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1,015 results on '"A. Gomez Pedro"'

1. Minimal Riesz and logarithmic energies on the Grassmannian $\operatorname{Gr}_{2,4}$

Author

Etayo, Ujué and López-Gómez, Pedro R.

Subjects
Mathematics - Classical Analysis and ODEs, Mathematics - Probability, 31C12, 60G55 (Primary) 33C50, 41A60, 42C10 (Secondary)
Abstract

We study the Riesz and logarithmic energies on the Grassmannian $\operatorname{Gr}_{2,4}$ of $2$-dimensional subspaces of $\mathbb{R}^4$. We prove that the continuous Riesz and logarithmic energies are uniquely minimized by the uniform measure, and we obtain asymptotic upper and lower bounds for the minimal discrete energies, with matching orders for the next-order terms. Additionally, we define a determinantal point process on $\operatorname{Gr}_{2,4}$ and compute the expected energy of the points coming from this random process, thereby obtaining explicit constants in the upper bounds for the Riesz and logarithmic energies., Comment: 47 pages

Published
2024

2. Community-engaged Research in Practice: Staffing Cooperative of the Los Angeles (LA) Garment Worker Center

Author

Banerjee, Nayantara, Calzada, Jonathan, Du, Justine Ligaya, Figueroa, Abigail, Gomez, Pedro Garcia, Gonzalez, Andres, Gonzalez, Tomas, Ortega Hernandez, Isabel, Hernandez, Julia, Hernandez, Juana Macavio, Macario, Marta Gonzalez, Morales, Bryzen Enzo, Santiago, Bilma, Session, Sherrod, and Woo, Miya

Subjects
Labor, High road, Co-op
Abstract

This community-engaged research is grounded in the experiences of Los Angeles (LA) garment workers. Therefore, we affirm the principle that workers should be actively involved in shaping their own working conditions. The purpose of this research, then, is to situate a Garment Worker Center (GWC) staffing cooperative (or staffing coop) in the LA garment industry to further advance workers’ rights and leverage their skills and expertise, by conducting an initial customer exploration. Doing so allows the GWC’s staffing cooperative to pursue a business model that is competitive and feasible in the LA garment industry.

Published
2024

4. Long distance electron-electron scattering detected with point contacts

Author

Ginzburg, Lev V., Wu, Yuze, Röösli, Marc P., Gomez, Pedro Rosso, Garreis, Rebekka, Tong, Chuyao, Stará, Veronika, Gold, Carolin, Nazaryan, Khachatur, Kryhin, Serhii, Overweg, Hiske, Reichl, Christian, Berl, Matthias, Taniguchi, Takashi, Watanabe, Kenji, Wegscheider, Werner, Ihn, Thomas, and Ensslin, Klaus

Subjects
Condensed Matter - Mesoscale and Nanoscale Physics
Abstract

We measure electron transport through point contacts in an electron gas in AlGaAs/GaAs heterostructures and graphene for a range of temperatures, magnetic fields and electron densities. We find a magnetoconductance peak around B = 0. With increasing temperature, the width of the peak increases monotonically, while its amplitude first increases and then decreases. For GaAs point contacts the peak is particularly sharp at relatively low temperatures $T\approx$1.5 K: the curve rounds on a scale of few tens of $\mu$T hinting at length scales of several millimeters for the corresponding scattering processes. We propose a model based on the transition between different transport regimes with increasing temperature: from ballistic transport to few electron-electron scatterings to hydrodynamic superballistic flow to hydrodynamic Poiseuille-like flow. The model is in qualitative and, in many cases, quantitative agreement with the experimental observations., Comment: 14 pages, 11 figures

Published
2023
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5. Measure-preserving mappings from the unit cube to some symmetric spaces

Author

Beltrán, Carlos, Ferizović, Damir, and López-Gómez, Pedro R.

Subjects
Mathematics - Classical Analysis and ODEs, Mathematics - Metric Geometry, 58C35, 52C35
Abstract

We construct measure-preserving mappings from the $d$-dimensional unit cube to the $d$-dimensional unit ball and the compact rank one symmetric spaces, namely the $d$-dimensional sphere, the real, complex, and quaternionic projective spaces, and the Cayley plane. We also give a procedure to generate measure-preserving mappings from the $d$-dimensional unit cube to product spaces and fiber bundles under certain conditions., Comment: 20 pages

Published
2023

7. Low energy points on the sphere and the real projective plane

Author

Beltrán, Carlos, Etayo, Ujué, and López-Gómez, Pedro R.

Subjects
Mathematics - Classical Analysis and ODEs, 31C12, 31C20, 41A60, 52C15, 52C35, 52A40
Abstract

We present a generalization of a family of points on $\mathbb{S}^2$, the Diamond ensemble, containing collections of $N$ points on $\mathbb{S}^2$ with very small logarithmic energy for all $N\in\mathbb{N}$. We extend this construction to the real projective plane $\mathbb{RP}^2$ and we obtain upper and lower bounds with explicit constants for the Green and logarithmic energy on this last space., Comment: 17 pages

Published
2022
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8. Genetic vulnerability and adverse mental health outcomes following mild traumatic brain injury: a meta-analysis of CENTER-TBI and TRACK-TBI cohorts

Author

Ackerlund, Cecilia, Adams, Hadie, Amrein, Krisztina, Andelic, Nada, Andreassen, Lasse, Anke, Audny, Antoni, Anna, Audibert, Gérard, Azouvi, Philippe, Azzolini, Maria Luisa, Bartels, Ronald, Barzó, Pál, Beauvais, Romuald, Beer, Ronny, Bellander, Bo-Michael, Belli, Antonio, Benali, Habib, Berardino, Maurizio, Beretta, Luigi, Blaabjerg, Morten, Bragge, Peter, Brazinova, Alexandra, Brinck, Vibeke, Brooker, Joanne, Brorsson, Camilla, Buki, Andras, Bullinger, Monika, Cabeleira, Manuel, Caccioppola, Alessio, Calappi, Emiliana, Calvi, Maria Rosa, Cameron, Peter, Lozano, Guillermo Carbayo, Carbonara, Marco, Castaño-León, Ana M., Cavallo, Simona, Chevallard, Giorgio, Chieregato, Arturo, Citerio, Giuseppe, Clusmann, Hans, Coburn, Mark Steven, Coles, Jonathan, Cooper, Jamie D., Correia, Marta, Čović, Amra, Curry, Nicola, Czeiter, Endre, Czosnyka, Marek, Dahyot-Fizelier, Claire, Dark, Paul, Dawes, Helen, De Keyser, Véronique, Degos, Vincent, Della Corte, Francesco, Boogert, Hugo den, Depreitere, Bart, Đilvesi, Đula, Dixit, Abhishek, Donoghue, Emma, Dreier, Jens, Dulière, Guy-Loup, Ercole, Ari, Esser, Patrick, Ezer, Erzsébet, Fabricius, Martin, Feigin, Valery L., Foks, Kelly, Frisvold, Shirin, Furmanov, Alex, Gagliardo, Pablo, Galanaud, Damien, Gantner, Dashiell, Gao, Guoyi, George, Pradeep, Ghuysen, Alexandre, Giga, Lelde, Glocker, Ben, Golubović, Jagoš, Gomez, Pedro A., Gratz, Johannes, Gravesteijn, Benjamin, Grossi, Francesca, Gruen, Russell L., Gupta, Deepak, Haagsma, Juanita A., Haitsma, Iain, Helbok, Raimund, Helseth, Eirik, Horton, Lindsay, Huijben, Jilske, Hutchinson, Peter J., Jacobs, Bram, Jankowski, Stefan, Jarrett, Mike, Jiang, Ji-yao, Johnson, Faye, Jones, Kelly, Karan, Mladen, Kolias, Angelos G., Kompanje, Erwin, Kondziella, Daniel, Koskinen, Lars-Owe, Kovács, Noémi, Kowark, Ana, Lagares, Alfonso, Lanyon, Linda, Laureys, Steven, Lecky, Fiona, Ledoux, Didier, Lefering, Rolf, Legrand, Valerie, Lejeune, Aurelie, Levi, Leon, Lightfoot, Roger, Lingsma, Hester, Maegele, Marc, Majdan, Marek, Manara, Alex, Maréchal, Hugues, Martino, Costanza, Mattern, Julia, McFadyen, Charles, McMahon, Catherine, Melegh, Béla, Menovsky, Tomas, Mikolic, Ana, Misset, Benoit, Muraleedharan, Visakh, Murray, Lynnette, Negru, Ancuta, Nelson, David, Newcombe, Virginia, Nieboer, Daan, Nyirádi, József, Oresic, Matej, Ortolano, Fabrizio, Otesile, Olubukola, Parizel, Paul M., Payen, Jean-François, Perera, Natascha, Perlbarg, Vincent, Persona, Paolo, Peul, Wilco, Piippo-Karjalainen, Anna, Pirinen, Matti, Pisica, Dana, Ples, Horia, Polinder, Suzanne, Pomposo, Inigo, Posti, Jussi P., Puybasset, Louis, Rădoi, Andreea, Ragauskas, Arminas, Raj, Rahul, Rambadagalla, Malinka, Rehorčíková, Veronika, Helmrich, Isabel Retel, Rhodes, Jonathan, Richter, Sophie, Rocka, Saulius, Roe, Cecilie, Roise, Olav, Rosenfeld, Jeffrey, Rosenlund, Christina, Rosenthal, Guy, Rossaint, Rolf, Rossi, Sandra, Rueckert, Daniel, Rusnák, Martin, Sahuquillo, Juan, Sakowitz, Oliver, Sanchez-Porras, Renan, Sandor, Janos, Schäfer, Nadine, Schmidt, Silke, Schoechl, Herbert, Schoonman, Guus, Schou, Rico Frederik, Schwendenwein, Elisabeth, Sewalt, Charlie, Singh, Ranjit D., Skandsen, Toril, Smielewski, Peter, Sorinola, Abayomi, Stamatakis, Emmanuel, Stanworth, Simon, Stevens, Robert, Stewart, William, Stocchetti, Nino, Sundström, Nina, Takala, Riikka, Tamás, Viktória, Tamosuitis, Tomas, Taylor, Mark Steven, Te Ao, Braden, Tenovuo, Olli, Theadom, Alice, Thibaut, Aurore, Thomas, Matt, Tibboel, Dick, Timmers, Marjolijn, Tolias, Christos, Trapani, Tony, Tudora, Cristina Maria, Unterberg, Andreas, Vajkoczy, Peter, Valeinis, Egils, Vallance, Shirley, Vámos, Zoltán, van der Jagt, Mathieu, van der Naalt, Joukje, Van der Steen, Gregory, van Dijck, Jeroen T.J.M., van Erp, Inge A., van Essen, Thomas A., Van Hecke, Wim, van Heugten, Caroline, Van Praag, Dominique, van Veen, Ernest, van Wijk, Roel, Vyvere, Thijs Vande, Vargiolu, Alessia, Vega, Emmanuel, Velt, Kimberley, Verheyden, Jan, Vespa, Paul M., Vik, Anne, Vilcinis, Rimantas, Volovici, Victor, von Steinbüchel, Nicole, Voormolen, Daphne, Vulekovic, Peter, Whitehouse, Daniel, Wiegers, Eveline, Williams, Guy, Wolf, Stefan, Yang, Zhihui, Ylén, Peter, Younsi, Alexander, Zeiler, Frederick A., Ziverte, Agate, Zoerle, Tommaso, Adeoye, Opeolu, Badjatia, Neeraj, Barber, Jason, Bergin, Michael, Boase, Kim, Bodien, Yelena, Chesnut, Randall, Corrigan, John, Crawford, Karen, Diaz-Arrastia, Ramon, Dikmen, Sureyya, Duhaime, Ann-Christine, Ellenbogen, Richard, Feeser, Venkata, Ferguson, Adam R., Foreman, Brandon, Gaudette, Etienne, Giacino, Joseph, Gonzalez, Luis, Gopinath, Shankar, Grandhi, Ramesh, Gullapalli, Rao, Hemphill, Claude, Hotz, Gillian, Huie, Russell, Jha, Ruchira, Keene, Dirk C., Kitagawa, Ryan, Korley, Frederick, Kramer, Joel, Kreitzer, Natalie, Levin, Harvey, Lindsell, Chris, Machamer, Joan, Madden, Christopher, Martin, Alastair, McAllister, Thomas, McCrea, Michael, Merchant, Randall, Mukherjee, Pratik, Nelson, Lindsay, Ngwenya, Laura B., Noel, Florence, Nolan, Amber, Okonkwo, David, Palacios, Eva, Perl, Daniel, Puccio, Ava, Rabinowitz, Miri, Robertson, Claudia, Ben Rodgers, Richard, Rosenthal, Eric, Sander, Angelle, Sandsmark, Danielle, Schneider, Andrea, Schnyer, David, Seabury, Seth, Sherer, Mark, Sugar, Gabriella, Temkin, Nancy, Toga, Arthur, Torres-Espin, Abel, Valadka, Alex, Vassar, Mary, Wang, Kevin, Wang, Vincent, Yue, John K., Yuh, Esther, Zafonte, Ross, Kals, Mart, Wilson, Lindsay, Levey, Daniel F., Parodi, Livia, Steyerberg, Ewout W., Richardson, Sylvia, He, Feng, Sun, Xiaoying, Jain, Sonia, Palotie, Aarno, Ripatti, Samuli, Rosand, Jonathan, Manley, Geoff T., Maas, Andrew I.R., Stein, Murray B., and Menon, David K.

Published
2024
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12. Parsimonious immune-response endotypes and global outcome in patients with traumatic brain injury

Author

Badjatia, Neeraj, Diaz-Arrastia, Ramon, Duhaime, Ann-Christine, Feeser, V Ramana, Gopinath, Shankar, Grandhi, Ramesh, Ha, Ruchira J., Keene, Dirk, Madden, Christopher, McCrea, Michael, Merchant, Randall, Ngwenya, Laura B., Rodgers, Richard B., Schnyer, David, Taylor, Sabrina R., Zafonte, Ross, Ackerlund, Cecilia, Amrein, Krisztina, Andelic, Nada, Andreassen, Lasse, Anke, Audny, Audibert, Gérard, Azouvi, Philippe, Azzolini, Maria Luisa, Bartels, Ronald, Beer, Ronny, Bellander, Bo-Michael, Benali, Habib, Berardino, Maurizio, Beretta, Luigi, Beqiri, Erta, Blaabjerg, Morten, Lund, Stine Borgen, Brorsson, Camilla, Buki, Andras, Cabeleira, Manuel, Caccioppola, Alessio, Calappi, Emiliana, Calvi, Maria Rosa, Cameron, Peter, Lozano, Guillermo Carbayo, Carbonara, Marco, Castaño-León, Ana M., Cavallo, Simona, Chevallard, Giorgio, Chieregato, Arturo, Citerio, Giuseppe, Clusmann, Hans, Coburn, Mark Steven, Coles, Jonathan, Cooper, Jamie D., Correia, Marta, Czeiter, Endre, Czosnyka, Marek, Dahyot-Fizelier, Claire, Dark, Paul, De Keyser, Véronique, Degos, Vincent, Della Corte, Francesco, Boogert, Hugo den, Depreitere, Bart, Đilvesi, Đula, Dixit, Abhishek, Dreier, Jens, Dulière, Guy-Loup, Ercole, Ari, Ezer, Erzsébet, Fabricius, Martin, Foks, Kelly, Frisvold, Shirin, Furmanov, Alex, Galanaud, Damien, Gantner, Dashiell, Ghuysen, Alexandre, Giga, Lelde, Golubović, Jagoš, Gomez, Pedro A., Gravesteijn, Benjamin, Grossi, Francesca, Gupta, Deepak, Haitsma, Iain, Helbok, Raimund, Helseth, Eirik, Huijben, Jilske, Hutchinson, Peter J., Jankowski, Stefan, Johnson, Faye, Karan, Mladen, Kolias, Angelos G., Kondziella, Daniel, Kornaropoulos, Evgenios, Koskinen, Lars-Owe, Kovács, Noémi, Kowark, Ana, Lagares, Alfonso, Laureys, Steven, Lecky, Fiona, Ledoux, Didier, Lightfoot, Roger, Lingsma, Hester, Maas, Andrew I.R., Manara, Alex, Maréchal, Hugues, Martino, Costanza, Mattern, Julia, McMahon, Catherine, Menon, David, Menovsky, Tomas, Misset, Benoit, Muraleedharan, Visakh, Murray, Lynnette, Negru, Ancuta, Nelson, David, Newcombe, Virginia, Nyirádi, József, Ortolano, Fabrizio, Payen, Jean-François, Perlbarg, Vincent, Persona, Paolo, Peul, Wilco, Piippo-Karjalainen, Anna, Ples, Horia, Pomposo, Inigo, Posti, Jussi P., Puybasset, Louis, Rădoi, Andreea, Ragauskas, Arminas, Raj, Rahul, Rhodes, Jonathan, Richter, Sophie, Rocka, Saulius, Roe, Cecilie, Roise, Olav, Rosenfeld, Jeffrey, Rosenlund, Christina, Rosenthal, Guy, Rossaint, Rolf, Rossi, Sandra, Sahuquillo, Juan, Sakowitz, Oliver, Sanchez-Porras, Renan, Sandrød, Oddrun, Schirmer-Mikalsen, Kari, Frederik Schou, Rico, Sewalt, Charlie, Smielewski, Peter, Sorinola, Abayomi, Stamatakis, Emmanuel, Steyerberg, Ewout W., Stocchetti, Nino, Sundström, Nina, Takala, Riikka, Tamás, Viktória, Tamosuitis, Tomas, Tenovuo, Olli, Thomas, Matt, Tibboel, Dick, Tolias, Christos, Trapani, Tony, Tudora, Cristina Maria, Unterberg, Andreas, Vajkoczy, Peter, Valeinis, Egils, Vallance, Shirley, Vámos, Zoltán, Van der Steen, Gregory, van Dijck, Jeroen T.J.M., van Essen, Thomas A., van Wijk, Roel, Vargiolu, Alessia, Vega, Emmanuel, Vik, Anne, Vilcinis, Rimantas, Volovici, Victor, Vulekovic, Peter, Wiegers, Eveline, Williams, Guy, Winzeck, Stefan, Wolf, Stefan, Younsi, Alexander, Zeiler, Frederick A., Ziverte, Agate, Zoerle, Tommaso, Samanta, Romit J., Chiollaz, Anne-Cécile, Needham, Edward, Yue, John K., Helmy, Adel, Zanier, Elisa R., Wang, Kevin K.W., Kobeissy, Firas, Summers, Charlotte, Manley, Geoffrey T., Maas, Andrew IR., Sanchez, Jean-Charles, and Menon, David K.

Published
2024
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14. Association of early blood-based biomarkers and six-month functional outcomes in conventional severity categories of traumatic brain injury: capturing the continuous spectrum of injury

Author

Ackerlund, Cecilia, Amrein, Krisztina, Andelic, Nada, Andreassen, Lasse, Anke, Audny, Antoni, Anna, Audibert, Gérard, Azouvi, Philippe, Azzolini, Maria Luisa, Bartels, Ronald, Barzó, Pál, Beauvais, Romuald, Beer, Ronny, Bellander, Bo-Michael, Belli, Antonio, Benali, Habib, Berardino, Maurizio, Beretta, Luigi, Blaabjerg, Morten, Bragge, Peter, Brazinova, Alexandra, Brinck, Vibeke, Brooker, Joanne, Brorsson, Camilla, Buki, Andras, Bullinger, Monika, Cabeleira, Manuel, Caccioppola, Alessio, Calappi, Emiliana, Calvi, Maria Rosa, Cameron, Peter, Lozano, Guillermo Carbayo, Carbonara, Marco, Castaño-León, Ana M., Cavallo, Simona, Chevallard, Giorgio, Chieregato, Arturo, Citerio, Giuseppe, Clusmann, Hans, Coburn, Mark Steven, Coles, Jonathan, Cooper, Jamie D., Correia, Marta, Čović, Amra, Curry, Nicola, Czeiter, Endre, Czosnyka, Marek, Dahyot-Fizelier, Claire, Dark, Paul, Dawes, Helen, De Keyser, Véronique, Degos, Vincent, Della Corte, Francesco, Boogert, Hugo den, Depreitere, Bart, Đilvesi, Đula, Dixit, Abhishek, Donoghue, Emma, Dreier, Jens, Dulière, Guy-Loup, Ercole, Ari, Esser, Patrick, Ezer, Erzsébet, Fabricius, Martin, Feigin, Valery L., Foks, Kelly, Frisvold, Shirin, Furmanov, Alex, Gagliardo, Pablo, Galanaud, Damien, Gantner, Dashiell, Gao, Guoyi, George, Pradeep, Ghuysen, Alexandre, Giga, Lelde, Glocker, Ben, Golubović, Jagoš, Gomez, Pedro A., Gratz, Johannes, Gravesteijn, Benjamin, Grossi, Francesca, Gruen, Russell L., Gupta, Deepak, Haagsma, Juanita A., Haitsma, Iain, Helbok, Raimund, Helseth, Eirik, Horton, Lindsay, Huijben, Jilske, Hutchinson, Peter J., Jacobs, Bram, Jankowski, Stefan, Jarrett, Mike, Jiang, Ji-yao, Johnson, Faye, Jones, Kelly, Karan, Mladen, Kolias, Angelos G., Kompanje, Erwin, Kondziella, Daniel, Kornaropoulos, Evgenios, Koskinen, Lars-Owe, Kovács, Noémi, Kowark, Ana, Lagares, Alfonso, Lanyon, Linda, Laureys, Steven, Lecky, Fiona, Ledoux, Didier, Lefering, Rolf, Legrand, Valerie, Lejeune, Aurelie, Levi, Leon, Lightfoot, Roger, Lingsma, Hester, Maegele, Marc, Majdan, Marek, Manara, Alex, Manley, Geoffrey, Maréchal, Hugues, Martino, Costanza, Mattern, Julia, McMahon, Catherine, Melegh, Béla, Menovsky, Tomas, Mikolic, Ana, Misset, Benoit, Muraleedharan, Visakh, Murray, Lynnette, Nair, Nandesh, Negru, Ancuta, Nelson, David, Nieboer, Daan, Nyirádi, József, Oresic, Matej, Ortolano, Fabrizio, Otesile, Olubukola, Palotie, Aarno, Parizel, Paul M., Payen, Jean-François, Perera, Natascha, Perlbarg, Vincent, Persona, Paolo, Peul, Wilco, Piippo-Karjalainen, Anna, Pirinen, Matti, Pisica, Dana, Ples, Horia, Polinder, Suzanne, Pomposo, Inigo, Posti, Jussi P., Puybasset, Louis, Rădoi, Andreea, Ragauskas, Arminas, Raj, Rahul, Rambadagalla, Malinka, Helmrich, Isabel Retel, Rhodes, Jonathan, Richardson, Sylvia, Richter, Sophie, Ripatti, Samuli, Rocka, Saulius, Roe, Cecilie, Roise, Olav, Rosand, Jonathan, Rosenfeld, Jeffrey, Rosenlund, Christina, Rosenthal, Guy, Rossaint, Rolf, Rossi, Sandra, Rueckert, Daniel, Rusnák, Martin, Sahuquillo, Juan, Sakowitz, Oliver, Sanchez-Porras, Renan, Sandor, Janos, Schäfer, Nadine, Schmidt, Silke, Schoechl, Herbert, Schoonman, Guus, Schou, Rico Frederik, Schwendenwein, Elisabeth, Singh, Ranjit D., Sewalt, Charlie, Skandsen, Toril, Smielewski, Peter, Sorinola, Abayomi, Stamatakis, Emmanuel, Stanworth, Simon, Stevens, Robert, Stewart, William, Steyerberg, Ewout W., Stocchetti, Nino, Sundström, Nina, Takala, Riikka, Tamás, Viktória, Tamosuitis, Tomas, Taylor, Mark Steven, Ao, Braden Te, Tenovuo, Olli, Theadom, Alice, Thomas, Matt, Thibaut, Aurore, Tibboel, Dick, Timmers, Marjolijn, Tolias, Christos, Trapani, Tony, Tudora, Cristina Maria, Unterberg, Andreas, Vajkoczy, Peter, Valeinis, Egils, Vallance, Shirley, Vámos, Zoltán, van der Jagt, Mathieu, van der Naalt, Joukje, Van der Steen, Gregory, van Dijck, Jeroen T.J.M., van Erp, Inge A., van Essen, Thomas A., Van Hecke, Wim, van Heugten, Caroline, Van Praag, Dominique, van Veen, Ernest, van Wijk, Roel P.J., Vyvere, Thijs Vande, Vargiolu, Alessia, Vega, Emmanuel, Velt, Kimberley, Verheyden, Jan, Vespa, Paul M., Vik, Anne, Vilcinis, Rimantas, Volovici, Victor, von Steinbüchel, Nicole, Voormolen, Daphne, Vulekovic, Peter, Wang, Kevin K.W., Wiegers, Eveline, Williams, Guy, Winzeck, Stefan, Wolf, Stefan, Yang, Zhihui, Ylén, Peter, Younsi, Alexander, Zeiler, Frederick A., Zelinkova, Veronika, Ziverte, Agate, Zoerle, Tommaso, Wilson, Lindsay, Newcombe, Virginia F.J., Whitehouse, Daniel P., Mondello, Stefania, Maas, Andrew I.R., and Menon, David K.

Published
2024
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17. Prospects and Future Questions

Author

Martínez-Gómez, Pedro, Sánchez-Pérez, Raquel, Fernández i Martí, Ángel, Sánchez-Pérez, Raquel, editor, Fernandez i Marti, Angel, editor, and Martinez-Gomez, Pedro, editor

Published
2023
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18. A genome-wide association study of outcome from traumatic brain injury

Author

Kals, Mart, Kunzmann, Kevin, Parodi, Livia, Radmanesh, Farid, Wilson, Lindsay, Izzy, Saef, Anderson, Christopher D, Puccio, Ava M, Okonkwo, David O, Temkin, Nancy, Steyerberg, Ewout W, Stein, Murray B, Manley, Geoff T, Maas, Andrew IR, Richardson, Sylvia, Diaz-Arrastia, Ramon, Palotie, Aarno, Ripatti, Samuli, Rosand, Jonathan, Menon, David K, Åkerlund, Cecilia, Amrein, Krisztina, Andelic, Nada, Andreassen, Lasse, Anke, Audny, Antoni, Anna, Audibert, Gérard, Azouvi, Philippe, Azzolini, Maria Luisa, Bartels, Ronald, Barzó, Pál, Beauvais, Romuald, Beer, Ronny, Bellander, Bo-Michael, Belli, Antonio, Benali, Habib, Berardino, Maurizio, Beretta, Luigi, Blaabjerg, Morten, Bragge, Peter, Brazinova, Alexandra, Brinck, Vibeke, Brooker, Joanne, Brorsson, Camilla, Buki, Andras, Bullinger, Monika, Cabeleira, Manuel, Caccioppola, Alessio, Calappi, Emiliana, Calvi, Maria Rosa, Cameron, Peter, Lozano, Guillermo Carbayo, Carbonara, Marco, Cavallo, Simona, Chevallard, Giorgio, Chieregato, Arturo, Citerio, Giuseppe, Clusmann, Hans, Coburn, Mark, Coles, Jonathan P, Cooper, Jamie D, Correia, Marta, Čović, Amra, Curry, Nicola, Czeiter, Endre, Czosnyka, Marek, DahyotFizelier, Claire, Dark, Paul, Dawes, Helen, De Keyser, Véronique, Degos, Vincent, Corte, Francesco Della, Boogert, Hugo den, Depreitere, Bart, Đilvesi, Đula, Dixit, Abhishek, Donoghue, Emma, Dreier, Jens, Dulière, GuyLoup, Ercole, Ari, Esser, Patrick, Ezer, Erzsébet, Fabricius, Martin, Feigin, Valery L, Foks, Kelly, Frisvold, Shirin, Furmanov, Alex, Gagliardo, Pablo, Galanaud, Damien, Gantner, Dashiell, Gao, Guoyi, George, Pradeep, Ghuysen, Alexandre, Giga, Lelde, Glocker, Ben, Golubovic, Jagoš, Gomez, Pedro A, Gratz, Johannes, Gravesteijn, Benjamin, and Grossi, Francesca

Subjects
Epidemiology, Health Sciences, Traumatic Head and Spine Injury, Genetics, Physical Injury - Accidents and Adverse Effects, Neurosciences, Brain Disorders, Human Genome, Traumatic Brain Injury (TBI), Aetiology, 2.1 Biological and endogenous factors, Good Health and Well Being, Brain Injuries, Traumatic, Genome-Wide Association Study, Humans, Mannose-Binding Lectin, Prospective Studies, Transcriptome, Traumatic brain injury, Genome-Wide association study, Outcome, Recovery, Consortia, Genetic Associations In Neurotrauma (GAIN) Consortium, Clinical Sciences, Public Health and Health Services, Clinical sciences
Abstract

BackgroundFactors such as age, pre-injury health, and injury severity, account for less than 35% of outcome variability in traumatic brain injury (TBI). While some residual outcome variability may be attributable to genetic factors, published candidate gene association studies have often been underpowered and subject to publication bias.MethodsWe performed the first genome- and transcriptome-wide association studies (GWAS, TWAS) of genetic effects on outcome in TBI. The study population consisted of 5268 patients from prospective European and US studies, who attended hospital within 24 h of TBI, and satisfied local protocols for computed tomography.FindingsThe estimated heritability of TBI outcome was 0·26. GWAS revealed no genetic variants with genome-wide significance (p < 5 × 10-8), but identified 83 variants in 13 independent loci which met a lower pre-specified sub-genomic statistical threshold (p < 10-5). Similarly, none of the genes tested in TWAS met tissue-wide significance. An exploratory analysis of 75 published candidate variants associated with 28 genes revealed one replicable variant (rs1800450 in the MBL2 gene) which retained significance after correction for multiple comparison (p = 5·24 × 10-4).InterpretationWhile multiple novel loci reached less stringent thresholds, none achieved genome-wide significance. The overall heritability estimate, however, is consistent with the hypothesis that common genetic variation substantially contributes to inter-individual variability in TBI outcome. The meta-analytic approach to the GWAS and the availability of summary data allows for a continuous extension with additional cohorts as data becomes available.FundingA full list of funding bodies that contributed to this study can be found in the Acknowledgements section.

Published
2022

19. Universal scaling laws rule explosive growth inhuman cancers

Author

Pérez-García, Víctor M., Calvo, Gabriel F., Bosque, Jesús J., León-Triana, Odelaisy, Jiménez, Juan, Pérez-Beteta, Julián, Belmonte-Beitia, Juan, Valiente, Manuel, Zhu, Lucía, García-Gómez, Pedro, Sánchez-Gómez, Pilar, Miguel, Esther Hernández-San, Hortigüela, Rafael, Azimzade, Youness, Molina-García, David, Martínez, Álvaro, Rojas, Ángel Acosta, de Mendivil, Ana Ortiz, Vallette, Francois, Schucht, Philippe, Murek, Michael, Pérez-Cano, María, Albillo, David, Martínez, Antonio F. Honguero, Londoño, Germán A. Jiménez, Arana, Estanislao, and Vicente, Ana M. García

Subjects
Quantitative Biology - Tissues and Organs
Abstract

Most physical and other natural systems are complex entities composed of a large number of interacting individual elements. It is a surprising fact that they often obey the so-called scaling laws relating an observable quantity with a measure of the size of the system. Here we describe the discovery of universal superlinear metabolic scaling laws in human cancers. This dependence underpins increasing tumour aggressiveness, due to evolutionary dynamics, which leads to an explosive growth as the disease progresses. We validated this dynamic using longitudinal volumetric data of different histologies from large cohorts of cancer patients. To explain our observations we put forward increasingly complex biologically-inspired mathematical models that captured the key processes governing tumor growth. Our models predicted that the emergence of superlinear allometric scaling laws is an inherently three-dimensional phenomenon. Moreover, the scaling laws thereby identified allowed us to define a set of metabolic metrics with prognostic value, thus providing added clinical utility to the base findings.

Published
2021
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25. Deep learning-based parameter mapping for joint relaxation and diffusion tensor MR Fingerprinting

Author

Pirkl, Carolin M., Gómez, Pedro A., Lipp, Ilona, Buonincontri, Guido, Molina-Romero, Miguel, Sekuboyina, Anjany, Waldmannstetter, Diana, Dannenberg, Jonathan, Endt, Sebastian, Merola, Alberto, Whittaker, Joseph R., Tomassini, Valentina, Tosetti, Michela, Jones, Derek K., Menze, Bjoern H., and Menzel, Marion I.

Subjects
Physics - Medical Physics, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Magnetic Resonance Fingerprinting (MRF) enables the simultaneous quantification of multiple properties of biological tissues. It relies on a pseudo-random acquisition and the matching of acquired signal evolutions to a precomputed dictionary. However, the dictionary is not scalable to higher-parametric spaces, limiting MRF to the simultaneous mapping of only a small number of parameters (proton density, T1 and T2 in general). Inspired by diffusion-weighted SSFP imaging, we present a proof-of-concept of a novel MRF sequence with embedded diffusion-encoding gradients along all three axes to efficiently encode orientational diffusion and T1 and T2 relaxation. We take advantage of a convolutional neural network (CNN) to reconstruct multiple quantitative maps from this single, highly undersampled acquisition. We bypass expensive dictionary matching by learning the implicit physical relationships between the spatiotemporal MRF data and the T1, T2 and diffusion tensor parameters. The predicted parameter maps and the derived scalar diffusion metrics agree well with state-of-the-art reference protocols. Orientational diffusion information is captured as seen from the estimated primary diffusion directions. In addition to this, the joint acquisition and reconstruction framework proves capable of preserving tissue abnormalities in multiple sclerosis lesions.

Published
2020

26. Pathological Computed Tomography Features Associated With Adverse Outcomes After Mild Traumatic Brain Injury

Author

Yuh, Esther L, Jain, Sonia, Sun, Xiaoying, Pisică, Dana, Harris, Mark H, Taylor, Sabrina R, Markowitz, Amy J, Mukherjee, Pratik, Verheyden, Jan, Giacino, Joseph T, Levin, Harvey S, McCrea, Michael, Stein, Murray B, Temkin, Nancy R, Diaz-Arrastia, Ramon, Robertson, Claudia S, Lingsma, Hester F, Okonkwo, David O, Maas, Andrew IR, Manley, Geoffrey T, Amrein, Krisztina, Andelic, Nada, Andreassen, Lasse, Anke, Audny, Antoni, Anna, Audibert, Gérard, Azouvi, Philippe, Azzolini, Maria Luisa, Bartels, Ronald, Barzó, Pál, Beauvais, Romuald, Beer, Ronny, Bellander, Bo-Michael, Belli, Antonio, Benal, Habib, Berardino, Maurizio, Beretta, Luigi, Blaabjerg, Morten, Bragge, Peter, Brazinova, Alexandra, Brinck, Vibeke, Brooker, Joanne, Brorsson, Camilla, Buki, Andras, Bullinger, Monika, Cabeleira, Manuel, Caccioppola, Alessio, Calappi, Emiliana, Calvi, Maria Rosa, Cameron, Peter, Carbayo Lozano, Guillermo, Carbonara, Marco, Castaño‑León, Ana M, Cavallo, Simona, Chevallard, Giorgio, Chieregato, Arturo, Citerio, Giuseppe, Clusmann, Hans, Coburn, Mark, Coles, Jonathan, Cooper, Jamie D, Correia, Marta, Čović, Amra, Curry, Nicola, Czeiter, Endre, Czosnyka, Marek, Dahyot‑Fizelier, Claire, Dark, Paul, Dawes, Helen, De Keyser, Véronique, Degos, Vincent, Della Corte, Francesco, den Boogert, Hugo, Depreitere, Bart, Đilvesi, Đula, Dixit, Abhishek, Donoghue, Emma, Dreier, Jens, Dulière, Guy‑Loup, Ercole, Ari, Esser, Patrick, Ezer, Erzsébet, Fabricius, Martin, Feigin, Valery L, Foks, Kelly, Frisvold, Shirin, Furmanov, Alex, Gagliardo, Pablo, Galanaud, Damien, Gantner, Dashiell, Gao, Guoyi, George, Pradeep, Ghuysen, Alexandre, Giga, Lelde, Glocker, Ben, Golubovic, Jagoš, Gomez, Pedro A, Gratz, Johannes, Gravesteijn, Benjamin, and Grossi, Francesca

Subjects
Biomedical and Clinical Sciences, Neurosciences, Clinical Sciences, Traumatic Head and Spine Injury, Biomedical Imaging, Physical Injury - Accidents and Adverse Effects, Traumatic Brain Injury (TBI), Brain Disorders, Cerebrovascular, 4.2 Evaluation of markers and technologies, Injuries and accidents, Good Health and Well Being, Adult, Aged, Brain Concussion, Cohort Studies, Female, Humans, Intracranial Hemorrhages, Male, Middle Aged, Prognosis, Recovery of Function, Tomography, X-Ray Computed, TRACK-TBI Investigators for the CENTER-TBI Investigators
Abstract

ImportanceA head computed tomography (CT) with positive results for acute intracranial hemorrhage is the gold-standard diagnostic biomarker for acute traumatic brain injury (TBI). In moderate to severe TBI (Glasgow Coma Scale [GCS] scores 3-12), some CT features have been shown to be associated with outcomes. In mild TBI (mTBI; GCS scores 13-15), distribution and co-occurrence of pathological CT features and their prognostic importance are not well understood.ObjectiveTo identify pathological CT features associated with adverse outcomes after mTBI.Design, setting, and participantsThe longitudinal, observational Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study enrolled patients with TBI, including those 17 years and older with GCS scores of 13 to 15 who presented to emergency departments at 18 US level 1 trauma centers between February 26, 2014, and August 8, 2018, and underwent head CT imaging within 24 hours of TBI. Evaluations of CT imaging used TBI Common Data Elements. Glasgow Outcome Scale-Extended (GOSE) scores were assessed at 2 weeks and 3, 6, and 12 months postinjury. External validation of results was performed via the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study. Data analyses were completed from February 2020 to February 2021.ExposuresAcute nonpenetrating head trauma.Main outcomes and measuresFrequency, co-occurrence, and clustering of CT features; incomplete recovery (GOSE scores

Published
2021

27. Ethical Principles and Governance for AI

Author

Francés-Gómez, Pedro, Gordijn, Bert, Series Editor, Roeser, Sabine, Series Editor, Birnbacher, Dieter, Editorial Board Member, Brownsword, Roger, Editorial Board Member, Dempsey, Paul Stephen, Editorial Board Member, Froomkin, Michael, Editorial Board Member, Gutwirth, Serge, Editorial Board Member, Knoppers, Bartha, Editorial Board Member, Laurie, Graeme, Editorial Board Member, Weckert, John, Editorial Board Member, Bovenkerk, Bernice, Editorial Board Member, Copeland, Samantha, Editorial Board Member, Carter, J. Adam, Editorial Board Member, Gardiner, Stephen M., Editorial Board Member, Heersmink, Richard, Editorial Board Member, Hillerbrand, Rafaela, Editorial Board Member, Möller, Niklas, Editorial Board Member, Fahlquist, Jessica Nihle-n, Editorial Board Member, Nyholm, Sven, Editorial Board Member, Saghai, Yashar, Editorial Board Member, Vallor, Shannon, Editorial Board Member, McKinnon, Catriona, Editorial Board Member, Sadowski, Jathan, Editorial Board Member, Lara, Francisco, editor, and Deckers, Jan, editor

Published
2023
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28. One-year employment outcome prediction after traumatic brain injury: A CENTER-TBI study

Author

Åkerlund, Cecilia, Amrein, Krisztina, Andelic, Nada, Andreassen, Lasse, Anke, Audny, Antoni, Anna, Audibert, Gérard, Azouvi, Philippe, Azzolini, Maria Luisa, Bartels, Ronald, Barzó, Pál, Beauvais, Romuald, Beer, Ronny, Bellander, Bo-Michael, Belli, Antonio, Benali, Habib, Berardino, Maurizio, Beretta, Luigi, Blaabjerg, Morten, Bragge, Peter, Brazinova, Alexandra, Brinck, Vibeke, Brooker, Joanne, Brorsson, Camilla, Buki, Andras, Bullinger, Monika, Cabeleira, Manuel, Caccioppola, Alessio, Calappi, Emiliana, Calvi, Maria Rosa, Cameron, Peter, Lozano, Guillermo Carbayo, Carbonara, Marco, Cavallo, Simona, Chevallard, Giorgio, Chieregato, Arturo, Citerio, Giuseppe, Clusmann, Hans, Coburn, Mark, Coles, Jonathan, Cooper, Jamie D., Correia, Marta, Čović, Amra, Curry, Nicola, Czeiter, Endre, Czosnyka, Marek, Dahyot-Fizelier, Claire, Dark, Paul, Dawes, Helen, De Keyser, Véronique, Degos, Vincent, Della Corte, Francesco, Boogert, Hugo den, Depreitere, Bart, Đilvesi, Đula, Dixit, Abhishek, Donoghue, Emma, Dreier, Jens, Dulière, Guy-Loup, Ercole, Ari, Esser, Patrick, Ezer, Erzsébet, Fabricius, Martin, Feigin, Valery L., Foks, Kelly, Frisvold, Shirin, Furmanov, Alex, Gagliardo, Pablo, Galanaud, Damien, Gantner, Dashiell, Gao, Guoyi, George, Pradeep, Ghuysen, Alexandre, Giga, Lelde, Glocker, Ben, Golubovic, Jagoš, Gomez, Pedro A., Gratz, Johannes, Gravesteijn, Benjamin, Grossi, Francesca, Gruen, Russell L., Gupta, Deepak, Haagsma, Juanita A., Haitsma, Iain, Helbok, Raimund, Helseth, Eirik, Horton, Lindsay, Huijben, Jilske, Hutchinson, Peter J., Jacobs, Bram, Jankowski, Stefan, Jarrett, Mike, Jiang, Ji-yao, Johnson, Faye, Jones, Kelly, Karan, Mladen, Kolias, Angelos G., Kompanje, Erwin, Kondziella, Daniel, Kornaropoulos, Evgenios, Koskinen, Lars-Owe, Kovács, Noémi, Kowark, Ana, Lagares, Alfonso, Lanyon, Linda, Laureys, Steven, Lecky, Fiona, Ledoux, Didier, Lefering, Rolf, Legrand, Valerie, Lejeune, Aurelie, Levi, Leon, Lightfoot, Roger, Lingsma, Hester, Maas, Andrew I.R., Castaño-León, Ana M., Maegele, Marc, Majdan, Marek, Manara, Alex, Manley, Geoffrey, Martino, Costanza, Maréchal, Hugues, Mattern, Julia, McMahon, Catherine, Melegh, Béla, Menon, David, Menovsky, Tomas, Mikolic, Ana, Misset, Benoit, Muraleedharan, Visakh, Murray, Lynnette, Negru, Ancuta, Nelson, David, Newcombe, Virginia, Nieboer, Daan, Nyirádi, József, Olubukola, Otesile, Oresic, Matej, Ortolano, Fabrizio, Palotie, Aarno, Parizel, Paul M., Payen, Jean-François, Perera, Natascha, Perlbarg, Vincent, Persona, Paolo, Peul, Wilco, Piippo-Karjalainen, Anna, Pirinen, Matti, Pisica, Dana, Ples, Horia, Polinder, Suzanne, Pomposo, Inigo, Posti, Jussi P., Puybasset, Louis, Radoi, Andreea, Ragauskas, Arminas, Raj, Rahul, Rambadagalla, Malinka, Helmrich, Isabel Retel, Rhodes, Jonathan, Richardson, Sylvia, Richter, Sophie, Ripatti, Samuli, Rocka, Saulius, Roe, Cecilie, Roise, Olav, Rosand, Jonathan, Rosenfeld, Jeffrey V., Rosenlund, Christina, Rosenthal, Guy, Rossaint, Rolf, Rossi, Sandra, Rueckert, Daniel, Rusnák, Martin, Sahuquillo, Juan, Sakowitz, Oliver, Sanchez-Porras, Renan, Sandor, Janos, Schäfer, Nadine, Schmidt, Silke, Schoechl, Herbert, Schoonman, Guus, Schou, Rico Frederik, Schwendenwein, Elisabeth, Sewalt, Charlie, Singh, Ranjit D., Skandsen, Toril, Smielewski, Peter, Sorinola, Abayomi, Stamatakis, Emmanuel, Stanworth, Simon, Stevens, Robert, Stewart, William, Steyerberg, Ewout W., Stocchetti, Nino, Sundström, Nina, Takala, Riikka, Tamás, Viktória, Tamosuitis, Tomas, Taylor, Mark Steven, Thibaut, Aurore, Ao, Braden Te, Tenovuo, Olli, Theadom, Alice, Thomas, Matt, Tibboel, Dick, Timmers, Marjolein, Tolias, Christos, Trapani, Tony, Tudora, Cristina Maria, Unterberg, Andreas, Vajkoczy, Peter, Vallance, Shirley, Valeinis, Egils, Vámos, Zoltán, van der Jagt, Mathieu, Van der Steen, Gregory, van der Naalt, Joukje, van Dijck, Jeroen T.J.M., van Erp, Inge A.M., van Essen, Thomas A., Van Hecke, Wim, van Heugten, Caroline, van Veen, Ernest, Vyvere, Thijs Vande, van Wijk, Roel P.J., Vargiolu, Alessia, Vega, Emmanuel, Velt, Kimberley, Verheyden, Jan, Vespa, Paul M., Vik, Anne, Vilcinis, Rimantas, Volovici, Victor, von Steinbüchel, Nicole, Voormolen, Daphne, Vulekovic, Petar, Wang, Kevin K.W., Whitehouse, Daniel, Wiegers, Eveline, Williams, Guy, Wilson, Lindsay, Winzeck, Stefan, Wolf, Stefan, Yang, Zhihui, Ylén, Peter, Younsi, Alexander, Zeiler, Frederick A., Zelinkova, Veronika, Ziverte, Agate, Zoerle, Tommaso, Van Deynse, Helena, Cools, Wilfried, De Deken, Viktor-Jan, Hubloue, Ives, Tisseghem, Ellen, and Putman, Koen

Published
2024
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31. Clinical descriptors of disease trajectories in patients with traumatic brain injury in the intensive care unit (CENTER-TBI): a multicentre observational cohort study

Author

Åkerlund, Cecilia, Amrein, Krisztina, Andelic, Nada, Andreassen, Lasse, Anke, Audny, Antoni, Anna, Audibert, Gérard, Azouvi, Philippe, Azzolini, Maria Luisa, Bartels, Ronald, Barzó, Pál, Beauvais, Romuald, Beer, Ronny, Bellander, Bo-Michael, Belli, Antonio, Benali, Habib, Berardino, Maurizio, Beretta, Luigi, Blaabjerg, Morten, Bragge, Peter, Brazinova, Alexandra, Brinck, Vibeke, Brooker, Joanne, Brorsson, Camilla, Buki, Andras, Bullinger, Monika, Cabeleira, Manuel, Caccioppola, Alessio, Calappi, Emiliana, Calvi, Maria Rosa, Cameron, Peter, Carbayo Lozano, Guillermo, Carbonara, Marco, Cavallo, Simona, Chevallard, Giorgio, Chieregato, Arturo, Citerio, Giuseppe, Clusmann, Hans, Coburn, Mark, Coles, Jonathan, Cooper, Jamie D., Correia, Marta, Čović, Amra, Curry, Nicola, Czeiter, Endre, Czosnyka, Marek, DahyotFizelier, Claire, Dark, Paul, Dawes, Helen, De Keyser, Véronique, Degos, Vincent, Della Corte, Francesco, den Boogert, Hugo, Depreitere, Bart, Đilvesi, Đula, Dixit, Abhishek, Donoghue, Emma, Dreier, Jens, Dulière, GuyLoup, Ercole, Ari, Esser, Patrick, Ezer, Erzsébet, Fabricius, Martin, Feigin, Valery L., Foks, Kelly, Frisvold, Shirin, Furmanov, Alex, Gagliardo, Pablo, Galanaud, Damien, Gantner, Dashiell, Gao, Guoyi, George, Pradeep, Ghuysen, Alexandre, Giga, Lelde, Glocker, Ben, Golubovic, Jagoš, Gomez, Pedro A., Gratz, Johannes, Gravesteijn, Benjamin, Grossi, Francesca, Gruen, Russell L., Gupta, Deepak, Haagsma, Juanita A., Haitsma, Iain, Helbok, Raimund, Helseth, Eirik, Horton, Lindsay, Huijben, Jilske, Hutchinson, Peter J., Jacobs, Bram, Jankowski, Stefan, Jarrett, Mike, Jiang, Jiyao, Johnson, Faye, Jones, Kelly, Karan, Mladen, Kolias, Angelos G., Kompanje, Erwin, Kondziella, Daniel, Kornaropoulos, Evgenios, Koskinen, LarsOwe, Kovács, Noémi, Kowark, Ana, Lagares, Alfonso, Lanyon, Linda, Laureys, Steven, Lecky, Fiona, Ledoux, Didier, Lefering, Rolf, Legrand, Valerie, Lejeune, Aurelie, Levi, Leon, Lightfoot, Roger, Lingsma, Hester, Maas, Andrew I.R., CastañoLeón, Ana M., Maegele, Marc, Majdan, Marek, Manara, Alex, Manley, Geoffrey, Martino, Costanza, Maréchal, Hugues, Mattern, Julia, McMahon, Catherine, Melegh, Béla, Menon, David, Menovsky, Tomas, Mikolic, Ana, Misset, Benoit, Muraleedharan, Visakh, Murray, Lynnette, Negru, Ancuta, Nelson, David, Newcombe, Virginia, Nieboer, Daan, Nyirádi, József, Olubukola, Otesile, Oresic, Matej, Ortolano, Fabrizio, Palotie, Aarno, Parizel, Paul M., Payen, JeanFrançois, Perera, Natascha, Perlbarg, Vincent, Persona, Paolo, Peul, Wilco, Piippo-Karjalainen, Anna, Pirinen, Matti, Pisica, Dana, Ples, Horia, Polinder, Suzanne, Pomposo, Inigo, Posti, Jussi P., Puybasset, Louis, Radoi, Andreea, Ragauskas, Arminas, Raj, Rahul, Rambadagalla, Malinka, Retel Helmrich, Isabel, Rhodes, Jonathan, Richardson, Sylvia, Richter, Sophie, Ripatti, Samuli, Rocka, Saulius, Roe, Cecilie, Roise, Olav, Rosand, Jonathan, Rosenfeld, Jeffrey V., Rosenlund, Christina, Rosenthal, Guy, Rossaint, Rolf, Rossi, Sandra, Rueckert, Daniel, Rusnák, Martin, Sahuquillo, Juan, Sakowitz, Oliver, SanchezPorras, Renan, Sandor, Janos, Schäfer, Nadine, Schmidt, Silke, Schoechl, Herbert, Schoonman, Guus, Schou, Rico Frederik, Schwendenwein, Elisabeth, Sewalt, Charlie, Singh, Ranjit D., Skandsen, Toril, Smielewski, Peter, Sorinola, Abayomi, Stamatakis, Emmanuel, Stanworth, Simon, Stevens, Robert, Stewart, William, Steyerberg, Ewout W., Stocchetti, Nino, Sundström, Nina, Takala, Riikka, Tamás, Viktória, Tamosuitis, Tomas, Taylor, Mark Steven, Ao, Braden Te, Tenovuo, Olli, Theadom, Alice, Thomas, Matt, Tibboel, Dick, Timmers, Marjolein, Tolias, Christos, Trapani, Tony, Tudora, Cristina Maria, Unterberg, Andreas, Vajkoczy, Peter, Vallance, Shirley, Valeinis, Egils, Vámos, Zoltán, van der Jagt, Mathieu, Van der Steen, Gregory, van der Naalt, Joukje, van Dijck, Jeroen T.J.M., van Erp, Inge A.M., van Essen, Thomas A., Van Hecke, Wim, van Heugten, Caroline, Van Praag, Dominique, van Veen, Ernest, Vande Vyvere, Thijs, van Wijk, Roel P.J., Vargiolu, Alessia, Vega, Emmanuel, Velt, Kimberley, Verheyden, Jan, Vespa, Paul M., Vik, Anne, Vilcinis, Rimantas, Volovici, Victor, von Steinbüchel, Nicole, Voormolen, Daphne, Vulekovic, Petar, Wang, Kevin K.W., Whitehouse, Daniel, Wiegers, Eveline, Williams, Guy, Wilson, Lindsay, Winzeck, Stefan, Wolf, Stefan, Yang, Zhihui, Ylén, Peter, Younsi, Alexander, Zeiler, Frederick A., Zelinkova, Veronika, Ziverte, Agate, Zoerle, Tommaso, Åkerlund, Cecilia A I, Holst, Anders, Bhattacharyay, Shubhayu, Steyerberg, Ewout, Menon, David K, and Nelson, David W

Published
2024
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33. Compressive MRI quantification using convex spatiotemporal priors and deep auto-encoders

Author

Golbabaee, Mohammad, Buonincontri, Guido, Pirkl, Carolin, Menzel, Marion, Menze, Bjoern, Davies, Mike, and Gomez, Pedro

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Physics - Medical Physics
Abstract

We propose a dictionary-matching-free pipeline for multi-parametric quantitative MRI image computing. Our approach has two stages based on compressed sensing reconstruction and deep learned quantitative inference. The reconstruction phase is convex and incorporates efficient spatiotemporal regularisations within an accelerated iterative shrinkage algorithm. This minimises the under-sampling (aliasing) artefacts from aggressively short scan times. The learned quantitative inference phase is purely trained on physical simulations (Bloch equations) that are flexible for producing rich training samples. We propose a deep and compact auto-encoder network with residual blocks in order to embed Bloch manifold projections through multiscale piecewise affine approximations, and to replace the nonscalable dictionary-matching baseline. Tested on a number of datasets we demonstrate effectiveness of the proposed scheme for recovering accurate and consistent quantitative information from novel and aggressively subsampled 2D/3D quantitative MRI acquisition protocols.

Published
2020

34. Rapid three-dimensional multiparametric MRI with quantitative transient-state imaging

Author

Gómez, Pedro A., Cencini, Matteo, Golbabaee, Mohammad, Schulte, Rolf F., Pirkl, Carolin, Horvath, Izabela, Fallo, Giada, Peretti, Luca, Tosetti, Michela, Menze, Bjoern H., and Buonincontri, Guido

Subjects
Physics - Medical Physics, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Novel methods for quantitative, transient-state multiparametric imaging are increasingly being demonstrated for assessment of disease and treatment efficacy. Here, we build on these by assessing the most common Non-Cartesian readout trajectories (2D/3D radials and spirals), demonstrating efficient anti-aliasing with a k-space view-sharing technique, and proposing novel methods for parameter inference with neural networks that incorporate the estimation of proton density. Our results show good agreement with gold standard and phantom references for all readout trajectories at 1.5T and 3T. Parameters inferred with the neural network were within 6.58% difference from the parameters inferred with a high-resolution dictionary. Concordance correlation coefficients were above 0.92 and the normalized root mean squared error ranged between 4.2% - 12.7% with respect to gold-standard phantom references for T1 and T2. In vivo acquisitions demonstrate sub-millimetric isotropic resolution in under five minutes with reconstruction and inference times < 7 minutes. Our 3D quantitative transient-state imaging approach could enable high-resolution multiparametric tissue quantification within clinically acceptable acquisition and reconstruction times., Comment: 43 pages, 12 Figures, 5 Tables

Published
2020

35. Comparative effectiveness of decompressive craniectomy versus craniotomy for traumatic acute subdural hematoma (CENTER-TBI): an observational cohort study

Author

Åkerlund, Cecilia, Amrein, Krisztina, Andelic, Nada, Andreassen, Lasse, Anke, Audny, Antoni, Anna, Audibert, Gérard, Azouvi, Philippe, Azzolini, Maria Luisa, Bartels, Ronald, Barzó, Pál, Beauvais, Romuald, Beer, Ronny, Bellander, Bo-Michael, Belli, Antonio, Benali, Habib, Berardino, Maurizio, Beretta, Luigi, Blaabjerg, Morten, Bragge, Peter, Brazinova, Alexandra, Brinck, Vibeke, Brooker, Joanne, Brorsson, Camilla, Buki, Andras, Bullinger, Monika, Cabeleira, Manuel, Caccioppola, Alessio, Calappi, Emiliana, Calvi, Maria Rosa, Cameron, Peter, Lozano, Guillermo Carbayo, Carbonara, Marco, Castaño-León, Ana M., Cavallo, Simona, Chevallard, Giorgio, Chieregato, Arturo, Citerio, Giuseppe, Clusmann, Hans, Coburn, Mark Steven, Coles, Jonathan, Cooper, Jamie D., Correia, Marta, Čović, Amra, Curry, Nicola, Czeiter, Endre, Czosnyka, Marek, Dahyot-Fizelier, Claire, Dark, Paul, Dawes, Helen, De Keyser, Véronique, Degos, Vincent, Della Corte, Francesco, Boogert, Hugo den, Depreitere, Bart, Đilvesi, Đula, Dixit, Abhishek, Donoghue, Emma, Dreier, Jens, Dulière, Guy-Loup, Ercole, Ari, Esser, Patrick, Ezer, Erzsébet, Fabricius, Martin, Feigin, Valery L., Foks, Kelly, Frisvold, Shirin, Furmanov, Alex, Gagliardo, Pablo, Galanaud, Damien, Gantner, Dashiell, Gao, Guoyi, George, Pradeep, Ghuysen, Alexandre, Giga, Lelde, Glocker, Ben, Golubović, Jagoš, Gomez, Pedro A., Gratz, Johannes, Gravesteijn, Benjamin, Grossi, Francesca, Gruen, Russell L., Gupta, Deepak, Haagsma, Juanita A., Haitsma, Iain, Helbok, Raimund, Helseth, Eirik, Horton, Lindsay, Huijben, Jilske, Hutchinson, Peter J., Jacobs, Bram, Jankowski, Stefan, Jarrett, Mike, Jiang, Ji-yao, Johnson, Faye, Jones, Kelly, Karan, Mladen, Kolias, Angelos G., Kompanje, Erwin, Kondziella, Daniel, Kornaropoulos, Evgenios, Koskinen, Lars-Owe, Kovács, Noémi, Lagares, Alfonso, Lanyon, Linda, Laureys, Steven, Lecky, Fiona, Ledoux, Didier, Lefering, Rolf, Legrand, Valerie, Lejeune, Aurelie, Levi, Leon, Lightfoot, Roger, Lingsma, Hester, Maas, Andrew I.R., Maegele, Marc, Majdan, Marek, Manara, Alex, Manley, Geoffrey, Maréchal, Hugues, Martino, Costanza, Mattern, Julia, McMahon, Catherine, Melegh, Béla, Menon, David, Menovsky, Tomas, Mikolic, Ana, Misset, Benoit, Muraleedharan, Visakh, Murray, Lynnette, Nair, Nandesh, Negru, Ancuta, Nelson, David, Newcombe, Virginia, Nieboer, Daan, Nyirádi, József, Oresic, Matej, Ortolano, Fabrizio, Otesile, Olubukola, Palotie, Aarno, Parizel, Paul M., Payen, Jean-François, Perera, Natascha, Perlbarg, Vincent, Persona, Paolo, Peul, Wilco, Piippo-Karjalainen, Anna, Pirinen, Matti, Pisica, Dana, Ples, Horia, Polinder, Suzanne, Pomposo, Inigo, Posti, Jussi P., Puybasset, Louis, Rădoi, Andreea, Ragauskas, Arminas, Raj, Rahul, Rambadagalla, Malinka, Rehorčíková, Veronika, Helmrich, Isabel Retel, Rhodes, Jonathan, Richardson, Sylvia, Richter, Sophie, Ripatti, Samuli, Rocka, Saulius, Roe, Cecilie, Roise, Olav, Rosand, Jonathan, Rosenfeld, Jeffrey, Rosenlund, Christina, Rosenthal, Guy, Rossaint, Rolf, Rossi, Sandra, Rueckert, Daniel, Rusnák, Martin, Sahuquillo, Juan, Sakowitz, Oliver, Sanchez-Porras, Renan, Sandor, Janos, Schäfer, Nadine, Schmidt, Silke, Schoechl, Herbert, Schoonman, Guus, Schou, Rico Frederik, Schwendenwein, Elisabeth, Sewalt, Charlie, Skandsen, Toril, Smielewski, Peter, Sorinola, Abayomi, Stamatakis, Emmanuel, Stanworth, Simon, Kowark, Ana, Stevens, Robert, Stewart, William, Steyerberg, Ewout W., Stocchetti, Nino, Sundström, Nina, Takala, Riikka, Tamás, Viktória, Tamosuitis, Tomas, Taylor, Mark Steven, Ao, Braden Te, Tenovuo, Olli, Theadom, Alice, Thomas, Matt, Tibboel, Dick, Timmers, Marjolijn, Tolias, Christos, Trapani, Tony, Tudora, Cristina Maria, Unterberg, Andreas, Vajkoczy, Peter, Valeinis, Egils, Vallance, Shirley, Vámos, Zoltán, Van der Jagt, Mathieu, van der Naalt, Joukje, Van der Steen, Gregory, van Dijck, Jeroen T.J.M., van Essen, Thomas A., Van Hecke, Wim, van Heugten, Caroline, Van Praag, Dominique, Van Veen, Ernest, van Wijk, Roel, Vyvere, Thijs Vande, Vargiolu, Alessia, Vega, Emmanuel, Velt, Kimberley, Verheyden, Jan, Vespa, Paul M., Vik, Anne, Vilcinis, Rimantas, Volovici, Victor, von Steinbüchel, Nicole, Voormolen, Daphne, Vulekovic, Petar, Wang, Kevin K.W., Wiegers, Eveline, Williams, Guy, Wilson, Lindsay, Winzeck, Stefan, Wolf, Stefan, Yang, Zhihui, Ylén, Peter, Younsi, Alexander, Zeiler, Frederick A., Ziverte, Agate, Zoerle, Tommaso, van Erp, Inge A.M., Lingsma, Hester F., Pisică, Dana, Yue, John K., Singh, Ranjit D., Kolias, Angelos, Peppel, Lianne D., Heijenbrok-Kal, Majanka, Ribbers, Gerard M., Menon, David K., Hutchinson, Peter J.A., Manley, Geoffrey T., de Ruiter, Godard C.W., and Peul, Wilco C.

Published
2023
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36. A Fully Convolutional Network for MR Fingerprinting

Author

Chen, Dongdong, Golbabaee, Mohammad, Gomez, Pedro A., Menzel, Marion I., and Davies, Mike E.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Magnetic Resonance Fingerprinting (MRF) methods typically rely on dictionary matching to map the temporal MRF signals to quantitative tissue parameters. These methods suffer from heavy storage and computation requirements as the dictionary size grows. To address these issues, we proposed an end to end fully convolutional neural network for MRF reconstruction (MRF-FCNN), which firstly employ linear dimensionality reduction and then use neural network to project the data into the tissue parameters manifold space. Experiments on the MAGIC data demonstrate the effectiveness of the method., Comment: The Signal Processing with Adaptive Sparse Structured Representations (SPARS'2019) workshop

Published
2019

37. Multi-shot Echo Planar Imaging for accelerated Cartesian MR Fingerprinting: an alternative to conventional spiral MR Fingerprinting

Author

Benjamin, Arnold Julian Vinoj, Gomez, Pedro A., Golbabaee, Mohammad, Mahbub, Zaid, Sprenger, Tim, Davies, Marion I. Menzel Michael, and Marshall, Ian

Subjects
Physics - Medical Physics, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Purpose: To develop an accelerated Cartesian MRF implementation using a multi-shot EPI sequence for rapid simultaneous quantification of T1 and T2 parameters. Methods: The proposed Cartesian MRF method involved the acquisition of highly subsampled MR images using a 16-shot EPI readout. A linearly varying flip angle train was used for rapid, simultaneous T1 and T2 quantification. The accuracy of parametric map estimations were improved by using an iterative projection algorithm. The results were compared to a conventional spiral MRF implementation. The acquisition time per slice was 8s and this method was validated on a phantom and a healthy volunteer brain in vivo. Results: Joint T1 and T2 estimations using the 16-shot EPI readout are in good agreement with the spiral implementation using the same acquisition parameters (deviation less than 3% for T1 and less than 4% for T2) for the healthy volunteer brain. The T1 and T2 values also agree with the conventional values previously reported in the literature. The visual quality of the multi-parametric maps generated by the multi-shot EPI-MRF and spiral-MRF implementations were comparable. Conclusion: The multi-shot EPI-MRF method generated accurate quantitative multi-parametric maps similar to conventional Spiral - MRF. This multi-shot approach achieved provides an alternative for performing MRF using an accelerated Cartesian readout, thereby increasing the potential usability of MRF., Comment: 12 pages, 11 main figures, 2 supplementry figures, preprint of accepted abstract

Published
2019
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40. Deep MR Fingerprinting with total-variation and low-rank subspace priors

Author

Golbabaee, Mohammad, Pirkl, Carolin M., Menzel, Marion I., Buonincontri, Guido, and Gómez, Pedro A.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Deep learning (DL) has recently emerged to address the heavy storage and computation requirements of the baseline dictionary-matching (DM) for Magnetic Resonance Fingerprinting (MRF) reconstruction. Fed with non-iterated back-projected images, the network is unable to fully resolve spatially-correlated corruptions caused from the undersampling artefacts. We propose an accelerated iterative reconstruction to minimize these artefacts before feeding into the network. This is done through a convex regularization that jointly promotes spatio-temporal regularities of the MRF time-series. Except for training, the rest of the parameter estimation pipeline is dictionary-free. We validate the proposed approach on synthetic and in-vivo datasets.

Published
2019

41. Designing contrasts for rapid, simultaneous parameter quantification and flow visualization with quantitative transient-state imaging

Author

Gómez, Pedro A., Molina-Romero, Miguel, Buonincontri, Guido, Menzel, Marion I., and Menze, Bjoern H.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Magnetic resonance imaging (MRI) is a remarkably powerful diagnostic technique: it generates wide-ranging information for the non-invasive study of tissue anatomy and physiology. Complementary data is normally obtained in separate measurements, either as contrast-weighted images, which are fast and simple to acquire, or as quantitative parametric maps, which offer an absolute quantification of underlying biophysical effects, such as relaxation times or flow. Here, we demonstrate how to acquire and reconstruct data in a transient-state with a dual purpose: 1 - to generate contrast-weighted images that can be adjusted to emphasise clinically relevant image biomarkers; exemplified with signal modulation according to flow to obtain angiography information, and 2 - to simultaneously infer multiple quantitative parameters with a single, highly accelerated acquisition. This is a achieved by introducing three novel elements: a model that accounts for flowing blood, a method for sequence design that incorporates both parameter encoding and signal contrast, and the reconstruction of temporally resolved contrast-weighted images. From these images we simultaneously obtain angiography projections and multiple quantitative maps. By doing so, we increase the amount of clinically relevant data without adding measurement time, creating new dimensions for biomarker exploration and adding value to MR examinations for patients and clinicians alike.

Published
2019

48. Balanced multi-shot EPI for accelerated Cartesian MRF: An alternative to spiral MRF

Author

Benjamin, Arnold Julian Vinoj, Gómez, Pedro A., Golbabaee, Mohammad, Sprenger, Tim, Menzel, Marion I., Davies, Mike E., and Marshall, Ian

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Machine Learning
Abstract

The main purpose of this study is to show that a highly accelerated Cartesian MRF scheme using a multi-shot EPI readout (i.e. multi-shot EPI-MRF) can produce good quality multi-parametric maps such as T1, T2 and proton density (PD) in a sufficiently short scan duration that is similar to conventional MRF. This multi-shot approach allows considerable subsampling while traversing the entire k-space trajectory, can yield better SNR, reduced blurring, less distortion and can also be used to collect higher resolution data compared to existing single-shot EPI-MRF implementations. The generated parametric maps are compared to an accelerated spiral MRF implementation with the same acquisition parameters to evaluate the performance of this method. Additionally, an iterative reconstruction algorithm is applied to improve the accuracy of parametric map estimations and the fast convergence of EPI-MRF is also demonstrated., Comment: Proceedings of the Joint Annual Meeting ISMRM-ESMRMB 2018 - Paris

Published
2018

49. Geometry of Deep Learning for Magnetic Resonance Fingerprinting

Author

Golbabaee, Mohammad, Chen, Dongdong, Gómez, Pedro A., Menzel, Marion I., and Davies, Mike E.

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Current popular methods for Magnetic Resonance Fingerprint (MRF) recovery are bottlenecked by the heavy storage and computation requirements of a dictionary-matching (DM) step due to the growing size and complexity of the fingerprint dictionaries in multi-parametric quantitative MRI applications. In this paper we study a deep learning approach to address these shortcomings. Coupled with a dimensionality reduction first layer, the proposed MRF-Net is able to reconstruct quantitative maps by saving more than 60 times in memory and computations required for a DM baseline. Fine-grid manifold enumeration i.e. the MRF dictionary is only used for training the network and not during image reconstruction. We show that the MRF-Net provides a piece-wise affine approximation to the Bloch response manifold projection and that rather than memorizing the dictionary, the network efficiently clusters this manifold and learns a set of hierarchical matched-filters for affine regression of the NMR characteristics in each segment.

Published
2018

50. Greedy Approximate Projection for Magnetic Resonance Fingerprinting with Partial Volumes

Author

Duarte, Roberto, Repetti, Audrey, Gómez, Pedro A., Davies, Mike, and Wiaux, Yves

Subjects
Electrical Engineering and Systems Science - Image and Video Processing
Abstract

In quantitative Magnetic Resonance Imaging, traditional methods suffer from the so-called Partial Volume Effect (PVE) due to spatial resolution limitations. As a consequence of PVE, the parameters of the voxels containing more than one tissue are not correctly estimated. Magnetic Resonance Fingerprinting (MRF) is not an exception. The existing methods addressing PVE are neither scalable nor accurate. We propose to formulate the recovery of multiple tissues per voxel as a nonconvex constrained least-squares minimisation problem. To solve this problem, we develop a memory efficient, greedy approximate projected gradient descent algorithm, dubbed GAP-MRF. Our method adaptively finds the regions of interest on the manifold of fingerprints defined by the MRF sequence. We generalise our method to compensate for phase errors appearing in the model, using an alternating minimisation approach. We show, through simulations on synthetic data with PVE, that our algorithm outperforms state-of-the-art methods. Our approach is validated on the EUROSPIN phantom and on in vivo datasets.

Published
2018
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