Heart attack diagnosis from DE-MRI images

Supervised by:Alain Lalande, PhD

Girona, 15 June 2011

Automatic Quantification of Myocardial Infarction from Delayed Enhancement MRI

Vanya Vabrina Valindria

IntroductionWhat is myocardial infarction (MI)?Heart attack, caused by coronary arthrosclerosis

Myocardium: heart muscleInfarction: tissue death, due to lack of oxygen

Heterogeneous infarct zones (HIA):

Infarct core Peri-infarct Microvascular obstruction

(no-reflow)

What is MI?What is DE-MRI?Problem definition

Delay-enhancement MRI (DE-MRI)

• Gold standard for MI viability• 10 – 15 minutes after contrast agent injection • Infarct area is shown as hyper-enhancement

IntroductionWhat is MI?What is DE-MRI? Problem definition

Acquisition of DE-MRI slices





Endocardium





Endocardium

Epicardium





Infarct in DE-MRI

Endocardium

Epicardium

Introduction

HIA is hard to be distinguished visually

No automatic solution available

What is MI?What is DE-MRI? Problem definition

Problem Definition

Introduction

HIA is hard to be distinguished visually

No automatic solution available

Develop automatic segmentation and quantification methods, by taking into account HIA.

Implement clinical software for automatic quantification of MI from DE-MR images

Project goals

!

!

What is MI?What is DE-MRI? Problem definition

Problem Definition

State of the Art

• Infarct segmentationDiagram + pictures

Infarct segmentationHIA segmentationQuantification & Representation

Kim, et al (1999)Beek, et al (2005, 2009)Heiberg, et al (2005, 2008)

State of the Art




Amado, et al (2004)

State of the Art




Amado, et al (2004) Hsu, et al (2006)

State of the Art





Doublier, et al (2003)Hennemuth, et al (2008)

Friman, et al (2008)Metwally, et al (2010)Elagoumi, et al (2010)

State of the Art





Doublier, et al (2003)Hennemuth, et al (2008)

Friman, et al (2008)Metwally, et al (2010)Elagoumi, et al (2010)

State of the Art

Microvascular obstruction (MO) NO exact threshold definition


HIA Segmentation

Simple intensity thresholdingYan, et al (2006) – SD basedSchmidt, et al (2007) – FWHM basedHundley, et al (2010) – SD based

Infarct core

State of the Art



HIA Segmentation

Peri-Infarct


Infarct core

State of the Art



HIA Segmentation

Peri-Infarct

No-reflow


Contiguous short-axis slices

State of the Art

Bull’s eye plot in 17-segment model


Quantification Representation

Slice thickness

Contiguous short-axis slices

State of the Art



Quantification Representation

Slice thickness

basal

mid-cavity

apical

apex

Methodology

20 patients

Ages: 53 +10 years

Acute MI (<2 weeks after heart attack)

Material

3 T MR MagnetPSIR sequence

Population study

MRI Protocol

MaterialPre-processingInfarct segmentationInfarct core segmentationPeri-infarct segmentationNo-reflow segmentationQuantificationRepresentations

Methodology

Input:Original image

256x216+ Myocardial contours

Increase resolution


Pre-processing

Methodology



Increase resolution

Contrast Enhancement


Pre-processing

Methodology



Increase resolution


Unregistered 3D MRI stack

Rigid registration

Motion compensation

Registered image slices, same center myocardium ROI location


Pre-processing

Methodology



Increase resolution


Unregistered 3D MRI stack

Rigid registration

Motion compensation

Registered image slices, same center myocardium ROI location

Myocardium ROI

Image filtering


Pre-processing

Mixture of Gaussian distribution:

Input:Pre-processed myocardium

Gaussian mixture model (GMM)

Estimate θ by iterative expectation-maximization (EM) algorithm

M-step:

E-step:

Gaussian parameters

Voxel intensity

MethodologyMaterialPre-processingInfarct segmentationInfarct core segmentationPeri-infarct segmentationNo-reflow segmentationQuantificationRepresentations

Gaussian distribution:

Infarct segmentation





M-step:

E-step:

Gaussian parameters

Voxel intensity








Output:Hyper-enhanced (HE) region

M-step:

E-step:

Gaussian parameters

Voxel intensity




Two-way 3D connectivity analysisFalse positive compensation noisy acquisition, blood pool artifact, or partial volume effect (PVE)Detected infarct continuous in 3D image stack



Two-way 3D connectivity analysisFalse positive compensation noisy acquisition, blood pool artifact, or partial volume effect (PVE)Detected infarct continuous in 3D image stack

Feature analysis• Minimum size• Sub-endocardial distance• Solidity



FWHM Thresholding

Feature analysis Inclusion of no-reflow areaMorphological filling and closing with endocardium

3D connectivity analysis Minimum size


Infarct core segmentation

FWHM Thresholding

Feature analysis Inclusion of no-reflow areaMorphological filling and closing with endocardium

3D connectivity analysis Minimum size


Infarct core segmentation

Spatial-weighted Fuzzy clustering

Spatial-weighted fuzzy membership:

Optimization of objective function O for the optimal cluster center c and degree of membership u:

Cluster: K = 2 normal and peri-infarct

Euclidean distance of myocardium pixels to the infarct core

Input


Peri-infarct segmentation

Spatial weight pik






Input



Output Spatial weight pik






Defuzzification

Input



Input: Infarct core

Dark region surrounded by infarct core


1

No-reflow segmentation

Input: Infarct core


Adjacent to endocardium


1

2


Input: Infarct core

Extent of MO ≠transmural



Spatial constraint Dist myo : normalized relative distance of myocardium pixels to endocardium

Limit for no-reflow region:


1

2

3


Input: Infarct core

Extent of MO ≠transmural



Spatial constraint Dist myo : normalized relative distance of myocardium pixels to endocardium

Limit for no-reflow region:

Output


1

2

3


Area in

MethodologyQuantification


A r e a

Area in

V o l u m e

Volume in Also, in % of myocardium

i = the current slice from N image slices in MRI stack.

MethodologyQuantification


A r e a

Volumetric quantification for the whole myocardium where,

Representation


Qualitative

Infarct Core

Peri-infarct

No-reflow

All

Quantitative


Results

Menu toolbar

Image display

Patient information

Navigation

Status GUI

Manual contouring

Analysis panelDisplay

segmentation Display quantification

GUI ImplementationEvaluation of Infarct Size QuantificationEvaluation of the Infarct Area SegmentationVisual Evaluation of HIA Segmentation

Results

Menu toolbar

Image display

Patient information

Navigation

Status GUI

Manual contouring




Results

Menu toolbar

Image display

Patient information

Navigation

Status GUI

Manual contouring




Results

Menu toolbar

Image display

Patient information

Navigation

Status GUI

Manual contouring




Results

Menu toolbar

Image display

Patient information

Navigation

Status GUI

Manual contouring




Results

Menu toolbar

Image display

Patient information

Navigation

Status GUI

Manual contouring




Results

Menu toolbar

Image display

Patient information

Navigation

Status GUI

Manual contouring




Vo l u m e

Comparison of our automatic method & ground truth (manual total-infarct tracing from 2 observers)

mean of the differences = 2.78 cm3SD of the differences = 3.27 cm3

ResultsGUI ImplementationEvaluation of Infarct Size QuantificationEvaluation of the Infarct Area SegmentationVisual Evaluation of HIA Segmentation

Evaluation of Infarct Size Quantification

mean of the differences = 0.45 cm2SD of the differences = 0.75 cm2

A r e a

ResultsEvaluation of Infarct Size Quantification


Evaluation of the Infarct Segmentation


Results

Kappa statistics for infarct-segmentation comparison of automatic methods and observers agreement

By Kappa coefficient, between the automatic segmentation results and ground-truths (manual total-infarct tracing from 2 observers)

Visual evaluation result for HIA segmentation from 116 images from 20 patients


Visual Evaluation of HIA Segmentation

Rating score for subjective-visual evaluation of HIA segmentation

Issues in HIA segmentation (errors are indicated with white arrow)

Error in the infarct core segmentation

Error in the peri-infact segmentation

Error in the no-reflow segmentation

Unconnected infarct regions



Example of correct infarct segmentation results with our method

Robustness of the infarct segmentation method neighboring slices by using two-way 3D connectivity

Image with narrow range of signal intensity



Improvements made in this thesis work: Clustering method used rather than strict threshold determination Elimination of false positive cases were tackled The definitions for peri-infarct and no-reflow segmentation give promising result

Conclusions

1 A fully automatic system for myocardial infarction quantification has been implemented

The automatic quantification and segmentation results had been evaluated and gave the best performance with fast computational time

2

Improvements made in this thesis work: Clustering method used rather than strict threshold determination Elimination of false positive cases were tackled The definitions for peri-infarct and no-reflow segmentation give promising result

Conclusions

Validation with histopathologyExtend the representation in 3D model Implementation in C++

F u t u r e w o r k s

1 A fully automatic system for myocardial infarction quantification has been implemented

The automatic quantification and segmentation results had been evaluated and gave the best performance with fast computational time

2

Thank you! Gracias! Merci!

Terima kasih!

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References

Infarct Segmentation Comparison

2 SD FWHM Combined threshold-Feature Analysis

Proposed method Ground truth 1 Ground truth 2

HIA Segmentation Comparison

Yan et al. (2006)

Schmidt et al. (2007)

Hundely et al. (2010)

Proposed method

Volume Evaluation - Regression

Volume Evaluation – Bland Altman

2 SD FWHM

FACT Proposed Method

Area Evaluation - Regression

Area Evaluation – Bland Altman

2 SD FWHM

FACT Proposed Method

Threshold management

HIA Segmentation

Bull’s Eye Calculation

Weight according to the location of the overlapped slice

Volumetric Quantification


Characteristics:

• The distribution of myocardium SI according to Gaussian

• Infarct always starts from endocardial

• Infarct regions are compact-shape of certain size

A = area of the region H = the convex hull area of the polygon approximating the region shape

MRF Segmentation

Health & Medicine

Heart attack diagnosis from DE-MRI images