Work

RAMI: Radiomics Predictive Model of Myocardial Infarction and Microvascular Obstruction

Year: 2024

Type: conference-paper

Abstract: Motivation: Late gadolinium Enhancement (LGE) is the method of choice for assessing myocardial infarction (MI) and viability, essential to guide revascularization decisions. LGE also shows no-reflow regions that occur when blood flow remains inadequate post-revascularization. Existing automatic segmentation methods identify regions of LGE uptake but ignore no-reflow regions. Goal(s): Our goal is to provide a robust and automated solution for the detection of MI and no-reflow. Approach: We propose two LGE-based radiomics models, RAMI and RAMI-NOR, to improve MI diagnosis and detect no-reflow, respectively. Results: RAMI distinguishes normal and pathological cases accurately. RAMI-NOR shows potential in assessing MI with no-reflow but needs further refinement. Impact: The proposed RAMI and RAMI-NOR methods extract radiomics features from LGE images to automatically detect infarcted and microvascular obstruction areas, essential for the diagnosis and treatment management of patients suffering from myocardial infarction.

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: 10.58530/2024/3386

Authors: Joao Santinha, Teresa Correia

Language: English

Topic: Radiomics and Machine Learning in Medical Imaging

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funder: Fundação para a Ciência e a Tecnologia

Awards: Centro de Investigação Marinha e Ambiental, G3464674575, Centro de Ciências do Mar do Algarve, Centro de Ciências do Mar do Algarve