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CN-122003720-A - Biological imaging system

CN122003720ACN 122003720 ACN122003720 ACN 122003720ACN-122003720-A

Abstract

There is provided a biometric imaging system for determining a spatial distribution of one or more physical properties within a biometric structure, the system comprising a data input configured to receive a plurality of measurements related to one or more physical fields, and a processing device configured to determine the spatial distribution of the one or more physical properties within the biometric structure using the plurality of measurements. The processing device forms a discretized full-order model of the biological structure, uses the model to describe a baseline spatial distribution of the biological structure, and determines an optimized value of a coefficient describing the baseline spatial distribution based on the plurality of measurements. The processing device also forms a hybrid model that includes a baseline spatial distribution coefficient and a set of bias full-order model coefficients that describe a bias relative to the baseline spatial distribution, determines an optimized value for the baseline spatial distribution coefficient and the bias full-order model coefficients of the hybrid model, and uses the optimized value for the coefficients to determine a spatial distribution of one or more physical properties within the biological structure.

Inventors

  • ZHONG YU
  • HUANG WENWEN

Assignees

  • 新菲涅(新加坡)私人有限公司

Dates

Publication Date
20260508
Application Date
20241011
Priority Date
20231011

Claims (20)

  1. 1. A biological imaging system for determining a spatial distribution of one or more physical properties within a biological structure, the system comprising: a data input configured to receive a plurality of measurements related to one or more physical fields, and A processing device configured to determine a spatial distribution of one or more physical properties within the biological structure using the plurality of measurements; wherein the processing device is configured to: Forming a discretized full-order model of the biological structure, the full-order model comprising a first number of full-order model coefficients; determining a reduced order model of the biological structure, the reduced order model comprising a second, lower number of reduced order model coefficients; determining an optimized value of the reduced-order model coefficient based on the plurality of measurements such that the reduced-order model coefficient describes a baseline spatial distribution of the one or more physical properties within the biological structure; Forming a hybrid model of the biological structure, the hybrid model comprising the reduced-order model coefficients and a set of full-order model coefficients describing deviations of the biological structure from the baseline spatial distribution; determining optimized values of reduced-order model coefficients and full-order model coefficients of the hybrid model based on the plurality of measurements, and The optimized values of the full-order model coefficients and reduced-order model coefficients of the hybrid model are used to determine a spatial distribution of one or more physical properties within the biological structure.
  2. 2. The bioimaging system of claim 1, wherein the reduced-order model coefficients describe a spatial distribution of the one or more physical properties within one or more expected uncertainty ranges associated with a healthy biological structure, and the full-order model coefficients of the hybrid model describe one or more anomalies in the biological structure that deviate from the expected uncertainty ranges.
  3. 3. The biological imaging system of claim 2, wherein the reduced-order model coefficients describe an enhanced context of the biological structure, the enhanced context comprising one or more regions of the spatial distribution of the one or more physical attributes that deviate from the expected uncertainty range.
  4. 4. The biological imaging system of any of the preceding claims, wherein forming the hybrid model includes assigning an initial value of zero to some or all of the full-order model coefficients.
  5. 5. The biological imaging system of any of the preceding claims, wherein initial values of some or all of the full-order model coefficients of the hybrid model are determined using previously determined optimized values of the reduced-order model coefficients.
  6. 6. The biological imaging system of any of the preceding claims, wherein the determined spatial distribution is one or more of the group consisting of density, refractive index, permittivity, resistivity, permittivity, and permeability.
  7. 7. The biological imaging system of any of the preceding claims, wherein the plurality of measurements comprises measurements relating to one or more of the group consisting of electric field strength and/or phase and/or direction, magnetic field strength and/or phase and/or direction, electromagnetic wave strength and/or propagation direction and/or frequency and/or phase and/or polarization, sound field strength and/or frequency and/or phase.
  8. 8. The biometric imaging system as in any one of the preceding claims, comprising one or more sensing devices configured to acquire one or more of said plurality of measurements and provide said measurements to said data input.
  9. 9. The biological imaging system of any of the preceding claims, wherein information about one or more sources of the field is used in determining the spatial distribution.
  10. 10. The biological imaging system of claim 9, wherein the reduced order model and/or the hybrid model accounts for one or more uncertainties associated with one or more sources of the physical field.
  11. 11. The biological imaging system of any of the preceding claims, comprising one or more sources of the one or more physical fields measured and configured to control the sources to provide specific stimuli to the biological structure.
  12. 12. The biological imaging system of claim 11, wherein one or more sources of the one or more physical fields are provided by a same physical device as one or more sensing devices configured to acquire one or more of the plurality of measurements and provide the measurements to the data input.
  13. 13. The biological imaging system of any of the preceding claims, wherein measurements related to the one or more physical fields are performed at a plurality of different locations outside the biological structure.
  14. 14. The biological imaging system of any of the preceding claims, wherein determining optimized values of the reduced order model coefficients and/or the full order model coefficients comprises forming an objective function that depends on the coefficients, and finding values of coefficients that approximate or reach a target for the values of the objective function.
  15. 15. The biometric imaging system of claim 14, wherein the objective function includes a measurement mismatch term that quantifies the accuracy of the recurrence of the plurality of measurements by the reduced order model and/or the hybrid model.
  16. 16. The biometric imaging system of claim 15, wherein the measurement mismatch term accounts for one or more transfer functions associated with the plurality of measurements.
  17. 17. The biological imaging system of any of claims 14 to 16, wherein the reduced order model and/or the hybrid model acts as a hard constraint for an optimization problem.
  18. 18. The biological imaging system of any of claims 14 to 17, wherein the objective function comprises a mismatch term for the reduced order model and/or a mismatch term for the hybrid model comprising a modeled physical field as a parameter.
  19. 19. The biological imaging system of any of claims 14 to 18, wherein the objective function includes one or more regularization terms configured to mitigate instability in the objective function and/or avoid non-comprehension.
  20. 20. The biological imaging system of any of claims 14 to 19, wherein the objective function comprises a sum of a plurality of mismatched terms, and different terms in the objective function are given different weights.

Description

Biological imaging system Technical Field The present invention relates to methods and systems for determining physical properties within a biological structure, such as a part of the human or animal body (e.g., head, foot, leg, etc.), or an organ, cell, tissue, or subcellular structure, etc. Background Reconstructing the spatial distribution of physical properties within a biological structure based on observations of the surrounding field of the structure is a subject of extensive research, for example for non-invasive disease diagnosis. For example, computed Tomography (CT) scanning is used to generate detailed images of a human or animal body part by detecting X-rays that pass through the human or animal body at a series of different angles. Determining the physical properties of a system based on measurements is often referred to as solving an "inverse problem" (i.e., as opposed to predicting a "positive problem" of measurements from known systems). Generally, for the firstThe behavior of a physical system can be modeled as a secondary measurement: (1) Wherein, the For fields in the system (which may be scalar or vector, depending on the type of physical field and/or the type of measurement being made),For linear operators independent of medium properties in the system,For another linear operator dependent on the properties of the mediumIs used for the firstSource of secondary measurement. Equation (1) may be a differential equation or an integral equation. For example, FIG. 1 schematically illustrates a physical system 100 having a domain of interest (domain). Region of interestIncluding a plurality of unknown scatterers (not shown) that scatter the incident field. FIG. 2 illustrates a method for utilizing a region of interestExternal measurements of electric fields to determine the spatial distribution of one or more physical properties (e.g., spatial distribution of refractive index) within the region of interest. In a first step 202, a method is used from a region of interestExternal sources having angular frequencyA kind of electronic deviceIrradiating the region with secondary incidence and irradiating the region of interestExternal multiple pairs of electric fields generated by these illuminationsSeveral measurements were made. In this example, the firstSub-illuminated electric fieldModeling can be performed using the Helmholtz wave equation: (2) Here the number of the elements to be processed is, And (2) and,Wavenumbers (assuming that the domain is substantially non-magnetic). In a second step 204, the model is discretized to make it suitable for numerical calculations. One of the discretization methods is a finite element method. For example, a region of interestCan be divided into a large number of small cells in which the field and physical properties are assumed to be constant. This discretization produces a model, commonly referred to as a full-order model (FOM), in which each cell is associated with one or more coefficients of the model. Direct estimation of the coefficients of this full-order model that produces a response that matches the measurement results is often computationally demanding due to the large number of unknowns involved. For example, modeling a three-dimensional domain with reasonably high accuracy may involve a discretized model having hundreds of thousands of cells. Thus, in step 206, a Reduced Order Model (ROM) of the system is determined by projecting the full order model onto a reduced order solution subspace by parameterizing the relevant media properties. The basis of the reduced solution space is carefully selected to capture the characteristic behavior of the system with significantly fewer dimensions than the fully discretized model. The coefficients of the reduced order model are a function of the media parameters. There are a number of known methods for generating Reduced Order Models (ROMs) of physical systems, such as eigen-orthogonal decomposition (POD). Model order reduction is discussed in literature "Basic ideas and tools for projection-based model reduction of parametric partial differential equations",Rozza、G.、Hess、M.、Stabile、G.、Tezzele、M. &Ballarin, F. (2021), in P.Benner, S. Grivet-Talocia, A.Quartroni, G. Rozza, W. SCHILDERS & L. Silveira (incorporated), volume 2, snapshot method and algorithm Volume 2: snapshot-Based Methods and Algorithms (pages 1 to 47), berlin, boston: de Gruyter https:// doi.org/10.1515/9783110671490-001. In step 208, the media parameters (the coefficients of the reduced order model are a function thereof) that best match the reduced order model to the measurement result are found by an optimization method, for example, using a newton-type optimization algorithm or a genetic optimization algorithm. The optimized parameters are then used to generate fields in the region of interest (e.g) The estimate in the solution subspace may then be projected back into the full order space and used to find the spatial distribution of one