Medical Image Layer Separation for Real-Time CNN-MLP Denoising
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Solution Overview
Problem
Medical imaging technologies like X-ray fluoroscopy suffer from noise due to hardware limitations and environmental factors, making real-time denoising challenging with deep learning due to computation complexity and hardware costs.
Innovation Solution
A method involving separating medical images into background and foreground layers, using different neural networks for denoising each layer, where a convolutional neural network (CNN) is used for the background and a multi-layer perceptron (MLP) for the foreground, followed by merging the denoised layers to improve denoising efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning techniques are used for medical image denoising, then denoising performance is improved, but computation complexity and hardware costs increase
Solution Approach 1:
The patent segments the medical image into foreground and background layers, applying different denoising strategies to each. The foreground layer (containing anatomical structures) is processed with a lightweight model to preserve details, while the background layer is processed separately. This segmentation reduces the overall computational complexity while maintaining denoising performance.
Solution Approach 2:
The patent applies different denoising approaches to different regions of the image based on their specific requirements. The foreground region receives a simplified denoising treatment optimized for structural preservation, while the background region receives different processing. This local differentiation reduces computation complexity by avoiding uniform heavy processing across the entire image.
2Measurement precision
If deep learning techniques are used for medical image denoising, then denoising performance is improved, but hardware costs increase
Solution Approach 1:
By segmenting the image into foreground and background and applying different processing strategies, the patent reduces the hardware resources required. The lightweight foreground processing requires fewer computational resources compared to applying a heavy deep learning model to the entire image, thereby reducing hardware costs while maintaining performance.
Solution Approach 2:
The patent employs a lightweight, simplified neural network model for foreground denoising that requires fewer hardware resources. This lighter model acts as a cost-effective alternative to heavy deep learning models, reducing hardware costs while still achieving acceptable denoising performance for the critical foreground regions.
3Speed
If real-time denoising is implemented, then processing speed is improved, but denoising quality may deteriorate
Solution Approach 1:
The patent segments processing into two parallel streams: foreground and background. The foreground processing uses a lightweight model that operates quickly to meet real-time requirements, while the background is processed separately. This segmentation enables real-time processing speed while maintaining quality in the critical foreground regions through optimized lightweight processing.
Solution Approach 2:
The patent applies local quality optimization by using a simplified model specifically tuned for foreground regions where anatomical details are critical. This localized approach ensures that the most important regions maintain high quality even with faster, lighter processing, while the background can tolerate slightly different processing characteristics to achieve real-time speeds.
Data Source
AI summary
Disclosed herein are systems, methods, and instrumentalities associated with medical image denoising. An apparatus configured to perform the medical image denoising task may be configured to obtain a medical image of an object and separate the medical image into a background layer and a foreground layer. The apparatus may then denoise the background layer using a first neural network pre-trained to suit the characteristics of the background layer, denoise the foreground layer using a second neural network pre-trained to suit the characteristics of the foreground layer, and merge the denoised background layer and the denoised foreground layer back into a clean medical image that depicts the object with improved image quality.