Title:
医用データ処理装置、磁気共鳴イメージング装置及び学習済みモデル生成方法
Document Type and Number:
Japanese Patent JP6545887
Kind Code:
B2
Abstract:
A medical data processing apparatus includes a memory unit and a processing unit. The memory unit stores a learned model including an input layer to which first MR data and second MR data having the same imaging target as the first MR data and an imaging parameter different from the first MR data are inputted, an output layer from which third MR data is output with a missing portion of the first MR data restored, and at least one intermediate layer arranged between the input layer and the output layer. The processing unit generates third MR data relating to the subject, from the first MR data serving as a process target and relating to the subject and the second MR data relating to the subject and acquired by an imaging parameter different from the first MR data serving as the process target, in accordance with the learned model.
Inventors:
Hidenori Takeshima
Application Number:
JP2018208903A
Publication Date:
July 17, 2019
Filing Date:
November 06, 2018
Export Citation:
Assignee:
Canon Medical Systems Corporation
International Classes:
A61B5/055
Domestic Patent References:
| JP2008167949A | ||||
| JP11031214A |
Foreign References:
| US20050100208 |
Other References:
TAKESHIMA, H,Integrating Spatial and Temporal Correlations into a Deep Neural Network for Low-delay Reconstruction of Highly Undersampled Radial Dynamic Images,Proc.Intl.Soc.Mag.Reson.Med.,2018年 6月,2796
RECZKO, M et al.,Neural Networks in MR Image Estimation from Sparsely Sampled Scans,Lecture Notes in Computer Science,1999年,vol.1715,pp.75-86
NIE, D et al.,Estimating CT Image from MRI Data Using 3D Fully Convolutional Networks,Deep Learn Data Label Med Appl,2016年 9月27日,2016,pp.170-178
XIANG, L et al.,Deep Auto-context Convolutional Neural Networks for Standard-Dose PET Image Estimation from Low-Dose PET/MRI,Neurocomputing,2017年 6月29日,vol.267,pp.406-416
笠原勇布ほか,ニューラルネットを用いたスタッキングによるMR画像再構成,電子情報通信学会技術研究報告,日本,2017年 1月11日,第116巻, 第393号,第29-30頁
RECZKO, M et al.,Neural Networks in MR Image Estimation from Sparsely Sampled Scans,Lecture Notes in Computer Science,1999年,vol.1715,pp.75-86
NIE, D et al.,Estimating CT Image from MRI Data Using 3D Fully Convolutional Networks,Deep Learn Data Label Med Appl,2016年 9月27日,2016,pp.170-178
XIANG, L et al.,Deep Auto-context Convolutional Neural Networks for Standard-Dose PET Image Estimation from Low-Dose PET/MRI,Neurocomputing,2017年 6月29日,vol.267,pp.406-416
笠原勇布ほか,ニューラルネットを用いたスタッキングによるMR画像再構成,電子情報通信学会技術研究報告,日本,2017年 1月11日,第116巻, 第393号,第29-30頁
Attorney, Agent or Firm:
Kurata Masatoshi
Nobuhisa Nogawa
Takashi Mine
Naoki Kono
Tadashi Inoue
Sanae Kaneko
Nobuhisa Nogawa
Takashi Mine
Naoki Kono
Tadashi Inoue
Sanae Kaneko