Ambiguity in Detection of Necrosis in IVUS

Plaque Characterization Algorithms

and SDH as Alternative Solution

Amin Katouzian, Ph.D., Debdoot Sheet, M.S., Abouzar Eslami, Ph.D.,

Athanasios Karamalis, M.Sc., Andreas König, MD,

Stephane G. Carlier, MD, Nassir Navab, Ph.D.

Conflict of interest

None

Background

• Intravascular ultrasound (IVUS)

– Provides information about arterial wall and extend of atherosclerosis.

– Grayscale and radiofrequency (RF) signals are used for

atherosclerotic tissue characterization (TC).

– Has potential to identify factors associated with vulnerable plaques.

• Lipid pool.

• Necrotic core.

– Existing TC techniques fail to detect it reliably.

• Calcification patterns.

• Thin cap fibroatheroma.

– Hard to detect due to resolution limitation.

• Shear stress.

3

Necrotic Core Formation • During formation, the extracellular matrix is degraded and

replaced with

– Lipid-rich cellular debris (represented by lipid pools).

– Dead cells (no nuclei) fragments.

4 [1] Falk E, Shah PK, Fuster V. Coronary plaque disruption. Circulation.1995;92:657– 671

[2] Virmani, R. et al. Arterioscler Thromb Vasc Biol 2000

Necrotic Core Formation • From IVUS perspective

– Lipidic tissue is relatively easy to detect (hypo-echoic).

– No ultrasound signal is received from dead cells due to lack of tissues

but strong backscattering if small calcifications: “black or white”

• From histopathology perspective

– NC is confluent.

5

In 122 cross-sections (12 arteries), lipid pools observed by histology in 30, revealed by IVUS in 19 (sensitivity 65% and specificity 95%) F Prati et al Heart 2001;85:567-

Major Challenges • At what stage of formation of NC are we looking at?

– Early?

– Late?

• There is no specific ultrasonic textural patterns associated with NC.

• The stage of formation will alter spectral information.

6

There are

speckles

(textures)

Hypoechoic area (due to lack

of tissue) and speckles within

NC

• We are able to generate VH-like images, coined Prognosis

Histology (PH)[1], based only on textural information.

• Correlation between detected tissues in PH and VH images in

155 cross sections collected in 4 patients.

– Calcified: 93.1±6.1%, Necrosis: 87.5±9.5%, Fibrotic: 78.4±17.6%, and

Fibrofatty: 61.3±21.3%.

• Observation:

– VH detects necrosis around calcified tissues.

– NC appears sparse and mainly superficially in VH images.

– VH systematically detects fibrotic followed by fibrofatty tissues in

shadowed regions behind arc of calcified plaques.

7

Iterative self-organizing atherosclerotic

tissue labeling: Prognosis Histology

[1] Amin Katouzian, Athanasios Karamalis, Debdoot Sheet, Elisa E. Konofagou, Babak Baseri, Stephane G. Carlier, Abouzar Eslami, Andreas König, Nassir Navab, Andrew F. Laine,

“Iterative self-organizing atherosclerotic tissue labeling in intravascular ultrasound images and comparison with virtual histology,” Accepted in IEEE TBME, 2012.

8

VH vs PH for the detection of NC

[1] Amin Katouzian, Athanasios Karamalis, Debdoot Sheet, Elisa E. Konofagou, Babak Baseri, Stephane G. Carlier, Abouzar Eslami, Andreas König, Nassir Navab, Andrew F. Laine,

“Iterative self-organizing atherosclerotic tissue labeling in intravascular ultrasound images and comparison with virtual histology,” Accepted in IEEE TBME, 2012.

VH PH

Histology Study VH Study

Number of cases 12 (in vitro, cadavers) 4 (in vivo, patients)

Number of cross sections 892 155

Average stenosis 40% 40%

Number of cross sections

containing NC (%) 156 (17.5%) 155 (100%)

Morphology pattern Confluent Spares and mainly around

calcified tissues

NC/artery (%) 1.6 ± 2.6 % 10.1 ± 21.8 %

9

Immediate Conclusion: 1) Excessive detection of NC in VH images.

2) NC is a rare tissue.

VH vs PH for the detection of NC

Histopathology Perspective

10 Thim T et al. Circ Cardiovasc Imaging

2010;3:384-391

Fibrous lesion with dense collagen

displayed as necrotic core by VH

IVUS. A, Gray-scale IVUS with lumen

and external elastic lamina borders.

Fibroatheroma with large necrotic core

missed by VH IVUS. A, Gray-scale

IVUS with lumen and external elastic

lamina border contours.

Granada J F et al. Arterioscler Thromb Vasc Biol

2007;27:387-393

Movat pentachrome section showing a fibrolipidic

plaque with no evidence of calcification.

Alternative Solution for Tissue

Classification and NC Identification

• Combination of both textural and RF information.

• Deployment of features with ultrasonic physics background.

• Superior machine learning algorithm.

• Reliability measure for estimation of tissues.

• Extensive and proper in vitro and in vivo validation.

11

IVUS Signal behind Calcium

12 Poster session 7: Invasive coronary imaging 29 Aug 08:30-12:30: P5462 Confidence estimation with random walks of IVUS based radio-frequency plaque characterization

RF Gray-scale VH

Distance

Sig

na

l Am

plit

ud

e

Confidence Map Estimation in

IVUS Images/Signals

13

• We introduce a novel uncertainty (confidence) estimation

method based on random walks constrained with

ultrasonic physic properties[1].

VH image PH image

Weighted

images by

confidence

values

[1] A. Karamalis, W. Wein, T. Klein, Nassir Navab, “Ultrasound confidence maps using random walks,” To be appeared in J. Med. Imag. Analys.

Ultrasound RF data Tissue labels (PH images[1])

Compute ultrasonic statistical physics primal sketch (i)Nakagami coefficients (Ω,m) for speckle statistics (ii)Ultrasonic signal confidence

Machine learning of tissue

specific ultrasonic statistical

physics primal sketch using

Random Forest

Compute ultrasonic statistical physics primal sketch (i)Nakagami coefficients (Ω,m) for speckle statistics (ii)Ultrasonic signal confidence

Ultrasound RF data

Predict tissue probabilities

using learnt Random Forest

Characterized tissues

[1] Amin Katouzian, Athanasios Karamalis, Debdoot Sheet, Elisa E. Konofagou, Babak Baseri, Stephane G. Carlier, Abouzar Eslami, Andreas König, Nassir Navab, Andrew F. Laine,

“Iterative self-organizing atherosclerotic tissue labeling in intravascular ultrasound images and comparison with virtual histology,” Accepted in IEEE TBME, 2012. 14

Ultrasonic Stochastic Driven Histology

(SDH)[1]

• Extraction of features with ultrasonic physics background.

• Superior machine learning algorithm.

• Reliability measure for estimation of tissues.

15

Probability of Calcified

tissues

Probability of Fibrotic

tissues

Probability of Lipidic

tissues Probability of Necrotic

tissues

[1] D. Sheet, Katouzian, A. Karamalis, A. Eslami, P. Noel, A. Koening, N. Navab, J. Chatterjee, A. Ray, A. Laine, S. G. Carlier, A. Katouzian,

“Joint Learning of Ultrasonic Statistical Physics and Signal Confidence Primal using Random Forests for Plaque Characterization in Intravascular Ultrasound,” under review.

Calcified

Lipidic

Fibrotic

Necrotic

Qualitative In Vitro Results

16

What If There Were No Confidence?

17

With confidence No Confidence

Without confidence maps and tissues

probabilities, we may

1) overestimate NC and other tissue types.

2) misinterpret tissues in shadowed regions.

Conclusion

• Our findings confirm the limitations of existing algorithms for plaque

characterization and in particular necrotic core detection[1,2]

• NC is a rare tissue and appears confluent.

• IVUS has potential to be used for reliable tissue characterization and

vulnerable plaque identification if

– Right textural+RF signatures with ultrasonic physic background are combined

with superior machine learning algorithm.

– Extensive and proper in vitro and in vivo validation is performed.

– Transducer center frequency is increased.

– Sampling in longitudinal direction is increased.

• We obtained encouraging and promising SDH results.

• Further characterization of Stochastic Driven Histology is warranted.

18 [1] T. Thim, M. and E. Falk, “Unreliable assessment of necrotic core by virtual histology intravascular ultrasound in porcine coronary artery disease,” Circulation Card. Imag., pp. 384-391, 2010

[2] Granada JF and Kaluza GL, “In vivo plaque characterization using IVUS-VH in a porcine model of complex coronary lesions. Arterioscler Thromb Vasc Biol. 2007;27:387–393

19

Amin Katouzian Debdoot Sheet Abouzar Eslami

Ph.D. MS Ph.D.

Athanasios Karamalis Nassir Navab

M.Sc. Ph.D.

THANK YOU!