Ambiguity in Detection of Necrosis in IVUS Plaque Characterization Algorithms and SDH as Alternative Solution

Transcription

1 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.

2 None Conflict of interest

3 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

4 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. [1] Falk E, Shah PK, Fuster V. Coronary plaque disruption. Circulation.1995;92: [2] Virmani, R. et al. Arterioscler Thromb Vasc Biol

5 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 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- From histopathology perspective NC is confluent. 5

6 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. There are speckles (textures) Hypoechoic area (due to lack of tissue) and speckles within NC 6

7 Iterative self-organizing atherosclerotic tissue labeling: Prognosis Histology 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. [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,

8 VH vs PH for the detection of NC VH PH [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,

9 VH vs PH for the detection of NC Histology Study VH Study Number of cases 12 (in vitro, cadavers) 4 (in vivo, patients) Number of cross sections 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 % Immediate Conclusion: 1) Excessive detection of NC in VH images. 2) NC is a rare tissue. 9

10 Histopathology Perspective Fibrous lesion with dense collagen displayed as necrotic core by VH IVUS. A, Gray-scale IVUS with lumen and external elastic lamina borders. Movat pentachrome section showing a fibrolipidic plaque with no evidence of calcification. 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: Thim T et al. Circ Cardiovasc Imaging 2010;3:

11 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

12 Signal Amplitude IVUS Signal behind Calcium Distance RF Gray-scale VH 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 12

13 Confidence Map Estimation in IVUS Images/Signals We introduce a novel uncertainty (confidence) estimation method based on random walks constrained with ultrasonic physic properties [1]. Weighted images by confidence values VH image PH image [1] A. Karamalis, W. Wein, T. Klein, Nassir Navab, Ultrasound confidence maps using random walks, To be appeared in J. Med. Imag. Analys. 13

14 Ultrasound RF data Compute ultrasonic statistical physics primal sketch (i)nakagami coefficients (Ω,m) for speckle statistics (ii)ultrasonic signal confidence Tissue labels (PH images [1] ) Ultrasound RF data Compute ultrasonic statistical physics primal sketch (i)nakagami coefficients (Ω,m) for speckle statistics (ii)ultrasonic signal confidence Predict tissue probabilities using learnt Random Forest Machine learning of tissue specific ultrasonic statistical physics primal sketch using 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,

15 Ultrasonic Stochastic Driven Histology (SDH) [1] Extraction of features with ultrasonic physics background. Superior machine learning algorithm. Reliability measure for estimation of tissues. Calcified Lipidic Fibrotic Necrotic Probability of of Calcified Necrotic Fibrotic Lipidic 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. 15

16 Qualitative In Vitro Results 16

17 What If There Were No Confidence? 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. 17

18 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 , 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:

19 Amin Katouzian Debdoot Sheet Abouzar Eslami Ph.D. MS Ph.D. THANK YOU! Athanasios Karamalis M.Sc. Nassir Navab Ph.D. 19