SlideShare a Scribd company logo
1 of 35
Pablo Lamata
Lecturer. Sir Henry Dale Fellow
 “All models are wrong”Denis Noble.
 “Nobody believes on the results of an
experimentalist, but him, and everybody
believes on the results of a modeller, but
himself”
 Take home message: … need to define the
problem and hypothesis as clear as possible!!
 Goal: clinical support. Stratifiation.
 Hypotheses:
 Models regularise / clean clinical data.
 Models unveil diagnostic metrics
 Examples:
 Shape
 Diastolic performance
 Non-invasive pressure
 Discussion:
 Opportunity to make a clinical impact
 Robustness!!
 Cardiac remodelling
 Development
 Disease
 State of art: coarse metrics
 Length, diameter, volume…
 Opportunities
 Myriad of shape patterns
 Tons of data
 Computational statistical atlas of anatomy [1]
 Clinicians will adopt novel shape coordinates
in this parametric space
[1] A. Young, A. Frangi. “Computational cardiac atlases: from patient to population and
back.” Exp. Physiol. (2009)
[2] P. Lamata, S. Niederer, et al., “An accurate, fast and robust method to generate
patient-specific cubic Hermite meshes,” Med. image Anal. (2011).
[3] P. Lamata, M. Sinclair, et al. “An automatic service for the personalization of
ventricular cardiac meshes.” J R Soc Interface (2014)
 Model: ellipsoid
 Meshing [2,3]
 Reduce noise and artifacts
 Smooth C1 representation
 Statistics: PCA
 Web-service
http://amdb.isd.kcl.ac.uk/
 Give me your short axis stack, and I’ll tell you
if you had a premature birth [4].
[4] A. Lewandovski, D. Augustine et al. “Preterm heart in adult life: cardiovascular
magnetic resonance reveals distinct differences in left ventricular mass, geometry, and
function.” Circulation (2013)
 Ventricle grow differently depending on
surgical choice in HLHS [5].
[5] J. Wong, P. Lamata et al. “Right ventricular morphology and function following
stage I palliation with a modified Blalock-Taussig shunt versus a right ventricle-to-
pulmonary artery conduit” Circ. Imaging (in review)
[5] J. Wong, P. Lamata et al. “Right ventricular morphology and function following
stage I palliation with a modified Blalock-Taussig shunt versus a right ventricle-to-
pulmonary artery conduit” Circ. Imaging (in review)
 HF with Normal Ejection Fraction
 Evidence of abnormal filling caused by
stiffer myocardium, delayed relaxation,
impaired atrio-ventricular conduit function.
 Diagnostic surrogates [6]:
• Lab: natruiretic peptides
• Echo: ratio early/late filling
• Catheters: LV pressure
 Stratification: on-going challenge [6]
[6] Maeder and Kaye, “Heart
Failure With Normal Left
Ventricular Ejection Fraction,” J.
Am. Coll. Cardiol. 2009
 State of art (catheter): exponential fitting
 Coupling between relaxation and stiffness
P
V
Passive elastic
Active fibre relaxation
Total LV pressure
 Myocardial properties (relaxation/stiffness)
 Input: deformation and pressure
 Method: Model personalization
 Output: Decouple relaxation / stiffness
[7] J. Xi, P. Lamata, et. al, “The estimation of patient-specific cardiac diastolic functions
from clinical measurements,” Med. image Anal., 17:133-146 (2013).
6 unknowns
4 data points
 Additional constraints [7]
 End diastole: null active tension
 Positive, and monotonically decaying active tension
 Criterion to choose reference configuration
[7] J. Xi, P. Lamata, et. al, “The estimation of patient-specific cardiac diastolic functions
from clinical measurements,” Med. image Anal., 17:133-146 (2013).
 Criterion to choose reference configuration
 Stiffness = f(deform., pressure)
 LV filling pressure: only catheter
 Two aims [8]:
 Hypothesis: P = f(V)
 Characterise impact of pressure
offset errors
[8] J. Xi, W. Shi, et. al, “Understanding the need of ventricular pressure for the estimation
of diastolic biomarkers,” Biomech. Model. Mechanobiol. (2013)
 Literature surrogate
 Able to differentiate
stiffness
 Stiffness = f(ejection
fraction)
 Unable to different.
active tension
[8] J. Xi, W. Shi, et. al, “Understanding the need of ventricular pressure for the estimation
of diastolic biomarkers,” Biomech. Model. Mechanobiol. (2013)
 Able to recover from pressure offset errors
 Need temporal resolution!
No pressure offset With pressure offset
[8] J. Xi, W. Shi, et. al, “Understanding the need of ventricular pressure for the estimation
of diastolic biomarkers,” Biomech. Model. Mechanobiol. (2013)
 Pressure:
important
biomarker
 What if…
 Central
pressure?
 Time + space
 Coarctation  Obstruction LVOT
∆P=P(B)-P(A)?
B
A
∆P=P(B)-P(A)?
P (mmHg) = 4 𝑽 𝒎𝒂𝒙
𝟐
(m/sec)
 PC-MRI
 Navier Stokes Eq.
x 4
 No need of
boundary conditions
 Arbitrary domains
 Includes viscous
effects
[8] S. Krittian, P. Lamata et al. “A FEM approach to the direct computation of relative
cardiovascular pressure from time-resolved MR velocity data.” Med. Im. Analysis (2012)
 Mass and momentum conservation:
Viscous forces
Convective acceleration
(in-space)
Transient acceleration
(in-time)
Inertial forces
t=1
t=0
[9] P. Lamata, A. Pitcher et al. “Aortic relative pressure components derived from 4D flow
cardiovascular magnetic resonance”. MRM (2014)
 Transient: pump action
and compliance
 Convective: vessel
geometry
 Viscous: inefficiencies
due to friction
[9] P. Lamata, A. Pitcher et al. “Aortic relative pressure components derived from 4D flow
cardiovascular magnetic resonance”. MRM (2014)
 Images are drivers of
modelling progress [10]
 Complexity vs. clinical
adoption
 Robustness!!
[10] P. Lamata, R. Casero et al, “Images as drivers of progress in cardiac
computational modelling”, Prog Biophys Mol Biol (2014)
 Meshes of high quality [11]
[11] P. Lamata, I. Roy et al. “Quality metrics for high order meshes: analysis of the
mechanical simulation of the heart beat.” IEEE Trans Med Imag (2013)
 More stable simulations: guide the optimizer
to enforce non-compressibility
[12] S. Land, S. Niederer et al. “Improving the stability of cardiac mechanical
simulations” IEEE Trans Biom Eng (accepted)
 Goal: clinical support. Stratifiation.
 Hypotheses:
 Models regularise / clean clinical data.
 Models unveil diagnostic metrics
 Examples:
 Shape
 Diastolic performance
 Non-invasive pressure
 Discussion:
 Opportunity to make a clinical impact
 Robustness!!
 Oxford / KCL
 Nic Smith
 Steve Niederer
 David Nordsletten
 Sander Land
 [Jiahe Xi]
 [Sebastian Krittian]
 [Ishani Roi]
 Imperial
 Daniel Rueckert
 Wenzhe Shi
 Clinicians
 Reza Razavi (KCL)
 Aldo Rinaldi (KCL)
 Paul Leeson (OXF)
 Adam Lewandovski (OXF)
 Stefan Neubauer (OXF)
 Alex Pitcher (OXF)

More Related Content

Viewers also liked

ICT for a global infrastructure for health research VPH Models, images and pe...
ICT for a global infrastructure for health research VPH Models, images and pe...ICT for a global infrastructure for health research VPH Models, images and pe...
ICT for a global infrastructure for health research VPH Models, images and pe...Plan de Calidad para el SNS
 
Mobicents Media Server theory, practice, cloud considerations, design discuss...
Mobicents Media Server theory, practice, cloud considerations, design discuss...Mobicents Media Server theory, practice, cloud considerations, design discuss...
Mobicents Media Server theory, practice, cloud considerations, design discuss...telestax
 
Oral presentation at STACOM10 (Invited talk)
Oral presentation at STACOM10 (Invited talk)Oral presentation at STACOM10 (Invited talk)
Oral presentation at STACOM10 (Invited talk)Mathieu De Craene
 
3 d virtual heart to predict sudden cardiac death risk
3 d virtual heart to predict sudden cardiac death risk3 d virtual heart to predict sudden cardiac death risk
3 d virtual heart to predict sudden cardiac death riskOther Mother
 
Modules for reusable and collaborative modeling of biological mathematical sy...
Modules for reusable and collaborative modeling of biological mathematical sy...Modules for reusable and collaborative modeling of biological mathematical sy...
Modules for reusable and collaborative modeling of biological mathematical sy...Daniele Gianni
 
The Process of Process Modeling
The Process of Process ModelingThe Process of Process Modeling
The Process of Process ModelingDirk Fahland
 
Computer aided drug designing
Computer aided drug designing Computer aided drug designing
Computer aided drug designing Ayesha Aftab
 
Applications Of Bioinformatics In Drug Discovery And Process
Applications Of Bioinformatics In Drug Discovery And ProcessApplications Of Bioinformatics In Drug Discovery And Process
Applications Of Bioinformatics In Drug Discovery And ProcessProf. Dr. Basavaraj Nanjwade
 
3D printer Technology _ A complete presentation
3D printer Technology _ A complete presentation3D printer Technology _ A complete presentation
3D printer Technology _ A complete presentationVijay Patil
 

Viewers also liked (9)

ICT for a global infrastructure for health research VPH Models, images and pe...
ICT for a global infrastructure for health research VPH Models, images and pe...ICT for a global infrastructure for health research VPH Models, images and pe...
ICT for a global infrastructure for health research VPH Models, images and pe...
 
Mobicents Media Server theory, practice, cloud considerations, design discuss...
Mobicents Media Server theory, practice, cloud considerations, design discuss...Mobicents Media Server theory, practice, cloud considerations, design discuss...
Mobicents Media Server theory, practice, cloud considerations, design discuss...
 
Oral presentation at STACOM10 (Invited talk)
Oral presentation at STACOM10 (Invited talk)Oral presentation at STACOM10 (Invited talk)
Oral presentation at STACOM10 (Invited talk)
 
3 d virtual heart to predict sudden cardiac death risk
3 d virtual heart to predict sudden cardiac death risk3 d virtual heart to predict sudden cardiac death risk
3 d virtual heart to predict sudden cardiac death risk
 
Modules for reusable and collaborative modeling of biological mathematical sy...
Modules for reusable and collaborative modeling of biological mathematical sy...Modules for reusable and collaborative modeling of biological mathematical sy...
Modules for reusable and collaborative modeling of biological mathematical sy...
 
The Process of Process Modeling
The Process of Process ModelingThe Process of Process Modeling
The Process of Process Modeling
 
Computer aided drug designing
Computer aided drug designing Computer aided drug designing
Computer aided drug designing
 
Applications Of Bioinformatics In Drug Discovery And Process
Applications Of Bioinformatics In Drug Discovery And ProcessApplications Of Bioinformatics In Drug Discovery And Process
Applications Of Bioinformatics In Drug Discovery And Process
 
3D printer Technology _ A complete presentation
3D printer Technology _ A complete presentation3D printer Technology _ A complete presentation
3D printer Technology _ A complete presentation
 

Similar to Imaging and modeling biomarkers

The Development of Computational Fluid Dynamic Models for Studying the Detect...
The Development of Computational Fluid Dynamic Models for Studying the Detect...The Development of Computational Fluid Dynamic Models for Studying the Detect...
The Development of Computational Fluid Dynamic Models for Studying the Detect...Cristina Staicu
 
1 s2.0-s0010482514001784-main
1 s2.0-s0010482514001784-main1 s2.0-s0010482514001784-main
1 s2.0-s0010482514001784-mainCMIB
 
Aortic Valve Modelling
Aortic Valve ModellingAortic Valve Modelling
Aortic Valve Modellingsp435
 
MWEBAZA VICTOR - Nuclear Cardiology The Basics-How To Set Up And Maintain A L...
MWEBAZA VICTOR - Nuclear Cardiology The Basics-How To Set Up And Maintain A L...MWEBAZA VICTOR - Nuclear Cardiology The Basics-How To Set Up And Maintain A L...
MWEBAZA VICTOR - Nuclear Cardiology The Basics-How To Set Up And Maintain A L...Dr. MWEBAZA VICTOR
 
2009--Recovery Trends in TSA Patients
2009--Recovery Trends in TSA Patients2009--Recovery Trends in TSA Patients
2009--Recovery Trends in TSA PatientsColin Ryan
 
Short Term Outcomes after Use of Intracardiac Bone Stem Cell Transplantation ...
Short Term Outcomes after Use of Intracardiac Bone Stem Cell Transplantation ...Short Term Outcomes after Use of Intracardiac Bone Stem Cell Transplantation ...
Short Term Outcomes after Use of Intracardiac Bone Stem Cell Transplantation ...crimsonpublishersOJCHD
 
ClearFlow ~ The Facts About Milking & Stripping Occluded Chest Tubes
ClearFlow ~ The Facts About Milking & Stripping Occluded Chest TubesClearFlow ~ The Facts About Milking & Stripping Occluded Chest Tubes
ClearFlow ~ The Facts About Milking & Stripping Occluded Chest TubesDavid Bigg
 
What is the real cardiac anatomy, clinical anatomy 2019
What is the real cardiac anatomy, clinical anatomy 2019What is the real cardiac anatomy, clinical anatomy 2019
What is the real cardiac anatomy, clinical anatomy 2019gisa_legal
 
Evidence-Based Practice_Lecture 3_slides
Evidence-Based Practice_Lecture 3_slidesEvidence-Based Practice_Lecture 3_slides
Evidence-Based Practice_Lecture 3_slidesCMDLearning
 
Computational Biomedicine Lab: Current Members, pumpsandpipesmdhc
Computational Biomedicine Lab: Current Members, pumpsandpipesmdhcComputational Biomedicine Lab: Current Members, pumpsandpipesmdhc
Computational Biomedicine Lab: Current Members, pumpsandpipesmdhctmhsweb
 
Mangement of chronic heart failure
Mangement of chronic heart failure Mangement of chronic heart failure
Mangement of chronic heart failure Irfan iftekhar
 
Mangement of chronic heart failure 93432-rephrased
Mangement of chronic heart failure 93432-rephrasedMangement of chronic heart failure 93432-rephrased
Mangement of chronic heart failure 93432-rephrasedIrfan iftekhar
 
Antegrage cerebral perfusion
Antegrage cerebral perfusionAntegrage cerebral perfusion
Antegrage cerebral perfusionmshihatasite
 
Essay On Heart Failure
Essay On Heart FailureEssay On Heart Failure
Essay On Heart FailureHeidi Owens
 

Similar to Imaging and modeling biomarkers (20)

The Development of Computational Fluid Dynamic Models for Studying the Detect...
The Development of Computational Fluid Dynamic Models for Studying the Detect...The Development of Computational Fluid Dynamic Models for Studying the Detect...
The Development of Computational Fluid Dynamic Models for Studying the Detect...
 
Nuclear imaging bck
Nuclear imaging bckNuclear imaging bck
Nuclear imaging bck
 
Residency Abstract
Residency AbstractResidency Abstract
Residency Abstract
 
1 s2.0-s0010482514001784-main
1 s2.0-s0010482514001784-main1 s2.0-s0010482514001784-main
1 s2.0-s0010482514001784-main
 
Aortic Valve Modelling
Aortic Valve ModellingAortic Valve Modelling
Aortic Valve Modelling
 
CV2016v4
CV2016v4CV2016v4
CV2016v4
 
MWEBAZA VICTOR - Nuclear Cardiology The Basics-How To Set Up And Maintain A L...
MWEBAZA VICTOR - Nuclear Cardiology The Basics-How To Set Up And Maintain A L...MWEBAZA VICTOR - Nuclear Cardiology The Basics-How To Set Up And Maintain A L...
MWEBAZA VICTOR - Nuclear Cardiology The Basics-How To Set Up And Maintain A L...
 
2009--Recovery Trends in TSA Patients
2009--Recovery Trends in TSA Patients2009--Recovery Trends in TSA Patients
2009--Recovery Trends in TSA Patients
 
Short Term Outcomes after Use of Intracardiac Bone Stem Cell Transplantation ...
Short Term Outcomes after Use of Intracardiac Bone Stem Cell Transplantation ...Short Term Outcomes after Use of Intracardiac Bone Stem Cell Transplantation ...
Short Term Outcomes after Use of Intracardiac Bone Stem Cell Transplantation ...
 
ClearFlow ~ The Facts About Milking & Stripping Occluded Chest Tubes
ClearFlow ~ The Facts About Milking & Stripping Occluded Chest TubesClearFlow ~ The Facts About Milking & Stripping Occluded Chest Tubes
ClearFlow ~ The Facts About Milking & Stripping Occluded Chest Tubes
 
What is the real cardiac anatomy, clinical anatomy 2019
What is the real cardiac anatomy, clinical anatomy 2019What is the real cardiac anatomy, clinical anatomy 2019
What is the real cardiac anatomy, clinical anatomy 2019
 
CCTGA dobutamine
CCTGA dobutamineCCTGA dobutamine
CCTGA dobutamine
 
Evidence-Based Practice_Lecture 3_slides
Evidence-Based Practice_Lecture 3_slidesEvidence-Based Practice_Lecture 3_slides
Evidence-Based Practice_Lecture 3_slides
 
Computational Biomedicine Lab: Current Members, pumpsandpipesmdhc
Computational Biomedicine Lab: Current Members, pumpsandpipesmdhcComputational Biomedicine Lab: Current Members, pumpsandpipesmdhc
Computational Biomedicine Lab: Current Members, pumpsandpipesmdhc
 
Mangement of chronic heart failure
Mangement of chronic heart failure Mangement of chronic heart failure
Mangement of chronic heart failure
 
Mangement of chronic heart failure 93432-rephrased
Mangement of chronic heart failure 93432-rephrasedMangement of chronic heart failure 93432-rephrased
Mangement of chronic heart failure 93432-rephrased
 
Antegrage cerebral perfusion
Antegrage cerebral perfusionAntegrage cerebral perfusion
Antegrage cerebral perfusion
 
Annotated bibliographies
Annotated bibliographiesAnnotated bibliographies
Annotated bibliographies
 
Annotated bibliographies
Annotated bibliographiesAnnotated bibliographies
Annotated bibliographies
 
Essay On Heart Failure
Essay On Heart FailureEssay On Heart Failure
Essay On Heart Failure
 

More from CMIB

Diastolic biomarkers
Diastolic biomarkersDiastolic biomarkers
Diastolic biomarkersCMIB
 
1 s2.0-s0010482514000766-main
1 s2.0-s0010482514000766-main1 s2.0-s0010482514000766-main
1 s2.0-s0010482514000766-mainCMIB
 
10.1016 j.media.2012.04.003 figure
10.1016 j.media.2012.04.003 figure10.1016 j.media.2012.04.003 figure
10.1016 j.media.2012.04.003 figureCMIB
 
10.1016 j.media.2012.08.001 figure
10.1016 j.media.2012.08.001 figure10.1016 j.media.2012.08.001 figure
10.1016 j.media.2012.08.001 figureCMIB
 
Fimh revealing differences
Fimh revealing differencesFimh revealing differences
Fimh revealing differencesCMIB
 
Atrial shape
Atrial shapeAtrial shape
Atrial shapeCMIB
 

More from CMIB (6)

Diastolic biomarkers
Diastolic biomarkersDiastolic biomarkers
Diastolic biomarkers
 
1 s2.0-s0010482514000766-main
1 s2.0-s0010482514000766-main1 s2.0-s0010482514000766-main
1 s2.0-s0010482514000766-main
 
10.1016 j.media.2012.04.003 figure
10.1016 j.media.2012.04.003 figure10.1016 j.media.2012.04.003 figure
10.1016 j.media.2012.04.003 figure
 
10.1016 j.media.2012.08.001 figure
10.1016 j.media.2012.08.001 figure10.1016 j.media.2012.08.001 figure
10.1016 j.media.2012.08.001 figure
 
Fimh revealing differences
Fimh revealing differencesFimh revealing differences
Fimh revealing differences
 
Atrial shape
Atrial shapeAtrial shape
Atrial shape
 

Recently uploaded

Mine Environment II Lab_MI10448MI__________.pptx
Mine Environment II Lab_MI10448MI__________.pptxMine Environment II Lab_MI10448MI__________.pptx
Mine Environment II Lab_MI10448MI__________.pptxRomil Mishra
 
complete construction, environmental and economics information of biomass com...
complete construction, environmental and economics information of biomass com...complete construction, environmental and economics information of biomass com...
complete construction, environmental and economics information of biomass com...asadnawaz62
 
Sachpazis Costas: Geotechnical Engineering: A student's Perspective Introduction
Sachpazis Costas: Geotechnical Engineering: A student's Perspective IntroductionSachpazis Costas: Geotechnical Engineering: A student's Perspective Introduction
Sachpazis Costas: Geotechnical Engineering: A student's Perspective IntroductionDr.Costas Sachpazis
 
Comparative study of High-rise Building Using ETABS,SAP200 and SAFE., SAFE an...
Comparative study of High-rise Building Using ETABS,SAP200 and SAFE., SAFE an...Comparative study of High-rise Building Using ETABS,SAP200 and SAFE., SAFE an...
Comparative study of High-rise Building Using ETABS,SAP200 and SAFE., SAFE an...Erbil Polytechnic University
 
11. Properties of Liquid Fuels in Energy Engineering.pdf
11. Properties of Liquid Fuels in Energy Engineering.pdf11. Properties of Liquid Fuels in Energy Engineering.pdf
11. Properties of Liquid Fuels in Energy Engineering.pdfHafizMudaserAhmad
 
Engineering Drawing section of solid
Engineering Drawing     section of solidEngineering Drawing     section of solid
Engineering Drawing section of solidnamansinghjarodiya
 
Ch10-Global Supply Chain - Cadena de Suministro.pdf
Ch10-Global Supply Chain - Cadena de Suministro.pdfCh10-Global Supply Chain - Cadena de Suministro.pdf
Ch10-Global Supply Chain - Cadena de Suministro.pdfChristianCDAM
 
Crushers to screens in aggregate production
Crushers to screens in aggregate productionCrushers to screens in aggregate production
Crushers to screens in aggregate productionChinnuNinan
 
Arduino_CSE ece ppt for working and principal of arduino.ppt
Arduino_CSE ece ppt for working and principal of arduino.pptArduino_CSE ece ppt for working and principal of arduino.ppt
Arduino_CSE ece ppt for working and principal of arduino.pptSAURABHKUMAR892774
 
TechTAC® CFD Report Summary: A Comparison of Two Types of Tubing Anchor Catchers
TechTAC® CFD Report Summary: A Comparison of Two Types of Tubing Anchor CatchersTechTAC® CFD Report Summary: A Comparison of Two Types of Tubing Anchor Catchers
TechTAC® CFD Report Summary: A Comparison of Two Types of Tubing Anchor Catcherssdickerson1
 
Internet of things -Arshdeep Bahga .pptx
Internet of things -Arshdeep Bahga .pptxInternet of things -Arshdeep Bahga .pptx
Internet of things -Arshdeep Bahga .pptxVelmuruganTECE
 
welding defects observed during the welding
welding defects observed during the weldingwelding defects observed during the welding
welding defects observed during the weldingMuhammadUzairLiaqat
 
Input Output Management in Operating System
Input Output Management in Operating SystemInput Output Management in Operating System
Input Output Management in Operating SystemRashmi Bhat
 
home automation using Arduino by Aditya Prasad
home automation using Arduino by Aditya Prasadhome automation using Arduino by Aditya Prasad
home automation using Arduino by Aditya Prasadaditya806802
 
IVE Industry Focused Event - Defence Sector 2024
IVE Industry Focused Event - Defence Sector 2024IVE Industry Focused Event - Defence Sector 2024
IVE Industry Focused Event - Defence Sector 2024Mark Billinghurst
 
Main Memory Management in Operating System
Main Memory Management in Operating SystemMain Memory Management in Operating System
Main Memory Management in Operating SystemRashmi Bhat
 
Work Experience-Dalton Park.pptxfvvvvvvv
Work Experience-Dalton Park.pptxfvvvvvvvWork Experience-Dalton Park.pptxfvvvvvvv
Work Experience-Dalton Park.pptxfvvvvvvvLewisJB
 
Configuration of IoT devices - Systems managament
Configuration of IoT devices - Systems managamentConfiguration of IoT devices - Systems managament
Configuration of IoT devices - Systems managamentBharaniDharan195623
 
Gurgaon ✡️9711147426✨Call In girls Gurgaon Sector 51 escort service
Gurgaon ✡️9711147426✨Call In girls Gurgaon Sector 51 escort serviceGurgaon ✡️9711147426✨Call In girls Gurgaon Sector 51 escort service
Gurgaon ✡️9711147426✨Call In girls Gurgaon Sector 51 escort servicejennyeacort
 

Recently uploaded (20)

Mine Environment II Lab_MI10448MI__________.pptx
Mine Environment II Lab_MI10448MI__________.pptxMine Environment II Lab_MI10448MI__________.pptx
Mine Environment II Lab_MI10448MI__________.pptx
 
complete construction, environmental and economics information of biomass com...
complete construction, environmental and economics information of biomass com...complete construction, environmental and economics information of biomass com...
complete construction, environmental and economics information of biomass com...
 
Sachpazis Costas: Geotechnical Engineering: A student's Perspective Introduction
Sachpazis Costas: Geotechnical Engineering: A student's Perspective IntroductionSachpazis Costas: Geotechnical Engineering: A student's Perspective Introduction
Sachpazis Costas: Geotechnical Engineering: A student's Perspective Introduction
 
Comparative study of High-rise Building Using ETABS,SAP200 and SAFE., SAFE an...
Comparative study of High-rise Building Using ETABS,SAP200 and SAFE., SAFE an...Comparative study of High-rise Building Using ETABS,SAP200 and SAFE., SAFE an...
Comparative study of High-rise Building Using ETABS,SAP200 and SAFE., SAFE an...
 
Designing pile caps according to ACI 318-19.pptx
Designing pile caps according to ACI 318-19.pptxDesigning pile caps according to ACI 318-19.pptx
Designing pile caps according to ACI 318-19.pptx
 
11. Properties of Liquid Fuels in Energy Engineering.pdf
11. Properties of Liquid Fuels in Energy Engineering.pdf11. Properties of Liquid Fuels in Energy Engineering.pdf
11. Properties of Liquid Fuels in Energy Engineering.pdf
 
Engineering Drawing section of solid
Engineering Drawing     section of solidEngineering Drawing     section of solid
Engineering Drawing section of solid
 
Ch10-Global Supply Chain - Cadena de Suministro.pdf
Ch10-Global Supply Chain - Cadena de Suministro.pdfCh10-Global Supply Chain - Cadena de Suministro.pdf
Ch10-Global Supply Chain - Cadena de Suministro.pdf
 
Crushers to screens in aggregate production
Crushers to screens in aggregate productionCrushers to screens in aggregate production
Crushers to screens in aggregate production
 
Arduino_CSE ece ppt for working and principal of arduino.ppt
Arduino_CSE ece ppt for working and principal of arduino.pptArduino_CSE ece ppt for working and principal of arduino.ppt
Arduino_CSE ece ppt for working and principal of arduino.ppt
 
TechTAC® CFD Report Summary: A Comparison of Two Types of Tubing Anchor Catchers
TechTAC® CFD Report Summary: A Comparison of Two Types of Tubing Anchor CatchersTechTAC® CFD Report Summary: A Comparison of Two Types of Tubing Anchor Catchers
TechTAC® CFD Report Summary: A Comparison of Two Types of Tubing Anchor Catchers
 
Internet of things -Arshdeep Bahga .pptx
Internet of things -Arshdeep Bahga .pptxInternet of things -Arshdeep Bahga .pptx
Internet of things -Arshdeep Bahga .pptx
 
welding defects observed during the welding
welding defects observed during the weldingwelding defects observed during the welding
welding defects observed during the welding
 
Input Output Management in Operating System
Input Output Management in Operating SystemInput Output Management in Operating System
Input Output Management in Operating System
 
home automation using Arduino by Aditya Prasad
home automation using Arduino by Aditya Prasadhome automation using Arduino by Aditya Prasad
home automation using Arduino by Aditya Prasad
 
IVE Industry Focused Event - Defence Sector 2024
IVE Industry Focused Event - Defence Sector 2024IVE Industry Focused Event - Defence Sector 2024
IVE Industry Focused Event - Defence Sector 2024
 
Main Memory Management in Operating System
Main Memory Management in Operating SystemMain Memory Management in Operating System
Main Memory Management in Operating System
 
Work Experience-Dalton Park.pptxfvvvvvvv
Work Experience-Dalton Park.pptxfvvvvvvvWork Experience-Dalton Park.pptxfvvvvvvv
Work Experience-Dalton Park.pptxfvvvvvvv
 
Configuration of IoT devices - Systems managament
Configuration of IoT devices - Systems managamentConfiguration of IoT devices - Systems managament
Configuration of IoT devices - Systems managament
 
Gurgaon ✡️9711147426✨Call In girls Gurgaon Sector 51 escort service
Gurgaon ✡️9711147426✨Call In girls Gurgaon Sector 51 escort serviceGurgaon ✡️9711147426✨Call In girls Gurgaon Sector 51 escort service
Gurgaon ✡️9711147426✨Call In girls Gurgaon Sector 51 escort service
 

Imaging and modeling biomarkers

  • 1. Pablo Lamata Lecturer. Sir Henry Dale Fellow
  • 2.
  • 3.  “All models are wrong”Denis Noble.  “Nobody believes on the results of an experimentalist, but him, and everybody believes on the results of a modeller, but himself”  Take home message: … need to define the problem and hypothesis as clear as possible!!
  • 4.  Goal: clinical support. Stratifiation.  Hypotheses:  Models regularise / clean clinical data.  Models unveil diagnostic metrics  Examples:  Shape  Diastolic performance  Non-invasive pressure  Discussion:  Opportunity to make a clinical impact  Robustness!!
  • 5.
  • 6.  Cardiac remodelling  Development  Disease  State of art: coarse metrics  Length, diameter, volume…  Opportunities  Myriad of shape patterns  Tons of data
  • 7.  Computational statistical atlas of anatomy [1]  Clinicians will adopt novel shape coordinates in this parametric space [1] A. Young, A. Frangi. “Computational cardiac atlases: from patient to population and back.” Exp. Physiol. (2009)
  • 8. [2] P. Lamata, S. Niederer, et al., “An accurate, fast and robust method to generate patient-specific cubic Hermite meshes,” Med. image Anal. (2011). [3] P. Lamata, M. Sinclair, et al. “An automatic service for the personalization of ventricular cardiac meshes.” J R Soc Interface (2014)  Model: ellipsoid  Meshing [2,3]  Reduce noise and artifacts  Smooth C1 representation  Statistics: PCA  Web-service http://amdb.isd.kcl.ac.uk/
  • 9.  Give me your short axis stack, and I’ll tell you if you had a premature birth [4]. [4] A. Lewandovski, D. Augustine et al. “Preterm heart in adult life: cardiovascular magnetic resonance reveals distinct differences in left ventricular mass, geometry, and function.” Circulation (2013)
  • 10.  Ventricle grow differently depending on surgical choice in HLHS [5]. [5] J. Wong, P. Lamata et al. “Right ventricular morphology and function following stage I palliation with a modified Blalock-Taussig shunt versus a right ventricle-to- pulmonary artery conduit” Circ. Imaging (in review)
  • 11. [5] J. Wong, P. Lamata et al. “Right ventricular morphology and function following stage I palliation with a modified Blalock-Taussig shunt versus a right ventricle-to- pulmonary artery conduit” Circ. Imaging (in review)
  • 12.
  • 13.  HF with Normal Ejection Fraction  Evidence of abnormal filling caused by stiffer myocardium, delayed relaxation, impaired atrio-ventricular conduit function.  Diagnostic surrogates [6]: • Lab: natruiretic peptides • Echo: ratio early/late filling • Catheters: LV pressure  Stratification: on-going challenge [6] [6] Maeder and Kaye, “Heart Failure With Normal Left Ventricular Ejection Fraction,” J. Am. Coll. Cardiol. 2009
  • 14.  State of art (catheter): exponential fitting  Coupling between relaxation and stiffness P V Passive elastic Active fibre relaxation Total LV pressure
  • 15.  Myocardial properties (relaxation/stiffness)  Input: deformation and pressure  Method: Model personalization  Output: Decouple relaxation / stiffness
  • 16. [7] J. Xi, P. Lamata, et. al, “The estimation of patient-specific cardiac diastolic functions from clinical measurements,” Med. image Anal., 17:133-146 (2013). 6 unknowns 4 data points  Additional constraints [7]  End diastole: null active tension  Positive, and monotonically decaying active tension  Criterion to choose reference configuration
  • 17. [7] J. Xi, P. Lamata, et. al, “The estimation of patient-specific cardiac diastolic functions from clinical measurements,” Med. image Anal., 17:133-146 (2013).  Criterion to choose reference configuration
  • 18.  Stiffness = f(deform., pressure)  LV filling pressure: only catheter  Two aims [8]:  Hypothesis: P = f(V)  Characterise impact of pressure offset errors [8] J. Xi, W. Shi, et. al, “Understanding the need of ventricular pressure for the estimation of diastolic biomarkers,” Biomech. Model. Mechanobiol. (2013)
  • 19.  Literature surrogate  Able to differentiate stiffness  Stiffness = f(ejection fraction)  Unable to different. active tension [8] J. Xi, W. Shi, et. al, “Understanding the need of ventricular pressure for the estimation of diastolic biomarkers,” Biomech. Model. Mechanobiol. (2013)
  • 20.  Able to recover from pressure offset errors  Need temporal resolution! No pressure offset With pressure offset [8] J. Xi, W. Shi, et. al, “Understanding the need of ventricular pressure for the estimation of diastolic biomarkers,” Biomech. Model. Mechanobiol. (2013)
  • 21.
  • 22.  Pressure: important biomarker  What if…  Central pressure?  Time + space
  • 23.  Coarctation  Obstruction LVOT ∆P=P(B)-P(A)? B A ∆P=P(B)-P(A)?
  • 24. P (mmHg) = 4 𝑽 𝒎𝒂𝒙 𝟐 (m/sec)
  • 25.  PC-MRI  Navier Stokes Eq. x 4
  • 26.  No need of boundary conditions  Arbitrary domains  Includes viscous effects [8] S. Krittian, P. Lamata et al. “A FEM approach to the direct computation of relative cardiovascular pressure from time-resolved MR velocity data.” Med. Im. Analysis (2012)
  • 27.  Mass and momentum conservation: Viscous forces Convective acceleration (in-space) Transient acceleration (in-time) Inertial forces t=1 t=0
  • 28. [9] P. Lamata, A. Pitcher et al. “Aortic relative pressure components derived from 4D flow cardiovascular magnetic resonance”. MRM (2014)
  • 29.  Transient: pump action and compliance  Convective: vessel geometry  Viscous: inefficiencies due to friction [9] P. Lamata, A. Pitcher et al. “Aortic relative pressure components derived from 4D flow cardiovascular magnetic resonance”. MRM (2014)
  • 30.
  • 31.  Images are drivers of modelling progress [10]  Complexity vs. clinical adoption  Robustness!! [10] P. Lamata, R. Casero et al, “Images as drivers of progress in cardiac computational modelling”, Prog Biophys Mol Biol (2014)
  • 32.  Meshes of high quality [11] [11] P. Lamata, I. Roy et al. “Quality metrics for high order meshes: analysis of the mechanical simulation of the heart beat.” IEEE Trans Med Imag (2013)
  • 33.  More stable simulations: guide the optimizer to enforce non-compressibility [12] S. Land, S. Niederer et al. “Improving the stability of cardiac mechanical simulations” IEEE Trans Biom Eng (accepted)
  • 34.  Goal: clinical support. Stratifiation.  Hypotheses:  Models regularise / clean clinical data.  Models unveil diagnostic metrics  Examples:  Shape  Diastolic performance  Non-invasive pressure  Discussion:  Opportunity to make a clinical impact  Robustness!!
  • 35.  Oxford / KCL  Nic Smith  Steve Niederer  David Nordsletten  Sander Land  [Jiahe Xi]  [Sebastian Krittian]  [Ishani Roi]  Imperial  Daniel Rueckert  Wenzhe Shi  Clinicians  Reza Razavi (KCL)  Aldo Rinaldi (KCL)  Paul Leeson (OXF)  Adam Lewandovski (OXF)  Stefan Neubauer (OXF)  Alex Pitcher (OXF)

Editor's Notes

  1. Thank you for the introduction, and for the opportunity to present my ideas and goals. The problem that I will address is the management of HF, a major health issue in the UK, which brings annual costs of £0.75 billion to our health system. The lifetime risk of developing HF is one in five, it is expected that 3 of us will develop some form of this disease. HF is the clinical condition in which the heart is not able to pump enough blood to meet the body demands. Two actions govern the mechanical pump function of the heart, ejection and filling. The scope of my work focuses on the second, which relates to the condition of HFNEF, that affects half of the population with HF (the other half have systolic HF). HFNEF patients have abnormal filling, caused by a stiffer myocardium, a delayed relaxation, or an impaired atrio-ventricular counduit function. Current diagnostic clinical guidelines for HFNEG use these surrogates to characterise an impaired filling of the ventricles: 1,2,3 The problem is that the characterization and stratification of patients is an on-going challenge. One of the fundamental reasons for it is that current diagnostic metrics are only surrogates of the mechanisms that impair ventricular filling. In my work, I plan to bridges this gap, estimating the fundamental mechanical properties that govern diastolic filling.
  2. The huge potential of the combination of models and images (observations)
  3. Therefore, my central hypothesis is that these new mechanical parameters will improve the characterization and stratification of patients, and therefore the management of HF. And therefore my objective is to develop a robust and clinically applicable methodology to characterise these mechanical parameters. I will address this ambitious goal combining three of my most recent contributions in the field of research overlapping medical imaging and mathematical modelling. First, the technology to personalise mechanical computational meshes to the anatomy of the patient, captured from medical images. A proof of the accuracy of this process, and also a significant contribution since the submission of the proposal, is the publication of a computational anatomical atlas of the left ventricle in Circulation, the leading journal in Cardiology, where I have clearly characterised the shape of the LV. Once the anatomy is captured, a methodology is used to automatically uncouple the active relaxation and passive inflation of the ventricle, the two interrelated mechanisms that rule diastolic filling. DIAGRAM: an overview of the model personalization technique is represented in this diagram. MOVIE: clinically available data of deformation, captured through dynamic MRI, and pressure, measured with catheters, is assimilated into the model: the fundamental physical parameters of the model are optimised by minimising the differences between the predicted deformation by the model, and the observed deformation in images. The outcome of this process is the myocardial stiffness and relaxation profile that best explain the data. I have already provided the proof of concept of this methodology with the comparison of two diseased and one healthy subject, as published in the leading journal in the field of Medical Image Analysis. 2. On the other hand, the characterization of the atria-ventricular conduit will be tackled using one of my recent contributions, a method for the computation of blood pressure differences from velocity data captured by PC-MRI. MOVIE 1: this imaging modality enables us to capture the velocity at each instant and voxel of the sequence. In this example we can see the streamlines of velocity colour coded by the magnitude of velocity. MOVIE 2: solving the fundamental physical Navier-Stokes equations, the pressure that explains the acceleration and viscous friction of that velocity is computed. Now we have the same streamlines colour coded by pressure. The idea is to characterise the presence of any impaired conduit function of the mitral valve through the existence of pressure drops. I’d like to finish this presentation with the envisioned solution combining these two technologies, where the pressure that is required to estimate myocardial parameteres, currently only available through invasive catheterised procedures, is estimated with the non-invasive methods that have been described before. This will then lead to a simple clinical workflow for patients, in which they will only require an MRI acquisition for about 20 minutes, and where a computational modelling post-processing step unveils these novel biomarkers. The majority of patients could then benefit from this diagnostic tool minimising any associated risks, and therefore maximising the impact of this envisioned solution
  4. Thank you for the introduction, and for the opportunity to present my ideas and goals. The problem that I will address is the management of HF, a major health issue in the UK, which brings annual costs of £0.75 billion to our health system. The lifetime risk of developing HF is one in five, it is expected that 3 of us will develop some form of this disease. HF is the clinical condition in which the heart is not able to pump enough blood to meet the body demands. Two actions govern the mechanical pump function of the heart, ejection and filling. The scope of my work focuses on the second, which relates to the condition of HFNEF, that affects half of the population with HF (the other half have systolic HF). HFNEF patients have abnormal filling, caused by a stiffer myocardium, a delayed relaxation, or an impaired atrio-ventricular counduit function. Current diagnostic clinical guidelines for HFNEG use these surrogates to characterise an impaired filling of the ventricles: 1,2,3 The problem is that the characterization and stratification of patients is an on-going challenge. One of the fundamental reasons for it is that current diagnostic metrics are only surrogates of the mechanisms that impair ventricular filling. In my work, I plan to bridges this gap, estimating the fundamental mechanical properties that govern diastolic filling.