Ge Precision Healthcare LLC

Florintina C

Dattesh Dayanand Shanbhag

Chitresh Bhushan

Deepa Anand

Sudhanya Chatterjee

Radhika Madhavan

Rohan Keshav Patil

Suresh Emmanuel Devadoss Joel

Preetham Shankapal

Megha Goel

Systems and methods are provided for reconstructing images from motion-affected k-space data. In one example, a method comprises obtaining k-space data of a spin echo magnetic resonance imaging (MRI) exam of a subject, the k-space data comprising a plurality of echo train lengths (ETLs), with each ETL comprising a subset of lines of the k-space data. The method further comprises identifying a subset of ETLs of the plurality of ETLs of the k-space data corresponding to a dominant pose of the subject, generating an undersampled version of the k-space data, the undersampled version including only the subset of ETLs, entering the undersampled version of the k-space data as input to a reconstruction model trained to output a reconstructed image based on the undersampled version of the k-space data, and displaying the reconstructed image on a display device and/or saving the reconstructed image in memory.

Systems and methods for motion-artifact reduction in magnetic resonance imaging

A medical imaging system includes at least one medical imaging device providing image data of a subject and a processing system programmed to generate a plurality of training images having simulated medical conditions by blending a pathology region from a plurality of template source images to a plurality of target images. The processing system is further programmed to train a deep learning network model using the plurality of training images and input the image data of the subject to the deep learning network model. The processing system is further programmed to generate a medical image of the subject based on the output of the deep learning network model.

Deep learning based medical system and method for image acquisition

Growing community of inventors

Bengaluru, India

Florintina C