Canon kabushiki kaisha (20240299011). ULTRASONIC DIAGNOSTIC APPARATUS, LEARNING APPARATUS, AND IMAGE PROCESSING METHOD simplified abstract
Contents
ULTRASONIC DIAGNOSTIC APPARATUS, LEARNING APPARATUS, AND IMAGE PROCESSING METHOD
Organization Name
Inventor(s)
Kenichi Nagae of Kanagawa (JP)
ULTRASONIC DIAGNOSTIC APPARATUS, LEARNING APPARATUS, AND IMAGE PROCESSING METHOD - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240299011 titled 'ULTRASONIC DIAGNOSTIC APPARATUS, LEARNING APPARATUS, AND IMAGE PROCESSING METHOD
Simplified Explanation: The ultrasonic diagnostic apparatus uses machine learning to generate estimated images based on received signals from multiple transmissions/receptions of ultrasonic waves.
- Utilizes an ultrasonic probe to scan an observation region in an object with ultrasonic waves.
- Employs a machine-learned model to generate estimated images equivalent to those obtained from multiple transmissions/receptions of ultrasonic waves.
- Generates estimated images based on received signals from different transmissions/receptions of ultrasonic waves.
Potential Applications: 1. Medical imaging for diagnostic purposes. 2. Non-destructive testing in industrial applications. 3. Veterinary medicine for imaging animals. 4. Quality control in manufacturing processes.
Problems Solved: 1. Enhances image quality and accuracy in ultrasonic diagnostics. 2. Reduces the need for multiple transmissions/receptions of ultrasonic waves. 3. Improves efficiency and reliability of diagnostic imaging.
Benefits: 1. Increased precision in diagnostic imaging. 2. Time-saving by generating estimated images from fewer transmissions/receptions. 3. Enhanced diagnostic capabilities in various fields.
Commercial Applications: The technology can be utilized in medical imaging devices, industrial testing equipment, and veterinary ultrasound machines. It has the potential to improve diagnostic accuracy and efficiency in various industries.
Questions about Ultrasonic Diagnostic Apparatus: 1. How does machine learning improve the generation of estimated images in ultrasonic diagnostics? 2. What are the key advantages of using estimated images based on multiple transmissions/receptions of ultrasonic waves?
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Original Abstract Submitted
an ultrasonic diagnostic apparatus, comprising: an ultrasonic probe which scans an observation region in an object with an ultrasonic wave; and an estimated image generating unit which, by using a model having been machine-learned using learning data including first data based on a first received signal that is obtained by first transmission/reception of an ultrasonic wave and second data based on a second received signal that is obtained by second transmission/reception that represents a larger number of transmissions/receptions than the first transmission/reception of the ultrasonic wave, generates an estimated image equivalent to image data obtained by the second transmission/reception from third data based on a third received signal that is obtained by transmission/reception equivalent to the first transmission/reception of the ultrasonic wave by the ultrasonic probe.
