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Multi-contrast MRI synthesis with channel-exchanging-network

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Dalmaz, Onat
Aytekin, İdil
Dar, Salman Ul Hassan
Erdem, Erkut
Çukur, Tolga

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Yürüme analizi için ivmeölçer verilerinden uzun adım süresi ve basma fazı oranı tespiti

Abstract

Magnetic resonance imaging (MRI) is used in many diagnostic applications as it has a high soft-tissue contrast and is a non-invasive medical imaging method. MR signal levels differs according to the parameters T1, T2 and PD that change with respect to the chemical structure of the tissues. However, long scan times might limit acquiring images from multiple contrasts or if the multi-contrasts images are acquired, the contrasts are noisy. To overcome this limitation of MRI, multi-contrast synthesis can be utilized. In this paper, we propose a deep learning method based on Channel-Exchanging-Network (CEN) for multi-contrast image synthesis. Demonstrations are provided on IXI dataset. The proposed model based on CEN is compared against alternative methods based on CNNs and GANs. Our results show that the proposed model achieves superior performance to the competing methods.

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Institute of Electrical and Electronics Engineers Inc.

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Radiology, Nuclear medicine, Imaging systems in medicine

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2022 30th Signal Processing and Communications Applications Conference, SIU 2022

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10.1109/SIU55565.2022.9864937

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