3D Neural Image Database Construction via Shape-Based Interpolation
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Solution Overview
Problem
Current image processing techniques for magnetic resonance imaging, particularly in constructing 3D brain image databases, face challenges in handling large volumes of neural images and accurately reconstructing neural networks due to insufficient views leading to streak artifacts and the need for robust preprocessing methods to align and trace neurons across multiple slices.
Innovation Solution
An image preprocessing system that includes an image fusion module for estimating missing values, an image stitching module for combining overlapping slices, a standardizing module for generating standard coordinates, an automatic aligning module for aligning images with a standard brain model, and a neuronal tracing module for tracing centerlines of neurons, utilizing Registration-based Shape-Based Interpolation and gray-level shape-based interpolation methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If projection reconstruction methods are used to acquire multi-angle views, then 3D neural image database construction is enabled, but streak artifacts are produced when insufficient views are acquired
Solution Approach 1:
The patent applies preliminary action by performing image fusion to estimate missing values before the actual image reconstruction process. The system pre-processes the acquired views by interpolating missing data using shape-based interpolation methods, thereby preventing streak artifacts from forming in the final reconstruction rather than correcting them afterward.
Solution Approach 2:
The patent introduces an intermediary processing step between data acquisition and final reconstruction. The image fusion module acts as a mediator that combines multiple acquired views and estimates missing information through shape-based interpolation, creating a complete intermediate dataset that eliminates streak artifacts before the reconstruction process begins.
2Quantity of substance
If tens of millions or billions of neural images are collected for whole brain reconstruction, then complete neural network coverage is achieved, but processing complexity and time requirements increase significantly
Solution Approach 1:
The patent merges multiple individual neural images into a unified 3D database through systematic integration. The image fusion module combines overlapping regions from numerous images while estimating missing values, consolidating tens of millions of separate datasets into a coherent whole-brain neural network representation that reduces processing complexity.
Solution Approach 2:
The patent applies segmentation by dividing the whole brain into multiple slices that are processed independently through confocal microscopy. Each slice is then individually pre-processed using shape-based interpolation before being integrated into the complete 3D database, allowing manageable processing of large quantities of neural images.
3Measurement precision
If confocal microscopy is used to acquire multi-slices of high resolution images, then cellular level detail is captured, but image alignment and stitching across slices become challenging
Solution Approach 1:
The patent introduces an intermediary image fusion module that serves as a bridge between raw confocal microscopy slices and the final aligned 3D reconstruction. This module performs shape-based interpolation to estimate missing values and automatically aligns slices by matching geometric features, eliminating the manual alignment difficulty while preserving high resolution.
Solution Approach 2:
The patent changes the parameter representation of image data by transforming raw slice coordinates into a standardized coordinate system. The shape-based interpolation method adjusts spatial parameters and intensity values to account for slight misalignments between slices, automatically correcting positioning errors while maintaining cellular-level resolution.
Data Source
AI summary
The present invention discloses an image preprocessing system, which includes a processing unit; an image preprocessing unit coupled to the processing unit to preprocess image slice data, wherein the image preprocessing unit includes an image fusion module to estimate missing values between different image slice data and an image stitching module to stitch different image slice data into stitched image data; and a database coupled to the processing unit to store the preprocessed image slice data.