Method and apparatus for accelerated acquisition and reconstruction of cine MRI using a deep learning based convolutional neural network

A system for recreating images from undersampled MRI image data comprising:

• an MRI imaging system comprising a plurality of magnets and RF coils configured to acquire undersampled MRI image data that include one or more images, each having a plurality of non-contiguous acquired scan lines;
• a processor and memory configured to execute software instructions that implement a series of cascading image enhancing stages that each produce enhanced output image data from input image data, a first stage receiving the undersampled MRI image data while each remaining stage receives the enhanced output image data from a previous stage, each stage including,
• a data consistency block configured to receive the enhanced output image data from the previous stage and generate multi-coil input images based on multiplying a sensitivity map by the enhanced output image data received from the previous stage, apply a first Fourier transform to convert the multi-coil input images to k-space data in a k-space domain, replace data in each image of the k-space data with the plurality of non-contiguous acquired scan lines at respective locations of each image of the k-space data, perform a second inverse Fourier transform to convert the k-space data to data-consistent images in an image domain, and output the data-consistent images including the plurality of non-contiguous acquired scan lines,
• a convolutional neural network (CNN) configured to estimate a regularizer term for the input image data, wherein the regularizer term is configured to minimize a difference between a true image and the output of the data consistency block, and
• a combinational block configured to combine the outputs of the data consistency block and CNN to create the enhanced output image data for the stage;
• and
• a memory configured to store recreated image data from a final stage of the cascading image enhancing stages.