Cardiac Strain Analysis Systems and Methods for Specialized Environments

This inventive concept adapts cardiac strain analysis using deep learning for specific, narrow markets or unique operational environments, such as extreme weather conditions, emergency response situations, high-security needs, disaster relief, and remote or isolated areas.

The original patent disclosed systems and methods for phase unwrapping for dense MRI using deep learning, enabling accurate cardiac strain analysis. However, these methods are limited to general clinical settings and do not account for specialized environments that require adapted solutions. The new inventive concept addresses this limitation by providing tailored systems and methods for cardiac strain analysis in various niche situations.

The inventive concept comprises systems and methods that integrate MRI scanners and deep learning modules to generate phase images for cardiac strain analysis in specialized environments. For example, in extreme weather conditions, the MRI scanner is configured to acquire displacement encoded MRI data in high winds or extreme temperatures, while the deep learning module generates phase images for strain analysis. In emergency response situations, the method rapidly acquires displacement encoded MRI data and uses a U-Net structured CNN to compute a wrapping label map for strain analysis in real-time. Similarly, in high-security environments, the deep learning module is encrypted to prevent unauthorized access. In disaster relief situations, a portable MRI scanner acquires displacement encoded MRI data, and a CNN configured for epicardial and endocardial segmentation assigns one of three classes to each pixel. In remote or isolated areas, a satellite-enabled MRI scanner acquires displacement encoded MRI data, and the deep learning module is connected to a cloud-based server for remote analysis.

The new inventive concept is novel and non-obvious in its adaptation of cardiac strain analysis using deep learning for specialized environments, which are not addressed by the original patent. The inventive step lies in the tailored design of systems and methods to accommodate unique operational requirements, ensuring accurate and reliable cardiac strain analysis in these niche situations.

Alternative embodiments of the inventive concept could include the use of different deep learning architectures, such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs), to improve the accuracy and speed of cardiac strain analysis. Variations could also include the integration of additional sensors or data sources, such as electrocardiogram (ECG) or blood pressure data, to enhance the robustness of the analysis.

The inventive concept has significant commercial potential in various industries, including healthcare, emergency response, disaster relief, and remote or isolated area services. The adapted systems and methods can provide accurate and reliable cardiac strain analysis in specialized environments, enabling healthcare professionals to make informed decisions in critical situations. The market for these adapted solutions is substantial, given the growing need for specialized healthcare services and the increasing adoption of deep learning in medical imaging.