Nora Tgavalekos1

Jose G. Venegas, Ph.D.2

Kenneth Lutchen,Ph.D.1

1 Respiratory and Physiological Systems Identification Laboratory

Biomedical Engineering, Boston University2 Massachusetts General Hospital, Anesthesia and Critical Care

Positron Emission Tomography (PET) Based Image Assisted Modeling of Lung Mechanics in Asthmatics

Physiological Implications of Asthma

Healthy Airway Asthmatic Airway

• Airway disease characterized by: airway smooth muscle hypertrophy, edema, mucous gland hypertrophy, and infiltration by eosinophils

• Airways are hyper-responsive to various stimuli

• During an asthma attack, airway smooth muscle contracts

Asymmetric Horsefield model

Human Airway Tree Models

Zw(n)

Z(n-1)

Z(n-1- )

Z(n) R(n)/2 I(n)/2

Cg(n)

R(n)/2 I(n)/2

Impedance of a Single Airway

• Airways Terminate on Alveoli with Viscoelastic Tissue

Previous Uses of Morphometric Tree Models

• Models suggest a relationship between the pattern of constriction and the impact on mechanical function

• Shapes are consistent with measured RL and ELin asthma

Frequency (Hz)

0 1 2 3 4 5

RL (c

mH

2O/l/

s)

0

5

10

15

homogeneousconstriction

heterogeneousconstriction

healthy

Raw

Frequency (Hz)

0 1 2 3 4 5

EL(c

mH

2O/l)

-10

0

10

20

30

40

50

airway wall shuntingairwayclosure

heterogeneousconstriction

healthy

Advances in Advances in Airway Tree Airway Tree ModelsModels

Kitaoka et al.(1999)

Creation 3-D Airway Trees

o

1 2

Q

Q Q

Q ~ dn

• Murray described a relationship between flow rate (Q) and diameter (d)

• Model determines branching angles and lengths based on a space filling algorithm

1 2

Advancing 3D Models for Computation of Prediction of Function

• Application of arbitrary number of distinct heterogeneous patterns to specific anatomic locations throughout the tree

• Prediction of dynamic lung properties during heterogeneous constriction

3-D Model was advanced to incorporate a combined parallel-serial stacking algorithm which allows the following:

• Airway walls are non-rigid and allow for gas compression

1) Healthy

Mechanical Impact of Regional Constriction

Frequency

0 2 4 6 8

04080

120160200240

0 2 4 6 80

10

20

30

40

Frequency

Mechanics

2) Cranial-Dorsal Only: M = 50%; SD = 70%

Front

Ventilation

% Reduction in Diameter

50 60 70 80 90 1000

20

40

60

80

100

% D

ista

l Alv

eoli

3) Case 2 & Remaining with: M = 25%; SD = 35%

4) Case 3 & Remaining with: M = 25%; SD =70%

Back

closure

baselineR

L(cm

H2O

/L/S

)

EL(

cmH

2O/L

)

Mild-Moderate Asthmatic Pre Challenge

Regional Ventilation via PET Imaging

EACH SLICE:

• Color intensity proportional to tracer washout rate calculated by integrating 32 time sequenced images

• Darker colors correspond to regions of low ventilation

• Lighter color correspond to high ventilated regions

Apex

Base

Regional Ventilation via PET ImagingPost Challenge

EACH SLICE:

• Color intensity proportional to tracer washout rate calculated by integrating 32 time sequenced images

• Darker colors correspond to regions of low ventilation

• Lighter color correspond to high ventilated regions

Quantifying PET Images

Baseline

Percent of Tracer Remaining

0 4 8 12 16 20

Perc

en

t o

f th

e L

ung

0

20

40

60

80

100

Percent of Tracer Remaining

0 20 40 60 80 100 120

Perc

en

t o

f th

e L

ung

0

20

40

60

80

100

Post-Challenge

17%

Image Assisted Modeling Challenges

Find a constriction pattern that :

• Creates closures primarily in the upper region of the lung with ~ 20 % of alveoli not communicating with the rest of the lung

• Matches subject specific RL and EL

0 2 4 6 80

10

20

30

40

Frequency (Hz)0 2 4 6 8

0

40

80

120

160

200

240

% Reduction in Diameter

50 60 70 80 90 1000

20

40

60

80

100

Frequency (Hz)

Asthmatic #1Post MCH: 2.56 mg/ml

Image Assisted Modeling I: Constricted AsthmaticImage Assisted Modeling I: Constricted Asthmatic

1) Baseline

3) Case 2 & Remaining with: M = 50%; SD = 40%

2) Cranial-Dorsal Only: M = 50%; SD = 70% (d<2mm)

4) Case 2 & Remaining with: M = 50%; SD = 60%

RL(

cmH

2O/L

/S)

EL(

cmH

2O/L

)

% D

ista

l Alv

eoli

Conclusions• An anatomically explicit airway tree model can now be used to predict RL and EL for anatomically applied patterns of constriction

• We now have the ability to predict structure –function on almost a personalized basis understand what range of constriction patterns are possible for different levels and degrees of asthma.

• This model has the potential to predict ventilation distributions in asthmatic patients

Acknowledgements

Boston University Mass. General Hospital

K. R. Lutchen, Ph.D. J. G. Venegas, Ph.D.

Bela Suki, Ph.D. Scott Harris, MD.

Heather Gillis, M.S. Dominick Layfield, Ph.D. Cand.

Andrew Jensen, M.S.

Cortney Henderson, Ph.D. Cand.

Lauren Black, MS Cand.

Carissa Belladrine, MS Cand.

Skyler Greene &Tina Lewis, BS Cand.