Cloud-based medical imaging reconstruction using deep neural networks

Medical imaging techniques like computed tomography (CT), magnetic resonance imaging (MRI), medical x-ray imaging, ultrasound imaging, and others are commonly used by doctors for various reasons. Some examples include detecting changes in the appearance of organs, tissues, and vessels, and detecting abnormalities such as tumors and various other type of pathologies. Before doctors can use the data from those techniques, the data needs to be transformed from its native raw form to a form that can be displayed as an image on a computer screen. This process is known as image reconstruction, and it plays a crucial role in a medical imaging workflow--it's the step that creates diagnostic images that can be then reviewed by doctors. In this post, we discuss a use case of MRI reconstruction, but the architectural concepts can be applied to other types of image reconstruction.