Work

Post-contrast multi-parametric mapping from only pre-contrast conventional weighted images

Year: 2024

Type: conference-paper

Abstract: Motivation: Gadolinium-based contrast agents (GBCAs) have become a cornerstone in clinical routine for lesions characterization and treatment monitoring. However, issues such as safety concerns related to deposition of GBCA in the body and brain, prolonged acquisitions, and cost increase advocate against its usage. Goal(s): To replace the usage of GBCAs in post-contrast imaging with parametric maps and deep learning. Approach: A cascade of two convolutional-neural-networks for pre- and post-contrast parametric mapping and the synthesis of post-contrast T1-weighted images from only two pre-contrast conventional weighted images. Results: The proposed approach presents potential for predicting post-contrast T1w-enhancement without the usage of GBCAs. Impact: The proposed deep learning approach provides both pre- and post-contrast parametric maps and, consequently, the capability of synthesizing any post-contrast image from only two pre-contrast conventional weighted images. Thus, it paves the way towards GBCAs-free acquisitions.

Source: Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition

DOI: 10.58530/2024/2809

Authors: Elisa Moya-Sáez, Laura Nunez-Gonzalez, Rodrigo de Luis-García, Carlos Alberola-López, Juan Hernandez-Tamames

Language: English

Topic: Advanced MRI Techniques and Applications

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funders: Fundación Científica Asociación Española Contra el Cáncer, Ministerio de Ciencia e Innovación, Fundação para a Ciência e a Tecnologia

Awards: G238486689, G3464674575, G5805502524