Method and system for correcting contrast-enhanced images

Patent metadata

Field	Value
Publication number	US-2024153037-A1
Application number	US-202218280512-A
Country	US
Kind code	A1
Filing date	Mar 3, 2022
Priority date	Mar 8, 2021
Publication date	May 9, 2024
Grant date	—

Abstract

A method and system for correcting a difference in contrast agent density in a sequence of contrast-enhanced image frames. A reference image frame is defined in the sequence of contrast-enhanced image frames, and segmentation is performed on the reference image frame to determine a location of a region of interest within the reference image frame. The region of interest is a region of the reference image frame that contains contrast agent. Other image frames in the sequence of contrast-enhanced images are corrected based on a difference in contrast agent density/image intensity in the region of interest relative to the region of interest in the reference image frame.

First claim

1 . A computer-implemented method for correcting a difference in contrast agent density in a sequence of contrast-enhanced image frames, the computer-implemented method comprising:
selecting a reference image frame from the sequence of contrast-enhanced image frames; performing segmentation on the reference image frame; identifying a region of interest in the reference image frame based on the segmentation, wherein the region of interest is a region of the reference image frame that contains the contrast agent; and correcting the difference in contrast agent density in a set of one or more image frames in the sequence of image frames based on a change in image intensity within the region of interest between each of the one or more contrast-enhanced image frames and the reference image frame, wherein the set of one or more image frames includes at least one image frame that is different to the reference image frame.

2 . The computer-implemented method of claim 1 , wherein the sequence of contrast-enhanced image frames is a sequence of ventricular opacification image frames.

3 . The computer-implemented method of claim 2 , wherein the reference image frame is selected by:
performing an initial segmentation on a subset of image frames comprising the first M frames in the sequence of contrast-enhanced image frames, wherein M is a predetermined number; determining which image frame of the subset of image frames corresponds to a first end-diastolic frame based on the initial segmentation; and selecting the determined image frame as the reference image frame.

4 . The computer-implemented method of claim 2 , wherein the region of interest comprises an estimated location of a blood pool in the reference image frame.

5 . The computer-implemented method of claim 4 , wherein the step of identifying the region of interest further comprises:
identifying an end-systolic frame in the sequence of contrast-enhanced image frames; estimating a location of the blood pool in the identified end-systolic frame; and defining the region of interest as a region comprising the estimated location of the blood pool in the reference image frame and the estimated location in the end-systolic frame.

6 . The computer-implemented method of claim 1 , wherein the reference image frame is selected by:
determining which image frame of the first N frames in the sequence of contrast-enhanced image frames has a largest average intensity, wherein N is a predetermined number; and selecting the determined image frame as the reference image frame.

7 . The computer-implemented method of claim 1 , wherein the reference image frame is an image frame in which the contrast agent density is the highest in the sequence of contrast-enhanced image frames.

8 . The computer-implemented method of claim 1 , wherein the step of correcting the set of one or more image frames in the sequence of image frames comprises:
defining, for each image frame in the set of one or more image frames, a non-linear transfer function of intensity values such that a mean uncorrected image intensity of the region of interest in the image frame is mapped to a mean image intensity of the region of interest in the reference image frame; applying, for each image frame in the set of one or more image frames, the non-linear transfer function defined for the image frame to at least a part of the image frame.

9 . The computer-implemented method of claim 1 , wherein the step of correcting the set of one or more image frames in the sequence of image frames comprises:
defining, for each image frame in the set of one or more image frames, a function based on histogram matching such that gray-value statistics of at least a part of the image frame are aligned to gray-value statistics of the region of interest in the reference image frame; and applying, for each image frame in the set of one or more image frames, the function defined for the image frame to at least a part of the image frame.

10 . The computer-implemented method of claim 1 , wherein the difference in contrast agent density is corrected in the whole of each image frame of the set of one or more image frames.

11 . The computer-implemented method of claim 1 , wherein the difference in contrast agent density is corrected in a part of each image frame of the set of one or more image frames, wherein the part includes the region of interest.

12 . A computer program product comprising code means which, when executed on a computing device having a processing system, cause the processing system to perform all of the steps of the method according to claim 1 .

13 . A processing system for correcting a difference in contrast agent density in a sequence of contrast-enhanced image frames, the processing system being configured to:
select a reference image frame from the sequence of contrast-enhanced image frames; perform segmentation on the reference image frame; identify a region of interest in the reference image frame based on the segmentation, wherein the region of interest is a region of the reference image frame that contains the contrast agent; and correct the difference in contrast agent density in a set of one or more image frames in the sequence of image frames based on a change in image intensity within the region of interest between each of the one or more contrast-enhanced image frames and the reference image frame, wherein the set of one or more image frames includes at least one image frame that is different to the reference image frame.

14 . The processing system of claim 13 , wherein the reference image frame is an image frame in which the contrast agent density is the highest in the sequence of contrast-enhanced image frames.

15 . A system comprising:
imaging apparatus for obtaining medical images of a subject; and the processing system of claim 13 , further configured to receive the sequence of contrast-enhanced image frames from the imaging apparatus.

Assignees

• Koninklijke Philips Nv

Inventors

• Wild Sebastian
• Weber Frank Michael
• Waechter-Stehle Irina
• Peters Jochen
• Ewald Arne
• Goossen André
• Lenga Matthias
• Wissel Tobias
• Prater David

Classifications

• G06T5/40 Primary

  using histogram techniques · CPC title

• G06T5/50

  using two or more images, e.g. averaging or subtraction · CPC title

• G06T7/10

  Segmentation; Edge detection (motion-based segmentation G06T7/215) · CPC title

• G06T7/73

  using feature-based methods · CPC title

• G06T2207/10016

  Video; Image sequence · CPC title