Image processing method for multi-frequency ultrasound probe device

Patent metadata

Field	Value
Publication number	US-12551194-B2
Application number	US-202318480313-A
Country	US
Kind code	B2
Filing date	Oct 3, 2023
Priority date	Oct 3, 2023
Publication date	Feb 17, 2026
Grant date	Feb 17, 2026

Abstract

A method for processing multiple image data sets, each image data set generated by a different ultrasound frequency emitter, to obtain a single merged image data set for use to display a single merged image. The method can also include steps to account for non-ideal alignment between the different ultrasound frequency emitters.

First claim

What is claimed is:

1 . A method for processing ultrasonic imaging using a multi-frequency probe device, comprising the steps of:
acquiring a first set of ultrasound raw data from a first frequency emitter on the multi-frequency probe device; acquiring a second set of ultrasound raw data from a second frequency emitter on the multi-frequency probe device, wherein the first set of ultrasound raw data and the second set of ultrasound raw data are filtered dynamically as they are acquired to account for at least two signal segments of a scanline divided at one or more depth-points to be analyzed; processing the first set of ultrasound raw data using one or more GPU cores on a multi-GPU core set, the multi-GPU core set being part of a system on a chip device to generate a first image data set; processing the second set of ultrasound raw data using one or more GPU cores on the multi-GPU core set in parallel with the processing of the first set of ultrasound raw data to generate a second image data set; determining a vertical defect angle between the first frequency emitter and the second frequency emitter; determining a level defect angle between the first frequency emitter and the second frequency emitter; applying a vertical calibration algorithm to the first set of ultrasound raw data and the second set of ultrasound raw data to correct for the vertical defect angle; applying a level calibration algorithm to the first set of ultrasound raw data and the second set of ultrasound raw data to correct for the level defect angle; fusing the first image data set and the second image data set to generate a fused image data set; using the fused image data set to generate a fused image; and displaying the fused image on a display for review by a user.

2 . The method of claim 1 , wherein the fused image data set is generated by applying a wavelet transform algorithm to the first and second data sets.

3 . The method of claim 2 , further comprising the steps of:
applying a speckle-noise suppression algorithm to the fused image data set.

4 . The method of claim 3 , wherein there are an arbitrary number of frequency emitters which each generate a corresponding set of ultrasound raw data, and the corresponding sets of raw data are each processed in parallel on the multi-GPU core set and fused to create the fused image data set.

5 . The method of claim 2 , wherein the first set of ultrasound raw data and the second set of ultrasound raw data are filtered dynamically as they are acquired to account for two or more-signal segments of a scanline divided at one or more depth-points to be analyzed.

6 . The method of claim 5 , wherein a bandpass filter with a higher center-frequency is applied to the first set of ultrasound raw data and a second bandpass filter with a lower center-frequency is applied to the second set of ultrasound raw data when dynamically filtering the first set of ultrasound raw data and the second set of ultrasound raw data.

7 . The method of claim 6 , wherein there are an arbitrary number of frequency emitters which each generate a corresponding set of ultrasound raw data, and the corresponding sets of raw data are each processed in parallel on the multi-GPU core set and fused to create the fused image data set.

8 . The method of claim 5 , wherein there are an arbitrary number of frequency emitters which each generate a corresponding set of ultrasound raw data, and the corresponding sets of raw data are each processed in parallel on the multi-GPU core set and fused to create the fused image data set.

9 . The method of claim 2 , wherein there are an arbitrary number of frequency emitters which each generate a corresponding set of ultrasound raw data, and the corresponding sets of raw data are each processed in parallel on the multi-GPU core set and fused to create the fused image data set.

10 . The method of claim 1 , wherein a bandpass filter with a higher center-frequency is applied to a first signal segment of the scanline and a second bandpass filter with a lower center-frequency is applied to a second segment of the scanline when dynamically filtering the first set of ultrasound raw data and the second set of ultrasound raw data.

11 . The method of claim 1 , wherein the first frequency emitter is placed back-to-back with the second frequency emitter.

12 . The method of claim 11 , wherein there are an arbitrary number of frequency emitters which each generate a corresponding set of ultrasound raw data, and the corresponding sets of raw data are each processed in parallel on the multi-GPU core set and fused to create the fused image data set.

13 . The method of claim 1 , further comprising the steps of:
acquiring a third set of ultrasound raw data from a third frequency emitter on the multi-frequency probe device; processing the third set of ultrasound raw data using one or more GPU cores on a multi-GPU core set in parallel with the processing of the first set of ultrasound raw data and the second set of ultrasound raw data to generate a third image data set; and fusing the first image data set, the second image data set, and the third image data set to generate the fused image data set.

14 . The method of claim 13 , wherein the first frequency emitter, the second frequency emitter, and the third frequency emitter are at an angle of 120 degrees to each other in the plane of the multi-frequency probe device.

15 . The method of claim 1 , further comprising the steps of:
applying an envelope detection algorithm to the first set of ultrasound raw data and the second set of ultrasound raw data; applying a log-compression algorithm to the first set of ultrasound raw data and the second set of ultrasound raw data; applying an interpolation algorithm to the first set of ultrasound raw data and the second set of ultrasound raw data; and applying a coordinate transformation algorithm to the first set of ultrasound raw data and the second set of ultrasound raw data.

16 . The method of claim 1 , wherein there are an arbitrary number of frequency emitters which each generate a corresponding set of ultrasound raw data, and the corresponding sets of raw data are each processed in parallel on the multi-GPU core set and fused to create the fused image data set.

Assignees

• Hong Kong Applied Science & Tech Research Inst Co Ltd
• Hong Kong Applied Science And Tech Research Institute Company

Inventors

• Chen Rihong
• Huang Hanqiang

Classifications

• G06T1/20 Primary

  Processor architectures; Processor configuration, e.g. pipelining · CPC title

• A61B8/5269

  involving detection or reduction of artifacts · CPC title

• A61B8/5238 Primary

  for combining image data of patient, e.g. merging several images from different acquisition modes into one image · CPC title

• A61B8/5207

  involving processing of raw data to produce diagnostic data, e.g. for generating an image · CPC title

• A61B8/12

  in body cavities or body tracts, e.g. by using catheters · CPC title