Subtle Medical, Inc. (20240221115). ULTRA-HIGH RESOLUTION CT RECONSTRUCTION USING GRADIENT GUIDANCE simplified abstract
Contents
ULTRA-HIGH RESOLUTION CT RECONSTRUCTION USING GRADIENT GUIDANCE
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ULTRA-HIGH RESOLUTION CT RECONSTRUCTION USING GRADIENT GUIDANCE - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240221115 titled 'ULTRA-HIGH RESOLUTION CT RECONSTRUCTION USING GRADIENT GUIDANCE
Simplified Explanation: This patent application describes a computer-implemented method for ultra-high resolution computed tomography using deep learning technology.
Key Features and Innovation:
- Acquiring a medical image of a subject with lower resolution using computed tomography.
- Processing the medical image with a deep learning network model to reconstruct an ultra-high resolution medical image.
- Training the deep learning network model using a generative adversarial network (GAN)-based framework with gradient guidance.
Potential Applications: This technology can be used in medical imaging for more detailed and accurate diagnostics, particularly in areas where high resolution is crucial.
Problems Solved: This technology addresses the limitation of lower resolution medical images in computed tomography, providing a solution for obtaining ultra-high resolution images.
Benefits:
- Improved accuracy in medical diagnostics.
- Enhanced visualization of anatomical structures.
- Better treatment planning based on detailed imaging.
Commercial Applications: Potential commercial applications include medical imaging equipment manufacturers, healthcare facilities, and research institutions looking to enhance their imaging capabilities.
Prior Art: Readers can start their search for prior art related to this technology by exploring patents in the field of medical imaging, deep learning in healthcare, and advanced imaging techniques.
Frequently Updated Research: Stay updated on advancements in deep learning applications in medical imaging, developments in GAN-based frameworks, and improvements in computed tomography technology.
Questions about Ultra-High Resolution Computed Tomography: 1. What are the key benefits of using deep learning technology in ultra-high resolution computed tomography? 2. How does this technology improve the accuracy of medical diagnostics compared to traditional methods?
Original Abstract Submitted
a computer-implemented method is provided for ultra-high resolution computed tomography. the method comprises: acquiring, using computed tomography (ct), a medical image of a subject, the medical image has a lower resolution; and processing the medical image, with aid of a deep learning network model, to reconstruct an ultra-high resolution medical image, where the deep learning network model is trained using a generative adversarial network (gan)-based framework with a gradient guidance.
