MRI Noise Reduction via Spatial SNR Distribution Calculation
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Solution Overview
Problem
Current noise reduction techniques for MRI images, especially those using parallel imaging methods, fail to effectively reduce noise due to varying signal-to-noise ratios (SNR) caused by different imaging conditions and the geometry factor, leading to degraded SNR and inefficient noise reduction processes that require multiple imaging sessions.
Innovation Solution
A method that calculates the spatial distribution of signal-to-noise ratio (SNR) based on noise and signal levels in MRI images, using a noise reduction unit to iteratively reduce noise based on this SNR distribution, incorporating the geometry factor and signal sensitivity of receiving coils to improve noise reduction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If parallel imaging method is applied to shorten imaging time, then imaging time is reduced, but SNR degrades in accordance with the g factor
Solution Approach 1:
The patent performs preliminary noise reduction processing on coil images before parallel imaging reconstruction. By calculating the SNR distribution from the coil images and applying noise reduction based on this distribution, the method prepares the data in advance to compensate for the SNR degradation that will occur during parallel imaging reconstruction, thus resolving the contradiction between shortened imaging time and maintained SNR.
2Object-generated harmful factors
If noise reduction is performed on coil images before parallel imaging reconstruction, then noise is reduced, but SNR still degrades after reconstruction due to g factor
Solution Approach 1:
The patent calculates the SNR distribution from the coil images and uses this calculated distribution as feedback to guide the noise reduction processing. The noise reduction unit iteratively reduces noise while referencing the SNR distribution, creating a feedback loop that ensures noise reduction is optimized for the specific imaging conditions and coil arrangement, thereby preventing SNR degradation after parallel imaging reconstruction.
3Measurement precision
If multiple imaging sessions are performed to calculate SNR, then accurate SNR is obtained, but imaging time and calculation load increase
Solution Approach 1:
The patent enables the system to calculate its own SNR distribution directly from the acquired coil images without requiring separate calibration scans or multiple imaging sessions. The SNR distribution calculation unit computes the SNR for each pixel based on the actual coil images, allowing the system to self-determine the appropriate noise reduction parameters from the imaging data itself, thus achieving accurate SNR measurement without additional time cost.
4Object-generated harmful factors
If conventional noise reduction filters are applied, then noise is reduced, but edge preservation and noise reduction effects are limited
Solution Approach 1:
The patent applies noise reduction processing with spatially varying characteristics based on the calculated SNR distribution. Different regions of the image receive customized noise reduction treatment according to their local SNR values, allowing aggressive noise reduction in low-SNR regions while preserving edges and details in high-SNR regions. This local quality approach overcomes the limitations of uniform conventional filters.
Data Source
AI summary
The present invention is to perform appropriate noise reduction processing on an image having different signal levels or noise levels depending on an imaging condition or a reconstruction condition. A magnetic resonance imaging apparatus according to the invention includes: a measurement unit that receives a nuclear magnetic resonance signal generated in a subject by a receiving coil; an image reconstruction unit that processes the nuclear magnetic resonance signal received by the receiving coil and reconstructs an image of the subject; an SNR spatial distribution calculation unit that calculates spatial distribution of a signal-to-noise ratio of the image using spatial distribution of a noise level and spatial distribution of the signal of the image; and a noise reduction unit that reduces noise from the image based on the spatial distribution of the signal-to-noise ratio.