Tomographic Image Reconstruction Noise Reduction
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Solution Overview
Problem
Current tomographic image reconstruction methods, such as iterative reconstruction, are time-consuming and struggle with noise reduction, particularly due to varying noise amounts across pixel values in X-ray images, which affects image quality and diagnostic performance.
Innovation Solution
An image processing apparatus and method that acquires noise data for projection images, estimates noise reconstruction data, and adjusts the reconstruction process based on noise amounts and pixel value differences to reduce noise adaptively, incorporating influence functions for regulation and noise suppression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If iterative reconstruction is used to generate tomographic images, then image quality with higher signal-to-noise ratio and suppressed artifacts is improved, but reconstruction time increases significantly compared to filtered back projection
Solution Approach 1:
The patent applies preliminary action by performing noise reduction processing on projection images before reconstruction. The system estimates noise amounts in projection images, creates noise-reduced projection images by subtracting estimated noise components, and then performs iterative reconstruction on these pre-processed images. This preliminary noise reduction step allows the reconstruction process to converge faster while maintaining high image quality, effectively resolving the contradiction between image quality and reconstruction time.
2Object-affected harmful factors
If regulation is strengthened in areas of small pixel value difference to reduce noise, then noise reduction is improved, but object structure conservation deteriorates
Solution Approach 1:
The patent applies local quality by making the regulation strength spatially variable based on local image characteristics. The system calculates pixel value differences between adjacent pixels and uses this information to adjust the regulation parameter locally during iterative reconstruction. In regions with small pixel value differences (likely noise), stronger regulation is applied for noise reduction, while in regions with large pixel value differences (likely object boundaries), weaker regulation preserves object structures. This local adaptation resolves the contradiction between noise reduction and structure conservation.
3Manufacturing precision
If noise reduction processing is applied during iterative reconstruction, then signal-to-noise ratio is improved, but the complexity of the reconstruction process increases
Solution Approach 1:
The patent applies segmentation by dividing the reconstruction process into distinct functional modules: (1) noise amount estimation module that calculates noise statistics from projection images, (2) noise-reduced projection image creation module that subtracts estimated noise components, and (3) iterative reconstruction module that processes the pre-processed images. This modular segmentation makes the complex noise reduction process more manageable and implementable, resolving the contradiction between improving signal-to-noise ratio and managing process complexity.
Data Source
AI summary
An image processing apparatus for reconstructing a tomographic image from a plurality of projection images acquires noise data for a plurality of projection images, respectively, acquires noise reconstruction data by executing reconstruction processing based on the acquired noise data, and reconstructs the tomographic image by executing the reconstruction processing based on the projection images and the noise reconstruction data.