公开(公告)号：US12109070B2

公开(公告)日：2024-10-08

申请号：US17763069

申请日：2020-09-28

Applicant: THE JOHNS HOPKINS UNIVERSITY

Inventor： Muyinatu Bell ,  Alycen Wiacek

IPC: A61B8/08 ,  A61B5/00 ,  A61B8/06 ,  G01S7/52 ,  G01S15/89

CPC classification number: A61B8/5207 ,  A61B5/0095 ,  A61B8/06 ,  A61B8/485 ,  G01S7/52026 ,  G01S7/52046 ,  G01S15/8915

Abstract: A computer-implemented method for training and using a neural network to predict a coherence function includes: training a neural network by mapping a plurality of different sets of training input samples to respective coherence function truths to generate a trained neural network; receiving an operational input sample; inputting the operational input sample into the trained neural network; obtaining, from the trained neural network, a coherence function mapped to the operational input sample in response to the inputting the operational input sample into the trained neural network; and executing a computer-based instruction based on obtaining the coherence function. The coherence function may be used to differentiate solid masses from fluid-filled masses.