Medical Image Noise Reduction via Signal Segmentation
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Solution Overview
Problem
Medical imaging methods face challenges in achieving consistent image noise and contrast due to varying imaging parameters, which complicates evaluation and requires restrictive dose regulation to maintain a uniform image impression, limiting the effectiveness of dose optimization.
Innovation Solution
A computer-implemented method that separately processes the noise and signal components of image data, allowing for independent adjustment of noise and contrast, enabling a more uniform image impression and flexible dose regulation while maintaining diagnostic quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If dose regulation is optimized to reduce radiation exposure, then radiation dose to the examination object is reduced, but image noise increases and contrast becomes variable, leading to non-uniform image impression
Solution Approach 1:
The image data set is segmented into signal component and noise component separately. The signal component undergoes contrast adjustment while the noise component is processed independently to maintain uniform noise characteristics. This segmentation allows independent optimization of contrast and noise, resolving the contradiction between dose reduction and image quality consistency.
Solution Approach 2:
The patent applies parameter changes by adjusting contrast parameters and noise parameters independently through separate processing pipelines. By changing the processing parameters for signal and noise separately, the system maintains consistent image impression across different dose levels while optimizing radiation exposure.
2Reliability
If contrast adjustment is applied to compensate for dose regulation changes, then contrast consistency is improved, but noise spectrum changes, limiting flexible dose regulation
Solution Approach 1:
By segmenting the image data into signal and noise components, the patent enables independent adjustment of contrast (applied to signal component) without affecting noise characteristics. This segmentation removes the coupling between contrast and noise adjustment, allowing flexible dose regulation while maintaining contrast consistency.
Solution Approach 2:
The patent introduces an intermediary processing step where the image data is transformed into signal and noise components. This intermediary representation allows independent manipulation of contrast and noise parameters, enabling both contrast consistency and dose regulation flexibility simultaneously.
3Reliability
If noise reduction filtering is applied to achieve uniform image impression, then image noise is reduced, but image data is falsified and artifacts are generated, complicating diagnosis
Solution Approach 1:
The patent segments image data into signal and noise components, allowing selective processing. The noise component is processed to achieve uniformity while the signal component retains its original diagnostic information. This segmentation prevents the loss of diagnostic information that occurs with conventional filtering approaches.
Solution Approach 2:
The patent extracts the noise component from the image data set for separate processing. By taking out the noise component, the system can reduce noise to achieve uniformity without applying filtering operations that would falsify the original image data or generate artifacts.
Data Source
AI summary
A method for providing an output data set as a function of an input data set includes applying an algorithm for reducing image noise to the input data set or an intermediate data set determined as a function of the input data set to determine a noise-reduced signal data set. The method includes determining a noise data set as difference between the input data set or the intermediate data set and the signal data set. The method includes determining a modified noise data set by applying a noise-processing algorithm to the noise data set and/or determining a modified signal data set by applying a signal processing algorithm to the signal data set. The method includes determining an output data set by adding the modified noise data set to the signal data set or the modified signal data set, or adding the noise data set to the modified signal data set.