Cardiac MRI Orientation Recognition and Standardization using Deep Neural Networks

TL;DR

This paper introduces a deep learning approach for recognizing and standardizing cardiac MRI orientations, employing transfer learning to adapt across multiple MRI modalities with high accuracy.

Contribution

It presents a novel transfer learning-based method for cardiac MRI orientation recognition that works across various modalities, improving robustness and accuracy.

Findings

Achieved 100% accuracy on bSSFP and T2 modalities.

Achieved 99.4% accuracy on LGE modality.

Demonstrated robustness across multiple MRI sequences.

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