Iterative ROI Data Estimation for Low-Dose Medical Imaging
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Solution Overview
Problem
Medical imaging apparatuses face challenges in acquiring high-quality images while minimizing exposure to X-rays, as high doses can damage body parts and low radiation doses result in image deterioration due to truncation and incomplete data.
Innovation Solution
A medical imaging apparatus and method that acquires measured data, estimates region of interest (ROI)-inside and ROI-outside data, and iteratively updates these data sets to reconstruct high-quality images, using techniques like iterative reconstruction to minimize the impact of truncation and low radiation doses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high dose X-ray is used for imaging, then image quality is improved, but harmful effects on body parts increase
Solution Approach 1:
The patent segments the measured data into ROI-inside data (within region of interest) and ROI-outside data (outside region of interest). By separating and independently processing these data segments, the system can apply different reconstruction strategies to minimize radiation dose while maintaining image quality in the critical ROI area.
Solution Approach 2:
The patent performs preliminary estimation of ROI-outside data before final image reconstruction. By pre-estimating the outside data and iteratively updating it, the system prepares corrected data in advance, enabling high-quality ROI imaging with reduced radiation dose.
2Object-affected harmful factors
If low radiation dose is used for imaging, then harmful effects on body parts are reduced, but image quality deteriorates due to truncation and incomplete data
Solution Approach 1:
The patent implements an iterative feedback mechanism where ROI-inside data is reconstructed, then used to update ROI-outside data estimates, which in turn are used to improve ROI-inside reconstruction. This closed-loop feedback process progressively refines the image quality even with low radiation dose input data.
Solution Approach 2:
The patent replaces traditional mechanical/data acquisition approaches with computational methods. Instead of acquiring complete high-quality data through higher radiation dose, the system uses iterative computational algorithms to reconstruct high-quality images from truncated low-dose data by substituting mathematical estimation for physical data collection.
3Measurement precision
If iterative reconstruction is performed to improve image quality from low dose data, then image quality is improved, but processing time and computational complexity increase
Solution Approach 1:
The patent segments the iterative reconstruction process into distinct phases: initial ROI-inside reconstruction, ROI-outside data estimation, and iterative updates. This segmentation allows the system to focus computational resources on the most critical reconstruction tasks, reducing overall processing time while maintaining image quality.
Solution Approach 2:
The patent applies partial iterative updates, performing a predetermined number of iterations or updates only when improvement exceeds a threshold. This partial action approach achieves sufficient image quality improvement without performing excessive iterations that would unnecessarily extend processing time.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The solution enables the acquisition of high-quality medical images with reduced X-ray exposure, improving image quality by addressing issues of truncation and low radiation dose effects, thereby enhancing safety and user satisfaction.
Implementation Method 1
measured data acquired by detecting X-ray transmitted by an X-ray source to an object
Data Source
AI summary
A medical imaging apparatus includes a data acquirer configured to acquire measured data acquired by detecting an X-ray transmitted by an X-ray source to an object, and an image processor configured to acquire an initial image based on the measured data, alternately estimate region of interest (ROI)-outside measured data and ROI-inside measured data based on the measured data and the initial image, and acquire a reconstructed image based on the ROI-inside measured data.