Granted Patent
B2
US 12,236,595 · App. 18/545,550 · Granted Feb 25, 2025
Characterizing permeability, neovascularization, necrosis, collagen breakdown, or inflammation
Inventors:
Andrew J. Buckler (Boston, MA); Kjell Johnson (Ann Arbor, MI); Xiaonan Ma (South Hamilton, MA); Keith A. Moulton (Amesbury, MA); David S. Paik (Boston, MA)
Assignee:
ELUCID BIOIMAGING INC.
G06T7/0012G06F18/211G06F18/2148G06F18/24G06N3/08G06N20/00G06T3/00G06T5/73G06T7/11G06V10/25G06V10/764G06V20/69G06T2207/10048G06T2207/10081G06T2207/10088G06T2207/10101G06T2207/10104G06T2207/10108G06T2207/10132G06T2207/20081G06T2207/30096G06T2207/30104
View Patent ↗
Loading inventors, assignments & file history…
Abstract
Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties/analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties/analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.
Claims (26)
1. A system for implementing a layered analytics framework, the system comprising:
a processor configured to:
receive imaging data of Computed Tomography (CT), Magnetic resonance (MR), Ultrasound (US), or Positron Emission Tomography (PET) of a patient, imaged at two or more points in time;
for each of the points in time, determine, based on the received imaging data at each respective point in time, a set of segmentations including lumen, wall, or a combination thereof, wherein the set of segmentation includes one or more cross sectional slices of a vessel;
determine, based on the set of segmentations, a change in a set of quantities that identify or characterize permeability, neovascularization, necrosis, collagen breakdown, or inflammation, across the points in time; and
output the identification or characterization of the permeability, neovascularization, necrosis, collagen breakdown, or inflammation for each of the points in time.
2. The system of claim 1 wherein the determination of the quantities is based on machine learning.
3. The system of claim 1 wherein the processor is further configured to determine hemodynamic properties based on the set of segmentations.
4. The system of claim 1 wherein the processor is further configured to determine one or more of blood pressure, blood flow velocity, flow reserve, or vessel wall shear stress based on the set of segmentations.
5. A method for implementing a layered analytics framework, the method comprising:
a processor configured to:
receiving imaging data of Computed Tomography (CT), Magnetic resonance (MR), Ultrasound (US), or Positron Emission Tomography (PET) of a patient, imaged at two or more points in time;
for each of the points in time, determining, based on the received imaging data at each respective point in time, a set of segmentations including lumen, wall, or a combination thereof, wherein the set of segmentation includes one or more cross sectional slices of a vessel;
determining, based on the set of segmentations, a change in a set of quantities that identify or characterize permeability, neovascularization, necrosis, collagen breakdown, or inflammation, across the points in time; and
outputting the identification or characterization of the permeability, neovascularization, necrosis, collagen breakdown, or inflammation for each of the points in time.
6. The method of claim 5 wherein determining the quantities further comprises applying one or more machine learning algorithms.
7. The method of claim 5 further comprising determining hemodynamic properties based on the set of segmentations.
8. The method of claim 5 further comprising determining one or more of blood pressure, blood flow velocity, flow reserve, or vessel wall shear stress based on the set of segmentations.
9. One or more non-transitory computer-readable storage media comprising instructions that are executable to cause one or more processors to:
receive imaging data of Computed Tomography (CT), Magnetic resonance (MR), Ultrasound (US), or Positron Emission Tomography (PET) of a patient, imaged at two or more points in time;
for each of the points in time, determine, based on the received imaging data at each respective point in time, a set of segmentations including lumen, wall, or a combination thereof, wherein the set of segmentation includes one or more cross sectional slices of a vessel;
determine, based on the set of segmentations, a change in a set of quantities that identify or characterize permeability, neovascularization, necrosis, collagen breakdown, or inflammation, across the points in time; and
output the identification or characterization of the permeability, neovascularization, necrosis, collagen breakdown, or inflammation for each of the points in timer inflammation.
10. The one or more non-transitory computer-readable storage media of claim 9 where the instructions when executed further cause one or more processors to determine the quantities based on machine learning.
11. The one or more non-transitory computer-readable storage media of claim 9 where the instructions when executed further cause one or more processors to determine hemodynamic properties based on the set of segmentations.
12. The one or more non-transitory computer-readable storage media of claim 9 where the instructions when executed further cause one or more processors to determine one or more of blood pressure, blood flow velocity, flow reserve, vessel wall shear stress based on the set of segmentations.
Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST
Recorded Dec 20, 2023
From: BUCKLER, ANDREW J.; JOHNSON, KJELL; MA, XIAONAN; MOULTON, KEITH A.; PAIK, DAVID S.
To: ELUCID BIOIMAGING INC.
Reel/Frame 065922/0496 →
Continuity (12)
Provisional Application
62771448
· Nov 26, 2018
Provisional Application
62676975
· May 27, 2018
Provisional Application
62219860
· Sep 17, 2015
Provisional Application
62205295
· Aug 14, 2015
Provisional Application
62205313
· Aug 14, 2015
Provisional Application
62205305
· Aug 14, 2015
Provisional Application
62205322
· Aug 14, 2015
References Cited (376)
US 6108635A
· Herren et al.
· 2000
[cited by applicant]
US 7127095B2
· Fakhri et al.
· 2006
[cited by applicant]
US 7519211B2
· Fakhri et al.
· 2009
[cited by applicant]
US 8157742B2
· Taylor
· 2012
[cited by applicant]
US 8175355B2
· Fakhri et al.
· 2012
[cited by applicant]
US 8249815B2
· Taylor
· 2012
[cited by applicant]
US 8311747B2
· Taylor
· 2012
[cited by applicant]
US 8311748B2
· Taylor et al.
· 2012
[cited by applicant]
US 8311750B2
· Taylor
· 2012
[cited by applicant]
US 8315812B2
· Taylor
· 2012
[cited by applicant]
US 8315813B2
· Taylor et al.
· 2012
[cited by applicant]
US 8315814B2
· Taylor et al.
· 2012
[cited by applicant]
US 8321150B2
· Taylor
· 2012
[cited by applicant]
US 8374689B2
· Gopinathan et al.
· 2013
[cited by applicant]
US 8386188B2
· Taylor et al.
· 2013
[cited by applicant]
US 8496594B2
· Taylor et al.
· 2013
[cited by applicant]
US 8523779B2
· Taylor et al.
· 2013
[cited by applicant]
US 8548778B1
· Hart et al.
· 2013
[cited by applicant]
US 8553832B2
· Camus et al.
· 2013
[cited by applicant]
US 8554490B2
· Tang et al.
· 2013
[cited by applicant]
US 8594950B2
· Taylor
· 2013
[cited by applicant]
US 8606530B2
· Taylor
· 2013
[cited by applicant]
US 8630812B2
· Taylor
· 2014
[cited by applicant]
US 8706457B2
· Hart et al.
· 2014
[cited by applicant]
US 8734356B2
· Taylor
· 2014
[cited by applicant]
US 8734357B2
· Taylor
· 2014
[cited by applicant]
US 8768669B1
· Hart et al.
· 2014
[cited by applicant]
US 8768670B1
· Hart et al.
· 2014
[cited by applicant]
US 8812245B2
· Taylor
· 2014
[cited by applicant]
US 8812246B2
· Taylor
· 2014
[cited by applicant]
US 8824752B1
· Fonte et al.
· 2014
[cited by applicant]
US 8825151B2
· Gopinathan et al.
· 2014
[cited by applicant]
US 8831314B1
· Fonte et al.
· 2014
[cited by applicant]
US 8831315B1
· Fonte et al.
· 2014
[cited by applicant]
US 8855984B2
· Hart et al.
· 2014
[cited by applicant]
US 8861820B2
· Fonte et al.
· 2014
[cited by applicant]
US 8914264B1
· Hart et al.
· 2014
[cited by applicant]
US 8970578B2
· Voros et al.
· 2015
[cited by applicant]
US 8977339B1
· Wu et al.
· 2015
[cited by applicant]
US 8983155B2
· McIntyre
· 2015
[cited by examiner]
US 9002690B2
· Hart et al.
· 2015
[cited by applicant]
US 9008405B2
· Fonte et al.
· 2015
[cited by applicant]
US 9042613B2
· Spilker et al.
· 2015
[cited by applicant]
US 9043190B2
· Grady et al.
· 2015
[cited by applicant]
US 9063634B2
· Hart et al.
· 2015
[cited by applicant]
US 9063635B2
· Hart et al.
· 2015
[cited by applicant]
US 9078564B2
· Taylor
· 2015
[cited by applicant]
US 9081882B2
· Taylor
· 2015
[cited by applicant]
US 9087147B1
· Fonte
· 2015
[cited by applicant]
US 9119540B2
· Sharma et al.
· 2015
[cited by applicant]
US 9149197B2
· Taylor
· 2015
[cited by applicant]
US 9152757B2
· Taylor
· 2015
[cited by applicant]
US 9155512B2
· Choi et al.
· 2015
[cited by applicant]
US 9167974B2
· Taylor
· 2015
[cited by applicant]
US 9168012B2
· Hart et al.
· 2015
[cited by applicant]
US 9189600B2
· Spilker et al.
· 2015
[cited by applicant]
US 9195801B1
· Sankaran et al.
· 2015
[cited by applicant]
US 9202010B2
· Taylor et al.
· 2015
[cited by applicant]
US 9220418B2
· Choi et al.
· 2015
[cited by applicant]
US 9220419B2
· Choi et al.
· 2015
[cited by applicant]
US 9226672B2
· Taylor
· 2016
[cited by applicant]
US 9235679B2
· Taylor
· 2016
[cited by applicant]
US 9239905B1
· Sankaran et al.
· 2016
[cited by applicant]
US 9247918B2
· Sharma et al.
· 2016
[cited by applicant]
US 9262581B2
· Kim et al.
· 2016
[cited by applicant]
US 9265473B2
· Mittal et al.
· 2016
[cited by applicant]
US 9268902B2
· Taylor et al.
· 2016
[cited by applicant]
US 9271657B2
· Taylor
· 2016
[cited by applicant]
US 9280639B2
· Sankaran et al.
· 2016
[cited by applicant]
US 9292659B1
· Grady et al.
· 2016
[cited by applicant]
US 9320487B2
· Mittal et al.
· 2016
[cited by applicant]
US 9336354B1
· Sankaran et al.
· 2016
[cited by applicant]
US 9339200B2
· Fonte
· 2016
[cited by applicant]
US 9349178B1
· Itu et al.
· 2016
[cited by applicant]
US 9386933B2
· Grady et al.
· 2016
[cited by applicant]
US 9390224B2
· Choi et al.
· 2016
[cited by applicant]
US 9390232B2
· Taylor et al.
· 2016
[cited by applicant]
US 9405886B2
· Taylor et al.
· 2016
[cited by applicant]
US 9424395B2
· Sankaran et al.
· 2016
[cited by applicant]
US 9449145B2
· Sankaran et al.
· 2016
[cited by applicant]
US 9449146B2
· Spilker et al.
· 2016
[cited by applicant]
US 9449147B2
· Taylor
· 2016
[cited by applicant]
US 9471999B2
· Ishi et al.
· 2016
[cited by applicant]
US 9501622B2
· Sankaran et al.
· 2016
[cited by applicant]
US 9517040B2
· Hart et al.
· 2016
[cited by applicant]
US 9538925B2
· Sharma et al.
· 2017
[cited by applicant]
US 9572495B2
· Schmitt et al.
· 2017
[cited by applicant]
US 9585623B2
· Fonte et al.
· 2017
[cited by applicant]
US 9585723B2
· Taylor
· 2017
[cited by applicant]
US 9594876B2
· Sankaran et al.
· 2017
[cited by applicant]
US 9595089B2
· Itu et al.
· 2017
[cited by applicant]
US 9607386B2
· Grady et al.
· 2017
[cited by applicant]
US 9649171B2
· Sankaran et al.
· 2017
[cited by applicant]
US 9613186B2
· Fonte
· 2017
[cited by applicant]
US 9629563B2
· Sharma et al.
· 2017
[cited by applicant]
US 9633454B2
· Lauritsch et al.
· 2017
[cited by applicant]
US 9668700B2
· Taylor
· 2017
[cited by applicant]
US 9672615B2
· Fonte et al.
· 2017
[cited by applicant]
US 9675301B2
· Fonte et al.
· 2017
[cited by applicant]
US 9679374B2
· Choi et al.
· 2017
[cited by applicant]
US 9697330B2
· Taylor
· 2017
[cited by applicant]
US 9700219B2
· Sharma et al.
· 2017
[cited by applicant]
US 9706925B2
· Taylor
· 2017
[cited by applicant]
US 9607130B2
· Grady et al.
· 2017
[cited by applicant]
US 9737276B2
· Mittal et al.
· 2017
[cited by applicant]
US 9743835B2
· Taylor
· 2017
[cited by applicant]
US 9747525B2
· Sauer et al.
· 2017
[cited by applicant]
US 9754082B2
· Taylor et al.
· 2017
[cited by applicant]
US 9757073B2
· Goshen et al.
· 2017
[cited by applicant]
US 9761048B2
· Igarashi
· 2017
[cited by applicant]
US 9770303B2
· Choi et al.
· 2017
[cited by applicant]
US 9773219B2
· Sankaran et al.
· 2017
[cited by applicant]
US 9785746B2
· Fonte et al.
· 2017
[cited by applicant]
US 9785748B2
· Koo et al.
· 2017
[cited by applicant]
US 9786068B2
· Ishi et al.
· 2017
[cited by applicant]
US 9788807B2
· Schmitt et al.
· 2017
[cited by applicant]
US 9801689B2
· Taylor
· 2017
[cited by applicant]
US 9805168B2
· Sankaran et al.
· 2017
[cited by applicant]
US 9805463B2
· Choi et al.
· 2017
[cited by applicant]
US 9836840B2
· Fonte et al.
· 2017
[cited by applicant]
US 9839399B2
· Fonte et al.
· 2017
[cited by applicant]
US 9839483B2
· Sankaran et al.
· 2017
[cited by applicant]
US 9839484B2
· Taylor
· 2017
[cited by applicant]
US 9842401B2
· Prevrhal et al.
· 2017
[cited by applicant]
US 9855105B2
· Taylor
· 2018
[cited by applicant]
US 9858387B2
· Lavi et al.
· 2018
[cited by applicant]
US 9861284B2
· Taylor
· 2018
[cited by applicant]
US 9864840B2
· Grady et al.
· 2018
[cited by applicant]
US 9877657B2
· Igarashi
· 2018
[cited by applicant]
US 9888968B2
· Sauer et al.
· 2018
[cited by applicant]
US 9888971B2
· Taylor
· 2018
[cited by applicant]
US 9891044B2
· Tu et al.
· 2018
[cited by applicant]
US 9913616B2
· Fonte et al.
· 2018
[cited by applicant]
US 9918690B2
· Itu et al.
· 2018
[cited by applicant]
US 9924869B2
· Kim et al.
· 2018
[cited by applicant]
US 9928593B2
· Ooga et al.
· 2018
[cited by applicant]
US 9940736B2
· Ishi et al.
· 2018
[cited by applicant]
US 9943233B2
· Lavi et al.
· 2018
[cited by applicant]
US 9949650B2
· Edic et al.
· 2018
[cited by applicant]
US 9965891B2
· Grady et al.
· 2018
[cited by applicant]
US 9974454B2
· Sharma et al.
· 2018
[cited by applicant]
US 9974508B2
· Kassab et al.
· 2018
[cited by applicant]
US 9974616B2
· Grady et al.
· 2018
[cited by applicant]
US 9977869B2
· Lavi et al.
· 2018
[cited by applicant]
US 9984465B1
· Ma et al.
· 2018
[cited by applicant]
US 9993303B2
· Sankaran et al.
· 2018
[cited by applicant]
US 9999361B2
· Sharma et al.
· 2018
[cited by applicant]
US 20010047137A1
· Moreno
· 2001
[cited by examiner]
US 20030082105A1
· Fischman
· 2003
[cited by examiner]
US 20030105638A1
· Taira
· 2003
[cited by applicant]
US 20050041844A1
· Yamanaka
· 2005
[cited by applicant]
US 20050118632A1
· Chen
· 2005
[cited by applicant]
US 20070130206A1
· Zhou et al.
· 2007
[cited by applicant]
US 20070208516A1
· Kutsyy et al.
· 2007
[cited by applicant]
US 20080027695A1
· Balgi et al.
· 2008
[cited by applicant]
US 20080085294A1
· Freyman
· 2008
[cited by examiner]
US 20080188762A1
· John
· 2008
[cited by examiner]
US 20080294038A1
· Weese et al.
· 2008
[cited by applicant]
US 20100070448A1
· Omoigui
· 2010
[cited by applicant]
US 20100088264A1
· Teverovskiy et al.
· 2010
[cited by applicant]
US 20100137711A1
· Hamilton
· 2010
[cited by examiner]
US 20140126770A1
· Odessky et al.
· 2014
[cited by applicant]
US 20140276121A1
· Kassab
· 2014
[cited by applicant]
US 20150234921A1
· Li
· 2015
[cited by applicant]
US 20150324527A1
· Siegel et al.
· 2015
[cited by applicant]
US 20160310018A1
· Fonte
· 2016
[cited by applicant]
CN 1952981
· 2007
[cited by applicant]
CN 106537450
· 2017
[cited by applicant]
CN 106682060
· 2017
[cited by applicant]
CN 107730489
· 2018
[cited by applicant]
CN 110140042
· 2019
[cited by applicant]
JP 2005065944
· 2005
[cited by applicant]
JP 2006102491
· 2006
[cited by applicant]
JP 2006142011
· 2006
[cited by applicant]
JP A2006181025
· 2006
[cited by applicant]
JP 2009056305
· 2009
[cited by applicant]
JP 2011206240
· 2011
[cited by applicant]
JP 2013000227
· 2013
[cited by applicant]
JP 2015033581
· 2015
[cited by applicant]
JP 2016022143
· 2016
[cited by applicant]
JP A2018011958
· 2018
[cited by applicant]
JP 2018504969
· 2018
[cited by applicant]
JP 2018506368
· 2018
[cited by applicant]
JP 2019520120
· 2019
[cited by applicant]
KR 102080021635
· 2018
[cited by applicant]
KR 1020190013837
· 2019
[cited by applicant]
WO WO2014002095
· 2014
[cited by applicant]
WO WO2015058151
· 2015
[cited by applicant]
WO WO2016011339
· 2016
[cited by applicant]
WO WO2016137972
· 2016
[cited by applicant]
WO WO2017150497
· 2017
[cited by applicant]
WO WO2017210225
· 2017
[cited by applicant]
WO WO2019016309
· 2019
[cited by applicant]
WO WO2019141747
· 2019
[cited by applicant]
WO WO2020242761
· 2020
[cited by applicant]
Office Action for U.S. Appl. No. 18/307,316 dated Jan. 24, 2024.
[cited by applicant]
Castellano et al. “Texture analysis of medical images,” Clinical Radiology, Dec. 1, 2004 (Dec. 1, 2004) vol. 59.
[cited by applicant]
Choi et al. “Multiscale image segmentation using wavelet-domain hidden Markov models” IEEE Trans Image Process, Sep. 1, 2001 (Sep. 1, 2001), vol. 10.
[cited by applicant]
Reedy et al. “Confidence guided enhancing brain tumor segmentation in multi-parametric MRI” Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2009, he…
[cited by applicant]
Khan et al., “Robust atlas-based brain segmentation using multi-structure confidence-weighted registration” Proceedings of the 12th International Conference on Medical Image Computing, Sep. 20, 2009.
[cited by applicant]
Ariff et al. “Carotid Artery Hemodynamics: Observing Patient-specific Changes with Amlodipine and Lisinopril by Using MRI Imaging Computation Fluid Dynamics.” Radiol. 257.3(2010):662-669.
[cited by applicant]
Bourque et al. “Usefulness of Cardiovascular Magnetic Resonance Imaging of the Superficial Femoral Artery for Screening Patients with Diabetes Mellitus for Artherosclerosis.” Am. J. Cardiol. 110.1(2012):50-56.
[cited by applicant]
Buckler et al. “A Collaborative Enterprise for Multi-Stakeholder Participation in the Advancement of Quantitative Imaging.” Radiol. 258.3(2011):906-914.
[cited by applicant]
Buckler et al. “Data Sets for the Qualification of CT as a Quantitative Imaging Biomarker in Lung Cancer.” Optics Exp.18.14(2010):16.
[cited by applicant]
Buckler et al. “Data Sets for the Qualification of Volumetric CT as a Quantitative Imaging Biomarker in Lung Cancer.” Optics Exp. 18.14(2010):15267-15282.
[cited by applicant]
Buckler et al. “Quantitative Imaging Test Approval and Biomarker Qualification: Interrelated but Distinct Activities.” Radiol. 259.3(2011):875-884.
[cited by applicant]
Buckler et al. “Standardization of Quantitative Imaging: The Time is Right and 18F-FDG PET/CT is a Good Place to Start.” J. Nuclear Med. 52.2(2011):171-172.
[cited by applicant]
Buckler et al. “The Use of Volumetric CT as an Imaging Biomarker in Lung Cancer.” Academic Radiol. 17.1(2010):100-106.
[cited by applicant]
Buckler et al. “Volumetric CT in Lung Cancer: An Example for the Qualification of Imaging as a Biomarker.” Academic Radiol. 17.1(2010):107-115.
[cited by applicant]
Buyse et al. “The Validation of Surrogate Endpoints in Meta-Analysis of Randomized Experiments.” Biostat. 1 (2000):1-1.
[cited by applicant]
Chan et al. “Active Contours without Edges.” IEEE Trans. Image Process. 10.2(2001):266-277.
[cited by applicant]
De Weert et al. “In Vivo Characterization and Quantification of Atherosclerotic Carotid Plaque Components with Multidetector Computed Tomography and Histopathlogical Correlation.” Arterioscler. Thromb. Vase. Biol. 26.10…
[cited by applicant]
Fleming. “Surrogate Endpoints and FDA's Accelerated Approval Process.” Health Affairs. 24.1{2005):67-78.
[cited by applicant]
Freedman et al. “Statistical Validation of Intermediate Endpoints for Chronic Diseases.” Stat. Med. 11(1992):167-178.
[cited by applicant]
Fuleihan et al. “Reproducibility of DXA Absorptiometry: A Model for Bone Loss Estimates.” J. Bone Miner. Res. 10.74 (1995):1004-1014.
[cited by applicant]
Horie et al. “Assessment of Carotid Plaque Stability Based on Dynamic Enhancement Pattern in Plaque Components with Multidetector CT Angiography.” Stroke. 43.2{2012):393-398.
[cited by applicant]
Hrace et al. “Human Common Carotid Wall Shear Stress as a Function of Age and Gender: A 12-year Follow-up Study.” Age. 34.6(2012):1553-1562.
[cited by applicant]
Jaffe, “Measures of Response: RECIST, WHO, and New Alternatives.” J.Clin. Oncol. 24.20(2006):3245-3251.
[cited by applicant]
Katz, “Biomarkers and Surrogate Markers: An FDA Perspective.” NeuroRx. 1.2(2004):189-195.
[cited by applicant]
Kerwin et al. “MRI of Carotid Artherosclerosis.” Am. J. Roentgenol. 200.3(2013):W304-W313.
[cited by applicant]
Kim et al. “A Curve Evolution-based variational approach to Simultaneous Image Restoration and Segmentation.” EEE Int. Conf. Image Proc. (2002):1-109.
[cited by applicant]
Lathia et al. “The Value, Qualification, and Regulatory Use of Surrogate End Points in Drug Development.” Clin Pharmacol. Therapeutics. 86.1(2009):32-43.
[cited by applicant]
Mozley et al. “Change in Lung Tumor Volume as a Biomarker of Treatment Response: A Critical Review of the Evidence.” Ann. Oncol. 21.9(2010):1751-1755.
[cited by applicant]
Phinikaridou et al. “Regions of Low Endothelial Shear Stress Colocalize with Positive Vascular Remodeling and Atherosclerotic Plaque Disruption: An in vivo Magnetic Resonance Imaging Study.” Circ. Cardiovasc. Imaging. 6…
[cited by applicant]
Prentice, “Surrogate Endpoints in Clinical Trials: Definition and Operational Criteria.” Stat. Med. 9(1989):431-440.
[cited by applicant]
Sargent et al. “Validation of Novel Imaging Methodologies for Use as Cancer Clinical Trial End-points.” Eur. J. Dis. 45 ('2009):290-299.
[cited by applicant]
Sui et al. “Assessment of Wall Shear Stress in the Common Carotid Artery of Healthy Subjects Using 3.0-Tesla Magentic Resonance.” Acta Radiologica. 49.4(2008):442-449.
[cited by applicant]
Sten Kate et al. “Noninvasive Imaging of the Vulnerable Atherosclerotic Plaque.” Current Problems Cardiol. 35.11(2010):556-591.
[cited by applicant]
Van Klavern et al. “Management of Lung Nodules Detected by Volume CT Scanning.” New Engl. J. Med. 361 (2009):23.
[cited by applicant]
Varma et al. “Coronary Vessel Wall Contrast Enhancement Imaging as a Potential Direct Marker of Coronary Involvement: Integration of Findings from CAD and SLE Patients.” JACC Cardiovasc. Imaging. 7.8(2014):762-770.
[cited by applicant]
Wintermark et al. “Carotid Plaque CT Imaging in Stroke and Non-Stroke Patients.” Ann. Neurol. 64.2(2008):149-157.
[cited by applicant]
Wintermark et al. “High-Resolution CT Imaging of Carotid Artery Atherosclerotic Plaques.” Am. J. Neuroradiol. 29.5 (2008):875-882.
[cited by applicant]
Wong et al. “Imaging in Drug Discovery, Preclinical, and Early Clinical Development.” J. Nuclear Med. 49.6 (2008):26N-28N.
[cited by applicant]
Woodcock et al. “The FDA Critical Path Initiative and its Influence on New Drug Development.” Annu. Rev. Med. 59 (2008):1-12.
[cited by applicant]
Zavodni et al. “Carotid Artery Plaque Morphology and Composition in Relation to Incident Cardiovascular Events: The Multi-Ethnic Study of Atherosclerosis (MESA).” Radiol. 271.2 (2014):361-389.
[cited by applicant]
Zhao et al. “Evaluating Variability in Tumor Measurements from Same-Day Repeat CT Scans of Patients with Non-Small Cell Lung Cancer.” Radiol. 252.1(2009):263-272.
[cited by applicant]
Aerts, H. J.W.L., et al., Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun, 2014. 5.
[cited by applicant]
Ahmadi, A., et al., Association of Coronary Stenosis and Plaque Morphology With Fractional Flow Reserve and Outcomes. JAMA Cardiol, 2016.1(3): p. 350-7.
[cited by applicant]
Ahmadi, A., et al., Lesion-Specific and Vessel-Related Determinants of Fractional Flow Reserve Beyond Coronary Artery Stenosis. JACC Cardiovasc Imaging,2018. 11 (4): p. 521-530.
[cited by applicant]
Albuquerque, L C , et al., Intraplaque hemorrhage assessed by high-resolution magnetic resonance imaging and C-reactive protein in carotid atherosclerosis. Journal of Vascular Surgery, 2007.46(6): p. 1130-1137.
[cited by applicant]
Alimohammadi, M., et al., Development of a Patient-Specific Multi-Scale Model to Understand Atherosclerosis and Calcification Locations: Comparison with In vivo Data in an Aortic Dissection Front Physiol, 2016. 7: p. 23.
[cited by applicant]
Aoki, T., et al., Peripheral Lung Adenocarcinoma: Correlation of Thin-Section CT Findings with Histologic Prognostic Factors and Survival 1. Radiology, 2001.220(3): p. 803-809.
[cited by applicant]
Atkinson, A.J., et al., Biomarkers and surrogate endpoints: Preferred definitions and conceptual framework*. Clinical Pharmacology & Therapeutics, 2001. 69(3): p. 89-95.
[cited by applicant]
Bittencourt, M.S., et al., Prognostic Value of Nonobstructive and Obstructive Coronary Artery Disease Detected by Coronary Computed Tomography Angiography to Identify Cardiovascular Events. Circulation: Cardiovascular I…
[cited by applicant]
Buckler,A., et al, A Novel Knowledge Representation Framework for the Statistical Validation of Quantitative Imaging Biomarkers. Journal of Digital Imaging, 2013. 26(4): p. 614-629.
[cited by applicant]
Buckler, A.J., et al., Quantitative imaging biomarker ontology (QIBO) for knowledge representation of biomedical imaging biomarkers. Journal of digital imaging : the official journal of the Society for Computer Applicat…
[cited by applicant]
Buckler, A.J., et al., Quantitative imaging test approval and biomarker qualification: interrelated but distinct activities. Radiology, 2011.259(3): p. 875-84.
[cited by applicant]
Cai, J., et al., In vivo quantitative measurement of intact fibrous cap and lipid-rich necrotic core size in atherosclerotic carotid plaque: comparison of high-resolution, contrast-enhanced magnetic resonance imaging an…
[cited by applicant]
Chan, T.F. and L.A. Vese, Active contours without edges. IEEE Trans Image Process,2001. 10(2): p. 266-77.
[cited by applicant]
Coenen, A., et al., Diagnostic accuracy of a machine-learning approach to coronary computed tomographic angiography-based fractional flow reserve: result from the Machine consortium. Circulation: Cardiovascular Imaging,…
[cited by applicant]
De Bono, B., et al., The Open Physiology workflow: modeling processes over physiology circuit boards of interoperable tissue units Front Physiol, 2015. 6: p. 24.
[cited by applicant]
De Graaf, M., et al., Automatic quantification and characterization of coronary atherosclerosis with computed tomography coronary angiography: cross-correlation with intravascular ultrasound virtual histology. Int J Car…
[cited by applicant]
DeMarco, J.K. and J. Huston, Imaging of high-risk carotid artery plaques: current status and future directions Neurosurgical Focus, 2014. 36(1): p. E1.
[cited by applicant]
Diaz-Zamudio, M., et al., Automated Quantitative Plaque Burden from Coronary CT Angiography Noninvasively Predicts Hemodynamic Significance by using Fractional Flow Reserve in Intermediate Coronary Lesions. Radiology, 2…
[cited by applicant]
Dong, L., et al., Carotid Artery Atherosclerosis: Effect of Intensive Lipid Therapy on the Vasa Vasorum—Evaluation by Using Dynamic Contrast-enhanced MR Imaging. Radiology, 2011.260(1): p. 224-231.
[cited by applicant]
Fiilardi, V., Carotid artery stenosis near a bifurcation investigated by fluid dynamic analyses. The Neuroradiology Journal, 2013. 26(4):p. 439-453.
[cited by applicant]
Fleming, T.R. and D.L. DeMets, Surrogate end points in clinical trials:are we being misled? Ann Intern Med, 1996. 125 (7): p. 605-13.
[cited by applicant]
Freimuth, R.R.,et al., Life sciences domain analysis model. J Am Med Inform Assoc,2012. 19(6): p. 1095-102.
[cited by applicant]
Fujimoto, S.,et al., A novel method for non-invasive plaque morphology analysis by coronary computed tomography angiography. Int J Cardiovasc Imaging, 2014. 30(7): p. 1373-1382.
[cited by applicant]
Gaston A. Rodriguez-Granillo, Patricia Carrascosa, Nico Bruining, and a.H.M.G.-G. Ron Waksman, Defining the non-vulnerable and vulnerable patients with computed tomography coronary angiography: evaluation of atheroscler…
[cited by applicant]
Gevaert, O., et al., Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data—methods and preliminary results Radiology, 2012 264(2): p. 387-396.
[cited by applicant]
Ghazalpour, A., et al., Thematic review series: The pathogenesis of atherosclerosis. Toward a biological network for atherosclerosis J Lipid Res, 2004 45(10): p. 1793-805.
[cited by applicant]
Gupta, A., et al., Carotid Plaque MRI and Stroke Risk A Systematic Review and Meta-analysis. Stroke, 2013.44(11): p. 3071-3077.
[cited by applicant]
Gupta, A., et al., Detection of Symptomatic Carotid Plaque Using Source Data from MR and CT Angiography: A Correlative Study Cerebrovasc Dis, 2015. 39(3-4): p. 151-61.
[cited by applicant]
Gupta, A., et al., Intraplaque high-intensity signal on 3D time-of-flight MR angiography is strongly associated with symptomatic carotid artery stenosis. American Journal of Neuroradiology, 2014. 35(3): p. 557-561.
[cited by applicant]
Hecht, H.S., Coronary artery calcium scanning: past, present, and future. JACC Cardiovasc Imaging, 2015. 8:p. 579-596.
[cited by applicant]
Hecht, H.S., J. Narula, and W.F. Fearon, Fractional Flow Reserve and Coronary Computed Tomographic Angiography, A Review and Critical Analysis. Circ Res, 2016.119(2): p. 300-16.
[cited by applicant]
Helft, G., et al., Progression and regression of atherosclerotic lesions: monitoring with serial noninvasive magnetic resonance imaging Circulation, 2002. 105(8): p. 993-8.
[cited by applicant]
Inoue, K., et al., Serial Coronary CT Angiography-Verified Changes in Plaque Characteristics as an End Point: Evaluation of Effect of Statin Intervention. JACC: Cardiovascular Imaging, 2010. 3(7): p. 691-698.
[cited by applicant]
Krizhevsky, A.I. Sutskever, and G.E. Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems 2012.
[cited by applicant]
Lobatto, ME., et al., Multimodal Clinical Imaging To Longitudinally Assess a Nanomedical Anti-Inflammatory Treatment in Experimental Atherosclerosis. Molecular Pharmaceutics, 2010. 7(6): p. 2020-2029.
[cited by applicant]
Ma,X.,et al Volumes Learned: It Takes More Than Size to “Size Up” Pulmonary Lesions. Acad Radiol, 2016. 23(9):p. 1190-8.
[cited by applicant]
Melander, O., et al., Novel and conventional biomarkers for prediction of incident cardiovascular events in the community. JAMA: the journal of the American Medical Association, 2009. 302(1): p. 49-57.
[cited by applicant]
Miao, C., et al., The Association of Pericardial Fat with Coronary Artery Plaque Index at MR Imaging: The Multi-Ethnic Study of Atherosclerosis (MESA). Radiology, 2011.261 (1): p. 109-115.
[cited by applicant]
Mono, M.L., et al., Plaque Characteristics of Asymptomatic Carotid Stenosis and Risk of Stroke. Cerebrovascular Diseases, 2012. 34(5-6):p. 343-350.
[cited by applicant]
Narula, J., et al., Histopathologic characteristics of atherosclerotic coronary disease and implications of the findings for the invasive and noninvasive detection of vulnerable plaques. Journal of the American College …
[cited by applicant]
Naylor, A.R., Identifying the high-risk carotid plaque. The Journal of Cardiovascular Surgery, 2014. 55(2): p. 11-20.
[cited by applicant]
Perera , R. and P. Nand, Recent Advances in Natural Language Generation: A Survey and Classification of the Empirical Literature. vol. 36.2017. 1-31.
[cited by applicant]
Prentice, R.L., Surrogate endpoints in clinical trials: definition and operational criteria. Statistics in medicine, 1989. 8 (4): p. 431-440.
[cited by applicant]
Prescott, J., Quantitative Imaging Biomarkers: The Application of Advanced Image Processing and Analysis to Clinical and Preclinical Decision Making Journal of Digital Imaging, 2013. 26(1):p. 97-108.
[cited by applicant]
Reddy et al. “Confidence guided enhancing brain tumor segmentation in multi-parametric MRI” Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2009, he…
[cited by applicant]
Saba, L., et al., Carotid Artery Plaque Characterization Using CT Multienergy Imaging American Journal of Neuroradiology, 2013 34(4): p. 855-859.
[cited by applicant]
Sadot, A., et al., Toward verified biological models. IEEE/ACM Trans Comput Biol Bioinform, 2008. 5(2): p. 223-34.
[cited by applicant]
Sargent, D., et al., Validation of novel imaging methodologies for use as cancer clinical trial end-points. European Journal of Cancer, 2009.45(2): p. 290-299.
[cited by applicant]
Stary, H.C., et al., A definition of advanced types of atherosclerotic lesions and a histological classification of atherosclerosis A report from the Committee on Vascular Lesions of the Council on Arteriosclerosis, Ame…
[cited by applicant]
Stary, H.C., Natural history and histological classification of atherosclerotic lesions: an update. Arterioscler Thromb Vasc Biol, 2000.20(5):p. 1177-8.
[cited by applicant]
Van't Klooster ,R., et al., Visualization of Local Changes in Vessel Wall Morphology and Plaque Progression in Serial Carotid Artery Magnetic Resonance Imaging. Stroke, 2014. 45(8): p. e160-e163.
[cited by applicant]
Virmani, R., et al., Pathology of the Vulnerable Plaque. JACC,2006.47(8): p. C13-8.
[cited by applicant]
Voros, S., et al., Coronary Atherosclerosis Imaging by Coronary CT Angiography. JACC Cardiovasc Imaging, 2011.4 (5): p. 537-48.
[cited by applicant]
William B. Kerr et al. “A Methodology and Metric for Quantitative Analysis and Parameter Optimization of Unsupervised, Multi-Region Image Segmentation”, Proceeding of the 8th IASTED International Conference on Signal an…
[cited by applicant]
Majd Zreik et al. “Automatic Detection and Characterization of Coronary Artery Plaque and Stenosis using a Recurrent Convolutional Neural Network in Coronary CT Angiography”, arxiv.org, Cornell University Library, 201 O…
[cited by applicant]
Wei Jun et al.:“Computerized detection of noncalcified plaques in coronary CT angiography: Evaluation of topologicals soft gradient prescreening method and luminal analysis”, Medical Physics., vol. 41, No. 8 Part 1, Jul…
[cited by applicant]
Filipovic et al. Hemodynamic Flow Modeling Through an Abdominal Aorta Aneurysm Using Data Mining Tools, IEEE Transactions on Information Technology in Biomedicine, vol. 15, No. 2, Mar. 2011, pp. 189-194.
[cited by applicant]
Bishop, Christopher M., Pattern Recognition and Machine Learning, Springer Science 2006, pp. 1-758.
[cited by applicant]
Office Action for U.S. Appl. No. 18/15,125 dated Apr. 30, 2024.
[cited by applicant]
Office Action for U.S. Appl. No. 18/415,137 dated May 1, 2024.
[cited by applicant]
Notice of Allowance for U.S. Appl. No. 18/309,903 dated Mar. 7, 2024.
[cited by applicant]
Office Action for U.S. Appl. No. 18/545,542 dated Mar. 15, 2024.
[cited by applicant]
Notice of Allowance from U.S. Appl. No. 18/415,137 dated Aug. 9, 2024.
[cited by applicant]
Office Action from U.S. Appl. No. 18/307,316 dated Jul. 2, 2024.
[cited by applicant]
Notice of Allowance from U.S. Appl. No. 18/415,125 dated Jul. 18, 2024.
[cited by applicant]
Notice of Allowance from U.S. Appl. No. 18/504,811 dated Jul. 8, 2024.
[cited by applicant]
Office Action from U.S. Appl. No. 18/415,125 dated Apr. 30, 2024.
[cited by applicant]
Notice of Allowance from U.S. Appl. No. 18/504,795 dated Jun. 20, 2024.
[cited by applicant]
Notice of Allowance from U.S. Appl. No. 18/545,563 dated May 31, 2024.
[cited by applicant]
Notice of Allowance from U.S. Appl. No. 18/307,316 dated Oct. 8, 2024.
[cited by applicant]