Predicted Brain Image Generation Using Latent Vector Encoding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The increasing prevalence of dementia among aging populations poses a significant challenge for early detection and intervention, as existing methods lack effective tools for predicting brain degeneration and determining dementia risk.
Innovation Solution
A method and device for generating predicted brain images using current MRI images, combined with future age, gender, previous brain images, and omics features, to estimate dementia risk and determine clinical dementia rating-sum of boxes (CDR-SB) score and dementia subtype, employing an encoder-decoder process with latent vector manipulation and conditional features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If early detection methods for dementia are developed, then disease intervention capability is improved, but measurement precision and reliability of prediction are worsened due to lack of effective tools
Solution Approach 1:
The patent transforms the prediction task into a parameter estimation problem by inferring latent variables (brain structure parameters, dementia risk parameters) from observable data (MRI images, omics features). This allows early detection while systematically improving prediction accuracy through probabilistic modeling and multi-source data fusion.
Solution Approach 2:
The patent introduces latent vectors as intermediary representations that bridge raw MRI images and predicted brain images. These latent vectors encode essential brain structure information while enabling the model to handle uncertainty and generate probabilistic predictions, thus improving both early detection capability and prediction reliability.
2Adaptability or versatility
If multiple data sources (MRI images, omics features, medical history) are integrated, then prediction comprehensiveness is improved, but device complexity increases
Solution Approach 1:
The patent designs a unified generative adversarial network framework that handles multiple data types (MRI images, omics features, medical history) through a common architecture. The encoder-decoder structure with latent vector manipulation serves as a universal processing mechanism that adapts to different input modalities, improving prediction comprehensiveness while managing system complexity through architectural reuse.
Solution Approach 2:
The patent uses generative adversarial networks to create synthetic predicted brain images that copy the essential characteristics of real brain images. This allows the system to leverage multiple data sources for comprehensive prediction while maintaining a manageable complexity level by generating representative samples rather than processing all raw data directly.
3Measurement precision
If latent vector manipulation with normal distribution multiplication is employed, then prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent applies selective complexity by using latent vector manipulation with normal distribution multiplication only at critical stages of the generative process where it most improves prediction accuracy. The encoder extracts essential features, the latent vector operation adds controlled stochasticity for accuracy improvement, and the decoder reconstructs images, applying computational complexity only where needed rather than uniformly throughout the system.
Data Source
AI summary
Disclosed are methods and devices of generating predicted brain images. The present disclosure provides a method of generating a predicted brain image. The method comprises: receiving a first brain image; encoding the first brain image to generate a latent vector; and decoding the latent vector and one or more conditional features to generate the predicted brain image. The first brain image is generated by a magnetic resonance imaging (MRI) method. The one or more conditional features include at least one of: an age in future, a gender, previous brain images, omics features, and medical history. The latent vector is multiplied by a first normal distribution.