Stomach Geometry Reconstruction Using Serosal Transmitting Coils and Magnetic Source Localization

OBJECTIVE

Bioelectric slow waves (SWs) are a key regulator of gastrointestinal motility, and disordered SW activity has been linked to motility disorders. There is currently a lack of practical options for the acquisition of the 3D stomach geometry during research studies when medical imaging is challenging. Accurately recording the geometry of the stomach and co-registering electrode and sensor positions would provide context for in-vivo studies and aid the development of non-invasive methods of gastric SW assessment.

METHODS

A stomach geometry reconstruction method based on the localization of transmitting coils placed on the gastric serosa was developed. The positions and orientations of the coils, which represented boundary points and surface-normal vectors, were estimated using a magnetic source localization algorithm. Coil localization results were then used to generate surface models. The reconstruction method was evaluated against four 3D-printed anatomically realistic human stomach models and applied in a proof of concept in-vivo pig study.

RESULTS

Over ten repeated reconstructions, average Hausdorff distance and average surface-normal vector error values were 4.7 ±0.2 mm and 18.7 ±0.7° for the whole stomach, and 3.6 ±0.2 mm and 14.6 ±0.6° for the corpus. Furthermore, mean intra-array localization error was 1.4 ±1.1 mm for the benchtop experiment and 1.7 ±1.6 mm in-vivo.

CONCLUSION AND SIGNIFICANCE

Results demonstrated that the proposed reconstruction method is accurate and feasible. The stomach models generated by this method, when co-registered with electrode and sensor positions, could enable the investigation and validation of novel inverse analysis techniques.

Inverse material parameter estimation of patient-specific finite element models at the carotid bifurcation: The impact of excluding the zero-pressure configuration and residual stress

The carotid bifurcation experiences a complex loading environment due to its anatomical structure. Previous in-vivo material parameter estimation methods often use simplified model geometries, isotropic hyperelastic constitutive equations or neglect key aspects of the vessel, such as the zero-pressure configuration or residual stress, all of which have independently been shown to alter the stress environment of the vessel wall. Characterizing the location of high stress in the vessel wall has often been proposed as a potential indicator of structural weakness. However, excluding the afore-mentioned zero-pressure configuration, residual stress and patient-specific material parameters can lead to an incorrect estimation of the true stress values observed, meaning that stress alone as a risk indicator of rupture is insufficient. In this study, we investigate how the estimated material parameters and overall stress distributions in geometries of carotid bifurcations, extracted from in-vivo MR images, alter with the inclusion of the zero-pressure configuration and residual stress. This approach consists of the following steps: (1) geometry segmentation and hexahedral meshing from in-vivo magnetic resonance images (MRI) at two known phases; (2) computation of the zero-pressure configuration and the associated residual stresses; (3) minimization of an objective function built on the difference between the stress states of an "almost true" stress field at two known phases and a "deformed" stress field by altering the input material parameters to determine patient-specific material properties; and (4) comparison of the stress distributions throughout these carotid bifurcations for all cases with estimated material parameters. This numerical approach provides insights into the need for estimation of both the zero-pressure configuration and residual stress for accurate material property estimation and stress analysis for the carotid bifurcation, establishing the reliability of stress as a rupture risk metric.

A semi-automated method for patient-specific computational flow modelling of left ventricles

Patient-specific computational fluid dynamics (CFD) modelling of the left ventricle (LV) is a promising technique for the visualisation of ventricular flow patterns throughout a cardiac cycle. While significant progress has been made in improving the physiological quality of such simulations, the methodologies involved for several key steps remain significantly operator-dependent to this day. This dependency limits both the efficiency of the process as well as the consistency of CFD results due to the labour-intensive nature of current methods as well as operator introduced uncertainties in the modelling process. In order to mitigate this dependency, we propose a semi-automated method for patient-specific computational flow modelling of the LV. Using magnetic resonance imaging derived coarse geometry data of a patient's LV endocardium shape throughout a cardiac cycle, we then proceed to refine the geometry to eliminate rough edges before reconstructing meshes for all time frames and finally numerically solving for the intra-ventricular flow. Using a sample of patient-specific volunteer data, we demonstrate that our semi-automated, minimal operator involvement approach is capable of yielding CFD results of the LV that are comparable to other clinically validated LV flow models in the literature.

Flow prediction in cerebral aneurysms based on geometry reconstruction from 3D rotational angiography

We present an immersed boundary (IB) method for the simulation of steady blood flow inside a realistic cerebral aneurysm. We reconstruct a segment of the cerebrovascular system that contains an aneurysm, by using medical images obtained with three dimensional rotational angiography (3DRA). The main focus is on evaluating the sensitivity of flow predictions to the various steps of the vascular reconstruction process. Starting from the raw medical data, we analyze the fluid-mechanical consequences of the steps needed to generate the IB masking function for our simulations. We illustrate the IB method by applying it to a realistic aneurysm and investigate the role of (i) numerical resolution of the geometry; (ii) the selection of the specific vascular segment used in the simulations; and (iii) the influence of the smoothness of the periodic vessel extension to complete the computational model. Because of an unavoidable degree of uncertainty in the medical images, the geometry of the vessels and the aneurysm can be reconstructed only approximately. We also incorporate these slight uncertainties in the masking function by introducing inner and outer 'bounding' geometries and analyze the sensitivity of the flow predictions to these variations in the masking function. The numerical solutions computed in the inner and outer bounding geometries provide practical upper and lower bounds for basic flow properties, thus quantifying the reliability of the numerical solution, subject to uncertainties in the geometry of the flow domain.

Acoustic echoes reveal room shape

Imagine that you are blindfolded inside an unknown room. You snap your fingers and listen to the room's response. Can you hear the shape of the room? Some people can do it naturally, but can we design computer algorithms that hear rooms? We show how to compute the shape of a convex polyhedral room from its response to a known sound, recorded by a few microphones. Geometric relationships between the arrival times of echoes enable us to "blindfoldedly" estimate the room geometry. This is achieved by exploiting the properties of Euclidean distance matrices. Furthermore, we show that under mild conditions, first-order echoes provide a unique description of convex polyhedral rooms. Our algorithm starts from the recorded impulse responses and proceeds by learning the correct assignment of echoes to walls. In contrast to earlier methods, the proposed algorithm reconstructs the full 3D geometry of the room from a single sound emission, and with an arbitrary geometry of the microphone array. As long as the microphones can hear the echoes, we can position them as we want. Besides answering a basic question about the inverse problem of room acoustics, our results find applications in areas such as architectural acoustics, indoor localization, virtual reality, and audio forensics.