WO2024121617 - ADAPTIVE ULTRASOUND BEAMFORMING
National phase entry:
Publication Number
WO/2024/121617
Publication Date
13.06.2024
International Application No.
PCT/IB2023/000713
International Filing Date
07.12.2023
Title **
[English]
ADAPTIVE ULTRASOUND BEAMFORMING
[Spanish]
ADAPTIVE ULTRASOUND BEAMFORMING
[French]
FORMATION DE FAISCEAU ULTRASONORE ADAPTATIVE
Applicants **
UNIVERSITY OF LEEDS
Inventors
SMITH, Elliott Ian James
FREEAR, Steven
Priority Data
2218330.5
06.12.2022
GB
Application details
| Total Number of Claims/PCT | * |
| Number of Independent Claims | * |
| Number of Priorities | * |
| Number of Multi-Dependent Claims | * |
| Number of Drawings | * |
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| Pages of Specification | * |
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International Searching Authority |
EPO
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| Recordal of a Change of the Applicant's Name/Address |
Change of Applicant's Name and Address
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| Type of Assignment |
The Standard Agent's Assignment
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| Applicant's Legal Status |
Legal Entity
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| * | |
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| Entry into National Phase under |
Chapter I
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| Patent Delivery |
Send the Letters Patent by Courier
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| Translation |
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Quotation for National Phase entry
| Country | Stages | Total | |
|---|---|---|---|
| China | Filing, Examination, Granting | 1880 | |
| EPO | Filing, Examination, Granting | 8113 | |
| Japan | Filing, Examination, Granting | 1998 | |
| South Korea | Filing, Examination, Granting | 1757 | |
| USA | Filing, Examination, Granting | 5340 |
Total:
19,088
The term for entry into the National Phase has expired. This quotation is for informational purposes only
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Abstract[English]
Ultrasound beamforming is performed by a deep learning architecture incorporating convolution layers and known operators. The network is trained to combine multiple observations of complex signal data in an optimal way into a single complex signal. This is achieved using convolutional layers with learnable parameters and by embedding and an operator to perform multiple weighted sum operations of subsets of the original observations and average these to produce a single complex signal. Known operators including a forward-backward operator are optionally incorporated into the architecture.[Spanish]
Ultrasound beamforming is performed by a deep learning architecture incorporating convolution layers and known operators. The network is trained to combine multiple observations of complex signal data in an optimal way into a single complex signal. This is achieved using convolutional layers with learnable parameters and by embedding and an operator to perform multiple weighted sum operations of subsets of the original observations and average these to produce a single complex signal. Known operators including a forward-backward operator are optionally incorporated into the architecture.[French]
L'invention concerne la formation de faisceaux ultrasonores par une architecture d'apprentissage profond incorporant des couches de convolution et des opérateurs connus. Le réseau est entraîné à combiner de manière optimale de multiples observations de données de signaux complexes en un seul signal complexe. Pour ce faire, on utilise des couches convolutives dont les paramètres peuvent être appris, ainsi qu'un opérateur permettant d'effectuer de multiples opérations de somme pondérée sur des sous-ensembles des observations originales et d'en faire la moyenne pour produire un seul signal complexe. Des opérateurs connus comprenant un opérateur avant-arrière sont éventuellement incorporés dans l'architecture.