Medical Image Acquisition Information via K-Space Neural Analysis
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Solution Overview
Problem
Existing methods for automating medical image processing workflows struggle with accurately extracting image acquisition information due to insufficient documentation and variability in imaging modalities, protocols, and preferences, leading to inefficient and inaccurate automation.
Innovation Solution
A method involving transforming medical images into the frequency domain using Fourier transforms to obtain k-space images, which are then processed by trained functions like convolutional neural networks to extract image acquisition information, enabling more accurate and automated processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If image acquisition information is extracted from documented metadata, then the extraction process is simple, but the accuracy and reliability of the extracted information is insufficient due to incomplete or incorrect documentation
Solution Approach 1:
The patent introduces k-space images as an intermediary representation that bridges the gap between raw image data and acquisition parameters. By transforming the medical image into the frequency domain (k-space), the system creates an intermediate representation that encodes acquisition information in a more reliable and complete manner, allowing accurate extraction without depending on potentially erroneous metadata documentation.
Solution Approach 2:
The patent replaces the mechanical approach of directly reading acquisition information from documentation with a computational transformation approach. Instead of extracting information through simple metadata parsing, the system applies Fourier transforms and trained neural network functions to the k-space image, substituting the manual/documentation-based extraction mechanism with an automated computational process that achieves higher accuracy.
2Reliability
If deep learning models are used to extract image acquisition information, then the reliability improves, but the computational complexity and processing time increase
Solution Approach 1:
The patent applies the Fourier transform to convert the medical image into k-space before feeding it to the neural network. This preliminary transformation prepares the data in a domain where acquisition information is more prominently represented, allowing the neural network to extract accurate information more efficiently. The pre-processing step simplifies the subsequent computational task by organizing information in a more favorable representation.
Solution Approach 2:
The patent segments the information extraction task into distinct functional components: the Fourier transform module that converts the image to k-space, and the trained neural network function that extracts specific acquisition parameters. This segmentation allows each component to be optimized independently - the transform handles the data conversion while the specialized network handles the parameter extraction, reducing overall computational complexity compared to a monolithic approach.
3Extent of automation
If automated processing is implemented without accurate image acquisition information, then workflow automation is achieved, but the processing accuracy and diagnostic quality deteriorates
Solution Approach 1:
The system enables automated processing to serve itself by automatically extracting accurate acquisition information from the k-space image representation. The trained neural network functions autonomously identify acquisition parameters such as MRI sequence type, contrast agent information, and imaging settings directly from the transformed image data, providing the automation system with the accurate information it needs without external intervention or reliance on potentially incorrect metadata.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the automation of medical image processing by providing reliable image acquisition information, improving workflow efficiency and reducing user intervention in diagnostic tasks.
Implementation Method 1
transforming the medical image into the frequency domain so as to obtain a k-space image
Data Source
AI summary
Computer-implemented methods and systems for providing an image acquisition information of a medical image are provided. The methods and systems implement a plurality of steps. One step is directed to receive the medical image. Another step is directed to transform the medical image (or data of the medical image) into a frequency domain to obtain a k-space image. Another step is directed to determine the image acquisition information by applying a trained function to the k-space image (or to data of the k-space image). Another step is directed to provide the image acquisition information.