Medical Image Processing Apparatus for Hyperacute Cerebral Infarction Detection
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Solution Overview
Problem
Current medical image diagnosis using X-ray CT and MRI apparatuses face challenges in accurately detecting hyperacute cerebral infarction due to high false negative rates with X-ray CT and longer diagnosis times with MRI, necessitating improved sensitivity and efficiency.
Innovation Solution
A medical image processing apparatus that acquires and processes medical image data using a learned model trained on both X-ray CT and MRI data, specifically identifying regions of interest with higher imaging sensitivity, enabling accurate detection of hyperacute cerebral infarction while maintaining rapid image reconstruction times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If X-ray CT apparatus is used for medical image diagnosis, then image reconstruction time is short, but interpretation ability is low and false negatives are many
Solution Approach 1:
The patent introduces a learned model as an intermediary between the X-ray CT apparatus and the radiologist. The model processes the raw data or images to generate processed images that highlight early ischemia signs, thereby compensating for the limited interpretation ability while preserving the fast reconstruction time of X-ray CT.
Solution Approach 2:
The patent replaces the mechanical/visual interpretation process performed by radiologists with an automated learned model. This substitution enables the system to detect early CT signs with high accuracy automatically, eliminating the dependency on human interpretation skills while maintaining rapid processing.
2Measurement precision
If MRI apparatus is used for medical image diagnosis, then ability to detect hyperacute cerebral infarction is high, but diagnosis time is long
Solution Approach 1:
The patent creates a processed image from X-ray CT data that copies or replicates the diagnostic information found in MRI DWI images. The learned model generates images that simulate the high-sensitivity detection capability of MRI, allowing X-ray CT to achieve similar detection accuracy without the long acquisition and processing time of MRI.
3Loss of time
If early CT sign is used for detection, then early stage detection is enabled, but high interpretation ability is required
Solution Approach 1:
The patent implements self-service by enabling the system to automatically detect and highlight early ischemia signs without requiring high-level human interpretation skills. The learned model autonomously processes the imaging data, identifies subtle early CT signs, and presents enhanced images that make diagnosis straightforward, thereby reducing both time loss and interpretation complexity.
Data Source
AI summary
A medical image processing apparatus according to an embodiment includes processing circuitry. The processing circuitry is configured to acquire first medical image data. The processing circuitry is configured to identify a region of interest in the first medical image data based on a learned model and the first medical image data. The learned model is trained based on second medical image data corresponding to the first medical image data and third medical image data different from the first medical image data in type and related to the same subject as a subject of the second medical image data. At least part of the third medical image data is higher in imaging sensitivity for a region of the subject corresponding to the region of interest than the second medical image data.