Brain Imaging System Deep Learning Feature Selection
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Solution Overview
Problem
Current brain imaging technologies, such as CT and MRI, face challenges in precision and efficiency due to the need for manual interpretation of two-dimensional images, which can lead to inaccuracies and inefficiencies in differentiating normal and tumor tissues.
Innovation Solution
A brain imaging system and method that combines CT and MRI devices with a processor to capture and process brain images using deep learning models for image pre-processing, enhancement, and feature selection, enabling the estimation of cerebral perfusion and identification of brain lesions, thereby improving image analysis and interpretation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual interpretation of two-dimensional images is used, then medical staff can analyze brain images, but precision and efficiency are adversely affected
Solution Approach 1:
The patent replaces the manual mechanical interpretation process with an automated deep learning-based image processing system. The processor automatically performs image pre-processing, enhancement, and feature selection using trained deep learning models, eliminating the need for manual analysis while improving both precision through consistent algorithmic application and efficiency through automated high-speed processing of three-dimensional image sets
Solution Approach 2:
The system enables self-service by allowing the brain imaging system to automatically analyze and interpret images without requiring medical staff intervention. The deep learning models autonomously perform feature extraction, lesion identification, and perfusion estimation, making the system self-sufficient in the interpretation task while medical staff can focus on higher-level decision-making
2Reliability
If two-dimensional images are used for brain imaging, then the imaging process is simple, but the ability to differentiate normal and tumor tissues is limited
Solution Approach 1:
The patent transitions from two-dimensional image analysis to three-dimensional volumetric image processing. By acquiring and processing three-dimensional image sets with multiple contrast agents, the system provides comprehensive spatial information that significantly improves the reliability of differentiating normal and tumor tissues, while the complexity is managed through automated deep learning algorithms
3Measurement precision
If deep learning models are used for image processing, then feature selection accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-training deep learning models with large datasets before actual image analysis. The models are trained offline to learn optimal feature extraction patterns, so during actual brain image processing, they can quickly and accurately select relevant features without requiring complex real-time computations, thus improving accuracy while managing computational complexity
Data Source
AI summary
A brain imaging system and a brain imaging method are provided. The brain imaging system includes a first imaging device, a second imaging device and a processor. The first imaging device captures a first brain image set by scanning a patient, and the second imaging device captures a second brain image set. The processor is configured to: pre-process and enhance first and second brain image sets; select first features that are optimal for estimating cerebral perfusion and select second features that are optimal for brain lesion identification; obtain, by performing calculations on first features, a plurality of brain perfusion indices; and identify, by inputting the second features to a third deep learning model having been trained, position information and volume information of one or more target brain lesions in the brain of the patient.