Multi Modality Medical Image Translation for Dicom Brain Images

Ninad Anklesaria, Yashvi Malu, Dhyey Nikalwala, Urmi Pathak, Jinal Patel, Nirali Nanavati, Preethi Srinivasan, Arnav Bhavsar

2022

Abstract

The acquisition time for different MRI (Magnetic Resonance Imaging) image modalities pose a unique challenge to the efficient usage of the contemporary radiology technologies. The ability to synthesize one modality from another can benefit the diagnostic utility of the scans. Currently, all the exploration in the field of medical image to image translation is focused on NIfTI (Neuroimaging Informatics Technology Initiative) images. However, DICOM (Bidgood et al., 1997) images are the prevalent image standard in MRI centers. Here, we propose a modified deep learning network based on U-Net architecture for T1-Weighted image (T1WI) modality to T2-Weighted image (T2WI) modality image to image translation for DICOM images and vice versa. Our deep learning model exploits the pixel wise features between T1W images and T2W images which are important to understand the brain structures. The observations indicate better performance of our approach to the previous state-of-the-art methods. Our approach can help to decrease the acquisition time required for the scans and thus, also avoid motion artifacts.

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Paper Citation


in Harvard Style

Anklesaria N., Malu Y., Nikalwala D., Pathak U., Patel J., Nanavati N., Srinivasan P. and Bhavsar A. (2022). Multi Modality Medical Image Translation for Dicom Brain Images. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING, ISBN 978-989-758-552-4, pages 168-173. DOI: 10.5220/0010906400003123


in Bibtex Style

@conference{bioimaging22,
author={Ninad Anklesaria and Yashvi Malu and Dhyey Nikalwala and Urmi Pathak and Jinal Patel and Nirali Nanavati and Preethi Srinivasan and Arnav Bhavsar},
title={Multi Modality Medical Image Translation for Dicom Brain Images},
booktitle={Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING,},
year={2022},
pages={168-173},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010906400003123},
isbn={978-989-758-552-4},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING,
TI - Multi Modality Medical Image Translation for Dicom Brain Images
SN - 978-989-758-552-4
AU - Anklesaria N.
AU - Malu Y.
AU - Nikalwala D.
AU - Pathak U.
AU - Patel J.
AU - Nanavati N.
AU - Srinivasan P.
AU - Bhavsar A.
PY - 2022
SP - 168
EP - 173
DO - 10.5220/0010906400003123