Optical Imaging Functions for 3D Flow Measurement
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Solution Overview
Problem
Existing methods for determining imaging functions in optical imaging systems fail to account for complex imaging errors such as defocusing and spherical aberration, leading to inaccuracies in reconstructing three-dimensional velocity fields and voxel-wise intensity distributions, especially in large measuring volumes.
Innovation Solution
A shape model parameterized by shape parameters is used to describe the imaging of particles onto detector surfaces, with shape parameters like ellipse axes and orientation being determined and applied to improve the imaging functions, accounting for imaging errors and providing a more accurate geometrical and optical representation of particle images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a simple geometric imaging function is used to map volume positions to detector positions, then the method is simple and fast, but it fails to account for complex imaging errors such as defocusing and spherical aberration, leading to inaccuracies in reconstruction
Solution Approach 1:
The patent transforms the imaging function from a simple geometric mapping to a parameterized model that includes shape parameters (a, b, c) describing the elliptical image shape and orientation parameters (alpha, beta, gamma). By changing the parameters of the imaging function to include these shape descriptors, the system can accurately model complex imaging errors like defocusing and spherical aberration while maintaining computational efficiency through analytical solutions.
Solution Approach 2:
The patent extends the traditional 2D geometric imaging function to a 6D parameter space by adding three shape parameters (a, b, c) and three orientation parameters (alpha, beta, gamma). This dimensional extension allows the imaging function to capture complex optical distortions and imaging errors that cannot be represented by simple geometric transformations alone.
2Area of stationary object
If the measuring volume is made large to capture more flow data, then the measurement coverage increases, but imaging errors such as defocusing and spherical aberration become more significant, reducing measurement accuracy
Solution Approach 1:
The patent introduces shape parameters (a, b, c) that describe the elliptical shape of particle images and orientation parameters (alpha, beta, gamma) that describe their orientation. These parameters allow the system to model and compensate for imaging errors that increase with measuring volume size, enabling accurate measurements across large volumes by accounting for defocusing and spherical aberration effects.
Solution Approach 2:
The patent uses the determined shape and orientation parameters to correct the imaging function iteratively. By comparing the modeled image characteristics with actual measurements and adjusting the parameters accordingly, the system compensates for imaging errors in large measuring volumes, maintaining accuracy despite the increased size.
3Measurement precision
If multiple detectors are used to observe the measuring volume at different angles, then the reconstruction accuracy improves through triangulation, but the complexity of determining and calibrating imaging functions increases
Solution Approach 1:
The patent transforms the calibration process by introducing shape parameters (a, b, c) and orientation parameters (alpha, beta, gamma) that can be determined analytically from the imaging data. This parameterization allows multiple detectors to be calibrated simultaneously using a unified approach, reducing the overall calibration complexity while maintaining the accuracy benefits of multi-angle observation.
Solution Approach 2:
The patent creates a universal imaging function model that can be applied to multiple detectors with different observation angles. By using a single parameterized framework that describes the elliptical shape and orientation of images across all detectors, the system simplifies the calibration process while preserving the advantages of multi-detector geometry for accurate volume 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
This approach effectively compensates for complex imaging errors, improving the accuracy of volume reconstruction and velocity field measurements by modeling the shape and position of particle images, leading to more precise results even in large measuring volumes.
Implementation Method 1
a plurality of optically detectable particles are distributed, onto the detector surfaces... The emitted light is then transmitted via suitable detection optics simultaneously to several planar detectors on which the measuring volume is imaged
Implementation Method 2
determining support positions, namely of the volume positions of at least a few particles, from image positions of corresponding particle images in the images of the measuring volume generated in step b by applying a triangulation method based upon the assignments determined in step a
Data Source
AI summary
The invention relates to a method for determining a set of optical imaging functions that describe the imaging of a measuring volume onto each of a plurality of detector surfaces on which the measuring volume can be imaged at in each case a different observation angle by means of detection optics. In addition to the assignment of in each case one image position (x, y) to each volume position (X, Y, Z), the method according to the invention envisages that the shape of the image of a punctiform particle in the measuring volume be described by shape parameter values (a, b, 100 , I) and that the corresponding set of shape parameter values be assigned to each volume position (X, Y. Z) for each detector surface.