ANALYSIS OF PULMONARY FIBROSIS IN MRI ... -...

ANALYSIS OF PULMONARY FIBROSIS IN MRI, USINGAN ELASTIC REGISTRATION TECHNIQUE IN A MODELOF FIBROSIS: Scleroderma

ORAL DEFENSE 8 th of September 2017

Charlotte MARTINSupervisor: Pr. MP REVEL

M2 Bio Medical ImagingSubtract: Imaging from molecular to human

2016-2017

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1. BACKGROUND - Scleroderma

Scleroderma also known as systemic sclerosis (SSc) is a multisystemic connective tissue disease

There is 70 to 100% of pulmonary involvement, which is the first cause of death

Infiltrative lung disease (ILD) in scleroderma progresses from non fibrotic ILD towards fibrotic ILD

HRCT and pulmonary function testing are the two mains examinations to evaluate pulmonary involvement

Background Materiel and methods Results Discussion Conclusion

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1. BACKGROUND – Scleroderma evaluation

HRCT :• Has high spatial and contrast resolution

• Provides radiation exposure and no functional information


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No ILD ILD with FibrosisILD without fibrosis

1. BACKGROUND – Scleroderma evaluation

Pulmonary Function Testing:• Is the Gold Standard for pulmonary

evaluation

• Is not very sensitive to small changes in pulmonary function

• The two mains parameters of pulmonary fibrosis evaluation are:

Forced vital capacity (FVC)

Corrected free diffusion of CO

(DLCOc)


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1. BACKGROUND - Lung MRI

MRI of lung parenchyma is not currently used in clinical practice, due to:• Lack of signal (low H+ proton density and short T2* relaxation time)

• Important artifacts (heterogeneity of the local magnetic field and respiratory movements)

New MR sequences named UTE (ultra-short time of echo) have been developed (1):• TE < 0.5 msec

• It allows minimizing the T2* signal decay and obtaining images from the lung parenchyma with sufficient signal intensity


5(1) Holmes JE, Bydder GM. MR imaging with ultrashort TE (UTE) pulse sequences: Basic principles.

Radiography. 1 août 2005;11(3):163-74

OBJECTIVES

To evaluate the feasibility of an elastic registration method based on MR images for the detection of pulmonary fibrosis in patients with scleroderma


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2. MATERIEL AND METHODS - Population

We evaluated scleroderma patients referred for cardiac MR as part of their routine follow-up

Patients were classified in 3 groups:• No pulmonary involvement (SSc-no-ILD)

• Infiltrative lung disease without fibrosis (SSC-ILD)

• Infiltrative lung disease with pulmonary fibrosis (SSc-fibrosis)

We also evaluated a cohort of healthy volunteers


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Two UTE sequences named spiral VIBE (volumetric Interpolated Breath-hold Examination) were performed, one at the end of full inspiration and another after full expiration on a 3T-unit

3D sequences, acquired on the coronal plane

Voxel size 2.14 x 2.14 x 2.5 mm

Echo time : 0.05ms

2. MATERIEL AND METHODS – MR examination


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Inspiration Expiration

2. MATERIEL AND METHODS – MR segmentation


We created lung masks for inspiratory and expiratory volumes

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We used an elastic registration algorithm (1)

We chose to make the registration from inspiratory to expiratory images• Because inspiratory images contain

more information

Registration was helped by the pre-processing masks

2. MATERIEL AND METHODS – Elastic registration


(1) Glocker B, Sotiras A, Komodakis N, Paragios N. Deformable Medical Image Registration: Setting the State of the Art with Discrete Methods. Annu Rev Biomed Eng. 2011;13(1):219-44. 10

Inspiratory+Expiratory

Expiratory+Registered

The first step was to evaluate the validity of the elastic registration algorithm

11 anatomic landmarks were manually placed by one observer on inspiratory images

Then, the same landmarks were placed on expiratory images by:

• the same observer (manually)

• another independent observer (manually)

• the algorithm (registered)

2. MATERIEL AND METHODS – Evaluation of elastic registration


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Mean distances between points were compared:• Distance A: Observer 1 - Observer 2

• Distance B: Observer 1 - Registered

• Distance C: Observer 2 - Registered

Variance test: • No significant difference

• p = 0,49

2. MATERIEL AND METHODS – Evaluation of elastic registration


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Inspiratory sequence11 landmarks

Observer 1

Expiratory sequence11 landmarks

Observer 1

Expiratory sequence11 landmarks

Observer 2

Registered sequence11 landmarks

Set of 11 expiratorypoints

Set of 11 expiratory points

Set of 11 registeredpoints

Manually placed Deformed by registration

Distance A Distance B Distance C

Elastic registration produces deformation maps from which the Jacobian determinant of each voxel is obtained.

Jacobian determinant is the quantitative value of the deformation matrix of each voxel

2. MATERIEL AND METHODS – Deformation analysis


Deformed images

DEFORMATION

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V0 = V1Jacobian

determinant =1Source image = V0





Deformed images

DEFORMATION

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V0>V1Jacobian

determinant [0;1]

V0 = V1Jacobian






Deformed images

DEFORMATION

V0<V1Jacobian

determinant > 1

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V0>V1Jacobian

determinant [0;1]

V0 = V1Jacobian




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Post-processing steps of Jacobian determinant maps:

• Step 1: Normalization of the Jacobian determinants of each patient using the ratio between its expiratory and inspiratory volumes, to minimize the effect of the quality of inspiration/expiration



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• Step 1: Normalization of the Jacobian determinants of each patient using the ratio between its expiratory and inspiratory volumes

• Step 2: Mapping to a common template (expiratory mask for a healthy volunteer) through elastic registration of the binary masks of the lung

Patient 1 Patient 4 Patient 29

Common mask



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Patient 1 Patient 4 Patient 29

y axis

x axis

z axis




• Step 3: For each patient, calculation of the mean of the Jacobian determinant along each axis (x; y; z)



SSc-Fibrosis=7

HealthyVolunteersn=11

SSc-ILDn=5

SSc-no-ILDn=9

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• Step 3: For each patient, calculation of the mean of the Jacobian determinant along each axis (x; y; z)

• Step 4: For each group of patients

Calculation of the mean of the Jacobian determinant for each voxel and representation with 3D color maps

Calculation of the mean of the Jacobian determinant along each axis.

2. MATERIEL AND METHODS – Quantitative analysis

Healthy volunteers group


We isolated lung regions with the most important positive values of the Jacobian determinant logarithm (= areas with the most important positive deformation), using a threshold of 0.15 :

• In the healthy volunteers group

• And for each patient

• We calculated similarity index, Dice coefficient, between the healthy volunteers group and each patient

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3. RESULTS - Population

33 subjects were included:• 11 healthy volunteers

• 22 patients

1 patient was excluded of the analysis because of poor quality of examination due to a misunderstanding of breathing instructions, so 32 subjects were evaluated

Total HealthyVolunteers SSc-no-ILD SSc-ILD SSc-Fibrosis pvalue

N=32 N=11 N=9 N=5 N=7

Gender 0,005

Female 19(59%) 2(18%) 7(78%) 5(100%) 5(71%)

Male 13(41%) 9(82%) 2(22%) 0(0%) 2(29%)

Age(years) 0,001

Mean(SD) 45,6(19,5) 29,1(9,3) 59(17,5) 43,6(14,7) 60(16,3)

BMI 0,79

Mean(SD) 21,9(2,5) 21,8(1,6) 22,6(2,3) 21,1(3,9) 21,8(2,9)

Smokers 7(21%) 3(27%) 4(44%) 0(0%) 3(42%) 0,042

N=21 N=9 N=5 N=7

FVC 0,012

Mean(SD) 2,76(1,47) 3,2(1,16) 2,83(0,24) 1,94(0,86)

DLCO 0,001

Mean(SD) 60(20) 67(14) 66(27) 38(6)


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Maps of the mean value logarithm of Jacobian determinant for each group of patients show:

• Differences in the repartition of the Jacobian determinant in each group

• In healthy volunteers, the most important stretching (red color) in the posterior part of lung bases

• In the corresponding areas, the lungs of SSc-fibrosis patients show only few changes, with Jacobian determinant near to 0 (green color)

SSc-Fibrosis=7

HealthyVolunteersn=11

SSc-ILDn=5

SSc-no-ILDn=9

3. RESULTS – Qualitative analysis


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3. RESULTS – Quantitative analysis

Disease + Disease -

Test + 11 5 16

Test - 1 15 16

12 20 32

Se = 91% Sp = 75%

A whole cohort analysis was performed with:• Disease+: SSc-fibrosis + SSc-ILD

• Disease-: SSc-no-ILD + Healthy Volunteers

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

0 0,2 0,4 0,6 0,8 1

Fra

cti

on

of

true

po

sit

ive

Fraction of false positive

ROCcurve/AUC=0.802


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Dice coefficient Best cut-off : 0.44

3. RESULTS – Quantitative analysis

A sub-group analysis was also performed:• Disease+: SSc-fibrosis

• Disease-: Healthy volunteers


0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

0 0,2 0,4 0,6 0,8 1

Fra

cti

on

of

true

po

sit

ive

Fraction of false positive

ROCcurve/AUC=0,929

Disease + Disease -

Test + 6 0 6

Test - 1 11 12

7 11 18

Se = 86% Sp = 100%

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Dice coefficientBest cut-off : 0.44

3. RESULTS – Correlation with PFT DLCOc could not be evaluated for 2 patients due to severity of

disease

Analysis was performed :• FVC: n=21

• DLCOc: n=19


0

0,1

0,2

0,3

0,4

0,5

0,6

0 1 2 3 4 5 6

Dic

e in

de

x

FCV

R= 0.12; p= 0.6

0

0,1

0,2

0,3

0,4

0,5

0,6

0 20 40 60 80 100 120 140

Dic

e c

oe

ffic

ien

t

DLCOc

R= 0.16; p= 0.5

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4. DISCUSSION

TO SUMMARIZE OUR FINDINGS

Feasibility of an elastic registration algorithm applied on MR inspiratory and expiratory sequences

Visual differences in Jacobian maps between subjects with and without ILD

Distinction between healthy and fibrotic subject with acceptable sensitivity and perfect specificity

No correlation between PFT and our method of evaluation of fibrosis

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4. DISCUSSION

OUR LIMITATIONS

Small size of the cohort

No evaluation of repeatability

Subjective choice of threshold for Dice coefficient

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4. DISCUSSION

OUR PERSPECTIVES

Evaluation on a larger cohort

Quantitative analysis of Jacobian determinant repartition analysis with no subjective threshold:• Principal component analysis of the whole volume

• Comparison of joint entropy

Application to other types of fibrosis

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5. CONCLUSION

First study to assess ILD using an elastic registration between inspiratory and expiratory lung MR images

Lung deformation pattern was different between healthy subjects and scleroderma patients with pulmonary fibrosis

This functional approach of lung deformation based on elastic registration allows diagnosis of pulmonary fibrosis with an acceptable sensitivity and a perfect specificity, and no radiation exposure


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Thank you for your attention


BIBLIOGRAPHIE• Giacomelli, R. et al. Interstitial lung disease in systemic sclerosis: current and future treatment.

Rheumatol. Int. 37, 853–863 (2017).

• Bastos, A. de L., Corrêa, R. de A. & Ferreira, G. A. Tomography patterns of lung disease in systemic sclerosis. Radiol. Bras. 49, 316–321 (2016).

• Behr, J. & Furst, D. E. Pulmonary function tests. Rheumatology 47, v65–v67 (2008).

• Qian, Y. & Boada, F. E. Acquisition-weighted stack of spirals for fast high-resolution three-dimensional ultra-short echo time MR imaging. Magn. Reson. Med. 60, 135–145 (2008).

• Glocker, B., Sotiras, A., Komodakis, N. & Paragios, N. Deformable Medical Image Registration: Setting the State of the Art with Discrete Methods. Annu. Rev. Biomed. Eng. 13, 219–244 (2011).

• Castillo, R. et al. A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets. Phys. Med. Biol. 54, 1849–1870 (2009).

• Dice, L. R. Measures of the Amount of Ecologic Association Between Species. Ecology 26, 297–302 (1945).

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