Methods and systems for multiplanar reformation with machine learning based image enhancement

Patent metadata

Field	Value
Publication number	US-12499608-B2
Application number	US-202318356083-A
Country	US
Kind code	B2
Filing date	Jul 20, 2023
Priority date	Jul 20, 2023
Publication date	Dec 16, 2025
Grant date	Dec 16, 2025

Abstract

The disclosure relates to multiplanar reformation of three-dimensional medical images. In particular, the invention provides a method for reformatting image sequences by determining a landmark plane intersecting a volume, acquiring an image sequence, reformatting the image sequence along the landmark plane to produce a first reformatted image sequence, perturbing the landmark plane to produce a perturbed landmark plane, reformatting the first reformatted image sequence along the perturbed landmark plane to produce a second reformatted image sequence, mapping the second reformatted image sequence, the image sequence, and the landmark plane, to a resolution enhanced image sequence using a trained image enhancement network, and displaying the resolution enhanced image sequence via a display device. The present disclosure provides approaches which may reduce image artifacts in retrospectively reformatted image sequences, particularly in cases of retrospective reformatting of medium or low-resolution image sequences, without relying on acquisition of high-resolution 3D images.

First claim

The invention claimed is:

1 . A method, comprising:
determining a landmark plane intersecting a volume; acquiring an image sequence; reformatting the image sequence along the landmark plane to produce a first reformatted image sequence; perturbing the landmark plane to produce a perturbed landmark plane; reformatting the first reformatted image sequence along the perturbed landmark plane to produce a second reformatted image sequence; mapping the second reformatted image sequence, the image sequence, and the landmark plane, to a resolution enhanced image sequence, using a trained image enhancement network; and displaying the resolution enhanced image sequence via a display device.

2 . The method of claim 1 , wherein the landmark plane is determined based on three-plane localizer images of the volume, using a landmark plane segmentation model, and wherein the landmark plane intersects a pre-determined anatomical region.

3 . The method of claim 1 , wherein the image sequence comprises a plurality of two-dimensional (2D) images, wherein each of the plurality of 2D images is parallel to a first plane, and wherein the landmark plane is not parallel to the first plane.

4 . The method of claim 1 , wherein the first reformatted image sequence comprises a plurality of two-dimensional (2D) images, wherein each of the plurality of 2D images is parallel to the landmark plane.

5 . The method of claim 1 , wherein the resolution enhanced image sequence comprises a plurality of two-dimensional (2D) images corresponding to the second reformatted image sequence, wherein each of the plurality of 2D images is parallel to the perturbed landmark plane.

6 . The method of claim 1 , wherein the trained image enhancement network includes a plurality of learned convolutional filters.

7 . The method of claim 6 , wherein the plurality of learned convolutional filters receive the second reformatted image sequence.

8 . A method for training an image enhancement network, comprising:
selecting a training data pair comprising input data and a ground truth image sequence, wherein the input data comprises:
a landmark plane;
a reformatted image sequence; and
a native image sequence comprising a first plurality of images parallel to a first plane; and
mapping the input data to a predicted resolution enhanced image sequence using the image enhancement network; determining a loss based on a difference between the predicted resolution enhanced image sequence and the ground truth image sequence; and updating parameters of the image enhancement network based on the loss to produce a trained image enhancement network.

9 . The method of claim 8 , wherein the landmark plane intersects a pre-determined anatomical region, and wherein the landmark plane is automatically determined using a landmark plane segmentation model.

10 . The method of claim 8 , the method further comprising:
generating the training data pair by:
receiving the native image sequence of a volume;
selecting the landmark plane intersecting the volume;
reformatting the native image sequence along the landmark plane to produce a second image sequence comprising a second plurality of images parallel to the landmark plane;
determining an inverse plane to the landmark plane;
reformatting the second image sequence along the inverse plane to produce the reformatted image sequence comprising a third plurality of images parallel to the first plane;
determining an overlap region of the native image sequence and the reformatted image sequence;
storing the overlap region as the ground truth image sequence of the training data pair, and
storing the landmark plane, the reformatted image sequence, and the native image sequence, as the input data of the training data pair.

11 . The method of claim 10 , wherein determining the inverse plane to the landmark plane comprises determining a set of transformations which map from the landmark plane to the first plane.

12 . The method of claim 10 , wherein determining the overlap region of the native image sequence and the reformatted image sequence comprises masking out regions of the native image sequence outside of an intersection between a foreground of the native image sequence and a foreground of the reformatted image sequence.

13 . The method of claim 8 , the method further comprising:
generating the training data pair by:
receiving a high-resolution native image sequence of a volume;
downsampling the high-resolution native image sequence to produce the native image sequence;
reformatting the native image sequence along the landmark plane to produce the reformatted image sequence comprising a second plurality of images parallel to the landmark plane;
reformatting the high-resolution native image sequence along the landmark plane to produce the ground truth image sequence; and
storing the ground truth image sequence, and the input data comprising the landmark plane, the reformatted image sequence, and the native image sequence, as the training data pair.

14 . The method of claim 13 , wherein downsampling the high-resolution native image sequence to produce the native image sequence further includes:
adding noise to the high-resolution native image sequence.

15 . The method of claim 13 , wherein the high-resolution native image sequence comprises a third plurality of images, and wherein downsampling the high-resolution native image sequence to produce the native image sequence comprises:
reducing spatial resolution of each of the third plurality of images; and reducing a number of the third plurality of images.

16 . An imaging system, comprising:
an imaging device; a display device; and an image processing system configured with a processor, and executable instructions stored in non-transitory memory, wherein, when executing the instructions, the processor causes the imaging system to: determine a landmark plane intersecting a volume; acquire an image sequence of the volume based on the landmark plane; reformat the image sequence along the landmark plane to produce a first reformatted image sequence; map the first reformatted image sequence, the image sequence, and the landmark plane, to a resolution enhanced image sequence, using a trained image enhancement network; and display the resolution enhanced image sequence via the display device.

17 . The imaging system of claim 16 , wherein the landmark plane is determined based on three-plane localizer images of the volume acquired via the imaging device, using a landmark plane segmentation model, and wherein the landmark plane intersects a pre-determined anatomical region.

18 . The imaging system of claim 16 , wherein the image sequence comprises a plurality of two-dimensional (2D) images, wherein each of the plurality of 2D images is parallel to a first plane, and wherein the landmark plane is not parallel the first plane.

19 . The imaging system of claim 16 , wherein the first reformatted image sequence comprises a plurality of two-dimensional (2D) images, wherein each of the plurality of 2D images is parallel to the landmark plane.

20 . The imaging system of claim 16 , wherein the resolution enhanced image sequence comprises a plurality of two-dimensional (2D) images corresponding to the first reformatted image sequence, wherein each of the plurality of 2D images is parallel to the landmark plane.

Assignees

• Ge Prec Healthcare Llc

Inventors

• Patil Rohan Keshav
• Chatterjee Sudhanya
• Shanbhag Dattesh Dayanand

Classifications

• G06T2207/10016

  Video; Image sequence · CPC title

• G06T2207/20081

  Training; Learning · CPC title

• G06T2207/20084

  Artificial neural networks [ANN] · CPC title

• G06T2207/10088

  Magnetic resonance imaging [MRI] · CPC title

• G06T7/11

  Region-based segmentation · CPC title