Systems and Methods for Multi-Kernel Synthesis and Kernel Conversion in Medical Imaging

Abstract

Systems and methods are provided for synthesizing information from multiple image series of different kernels into a single image series using deep-learning based methods trained using a task-based loss function that includes a sharp loss term and a smooth loss term that parameterize training. For multi-kernel synthesis, a single set of images with desired high spatial resolution and low image noise can be synthesized from multiple image series of different kernels. The synthesized kernel is sufficient for a wide variety of clinical tasks, even in circumstances that would otherwise require many separate image sets. Kernel conversion may be configured to generate images with arbitrary reconstruction kernels from a single baseline kernel. This would reduce the burden on the CT scanner and the archival system, and greatly simplify the clinical workflow.

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