Automatic 3D Registration of Dental CBCT and Face Scan Data using 2D Projection images

This paper presents a fully automatic registration method of dental cone-beam computed tomography (CBCT) and face scan data. It can be used for a digital platform of 3D jaw-teeth-face models in a variety of applications, including 3D digital treatment planning and orthognathic surgery. Difficulties in accurately merging facial scans and CBCT images are due to the different image acquisition methods and limited area of correspondence between the two facial surfaces. In addition, it is difficult to use machine learning techniques because they use face-related 3D medical data with radiation exposure, which are difficult to obtain for training. The proposed method addresses these problems by reusing an existing machine-learning-based 2D landmark detection algorithm in an open-source library and developing a novel mathematical algorithm that identifies paired 3D landmarks from knowledge of the corresponding 2D landmarks. A main contribution of this study is that the proposed method does not require annotated training data of facial landmarks because it uses a pre-trained facial landmark detection algorithm that is known to be robust and generalized to various 2D face image models. Note that this reduces a 3D landmark detection problem to a 2D problem of identifying the corresponding landmarks on two 2D projection images generated from two different projection angles. Here, the 3D landmarks for registration were selected from the sub-surfaces with the least geometric change under the CBCT and face scan environments. For the final fine-tuning of the registration, the Iterative Closest Point method was applied, which utilizes geometrical information around the 3D landmarks. The experimental results show that the proposed method achieved an averaged surface distance error of 0.74 mm for three pairs of CBCT and face scan datasets.Comment: 8 pages, 6 figures, 2 table

Automatic Three-Dimensional Cephalometric Annotation System Using Three-Dimensional Convolutional Neural Networks

Background: Three-dimensional (3D) cephalometric analysis using computerized tomography data has been rapidly adopted for dysmorphosis and anthropometry. Several different approaches to automatic 3D annotation have been proposed to overcome the limitations of traditional cephalometry. The purpose of this study was to evaluate the accuracy of our newly-developed system using a deep learning algorithm for automatic 3D cephalometric annotation. Methods: To overcome current technical limitations, some measures were developed to directly annotate 3D human skull data. Our deep learning-based model system mainly consisted of a 3D convolutional neural network and image data resampling. Results: The discrepancies between the referenced and predicted coordinate values in three axes and in 3D distance were calculated to evaluate system accuracy. Our new model system yielded prediction errors of 3.26, 3.18, and 4.81 mm (for three axes) and 7.61 mm (for 3D). Moreover, there was no difference among the landmarks of the three groups, including the midsagittal plane, horizontal plane, and mandible (p>0.05). Conclusion: A new 3D convolutional neural network-based automatic annotation system for 3D cephalometry was developed. The strategies used to implement the system were detailed and measurement results were evaluated for accuracy. Further development of this system is planned for full clinical application of automatic 3D cephalometric annotation

One-ninth magnetization plateau stabilized by spin entanglement in a kagome antiferromagnet

The spin-1/2 antiferromagnetic Heisenberg model on a Kagome lattice is geometrically frustrated, which is expected to promote the formation of many-body quantum entangled states. The most sought-after among these is the quantum spin liquid phase, but magnetic analogs of liquid, solid, and supersolid phases may also occur, producing fractional plateaus in the magnetization. Here, we investigate the experimental realization of these predicted phases in the Kagome material YCu3(OD)6+xBr3-x (x=0.5). By combining thermodynamic and Raman spectroscopic techniques, we provide evidence for fractionalized spinon excitations and observe the emergence of a 1/9 magnetization plateau. These observations establish YCu3(OD)6+xBr3-x as a model material for exploring the 1/9 plateau phase.Comment: to appear in Nature Physics, 33 pagses, 15 figure

CoReHA 2.0: A Software Package for In Vivo MREIT Experiments

Magnetic resonance electrical impedance tomography (MREIT) is a new medical imaging modality visualizing static conductivity images of electrically conducting subjects. Recently, MREIT has rapidly progressed in its theory, algorithm, and experiment technique and now reached to the stage of in vivo animal experiments. In this paper, we present a software, named CoReHA 2.0 standing for the second version of conductivity reconstructor using harmonic algorithms, to facilitate in vivo MREIT reconstruction of conductivity image. This software offers various computational tools including preprocessing of MREIT data, identification of 2D geometry of the imaging domain and electrode positions, and reconstruction of cross-sectional scaled conductivity images from MREIT data. In particular, in the new version, we added several tools including ramp-preserving denoising, harmonic inpainting, and local harmonic Bz algorithm to deal with data from in vivo experiments. The presented software will be useful to researchers in the field of MREIT for simulation, validation, and further technical development

Division of Applied Mathematics,

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Automatic 3D Registration of Dental CBCT and Face Scan Data Using 2D Projection Images

This paper presents a fully automatic registration method of dental cone-beam computed tomography (CBCT) and face scan data. Difficulties in accurately merging facial scans and CBCT images arise from the different image acquisition methods and limited area of correspondence between the two facial surfaces. In addition, it is difficult to use machine learning techniques because they use face-related 3D medical data with radiation exposure, which are difficult to obtain for training. The proposed method addresses these problems by reusing an existing machine-learning-based 2D landmark detection algorithm in an open-source library and developing a novel mathematical algorithm that identifies paired 3D landmarks from knowledge of the corresponding 2D landmarks. A main contribution of this study is that the proposed method does not require annotated training data of facial landmarks because it uses a pre-trained facial landmark detection algorithm that is known to be robust and generalized to various 2D face image models. Note that this reduces a 3D landmark detection problem to a 2D problem of identifying the corresponding landmarks on two 2D projection images generated from two different projection angles. Here, the 3D landmarks for registration were selected from the sub-surfaces with the least geometric change under the CBCT and face scan environments. For the final fine-tuning of the registration, the Iterative Closest Point method was applied, which utilizes geometrical information around the 3D landmarks. The proposed method achieved an averaged mean surface distance error of 0.74 mm for three pairs of CBCT and face scan datasets

Machine learning-based signal quality assessment for cardiac volume monitoring in electrical impedance tomography

Owing to recent advances in thoracic electrical impedance tomography (EIT), a patient’s hemodynamic function can be noninvasively and continuously estimated in real-time by surveilling a cardiac volume signal (CVS) associated with stroke volume and cardiac output. In clinical applications, however, a CVS is often of low quality, mainly because of the patient’s deliberate movements or inevitable motions during clinical interventions. This study aims to develop a signal quality indexing method that assesses the influence of motion artifacts on transient CVSs. The assessment is performed on each cardiac cycle to take advantage of the periodicity and regularity in cardiac volume changes. Time intervals are identified using the synchronized electrocardiography system. We apply divergent machine-learning methods, which can be sorted into discriminative-model and manifold-learning approaches. The use of machine-learning could be suitable for our real-time monitoring application that requires fast inference and automation as well as high accuracy. In the clinical environment, the proposed method can be utilized to provide immediate warnings so that clinicians can minimize confusion regarding patients’ conditions, reduce clinical resource utilization, and improve the confidence level of the monitoring system. Numerous experiments using actual EIT data validate the capability of CVSs degraded by motion artifacts to be accurately and automatically assessed in real-time by machine learning. The best model achieved an accuracy of 0.95, positive and negative predictive values of 0.96 and 0.86, sensitivity of 0.98, specificity of 0.77, and AUC of 0.96

A Reconstruction Method of Blood Flow Velocity in Left Ventricle Using Color Flow Ultrasound

Vortex flow imaging is a relatively new medical imaging method for the dynamic visualization of intracardiac blood flow, a potentially useful index of cardiac dysfunction. A reconstruction method is proposed here to quantify the distribution of blood flow velocity fields inside the left ventricle from color flow images compiled from ultrasound measurements. In this paper, a 2D incompressible Navier-Stokes equation with a mass source term is proposed to utilize the measurable color flow ultrasound data in a plane along with the moving boundary condition. The proposed model reflects out-of-plane blood flows on the imaging plane through the mass source term. The boundary conditions to solve the system of equations are derived from the dimensions of the ventricle extracted from 2D echocardiography data. The performance of the proposed method is evaluated numerically using synthetic flow data acquired from simulating left ventricle flows. The numerical simulations show the feasibility and potential usefulness of the proposed method of reconstructing the intracardiac flow fields. Of particular note is the finding that the mass source term in the proposed model improves the reconstruction performance