公开(公告)号：US11058392B2

公开(公告)日：2021-07-13

申请号：US16471526

申请日：2017-12-19

Applicant: Mayo Foundation for Medical Education and Research

Inventor： Kenneth A. Fetterly ,  Christopher P. Favazza

IPC: A61B6/00

Abstract: Systems and methods for correcting images acquired with an imaging system for measurement bias from non-stationary noise in model observers are described. A biased detectability index is estimated from signal present and signal absent condition images and an estimate of the bias from non-stationary noise is also estimated. This bias is then removed from the detectability index to provide an assessment of detectability of the imaging system.

公开(公告)号：US20240135603A1

公开(公告)日：2024-04-25

申请号：US18546376

申请日：2022-02-14

Applicant: Mayo Foundation for Medical Education and Research

Inventor： Christopher P. Favazza ,  Andrea Ferrero ,  Liqiang Ren

IPC: G06T11/00 ,  G06V10/774 ,  G06V10/82

CPC classification number: G06T11/005 ,  G06V10/774 ,  G06V10/82 ,  G06T2211/441 ,  G06T2211/448 ,  G06V2201/03

Abstract: Metal artifacts are reduced in x-ray computed tomography (“CT”) images using a suitably trained neural network, such as a convolutional neural network (“CNN”). Virtual metal DATA objects are inserted to either the raw projection data or CT image data (e.g., from pre-procedural CT scans) to generate sets of matching artifact-corrupted and artifact-uncorrupted images, and a CNN, or other neural network, is trained to separate the contribution to each image pixel due to patient anatomy, metal object, or metal object-induced artifact. The contributions from metal object-induced artifacts can then be removed to generate a final, artifact-reduced image.

公开(公告)号：US20190320996A1

公开(公告)日：2019-10-24

申请号：US16471526

申请日：2017-12-19

Applicant: Mayo Foundation for Medical Education and Research

Inventor： Kenneth A. Fetterly ,  Christopher P. Favazza

IPC: A61B6/00

Abstract: Systems and methods for correcting images acquired with an imaging system for measurement bias from non-stationary noise in model observers are described. A biased detectability index is estimated from signal present and signal absent condition images and an estimate of the bias from non-stationary noise is also estimated. This bias is then removed from the detectability index to provide an assessment of detectability of the imaging system.