Placental Flattening via Volumetric Parameterization

We present a volumetric mesh-based algorithm for flattening the placenta to a canonical template to enable effective visualization of local anatomy and function. Monitoring placental function in vivo promises to support pregnancy assessment and to improve care outcomes. We aim to alleviate visualization and interpretation challenges presented by the shape of the placenta when it is attached to the curved uterine wall. To do so, we flatten the volumetric mesh that captures placental shape to resemble the well-studied ex vivo shape. We formulate our method as a map from the in vivo shape to a flattened template that minimizes the symmetric Dirichlet energy to control distortion throughout the volume. Local injectivity is enforced via constrained line search during gradient descent. We evaluate the proposed method on 28 placenta shapes extracted from MRI images in a clinical study of placental function. We achieve sub-voxel accuracy in mapping the boundary of the placenta to the template while successfully controlling distortion throughout the volume. We illustrate how the resulting mapping of the placenta enhances visualization of placental anatomy and function. Our code is freely available at https://github.com/mabulnaga/placenta-flattening .Comment: MICCAI 201

Relative equilibria in the unrestricted problem of a sphere and symmetric rigid body

We consider the unrestricted problem of two mutually attracting rigid bodies, an uniform sphere (or a point mass) and an axially symmetric body. We present a global, geometric approach for finding all relative equilibria (stationary solutions) in this model, which was already studied by Kinoshita (1970). We extend and generalize his results, showing that the equilibria solutions may be found by solving at most two non-linear, algebraic equations, assuming that the potential function of the symmetric rigid body is known explicitly. We demonstrate that there are three classes of the relative equilibria, which we call "cylindrical", "inclined co-planar", and "conic" precessions, respectively. Moreover, we also show that in the case of conic precession, although the relative orbit is circular, the point-mass and the mass center of the body move in different parallel planes. This solution has been yet not known in the literature.Comment: The manuscript with 10 pages, 5 figures; accepted to the Monthly Notices of the Royal Astronomical Societ

Hybrid III-V diamond photonic platform for quantum nodes based on neutral silicon vacancy centers in diamond

Integrating atomic quantum memories based on color centers in diamond with on-chip photonic devices would enable entanglement distribution over long distances. However, efforts towards integration have been challenging because color centers can be highly sensitive to their environment, and their properties degrade in nanofabricated structures. Here, we describe a heterogeneously integrated, on-chip, III-V diamond platform designed for neutral silicon vacancy (SiV0) centers in diamond that circumvents the need for etching the diamond substrate. Through evanescent coupling to SiV0 centers near the surface of diamond, the platform will enable Purcell enhancement of SiV0 emission and efficient frequency conversion to the telecommunication C-band. The proposed structures can be realized with readily available fabrication techniques

AnyStar: Domain randomized universal star-convex 3D instance segmentation

Star-convex shapes arise across bio-microscopy and radiology in the form of nuclei, nodules, metastases, and other units. Existing instance segmentation networks for such structures train on densely labeled instances for each dataset, which requires substantial and often impractical manual annotation effort. Further, significant reengineering or finetuning is needed when presented with new datasets and imaging modalities due to changes in contrast, shape, orientation, resolution, and density. We present AnyStar, a domain-randomized generative model that simulates synthetic training data of blob-like objects with randomized appearance, environments, and imaging physics to train general-purpose star-convex instance segmentation networks. As a result, networks trained using our generative model do not require annotated images from unseen datasets. A single network trained on our synthesized data accurately 3D segments C. elegans and P. dumerilii nuclei in fluorescence microscopy, mouse cortical nuclei in micro-CT, zebrafish brain nuclei in EM, and placental cotyledons in human fetal MRI, all without any retraining, finetuning, transfer learning, or domain adaptation. Code is available at https://github.com/neel-dey/AnyStar.Comment: Code available at https://github.com/neel-dey/AnySta

Dynamic Neural Fields for Learning Atlases of 4D Fetal MRI Time-series

We present a method for fast biomedical image atlas construction using neural fields. Atlases are key to biomedical image analysis tasks, yet conventional and deep network estimation methods remain time-intensive. In this preliminary work, we frame subject-specific atlas building as learning a neural field of deformable spatiotemporal observations. We apply our method to learning subject-specific atlases and motion stabilization of dynamic BOLD MRI time-series of fetuses in utero. Our method yields high-quality atlases of fetal BOLD time-series with $\sim$5-7$\times$ faster convergence compared to existing work. While our method slightly underperforms well-tuned baselines in terms of anatomical overlap, it estimates templates significantly faster, thus enabling rapid processing and stabilization of large databases of 4D dynamic MRI acquisitions. Code is available at https://github.com/Kidrauh/neural-atlasingComment: 6 pages, 2 figures. Accepted by Medical Imaging Meets NeurIPS 202

Hybrid Integration of GaP Photonic Crystal Cavities with Silicon-Vacancy Centers in Diamond by Stamp-Transfer

Optically addressable solid-state defects are emerging as one of the most promising qubit platforms for quantum networks. Maximizing photon-defect interaction by nanophotonic cavity coupling is key to network efficiency. We demonstrate fabrication of gallium phosphide 1-D photonic crystal waveguide cavities on a silicon oxide carrier and subsequent integration with implanted silicon-vacancy (SiV) centers in diamond using a stamp-transfer technique. The stamping process avoids diamond etching and allows fine-tuning of the cavities prior to integration. After transfer to diamond, we measure cavity quality factors ($Q$) of up to 8900 and perform resonant excitation of single SiV centers coupled to these cavities. For a cavity with $Q$ of 4100, we observe a three-fold lifetime reduction on-resonance, corresponding to a maximum potential cooperativity of $C = 2$. These results indicate promise for high photon-defect interaction in a platform which avoids fabrication of the quantum defect host crystal

Visualization of Horizontal Settling Slurry Flow Using Electrical Resistance Tomography

Settling slurry flow is very common and important in many industries, especially in transportation, which need to be monitored in practical operation. An investigation on visualization of horizontal settling slurry flow in pipeline using electrical resistance tomography was made in this paper. The internal images of fluid structure were displayed to operators with measurement of the solids concentration distribution and solids velocity distribution in pipe cross section. Experimental investigation with 5% solids loading concentration at various transport velocities was conducted. Meanwhile, the results of photography and other flow measurement methods were compared with the results obtained from electrical resistance tomography