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Background: Patient motions are a repeatedly reported phenomenon in oral and maxillofacial cone beam CT scans, leading to reconstructions of limited usability. In certain cases, independent movements of the mandible induce unpredictable motion patterns. Previous motion correction methods are not able to handle such complex cases of patient movements., Purpose: Our goal was to design a combined motion estimation and motion correction approach for separate cranial and mandibular motions, solely based on the 2D projection images from a single scan., Methods: Our iterative three-step motion correction algorithm models the two articulated motions as independent rigid motions. First of all, we segment cranium and mandible in the projection images using a deep neural network. Next, we compute a 3D reconstruction with the poses of the object's trajectories fixed. Third, we improve all poses by minimizing the projection error while keeping the reconstruction fixed. Step two and three are repeated alternately., Results: We find that our marker-free approach delivers reconstructions of up to 85% higher quality, with respect to the projection error, and can improve on already existing techniques, which model only a single rigid motion. We show results of both synthetic and real data created in different scenarios. The reconstruction of motion parameters in a real environment was evaluated on acquisitions of a skull mounted on a hexapod, creating a realistic, easily reproducible motion profile., Conclusions: The proposed algorithm consistently enhances the visual quality of motion impaired cone beam computed tomography scans, thus eliminating the need for a re-scan in certain cases, considerably lowering radiation dosage for the patient. It can flexibly be used with differently sized regions of interest and is even applicable to local tomography., (© 2023 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Purpose: Computed tomography (CT) and, in particular, cone beam CT (CBCT) have been increasingly used as a diagnostic tool in recent years. Patient motion during acquisition is common in CBCT due to long scan times. This results in degraded image quality and may potentially increase the number of retakes. Our aim was to develop a marker-free iterative motion correction algorithm that works on the projection images and is suitable for local tomography., Methods: We present an iterative motion correction algorithm that allows the patient's motion to be detected and taken into account during reconstruction. The core of our method is a fast GPU-accelerated three-dimensional reconstruction algorithm. Assuming rigid motion, motion correction is performed by minimizing a pixel-wise cost function between all captured x-ray images and parameterized projections of the reconstructed volume., Results: Our method is marker-free and requires only projection images. Furthermore, it can deal with local tomography data. We demonstrate the effectiveness of our approach on both simulated and real motion-beset patient images. The results show that our new motion correction algorithm leads to accurate reconstructions with sharper edges, better contrasts and more detail., Conclusions: The presented method allows for correction of patient motion with observable improvements in image quality compared to uncorrected reconstructions. Potentially, this may reduce the number of retakes caused by corrupted reconstructions due to patient movements., (© 2019 The Authors. Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.)

We propose an algorithm for real-time 6DOF pose tracking of rigid 3D objects using a monocular RGB camera. The key idea is to derive a region-based cost function using temporally consistent local color histograms. While such region-based cost functions are commonly optimized using first-order gradient descent techniques, we systematically derive a Gauss-Newton optimization scheme which gives rise to drastically faster convergence and highly accurate and robust tracking performance. We furthermore propose a novel complex dataset dedicated for the task of monocular object pose tracking and make it publicly available to the community. To our knowledge, it is the first to address the common and important scenario in which both the camera as well as the objects are moving simultaneously in cluttered scenes. In numerous experiments-including our own proposed dataset-we demonstrate that the proposed Gauss-Newton approach outperforms existing approaches, in particular in the presence of cluttered backgrounds, heterogeneous objects and partial occlusions.

In this paper, we present a method for automated estimation of a human face given a skull remain. Our proposed method is based on three statistical models. A volumetric (tetrahedral) skull model encoding the variations of different skulls, a surface head model encoding the head variations, and a dense statistic of facial soft tissue thickness (FSTT). All data are automatically derived from computed tomography (CT) head scans and optical face scans. In order to obtain a proper dense FSTT statistic, we register a skull model to each skull extracted from a CT scan and determine the FSTT value for each vertex of the skull model towards the associated extracted skin surface. The FSTT values at predefined landmarks from our statistic are well in agreement with data from the literature. To recover a face from a skull remain, we first fit our skull model to the given skull. Next, we generate spheres with radius of the respective FSTT value obtained from our statistic at each vertex of the registered skull. Finally, we fit a head model to the union of all spheres. The proposed automated method enables a probabilistic face-estimation that facilitates forensic recovery even from incomplete skull remains. The FSTT statistic allows the generation of plausible head variants, which can be adjusted intuitively using principal component analysis. We validate our face recovery process using an anonymized head CT scan. The estimation generated from the given skull visually compares well with the skin surface extracted from the CT scan itself., Competing Interests: The authors have declared that no competing interests exist.

Vision unfolds as an intricate pattern of information processing over time. Studying vision and visual cognition therefore requires precise manipulations of the timing of visual stimulus presentation. Although standard computer display technologies offer great accuracy and precision of visual presentation, their temporal resolution is limited. This limitation stems from the fact that the presentation of rendered stimuli has to wait until the next refresh of the computer screen. We present a novel method for presenting visual stimuli with ultrahigh temporal resolution (<1 ms) on newly available gaming monitors. The method capitalizes on the G-Sync technology, which allows for presenting stimuli as soon as they have been rendered by the computer's graphics card, without having to wait for the next screen refresh. We provide software implementations in the three programming languages C++, Python (using PsychoPy2), and Matlab (using Psychtoolbox3). For all implementations, we confirmed the ultrahigh temporal resolution of visual presentation with external measurements by using a photodiode. Moreover, a psychophysical experiment revealed that the ultrahigh temporal resolution impacts on human visual performance. Specifically, observers' object recognition performance improved over fine-grained increases of object presentation duration in a theoretically predicted way. Taken together, the present study shows that the G-Sync-based presentation method enables researchers to investigate visual processes whose data patterns were concealed by the low temporal resolution of previous technologies. Therefore, this new presentation method may be a valuable tool for experimental psychologists and neuroscientists studying vision and its temporal characteristics.

Objectives: To develop an automated procedure to detect patient motion on the projection images acquired during a cone beam computed tomography (CBCT) scan and to evaluate the method's feasibility on small real-world CBCT images in relation to visual assessment., Methods: Based on optical flow theory, software was developed using the sequence of the projection images of a CBCT machine for automated detection of patient motion. Averaged acceleration vectors were used as measurement data and compared with visual assessment of the projection images displayed as video. Seventy-nine CBCT data sets (small field-of-view: 40 mm) from our patient database were selected in a sequential fashion and evaluated with the software., Results: 10 out of 79 (13%) were allocated to a patient movement. A threshold of 0.4 pixel/frame transition was empirically determined as indicating motion by visual assessment of the image sequence. Relative to this standard of reference, the software reached 80% sensitivity versus 67% specificity., Conclusions: Optical flow seems to be an efficient concept for automated detection of patient motion on the projection images acquired during a CBCT scan., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Objective: The present study tested the reliability of an optical scanning device for the objective assessment of postoperative facial swelling., Study Design: Twenty control subjects bearing a defined volume of water (10-30 mL) in an intraorally carried balloon were tested to assess the measurement accuracy of the device. As a proof of concept, facial volumes of 59 surgical cases were recorded before osteotomy and 1 and 7 days after intervention with the use of a structured light scanner., Results: The median difference between the applied and the measured volumes was 0.67 mL for the control test with the artificial swelling simulated using water balloons. For subjects having third molar osteotomy, extraoral volume increased to 5.29 cm(3) 1 day after surgery (95% CI 5.22-8.52) and decreased to 0.00 mL (95% CI 0.85-2.55) after 7 days., Conclusions: Contact-free visible-light 3-dimensional scanning is reliable for the objective assessment of postoperative facial swelling., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Purpose: This paper introduces a novel autocalibration method for cone-beam-CTs (CBCT) or flat-panel CTs, assuming a perfect rotation. The method is based on ellipse-fitting. Autocalibration refers to accurate recovery of the geometric alignment of a CBCT device from projection images alone, without any manual measurements., Methods: The authors use test objects containing small arbitrarily positioned radio-opaque markers. No information regarding the relative positions of the markers is used. In practice, the authors use three to eight metal ball bearings (diameter of 1 mm), e.g., positioned roughly in a vertical line such that their projection image curves on the detector preferably form large ellipses over the circular orbit. From this ellipse-to-curve mapping and also from its inversion the authors derive an explicit formula. Nonlinear optimization based on this mapping enables them to determine the six relevant parameters of the system up to the device rotation angle, which is sufficient to define the geometry of a CBCT-machine assuming a perfect rotational movement. These parameters also include out-of-plane rotations. The authors evaluate their method by simulation based on data used in two similar approaches [L. Smekal, M. Kachelriess, S. E, and K. Wa, "Geometric misalignment and calibration in cone-beam tomography," Med. Phys. 31(12), 3242-3266 (2004); K. Yang, A. L. C. Kwan, D. F. Miller, and J. M. Boone, "A geometric calibration method for cone beam CT systems," Med. Phys. 33(6), 1695-1706 (2006)]. This allows a direct comparison of accuracy. Furthermore, the authors present real-world 3D reconstructions of a dry human spine segment and an electronic device. The reconstructions were computed from projections taken with a commercial dental CBCT device having two different focus-to-detector distances that were both calibrated with their method. The authors compare their reconstruction with a reconstruction computed by the manufacturer of the CBCT device to demonstrate the achievable spatial resolution of their calibration procedure., Results: Compared to the results published in the most closely related work [K. Yang, A. L. C. Kwan, D. F. Miller, and J. M. Boone, "A geometric calibration method for cone beam CT systems," Med. Phys. 33(6), 1695-1706 (2006)], the simulation proved the greater accuracy of their method, as well as a lower standard deviation of roughly 1 order of magnitude. When compared to another similar approach [L. Smekal, M. Kachelriess, S. E, and K. Wa, "Geometric misalignment and calibration in cone-beam tomography," Med. Phys. 31(12), 3242-3266 (2004)], their results were roughly of the same order of accuracy. Their analysis revealed that the method is capable of sufficiently calibrating out-of-plane angles in cases of larger cone angles when neglecting these angles negatively affects the reconstruction. Fine details in the 3D reconstruction of the spine segment and an electronic device indicate a high geometric calibration accuracy and the capability to produce state-of-the-art reconstructions., Conclusions: The method introduced here makes no requirements on the accuracy of the test object. In contrast to many previous autocalibration methods their approach also includes out-of-plane rotations of the detector. Although assuming a perfect rotation, the method seems to be sufficiently accurate for a commercial CBCT scanner. For devices which require higher dimensional geometry models, the method could be used as a initial calibration procedure.

Artefacts are common in today's cone beam CT (CBCT). They are induced by discrepancies between the mathematical modelling and the actual physical imaging process. Since artefacts may interfere with the diagnostic process performed on CBCT data sets, every user should be aware of their presence. This article aims to discuss the most prominent artefacts identified in the scientific literature and review the existing knowledge on these artefacts. We also briefly review the basic three-dimensional (3D) reconstruction concept applied by today's CBCT scanners, as all artefacts are more or less directly related to it.

This article illustrates a new method to align and merge two partially overlapping volumes each of them generated by cone beam computed tomography (CBCT). The aggregate volume covers a larger area of investigation and is determined by localizing one fixed LEGO brick in both of the primal volumes. Based on the LEGO brick an approximate registration of the volumes is determined. Afterwards we improve the transformation by minimizing the difference in overlapping space. In this paper we present a method which automates these two steps and provides an aligned volume., (Copyright (c) 2010 Elsevier Ltd. All rights reserved.)

Precise registration of radiographic projection images acquired in almost arbitrary geometries for the purpose of three-dimensional (3D) reconstruction is beset with difficulties. We modify and enhance a registration method [R. Schulze, D. D. Bruellmann, F. Roeder, and B. d'Hoedt, Med. Phys. 31, 2849-2854 (2004)] based on coupling a minimum amount of three reference spheres in arbitrary positions to a rigid object under study for precise a posteriori pose estimation. Two consecutive optimization procedures (a, initial guess; b, iterative coordinate refinement) are applied to completely exploit the reference's shadow information for precise registration of the projections. The modification has been extensive, i.e., only the idea of using the sphere shadows to locate each sphere in three dimensions from each projection was retained whereas the approach to extract the shadow information has been changed completely and extended. The registration information is used for subsequent algebraic reconstruction of the 3D information inherent in the projections. We present a detailed mathematical theory of the registration process as well as simulated data investigating its performance in the presence of error. Simulation of the initial guess revealed a mean relative error in the critical depth coordinate ranging between 2.1% and 4.4%, and an evident error reduction by the subsequent iterative coordinate refinement. To prove the applicability of the method for real-world data, algebraic 3D reconstructions from few (< or = 9) projection radiographs of a human skull, a human mandible and a teeth-containing mandible segment are presented. The method facilitates extraction of 3D information from only few projections obtained from off-the-shelf radiographic projection units without the need for costly hardware. Technical requirements as well as radiation dose are low.