Implicit Neural Representation Learning with Prior Embedding for Sparse Image Reconstruction
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Solution Overview
Problem
Current deep learning-based image reconstruction methods face challenges such as data-intensive training requirements, limited generalizability across different imaging modalities and anatomical sites, and instability in capturing subtle structural changes like tumor progression.
Innovation Solution
The proposed implicit Neural Representation learning methodology with Prior embedding (NeRP) reconstructs images from sparsely sampled measurements by leveraging internal image prior information and physics of measurements, eliminating the need for large-scale training data and enabling generalization across different imaging modalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional deep learning-based image reconstruction methods are used, then image reconstruction performance is improved, but data-intensive training requirements worsen the applicability
Solution Approach 1:
The patent applies preliminary action by pre-training a neural network on a large dataset of fully sampled images and their ground truth reconstructions before applying it to sparse measurement data. This pre-training phase captures general image priors and reconstruction patterns, enabling the model to perform reliably on sparse data without requiring large amounts of task-specific training data, thus resolving the contradiction between reconstruction performance and applicability.
Solution Approach 2:
The patent uses copying by creating a transferable neural network model that can be copied and applied across different imaging modalities and anatomical sites. The pre-trained network serves as a universal template that can be fine-tuned with minimal data for new applications, eliminating the need to train separate models for each specific task and thereby improving both performance and adaptability.
2Measurement precision
If deep neural networks are trained on large-scale datasets, then reconstruction accuracy is improved, but the difficulty of data collection increases
Solution Approach 1:
The patent performs preliminary action by conducting extensive pre-training on readily available fully sampled image data before the actual application. This preliminary training phase accumulates the necessary learning from abundant data sources, allowing the model to achieve high reconstruction accuracy on sparse measurements without requiring difficult-to-obtain task-specific training data for each application scenario.
Solution Approach 2:
The patent achieves universality by developing a single pre-trained neural network model that can serve multiple imaging modalities and anatomical sites. This universal model leverages the diversity of data from various sources during pre-training, enabling it to generalize and maintain high reconstruction accuracy across different applications without requiring separate data collection efforts for each modality.
3Reliability
If deep learning models are specialized for specific imaging modalities, then reconstruction reliability is improved, but generalization capability deteriorates
Solution Approach 1:
The patent implements universality by training a single neural network on diverse imaging data from multiple modalities and anatomical sites during the pre-training phase. This diverse training enables the model to learn universal image priors and reconstruction patterns that are transferable across different applications. The model maintains high reliability for each specific modality while also possessing strong generalization capability to handle new modalities and sites that were not seen during training.
Solution Approach 2:
The patent applies preliminary action by pre-training the network on a comprehensive diversity of imaging data before applying it to specific tasks. This preliminary exposure to multiple imaging modalities and anatomical structures during pre-training builds a robust foundation that enables the model to maintain specialized performance for each modality while also being capable of generalizing to new applications without requiring retraining.
4Productivity
If conventional deep learning methods are used, then image reconstruction is achieved, but robustness in capturing subtle structural changes deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network on fully sampled images with ground truth labels, allowing the model to learn precise relationships between image structures and their representations. This pre-training on high-quality data enables the network to capture subtle structural variations and patterns during the subsequent sparse measurement reconstruction phase, improving its robustness in detecting changes such as tumor progression while maintaining efficient reconstruction capability.
Data Source
AI summary
A method for diagnostic imaging reconstruction uses a prior image xpr from a scan of a subject to initialize parameters of a neural network which maps coordinates in image space to corresponding intensity values in the prior image. The parameters are initialized by minimizing an objective function representing a difference between intensity values of the prior image and predicted intensity values output from the neural network. The neural network is then trained using subsampled (sparse) measurements of the subject to learn a neural representation of a reconstructed image. The training includes minimizing an objective function representing a difference between the subsampled measurements and a forward model applied to predicted image intensity values output from the neural network. Image intensity values output from the trained neural network from coordinates in image space input to the trained neural network are computed to produce predicted image intensity values.