Skip to main content

CORRECTION article

Front. Oncol., 05 September 2022
Sec. Radiation Oncology
Volume 12 - 2022 | https://doi.org/10.3389/fonc.2022.1005287

Corrigendum: Investigation of a novel deep learning-based computed tomography perfusion mapping framework for functional lung avoidance radiotherapy

This article is a correction to:

  1. Investigation of a Novel Deep Learning-Based Computed Tomography Perfusion Mapping Framework for Functional Lung Avoidance Radiotherapy

    1. Read original article
Ge RenGe Ren1Sai-kit LamSai-kit Lam1Jiang ZhangJiang Zhang1Haonan XiaoHaonan Xiao1Andy Lai-yin CheungAndy Lai-yin Cheung1Wai-yin HoWai-yin Ho2Jing QinJing Qin3Jing Cai*Jing Cai1*
  • 1Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
  • 2Department of Nuclear Medicine, Queen Mary Hospital, Hong Kong, Hong Kong SAR, China
  • 3School of Nursing, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China

A Corrigendum on:
Investigation of a novel deep learning-based computed tomography perfusion mapping framework for functional lung avoidance radiotherapy.

By Ren G, Lam S-k, Zhang J, Xiao H, Cheung AL-y, Ho W-y, Qin J and Cai J (2021) Front. Oncol. 11:644703. doi: 10.3389/fonc.2021.644703

In the published article, there was an error in the Funding statement. The funding statement for the General Research Fund was displayed as “GRF 151022/19M”. The correct Funding statement appears below.

Funding

This work is supported by the Health and Medical Research Fund (HMRF 07183266); the General Research Fund (GRF 15103520).

The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Keywords: perfusion imaging, lung function imaging, deep learning, perfusion synthesis, CT based image analysis, functional lung avoidance radiation therapy

Citation: Ren G, Lam S-k, Zhang J, Xiao H, Cheung AL-y, Ho W-y, Qin J and Cai J (2022) Corrigendum: Investigation of a novel deep learning-based computed tomography perfusion mapping framework for functional lung avoidance radiotherapy. Front. Oncol. 12:1005287. doi: 10.3389/fonc.2022.1005287

Received: 28 July 2022; Accepted: 12 August 2022;
Published: 05 September 2022.

Approved by:

Frontiers Editorial Office, Frontiers Media SA, Switzerland

Copyright © 2022 Ren, Lam, Zhang, Xiao, Cheung, Ho, Qin and Cai. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Jing Cai, amluZy5jYWlAcG9seXUuZWR1Lmhr

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.