-tense artifacts despite the artifact reduction algorithm. To conclude, different dental restoration materials cause image hampering artifacts of different intensities in CBCT images. Zirconia is especially problem-atic in CBCT images. More studies are needed on artifact reduction methods to achieve an image quality without artifacts to make the correct diagnosis. In addition, the consequences of restoration and implant material options should be considered in postoperative CBCT images

Deep Learning Method for Mandibular Canal Segmentation in Dental Cone Beam Computed Tomography Volumes

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