Ml-based methods for pseudo-ct and hr mr image estimation

Patent metadata

Field	Value
Publication number	US-2020034948-A1
Application number	US-201916525562-A
Country	US
Kind code	A1
Filing date	Jul 29, 2019
Priority date	Jul 27, 2018
Publication date	Jan 30, 2020
Grant date	—

Abstract

The present disclosure describes a computer-implemented method of transforming a low-resolution MR image to a high-resolution MR image using a deep CNN-based MRI SR network and a computer-implemented method of transforming an MR image to a pseudo-CT (sCT) image using a deep CNN-based sCT network. The present disclosure further describes a MR image-guided radiation treatment system that includes a computing device to implement the MRI SR and CT networks and to produce a radiation plan based in the resulting high resolution MR images and sCT images.

First claim

What is claimed is:

1 . A computer-implemented method of transforming a low-resolution MR image into a super-resolution MR image using an MRI SR deep CNN system comprising a deep CNN-based de-noising auto-encoder (DAE) network and a deep CNN-based super-resolution generative network (SRG), the method comprising:
receiving, using a computing device, a low resolution MR image; transforming, using the computing device, the low resolution MR image into a de-noised MR image using the DAE network; and transforming, using the computing device, the de-noised MR data into the super-resolution MR image using the SRG network.

2 . The computer-implemented method of claim 1 , wherein the DAE network comprises:
six convolutional encoder layers with 4×4 filters and six de-convolutional decoder layers with 4×4 filters, wherein:
each convolutional encoder layer and each de-convolution decoder layer comprises a single convolutional/deconvolutional filter with stride 2; and
each convolutional encoder layer and each de-convolution decoder layer ends with a leaky and standard rectified linear unit (ReLU).

3 . The computer-implemented method of claim 1 , wherein the SRG network comprises:
two up-sampling layers, eight residual blocks, each residual block comprising two 3×3 convolutional filters separated by a ReLU activation with an elementwise sum operator attached at the end of the layer; and two output layers, each output layer comprising a 3×3 convolutional filter, ReLU activation, and a subpixel operator up-sampling layer.

4 . The computer-implemented method of claim 2 , further comprising training the DAE network by:
receiving, using the computing device, a set of noisy low resolution MR images; transforming, using the computing device, each noisy MR image into a de-noised MR image using a noise filter, wherein each noisy MR image and corresponding de-noised MR image together form a noisy/de-noised MR image pair; and training, using the computing device, the DAE network to minimize a reconstruction error given by ∥g θ g (f θ f ({tilde over (x)}))−x∥ for each matched noisy/de-noised low resolution image pair, where x denotes each de-noised MR image, {tilde over (x)} denotes each noisy MR image, and f θ f and g θ g denote the encoding and decoding network parameterized by θ f and θ g , respectively.

5 . The computer-implemented method of claim 4 , wherein the noise filter comprises a non-local means filter.

6 . The computer-implemented method of claim 3 , further comprising training the SRG network by:
receiving, using the computing device, a set of matched low resolution/high resolution MR image pairs; forming, using the computing device, a generative adversarial network (GAN) including a generative model G parametrized by θ G and comprising the SRG network and a discriminative model D parametrized by θ D , the discriminative model D configured to determine a probability that a high resolution MR image is a high resolution image or an SRG-transformed low resolution MR image from a matched low resolution/high resolution MR image pair; and training, using the computing device, the GAN to solve the optimization problem given by

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by updating D and G in alternating steps while fixing the other parameter, wherein the GAN is trained if D is unable to determine whether each high resolution MR image is the selected high resolution MR image or the transformed low resolution MR image from each matched low resolution/high resolution MR image pair.

7 . The computer-implemented method of claim 6 , wherein the set of matched low resolution/high resolution MR image pairs is produced by:
transforming, using the computing device, a high resolution MR image to a low resolution MR image training using a deep CNN-based down-sampling network (DSN), the DSN comprising:
two down-sampling layers, each down-sampling layer comprising a 3×3 convolutional filter of stride 2 followed by a ReLU activation;
two residual blocks, each residual block comprising two 3×3 convolutional filters separated by a ReLU activation and followed by an elementwise sum operator; and
an output layer.

8 . A computer-implemented method of transforming a low-resolution MR data into a pseudo-CT (sCT) using a deep-CNN based sCT system, the sCT system comprising a deep CNN-based sCT generative network, the method comprising:
receiving, using a computing device,

Assignees

• Park Chunjoo
• Mutic Sasa
• Zhang Hao
• Green Olga
• Univ Washington

Inventors

• Park Chunjoo
• Mutic Sasa
• Zhang Hao
• Green Olga

Classifications

• A61B6/032

  Transmission computed tomography [CT] · CPC title

• A61N5/1039

  using functional images, e.g. PET or MRI · CPC title

• A61B6/5247

  combining images from an ionising-radiation diagnostic technique and a non-ionising radiation diagnostic technique, e.g. X-ray and ultrasound · CPC title

• G06T3/4053 Primary

  based on super-resolution, i.e. the output image resolution being higher than the sensor resolution · CPC title

• A61N2005/1055

  using magnetic resonance imaging [MRI] · CPC title