Feasibility of deep learning k-space-to-image reconstruction for diffusion weighted imaging in patients with breast cancers: Focus on image quality and reduced scan time.

Journal: European journal of radiology

Published Date: Nov 13, 2022

Abstract

PURPOSE: This study aimed to evaluate the feasibility of accelerated DLR (deep learning reconstruction) single-shot echo planar imaging (ss-EPI) for diffusion-weighted image (DWI) in patients with breast cancers in comparison to conventional ss-EPI.

Authors

Eun Ji Lee

Department of Pathology, Green Cross Laboratories, Yongin, Gyeonggi, South Korea.
Yun-Woo Chang

Department of Radiology, Soonchunhyang University Seoul Hospital, 59 Daesakwan-ro, Yongsan-ku, Seoul 04401, Korea. Electronic address: ywchang@schmc.ac.kr.
Jae Kon Sung

Siemens Healthineers Ltd, Seoul, Korea.
Benkert Thomas

MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany.

Keywords

Breast Neoplasms Deep Learning Diffusion Magnetic Resonance Imaging Feasibility Studies Female Humans Image Processing, Computer-Assisted

External Resources

View on PubMed Access via DOI PubMed (36403564)

Feasibility of deep learning k-space-to-image reconstruction for diffusion weighted imaging in patients with breast cancers: Focus on image quality and reduced scan time.

Abstract

Authors

Keywords

External Resources

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