Toward deep learning replacement of gadolinium in neuro-oncology: A review of contrast-enhanced synthetic MRI

Elisa Moya-Sáez; Rodrigo de Luis-García; Carlos Alberola-López

doi:10.3389/fnimg.2023.1055463

Toward deep learning replacement of gadolinium in neuro-oncology: A review of contrast-enhanced synthetic MRI

Front Neuroimaging. 2023 Jan 23:2:1055463. doi: 10.3389/fnimg.2023.1055463. eCollection 2023.

Authors

Elisa Moya-Sáez¹, Rodrigo de Luis-García¹, Carlos Alberola-López¹

Affiliation

¹ Laboratorio de Procesado de Imagen, ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain.

Abstract

Gadolinium-based contrast agents (GBCAs) have become a crucial part of MRI acquisitions in neuro-oncology for the detection, characterization and monitoring of brain tumors. However, contrast-enhanced (CE) acquisitions not only raise safety concerns, but also lead to patient discomfort, the need of more skilled manpower and cost increase. Recently, several proposed deep learning works intend to reduce, or even eliminate, the need of GBCAs. This study reviews the published works related to the synthesis of CE images from low-dose and/or their native -non CE- counterparts. The data, type of neural network, and number of input modalities for each method are summarized as well as the evaluation methods. Based on this analysis, we discuss the main issues that these methods need to overcome in order to become suitable for their clinical usage. We also hypothesize some future trends that research on this topic may follow.

Keywords: brain tumors; contrast enhancement; deep learning; gadolinium-based contrast agents (GBCAs); neuro-oncology; synthesis; synthetic MRI.

Publication types

Review

Grants and funding

The authors acknowledge Asociación Española Contra el Cáncer (AECC), Junta Provincial de Valladolid, and the Fundación Científica AECC for the predoctoral contract of the EM-S (grant PRDVL19001MOYA). In addition, the authors also acknowledge grants PID2020-115339RB-I00, TED2021-130090B-I00, and PID2021-124407NB-I00 from the Ministerio de Ciencia e Innovación of Spain, as well as the company ESAOTE for grant 18IQBM. ESAOTE was not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication.