Diffuse Optical Tomography With Ultrasound-Guided Sparse Regularization
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Solution Overview
Problem
Existing diffuse optical tomography (DOT) systems face challenges in accurately localizing lesions due to intense light scattering in breast tissue, particularly when lesions are large and highly absorbing, leading to underestimation of absorption coefficients, and current sparse regularization methods are inadequate for improving reconstruction accuracy and robustness.
Innovation Solution
A DOT system employing a target-shape-regularized Fast Iterative Shrinkage-Thresholding algorithm (FISTA) combined with Finite Difference Method (FDM) or Finite Element Method (FEM) for iterative reconstruction, utilizing depth-dependent sparse regularization based on ultrasound-guided lesion depth and shape information to enhance image accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If linear Born approximation is used to compute the weight matrix, then the reconstruction process is computationally efficient, but the absorption coefficients are underestimated when the lesion is large and highly absorbing
Solution Approach 1:
The patent transitions from a static linear Born approximation to a dynamic iterative reconstruction process. The algorithm dynamically updates the weight matrix and absorption coefficient estimates through multiple iterations, allowing the system to adapt to large and highly absorbing lesions while maintaining computational feasibility through efficient matrix operations.
Solution Approach 2:
The patent changes the mathematical parameters and models used in reconstruction. It employs a non-linear Born approximation instead of the linear version, and introduces L1-norm regularization with depth-dependent weighting parameters. These parameter changes enable accurate reconstruction of large, highly absorbing lesions by correcting the underestimation issue while maintaining computational efficiency through optimized algorithms.
2Reliability
If sparse regularization is applied to improve DOT reconstruction accuracy, then the robustness is enhanced, but the current methods are inadequate for large and highly absorbing lesions
Solution Approach 1:
The patent applies local quality by introducing depth-dependent weighting in the L1-norm regularization term. Different depths receive different weighting factors, allowing the regularization to be adapted to local tissue properties and lesion characteristics. This local adaptation enables accurate reconstruction of large and highly absorbing lesions while maintaining robustness across varying tissue conditions.
Solution Approach 2:
The patent combines multiple methodological components into a composite reconstruction framework: non-linear Born approximation, L1-norm sparse regularization, depth-dependent weighting, and iterative optimization. This composite approach integrates the strengths of each component to achieve both robustness and high accuracy in reconstructing challenging lesions that neither method could handle alone.
3Manufacturing precision
If iterative reconstruction methods are used to improve image accuracy, then the resolution is enhanced, but the computational complexity increases
Solution Approach 1:
The patent segments the reconstruction process into distinct iterative steps: forward modeling, gradient computation, L1-norm thresholding, and weight matrix updates. Each segment performs a specific function, making the overall complex algorithm more manageable and efficient. This segmentation allows for optimized implementation of each step while maintaining the accuracy benefits of iterative reconstruction.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The proposed method achieves more accurate and robust reconstruction of absorption maps, improving lesion localization and providing better resolution for breast cancer diagnosis by iteratively updating photon-density waves and refining weight matrices.
Implementation Method 1
due to the intense light scattering in breast tissue
Implementation Method 2
the reflected or transmitted light is measured at the tissue surface
Implementation Method 3
a detection subsystem configured to convert the optical waves detected by the probe to digital signals
Data Source
AI summary
A diffuse optical tomography (DOT) system for generating a functional image of a lesion region of a subject includes a source subsystem configured to generate optical waves, a probe coupled to the source subsystem and configured to emit the optical waves generated by the source subsystem toward the lesion region and to detect optical waves reflected by the lesion region, a detection subsystem configured to convert the optical waves detected by the probe to digital signals, and a computing device including a processor and a memory. The memory includes instructions that program the processor to receive the digital signals sent from the detection subsystem, calculate an initial estimate of the functional image by solving an inverse optimization problem with a shape-regularized Fast Iterative Shrinkage-Thresholding algorithm (FISTA), reconstruct the functional image iteratively using a Finite Difference Method (FDM) or a Finite Element Method (FEM), and display the functional image.