Enhanced Systems and Methods for Optimizing Functional Images of a Lesion Region using Guided Diffuse Optical Tomography

This inventive concept improves upon existing guided diffuse optical tomography (DOT) systems by integrating machine learning, artificial intelligence, deep learning, and computer vision modules to enhance image quality, reduce noise, and optimize functional images of a lesion region.

The original patent, 'Systems and methods of optimizing functional images of a lesion region using guided diffuse optical tomography,' has limitations in image quality and noise reduction. This new inventive concept addresses these limitations by leveraging advanced computational techniques to improve the accuracy and efficiency of DOT systems.

The enhanced system comprises a DOT device integrated with a machine learning module, an artificial intelligence module, a deep learning module, or a computer vision module. These advanced computational modules are trained using datasets of images from multiple subjects to improve image quality and reduce noise. The system acquires lesion functional data from the lesion region and reference functional data from a healthy tissue region, applying the advanced computational techniques to generate optimized functional images. This integration enables the system to better distinguish between healthy and diseased tissues, leading to more accurate diagnoses and treatments.

The novelty of this inventive concept lies in the integration of advanced computational modules with DOT devices to improve image quality and reduce noise. This integration is non-obvious and provides a significant improvement over the original patent, enabling more accurate and efficient diagnoses.

Alternative embodiments of this inventive concept could include integrating different types of machine learning algorithms, using various deep learning architectures, or incorporating computer vision techniques for image segmentation. Additionally, the system could be adapted for use in other medical imaging applications, such as imaging other types of cancer or diseases.

This enhanced system has significant commercial potential in the medical imaging industry, particularly in the diagnosis and treatment of breast cancer. The improved image quality and reduced noise enable more accurate diagnoses, leading to better patient outcomes and reduced healthcare costs. The system could be marketed to hospitals, clinics, and research institutions, and could also be used in other medical imaging applications.