Cascaded Spatial Transformer Network for Stent Visualization
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Solution Overview
Problem
Current stent visualization methods during stent placement procedures face challenges in clearly visualizing stents due to motions like heart beating and breathing, which affect fluoroscopy image clarity, and require iterative online optimization for separating stent and non-stent layers, leading to slow processing.
Innovation Solution
An apparatus using a cascaded spatial transformer network (STN) to transform stent images and background images, separating the stent layer from the non-stent layer without explicit online optimization, utilizing neural networks to generate clear stent images from image sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional N-fluoroscopy alignment methods are used to separate stent and non-stent layers, then stent visualization is achieved, but processing speed is slow due to iterative online optimization
Solution Approach 1:
The patent pre-trains two spatial transformer networks (STN0 and STN1) offline using大量的training data before actual stent placement procedures. STN0 is trained to estimate stent motion, while STN1 estimates non-stent layer motion. This preliminary training eliminates the need for slow iterative online optimization during procedures, as the networks can directly process images in real-time with pre-learned motion estimation capabilities.
2Manufacturing precision
If iterative online optimization is performed to estimate stent and non-stent motion, then accurate layer separation is achieved, but computational time increases significantly
Solution Approach 1:
The patent replaces the traditional mechanical iterative optimization process with a neural network-based system. Instead of performing iterative mathematical optimization to estimate stent and non-stent motion, the pre-trained STN0 and STN1 networks directly predict motion parameters from input images. This substitution of mechanical optimization with neural network inference dramatically reduces computational time while maintaining accurate layer separation.
3Difficulty of detecting and measuring
If balloon markers are used for stent localization, then stent position can be identified, but clear visualization of the stent itself remains challenging due to motion artifacts
Solution Approach 1:
The patent employs two separate spatial transformer networks to segment and independently process different layers: STN0 specifically targets the stent layer while STN1 processes the non-stent background layer. This segmentation allows the system to separately estimate and correct motion for the stent and surrounding tissues, thereby improving stent image clarity by removing motion artifacts from the background while preserving stent features.
Data Source
AI summary
An apparatus for stent visualization includes a hardware processor that is configured to input one or more stent images from a sequence of X-ray images and corresponding balloon marker location data to a cascaded spatial transform network. The background is separated from the one or more stent images using the cascaded spatial transform network and a transformed stent image with a clear background and a non-stent background image is generated. The stent layer and non-stent layer are generated using a neural network without online optimization. A mapping function f maps the inputs, the sequence images and marker coordinates, into the two single image outputs.