公开(公告)号：US20140126794A1

公开(公告)日：2014-05-08

申请号：US13667757

申请日：2012-11-02

Applicant: GENERAL ELECTRIC COMPANY

Inventor： Sangtae Ahn ,  Evren Asma ,  Ravindra Manjeshwar ,  Steven Ross

IPC: G06T11/00

CPC classification number: G06T11/008

Abstract: A computer-implemented method for partial volume correction in Positron Emission Tomography (PET) image reconstruction includes receiving emission data related to an activity distribution, reconstructing the activity distribution from the emission data by maximizing a penalized-likelihood objective function to produce a reconstructed PET image, quantifying an activity concentration in a region of interest of the reconstructed PET image to produce an uncorrected quantitation, and correcting the uncorrected quantitation based on a pre-calculated contrast recovery coefficient value to account for a partial volume error in the uncorrected quantitation.

Abstract translation: 用于正电子发射断层扫描（PET）图像重建中的部分体积校正的计算机实现的方法包括接收与活动分布相关的发射数据，通过最大化惩罚似然度目标函数来重建来自发射数据的活动分布，以产生重建的PET图像 量化重构的PET图像的感兴趣区域中的活性浓度以产生未校正的定量，并且基于预计算的对比度恢复系数值来校正未校正的定量，以解决未校正的定量中的部分体积误差。

公开(公告)号：US20140126793A1

公开(公告)日：2014-05-08

申请号：US13667737

申请日：2012-11-02

Applicant: GENERAL ELECTRIC COMPANY

Inventor： Sangtae Ahn ,  Evren Asma ,  Ravindra Manjeshwar ,  Steven Ross

IPC: G06T7/00

CPC classification number: G06T11/006 ,  G06T2211/424

Abstract: A computer-implemented method for penalized-likelihood reconstruction of a Positron Emission Tomography (PET) image includes generating a regularization function in which a smoothing parameter is modulated by one or more data-independent spatially variable modulation factors to compensate for sensitivity variations in a PET voxel dataset, and reconstructing the PET image from the PET emission dataset using the regularization function.

Abstract translation: 用于正电子发射断层摄影（PET）图像的惩罚似然重建的计算机实现方法包括生成正则化函数，其中平滑参数由一个或多个数据无关的空间可变调制因子调制以补偿PET中的灵敏度变化 体素数据集，并使用正则化函数从PET发射数据集重建PET图像。

公开(公告)号：US20210035339A1

公开(公告)日：2021-02-04

申请号：US16528329

申请日：2019-07-31

Applicant: General Electric Company

Inventor： Sangtae Ahn ,  Bruno Kristiaan Bernard De Man

IPC: G06T11/00 ,  G06T7/00 ,  G06N3/08

Abstract: The present disclosure relates to performing an unrolled iterative reconstruction of image data. Such approaches may include use of neural networks used for one or both of data fidelity and/or image update steps, resulting in improved image quality and accelerated reconstruction for various imaging modalities including CT, PET and MR.

公开(公告)号：US20190122399A1

公开(公告)日：2019-04-25

申请号：US15792363

申请日：2017-10-24

Applicant: General Electric Company

Inventor： Nitin Jain ,  Sangtae Ahn ,  Steven Ross

IPC: G06T11/00 ,  G06T3/40 ,  A61B6/03

Abstract: An imaging system is provided that includes at least one detector configured to acquire imaging information, a processing unit, and a display unit. The processing unit is operably coupled to the at least one detector, and is configured to reconstruct an image using the imaging information. The image is organized into voxels having non-uniform dimensions. The processing unit is configured to perform a penalized likelihood (PL) image reconstruction using the imaging information. The PL image reconstruction includes a penalty function. Performing the penalty function includes interpolating a voxel size in at least one dimension from an original size to an interpolated size before determining a penalty function, determining the penalty function using the interpolated size to provide an initial penalty, interpolating the initial penalty to the original size to provide a modified penalty, and applying the modified penalty in the PL image reconstruction.

公开(公告)号：US09595121B2

公开(公告)日：2017-03-14

申请号：US13667737

申请日：2012-11-02

Applicant: General Electric Company

Inventor： Sangtae Ahn ,  Evren Asma ,  Ravindra Manjeshwar ,  Steven Ross

IPC: G06K9/00 ,  G06T11/00

CPC classification number: G06T11/006 ,  G06T2211/424

Abstract: A computer-implemented method for penalized-likelihood reconstruction of a Positron Emission Tomography (PET) image includes generating a regularization function in which a smoothing parameter is modulated by one or more data-independent spatially variable modulation factors to compensate for sensitivity variations in a PET voxel dataset, and reconstructing the PET image from the PET emission dataset using the regularization function.

Abstract translation: 用于正电子发射断层摄影（PET）图像的惩罚似然重建的计算机实现方法包括生成正则化函数，其中平滑参数由一个或多个数据无关的空间可变调制因子调制以补偿PET中的灵敏度变化 体素数据集，并使用正则化函数从PET发射数据集重建PET图像。

公开(公告)号：US20160174919A1

公开(公告)日：2016-06-23

申请号：US14579039

申请日：2014-12-22

Applicant: General Electric Company

Inventor： Sangtae Ahn ,  Ravindra Mohan Manjeshwar ,  Florian Wiesinger ,  Dattesh Dayanand Shanbhag ,  Sandeep Suryanarayana Kaushik ,  Hua Qian ,  Anne Menini

IPC: A61B6/00 ,  G01T1/24 ,  G06T7/00 ,  A61B8/00 ,  A61B6/03 ,  A61B5/055 ,  A61B5/00 ,  A61B5/053 ,  G01R33/56 ,  G06T11/00

CPC classification number: A61B6/4417 ,  A61B5/0059 ,  A61B5/0536 ,  A61B5/055 ,  A61B6/032 ,  A61B6/037 ,  A61B6/5205 ,  A61B6/5258 ,  A61B8/4416 ,  G01R33/481 ,  G01R33/56 ,  G01T1/1603 ,  G01T1/1647 ,  G01T1/249 ,  G06T7/0012 ,  G06T11/003 ,  G06T11/006 ,  G06T2200/04 ,  G06T2207/10081 ,  G06T2207/10088 ,  G06T2207/10104 ,  G06T2207/30024 ,  G06T2207/30061 ,  G06T2211/40 ,  G06T2211/424

Abstract: Imaging system and method are presented. Emission scan (ES) and anatomical scan (AS) data corresponding to a target volume in a subject are received. One or more at least partial AS images are reconstructed using AS data. An image-space certainty (IC) map representing a confidence level (CL) for attenuation coefficients of selected voxels in AS images and a preliminary attenuation (PA) map based on AS images are generated. One or more of selected attenuation factors (AF) in projection-space are initialized based on PA map. A projection-space certainty (PC) map representing CL for the selected AF is generated based on IC map. An emission image of the target volume is initialized. The selected AF and emission image are iteratively updated based on the ES data, PC map, initial AF, and/or initial emission image. A desired emission image and/or AF values are determined based on the iteratively updated AF and/or emission image.

Abstract translation: 介绍了成像系统和方法。 接收与对象中的目标体积对应的发射扫描（ES）和解剖扫描（AS）数据。 使用AS数据重构一个或多个至少部分AS图像。 生成表示AS图像中的选定体素的衰减系数的置信水平（CL）和基于AS图像的初步衰减（PA）图的图像空间确定性（IC）图。 基于PA映射初始化投影空间中的一个或多个选择的衰减因子（AF）。 基于IC映射生成表示所选AF的CL的投影空间确定性（PC）映射。 初始化目标体积的发射图像。 基于ES数据，PC映射，初始AF和/或初始发射图像迭代地更新所选择的AF和发​​射图像。 基于迭代更新的AF和/或发射图像来确定期望的发射图像和/或AF值。

公开(公告)号：US11696700B2

公开(公告)日：2023-07-11

申请号：US16394774

申请日：2019-04-25

Applicant: General Electric Company

Inventor： Michael Rotman ,  Rafael Shmuel Brada ,  Sangtae Ahn ,  Christopher Judson Hardy ,  Itzik Malkiel ,  Ron Wein

IPC: A61B5/055 ,  G01R33/565 ,  G01R33/48 ,  G01R33/56 ,  G01R33/567

CPC classification number: A61B5/055 ,  G01R33/4818 ,  G01R33/56509 ,  G01R33/5608 ,  G01R33/5676

Abstract: K-space data obtained from a magnetic resonance imaging scan where motion was detected is split into two parts in accordance with the timing of the motion to produce first and second sets of k-space data corresponding to different poses. Sub-images are reconstructed from the k first and second sets of k-space data, which are used as inputs to a deep neural network which transforms them into a motion-corrected image.

公开(公告)号：US20210208227A1

公开(公告)日：2021-07-08

申请号：US16733037

申请日：2020-01-02

Applicant: General Electric Company

Inventor： Christopher Judson Hardy ,  Sangtae Ahn

IPC: G01R33/56 ,  G06T5/50 ,  G06T11/00 ,  G06N20/00

Abstract: The subject matter discussed herein relates to a fast magnetic resonance imaging (MRI) method to suppress fine-line artifact in Fast-Spin-Echo (FSE) images reconstructed with a deep-learning network. The network is trained using fully sampled NEX=2 (Number of Excitations equals to 2) data. In each case, the two excitations are combined to generate fully sampled ground-truth images with no fine-line artifact, which are used for comparison with the network generated image in the loss function. However, only one of the excitations is retrospectively undersampled and inputted into the network during training. In this way, the network learns to remove both undersampling and fine-line artifacts. At inferencing, only NEX=1 undersampled data are acquired and reconstructed.

公开(公告)号：US10996306B2

公开(公告)日：2021-05-04

申请号：US16394791

申请日：2019-04-25

Applicant: General Electric Company

Inventor： Isabelle Heukensfeldt Jansen ,  Sangtae Ahn ,  Christopher Judson Hardy ,  Itzik Malkiel ,  Rafael Shmuel Brada ,  Ron Wein ,  Michael Rotman

IPC: G06T7/20 ,  G01R33/565 ,  G01R33/56 ,  G01R33/48 ,  G06T11/00 ,  G06T7/00 ,  G06K9/62 ,  G06N3/08

Abstract: A magnetic resonance imaging (MRI) system includes control and analysis circuitry having programming to acquire magnetic resonance (MR) data using coil elements of the MRI system, analyze the MR data, and reconstruct the MR data into MR sub-images. The system also includes a trained neural network associated with the control and analysis circuitry to transform the MR sub-images into a prediction relating to a presence and extent of motion corruption in the MR sub-images. The programming of the control and analysis circuitry includes instructions to control operations of the MRI system based at least in part on the prediction of the trained neural network.

公开(公告)号：US10970885B2

公开(公告)日：2021-04-06

申请号：US16528329

申请日：2019-07-31

Applicant: General Electric Company

Inventor： Sangtae Ahn ,  Bruno Kristiaan Bernard De Man

IPC: G06K9/00 ,  G06T11/00 ,  G06T7/00 ,  G06N3/08

Abstract: The present disclosure relates to performing an unrolled iterative reconstruction of image data. Such approaches may include use of neural networks used for one or both of data fidelity and/or image update steps, resulting in improved image quality and accelerated reconstruction for various imaging modalities including CT, PET and MR.