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1. Deep learning for dose assessment in radiotherapy by the super-localization of vaporized nanodroplets in high frame rate ultrasound imaging

Author

UCL - SSS/IREC/MIRO - Pôle d'imagerie moléculaire, radiothérapie et oncologie, Van Der Heyden, B., Heymans, S.V., Carlier, B., Collado-Lara, G., Sterpin, Edmond, Dhooge, J., UCL - SSS/IREC/MIRO - Pôle d'imagerie moléculaire, radiothérapie et oncologie, Van Der Heyden, B., Heymans, S.V., Carlier, B., Collado-Lara, G., Sterpin, Edmond, and Dhooge, J.

Published
2022

4. Differentiation of hypertensive heart disease and hypertrophic cardiomyopathy with myocardial stiffness measurements: a shear wave imaging study using ultra-high frame rate echocardiography

Author

Cvijic, M, primary, Bezy, S, additional, Petrescu, A, additional, Santos, P, additional, Orlowska, M, additional, Chakraborty, B, additional, Duchenne, J, additional, Pedrosa, J, additional, Vanassche, T, additional, Van Cleemput, J, additional, Dhooge, J, additional, and Voigt, J.U, additional

Published
2020
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5. 417 Can myocardial stiffness measurements distinguish the underlying pathology in hearts with thick walls? A shear wave imaging study using ultra-high frame rate echocardiography

Author

Cvijic, M, primary, Bezy, S, additional, Petrescu, A, additional, Santos, P, additional, Orlowska, M, additional, Chakraborty, B, additional, Duchenne, J, additional, Pedrosa, J, additional, Vanassche, T, additional, Delforge, M, additional, Van Cleemput, J, additional, Dhooge, J, additional, and Voigt, J U, additional

Published
2020
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12. TISSUE DOPPLER INDEXES OF LEFT VENTRICULAR SYSTOLIC FUNCTION IN RELATION TO THE PULSATILE AND STEADY COMPONENTS OF BLOOD PRESSURE IN A GENERAL POPULATION

Author

Kuznetsova, T., primary, Sakiewicz, W., additional, Kloch-Badelek, M., additional, Dhooge, J., additional, Ryabikov, A., additional, Kunicka, K., additional, Swierblewska, E., additional, Thijs, L., additional, Malyutina, S., additional, Stolarz-Skrzypek, K., additional, Kawecka-Jaszcz, K., additional, Narkiewicz, K., additional, and Staessen, J. A., additional

Published
2011
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40. Installation Restoration: Navy Landfills and EPA Cover Guidance

Author

NAVAL CIVIL ENGINEERING LAB PORT HUENEME CA, Karr, L., Dhooge, J., Walker, A., NAVAL CIVIL ENGINEERING LAB PORT HUENEME CA, Karr, L., Dhooge, J., and Walker, A.

Abstract

The Navy has 229 hazardous waste landfills that must be treated or capped. Treatment of these wastes is often complicated and expensive due to the wide range of contaminants present. Current technology does not meet the needs for remediating all 229 sites. An alternative to treatment is to design a long-term cover, or cap, for the landfill. Current EPA guidance allows for modifications which may reduce costs and increase life expectancy of a final cover system.

Published
1992

42. Poster session Thursday 12 December - AM: 12/12/2013, 08:30-12:30 * Location: Poster area

Author

Abdovic, E, Abdovic, S, Hristova, K, Hristova, K, Katova, TZ, Katova, TZ, Gocheva, N, Gocheva, N, Pavlova, M, Pavlova, M, Gurzun, M M, Ionescu, A, Canpolat, U, Yorgun, H, Sunman, H, Sahiner, L, Kaya, EB, Ozer, N, Tokgozoglu, L, Kabakci, G, Aytemir, K, Oto, A, Gonella, A, Dascenzo, F, Casasso, F, Conte, E, Margaria, F, Grosso Marra, W, Frea, S, Morello, M, Bobbio, M, Gaita, F, Seo, HY, Lee, SP, Lee, JM, Yoon, YE, Park, E, Kim, HK, Park, SJ, Lee, H, Kim, YJ, Sohn, DW, Nemes, A, Domsik, P, Kalapos, A, Orosz, A, Lengyel, C, Forster, T, Enache, R, Muraru, D, Popescu, BA, Calin, A, Nastase, O, Botezatu, D, Purcarea, F, Rosca, M, Beladan, CC, Ginghina, C, Canpolat, U, Aytemir, K, Ozer, N, Yorgun, H, Sahiner, L, Kaya, EB, Oto, A, Trial, Turkish Atrial Fibrosis, Muraru, D, Piasentini, E, Mihaila, S, Padayattil Jose, S, Peluso, D, Ucci, L, Naso, P, Puma, L, Iliceto, S, Badano, LP, Cikes, M, Jakus, N, Sutherland, GR, Haemers, P, Dhooge, J, Claus, P, Yurdakul, S, Oner, FATMA, Direskeneli, HANER, Sahin, TAYLAN, Cengiz, BETUL, Ercan, G, Bozkurt, AYSEN, Aytekin, SAIDE, Osa Saez, A M, Rodriguez-Serrano, M, Lopez-Vilella, R, Buendia-Fuentes, F, Domingo-Valero, D, Quesada-Carmona, A, Miro-Palau, VE, Arnau-Vives, MA, Palencia-Perez, M, Rueda-Soriano, J, Lipczynska, M, Piotr Szymanski, PS, Anna Klisiewicz, AK, Lukasz Mazurkiewicz, LM, Piotr Hoffman, PH, Kim, KH, Cho, SK, Ahn, Y, Jeong, MH, Cho, JG, Park, JC, Chinali, M, Franceschini, A, Matteucci, MC, Doyon, A, Esposito, C, Del Pasqua, A, Rinelli, G, Schaefer, F, group, the 4C study, Kowalik, E, Klisiewicz, A, Rybicka, J, Szymanski, P, Biernacka, EK, Hoffman, P, Lee, S, Kim, W, Yun, H, Jung, L, Kim, E, Ko, J, Ruddox, V, Norum, IB, Edvardsen, T, Baekkevar, M, Otterstad, JE, Erdei, T, Edwards, J, Braim, D, Yousef, Z, Fraser, AG, Cardiff, Investigators, MEDIA, Melcher, A, Reiner, B, Hansen, A, Strandberg, LE, Caidahl, K, Wellnhofer, E, Kriatselis, C, Gerd-Li, H, Furundzija, V, Thnabalasingam, U, Fleck, E, Graefe, M, Park, YJ, Moon, JG, Ahn, TH, Baydar, O, Kadriye Kilickesmez, KK, Ugur Coskun, UC, Polat Canbolat, PC, Veysel Oktay, VO, Umit Yasar Sinan, US, Okay Abaci, OA, Cuneyt Kocas, CK, Sinan Uner, SU, Serdar Kucukoglu, SK, Ferferieva, V, Claus, P, Rademakers, F, Dhooge, J, Le, T T, Wong, P, Tee, N, Huang, F, Tan, RS, Altman, M, Logeart, D, Bergerot, C, Gellen, B, Pare, C, Gerard, S, Sirol, M, Vicaut, E, Mercadier, JJ, Derumeaux, G A, investigators, PREGICA, Park, T-H, Park, J-I, Shin, S-W, Yun, S-H, Lee, J-E, Makavos, G, Kouris, N, Keramida, K, Dagre, A, Ntarladimas, I, Kostopoulos, V, Damaskos, D, Olympios, CD, Leong, DP, Piers, SRD, Hoogslag, GE, Hoke, U, Thijssen, J, Ajmone Marsan, N, Schalij, MJ, Bax, JJ, Zeppenfeld, K, Delgado, V, Rio, P, Branco, L, Galrinho, A, Cacela, D, Abreu, J, Timoteo, A, Teixeira, P, Pereira-Da-Silva, T, Selas, M, Cruz Ferreira, R, Popa, B A, Zamfir, L, Novelli, E, Lanzillo, G, Karazanishvili, L, Musica, G, Stelian, E, Benea, D, Diena, M, Cerin, G, Fusini, L, Mirea, O, Tamborini, G, Muratori, M, Gripari, P, Ghulam Ali, S, Cefalu, C, Maffessanti, F, Andreini, D, Pepi, M, Mamdoo, F, Goncalves, A, Peters, F, Matioda, H, Govender, S, Dos Santos, C, Essop, MR, Kuznetsov, V A, Yaroslavskaya, E I, Pushkarev, G S, Krinochkin, D V, Kolunin, G V, Bennadji, A, Hascoet, S, Dulac, Y, Hadeed, K, Peyre, M, Ricco, L, Clement, L, Acar, P, Ding, WH, Zhao, Y, Lindqvist, P, Nilson, J, Winter, R, Holmgren, A, Ruck, A, Henein, MY, Illatopa, V, Cordova, F, Espinoza, D, Ortega, J, Cavalcante, JL, Patel, MT, Katz, W, Schindler, J, Crock, F, Khanna, MK, Khandhar, S, Tsuruta, H, Kohsaka, S, Murata, M, Yasuda, R, Tokuda, H, Kawamura, A, Maekawa, Y, Hayashida, K, Fukuda, K, Le Tourneau, T, Kyndt, F, Lecointe, S, Duval, D, Rimbert, A, Merot, J, Trochu, JN, Probst, V, Le Marec, H, Schott, JJ, Veronesi, F, Addetia, K, Corsi, C, Lamberti, C, Lang, RM, Mor-Avi, V, Gjerdalen, G F, Hisdal, J, Solberg, EE, Andersen, TE, Radunovic, Z, Steine, K, Maffessanti, F, Gripari, P, Tamborini, G, Muratori, M, Fusini, L, Ferrari, C, Caiani, EG, Alamanni, F, Bartorelli, AL, Pepi, M, Dascenzi, F, Cameli, M, Iadanza, A, Lisi, M, Reccia, R, Curci, V, Sinicropi, G, Henein, M, Pierli, C, Mondillo, S, Rekhraj, S, Hoole, SP, Mcnab, DC, Densem, CG, Boyd, J, Parker, K, Shapiro, LM, Rana, BS, Kotrc, M, Vandendriessche, T, Bartunek, J, Claeys, MJ, Vanderheyden, M, Paelinck, B, De Bock, D, De Maeyer, C, Vrints, C, Penicka, M, Silveira, C, Albuquerque, ESA, Lamprea, DL, Larangeiras, VL, Moreira, CRPM, Victor Filho, MVF, Alencar, BMA, Silveira, AQMS, Castillo, JMDC, Zambon, E, Iorio, A, Carriere, C, Pantano, A, Barbati, G, Bobbo, M, Abate, E, Pinamonti, B, Di Lenarda, A, Sinagra, G, Salemi, V M C, Tavares, L, Ferreira Filho, JCA, Oliveira, AM, Pessoa, FG, Ramires, F, Fernandes, F, Mady, C, Cavarretta, E, Lotrionte, M, Abbate, A, Mezzaroma, E, De Marco, E, Peruzzi, M, Loperfido, F, Biondi-Zoccai, G, Frati, G, Palazzoni, G, Park, T-H, Lee, J-E, Lee, D-H, Park, J-S, Park, K, Kim, M-H, Kim, Y-D, Van T Sant, J, Gathier, WA, Leenders, GE, Meine, M, Doevendans, PA, Cramer, MJ, Poyhonen, P, Kivisto, S, Holmstrom, M, Hanninen, H, Schnell, F, Betancur, J, Daudin, M, Simon, A, Carre, F, Tavard, F, Hernandez, A, Garreau, M, Donal, E, Calore, C, Muraru, D, Badano, LP, Melacini, P, Mihaila, S, Denas, G, Naso, P, Casablanca, S, Santi, F, Iliceto, S, Aggeli, C, Venieri, E, Felekos, I, Anastasakis, A, Ritsatos, K, Kakiouzi, V, Kastellanos, S, Cutajar, I, Stefanadis, C, Palecek, T, Honzikova, J, Poupetova, H, Vlaskova, H, Kuchynka, P, Linhart, A, Elmasry, O, Mohamed, MH, Elguindy, WM, Bishara, PNI, Garcia-Gonzalez, P, Cozar-Santiago, P, Bochard-Villanueva, B, Fabregat-Andres, O, Cubillos-Arango, A, Valle-Munoz, A, Ferrer-Rebolleda, J, Paya-Serrano, R, Estornell-Erill, J, Ridocci-Soriano, F, Jensen, M, Havndrup, O, Christiansen, M, Andersen, PS, Axelsson, A, Kober, L, Bundgaard, H, Karapinar, H, Kaya, A, Uysal, EB, Guven, AS, Kucukdurmaz, Z, Oflaz, MB, Deveci, K, Sancakdar, E, Gul, I, Yilmaz, A, Tigen, M K, Karaahmet, T, Dundar, C, Yalcinsoy, M, Tasar, O, Bulut, M, Takir, M, Akkaya, E, Jedrzejewska, I, Braksator, W, Krol, W, Swiatowiec, A, Dluzniewski, M, Lipari, P, Bonapace, S, Zenari, L, Valbusa, F, Rossi, A, Lanzoni, L, Molon, G, Canali, G, Campopiano, E, Barbieri, E, Rueda Calle, E, Alfaro Rubio, F, Gomez Gonzalez, J, Gonzalez Santos, P, Cameli, M, Lisi, M, Focardi, M, Dascenzi, F, Solari, M, Galderisi, M, Mondillo, S, Pratali, L, Bruno, R M, Corciu, AI, Comassi, M, Passera, M, Gastaldelli, A, Mrakic-Sposta, S, Vezzoli, A, Picano, E, Perry, R, Penhall, A, De Pasquale, C, Selvanayagam, J, Joseph, M, Simova, I I, Katova, T M, Kostova, V, Hristova, K, Lalov, I, Dascenzi, F, Pelliccia, A, Natali, BM, Cameli, M, Alvino, F, Zorzi, A, Corrado, D, Bonifazi, M, Mondillo, S, Rees, E, Rakebrandt, F, Rees, DA, Halcox, JP, Fraser, AG, Odriscoll, J, Lau, N, Perez-Lopez, M, Sharma, R, Lichodziejewska, B, Goliszek, S, Kurnicka, K, Kostrubiec, M, Dzikowska Diduch, O, Krupa, M, Grudzka, K, Ciurzynski, M, Palczewski, P, Pruszczyk, P, Gheorghe, LL, Castillo Ortiz, J, Del Pozo Contreras, R, Calle Perez, G, Sancho Jaldon, M, Cabeza Lainez, P, Vazquez Garcia, R, Fernandez Garcia, P, Chueca Gonzalez, E, Arana Granados, R, Zhao, XX, Xu, XD, Bai, Y, Qin, YW, Leren, IS, Hasselberg, NE, Saberniak, J, Leren, TP, Edvardsen, T, Haugaa, KH, Daraban, A M, Sutherland, GR, Claus, P, Werner, B, Gewillig, M, Voigt, JU, Santoro, A, Ierano, P, De Stefano, F, Esposito, R, De Palma, D, Ippolito, R, Tufano, A, Galderisi, M, Costa, R, Fischer, C, Rodrigues, A, Monaco, C, Lira Filho, E, Vieira, M, Cordovil, A, Oliveira, E, Mohry, S, Gaudron, P, Niemann, M, Herrmann, S, Strotmann, J, Beer, M, Hu, K, Bijnens, B, Ertl, G, Weidemann, F, Baktir, AO, Sarli, B, Cicek, M, Karakas, MS, Saglam, H, Arinc, H, Akil, MA, Kaya, H, Ertas, F, Bilik, MZ, Yildiz, A, Oylumlu, M, Acet, H, Aydin, M, Yuksel, M, Alan, S, Odriscoll, J, Gravina, A, Di Fino, S, Thompson, M, Karthigelasingham, A, Ray, K, Sharma, R, De Chiara, B, Russo, CF, Alloni, M, Belli, O, Spano, F, Botta, L, Palmieri, B, Martinelli, L, Giannattasio, C, Moreo, A, Mateescu, AD, La Carrubba, S, Vriz, O, Di Bello, V, Carerj, S, Zito, C, Ginghina, C, Popescu, BA, Nicolosi, GL, Antonini-Canterin, F, Malev, E, Omelchenko, M, Vasina, L, Luneva, E, Zemtsovsky, E, Cikes, M, Velagic, V, Gasparovic, H, Kopjar, T, Colak, Z, Hlupic, LJ, Biocina, B, Milicic, D, Tomaszewski, A, Kutarski, A, Poterala, M, Tomaszewski, M, Brzozowski, W, Kijima, Y, Akagi, T, Nakagawa, K, Ikeda, M, Watanabe, N, Ueoka, A, Takaya, Y, Oe, H, Toh, N, Ito, H, Bochard Villanueva, B, Paya-Serrano, R, Fabregat-Andres, O, Garcia-Gonzalez, P, Perez-Bosca, JL, Cubillos-Arango, A, Chacon-Hernandez, N, Higueras-Ortega, L, De La Espriella-Juan, R, Ridocci-Soriano, F, Noack, T, Mukherjee, C, Ionasec, RI, Voigt, I, Kiefer, P, Hoebartner, M, Misfeld, M, Mohr, F-W, Seeburger, J, Daraban, A M, Baltussen, L, Amzulescu, MS, Bogaert, J, Jassens, S, Voigt, JU, Duchateau, N, Giraldeau, G, Gabrielli, L, Penela, D, Evertz, R, Mont, L, Brugada, J, Berruezo, A, Bijnens, BH, Sitges, M, Yoshikawa, H, Suzuki, M, Hashimoto, G, Kusunose, Y, Otsuka, T, Nakamura, M, Sugi, K, Ruiz Ortiz, M, Mesa, D, Romo, E, Delgado, M, Seoane, T, Martin, M, Carrasco, F, Lopez Granados, A, Arizon, JM, Suarez De Lezo, J, Magalhaes, A, Cortez-Dias, N, Silva, D, Menezes, M, Saraiva, M, Santos, L, Costa, A, Costa, L, Nunes Diogo, A, Fiuza, M, Ren, B, De Groot-De Laat, LE, Mcghie, J, Vletter, WB, Geleijnse, ML, Toda, H, Oe, H, Osawa, K, Miyoshi, T, Ugawa, S, Toh, N, Nakamura, K, Kohno, K, Morita, H, Ito, H, El Ghannudi, S, Germain, P, Samet, H, Jeung, M, Roy, C, Gangi, A, Orii, M, Hirata, K, Yamano, T, Tanimoto, T, Ino, Y, Yamaguchi, T, Kubo, T, Imanishi, T, Akasaka, T, Sunbul, M, Kivrak, T, Oguz, M, Ozguven, S, Gungor, S, Dede, F, Turoglu, HT, Yildizeli, B, Mutlu, B, Mihaila, S, Muraru, D, Piasentini, E, Peluso, D, Cucchini, U, Casablanca, S, Naso, P, Iliceto, S, Vinereanu, D, Badano, LP, Rodriguez Munoz, DA, Moya Mur, JL, Becker Filho, D, Gonzalez, A, Casas Rojo, E, Garcia Martin, A, Recio Vazquez, M, Rincon, LM, Fernandez Golfin, C, Zamorano Gomez, JL, Ledakowicz-Polak, A, Polak, L, Zielinska, M, Kamiyama, T, Nakade, T, Nakamura, Y, Ando, T, Kirimura, M, Inoue, Y, Sasaki, O, Nishioka, T, Farouk, H, Sakr, B, Elchilali, K, Said, K, Sorour, K, Salah, H, Mahmoud, G, Casanova Rodriguez, C, Cano Carrizal, R, Iglesias Del Valle, D, Martin Penato Molina, A, Garcia Garcia, A, Prieto Moriche, E, Alvarez Rubio, J, De Juan Bagua, J, Tejero Romero, C, Plaza Perez, I, Korlou, P, Stefanidis, A, Mpikakis, N, Ikonomidis, I, Anastasiadis, S, Komninos, K, Nikoloudi, P, Margos, P, and Pentzeridis, P

Abstract

Purpose: Atrial fibrillation (AF) is the most prevalent sustained arrhythmia. It is a disease of the elderly and it is common in patients (pts) with structural heart disease. Hypertension (HA), hypertensive heart disease (HHD), diabetes mellitus (DM), coronary artery disease (CAD), heart failure (HF), and valvular heart disease (VHD) are recognized predisposing factors to AF. Objectives: To echocardiographicly disclose the most common predisposing morbidities to AF in our population sample. Methods: From June 2000 to February 2013, 3755 consecutive pts with AF were studied during echocardiographic check-up. According to transthoracic echo, pts were divided in groups based on dominative underlying heart diseases. Electrocardiographically documented AF was subdivided in two groups: transitory and chronic. Transitory AF fulfilled criteria for paroxysmal or persistent AF. Chronic AF were cases of long-standing persistent or permanent AF. Results: The median age was 72 years, age range between 16 and 96 years. There were 51.4% of females. Chronic AF was observed in 68.3% pts. Distribution of underlying heart diseases is shown in figure. Lone AF was diagnosed in only 25 pts, mostly in younger males (median age 48 years, range 29–59, men 80%). Chronic AF was predominant in groups with advanced cardiac remodeling such as dilatative cardiomyopaty (DCM) and VHD, mostly in elderly. HA and DM were found in 75.4% and 18.8%, respectively. Almost 1/2 of pts with AF had HF and 59.2% had diastolic HF. Conclusion: Up to now, echocardiographic categorization of the predisposing factors to AF was not reported. Echocardiographic evaluation of patients with AF could facilitate in identification and well-timed treatment of predisposing comorbidites. Figure Etiological distribution of AF

Published
2013
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43. Young Investigator Award session - Basic Science: 11/12/2013, 12:45-13:45 * Location: Manisa

Author

Cikes, M, Sutherland, GR, Jakus, N, Haemers, P, Dhooge, J, Claus, P, Sorensen, L L, Bedja, D, Shah, PS, Abraham, TP, Abraham, MR, Gabrielson, KL, Brugger, N, De Marchi, S, Steck, H, Zumstein, D, and Seiler, C

Abstract

Purpose: Chronic pressure overload as in hypertension leads to regional LV remodelling, primarily affecting the basal iv. septum (IVS) which hypertrophies and shows reduced longitudinal function. This can be compensated by increased radial function, particularly in early disease. Measurement of systolic upstroke and diastolic downstroke of LV wall motion by M-mode might provide similar radial data. We have tested these easily obtainable measurements on our closed chest/closed pericardium pig model, pertaining as close to physiology as possible. In this model, we aimed to study LV wall systolic thickening and diastolic thinning velocities in acute pressure overload. Methods: 7 anesthetised, closed chest/closed pericardium pigs were instrumented with a descending aorta balloon, partially inflated during 5-10 heartbeats creating an 30% LV pressure increase. Echocardiographic LV LAX B-mode cine-loops were acquired (5 pre-, 5-10 inflation, 10 postinflation beats). The velocity of systolic thickening upstroke and diastolic thinning downstroke of the IVS and the LV posterior wall (LVPW) were measured from anatomic M-mode images of the LV base, mid and apex. Results: During balloon inflation, a reduction in the upstroke and downstroke velocities occurred in the basal LVPW, mid and apical IVS and LVPW, recovering after balloon deflation. However, the basal IVS showed the opposite-increased thickening and thinning velocities in response to afterload (Table 1). Conclusions: The basal LV septum differs from the remaining LV as the segment with the highest wall stress. In this model, we have demonstrated the increase in radial thickening and relaxation of the basal IVS with acute afterload, as opposed to the other LV segments where these parameters acutely decrease. These findings provide better insight into LV functional remodelling in acute afterload and, due to the selected animal model, might provide better translational data for the clinical setting. Table. Preinflation BasePreinflation MidPreinflation ApexInflation BaseInflation MidInflation ApexPostinflation BasePostinflation MidPostinflation ApexIVS S-slope15.3±10.8*28.9±14.5*27.0±14.2*23.6±13.720.9±12.619.6±8.418.4±14.8*28.3±23.426.4±10.4*IVS D-slope27.6±10.0*54.9±18.0*67.9±40.745.4±31.742.0±22.952.4±27.026.4±16.566.4±33.962.9±42.1LVPW S-slope48.7±10.2*43.3±16.0*36.3±16.5*35.6±11.422.0±13.022.4±12.443.4±15.9*39.6±16.3*39.4±19.3*LVPW D-slope75.7±49.7*76.4±22.985.0±51.0*37.3±27.758.7±57.138.0±25.365.9±30.0*70.4±33.466.9±27.4* Regional LV wall upstroke and downstroke velocities (mm/s) before, during and after acute pressure overload. * P<0.05 vs. inflation

Published
2013
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44. Poster Session Saturday 14 December - AM: 14/12/2013, 08:30-12:30 * Location: Poster area

Author

Muraru, D, Addetia, K, Veronesi, F, Corsi, C, Mor-Avi, V, Yamat, M, Weinert, L, Lang, RM, Badano, LP, Faita, F, Di Lascio, N, Bruno, RM, Bianchini, E, Ghiadoni, L, Sicari, R, Gemignani, V, Angelis, A, Ageli, K, Ioakimidis, N, Chrysohoou, C, Agelakas, A, Felekos, I, Vaina, S, Aznaourides, K, Vlachopoulos, C, Stefanadis, C, Nemes, A, Szolnoky, G, Gavaller, H, Gonczy, A, Kemeny, L, Forster, T, Ramalho, A, Placido, R, Marta, L, Menezes, M, Magalhaes, A, Cortez Dias, N, Martins, S, Almeida, A, Pinto, F, Nunes Diogo, A, Botezatu, C-D, Enache, R, Popescu, BA, Nastase, O, Coman, MC, Ghiorghiu, I, Calin, A, Rosca, M, Beladan, C, Ginghina, C, Grapsa, J, Cabrita, IZ, Durighel, G, Oregan, D, Dawson, D, Nihoyannopoulos, P, Pellicori, P, Kallvikbacka-Bennett, A, Zhang, J, Lukaschuk, E, Joseph, A, Bourantas, C, Loh, H, Bragadeesh, T, Clark, A, Cleland, JG, Kallvikbacka-Bennett, A, Pellicori, P, Lomax, S, Putzu, P, Diercx, R, Parsons, S, Dicken, B, Zhang, J, Clark, A, Cleland, JG, Vered, Z, Adirevitz, L, Dragu, R, Blatt, A, Karev, E, Malca, Y, Roytvarf, A, Marek, D, Sovova, E, Berkova, M, Cihalik, C, Taborsky, M, Lindqvist, P, Tossavainen, ERIK, Soderberg, S, Gonzales, M, Gustavsson, S, Henein, MY, Sonne, C, Bott-Fluegel, L, Hauck, S, Lesevic, H, Hadamitzky, M, Wolf, P, Kolb, C, Bandera, F, Pellegrino, M, Generati, G, Donghi, V, Alfonzetti, E, Castelvecchio, S, Menicanti, L, Guazzi, M, Buchyte, S, Rinkuniene, D, Jurkevicius, R, Smarz, K, Zaborska, B, Jaxa-Chamiec, T, Maciejewski, P, Budaj, A, Santoro, A, Federico Alvino, FA, Giovanni Antonelli, GA, Roberta Molle, RM, Matteo Bertini, MB, Stefano Lunghetti, SL, Sergio Mondillo, SM, Henri, C, Magne, J, Dulgheru, R, Laaraibi, S, Voilliot, D, Kou, S, Pierard, L, Lancellotti, P, Szulik, M, Stabryla-Deska, J, Kalinowski, M, Sliwinska, A, Szymala, M, Lenarczyk, R, Kalarus, Z, Kukulski, T, Investigators, TRUST CRT, Yiangou, K, Azina, C, Yiangou, A, Ioannides, M, Chimonides, S, Baysal, S, Pirat, B, Okyay, K, Bal, U, Muderrisoglu, H, Popovic, D, Ostojic, M, Petrovic, M, Vujisic-Tesic, B, Arandjelovic, A, Petrovic, I, Banovic, M, Popovic, B, Vukcevic, V, Damjanovic, S, Velasco Del Castillo, S, Onaindia Gandarias, JJ, Arana Achaga, X, Laraudogoitia Zaldumbide, E, Rodriguez Sanchez, I, Cacicedo De Bobadilla, A, Romero Pereiro, A, Aguirre Larracoechea, U, Salinas, T, Subinas, A, Elzbieciak, M, Wita, K, Grabka, M, Chmurawa, J, Doruchowska, A, Turski, M, Filipecki, A, Wybraniec, M, Mizia-Stec, K, Varho, VV, Karjalainen, PP, Lehtinen, T, Airaksinen, JKE, Ylitalo, A, Kiviniemi, TO, Gargiulo, P, Galderisi, M, D Amore, C, Lo Iudice, F, Savarese, G, Casaretti, L, Pellegrino, AM, Fabiani, I, La Mura, L, Perrone Filardi, P, Kim, J Y, Chung, WB, Yu, JS, Choi, YS, Park, CS, Youn, HJ, Lee, MY, Nagy, AI, Manouras, A, Gunyeli, E, Gustafsson, U, Shahgaldi, K, Winter, R, Johnsson, J, Zagatina, A, Krylova, L, Zhuravskaya, N, Vareldzyan, Y, Tyurina, TV, Clitsenko, O, Khalifa, E A, Ashour, Z, Elnagar, W, Jung, IH, Seo, HS, Lee, SJ, Lim, DS, Mizariene, V, Verseckaite, R, Janenaite, J, Jonkaitiene, R, Jurkevicius, R, Sanchez Espino, AD, Bonaque Gonzalez, JC, Merchan Ortega, G, Bolivar Herrera, N, Ikuta, I, Macancela Quinones, JJ, Gomez Recio, M, Silva Fazendas Adame, P R, Caldeira, D, Stuart, B, Almeida, S, Cruz, I, Ferreira, A, Freire, G, Lopes, L, Cotrim, C, Pereira, H, Mediratta, A, Addetia, K, Moss, JD, Nayak, HM, Yamat, M, Weinert, L, Mor-Avi, V, Lang, RM, Al Amri, I, Debonnaire, P, Van Der Kley, F, Schalij, MJ, Bax, JJ, Ajmone Marsan, N, Delgado, V, Schmidt, F P, Gniewosz, T, Jabs, A, Munzel, T, Jansen, T, Kaempfner, D, Hink, U, Von Bardeleben, RS, Jose, J, George, OK, Joseph, G, Jose, J, Adawi, S, Najjar, R, Ahronson, D, Shiran, A, Van Riel, ACMJ, Boerlage - Van Dijk, K, De Bruin - Bon, HACM, Araki, M, Meregalli, PG, Koch, KT, Vis, MM, Mulder, BJM, Baan, J, Bouma, BJ, Marciniak, A, Elton, D, Glover, K, Campbell, I, Sharma, R, Batalha, S, Lourenco, C, Oliveira Da Silva, C, Manouras, A, Shahgaldi, K, Caballero, L, Garcia-Lara, J, Gonzalez-Carrillo, J, Oliva, MJ, Saura, D, Garcia-Navarro, M, Espinosa, MD, Pinar, E, Valdes, M, De La Morena, G, Barreiro Perez, M, Lopez Perez, M, Roy, D, Brecker, S, Sharma, R, Venkateshvaran, A, Dash, P K, Sola, S, Barooah, B, Govind, S C, Winter, R, Shahgaldi, K, Brodin, L A, Manouras, A, Saura Espin, D, Caballero Jimenez, L, Gonzalez Carrillo, J, Oliva Sandoval, MJ, Lopez Ruiz, M, Garcia Navarro, M, Espinosa Garcia, MD, Valdes Chavarri, M, De La Morena Valenzuela, G, Gatti, G, Dellangela, L, Pinamonti, B, Benussi, B, Sinagra, G, Pappalardo, A, Group, Heart Muscle Disease Study, Hernandez, V, Saavedra, J, Gonzalez, A, Iglesias, P, Civantos, S, Guijarro, G, Monereo, S, Ikeda, M, Toh, N, Oe, H, Tanabe, Y, Watanabe, N, Ito, H, Ciampi, Q, Cortigiani, L, Pratali, L, Rigo, F, Villari, B, Picano, E, Sicari, R, Yoon, JH, Sohn, JW, Kim, YJ, Chang, HJ, Hong, GR, Kim, TH, Ha, JW, Choi, BW, Rim, SJ, Choi, EY, Tibazarwa, K, Sliwa, K, Wonkam, A, Mayosi, BM, Oryshchyn, N, Ivaniv, Y, Pavlyk, S, Lourenco, M R, Azevedo, O, Moutinho, J, Nogueira, I, Fernandes, M, Pereira, V, Quelhas, I, Lourenco, A, Sunbul, M, Tigen, K, Karaahmet, T, Dundar, C, Ozben, B, Guler, A, Cincin, A, Bulut, M, Sari, I, Basaran, Y, Baydar, O, Kadriye Kilickesmez, KK, Ugur Coskun, UC, Polat Canbolat, PC, Veysel Oktay, VO, Umit Yasar Sinan, US, Okay Abaci, OA, Cuneyt Kocas, CK, Sinan Uner, SU, Serdar Kucukoglu, SK, Zaroui, A, Mourali, MS, Ben Said, R, Asmi, M, Aloui, H, Kaabachi, N, Mechmeche, R, Saberniak, J, Hasselberg, NE, Borgquist, R, Platonov, PG, Holst, AG, Edvardsen, T, Haugaa, KH, Lourenco, M R, Azevedo, O, Nogueira, I, Moutinho, J, Fernandes, M, Pereira, V, Quelhas, I, Lourenco, A, Eran, A, Yueksel, D, Er, F, Gassanov, N, Rosenkranz, S, Baldus, S, Guedelhoefer, H, Faust, M, Caglayan, E, Matveeva, N, Nartsissova, G, Chernjavskij, A, Ippolito, R, De Palma, D, Muscariello, R, Santoro, C, Raia, R, Schiano-Lomoriello, V, Gargiulo, F, Galderisi, M, Lipari, P, Bonapace, S, Zenari, L, Valbusa, F, Rossi, A, Lanzoni, L, Canali, G, Molon, G, Campopiano, E, Barbieri, E, Ikonomidis, I, Varoudi, M, Papadavid, E, Theodoropoulos, K, Papadakis, I, Pavlidis, G, Triantafyllidi, H, Anastasiou - Nana, M, Rigopoulos, D, Lekakis, J, Sunbul, M, Tigen, K, Ozen, G, Durmus, E, Kivrak, T, Cincin, A, Ozben, B, Atas, H, Direskeneli, H, Basaran, Y, Stevanovic, A, Dekleva, M, Trajic, S, Paunovic, N, Simic, A, Khan, SG, Mushemi-Blake, S, Jouhra, F, Dennes, W, Monaghan, M, Melikian, N, Shah, AM, Division, Cardiovascular, Excellence, King’s BHF Centre of, Maceira Gonzalez, A M, Lopez-Lereu, MP, Monmeneu, JV, Igual, B, Estornell, J, Boraita, A, Kosmala, W, Rojek, A, Bialy, D, Mysiak, A, Przewlocka-Kosmala, M, Popescu, I, Mancas, S, Mornos, C, Serbescu, I, Ionescu, G, Ionac, A, Gaudron, P, Niemann, M, Herrmann, S, Hu, K, Liu, D, Wojciech, K, Frantz, S, Bijnens, B, Ertl, G, Weidemann, F, Maceira Gonzalez, A M, Cosin-Sales, J, Ruvira, J, Diago, JL, Aguilar, J, Igual, B, Lopez-Lereu, MP, Monmeneu, J, Estornell, J, Cruz, C, Pinho, T, Madureira, AJ, Lebreiro, A, Dias, CC, Ramos, I, Silva Cardoso, J, Julia Maciel, M, De Meester, P, Van De Bruaene, A, Herijgers, P, Voigt, J-U, Budts, W, Franzoso, F, Voser, EM, Wohlmut, C, Kellenberger, CJ, Valsangiacomo Buechel, E, Carrero, C, Benger, J, Parcerisa, MF, Falconi, M, Oberti, PF, Granja, M, Cagide, AM, Del Pasqua, A, Secinaro, A, Antonelli, G, Iacomino, M, Toscano, A, Chinali, M, Esposito, C, Carotti, A, Pongiglione, G, Rinelli, G, Youssef Moustafa, A, Al Murayeh, M, Al Masswary, A, Al Sheikh, K, Moselhy, M, Dardir, MD, Deising, J, Butz, T, Suermeci, G, Liebeton, J, Wennemann, R, Tzikas, S, Van Bracht, M, Prull, MW, Trappe, H-J, Martin Hidalgo, M, Delgado Ortega, M, Ruiz Ortiz, M, Mesa Rubio, D, Carrasco Avalos, F, Seoane Garcia, T, Pan Alvarez-Ossorio, M, Lopez Aguilera, J, Puentes Chiachio, M, Suarez De Lezo Cruz Conde, J, Petrovic, M T, Giga, V, Stepanovic, J, Tesic, M, Jovanovic, I, Djordjevic-Dikic, A, Generati, G, Pellegrino, M, Bandera, F, Donghi, V, Alfonzetti, E, Guazzi, M, Piatkowski, R, Kochanowski, J, Scislo, P, Opolski, G, Zagatina, A, Zhuravskaya, N, Krylova, L, Vareldzhyan, Y, Tyurina, TV, Clitsenko, O, Bombardini, T, Gherardi, S, Leone, O, Picano, E, Michelotto, E, Ciccarone, A, Tarantino, N, Ostuni, V, Rubino, M, Genco, W, Santoro, G, Carretta, D, Romito, R, Colonna, P, foundation, Cassa di Risparmio di Puglia, Cameli, M, Lunghetti, S, Lisi, M, Curci, V, Cameli, P, Focardi, M, Favilli, R, Galderisi, M, Mondillo, S, Hoffmann, R, Barletta, G, Von Bardeleben, S, Kasprzak, J, Greis, C, Vanoverschelde, J, Becher, H, Machida, T, Izumo, M, Suzuki, K, Kaimijima, R, Mizukoshi, K, Manabe-Uematsu, M, Takai, M, Harada, T, Akashi, YJ, Medicine., St. Marianna University School of, Cardiology, Division of, Martin Garcia, A, Arribas-Jimenez, A, Cruz-Gonzalez, I, Nieto, F, Iscar, A, Merchan, S, Martin-Luengo, C, Brecht, A, Theres, L, Spethmann, S, Dreger, H, Baumann, G, Knebel, F, Jasaityte, R, Heyde, B, Rademakers, F, Claus, P, Dhooge, J, Lervik Nilsen, L C, Lund, J, Brekke, B, Stoylen, A, Giraldeau, G, Duchateau, N, Gabrielli, L, Penela, D, Evertz, R, Mont, L, Brugada, J, Berruezo, A, Bijnens, BH, Sitges, M, Kordybach, M, Kowalski, M, Hoffman, P, Pilichowska, E, Zaborska, B, Baran, J, Kulakowski, P, Budaj, A, Wahi, S, Vollbon, W, Leano, R, Thomas, A, Bricknell, K, Holland, D, Napier, S, Stanton, T, Teferici, D, Qirko, S, Petrela, E, Dibra, A, Bajraktari, G, Bara, P, Sanchis Ruiz, L, Gabrielli, L, Andrea, R, Falces, C, Duchateau, N, Perez-Villa, F, Bijnens, B, Sitges, M, Sulemane, S, Panoulas, VF, Bratsas, AH, Tam, FW, Nihoyannopoulos, P, Abduch, MCD, Alencar, AM, Coracin, FL, Barban, A, Saboya, R, Dulley, FL, Mathias, W, Vieira, MLC, Buccheri, S, Mangiafico, S, Arcidiacono, A, Bottari, VE, Leggio, S, Tamburino, C, Monte, I P, Cruz, C, Lebreiro, A, Pinho, T, Dias, CC, Silva Cardoso, J, Julia Maciel, M, Spitzer, E, Beitzke, D, Kaneider, A, Pavo, N, Gottsauner-Wolf, M, Wolf, F, Loewe, C, Mushtaq, S, Andreini, D, Pontone, G, Bertella, E, Conte, E, Baggiano, A, Annoni, A, Cortinovis, S, Fiorentini, C, Pepi, M, Gustafsson, M, Alehagen, U, Dahlstrom, U, Johansson, P, Faden, G, Faggiano, P, Albertini, L, Reverberi, C, Gaibazzi, N, Taylor, R J, Moody, WE, Umar, F, Edwards, NC, Townend, JN, Steeds, RP, Leyva, F, Mihaila, S, Muraru, D, Piasentini, E, Peluso, D, Casablanca, S, Naso, P, Puma, L, Iliceto, S, Vinereanu, D, Badano, LP, Ciciarello, F L, Agati, L, Cimino, S, De Luca, L, Petronilli, V, Fedele, F, and Tsverava, M

Abstract

Purpose: Transthoracic 3D echocardiography (3DE) allows an unparalleled opportunity for quantifying the dynamic changes of the tricuspid annulus (TA). Accordingly, our aims were: (I) to assess the determinants of TA size during cardiac cycle in healthy subjects; (II) to propose an approach and timing for TA sizing using 3DE. Methods: In 50 healthy volunteers (45±14 yrs, range 18-74, 27 males, with no risk factors, symptoms, signs or history of cardiovascular disease and on no medication), a full-volume dataset of the right ventricle (RV) containing the tricuspid valve (TV) was acquired (Vivid E9, GE Healthcare). TA diameters (septo-lateral, SL; antero-posterior, AP) and areas were measured on multiplanar images (Flexi-slice, EchoPac BT12, GE Healthcare) at 5 time points during the cardiac cycle: OS (onset of systole, at TV closure); MS (mid-systole); ES (end-systole); ED (onset of diastole); LD (late diastole, after the P wave). RV volumes and ejection fraction (EF) were analyzed with commercial software (4D RV analysis, TomTec, D). Results: Temporal resolution of the 3D datasets was 32±4 vps (range 24-53). TA areas were more closely correlated with RV volumes and body surface area (BSA) than with either SL or AP diameters. TA areas increased during systole from OS (3.9±0.6 cm2/m2) to ES (4.9±0.8 cm2/m2) and reached its largest area in LD (6.7±1.0 cm2/m2). All 5 TA areas were correlated with BSA (r range 0.57-0.62) and RV volumes (r ranges 0.53-0.60 for end-diastolic volume and 0.43-0.50 for end-systolic volume, p<0.0001 for all). Indexed TA areas were not related to either age or gender. With multivariable analysis, both RV end-diastolic volume and BSA determined TA areas during systole and early diastole, while TA area at LD and at OS were independently related with BSA only. Conclusions: In healthy subjects, the main determinants of TA size are RV volume and BSA. The largest TA area occurs at LD and is independently related with BSA only. Therefore, normative values should be based on TA areas measured at LD and indexed for BSA. However, the rapid change in TA areas occurring from LD to OS underscores the importance of adequate temporal resolution of 3DE data sets for reliable TA measurements.

Published
2013
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45. Assessment of strain and strain rate by two-dimensional speckle tracking in mice: comparison with tissue Doppler echocardiography and conductance catheter measurements

Author

Ferferieva, V., Van den Bergh, A., Claus, P., Jasaityte, R., La Gerche, A., Rademakers, F., Herijgers, P., and Dhooge, J.

Abstract

Aims This study was designed in order to compare the strain and strain rate deformation parameters assessed by speckle tracking imaging (STI) with those of tissue Doppler imaging (TDI) and conductance catheter measurements in chronic murine models of left ventricular (LV) dysfunction. Methods and results Twenty-four male C57BL/6J mice were assigned to wild-type (n = 8), myocardial infarction (n = 8) and transaortic constriction (n = 8) groups. Echocardiographic and conductance measurements were simultaneously performed at rest and during dobutamine infusion (5 µg/kg/min) in all animals 10 weeks post-surgery. The LV circumferential strain (Scirc) and the strain rate (SRcirc) were derived from grey scale and tissue Doppler data at frame rates of 224 and 375 Hz, respectively. Scirc and SRcirc by TDI/STI correlated well with the preload recruitable stroke work (PRSW) (r = −0.64 and −0.71 for TDI; r = −0.46 and −0.50 for STI, P < 0.05). Both modalities showed a good agreement with respect to Scirc and SRcirc (r = 0.60 and r = 0.63, P < 0.05). During stress, however, TDI-estimated Scirc and SRcirc values were predominantly higher than those measured by STI (P < 0.05). The similarity of Scirc and SRcirc measurements with respect to the STI/TDI data was examined by the Bland–Altman analysis. Conclusion In mice, the STI- and TDI-derived strain and strain rate deformation parameters relate closely to intrinsic myocardial function. At low heart rate-to-frame rate ratios (HR/FR), both STI and TDI are equally acceptable for assessing the LV function non-invasively in these animals. At HR/FR (e.g. dobutamine challenge), however, these methods cannot be used interchangeably as STI underestimates S and SR at high values.

Published
2013
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46. Using high-power light emitting diodes for photoacoustic imaging

Author

René Skov Hansen, Dhooge, J., and Doyley, M. M.

Subjects
Physics, business.industry, Photoacoustic, Pulsed power, Laser, Imaging phantom, Semiconductor laser theory, law.invention, Lens (optics), Wavelength, Optics, Transducer, law, Optoelectronics, Foto akustik, business, Light-emitting diode
Abstract

Photoacoustic imaging will become an important supplement to conventional ultrasound imaging. However, the equipment needed is still too delicate to bring this technique into the daily clinical work. The pulsed light source is the area of interest in the present report. Usually, large and costly laser systems are used to provide high-energy light pulses with a chosen wavelength. Pulsed semiconductor lasers have been demonstrated as a possible alternative light source for the photoacoustic imaging purpose. As an alternative to laser sources, the preliminary result of using a pulsed high-power light emitting diode, LED, for photoacoustic imaging is presented here. The pulsed light source is created from a Luxeon LXHL_PD09 red LED (250mW optical output power at 1 Amp current). The LED is supplied with current pulses 60ns wide and 40A peak. The LED delivers 60ns light pulses with approximately 6W peak power. The phantom used consists of a thin stripe (3mm high x 5mm wide) of green colored gelatin overlaid by a 3cm layer of un-colored gelatin. The light pulses from the LED are collected by a lens system and focused on the green gelatin from beneath the sample. The acoustic response from the green gelatin is detected with a single focused transducer on the upper surface of the 3cm thick colorless gelatin layer. The response is clearly observed when the measurement is taken as an average of 50,000 pulses. Is it concluded that despite the relatively low pulse power, for some purposes, a combination of of LED's could be a candidate for an inexpensive light source.

Published
2011

47. MFP-Unet: A novel deep learning based approach for left ventricle segmentation in echocardiography.

Author

Moradi S, Oghli MG, Alizadehasl A, Shiri I, Oveisi N, Oveisi M, Maleki M, and Dhooge J

Subjects
Deep Learning, Echocardiography, Heart Ventricles diagnostic imaging, Image Processing, Computer-Assisted methods
Abstract

Segmentation of the Left ventricle (LV) is a crucial step for quantitative measurements such as area, volume, and ejection fraction. However, the automatic LV segmentation in 2D echocardiographic images is a challenging task due to ill-defined borders, and operator dependence issues (insufficient reproducibility). U-net, which is a well-known architecture in medical image segmentation, addressed this problem through an encoder-decoder path. Despite outstanding overall performance, U-net ignores the contribution of all semantic strengths in the segmentation procedure. In the present study, we have proposed a novel architecture to tackle this drawback. Feature maps in all levels of the decoder path of U-net are concatenated, their depths are equalized, and up-sampled to a fixed dimension. This stack of feature maps would be the input of the semantic segmentation layer. The performance of the proposed model was evaluated using two sets of echocardiographic images: one public dataset and one prepared dataset. The proposed network yielded significantly improved results when comparing with results from U-net, dilated U-net, Unet++, ACNN, SHG, and deeplabv3. An average Dice Metric (DM) of 0.953, Hausdorff Distance (HD) of 3.49, and Mean Absolute Distance (MAD) of 1.12 are achieved in the public dataset. The correlation graph, bland-altman analysis, and box plot showed a great agreement between automatic and manually calculated volume, area, and length., (Copyright © 2019 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.)

Published
2019
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48. Deep Learning for Segmentation Using an Open Large-Scale Dataset in 2D Echocardiography.

Author

Leclerc S, Smistad E, Pedrosa J, Ostvik A, Cervenansky F, Espinosa F, Espeland T, Berg EAR, Jodoin PM, Grenier T, Lartizien C, Dhooge J, Lovstakken L, and Bernard O

Subjects
Algorithms, Databases, Factual, Heart diagnostic imaging, Humans, Deep Learning, Echocardiography methods, Image Processing, Computer-Assisted methods
Abstract

Delineation of the cardiac structures from 2D echocardiographic images is a common clinical task to establish a diagnosis. Over the past decades, the automation of this task has been the subject of intense research. In this paper, we evaluate how far the state-of-the-art encoder-decoder deep convolutional neural network methods can go at assessing 2D echocardiographic images, i.e., segmenting cardiac structures and estimating clinical indices, on a dataset, especially, designed to answer this objective. We, therefore, introduce the cardiac acquisitions for multi-structure ultrasound segmentation dataset, the largest publicly-available and fully-annotated dataset for the purpose of echocardiographic assessment. The dataset contains two and four-chamber acquisitions from 500 patients with reference measurements from one cardiologist on the full dataset and from three cardiologists on a fold of 50 patients. Results show that encoder-decoder-based architectures outperform state-of-the-art non-deep learning methods and faithfully reproduce the expert analysis for the end-diastolic and end-systolic left ventricular volumes, with a mean correlation of 0.95 and an absolute mean error of 9.5 ml. Concerning the ejection fraction of the left ventricle, results are more contrasted with a mean correlation coefficient of 0.80 and an absolute mean error of 5.6%. Although these results are below the inter-observer scores, they remain slightly worse than the intra-observer's ones. Based on this observation, areas for improvement are defined, which open the door for accurate and fully-automatic analysis of 2D echocardiographic images.

Published
2019
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49. Fully Automatic 3-D-TEE Segmentation for the Planning of Transcatheter Aortic Valve Implantation.

Author

Queiros S, Papachristidis A, Morais P, Theodoropoulos KC, Fonseca JC, Monaghan MJ, Vilaca JL, and Dhooge J

Subjects
Humans, Image Interpretation, Computer-Assisted methods, Machine Learning, Pattern Recognition, Automated methods, Preoperative Care methods, Reproducibility of Results, Sensitivity and Specificity, Aortic Valve diagnostic imaging, Aortic Valve surgery, Echocardiography, Three-Dimensional methods, Echocardiography, Transesophageal methods, Surgery, Computer-Assisted methods, Transcatheter Aortic Valve Replacement methods
Abstract

A novel fully automatic framework for aortic valve (AV) trunk segmentation in three-dimensional (3-D) transesophageal echocardiography (TEE) datasets is proposed. The methodology combines a previously presented semiautomatic segmentation strategy by using shape-based B-spline Explicit Active Surfaces with two novel algorithms to automate the quantification of relevant AV measures. The first combines a fast rotation-invariant 3-D generalized Hough transform with a vessel-like dark tube detector to initialize the segmentation. After segmenting the AV wall, the second algorithm focuses on aligning this surface with the reference ones in order to estimate the short-axis (SAx) planes (at the left ventricular outflow tract, annulus, sinuses of Valsalva, and sinotubular junction) in which to perform the measurements. The framework has been tested in 20 3-D-TEE datasets with both stenotic and nonstenotic AVs. The initialization algorithm presented a median error of around 3 mm for the AV axis endpoints, with an overall feasibility of 90%. In its turn, the SAx detection algorithm showed to be highly reproducible, with indistinguishable results compared with the variability found between the experts' defined planes. Automatically extracted measures at the four levels showed a good agreement with the experts' ones, with limits of agreement similar to the interobserver variability. Moreover, a validation set of 20 additional stenotic AV datasets corroborated the method's applicability and accuracy. The proposed approach mitigates the variability associated with the manual quantification while significantly reducing the required analysis time (12 s versus 5 to 10 min), which shows its appeal for automatic dimensioning of the AV morphology in 3-D-TEE for the planning of transcatheter AV implantation.

Published
2017
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50. Detailed Evaluation of Five 3D Speckle Tracking Algorithms Using Synthetic Echocardiographic Recordings.

Author

Alessandrini M, Heyde B, Queiros S, Cygan S, Zontak M, Somphone O, Bernard O, Sermesant M, Delingette H, Barbosa D, De Craene M, ODonnell M, and Dhooge J

Subjects
Algorithms, Heart, Humans, Echocardiography, Three-Dimensional
Abstract

A plethora of techniques for cardiac deformation imaging with 3D ultrasound, typically referred to as 3D speckle tracking techniques, are available from academia and industry. Although the benefits of single methods over alternative ones have been reported in separate publications, the intrinsic differences in the data and definitions used makes it hard to compare the relative performance of different solutions. To address this issue, we have recently proposed a framework to simulate realistic 3D echocardiographic recordings and used it to generate a common set of ground-truth data for 3D speckle tracking algorithms, which was made available online. The aim of this study was therefore to use the newly developed database to contrast non-commercial speckle tracking solutions from research groups with leading expertise in the field. The five techniques involved cover the most representative families of existing approaches, namely block-matching, radio-frequency tracking, optical flow and elastic image registration. The techniques were contrasted in terms of tracking and strain accuracy. The feasibility of the obtained strain measurements to diagnose pathology was also tested for ischemia and dyssynchrony.

Published
2016
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