The topological derivative-based non-iterative imaging algorithm has demonstrated its applicability in limited-aperture inverse scattering problems. However, this has been confirmed through many experimental simulation results, and the reason behind this applicability has not been satisfactorily explained. In this paper, we identify the mathematical structure and certain properties of topological derivatives for the imaging of two-dimensional crack-like thin penetrable electromagnetic inhomogeneities that are completely embedded in a homogeneous material. To this end, we establish a relationship with an infinite series of Bessel functions of integer order of the first kind. Based on the derived structure, we discover a necessary condition for applying topological derivatives in limited-aperture inverse scattering problems, and thus confirm why topological derivatives can be applied. Furthermore, we analyze the structure of multi-frequency topological derivative, and identify why this improves the single-frequency topological derivative in limited-aperture inverse scattering problems. Various numerical simulations are conducted with noisy data, and the results support the derived structure and exhibit certain properties of single- and multi-frequency topological derivatives.
Accurate and robust identification of cephalometric landmarks for three-dimensional (3D) computerized tomography (CT) images is an important task for diagnosis, surgical planning, growth analysis, and treatment evaluation. Recent advances in imaging technology have led to the transition from two-dimensional (2D) cephalometry to a 3D one using CT scan images. 3D cephalometry holds several advantages, including the accurate identification of anatomical structures, avoidance of geometric distortion of the image, and ability to evaluate complicated facial structure. However, 3D cephalometry being a manual operation, it requires timeconsuming and labor-intensive work.Cephalometric analysis is basically conducted through cephalometric annotation, i.e. landmark detection for meaningful anatomical structures. It requires a high level of expertise, experience, and time. As 2D cephalometric analysis gives way to 3D, these difficulties are aggravated by a remarkable increase in data volume and geometric complexity. Several different automatic 3D annotation approaches have been proposed to address these limitations (
SUMMARYWe solve elliptic interface problems using a discontinuous Galerkin (DG) method, for which discontinuities in the solution and in its normal derivatives are prescribed on an interface inside the domain. Standard ways to solve interface problems with finite element methods consist in enforcing the prescribed discontinuity of the solution in the finite element space. Here, we show that the DG method provides a natural framework to enforce both discontinuities weakly in the DG formulation, provided the triangulation of the domain is fitted to the interface. The resulting discretization leads to a symmetric system that can be efficiently solved with standard algorithms. The method is shown to be optimally convergent in the L 2 -norm. We apply our method to the numerical study of electroporation, a widely used medical technique with applications to gene therapy and cancer treatment. Mathematical models of electroporation involve elliptic problems with dynamic interface conditions. We discretize such problems into a sequence of elliptic interface problems that can be solved by our method. We obtain numerical results that agree with known exact solutions.
The proposed structure of lasonolide A was synthesized employing radical cyclization reactions of beta-alkoxyacrylates for preparation of the tetrahydropyranyl units A and B, but the spectroscopic data did not match those of the natural product. Both enantiomers of a revised structure featuring 17E,25Z double bonds were synthesized, and the (-)-isomer was found to be the biologically active enantiomer.
The lengthy time needed for manual landmarking has delayed the widespread adoption of three-dimensional (3D) cephalometry. We here propose an automatic 3D cephalometric annotation system based on multi-stage deep reinforcement learning (DRL) and volume-rendered imaging. This system considers geometrical characteristics of landmarks and simulates the sequential decision process underlying human professional landmarking patterns. It consists mainly of constructing an appropriate two-dimensional cutaway or 3D model view, then implementing single-stage DRL with gradient-based boundary estimation or multi-stage DRL to dictate the 3D coordinates of target landmarks. This system clearly shows sufficient detection accuracy and stability for direct clinical applications, with a low level of detection error and low inter-individual variation (1.96 ± 0.78 mm). Our system, moreover, requires no additional steps of segmentation and 3D mesh-object construction for landmark detection. We believe these system features will enable fast-track cephalometric analysis and planning and expect it to achieve greater accuracy as larger CT datasets become available for training and testing.
Objectives To evaluate standard dose-like computed tomography (CT) images generated by a deep learning method, trained using unpaired low-dose CT (LDCT) and standard-dose CT (SDCT) images. Materials and methods LDCT (80 kVp, 100 mAs, n = 83) and SDCT (120 kVp, 200 mAs, n = 42) images were divided into training (42 LDCT and 42 SDCT) and validation (41 LDCT) sets. A generative adversarial network framework was used to train unpaired datasets. The trained deep learning method generated virtual SDCT images (VIs) from the original LDCT images (OIs). To test the proposed method, LDCT images (80 kVp, 262 mAs, n = 33) were collected from another CT scanner using iterative reconstruction (IR). Image analyses were performed to evaluate the qualities of VIs in the validation set and to compare the performance of deep learning and IR in the test set. Results The noise of the VIs was the lowest in both validation and test sets (all p<0.001). The mean CT number of the VIs for the portal vein and liver was lower than that of OIs in both validation and test sets (all p<0.001) and was similar to those of SDCT. The contrast-to-noise ratio of portal vein and the signal-to-noise ratio (SNR) of portal vein and liver of VIs were higher than those of SDCT (all p<0.05). The SNR of VIs in test sets was the highest among three images. Conclusion The deep learning method trained by unpaired datasets could reduce noise of LDCT images and showed similar performance to SAFIRE. It can be applied to LDCT images of older CT scanners without IR.
Magnetic resonance electrical impedance tomography (MREIT) is a new bio-imaging modality providing cross-sectional conductivity images from measurements of internal magnetic flux densities produced by externally injected currents. Recent experimental results of postmortem and in vivo imaging of the canine brain demonstrated its feasibility by showing conductivity images with meaningful contrast among different brain tissues. MREIT image reconstructions involve a series of data processing steps such as k-space data handling, phase unwrapping, image segmentation, meshing, modelling, finite element computation, denoising and so on. To facilitate experimental studies, we need a software tool that automates these data processing steps. In this paper, we summarize such an MREIT software package called CoReHA (conductivity reconstructor using harmonic algorithms). Performing imaging experiments of the postmortem canine abdomen, we demonstrate how CoReHA can be utilized in MREIT. The abdomen with a relatively large field of view and various organs imposes new technical challenges when it is chosen as an imaging domain. Summarizing a few improvements in the experimental MREIT technique, we report our first conductivity images of the postmortem canine abdomen. Illustrating reconstructed conductivity images, we discuss how they discern different organs including the kidney, spleen, stomach and small intestine. We elaborate, as an example, that conductivity images of the kidney show clear contrast among cortex, internal medulla, renal pelvis and urethra. We end this paper with a brief discussion on future work using different animal models.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.