IP Library Granted Patent US 12,268,552
Granted Patent B2
US 12,268,552 · App. 17/084,299 · Granted Apr 8, 2025

Method and apparatus for the automatic detection of atheromas in peripheral arteries

Inventors: Ram L. Bedi (Issaquah, WA); Todor Jeliaskov (Scottsdale, AZ); Charles D. Emery (Gilbert, AZ)
Assignee: Atherosys, Inc.
A61B8/0891A61B8/02A61B8/06A61B8/085A61B8/485A61B8/488A61B8/5223A61B8/5269G16H30/40G16H50/20
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,268,552
App. No.
17/084,299
Granted
Apr 8, 2025
Kind
B2
Abstract

A method for detecting the presence of an atheroma in an artery of interest using an ultrasound apparatus can include performing a first procedure on a first day. The first procedure can include detecting one or more blood vessels in a target region of a patient's body. The procedure can include identifying, from the one or more blood vessels, an artery of interest; automatically detecting spatial boundaries of constituent layers of an arterial wall of the artery of interest and calculating, via a processor of the ultrasound apparatus, a cross-sectional intima-media area (IMA) of the artery of interest at a first location along a length of the artery of interest. The procedure can include automatically calculating, via the processor of the ultrasound apparatus, IMA of the artery of interest at a second or more locations along the length of the artery of interest. In some embodiments, the procedure includes automatically calculating, via the processor and based at least in part on the calculations of the IMAs of the artery of interest at the first location and at the second or more locations, an intima-media volume (IMV), arterial volume, and luminal volume of the artery of interest over a predetermined length of the artery of interest.

Claims (70)

1. A method for detecting a presence of an atheroma using an ultrasound apparatus, the method comprising:

performing a procedure comprising—

repeatedly obtaining echo signals produced in response to ultrasound signals generated via a transducer;

based on the obtained echo signals, producing ultrasound image data via processing circuitry;

detecting, via the processing circuitry and without user input, a volume of interest and one or more blood vessels from the ultrasound image data in a target region of a patient's body as the echo signals are being obtained;

identifying, via the processing circuitry and from the one or more blood vessels, an artery of interest based on:

a brightness of a wall of the artery of interest,

a darkness in a lumen of the artery of interest, and

a difference between the brightness of the artery wall and the darkness of the artery lumen;

tracking the artery of interest via radio frequency (RF) correlation of contiguous 2-dimensional (2D) frames of a 3D dataset, wherein the 2D frames include at least a first frame, a second frame, and a third frame, and wherein a tracked length of the artery of interest encompasses at least a segment of a common carotid region of the carotid artery of the patient and a bifurcated region of the carotid artery;

determining via cross-correlation of the first frame to the second frame, the first frame to the third frame, and the second frame to the third frame, that each of the first frame, the second frame, and the third frame include the artery of interest;

identifying an atheroma in the artery of interest;

obtaining a plurality of intima-media thicknesses (IMTs) along a longitudinal axis of the artery of interest, wherein each of the IMTs is based on a distance between edges of (i) a first peak of a first echo signal corresponding to a media-adventitia boundary and (ii) a second peak corresponding to a lumen-intima boundary;

based at least in part on the obtained IMTs, calculating, via the processing circuitry, a first cross-sectional intima-media area (IMA) of the artery of interest at a first location along a length of the artery of interest;

based at least in part on the obtained IMTs, calculating, via the processing circuitry, a second IMA of the artery of interest at a second or more locations along the length of the artery of interest; and

based at least in part on the calculations of the first and second IMAs of the artery of interest, calculating, via the processing circuitry, an intima-media volume (IMV), arterial volume, and luminal volume of the artery of interest over a predetermined length of the artery of interest.

2. The method of claim 1 , wherein the length of the artery of interest includes one or more anatomical markers for registering a coordinate system in the artery of interest to enable accurate comparisons of measurements between first and subsequent procedures.

3. The method of claim 2 , wherein at least one of the one or more anatomical markers is a bifurcation detected by skeletonizing the artery of interest.

4. The method of claim 2 , wherein at least one of the one or more anatomical marker is a localized increase in diameter of the artery of interest.

5. The method of claim 1 , wherein identifying the artery of interest comprises utilizing a machine learning method of neural network trained to identify vessels in ultrasound echography images by using self-derived features and standard vessel characteristics.

6. The method of claim 1 , wherein identifying the artery of interest comprises utilizing deterministic methods using known anatomical attributes of vessels.

7. The method of claim 1 wherein detecting the one or more blood vessels comprises identifying at least one vessel characteristic without user input.

8. The method of claim 1 , wherein detecting the one or more blood vessels comprises identifying, via the processor, blood flow data as represented by a color map of Doppler signal analysis.

9. The method of claim 1 , wherein detecting the one or more blood vessels comprises correlating frame-to-frame radio frequency (RF) data and combining it with a noise map.

10. The method of claim 1 , wherein identifying the artery of interest comprises identifying at least one vessel characteristic including vessel wall elastic properties.

11. The method of claim 1 , wherein identifying the artery of interest comprises identifying at least one vessel characteristic including vessel pulsatility.

12. The method of claim 1 , wherein identifying the artery of interest comprises identifying at least one vessel characteristic including vessel cross-sectional diameter.

13. The method of claim 1 , wherein identifying the artery of interest comprises identifying at least one vessel characteristic including vessel location in spatially registered data sets.

14. The method of claim 1 , wherein identifying the artery of interest comprises identifying at least one vessel characteristic including blood flow dynamics.

15. The method of claim 1 , wherein identifying the artery of interest comprises identifying at least one vessel characteristic including correlation of radio frequency (RF) data showing speckle decorrelation.

16. The method of claim 1 , wherein identifying the artery of interest comprises identifying, without user input, at least one vessel characteristic including a vessel bifurcation when present.

17. The method of claim 1 , wherein identifying the artery of interest comprises identifying at least one vessel characteristic including a localized increase in vessel diameter.

18. The method of claim 1 , wherein identifying the artery of interest comprises identifying a vessel that has diameter and depth within a predetermined range.

19. The method of claim 1 , wherein identifying the artery of interest comprises creating vessel skeletons and identifying vessels that bifurcate or have a bulb.

20. The method of claim 1 , wherein identifying the artery of interest comprises analyzing vessel pulsatility.

21. The method of claim 1 , wherein identifying the artery of interest comprises tracking vessels through a 3D dataset using frame-to-frame radio frequency (RF) correlation.

22. The method of claim 1 , further comprising spatially relocating the transducer, wherein tracking the artery of interest occurs as the transducer is spatially relocated.

23. A method for detecting a presence of an atheroma using an ultrasound apparatus, the method comprising:

repeatedly obtaining echo signals produced in response to ultrasound signals generated via a transducer;

based on the obtained echo signals, producing ultrasound image data via processing circuitry;

detecting, without user input and via the processing circuitry, one or more blood vessels from the ultrasound image data at a target region of a patient;

identifying, without user input and via the processing circuitry, an artery of interest from the one or more blood vessels as the echo signals are being obtained by (i) creating a 3D dataset of the one or more blood vessels using radio frequency (RF) correlation, and (ii) creating virtual 2-dimensional (2D) frames out of the 3D dataset, wherein the 2D frames include at least a first frame, a second frame, and a third frame, and wherein the artery of interest includes at least two of (i) a bulb of the carotid artery of the patient, (ii) a bifurcation of the carotid artery, or (iii) a common carotid region of the carotid artery, wherein identifying the artery of interest is based on (i) a brightness of a wall of the artery, (ii) a darkness in a lumen of the artery, and (iii) a difference between the brightness of the artery wall and the darkness of the artery lumen;

determining via cross-correlation of the first frame to the second frame, the first frame to the third frame, and the second frame to the third frame, that each of the first frame, the second frame, and the third frame include the artery of interest;

identifying an atheroma in the artery of interest;

obtaining a plurality of intima-media thicknesses (IMTs) along a longitudinal axis of the artery of interest, wherein each of the IMTs is based on a distance between (i) an edge of a first peak of a first echo signal corresponding to a media-adventitia boundary and (ii) an edge of a second peak of a second echo signal corresponding to a lumen-intima boundary;

based on the obtained IMTs, obtaining (i) a first intima-media area (IMA) of the artery of interest at a first location of the artery of interest and (ii) a second IMA of the artery of interest at a second location different than the first location; and

based at least in part on the first IMA and the second IMA, calculating an intima-media volume (IMV) of the artery of interest over a predetermined length of the artery of interest.

24. The method of claim 23 , wherein identifying the artery of interest occurs at a first time and wherein the IMV is a first IMV, the method further comprising:

identifying the artery of interest from the one or blood vessels at a second time after the first time;

obtaining a third IMA of the artery of interest at a third location of the artery of interest;

obtaining a fourth IMA of the artery of interest at a fourth location different than the third location; and

based at least in part on the third IMA and the fourth IMA, obtaining a second IMV of the artery of interest over a predetermined length of the artery of interest; and

comparing the first IMV with the second IMV to determine progression or regression of atherosclerosis.

25. The method of claim 23 , further comprising detecting physiological changes associated with the cardiac cycle of the patient, wherein obtaining the first IMA and/or obtaining the second IMA is based at least in part on the detected physiological changes associated with the cardiac cycle of the patient.

26. The method of claim 25 , wherein detecting the physiological changes comprising detecting a change in a luminal diameter during an intra-cardiac cycle and/or an inter-cardiac cycle.

27. The method of claim 25 , wherein detecting the physiological changes comprising detecting a change in intima-media thickness during an intra-cardiac cycle and/or an inter-cardiac cycle.

28. The method of claim 25 , wherein detecting the physiological changes comprising detecting a change in an arterial wall diameter during an intra-cardiac cycle and/or an inter-cardiac cycle.

29. The method of claim 25 , wherein detecting the physiological changes comprising detecting a change in arterial volume during an intra-cardiac cycle and/or an inter-cardiac cycle.

30. The method of claim 23 , wherein obtaining the IMV comprises obtaining the IMV via the processing circuitry and without user input.

31. The method of claim 23 , wherein the IMV corresponds at least in part to a volume of the atheroma.

32. A method for detecting a presence of an atheroma using an ultrasound apparatus, the method comprising:

repeatedly obtaining echo signals produced in response to ultrasound signals generated via a transducer;

based on the obtained echo signals, producing ultrasound image data via processing circuitry;

detecting, without user input and via the processing circuitry, one or more blood vessels from the ultrasound image data at a target region of a patient;

identifying, without user input and via the processing circuitry, an artery of interest from the one or more blood vessels as the echo signals are being obtained by (i) creating a 3D dataset of the one or more blood vessels using radio frequency (RF) correlation, and (ii) creating virtual 2-dimensional (2D) frames out of the 3D dataset, wherein the 2D frames include at least a first frame, a second frame, and a third frame, wherein identifying the artery of interest is based on (i) a brightness of a wall of the artery, (ii) a darkness in a lumen of the artery, and (iii) a difference between the brightness of the artery wall and the darkness of the artery lumen, and wherein a tracked length of the artery of interest encompasses at least a segment of a common carotid region of the carotid artery of the patient and a bifurcated region of the carotid artery;

determining via cross-correlation of the first frame to the second frame, the first frame to the third frame, and the second frame to the third frame, that each of the first frame, the second frame, and the third frame include the artery of interest;

identifying an atheroma in the artery of interest;

obtaining a plurality of intima-media thicknesses (IMTs) along a longitudinal axis of the artery of interest, wherein each of the IMTs is based on a distance between (i) an edge of a first peak of a first echo signal corresponding to a media-adventitia boundary and (ii) an edge of a second peak of a second echo signal corresponding to a lumen-intima boundary;

based on the obtained IMTs, obtaining (i) a first intima-media area (IMA) of the artery of interest at a first location of the artery of interest and (ii) a second IMA of the artery of interest at a second location different than the first location; and

based at least in part on the first IMA and the second IMA, calculating an intima-media volume (IMV) of the artery of interest over a predetermined length of the artery of interest.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Nov 10, 2020
From: BEDI, RAM L.; JELIASKOV, TODOR; EMERY, CHARLES D.
To: ATHEROSYS, INC.
Reel/Frame 054371/0628 →
Continuity (3)
Continuation PCTUS2019029739 · Apr 29, 2019
Provisional Application 62664616 · Apr 30, 2018
Related Publication 20210045710A1 · Feb 18, 2021
References Cited (113)
US 4945478A · Merickel · 1990 [cited by examiner]
US 5954653A · Hatfield et al. · 1999 [cited by applicant]
US 6023968A · Spratt · 2000 [cited by examiner]
US 6139496A · Chen et al. · 2000 [cited by applicant]
US 6537220B1 · Friemel et al. · 2003 [cited by applicant]
US 6695784B1 · Michaell · 2004 [cited by applicant]
US 7356367B2 · Liang et al. · 2008 [cited by applicant]
US 7569016B2 · Watanabe et al. · 2009 [cited by applicant]
US 7925064B2 · Cloutier et al. · 2011 [cited by applicant]
US 8075488B2 · Burton · 2011 [cited by applicant]
US 8167805B2 · Emery et al. · 2012 [cited by applicant]
US 8491484B2 · Lewis · 2013 [cited by applicant]
US 8687862B2 · Hsu · 2014 [cited by examiner]
US 8740796B2 · Fukumoto et al. · 2014 [cited by applicant]
US 9179889B2 · Fukumoto et al. · 2015 [cited by applicant]
US 9192352B2 · Yao et al. · 2015 [cited by applicant]
US 9220477B2 · Urabe et al. · 2015 [cited by applicant]
US 9357980B2 · Toji et al. · 2016 [cited by applicant]
US 9498185B2 · Kimoto et al. · 2016 [cited by applicant]
US 9693755B2 · Kondoh · 2017 [cited by applicant]
US 9770227B2 · Kawabata et al. · 2017 [cited by applicant]
US 10722209B2 · Chen et al. · 2020 [cited by applicant]
US 11771399B2 · Bedi et al. · 2023 [cited by applicant]
US 20040116813A1 · Selzer · 2004 [cited by examiner]
US 20050096528A1 · Fritz et al. · 2005 [cited by applicant]
US 20050182319A1 · Glossop · 2005 [cited by examiner]
US 20080171939A1 · Ishihara · 2008 [cited by applicant]
US 20090105579A1 · Garibaldi · 2009 [cited by applicant]
US 20090275834A1 · Watanabe · 2009 [cited by examiner]
US 20090306509A1 · Pedersen et al. · 2009 [cited by applicant]
US 20100113930A1 · Miyachi · 2010 [cited by applicant]
US 20100210946A1 · Harada et al. · 2010 [cited by applicant]
US 20100240992A1 · Hao · 2010 [cited by examiner]
US 20110257527A1 · Suri · 2011 [cited by applicant]
US 20110299754A1 · Suri · 2011 [cited by applicant]
US 20120078099A1 · Suri · 2012 [cited by applicant]
US 20120179042A1 · Fukumoto et al. · 2012 [cited by applicant]
US 20120296214A1 · Urabe et al. · 2012 [cited by applicant]
US 20130046168A1 · Sui · 2013 [cited by applicant]
US 20130218024A1 · Boctor · 2013 [cited by examiner]
US 20130321262A1 · Scheter · 2013 [cited by applicant]
US 20140066770A1 · Watanabe et al. · 2014 [cited by applicant]
US 20140081142A1 · Toma · 2014 [cited by examiner]
US 20140100440A1 · Cheline et al. · 2014 [cited by applicant]
US 20140249417A1 · Ookouchi et al. · 2014 [cited by applicant]
US 20140275986A1 · Vertikov · 2014 [cited by examiner]
US 20140276059A1 · Sheehan · 2014 [cited by examiner]
US 20140276062A1 · Kondoh · 2014 [cited by applicant]
US 20140303499A1 · Toma et al. · 2014 [cited by applicant]
US 20140369583A1 · Toji et al. · 2014 [cited by applicant]
US 20140371593A1 · Kondoh · 2014 [cited by applicant]
US 20150009997A1 · Balassanian · 2015 [cited by applicant]
US 20150025380A1 · Azegami et al. · 2015 [cited by applicant]
US 20150055846A1 · Haque · 2015 [cited by applicant]
US 20150099974A1 · Kelly et al. · 2015 [cited by applicant]
US 20150209004A1 · Tamada · 2015 [cited by applicant]
US 20150310581A1 · Radulescu et al. · 2015 [cited by applicant]
US 20150359512A1 · Boctor et al. · 2015 [cited by applicant]
US 20150359605A1 · O'Brien-Coon et al. · 2015 [cited by applicant]
US 20160000408A1 · Matsunaga et al. · 2016 [cited by applicant]
US 20160157814A1 · Palanisamy et al. · 2016 [cited by applicant]
US 20160157826A1 · Sisodia et al. · 2016 [cited by applicant]
US 20160331469A1 · Hall et al. · 2016 [cited by applicant]
US 20160374562A1 · Vertikov · 2016 [cited by applicant]
US 20170032995A1 · Cox · 2017 [cited by applicant]
US 20170086785A1 · Bjaerum · 2017 [cited by applicant]
US 20170090571A1 · Bjaerum et al. · 2017 [cited by applicant]
US 20170265831A1 · Sankaran · 2017 [cited by examiner]
US 20170372475A1 · Gulsun · 2017 [cited by examiner]
US 20180014810A1 · Chen et al. · 2018 [cited by applicant]
US 20180070915A1 · Miyachi · 2018 [cited by applicant]
US 20180220991A1 · O'Brien · 2018 [cited by examiner]
US 20190015078A1 · Saad et al. · 2019 [cited by applicant]
US 20190046153A1 · Tanaka et al. · 2019 [cited by applicant]
US 20190239848A1 · Bedi et al. · 2019 [cited by applicant]
US 20200151872A1 · Ma · 2020 [cited by examiner]
CN 106388867A · 2017 [cited by applicant]
EP 2163202 · 2010 [cited by applicant]
EP 1997436B1 · 2014 [cited by applicant]
JP 2008161220 · 2008 [cited by applicant]
KR 20140103932 · 2014 [cited by applicant]
WO 2013067419A1 · 2013 [cited by applicant]
WO 2019212992 · 2019 [cited by applicant]
Molinari F, Zeng G, Suri JS. A state of the art review on intima-media thickness (IMT) measurement and wall segmentation techniques for carotid ultrasound. Comput Methods Programs Biomed. Dec. 2010;100(3):201-21. doi: 1… [cited by examiner]
Akosha, et al., “Carotid ultrasound for risk clarification in young to middle-aged adults undergoing elective coronary angiography,” Am J. Hypertens 19(12):1256-61, Dec. 2006. [cited by applicant]
Akosha, et al., “Pilot results of the Early Detection by Ultrasound of Carotid Artery Intima-Media Thickness Evaluation (EDUCATE) study,” Am. J. Hypertens 20:1183-1188, Nov. 2007. [cited by applicant]
Camacho, et al., “Phase Coherence Imaging,” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 56, No. 5, pp. 958-974, May 2009. [cited by applicant]
Canny, J., “A Computational Approach to Edge Detection,” IEEE Trans. Pattern Anal. Mach. Intell., Jun. 1986;8(6):679-98. [cited by applicant]
Demi, et al., “The first order absolute moment in low-level image processing,” in Proceedings of 13th International Conference on Digital Signal Processing, vol. 2, No. 1, pp. 511-514, 1997. [cited by applicant]
Drechsler, et al., “Hierarchical decomposition of vessel skeletons for graph creation and feature extraction,” Proc.—2010 IEEE Int. Conf. on Bioinformatics and Biomedicine BIBM 2010, pp. 456-461, 2010. [cited by applicant]
Ibanez, et al., “Diagnosis of Atherosclerosis by Imaging,” The American Journal of Medicine, vol. 122, Issue 1, Supplement, Jan. 2009, pp. S15-S25. [cited by applicant]
Illea, et al., “Fully Automated Segmentation and Tracking of the Intima Media Thickness in Ultrasound Video Sequences of the Common Carotid Artery,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 60, No. 1, pp. … [cited by applicant]
Jespersen, et al., “Multi-Angle Compound Imaging,” Ultrasonic Imaging, 20(2):81-102, May 1998. [cited by applicant]
Kaehler, et. al., “Learning OpenCV 3″ Computer Vision in C++ with the OpenCV Library,” O'Reilly Media, Inc., 2016. [cited by applicant]
Li, et al., “Adaptive Imaging Using the Generalized Coherence Factor,” IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, vol. 50, No. 2, pp. 128-141, 2003. [cited by applicant]
Lorenz, et al., “A Guassian model approach for the prediction of speckle reduction with spatial and frequency compounding,” Proceedings of the IEEE Ultrasonics Symposium 2:1097-1101 vol. 2, Dec. 1996. [cited by applicant]
Mitzev, et al., “Concatenated decision paths classification for datasets with small number of class labels,” ICPRAM 2017, Porto, Portugal, Feb. 2017, pp. 410-417. [cited by applicant]
Molinari, et al., “A state of the art review on intima-media thickness (IMT) measurement and wall segmentation techniques for carotid ultrasound,” Comput. Methods Programs Biomed., vol. 100, No. 3, pp. 201-221, 2010. [cited by applicant]
Neuschler, et al., “Diagnosis of Breast Masses Using Opto-Acoustics,” American Roentgen Ray Society, MS Powerpoint Presentation, pp. 1-40. [cited by applicant]
Oraevsky, et al., “Optoacoustic Tomography,” in Biomedical Photonics Handbook, ed. by T. VoDink, CRC Press, Boca Raton, Florida, vol. PM125, Chapter 34, pp. 34/1-34/34. [cited by applicant]
Otsu, N., “A Threshold Selection Method from Gray-Level Histograms,” IEEE Trans. Sys. Man. Cyber, vol. SMC-9, No. 1, pp. 62-66, 1979. [cited by applicant]
Rossi, et al., “Automatic localization of intimal and adventitial carotid artery layers with noninvasive ultrasound: a novel of algorithm providing scan quality control,” Ultrasound Med. Biol., vol. 36, No. 3, pp. 467-4… [cited by applicant]
Touboul, et al., “Mannheim Carotid Intima-Media Thickness Consensus (2004-2006),” Cardiovascular Diseases, vol. 23, pp. 75-80, 2007. [cited by applicant]
Best, S. , ““The Future of Medical Scans? Nintendo Wi-inspired 7 Pound Microchip Turns 2D Ultrasound Machines into 3D Imaging Devices,””, DailyMail.com, Oct. 31, 2017, http://www.dailymail.co.uk/sciencetech/article-5035… [cited by applicant]
Hasegawa, H. , et al., “Detection of lumen-intima interface of posterior wall for measurement of elasticity of the humad carotid artery”, IEEE Transactions on Ultrasonic, Ferroelectrics, and Frequency Control, IEEE, USA… [cited by applicant]
B-mode ultrasound common carotid artery intima-media thickness and external diameter: cross-sectional and longitudinal associations with carotid atherosclerosis in a large population sample (Eigenbrodt, M. L. et al.) Ca… [cited by applicant]
International Searching Authority, International Search Report and Written Opinion of PCT/US2019/029739, mailed Jul. 8, 2019; 13 pages. [cited by applicant]
U.S. Appl. No. 62/627,457, filed Feb. 7, 2018. [cited by applicant]
International Searching Authority, International Search Report and Written Opinion of PCT/US2019/016663 mailed Apr. 24, 2019, 10 pages. [cited by applicant]
F. Faita et al, “Real-time Measurement System for Evaluation of the Carotid Intima-Media Thickness With a Robust Edge Operator”, Journal of Ultrasound Medicine, vol. 27, p. 1353-1361, 2008. [cited by applicant]
F. Molinari et al, “Automated carotid IMT measurement and its validation in low contrast ultrasound database of patient indian population epidemiological study: results of AtheroEdge™ Software”, Int'l Angiology, vol. 31… [cited by applicant]
R. Menchon-Lara et al, “Automatic detection of the inti ma media thickness in ultrasound images of the common carotid artery using neural networks”, Medicine & Biological Engineering & Computing, vol. 52, pp. 169-181, N… [cited by applicant]
R. Menchon-Lara et al, “Early-stage atherosclerosis detection using deep learning over carotid ultrasound images”, Applied Soft Computing, vol. 49, pp. 616-628, Sep. 2016. [cited by applicant]