Cardiac MRI Orientation Recognition and Standardization using Deep Neural Networks
This work addresses a domain-specific problem for medical imaging practitioners, offering an incremental improvement with high accuracy in standardizing MRI orientations.
The paper tackles the challenge of orientation recognition and standardization in cardiac MRI by using deep neural networks, achieving validation accuracies of 100.0%, 100.0%, and 99.4% across different modalities.
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