Echocardiography to Cardiac MRI View Transformation for Real-Time Blind Restoration
Ilke Adalioglu, Serkan Kiranyaz, Mete Ahishali, Aysen Degerli, Tahir, Hamid, Rahmat Ghaffar, Ridha Hamila, and Moncef Gabbouj

TL;DR
This paper introduces a Cycle-GAN based method to transform echocardiography images into high-quality, artifact-free cardiac MRI views, enhancing diagnostic capabilities and accessibility.
Contribution
It presents the first deep learning approach to convert echocardiography into cardiac MRI views using a novel dataset and Cycle-GAN architecture.
Findings
Synthetic MRI views are indistinguishable from real ones according to cardiologists.
The method achieves high-quality, artifact-free MRI synthesis from echocardiography.
78.9% of diagnoses preferred synthetic MRI over original echocardiography.
Abstract
Echocardiography is the most widely used imaging to monitor cardiac functions, serving as the first line in early detection of myocardial ischemia and infarction. However, echocardiography often suffers from several artifacts including sensor noise, lack of contrast, severe saturation, and missing myocardial segments which severely limit its usage in clinical diagnosis. In recent years, several machine learning methods have been proposed to improve echocardiography views. Yet, these methods usually address only a specific problem (e.g. denoising) and thus cannot provide a robust and reliable restoration in general. On the other hand, cardiac MRI provides a clean view of the heart without suffering such severe issues. However, due to its significantly higher cost, it is often only afforded by a few major hospitals, hence hindering its use and accessibility. In this pilot study, we…
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Taxonomy
TopicsAdvanced MRI Techniques and Applications · Cardiac Imaging and Diagnostics · Atomic and Subatomic Physics Research
MethodsSparse Evolutionary Training
