In diagnosing and treating prostate cancer the flexible bevel tip needle insertion surgical technique is commonly used. Bevel tip needles experience asymmetric loading on the needle's tip, inducing natural bending of the needle and enabling control mechanisms for precise placement of the needle during surgery. Several methods leverage the needles natural bending to provide autonomous control of needle insertion for accurate needle placement in an effort to reduce excess tissue damage and improve patient outcomes from needle insertion intraventions. Moreover, control methods using lateral deflection of the needle intra-operatively to steer the needle during insertion have been studied and have shown promising results. Thus, to enable these autonomous control methods, real-time, intra-operative shape-sensing feedback is pivotal for optimal performance of the needle insertion control. This work presents an extension of our proven Lie-group theoretic shape-sensing model to handle lateral deflection of the needle during needle insertion and validate this extension with robotic needle insertions in phantom tissue using stereo vision as a ground truth. Furthermore, the system configuration for real-time shape-sensing is implemented using ROS 2, demonstrating average feedback frequency of 15 ± 8 Hz. Average needle shape errors realized from this extension under 1 mm, validating the shape-sensing models' extension.

Architected materials derive performance characteristics from material properties and internal geometry. These materials are increasingly prevalent across a wide variety of domains. Many intricate feature geometries associated with architected materials can be explored using additive manufacturing (AM) processes. However, current AM methods generally cannot fabricate geometries with completely closed voids without introducing a support structure. This paper describes a new, support-free approach to AM capable of creating structures with closed voids. This work limits part geometry to three-dimensional (3D) geometries defined by a revolution about a single axis. This limitation enables planar analysis within a three-degree-of-freedom (3-DoF) task space. Part geometry in 3-DoF task space is constrained to a convex arch. Task space geometry is divided into an ordered set of sub-regions, considering feasible deposition orientations and collision constraints. The use of 3-DoF task space provides planar translation and rotation of the component during fabrication. The introduction of this rotational DoF addresses AM overhang constraints imposed by gravity. Methods for generating, ordering, and layering sub-regions suitable for printing a part with a closed hole are presented. Layers derived in the 3-DoF task space analysis are then extended to 3D deposition paths using the axis of revolution defined by the original part. The method of hole closure relies on the concept of a "keystone" which requires a 45° nozzle offset for collision-free deposition within keystone-adjacent sub-regions. The feasibility of deposition using a 45° nozzle offset is explored experimentally, and results demonstrate feasibility.

Complex needle shape prediction remains an issue for planning of surgical interventions of flexible needles. In this paper, we validate a theoretical method for flexible needle shape prediction allowing for non-uniform curvatures, extending upon a previous sensor-based model which combines curvature measurements from fiber Bragg grating (FBG) sensors and the mechanics of an inextensible elastic rod to determine and predict the 3D needle shape during insertion. We evaluate the model's effectiveness in single-layer isotropic tissue for shape sensing and shape prediction capabilities. Experiments on a four-active area, FBG-sensorized needle were performed in varying single-layer isotropic tissues under stereo vision to provide 3D ground truth of the needle shape. The results validate a viable 3D needle shape prediction model accounting for non-uniform curvatures in flexible needles with mean needle shape sensing and prediction root-mean-square errors of 0.479 mm and 0.892 mm, respectively.

The determination of a flexible needle shape during the insertion is an important issue in minimally invasive surgery techniques. This is especially critical when considering conventional surgery procedures where a surgeon usually determines how to proceed needle insertion further based on the current needle trajectory inserted into tissue during biopsy and removal of malignant tissues in the body. In this paper, we propose a new method to determine the shape of a needle that is being inserted, together with the curvature measurement data obtained by fiber Bragg gratings (FBG) sensors inside the needle. This approach can be particularly advantageous to the situations where visual guidance (such as by ultrasound probes) is not easily applicable. The description of a needle shape is based on the elastic rod theory and Lie-group-theoretic approach. We also present the comparison between two different calibration methods, which have an impact on the quality of the results of the proposed method. In order to verify the proposed method, needle trajectories by the model are compared with experimental data obtained by image analysis, which in turn emphasizes the capability of the proposed method.

Assessing preferred relative rigid-body position and orientation is important in the description of biomolecular structures (such as proteins) and their interactions. For that purpose, techniques from the kinematics community are often used. In this paper, we review parameterization methods that are widely used to describe relative rigid body motions (in particular, orientations). Then we present the extended and updated review of a `symmetrical parameterization' which was newly introduced in the kinematics community. This parameterization is useful in describing the relative biomolecular rigid body motions, where the parameters are symmetrical in the sense that the subunits of a complex biomolecular structure are described in the same way for the corresponding motion and its inverse. The properties of this new parameterization, singularity analysis and inverse kinematics, are also investigated in more detail. Finally the parameterization is applied to real biomolecular structures to show the efficacy of the symmetrical parameterization in the field of computational structural biology.

Cryo-Electron Microscopy (EM) and Small Angle X-ray Scattering (SAXS) are two different data acquisition modalities often used to glean information about the structure of large biomolecular complexes in their native states. A SAXS experiment is generally considered fast and easy but unveiling the structure at very low resolution, whereas a cryo-EM experiment needs more extensive preparation and post-acquisition computation to yield a 3D density map at higher resolution. In certain applications, one may need to verify if the data acquired in the SAXS and cryo-EM experiments correspond to the same structure (e.g., prior to reconstructing the 3D density map in EM). In this paper, a simple and fast method is proposed to verify the compatibility of the SAXS and EM experiments. The method is based on averaging the 2D correlation of EM images and the Abel transform of the SAXS data. The results are verified on simulations of conformational states of large biomolecular complexes.

Fine needles facilitate diagnosis and therapy because they enable minimally invasive surgical interventions. This paper formulates the problem of steering a very flexible needle through firm tissue as a nonholonomic kinematics problem, and demonstrates how planning can be accomplished using diffusion-based motion planning on the Euclidean group, SE(3). In the present formulation, the tissue is treated as isotropic and no obstacles are present. The bevel tip of the needle is treated as a nonholonomic constraint that can be viewed as a 3D extension of the standard kinematic cart or unicycle. A deterministic model is used as the starting point, and reachability criteria are established. A stochastic differential equation and its corresponding Fokker-Planck equation are derived. The Euler-Maruyama method is used to generate the ensemble of reachable states of the needle tip. Inverse kinematics methods developed previously for hyper-redundant and binary manipulators that use this probability density information are applied to generate needle tip paths that reach the desired targets.