Time-of-Flight Based Calibration of an Ultrasonic Computed Tomography System

The paper presents a novel method for calibration of measuring geometry and of individual signal delays of transducers in ultrasonic computed tomography (USCT) systems via computational processing of multiple time-of-flight measurements of ultrasonic (US) impulses. The positions and time-delay parameters of thousands of ultrasonic transducers inside the USCT tank are calibrated by this approach with a high precision required for the tomographic reconstruction; such accuracy cannot be provided by any other known method. Although utilising similar basic principles as the global positioning system (GPS), the method is importantly generalised in treating all transducer parameters as the to-be calibrated (floating) unknowns, without any a-priori known positions and delays. The calibration is formulated as a non-linear least-squares problem, minimizing the differences between the calculated and measured time-of-arrivals of ultrasonic pulses. The paper provides detailed derivation of the method, and compares two implemented approaches (earlier calibration of individual transducers with the new approach calibrating rigid transducer arrays) via detailed simulations, aimed at testing the convergence properties and noise robustness of both approaches. Calibration using real US signals is described and, as an illustration of the utility of the presented method, a comparison is shown of two image reconstructions using the tomographic US data from a concrete experimental USCT system measuring a 3D phantom, without and after the calibration

Position-based simulation of deformations for autonomous robotic ultrasound scanning

Realistic and fast simulation of anatomical deformations due to ultrasound probe pressure is of outstanding importance for testing and validation of autonomous robotic ultrasound systems. We propose a deformation model which relies on the position-based dynamics (PBD) approach to simulate the probetissue interaction and predict the displacement of internal targets during US acquisition. Performances of the patient-specific PBD anatomical model are evaluated in comparison to two different simulations relying on the traditional finite element (FE) method, in the context of breast ultrasound scanning. Localization error obtained when applying the PBD model remains below 11 mm for all the tumors even for input displacements in the order of 30 mm. The proposed method is able to achieve a better trade-off among accuracy, computation time and generalization capabilities with respect to the two FE models. Position-based dynamics approach has proved to be successful in modeling breast tissue deformations during US acquisition. It represents a valid alternative to classical FE methods for simulating the interaction between US probe and tissues

Image-driven Stochastic Identification of Boundary Conditions for Predictive Simulation

International audienceIn computer-aided interventions, biomechanical models reconstructed from the pre-operative data are used via augmented reality to facilitate the intra-operative navigation. The predictive power of such models highly depends on the knowledge of boundary conditions. However , in the context of patient-specific modeling, neither the pre-operative nor the intra-operative modalities provide a reliable information about the location and mechanical properties of the organ attachments. We present a novel image-driven method for fast identification of boundary conditions which are modelled as stochastic parameters. The method employs the reduced-order unscented Kalman filter to transform in real-time the probability distributions of the parameters, given observations extracted from intra-operative images. The method is evaluated using synthetic, phantom and real data acquired in vivo on a porcine liver. A quantitative assessment is presented and it is shown that the method significantly increases the predictive power of the biomechanical model

Category Theoretic Analysis of Hierarchical Protein Materials and Social Networks

Materials in biology span all the scales from Angstroms to meters and typically consist of complex hierarchical assemblies of simple building blocks. Here we describe an application of category theory to describe structural and resulting functional properties of biological protein materials by developing so-called ologs. An olog is like a “concept web” or “semantic network” except that it follows a rigorous mathematical formulation based on category theory. This key difference ensures that an olog is unambiguous, highly adaptable to evolution and change, and suitable for sharing concepts with other olog. We consider simple cases of beta-helical and amyloid-like protein filaments subjected to axial extension and develop an olog representation of their structural and resulting mechanical properties. We also construct a representation of a social network in which people send text-messages to their nearest neighbors and act as a team to perform a task. We show that the olog for the protein and the olog for the social network feature identical category-theoretic representations, and we proceed to precisely explicate the analogy or isomorphism between them. The examples presented here demonstrate that the intrinsic nature of a complex system, which in particular includes a precise relationship between structure and function at different hierarchical levels, can be effectively represented by an olog. This, in turn, allows for comparative studies between disparate materials or fields of application, and results in novel approaches to derive functionality in the design of de novo hierarchical systems. We discuss opportunities and challenges associated with the description of complex biological materials by using ologs as a powerful tool for analysis and design in the context of materiomics, and we present the potential impact of this approach for engineering, life sciences, and medicine.Presidential Early Career Award for Scientists and Engineers (N000141010562)United States. Army Research Office. Multidisciplinary University Research Initiative (W911NF0910541)United States. Office of Naval Research (grant N000141010841)Massachusetts Institute of Technology. Dept. of MathematicsStudienstiftung des deutschen VolkesClark BarwickJacob Luri

Regularized Image Reconstruction for Ultrasound Attenuation Transmission Tomography

The paper is focused on ultrasonic transmission tomography as a potential medical imaging modality, namely for breast cancer diagnosis. Ultrasound attenuation coefficient is one of the tissue parameters which are related to the pathological tissue state. A technique to reconstruct images of attenuation distribution is presented. Furthermore, an alternative to the commonly used filtered backprojection or algebraic reconstruction techniques is proposed. It is based on regularization of the image reconstruction problem which imposes smoothness in the resulting images while preserving edges. The approach is analyzed on synthetic data sets. The results show that it stabilizes the image restoration by compensating for main sources of estimation errors in this imaging modality

Toward Robust Fully 3D Filopodium Segmentation and Tracking in Time-Lapse Fluorescence Microscopy.

Development, parameter tuning, and objective benchmarking of bioimage analysis workflows heavily rely on the availability of diverse bioimage datasets accompanied by reference annotations. In this paper, we present a new benchmark dataset, FiloData3D, designed for in-depth performance assessments of fully 3D filopodium segmentation and tracking algorithms that emerged recently in the field. It consists of 180 synthetic, fully annotated, 3D time-lapse sequences of single lung cancer cells, combining different cell shapes, signal-to-noise ratios, and anisotropy ratios, which are the well-known factors that influence the quality of segmentation and tracking results. Using FiloData3D, we show that the number of filopodia and their lengths extracted are significantly underestimated in the case of traditional 2D protocols that prevail in daily practice compared to fully 3D measurements, calling for a procedural change in filopodial analyses of 3D+t bioimage data