Cardiac MRI Orientation Recognition and Standardization using Deep Neural Networks

• URL: http://arxiv.org/abs/2308.00615v1
• Date: Mon, 31 Jul 2023 00:01:49 GMT
• Title: Cardiac MRI Orientation Recognition and Standardization using Deep Neural Networks
• Authors: Ruoxuan Zhen
• Abstract summary: We present a method that employs deep neural networks to categorize and standardize the orientation of cardiac MRI images. We conducted comprehensive experiments on CMR images from various modalities, including bSSFP, T2, and LGE. The validation accuracies achieved were 100.0%, 100.0%, and 99.4%, confirming the robustness and effectiveness of our model.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Orientation recognition and standardization play a crucial role in the effectiveness of medical image processing tasks. Deep learning-based methods have proven highly advantageous in orientation recognition and prediction tasks. In this paper, we address the challenge of imaging orientation in cardiac MRI and present a method that employs deep neural networks to categorize and standardize the orientation. To cater to multiple sequences and modalities of MRI, we propose a transfer learning strategy, enabling adaptation of our model from a single modality to diverse modalities. We conducted comprehensive experiments on CMR images from various modalities, including bSSFP, T2, and LGE. The validation accuracies achieved were 100.0\%, 100.0\%, and 99.4\%, confirming the robustness and effectiveness of our model. Our source code and network models are available at https://github.com/rxzhen/MSCMR-orient

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