View source for 18666676. ULTRASONIC DIAGNOSTIC APPARATUS, LEARNING APPARATUS, AND IMAGE PROCESSING METHOD simplified abstract (CANON KABUSHIKI KAISHA)

=ULTRASONIC DIAGNOSTIC APPARATUS, LEARNING APPARATUS, AND IMAGE PROCESSING METHOD=


==Organization Name==

[[:Category:CANON KABUSHIKI KAISHA|CANON KABUSHIKI KAISHA]]


[[Category:CANON KABUSHIKI KAISHA]]

==Inventor(s)==

[[:Category:Shoya Sasaki of Kanagawa (JP)|Shoya Sasaki of Kanagawa (JP)]][[Category:Shoya Sasaki of Kanagawa (JP)]]

[[:Category:Naoya Iizuka of Kanagawa (JP)|Naoya Iizuka of Kanagawa (JP)]][[Category:Naoya Iizuka of Kanagawa (JP)]]

[[:Category:Kenichi Nagae of Kanagawa (JP)|Kenichi Nagae of Kanagawa (JP)]][[Category: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 18666676 titled 'ULTRASONIC DIAGNOSTIC APPARATUS, LEARNING APPARATUS, AND IMAGE PROCESSING METHOD

==Simplified Explanation==
The patent application describes an ultrasonic diagnostic apparatus that uses machine learning to generate estimated images based on received signals from multiple ultrasonic wave transmissions.

* Ultrasonic probe scans an object with ultrasonic waves
* Estimated image generating unit uses machine-learned model to generate estimated images
* Learning data includes signals from multiple ultrasonic wave transmissions
* Estimated images equivalent to those obtained from more transmissions are generated

==Key Features and Innovation==
* Utilizes machine learning to generate estimated images
* Improves image quality by using signals from multiple ultrasonic wave transmissions
* Enhances diagnostic capabilities of the ultrasonic diagnostic apparatus

==Potential Applications==
* Medical imaging for diagnostic purposes
* Non-destructive testing in industrial applications
* Veterinary medicine for animal health assessments

==Problems Solved==
* Enhances image quality and diagnostic accuracy
* Reduces the need for multiple ultrasonic wave transmissions
* Improves efficiency and effectiveness of ultrasonic imaging

==Benefits==
* Accurate and detailed imaging results
* Time-saving and cost-effective diagnostic process
* Enhanced capabilities for medical and industrial applications

==Commercial Applications==
Ultrasonic diagnostic equipment manufacturers can integrate this technology into their products to offer advanced imaging capabilities for medical, industrial, and veterinary applications.

==Questions about Ultrasonic Diagnostic Apparatus==
===How does the machine-learned model improve image generation in the ultrasonic diagnostic apparatus?===
The machine-learned model enhances image generation by analyzing signals from multiple ultrasonic wave transmissions to generate more accurate and detailed estimated images.

===What are the potential benefits of using the estimated image generating unit in ultrasonic diagnostic equipment?===
The estimated image generating unit can improve diagnostic accuracy, reduce the need for multiple transmissions, and enhance imaging capabilities for various applications.


==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.



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[[Category:A61B8/00]]
[[Category:G06N3/08]]
[[Category:CPC_A61B8/5246]]