3D Image Generation Model Using Down-Sampling and Up-Sampling Blocks
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Solution Overview
Problem
Existing methods for generating 3D images from 2D images, such as in medical imaging, face limitations in accuracy and efficiency, often requiring multiple 2D images and significant computational resources.
Innovation Solution
A method utilizing a 3D image generation model with a specific configuration, including down-sampling, connection, and up-sampling blocks, to process a single target 2D image and generate a 3D image, thereby reducing the need for multiple 2D images and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple 2D images are used to generate 3D images, then the accuracy of the 3D image is improved, but the scanning time and processing complexity increase
Solution Approach 1:
The patent transforms the problem from acquiring multiple 2D images to generating a 3D image directly from a single 2D image by introducing a deep learning-based 3D image generation model. This dimensional transformation allows the system to bypass the time-consuming process of acquiring multiple 2D images while still producing accurate 3D reconstructions through learned patterns and priors embedded in the neural network.
Solution Approach 2:
The patent uses a pre-trained 3D image generation model that has been trained on large datasets of paired 2D and 3D images. The model learns to copy and reconstruct 3D image characteristics from single 2D inputs, effectively creating a synthetic copy of what would normally require multiple actual 2D images to capture, thereby reducing scanning time while maintaining accuracy.
2Measurement precision
If multiple 2D images are used to generate 3D images, then the accuracy of the 3D image is improved, but the computational complexity and costs increase
Solution Approach 1:
The patent employs a pre-trained 3D image generation model that has already learned the complex mappings from 2D to 3D images during an offline training phase. This preliminary action transfers the heavy computational burden to the training stage, allowing the actual 3D reconstruction from single 2D images to be performed efficiently during operation, thereby reducing real-time computational complexity while maintaining high accuracy.
3Productivity
If a single 2D image is used to generate 3D images, then the scanning time and computational complexity are reduced, but the accuracy may deteriorate
Solution Approach 1:
The patent changes the parameters of the image generation process by using a deep learning model with multiple layers (including down-sampling, connection, and up-sampling blocks) that can extract and reconstruct fine details from single 2D images. The model transforms the input 2D image through various computational layers, changing the representation parameters to generate high-quality 3D images that maintain accuracy despite the reduced input information.
Data Source
AI summary
The present disclosure relates to systems and methods for generating a three-dimensional (3D) image. The method may include obtaining a target two-dimensional (2D) image of a subject; obtaining a 3D image generation model that includes a plurality of sequentially connected layers, at least one layer of which may include a down-sampling block, a connection block, and an up-sampling block; and determining a target 3D image of the subject by processing the target 2D image using the 3D image generation model. The down-sampling block may be configured to generate a plurality of 2D feature maps by coding an input of the down-sampling block. The connection block may be configured to generate at least one 3D feature map based on the plurality of 2D feature maps. The up-sampling block may be configured to generate at least one target 3D feature map by processing the at least one 3D feature map.