Dopamine Transporter Check System for Objective Diagnosis
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Solution Overview
Problem
Diagnosing Parkinson's disease using dopamine transporter scans is challenging due to subjective interpretation of imaging results, which can be affected by ambient lighting, fatigue, and varying expertise, leading to inconsistencies in diagnosis.
Innovation Solution
A dopamine transporter check system and method that includes a processor and memory circuit for obtaining and processing three-dimensional brain scan images, aligning them to a standard space, performing intensity normalization, and establishing a dopamine neuron loss degree measurement model through transfer learning to classify and grade neuron loss, thereby providing an objective diagnostic aid.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If visual reading and semi-quantitative analysis are used for imaging diagnosis, then the diagnostic process is simple and quick, but the interpretation results show significant differences due to ambient lighting, screen brightness, observer fatigue, and varying expertise
Solution Approach 1:
The patent replaces the manual visual reading mechanism with an automated image processing system that performs spatial normalization, intensity normalization, and deep learning-based classification. This substitution eliminates human factors (fatigue, lighting conditions, screen brightness) that cause interpretation variability, thereby improving measurement precision while maintaining diagnostic throughput
Solution Approach 2:
The patent transforms the diagnostic approach by changing key parameters: converting 3D scan images to standardized 2D images with normalized intensity values, extracting specific features from defined regions of interest, and using these standardized parameters as input for deep learning models. This parameter standardization ensures consistent interpretation across different observers and conditions
2Measurement precision
If deep learning models are trained from scratch for each application, then the model can be highly specialized, but the training time and computational resources are excessive
Solution Approach 1:
The patent applies transfer learning by pre-training deep learning models on large datasets of brain images and then fine-tuning them for dopamine transporter evaluation. This preliminary training on general brain imaging data provides a strong foundation that can be quickly adapted to specific diagnostic tasks, reducing training time while maintaining high classification accuracy
Solution Approach 2:
The patent leverages knowledge and features learned from existing deep learning models trained on large-scale brain imaging data. By copying and adapting pre-trained model weights and architectures, the system achieves high accuracy without needing to train from scratch, significantly reducing computational resources and training time
Data Source
AI summary
The present disclosure provides an operating method of a dopamine transporter check system, and the operation method includes steps as follows. A scan image of a subject's brain is obtained from a scan machine, and the scan image is a three-dimensional image. The scan image is aligned to a standard brain space to obtain a standardized scan image. Intensity normalization is performed on the standardized scan image. The standardized scan image after the intensity normalization is converted into a two-dimensional image. A plurality of image data are got from at least one region of interest in the two-dimensional image, and the at least one region of interest includes a left caudate, a left putamen, a right caudate and a right putamen. A dopamine neuron loss degree measurement and evaluation model based on the image data is established through a transfer learning.