Highly-scalable image reconstruction using deep convolutional neural networks with bandpass filtering

Abstract

A method for magnetic resonance imaging (MRI) scans a field of view and acquires sub-sampled multi-channel k-space data U. An imaging model A is estimated. Sub-sampled multi-channel k-space data U is divided into sub-sampled k-space patches, each of which is processed using a deep convolutional neural network (ConvNet) to produce corresponding fully-sampled k-space patches, which are assembled to form fully-sampled k-space data V, which is transformed to image space using the imaging model adjoint Aadj to produce an image domain MRI image. The processing of each k-space patch ui preferably includes applying the k-space patch ui as input to the ConvNet to infer an image space bandpass-filtered image yi, where the ConvNet comprises repeated de-noising blocks and data-consistency blocks; and estimating the fully-sampled k-space patch vi from the image space bandpass-filtered image yi using the imaging model A and a mask matrix.

Description

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT[0001]This invention was made with Government support under contracts R01 EB019241 and R01-EB009690 awarded by the National Institutes of Health. The Government has certain rights in the invention.FIELD OF THE INVENTION[0002]The present invention relates generally to techniques for magnetic resonance imaging. More specifically, it relates to improved methods for magnetic resonance image reconstruction and artifact reduction.BACKGROUND OF THE INVENTION[0003]The ability to reconstruct magnetic resonance (MR) images from vastly undersampled acquisitions has significant clinical value. It allows the duration of the MR scan to be reduced and enables the visualization of rapid hemodynamics.[0004]Using advanced image reconstruction algorithms, images can be reconstructed with negligible loss in image quality despite high undersampling factors (R>6). To achieve this performance, algorithms exploit the data acquisition model w...

AI Technical Summary

Benefits of technology

The present invention describes a new technique for magnetic resonance imaging (MRI) that uses a deep learning network to reconstruct high-resolution images from undersampled data. The technique divides the imaging data into frequency-domain patches, ensuring that the reconstructed images are consistent with the measurement data. It also leverages the imaging model to account for images with different resolutions and sizes, making the network more flexible and efficient. By using this technique, MRI scans can be performed faster and at lower costs, and the images can be enlarged and printed without compromising their quality. The technique can be applied to other imaging applications where the measurement is performed in the image frequency domain.

Problems solved by technology

These traditional iterative algorithms, however, have considerable computational complexity for undersampled data.

There are various challenges in applying ConvNets to MRI reconstruction, however.

Consequently, many of the known techniques for image processing with ConvNets do not directly translate to MRI image reconstruction.

Without a data consistency step, the ConvNets may “hallucinate” new structures in the image or remove existing ones, leading to erroneous diagnosis.

On the other hand, if an attempt is made to use a data consistency step, the training and application can not be image-patch based, because if only small image patches are used, known information in the measurement domain (k-space domain) is lost.

This limitation increases the memory footprint of the ConvNet and decreases the speed of training and inference.

In addition, existing ConvNet techniques are not easily extendable to high-dimensional MR images and multi-dimensional MR images, because the training and inference of the ConvNet can never be fully parallelized: specific steps within the ConvNet (such as transforming from k-space domain to image domain) requires the gathering of all data before proceeding to the next step of the network.

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