Slice-and-View 3D Reconstruction With Depth Blur Reduction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Slice and view charged particle imaging experiences z-blur due to electron interaction depth exceeding slice thickness, leading to reduced x-y resolution and signal-to-noise ratio when attempting to reduce blur, making it difficult to achieve high-resolution 3D reconstructions of samples.
Innovation Solution
Employ a blur reducing algorithm, trained on specific microscope and sample conditions, to enhance data acquired through slice and view imaging, reducing blur caused by electron interactions outside the sample layer, allowing for higher energy beams without decreasing resolution or signal-to-noise ratio.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If the thickness of the slice of sample removed is reduced below the electron interaction depth, then the z-blur in 3D reconstruction increases, but the x-y resolution and signal-to-noise ratio deteriorate
Solution Approach 1:
The patent introduces an intermediary computational process (blur reduction algorithm) that processes the raw imaging data to separate and remove blur caused by electron interactions outside the target layer. This intermediary processing step allows the system to maintain high x-y resolution while achieving thin slice thickness, as the algorithm acts as a mediator between the physical imaging process and the final 3D reconstruction.
Solution Approach 2:
The patent replaces the mechanical approach of physically adjusting beam parameters (voltage, focus) to control electron interaction depth with a computational approach. Instead of mechanically modifying the imaging process to reduce blur, the system uses data processing and algorithms to computationally remove blur artifacts, substituting mechanical control with information processing.
2Length of stationary object
If the voltage of the charged particle beam is reduced to decrease electron interaction area, then the depth of interaction is reduced, but the signal-to-noise ratio deteriorates
Solution Approach 1:
The blur reduction algorithm serves as an intermediary that processes the low-voltage imaging data to recover and enhance the signal-to-noise ratio. By computationally removing blur and enhancing relevant features, the algorithm compensates for the reduced signal strength inherent in low-voltage imaging, allowing thin slice imaging without sacrificing measurement precision.
Solution Approach 2:
The patent changes the parameter of beam voltage to a lower value to reduce electron interaction depth, then uses parameter changes in the computational domain (algorithm parameters, processing parameters) to compensate for the resulting signal-to-noise ratio degradation. This dual-parameter approach allows simultaneous achievement of shallow interaction depth and high signal quality.
3Area of stationary object
If the voltage of the charged particle beam is reduced, then the electron interaction area is reduced, but the beam shaping capability deteriorates
Solution Approach 1:
The patent substitutes mechanical beam shaping (physical control of beam parameters) with computational beam shaping (algorithmic processing of image data). Instead of relying on physical beam focus and shape control that deteriorates at low voltages, the system uses computational methods to achieve and maintain precise beam shape and high manufacturing precision in the final reconstruction.
4Manufacturing precision
If thin slices of sample are removed to improve z-axis resolution, then the z-blur increases, but the x-y resolution and signal-to-noise ratio decrease
Solution Approach 1:
The blur reduction algorithm acts as an intermediary processing step that receives raw data from thin slice imaging and outputs enhanced data with improved reliability. This intermediary computational layer restores image quality by removing artifacts and enhancing signals, allowing the system to achieve thin slice thickness while maintaining high image quality reliability in the final 3D 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
Enables high-resolution 3D reconstructions of samples with reduced z-dimensional blurring, maintaining x-y resolution and signal-to-noise ratio by effectively separating image information from electron interactions within and outside the sample layer.
Implementation Method 1
the electron interaction depth of the charged particle beam is larger than each of the thickness of the first layer and the thickness of the second layer
Implementation Method 2
the acquired images and/or the data they are generated from can be used to reconstruct a 3D representation of the sample
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Methods and systems for generating high resolution reconstructions of 3D samples imaged using slice and view processes where the electron interaction depth of the imaging beam is greater than slice thicknesses. Data obtained via such slice and view processes is enhanced with a depth blur reducing algorithm, that is configured to reduce depth blur caused by portions of the first data and second data that are resultant from electron interactions outside the first layer and second layer, respectively, to create enhanced first data and second enhanced data. A high-resolution 3D reconstruction of the sample is then generated using the enhanced first data and the enhanced second data. In some embodiments, the depth blur reducing algorithm may be selected from a set of such algorithms that have been individually configured for certain microscope conditions, sample conditions, or a combination thereof.