Lei Xing

Title: Lei Xing: Innovator in Tomographic Imaging and Magnetic Resonance

Introduction

Lei Xing, based in Palo Alto, CA, is a prominent inventor with a remarkable portfolio of 26 patents. His innovative contributions primarily focus on tomographic imaging and magnetic resonance technologies, utilizing advanced methods such as deep learning to enhance imaging quality and efficiency.

Latest Patents

Among his latest patents is a groundbreaking method for single or few views computed tomography imaging that employs a deep neural network. This method involves acquiring a set of one or more 2D projection images and reconstructing a 3D volumetric image using a residual deep learning network with an encoder network, a transform module, and a decoder network. The process optimally transforms and maps 2D features into 3D features to generate a high-quality 3D image. In addition, he has developed a method for qualitative and quantitative MRI using deep learning, where a conventional pulse sequence is utilized to obtain a qualitative MR image, which is then processed through a deep convolutional neural network to produce a quantitative MR relaxation parametric map.

Career Highlights

Lei Xing has made significant strides in his career, notably through his work at esteemed institutions such as Leland Stanford Junior University and the University of California. His expertise and innovative approaches in imaging technologies have positioned him as a leading figure in the field.

Collaborations

Throughout his career, Lei has collaborated with distinguished colleagues, including Karl Kenneth Bush and Michael J. Hadsell. These partnerships have furthered the advancements in the imaging domain, enabling contributions that are pivotal in both research and clinical applications.

Conclusion

In summary, Lei Xing stands out as an influential inventor in the realm of medical imaging, with a strong emphasis on deep learning applications. His extensive patent portfolio not only reflects his innovative spirit but also his commitment to improving imaging techniques, ultimately benefiting both the healthcare industry and research communities.