MRI Noise Reduction Using Visual Characteristics
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Solution Overview
Problem
Magnetic Resonance Imaging (MRI) devices face a challenge in maintaining image quality while reducing noise, which leads to increased imaging time due to the need for higher Number of Excitations (NEX), causing longer processing times and decreased edge and luminance differences in post-processing.
Innovation Solution
A diagnostic imaging device equipped with a noise reduction unit and an image correction unit that separates broad luminance and local variation components, calculates correction levels, and performs correction processing using visual characteristics to enhance image quality and reduce processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the Number of Excitations (NEX) is increased to improve the Signal to Noise Ratio (SNR), then the image quality is improved, but the imaging time is lengthened
Solution Approach 1:
The patent applies preliminary action by performing noise reduction processing on the observed data before image reconstruction. The noise reduction unit processes the raw MRI data to suppress noise components, allowing the system to achieve acceptable SNR with fewer excitations, thereby reducing imaging time while maintaining image quality
Solution Approach 2:
The patent extracts and removes noise components from the observed data through dedicated noise reduction processing. By separating and eliminating noise from the signal before reconstruction, the system achieves improved SNR without requiring increased NEX, thus resolving the time-quality tradeoff
2Loss of time
If noise reduction processing is performed on observed data to shorten imaging time, then the imaging time is reduced, but edge and luminance difference (contrast) are reduced
Solution Approach 1:
The patent applies local quality by implementing different processing strategies for different image components. The noise reduction unit selectively processes noise while preserving edges and luminance differences through adaptive filtering that adjusts processing intensity based on local image characteristics, maintaining contrast and edge definition while reducing noise
Solution Approach 2:
The patent performs preliminary noise reduction on raw data before reconstruction, followed by correction processing that restores edge and luminance information. This two-stage approach (noise reduction then correction) maintains time efficiency while preserving critical image features that would otherwise be degraded
3Measurement precision
If correction processing using Retinex theory with multiple blur filters is performed to achieve adaptive correction, then image quality is improved, but processing load and processing time are increased
Solution Approach 1:
The patent segments the correction processing into distinct functional units: a noise reduction unit for initial noise suppression, a separating unit for decomposing image components, a correction level calculator for determining processing parameters, and a correction processing unit for final enhancement. This modular segmentation improves efficiency by optimizing each stage independently
Solution Approach 2:
The patent extracts and processes different image components (broad luminance component and local variation component) separately through dedicated units. By handling each component through specialized processing rather than applying uniform complex filtering, the system achieves effective correction with reduced computational load and faster processing
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 MRI images in a shorter time, reducing the burden on patients and improving diagnostic efficiency by effectively managing noise and processing time.
Implementation Method 1
A Magnetic Resonance Imaging (MRI), one of the medical diagnostic imaging devices, is a method that uses a Nuclear Magnetic Resonance (NMR) phenomenon to convert information of a test object, such as a living body, into an image.
Data Source
AI summary
In a diagnostic imaging device, such as an MRI device, correction processing improves an image to be a high-quality image and an imaging time is shortened. In the diagnostic imaging device, a noise reduction unit 201 reduces a noise in observation data acquired with an observation unit 100 and converted into an image, and the image correction unit 202 corrects the noise reduced data by correction processing that uses visual characteristics of human. The image correction unit 202 separates the noise reduced data into a broad luminance component and a local variation component, and generates a correction level map using the broad luminance component. Correcting the noise reduced data using this correction level map and the local variation component acquires a high-quality image that is competent in the clinical field.