Author: "Adluru A" / Publication Type: Electronic Resources - Searchworks@Jio Institute Digital Library Search Results

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23 results on '"Adluru A"'

1. Tractography passes the test:Results from the diffusion-simulated connectivity (disco) challenge

Author: Girard, Gabriel, Rafael-Patiño, Jonathan, Truffet, Raphaël, Aydogan, Dogu Baran, Adluru, Nagesh, Nair, Veena A., Prabhakaran, Vivek, Bendlin, Barbara B., Alexander, Andrew L., Bosticardo, Sara, Gabusi, Ilaria, Ocampo-Pineda, Mario, Battocchio, Matteo, Piskorova, Zuzana, Bontempi, Pietro, Schiavi, Simona, Daducci, Alessandro, Stafiej, Aleksandra, Ciupek, Dominika, Bogusz, Fabian, Pieciak, Tomasz, Frigo, Matteo, Sedlar, Sara, Deslauriers-Gauthier, Samuel, Kojčić, Ivana, Zucchelli, Mauro, Laghrissi, Hiba, Ji, Yang, Deriche, Rachid, Schilling, Kurt G., Landman, Bennett A., Cacciola, Alberto, Basile, Gianpaolo Antonio, Bertino, Salvatore, Newlin, Nancy, Kanakaraj, Praitayini, Rheault, Francois, Filipiak, Patryk, Shepherd, Timothy M., Lin, Ying Chia, Placantonakis, Dimitris G., Boada, Fernando E., Baete, Steven H., Hernández-Gutiérrez, Erick, Ramírez-Manzanares, Alonso, Coronado-Leija, Ricardo, Stack-Sánchez, Pablo, Concha, Luis, Descoteaux, Maxime, Mansour L．, Sina, Seguin, Caio, Zalesky, Andrew, Marshall, Kenji, Canales-Rodríguez, Erick J., Wu, Ye, Ahmad, Sahar, Yap, Pew Thian, Théberge, Antoine, Gagnon, Florence, Massi, Frédéric, Fischi-Gomez, Elda, Gardier, Rémy, Haro, Juan Luis Villarreal, Pizzolato, Marco, Caruyer, Emmanuel, Thiran, Jean Philippe, Girard, Gabriel, Rafael-Patiño, Jonathan, Truffet, Raphaël, Aydogan, Dogu Baran, Adluru, Nagesh, Nair, Veena A., Prabhakaran, Vivek, Bendlin, Barbara B., Alexander, Andrew L., Bosticardo, Sara, Gabusi, Ilaria, Ocampo-Pineda, Mario, Battocchio, Matteo, Piskorova, Zuzana, Bontempi, Pietro, Schiavi, Simona, Daducci, Alessandro, Stafiej, Aleksandra, Ciupek, Dominika, Bogusz, Fabian, Pieciak, Tomasz, Frigo, Matteo, Sedlar, Sara, Deslauriers-Gauthier, Samuel, Kojčić, Ivana, Zucchelli, Mauro, Laghrissi, Hiba, Ji, Yang, Deriche, Rachid, Schilling, Kurt G., Landman, Bennett A., Cacciola, Alberto, Basile, Gianpaolo Antonio, Bertino, Salvatore, Newlin, Nancy, Kanakaraj, Praitayini, Rheault, Francois, Filipiak, Patryk, Shepherd, Timothy M., Lin, Ying Chia, Placantonakis, Dimitris G., Boada, Fernando E., Baete, Steven H., Hernández-Gutiérrez, Erick, Ramírez-Manzanares, Alonso, Coronado-Leija, Ricardo, Stack-Sánchez, Pablo, Concha, Luis, Descoteaux, Maxime, Mansour L．, Sina, Seguin, Caio, Zalesky, Andrew, Marshall, Kenji, Canales-Rodríguez, Erick J., Wu, Ye, Ahmad, Sahar, Yap, Pew Thian, Théberge, Antoine, Gagnon, Florence, Massi, Frédéric, Fischi-Gomez, Elda, Gardier, Rémy, Haro, Juan Luis Villarreal, Pizzolato, Marco, Caruyer, Emmanuel, and Thiran, Jean Philippe
Abstract: Estimating structural connectivity from diffusion-weighted magnetic resonance imaging is a challenging task, partly due to the presence of false-positive connections and the misestimation of connection weights. Building on previous efforts, the MICCAI-CDMRI Diffusion-Simulated Connectivity (DiSCo) challenge was carried out to evaluate state-of-the-art connectivity methods using novel large-scale numerical phantoms. The diffusion signal for the phantoms was obtained from Monte Carlo simulations. The results of the challenge suggest that methods selected by the 14 teams participating in the challenge can provide high correlations between estimated and ground-truth connectivity weights, in complex numerical environments. Additionally, the methods used by the participating teams were able to accurately identify the binary connectivity of the numerical dataset. However, specific false positive and false negative connections were consistently estimated across all methods. Although the challenge dataset doesn't capture the complexity of a real brain, it provided unique data with known macrostructure and microstructure ground-truth properties to facilitate the development of connectivity estimation methods.
Published: 2023

2. Vision Backbone Enhancement via Multi-Stage Cross-Scale Attention

Author: Shang, Liang, Liu, Yanli, Lou, Zhengyang, Quan, Shuxue, Adluru, Nagesh, Guan, Bochen, Sethares, William A., Shang, Liang, Liu, Yanli, Lou, Zhengyang, Quan, Shuxue, Adluru, Nagesh, Guan, Bochen, and Sethares, William A.
Abstract: Convolutional neural networks (CNNs) and vision transformers (ViTs) have achieved remarkable success in various vision tasks. However, many architectures do not consider interactions between feature maps from different stages and scales, which may limit their performance. In this work, we propose a simple add-on attention module to overcome these limitations via multi-stage and cross-scale interactions. Specifically, the proposed Multi-Stage Cross-Scale Attention (MSCSA) module takes feature maps from different stages to enable multi-stage interactions and achieves cross-scale interactions by computing self-attention at different scales based on the multi-stage feature maps. Our experiments on several downstream tasks show that MSCSA provides a significant performance boost with modest additional FLOPs and runtime.
Published: 2023

3. Age-related differences in white matter microstructure measured by advanced diffusion MRI in healthy older adults at risk for Alzheimer’s disease

Author: Motovylyak, Alice, Vogt, Nicholas M., Adluru, Nagesh, Ma, Yue, Wang, Rui, Oh, Jennifer M., Kecskemeti, Steven R., Alexander, Andrew L., Dean, Douglas C., Gallagher, Catherine L., Sager, Mark A., Hermann, Bruce P., Rowley, Howard A., Johnson, Sterling C., Asthana, Sanjay, Bendlin, Barbara B., Okonkwo, Ozioma C., Motovylyak, Alice, Vogt, Nicholas M., Adluru, Nagesh, Ma, Yue, Wang, Rui, Oh, Jennifer M., Kecskemeti, Steven R., Alexander, Andrew L., Dean, Douglas C., Gallagher, Catherine L., Sager, Mark A., Hermann, Bruce P., Rowley, Howard A., Johnson, Sterling C., Asthana, Sanjay, Bendlin, Barbara B., and Okonkwo, Ozioma C.
Abstract: Neurite orientation dispersion and density imaging (NODDI) is an advanced diffusion imaging technique, which can detect more distinct microstructural features compared to conventional Diffusion Tensor Imaging (DTI). NODDI allows the signal to be divided into multiple water compartments and derive measures for orientation dispersion index (ODI), neurite density index (NDI) and volume fraction of isotropic diffusion compartment (FISO). This study aimed to investigate which diffusion metric—fractional anisotropy (FA), mean diffusivity (MD), NDI, ODI, or FISO—is most influenced by aging and reflects cognitive function in a population of healthy older adults at risk for Alzheimer’s disease (AD). Age was significantly associated with all but one diffusion parameters and regions of interest. NDI and MD in the cingulate region adjacent to the cingulate cortex showed a significant association with a composite measure of Executive Function and was proven to partially mediate the relationship between aging and Executive Function decline. These results suggest that both DTI and NODDI parameters are sensitive to age-related differences in white matter regions vulnerable to aging, particularly among older adults at risk for AD.
Published: 2022
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4. Age-related differences in white matter microstructure measured by advanced diffusion MRI in healthy older adults at risk for Alzheimer’s disease

Author: Motovylyak, Alice, Vogt, Nicholas M., Adluru, Nagesh, Ma, Yue, Wang, Rui, Oh, Jennifer M., Kecskemeti, Steven R., Alexander, Andrew L., Dean, Douglas C., Gallagher, Catherine L., Sager, Mark A., Hermann, Bruce P., Rowley, Howard A., Johnson, Sterling C., Asthana, Sanjay, Bendlin, Barbara B., Okonkwo, Ozioma C., Motovylyak, Alice, Vogt, Nicholas M., Adluru, Nagesh, Ma, Yue, Wang, Rui, Oh, Jennifer M., Kecskemeti, Steven R., Alexander, Andrew L., Dean, Douglas C., Gallagher, Catherine L., Sager, Mark A., Hermann, Bruce P., Rowley, Howard A., Johnson, Sterling C., Asthana, Sanjay, Bendlin, Barbara B., and Okonkwo, Ozioma C.
Abstract: Neurite orientation dispersion and density imaging (NODDI) is an advanced diffusion imaging technique, which can detect more distinct microstructural features compared to conventional Diffusion Tensor Imaging (DTI). NODDI allows the signal to be divided into multiple water compartments and derive measures for orientation dispersion index (ODI), neurite density index (NDI) and volume fraction of isotropic diffusion compartment (FISO). This study aimed to investigate which diffusion metric—fractional anisotropy (FA), mean diffusivity (MD), NDI, ODI, or FISO—is most influenced by aging and reflects cognitive function in a population of healthy older adults at risk for Alzheimer’s disease (AD). Age was significantly associated with all but one diffusion parameters and regions of interest. NDI and MD in the cingulate region adjacent to the cingulate cortex showed a significant association with a composite measure of Executive Function and was proven to partially mediate the relationship between aging and Executive Function decline. These results suggest that both DTI and NODDI parameters are sensitive to age-related differences in white matter regions vulnerable to aging, particularly among older adults at risk for AD.
Published: 2022
Full Text: View/download PDF

5. Biomedical image analysis competitions: The state of current participation practice

Author: Eisenmann, Matthias, Reinke, Annika, Weru, Vivienn, Tizabi, Minu Dietlinde, Isensee, Fabian, Adler, Tim J., Godau, Patrick, Cheplygina, Veronika, Kozubek, Michal, Ali, Sharib, Gupta, Anubha, Kybic, Jan, Noble, Alison, de Solórzano, Carlos Ortiz, Pachade, Samiksha, Petitjean, Caroline, Sage, Daniel, Wei, Donglai, Wilden, Elizabeth, Alapatt, Deepak, Andrearczyk, Vincent, Baid, Ujjwal, Bakas, Spyridon, Balu, Niranjan, Bano, Sophia, Bawa, Vivek Singh, Bernal, Jorge, Bodenstedt, Sebastian, Casella, Alessandro, Choi, Jinwook, Commowick, Olivier, Daum, Marie, Depeursinge, Adrien, Dorent, Reuben, Egger, Jan, Eichhorn, Hannah, Engelhardt, Sandy, Ganz, Melanie, Girard, Gabriel, Hansen, Lasse, Heinrich, Mattias, Heller, Nicholas, Hering, Alessa, Huaulmé, Arnaud, Kim, Hyunjeong, Landman, Bennett, Li, Hongwei Bran, Li, Jianning, Ma, Jun, Martel, Anne, Martín-Isla, Carlos, Menze, Bjoern, Nwoye, Chinedu Innocent, Oreiller, Valentin, Padoy, Nicolas, Pati, Sarthak, Payette, Kelly, Sudre, Carole, van Wijnen, Kimberlin, Vardazaryan, Armine, Vercauteren, Tom, Wagner, Martin, Wang, Chuanbo, Yap, Moi Hoon, Yu, Zeyun, Yuan, Chun, Zenk, Maximilian, Zia, Aneeq, Zimmerer, David, Bao, Rina, Choi, Chanyeol, Cohen, Andrew, Dzyubachyk, Oleh, Galdran, Adrian, Gan, Tianyuan, Guo, Tianqi, Gupta, Pradyumna, Haithami, Mahmood, Ho, Edward, Jang, Ikbeom, Li, Zhili, Luo, Zhengbo, Lux, Filip, Makrogiannis, Sokratis, Müller, Dominik, Oh, Young-tack, Pang, Subeen, Pape, Constantin, Polat, Gorkem, Reed, Charlotte Rosalie, Ryu, Kanghyun, Scherr, Tim, Thambawita, Vajira, Wang, Haoyu, Wang, Xinliang, Xu, Kele, Yeh, Hung, Yeo, Doyeob, Yuan, Yixuan, Zeng, Yan, Zhao, Xin, Abbing, Julian, Adam, Jannes, Adluru, Nagesh, Agethen, Niklas, Ahmed, Salman, Khalil, Yasmina Al, Alenyà, Mireia, Alhoniemi, Esa, An, Chengyang, Anwar, Talha, Arega, Tewodros Weldebirhan, Avisdris, Netanell, Aydogan, Dogu Baran, Bai, Yingbin, Calisto, Maria Baldeon, Basaran, Berke Doga, Beetz, Marcel, Bian, Cheng, Bian, Hao, Blansit, Kevin, Bloch, Louise, Bohnsack, Robert, Bosticardo, Sara, Breen, Jack, Brudfors, Mikael, Brüngel, Raphael, Cabezas, Mariano, Cacciola, Alberto, Chen, Zhiwei, Chen, Yucong, Chen, Daniel Tianming, Cho, Minjeong, Choi, Min-Kook, Xie, Chuantao Xie Chuantao, Cobzas, Dana, Cohen-Adad, Julien, Acero, Jorge Corral, Das, Sujit Kumar, de Oliveira, Marcela, Deng, Hanqiu, Dong, Guiming, Doorenbos, Lars, Efird, Cory, Escalera, Sergio, Fan, Di, Serj, Mehdi Fatan, Fenneteau, Alexandre, Fidon, Lucas, Filipiak, Patryk, Finzel, René, Freitas, Nuno R., Friedrich, Christoph M., Fulton, Mitchell, Gaida, Finn, Galati, Francesco, Galazis, Christoforos, Gan, Chang Hee, Gao, Zheyao, Gao, Shengbo, Gazda, Matej, Gerats, Beerend, Getty, Neil, Gibicar, Adam, Gifford, Ryan, Gohil, Sajan, Grammatikopoulou, Maria, Grzech, Daniel, Güley, Orhun, Günnemann, Timo, Guo, Chunxu, Guy, Sylvain, Ha, Heonjin, Han, Luyi, Han, Il Song, Hatamizadeh, Ali, He, Tian, Heo, Jimin, Hitziger, Sebastian, Hong, SeulGi, Hong, SeungBum, Huang, Rian, Huang, Ziyan, Huellebrand, Markus, Huschauer, Stephan, Hussain, Mustaffa, Inubushi, Tomoo, Polat, Ece Isik, Jafaritadi, Mojtaba, Jeong, SeongHun, Jian, Bailiang, Jiang, Yuanhong, Jiang, Zhifan, Jin, Yueming, Joshi, Smriti, Kadkhodamohammadi, Abdolrahim, Kamraoui, Reda Abdellah, Kang, Inha, Kang, Junghwa, Karimi, Davood, Khademi, April, Khan, Muhammad Irfan, Khan, Suleiman A., Khantwal, Rishab, Kim, Kwang-Ju, Kline, Timothy, Kondo, Satoshi, Kontio, Elina, Krenzer, Adrian, Kroviakov, Artem, Kuijf, Hugo, Kumar, Satyadwyoom, La Rosa, Francesco, Lad, Abhi, Lee, Doohee, Lee, Minho, Lena, Chiara, Li, Hao, Li, Ling, Li, Xingyu, Liao, Fuyuan, Liao, KuanLun, Oliveira, Arlindo Limede, Lin, Chaonan, Lin, Shan, Linardos, Akis, Linguraru, Marius George, Liu, Han, Liu, Tao, Liu, Di, Liu, Yanling, Lourenço-Silva, João, Lu, Jingpei, Lu, Jiangshan, Luengo, Imanol, Lund, Christina B., Luu, Huan Minh, Lv, Yi, Macar, Uzay, Maechler, Leon, L., Sina Mansour, Marshall, Kenji, Mazher, Moona, McKinley, Richard, Medela, Alfonso, Meissen, Felix, Meng, Mingyuan, Miller, Dylan, Mirjahanmardi, Seyed Hossein, Mishra, Arnab, Mitha, Samir, Mohy-ud-Din, Hassan, Mok, Tony Chi Wing, Murugesan, Gowtham Krishnan, Karthik, Enamundram Naga, Nalawade, Sahil, Nalepa, Jakub, Naser, Mohamed, Nateghi, Ramin, Naveed, Hammad, Nguyen, Quang-Minh, Quoc, Cuong Nguyen, Nichyporuk, Brennan, Oliveira, Bruno, Owen, David, Pal, Jimut Bahan, Pan, Junwen, Pan, Wentao, Pang, Winnie, Park, Bogyu, Pawar, Vivek, Pawar, Kamlesh, Peven, Michael, Philipp, Lena, Pieciak, Tomasz, Plotka, Szymon, Plutat, Marcel, Pourakpour, Fattaneh, Preložnik, Domen, Punithakumar, Kumaradevan, Qayyum, Abdul, Queirós, Sandro, Rahmim, Arman, Razavi, Salar, Ren, Jintao, Rezaei, Mina, Rico, Jonathan Adam, Rieu, ZunHyan, Rink, Markus, Roth, Johannes, Ruiz-Gonzalez, Yusely, Saeed, Numan, Saha, Anindo, Salem, Mostafa, Sanchez-Matilla, Ricardo, Schilling, Kurt, Shao, Wei, Shen, Zhiqiang, Shi, Ruize, Shi, Pengcheng, Sobotka, Daniel, Soulier, Théodore, Fadida, Bella Specktor, Stoyanov, Danail, Mun, Timothy Sum Hon, Sun, Xiaowu, Tao, Rong, Thaler, Franz, Théberge, Antoine, Thielke, Felix, Torres, Helena, Wahid, Kareem A., Wang, Jiacheng, Wang, YiFei, Wang, Wei, Wang, Xiong, Wen, Jianhui, Wen, Ning, Wodzinski, Marek, Wu, Ye, Xia, Fangfang, Xiang, Tianqi, Xiaofei, Chen, Xu, Lizhan, Xue, Tingting, Yang, Yuxuan, Yang, Lin, Yao, Kai, Yao, Huifeng, Yazdani, Amirsaeed, Yip, Michael, Yoo, Hwanseung, Yousefirizi, Fereshteh, Yu, Shunkai, Yu, Lei, Zamora, Jonathan, Zeineldin, Ramy Ashraf, Zeng, Dewen, Zhang, Jianpeng, Zhang, Bokai, Zhang, Jiapeng, Zhang, Fan, Zhang, Huahong, Zhao, Zhongchen, Zhao, Zixuan, Zhao, Jiachen, Zhao, Can, Zheng, Qingshuo, Zhi, Yuheng, Zhou, Ziqi, Zou, Baosheng, Maier-Hein, Klaus, Jäger, Paul F., Kopp-Schneider, Annette, Maier-Hein, Lena, Eisenmann, Matthias, Reinke, Annika, Weru, Vivienn, Tizabi, Minu Dietlinde, Isensee, Fabian, Adler, Tim J., Godau, Patrick, Cheplygina, Veronika, Kozubek, Michal, Ali, Sharib, Gupta, Anubha, Kybic, Jan, Noble, Alison, de Solórzano, Carlos Ortiz, Pachade, Samiksha, Petitjean, Caroline, Sage, Daniel, Wei, Donglai, Wilden, Elizabeth, Alapatt, Deepak, Andrearczyk, Vincent, Baid, Ujjwal, Bakas, Spyridon, Balu, Niranjan, Bano, Sophia, Bawa, Vivek Singh, Bernal, Jorge, Bodenstedt, Sebastian, Casella, Alessandro, Choi, Jinwook, Commowick, Olivier, Daum, Marie, Depeursinge, Adrien, Dorent, Reuben, Egger, Jan, Eichhorn, Hannah, Engelhardt, Sandy, Ganz, Melanie, Girard, Gabriel, Hansen, Lasse, Heinrich, Mattias, Heller, Nicholas, Hering, Alessa, Huaulmé, Arnaud, Kim, Hyunjeong, Landman, Bennett, Li, Hongwei Bran, Li, Jianning, Ma, Jun, Martel, Anne, Martín-Isla, Carlos, Menze, Bjoern, Nwoye, Chinedu Innocent, Oreiller, Valentin, Padoy, Nicolas, Pati, Sarthak, Payette, Kelly, Sudre, Carole, van Wijnen, Kimberlin, Vardazaryan, Armine, Vercauteren, Tom, Wagner, Martin, Wang, Chuanbo, Yap, Moi Hoon, Yu, Zeyun, Yuan, Chun, Zenk, Maximilian, Zia, Aneeq, Zimmerer, David, Bao, Rina, Choi, Chanyeol, Cohen, Andrew, Dzyubachyk, Oleh, Galdran, Adrian, Gan, Tianyuan, Guo, Tianqi, Gupta, Pradyumna, Haithami, Mahmood, Ho, Edward, Jang, Ikbeom, Li, Zhili, Luo, Zhengbo, Lux, Filip, Makrogiannis, Sokratis, Müller, Dominik, Oh, Young-tack, Pang, Subeen, Pape, Constantin, Polat, Gorkem, Reed, Charlotte Rosalie, Ryu, Kanghyun, Scherr, Tim, Thambawita, Vajira, Wang, Haoyu, Wang, Xinliang, Xu, Kele, Yeh, Hung, Yeo, Doyeob, Yuan, Yixuan, Zeng, Yan, Zhao, Xin, Abbing, Julian, Adam, Jannes, Adluru, Nagesh, Agethen, Niklas, Ahmed, Salman, Khalil, Yasmina Al, Alenyà, Mireia, Alhoniemi, Esa, An, Chengyang, Anwar, Talha, Arega, Tewodros Weldebirhan, Avisdris, Netanell, Aydogan, Dogu Baran, Bai, Yingbin, Calisto, Maria Baldeon, Basaran, Berke Doga, Beetz, Marcel, Bian, Cheng, Bian, Hao, Blansit, Kevin, Bloch, Louise, Bohnsack, Robert, Bosticardo, Sara, Breen, Jack, Brudfors, Mikael, Brüngel, Raphael, Cabezas, Mariano, Cacciola, Alberto, Chen, Zhiwei, Chen, Yucong, Chen, Daniel Tianming, Cho, Minjeong, Choi, Min-Kook, Xie, Chuantao Xie Chuantao, Cobzas, Dana, Cohen-Adad, Julien, Acero, Jorge Corral, Das, Sujit Kumar, de Oliveira, Marcela, Deng, Hanqiu, Dong, Guiming, Doorenbos, Lars, Efird, Cory, Escalera, Sergio, Fan, Di, Serj, Mehdi Fatan, Fenneteau, Alexandre, Fidon, Lucas, Filipiak, Patryk, Finzel, René, Freitas, Nuno R., Friedrich, Christoph M., Fulton, Mitchell, Gaida, Finn, Galati, Francesco, Galazis, Christoforos, Gan, Chang Hee, Gao, Zheyao, Gao, Shengbo, Gazda, Matej, Gerats, Beerend, Getty, Neil, Gibicar, Adam, Gifford, Ryan, Gohil, Sajan, Grammatikopoulou, Maria, Grzech, Daniel, Güley, Orhun, Günnemann, Timo, Guo, Chunxu, Guy, Sylvain, Ha, Heonjin, Han, Luyi, Han, Il Song, Hatamizadeh, Ali, He, Tian, Heo, Jimin, Hitziger, Sebastian, Hong, SeulGi, Hong, SeungBum, Huang, Rian, Huang, Ziyan, Huellebrand, Markus, Huschauer, Stephan, Hussain, Mustaffa, Inubushi, Tomoo, Polat, Ece Isik, Jafaritadi, Mojtaba, Jeong, SeongHun, Jian, Bailiang, Jiang, Yuanhong, Jiang, Zhifan, Jin, Yueming, Joshi, Smriti, Kadkhodamohammadi, Abdolrahim, Kamraoui, Reda Abdellah, Kang, Inha, Kang, Junghwa, Karimi, Davood, Khademi, April, Khan, Muhammad Irfan, Khan, Suleiman A., Khantwal, Rishab, Kim, Kwang-Ju, Kline, Timothy, Kondo, Satoshi, Kontio, Elina, Krenzer, Adrian, Kroviakov, Artem, Kuijf, Hugo, Kumar, Satyadwyoom, La Rosa, Francesco, Lad, Abhi, Lee, Doohee, Lee, Minho, Lena, Chiara, Li, Hao, Li, Ling, Li, Xingyu, Liao, Fuyuan, Liao, KuanLun, Oliveira, Arlindo Limede, Lin, Chaonan, Lin, Shan, Linardos, Akis, Linguraru, Marius George, Liu, Han, Liu, Tao, Liu, Di, Liu, Yanling, Lourenço-Silva, João, Lu, Jingpei, Lu, Jiangshan, Luengo, Imanol, Lund, Christina B., Luu, Huan Minh, Lv, Yi, Macar, Uzay, Maechler, Leon, L., Sina Mansour, Marshall, Kenji, Mazher, Moona, McKinley, Richard, Medela, Alfonso, Meissen, Felix, Meng, Mingyuan, Miller, Dylan, Mirjahanmardi, Seyed Hossein, Mishra, Arnab, Mitha, Samir, Mohy-ud-Din, Hassan, Mok, Tony Chi Wing, Murugesan, Gowtham Krishnan, Karthik, Enamundram Naga, Nalawade, Sahil, Nalepa, Jakub, Naser, Mohamed, Nateghi, Ramin, Naveed, Hammad, Nguyen, Quang-Minh, Quoc, Cuong Nguyen, Nichyporuk, Brennan, Oliveira, Bruno, Owen, David, Pal, Jimut Bahan, Pan, Junwen, Pan, Wentao, Pang, Winnie, Park, Bogyu, Pawar, Vivek, Pawar, Kamlesh, Peven, Michael, Philipp, Lena, Pieciak, Tomasz, Plotka, Szymon, Plutat, Marcel, Pourakpour, Fattaneh, Preložnik, Domen, Punithakumar, Kumaradevan, Qayyum, Abdul, Queirós, Sandro, Rahmim, Arman, Razavi, Salar, Ren, Jintao, Rezaei, Mina, Rico, Jonathan Adam, Rieu, ZunHyan, Rink, Markus, Roth, Johannes, Ruiz-Gonzalez, Yusely, Saeed, Numan, Saha, Anindo, Salem, Mostafa, Sanchez-Matilla, Ricardo, Schilling, Kurt, Shao, Wei, Shen, Zhiqiang, Shi, Ruize, Shi, Pengcheng, Sobotka, Daniel, Soulier, Théodore, Fadida, Bella Specktor, Stoyanov, Danail, Mun, Timothy Sum Hon, Sun, Xiaowu, Tao, Rong, Thaler, Franz, Théberge, Antoine, Thielke, Felix, Torres, Helena, Wahid, Kareem A., Wang, Jiacheng, Wang, YiFei, Wang, Wei, Wang, Xiong, Wen, Jianhui, Wen, Ning, Wodzinski, Marek, Wu, Ye, Xia, Fangfang, Xiang, Tianqi, Xiaofei, Chen, Xu, Lizhan, Xue, Tingting, Yang, Yuxuan, Yang, Lin, Yao, Kai, Yao, Huifeng, Yazdani, Amirsaeed, Yip, Michael, Yoo, Hwanseung, Yousefirizi, Fereshteh, Yu, Shunkai, Yu, Lei, Zamora, Jonathan, Zeineldin, Ramy Ashraf, Zeng, Dewen, Zhang, Jianpeng, Zhang, Bokai, Zhang, Jiapeng, Zhang, Fan, Zhang, Huahong, Zhao, Zhongchen, Zhao, Zixuan, Zhao, Jiachen, Zhao, Can, Zheng, Qingshuo, Zhi, Yuheng, Zhou, Ziqi, Zou, Baosheng, Maier-Hein, Klaus, Jäger, Paul F., Kopp-Schneider, Annette, and Maier-Hein, Lena
Abstract: The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.
Published: 2022

6. Age-related differences in white matter microstructure measured by advanced diffusion MRI in healthy older adults at risk for Alzheimer’s disease

Author: Motovylyak, Alice, Vogt, Nicholas M., Adluru, Nagesh, Ma, Yue, Wang, Rui, Oh, Jennifer M., Kecskemeti, Steven R., Alexander, Andrew L., Dean, Douglas C., Gallagher, Catherine L., Sager, Mark A., Hermann, Bruce P., Rowley, Howard A., Johnson, Sterling C., Asthana, Sanjay, Bendlin, Barbara B., Okonkwo, Ozioma C., Motovylyak, Alice, Vogt, Nicholas M., Adluru, Nagesh, Ma, Yue, Wang, Rui, Oh, Jennifer M., Kecskemeti, Steven R., Alexander, Andrew L., Dean, Douglas C., Gallagher, Catherine L., Sager, Mark A., Hermann, Bruce P., Rowley, Howard A., Johnson, Sterling C., Asthana, Sanjay, Bendlin, Barbara B., and Okonkwo, Ozioma C.
Abstract: Neurite orientation dispersion and density imaging (NODDI) is an advanced diffusion imaging technique, which can detect more distinct microstructural features compared to conventional Diffusion Tensor Imaging (DTI). NODDI allows the signal to be divided into multiple water compartments and derive measures for orientation dispersion index (ODI), neurite density index (NDI) and volume fraction of isotropic diffusion compartment (FISO). This study aimed to investigate which diffusion metric—fractional anisotropy (FA), mean diffusivity (MD), NDI, ODI, or FISO—is most influenced by aging and reflects cognitive function in a population of healthy older adults at risk for Alzheimer’s disease (AD). Age was significantly associated with all but one diffusion parameters and regions of interest. NDI and MD in the cingulate region adjacent to the cingulate cortex showed a significant association with a composite measure of Executive Function and was proven to partially mediate the relationship between aging and Executive Function decline. These results suggest that both DTI and NODDI parameters are sensitive to age-related differences in white matter regions vulnerable to aging, particularly among older adults at risk for AD.
Published: 2022
Full Text: View/download PDF

7. Age-related differences in white matter microstructure measured by advanced diffusion MRI in healthy older adults at risk for Alzheimer’s disease

Author: Motovylyak, Alice, Vogt, Nicholas M., Adluru, Nagesh, Ma, Yue, Wang, Rui, Oh, Jennifer M., Kecskemeti, Steven R., Alexander, Andrew L., Dean, Douglas C., Gallagher, Catherine L., Sager, Mark A., Hermann, Bruce P., Rowley, Howard A., Johnson, Sterling C., Asthana, Sanjay, Bendlin, Barbara B., Okonkwo, Ozioma C., Motovylyak, Alice, Vogt, Nicholas M., Adluru, Nagesh, Ma, Yue, Wang, Rui, Oh, Jennifer M., Kecskemeti, Steven R., Alexander, Andrew L., Dean, Douglas C., Gallagher, Catherine L., Sager, Mark A., Hermann, Bruce P., Rowley, Howard A., Johnson, Sterling C., Asthana, Sanjay, Bendlin, Barbara B., and Okonkwo, Ozioma C.
Abstract: Neurite orientation dispersion and density imaging (NODDI) is an advanced diffusion imaging technique, which can detect more distinct microstructural features compared to conventional Diffusion Tensor Imaging (DTI). NODDI allows the signal to be divided into multiple water compartments and derive measures for orientation dispersion index (ODI), neurite density index (NDI) and volume fraction of isotropic diffusion compartment (FISO). This study aimed to investigate which diffusion metric—fractional anisotropy (FA), mean diffusivity (MD), NDI, ODI, or FISO—is most influenced by aging and reflects cognitive function in a population of healthy older adults at risk for Alzheimer’s disease (AD). Age was significantly associated with all but one diffusion parameters and regions of interest. NDI and MD in the cingulate region adjacent to the cingulate cortex showed a significant association with a composite measure of Executive Function and was proven to partially mediate the relationship between aging and Executive Function decline. These results suggest that both DTI and NODDI parameters are sensitive to age-related differences in white matter regions vulnerable to aging, particularly among older adults at risk for AD.
Published: 2022
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8. Insights from the IronTract challenge:Optimal methods for mapping brain pathways from multi-shell diffusion MRI

Author: Maffei, Chiara, Girard, Gabriel, Schilling, Kurt G, Aydogan, Dogu Baran, Adluru, Nagesh, Zhylka, Andrey, Wu, Ye, Mancini, Matteo, Hamamci, Andac, Sarica, Alessia, Teillac, Achille, Baete, Steven H, Karimi, Davood, Yeh, Fang-Cheng, Yildiz, Mert E, Gholipour, Ali, Bihan-Poudec, Yann, Hiba, Bassem, Quattrone, Andrea, Quattrone, Aldo, Boshkovski, Tommy, Stikov, Nikola, Yap, Pew-Thian, de Luca, Alberto, Pluim, Josien, Leemans, Alexander, Prabhakaran, Vivek, Bendlin, Barbara B, Alexander, Andrew L, Landman, Bennett A, Canales-Rodríguez, Erick J, Barakovic, Muhamed, Rafael-Patino, Jonathan, Yu, Thomas, Rensonnet, Gaëtan, Schiavi, Simona, Daducci, Alessandro, Pizzolato, Marco, Fischi-Gomez, Elda, Thiran, Jean-Philippe, Dai, George, Grisot, Giorgia, Lazovski, Nikola, Puch, Santi, Ramos, Marc, Rodrigues, Paulo, Prčkovska, Vesna, Jones, Robert, Lehman, Julia, Haber, Suzanne N, Yendiki, Anastasia, Maffei, Chiara, Girard, Gabriel, Schilling, Kurt G, Aydogan, Dogu Baran, Adluru, Nagesh, Zhylka, Andrey, Wu, Ye, Mancini, Matteo, Hamamci, Andac, Sarica, Alessia, Teillac, Achille, Baete, Steven H, Karimi, Davood, Yeh, Fang-Cheng, Yildiz, Mert E, Gholipour, Ali, Bihan-Poudec, Yann, Hiba, Bassem, Quattrone, Andrea, Quattrone, Aldo, Boshkovski, Tommy, Stikov, Nikola, Yap, Pew-Thian, de Luca, Alberto, Pluim, Josien, Leemans, Alexander, Prabhakaran, Vivek, Bendlin, Barbara B, Alexander, Andrew L, Landman, Bennett A, Canales-Rodríguez, Erick J, Barakovic, Muhamed, Rafael-Patino, Jonathan, Yu, Thomas, Rensonnet, Gaëtan, Schiavi, Simona, Daducci, Alessandro, Pizzolato, Marco, Fischi-Gomez, Elda, Thiran, Jean-Philippe, Dai, George, Grisot, Giorgia, Lazovski, Nikola, Puch, Santi, Ramos, Marc, Rodrigues, Paulo, Prčkovska, Vesna, Jones, Robert, Lehman, Julia, Haber, Suzanne N, and Yendiki, Anastasia
Abstract: Limitations in the accuracy of brain pathways reconstructed by diffusion MRI (dMRI) tractography have received considerable attention. While the technical advances spearheaded by the Human Connectome Project (HCP) led to significant improvements in dMRI data quality, it remains unclear how these data should be analyzed to maximize tractography accuracy. Over a period of two years, we have engaged the dMRI community in the IronTract Challenge, which aims to answer this question by leveraging a unique dataset. Macaque brains that have received both tracer injections and ex vivo dMRI at high spatial and angular resolution allow a comprehensive, quantitative assessment of tractography accuracy on state-of-the-art dMRI acquisition schemes. We find that, when analysis methods are carefully optimized, the HCP scheme can achieve similar accuracy as a more time-consuming, Cartesian-grid scheme. Importantly, we show that simple pre- and post-processing strategies can improve the accuracy and robustness of many tractography methods. Finally, we find that fiber configurations that go beyond crossing (e.g., fanning, branching) are the most challenging for tractography. The IronTract Challenge remains open and we hope that it can serve as a valuable validation tool for both users and developers of dMRI analysis methods.
Published: 2022

9. Insights from the IronTract challenge: Optimal methods for mapping brain pathways from multi-shell diffusion MRI

Author: Maffei, Chiara, Girard, Gabriel, Schilling, Kurt G., Aydogan, Dogu Baran, Adluru, Nagesh, Zhylka, Andrey, Wu, Ye, Mancini, Matteo, Hamamci, Andac, Sarica, Alessia, Teillac, Achille, Baete, Steven H., Karimi, Davood, Yeh, Fang Cheng, Yildiz, Mert E., Gholipour, Ali, Bihan-Poudec, Yann, Hiba, Bassem, Quattrone, Andrea, Quattrone, Aldo, Boshkovski, Tommy, Stikov, Nikola, Yap, Pew Thian, de Luca, Alberto, Pluim, Josien, Leemans, Alexander, Prabhakaran, Vivek, Bendlin, Barbara B., Alexander, Andrew L., Landman, Bennett A., Canales-Rodríguez, Erick J., Barakovic, Muhamed, Rafael-Patino, Jonathan, Yu, Thomas, Rensonnet, Gaëtan, Schiavi, Simona, Daducci, Alessandro, Pizzolato, Marco, Fischi-Gomez, Elda, Thiran, Jean Philippe, Dai, George, Grisot, Giorgia, Lazovski, Nikola, Puch, Santi, Ramos, Marc, Rodrigues, Paulo, Prčkovska, Vesna, Jones, Robert, Lehman, Julia, Haber, Suzanne N., Yendiki, Anastasia, Maffei, Chiara, Girard, Gabriel, Schilling, Kurt G., Aydogan, Dogu Baran, Adluru, Nagesh, Zhylka, Andrey, Wu, Ye, Mancini, Matteo, Hamamci, Andac, Sarica, Alessia, Teillac, Achille, Baete, Steven H., Karimi, Davood, Yeh, Fang Cheng, Yildiz, Mert E., Gholipour, Ali, Bihan-Poudec, Yann, Hiba, Bassem, Quattrone, Andrea, Quattrone, Aldo, Boshkovski, Tommy, Stikov, Nikola, Yap, Pew Thian, de Luca, Alberto, Pluim, Josien, Leemans, Alexander, Prabhakaran, Vivek, Bendlin, Barbara B., Alexander, Andrew L., Landman, Bennett A., Canales-Rodríguez, Erick J., Barakovic, Muhamed, Rafael-Patino, Jonathan, Yu, Thomas, Rensonnet, Gaëtan, Schiavi, Simona, Daducci, Alessandro, Pizzolato, Marco, Fischi-Gomez, Elda, Thiran, Jean Philippe, Dai, George, Grisot, Giorgia, Lazovski, Nikola, Puch, Santi, Ramos, Marc, Rodrigues, Paulo, Prčkovska, Vesna, Jones, Robert, Lehman, Julia, Haber, Suzanne N., and Yendiki, Anastasia
Abstract: Limitations in the accuracy of brain pathways reconstructed by diffusion MRI (dMRI) tractography have received considerable attention. While the technical advances spearheaded by the Human Connectome Project (HCP) led to significant improvements in dMRI data quality, it remains unclear how these data should be analyzed to maximize tractography accuracy. Over a period of two years, we have engaged the dMRI community in the IronTract Challenge, which aims to answer this question by leveraging a unique dataset. Macaque brains that have received both tracer injections and ex vivo dMRI at high spatial and angular resolution allow a comprehensive, quantitative assessment of tractography accuracy on state-of-the-art dMRI acquisition schemes. We find that, when analysis methods are carefully optimized, the HCP scheme can achieve similar accuracy as a more time-consuming, Cartesian-grid scheme. Importantly, we show that simple pre- and post-processing strategies can improve the accuracy and robustness of many tractography methods. Finally, we find that fiber configurations that go beyond crossing (e.g., fanning, branching) are the most challenging for tractography. The IronTract Challenge remains open and we hope that it can serve as a valuable validation tool for both users and developers of dMRI analysis methods.
Published: 2022

10. Age-related differences in white matter microstructure measured by advanced diffusion MRI in healthy older adults at risk for Alzheimer’s disease

Author: Motovylyak, Alice, Vogt, Nicholas M., Adluru, Nagesh, Ma, Yue, Wang, Rui, Oh, Jennifer M., Kecskemeti, Steven R., Alexander, Andrew L., Dean, Douglas C., Gallagher, Catherine L., Sager, Mark A., Hermann, Bruce P., Rowley, Howard A., Johnson, Sterling C., Asthana, Sanjay, Bendlin, Barbara B., Okonkwo, Ozioma C., Motovylyak, Alice, Vogt, Nicholas M., Adluru, Nagesh, Ma, Yue, Wang, Rui, Oh, Jennifer M., Kecskemeti, Steven R., Alexander, Andrew L., Dean, Douglas C., Gallagher, Catherine L., Sager, Mark A., Hermann, Bruce P., Rowley, Howard A., Johnson, Sterling C., Asthana, Sanjay, Bendlin, Barbara B., and Okonkwo, Ozioma C.
Abstract: Neurite orientation dispersion and density imaging (NODDI) is an advanced diffusion imaging technique, which can detect more distinct microstructural features compared to conventional Diffusion Tensor Imaging (DTI). NODDI allows the signal to be divided into multiple water compartments and derive measures for orientation dispersion index (ODI), neurite density index (NDI) and volume fraction of isotropic diffusion compartment (FISO). This study aimed to investigate which diffusion metric—fractional anisotropy (FA), mean diffusivity (MD), NDI, ODI, or FISO—is most influenced by aging and reflects cognitive function in a population of healthy older adults at risk for Alzheimer’s disease (AD). Age was significantly associated with all but one diffusion parameters and regions of interest. NDI and MD in the cingulate region adjacent to the cingulate cortex showed a significant association with a composite measure of Executive Function and was proven to partially mediate the relationship between aging and Executive Function decline. These results suggest that both DTI and NODDI parameters are sensitive to age-related differences in white matter regions vulnerable to aging, particularly among older adults at risk for AD.
Published: 2022
Full Text: View/download PDF

11. Insights from the IronTract challenge: Optimal methods for mapping brain pathways from multi-shell diffusion MRI

Author: Maffei, Chiara, Girard, Gabriel, Schilling, Kurt G., Aydogan, Dogu Baran, Adluru, Nagesh, Zhylka, Andrey, Wu, Ye, Mancini, Matteo, Hamamci, Andac, Sarica, Alessia, Teillac, Achille, Baete, Steven H., Karimi, Davood, Yeh, Fang-Cheng, Yildiz, Mert E., Gholipour, Ali, Bihan-Poudec, Yann, Hiba, Bassem, Quattrone, Andrea, Quattrone, Aldo, Boshkovski, Tommy, Stikov, Nikola, Yap, Pew-Thian, de Luca, Alberto, Pluim, Josien, Leemans, Alexander, Prabhakaran, Vivek, Bendlin, Barbara B., Alexander, Andrew L., Landman, Bennett A., Canales-Rodríguez, Erick J., Barakovic, Muhamed, Rafael-Patino, Jonathan, Yu, Thomas, Rensonnet, Gaëtan, Schiavi, Simona, Daducci, Alessandro, Pizzolato, Marco, Fischi-Gomez, Elda, Thiran, Jean-Philippe, Dai, George, Grisot, Giorgia, Lazovski, Nikola, Puch, Santi, Ramos, Marc, Rodrigues, Paulo, Prčkovska, Vesna, Jones, Robert, Lehman, Julia, Haber, Suzanne N., Yendiki, Anastasia, Maffei, Chiara, Girard, Gabriel, Schilling, Kurt G., Aydogan, Dogu Baran, Adluru, Nagesh, Zhylka, Andrey, Wu, Ye, Mancini, Matteo, Hamamci, Andac, Sarica, Alessia, Teillac, Achille, Baete, Steven H., Karimi, Davood, Yeh, Fang-Cheng, Yildiz, Mert E., Gholipour, Ali, Bihan-Poudec, Yann, Hiba, Bassem, Quattrone, Andrea, Quattrone, Aldo, Boshkovski, Tommy, Stikov, Nikola, Yap, Pew-Thian, de Luca, Alberto, Pluim, Josien, Leemans, Alexander, Prabhakaran, Vivek, Bendlin, Barbara B., Alexander, Andrew L., Landman, Bennett A., Canales-Rodríguez, Erick J., Barakovic, Muhamed, Rafael-Patino, Jonathan, Yu, Thomas, Rensonnet, Gaëtan, Schiavi, Simona, Daducci, Alessandro, Pizzolato, Marco, Fischi-Gomez, Elda, Thiran, Jean-Philippe, Dai, George, Grisot, Giorgia, Lazovski, Nikola, Puch, Santi, Ramos, Marc, Rodrigues, Paulo, Prčkovska, Vesna, Jones, Robert, Lehman, Julia, Haber, Suzanne N., and Yendiki, Anastasia
Abstract: Limitations in the accuracy of brain pathways reconstructed by diffusion MRI (dMRI) tractography have received considerable attention. While the technical advances spearheaded by the Human Connectome Project (HCP) led to significant improvements in dMRI data quality, it remains unclear how these data should be analyzed to maximize tractography accuracy. Over a period of two years, we have engaged the dMRI community in the IronTract Challenge, which aims to answer this question by leveraging a unique dataset. Macaque brains that have received both tracer injections and ex vivo dMRI at high spatial and angular resolution allow a comprehensive, quantitative assessment of tractography accuracy on state-of-the-art dMRI acquisition schemes. We find that, when analysis methods are carefully optimized, the HCP scheme can achieve similar accuracy as a more time-consuming, Cartesian-grid scheme. Importantly, we show that simple pre- and post-processing strategies can improve the accuracy and robustness of many tractography methods. Finally, we find that fiber configurations that go beyond crossing (e.g., fanning, branching) are the most challenging for tractography. The IronTract Challenge remains open and we hope that it can serve as a valuable validation tool for both users and developers of dMRI analysis methods.
Published: 2022

12. Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset?

Author: Schilling, Kurt G., Rheault, François, Petit, Laurent, Hansen, Colin B., Nath, Vishwesh, Yeh, Fang-Cheng, Girard, Gabriel, Barakovic, Muhamed, Rafael-Patino, Jonathan, Yu, Thomas, Fischi-Gomez, Elda, Pizzolato, Marco, Ocampo-Pineda, Mario, Schiavi, Simona, Canales-Rodríguez, Erick J., Daducci, Alessandro, Granziera, Cristina, Innocenti, Giorgio, Thiran, Jean-Philippe, Mancini, Laura, Wastling, Stephen, Cocozza, Sirio, Petracca, Maria, Pontillo, Giuseppe, Mancini, Matteo, Vos, Sjoerd B., Vakharia, Vejay N., Duncan, John S., Melero Carrasco, Helena, Manzanedo, Lidia, Sanz-Morales, Emilio, Peña-Melián, Ángel, Calamante, Fernando, Attyé, Arnaud, Cabeen, Ryan P., Korobova, Laura, Toga, Arthur W., Vijayakumari, Anupa Ambili, Parker, Drew, Verma, Ragini, Radwan, Ahmed, Sunaert, Stefan, Emsell, Louise, De Luca, Alberto, Leemans, Alexander, Bajada, Claude J., Haroon, Hamied, Azadbakht, Hojjatollah, Chamberland, Maxime, Genc, Sila, Tax, Chantal M.W., Yeh, Ping-Hong, Srikanchana, Rujirutana, Mcknight, Colin D., Yang, Joseph Yuan-Mou, Chen, Jian, Kelly, Claire E., Yeh, Chun-Hung, Cochereau, Jerome, Maller, Jerome J., Welton, Thomas, Almairac, Fabien, Seunarine, Kiran K, Clark, Chris A., Zhang, Fan, Makris, Nikos, Golby, Alexandra, Rathi, Yogesh, O'Donnell, Lauren J., Xia, Yihao, Aydogan, Dogu Baran, Shi, Yonggang, Guerreiro Fernandes, Francisco, Raemaekers, Mathijs, Warrington, Shaun, Michielse, Stijn, Ramírez-Manzanares, Alonso, Concha, Luis, Aranda, Ramón, Rivera Meraz, Mariano, Lerma-Usabiaga, Garikoitz, Roitman, Lucas, Fekonja, Lucius S., Calarco, Navona, Joseph, Michael, Nakua, Hajer, Voineskos, Aristotle N., Karan, Philippe, Grenier, Gabrielle, Legarreta, Jon Haitz, Adluru, Nagesh, Nair, Veena A., Prabhakaran, Vivek, Alexander, Andrew L., Kamagata, Koji, Saito, Yuya, Uchida, Wataru, Andica, Christina, Abe, Masahiro, Bayrak, Roza G., Gandini Wheeler-Kingshott, Claudia A.M., D'Angelo, Egidio, Palesi, Fulvia, Savini, Giovanni, Rolandi, Nicolò, Guevara, Pamela, Houenou, Josselin, López-López, Narciso, Mangin, Jean-François, Poupon, Cyril, Claudio Román, Claudio, Vázquez, Andrea, Maffei, Chiara, Arantes, Mavilde, Andrade, José Paulo, Silva, Susana Maria, Calhoun, Vince D., Caverzasi, Eduardo, Sacco, Simone, Lauricella, Michael, Pestilli, Franco, Daniel Bullock, Daniel, Zhan, Yang, Brignoni-Perez, Edith, Lebel, Catherine, Reynolds, Jess E, Nestrasil, Igor, Labounek, René, Lenglet, Christophe, Paulson, Amy, Aulicka, Stefania, Heilbronner, Sarah R., Heuer, Katja, Chandio, Bramsh Qamar, Guaje, Javier, Tang, Wei, Garyfallidis, Eleftherios, Raja, Rajikha, Anderson, Adam W., Landman, Bennett A., Descoteaux, Maxime, Schilling, Kurt G., Rheault, François, Petit, Laurent, Hansen, Colin B., Nath, Vishwesh, Yeh, Fang-Cheng, Girard, Gabriel, Barakovic, Muhamed, Rafael-Patino, Jonathan, Yu, Thomas, Fischi-Gomez, Elda, Pizzolato, Marco, Ocampo-Pineda, Mario, Schiavi, Simona, Canales-Rodríguez, Erick J., Daducci, Alessandro, Granziera, Cristina, Innocenti, Giorgio, Thiran, Jean-Philippe, Mancini, Laura, Wastling, Stephen, Cocozza, Sirio, Petracca, Maria, Pontillo, Giuseppe, Mancini, Matteo, Vos, Sjoerd B., Vakharia, Vejay N., Duncan, John S., Melero Carrasco, Helena, Manzanedo, Lidia, Sanz-Morales, Emilio, Peña-Melián, Ángel, Calamante, Fernando, Attyé, Arnaud, Cabeen, Ryan P., Korobova, Laura, Toga, Arthur W., Vijayakumari, Anupa Ambili, Parker, Drew, Verma, Ragini, Radwan, Ahmed, Sunaert, Stefan, Emsell, Louise, De Luca, Alberto, Leemans, Alexander, Bajada, Claude J., Haroon, Hamied, Azadbakht, Hojjatollah, Chamberland, Maxime, Genc, Sila, Tax, Chantal M.W., Yeh, Ping-Hong, Srikanchana, Rujirutana, Mcknight, Colin D., Yang, Joseph Yuan-Mou, Chen, Jian, Kelly, Claire E., Yeh, Chun-Hung, Cochereau, Jerome, Maller, Jerome J., Welton, Thomas, Almairac, Fabien, Seunarine, Kiran K, Clark, Chris A., Zhang, Fan, Makris, Nikos, Golby, Alexandra, Rathi, Yogesh, O'Donnell, Lauren J., Xia, Yihao, Aydogan, Dogu Baran, Shi, Yonggang, Guerreiro Fernandes, Francisco, Raemaekers, Mathijs, Warrington, Shaun, Michielse, Stijn, Ramírez-Manzanares, Alonso, Concha, Luis, Aranda, Ramón, Rivera Meraz, Mariano, Lerma-Usabiaga, Garikoitz, Roitman, Lucas, Fekonja, Lucius S., Calarco, Navona, Joseph, Michael, Nakua, Hajer, Voineskos, Aristotle N., Karan, Philippe, Grenier, Gabrielle, Legarreta, Jon Haitz, Adluru, Nagesh, Nair, Veena A., Prabhakaran, Vivek, Alexander, Andrew L., Kamagata, Koji, Saito, Yuya, Uchida, Wataru, Andica, Christina, Abe, Masahiro, Bayrak, Roza G., Gandini Wheeler-Kingshott, Claudia A.M., D'Angelo, Egidio, Palesi, Fulvia, Savini, Giovanni, Rolandi, Nicolò, Guevara, Pamela, Houenou, Josselin, López-López, Narciso, Mangin, Jean-François, Poupon, Cyril, Claudio Román, Claudio, Vázquez, Andrea, Maffei, Chiara, Arantes, Mavilde, Andrade, José Paulo, Silva, Susana Maria, Calhoun, Vince D., Caverzasi, Eduardo, Sacco, Simone, Lauricella, Michael, Pestilli, Franco, Daniel Bullock, Daniel, Zhan, Yang, Brignoni-Perez, Edith, Lebel, Catherine, Reynolds, Jess E, Nestrasil, Igor, Labounek, René, Lenglet, Christophe, Paulson, Amy, Aulicka, Stefania, Heilbronner, Sarah R., Heuer, Katja, Chandio, Bramsh Qamar, Guaje, Javier, Tang, Wei, Garyfallidis, Eleftherios, Raja, Rajikha, Anderson, Adam W., Landman, Bennett A., and Descoteaux, Maxime
Abstract: White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process, Depto. de Psicobiología y Metodología en Ciencias del Comportamiento, Fac. de Psicología, TRUE, pub
Published: 2021

13. Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset?

Author: Schilling, Kurt G., Rheault, François, Petit, Laurent, Hansen, Colin B., Nath, Vishwesh, Yeh, Fang-Cheng, Girard, Gabriel, Barakovic, Muhamed, Rafael-Patino, Jonathan, Yu, Thomas, Fischi-Gomez, Elda, Pizzolato, Marco, Ocampo-Pineda, Mario, Schiavi, Simona, Canales-Rodríguez, Erick J., Daducci, Alessandro, Granziera, Cristina, Innocenti, Giorgio, Thiran, Jean-Philippe, Mancini, Laura, Wastling, Stephen, Cocozza, Sirio, Petracca, Maria, Pontillo, Giuseppe, Mancini, Matteo, Vos, Sjoerd B., Vakharia, Vejay N., Duncan, John S., Melero, Helena, Manzanedo, Lidia, Sanz-Morales, Emilio, Peña-Melián, Ángel, Calamante, Fernando, Attyé, Arnaud, Cabeen, Ryan P., Korobova, Laura, Toga, Arthur W., Vijayakumari, Anupa Ambili, Parker, Drew, Verma, Ragini, Radwan, Ahmed, Sunaert, Stefan, Emsell, Louise, De Luca, Alberto, Leemans, Alexander, Bajada, Claude J., Haroon, Hamied, Azadbakht, Hojjatollah, Chamberland, Maxime, Genc, Sila, Tax, Chantal M.W., Yeh, Ping-Hong, Srikanchana, Rujirutana, Mcknight, Colin D., Yang, Joseph Yuan-Mou, Chen, Jian, Kelly, Claire E., Yeh, Chun-Hung, Cochereau, Jerome, Maller, Jerome J., Welton, Thomas, Almairac, Fabien, Seunarine, Kiran K, Clark, Chris A., Zhang, Fan, Makris, Nikos, Golby, Alexandra, Rathi, Yogesh, O'Donnell, Lauren J., Xia, Yihao, Aydogan, Dogu Baran, Shi, Yonggang, Guerreiro Fernandes, Francisco, Raemaekers, Mathijs, Warrington, Shaun, Michielse, Stijn, Ramírez-Manzanares, Alonso, Concha, Luis, Aranda, Ramón, Rivera Meraz, Mariano, Lerma-Usabiaga, Garikoitz, Roitman, Lucas, Fekonja, Lucius S., Calarco, Navona, Joseph, Michael, Nakua, Hajer, Voineskos, Aristotle N., Karan, Philippe, Grenier, Gabrielle, Legarreta, Jon Haitz, Adluru, Nagesh, Nair, Veena A., Prabhakaran, Vivek, Alexander, Andrew L., Kamagata, Koji, Saito, Yuya, Uchida, Wataru, Andica, Christina, Abe, Masahiro, Bayrak, Roza G., Gandini Wheeler-Kingshott, Claudia A.M., D'Angelo, Egidio, Palesi, Fulvia, Savini, Giovanni, Rolandi, Nicolò, Guevara, Pamela, Houenou, Josselin, López-López, Narciso, Mangin, Jean-François, Poupon, Cyril, Román, Claudio, Vázquez, Andrea, Maffei, Chiara, Arantes, Mavilde, Andrade, José Paulo, Silva, Susana Maria, Calhoun, Vince D., Caverzasi, Eduardo, Sacco, Simone, Lauricella, Michael, Pestilli, Franco, Bullock, Daniel, Zhan, Yang, Brignoni-Perez, Edith, Lebel, Catherine, Reynolds, Jess E, Nestrasil, Igor, Labounek, René, Lenglet, Christophe, Paulson, Amy, Aulicka, Stefania, Heilbronner, Sarah R., Heuer, Katja, Chandio, Bramsh Qamar, Guaje, Javier, Tang, Wei, Garyfallidis, Eleftherios, Raja, Rajikha, Anderson, Adam W., Landman, Bennett A., Descoteaux, Maxime, Schilling, Kurt G., Rheault, François, Petit, Laurent, Hansen, Colin B., Nath, Vishwesh, Yeh, Fang-Cheng, Girard, Gabriel, Barakovic, Muhamed, Rafael-Patino, Jonathan, Yu, Thomas, Fischi-Gomez, Elda, Pizzolato, Marco, Ocampo-Pineda, Mario, Schiavi, Simona, Canales-Rodríguez, Erick J., Daducci, Alessandro, Granziera, Cristina, Innocenti, Giorgio, Thiran, Jean-Philippe, Mancini, Laura, Wastling, Stephen, Cocozza, Sirio, Petracca, Maria, Pontillo, Giuseppe, Mancini, Matteo, Vos, Sjoerd B., Vakharia, Vejay N., Duncan, John S., Melero, Helena, Manzanedo, Lidia, Sanz-Morales, Emilio, Peña-Melián, Ángel, Calamante, Fernando, Attyé, Arnaud, Cabeen, Ryan P., Korobova, Laura, Toga, Arthur W., Vijayakumari, Anupa Ambili, Parker, Drew, Verma, Ragini, Radwan, Ahmed, Sunaert, Stefan, Emsell, Louise, De Luca, Alberto, Leemans, Alexander, Bajada, Claude J., Haroon, Hamied, Azadbakht, Hojjatollah, Chamberland, Maxime, Genc, Sila, Tax, Chantal M.W., Yeh, Ping-Hong, Srikanchana, Rujirutana, Mcknight, Colin D., Yang, Joseph Yuan-Mou, Chen, Jian, Kelly, Claire E., Yeh, Chun-Hung, Cochereau, Jerome, Maller, Jerome J., Welton, Thomas, Almairac, Fabien, Seunarine, Kiran K, Clark, Chris A., Zhang, Fan, Makris, Nikos, Golby, Alexandra, Rathi, Yogesh, O'Donnell, Lauren J., Xia, Yihao, Aydogan, Dogu Baran, Shi, Yonggang, Guerreiro Fernandes, Francisco, Raemaekers, Mathijs, Warrington, Shaun, Michielse, Stijn, Ramírez-Manzanares, Alonso, Concha, Luis, Aranda, Ramón, Rivera Meraz, Mariano, Lerma-Usabiaga, Garikoitz, Roitman, Lucas, Fekonja, Lucius S., Calarco, Navona, Joseph, Michael, Nakua, Hajer, Voineskos, Aristotle N., Karan, Philippe, Grenier, Gabrielle, Legarreta, Jon Haitz, Adluru, Nagesh, Nair, Veena A., Prabhakaran, Vivek, Alexander, Andrew L., Kamagata, Koji, Saito, Yuya, Uchida, Wataru, Andica, Christina, Abe, Masahiro, Bayrak, Roza G., Gandini Wheeler-Kingshott, Claudia A.M., D'Angelo, Egidio, Palesi, Fulvia, Savini, Giovanni, Rolandi, Nicolò, Guevara, Pamela, Houenou, Josselin, López-López, Narciso, Mangin, Jean-François, Poupon, Cyril, Román, Claudio, Vázquez, Andrea, Maffei, Chiara, Arantes, Mavilde, Andrade, José Paulo, Silva, Susana Maria, Calhoun, Vince D., Caverzasi, Eduardo, Sacco, Simone, Lauricella, Michael, Pestilli, Franco, Bullock, Daniel, Zhan, Yang, Brignoni-Perez, Edith, Lebel, Catherine, Reynolds, Jess E, Nestrasil, Igor, Labounek, René, Lenglet, Christophe, Paulson, Amy, Aulicka, Stefania, Heilbronner, Sarah R., Heuer, Katja, Chandio, Bramsh Qamar, Guaje, Javier, Tang, Wei, Garyfallidis, Eleftherios, Raja, Rajikha, Anderson, Adam W., Landman, Bennett A., and Descoteaux, Maxime
Abstract: Available online 22 August 2021., White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process.
Published: 2021

14. Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset?

Author: Schilling, KG, Rheault, F, Petit, L, Hansen, CB, Nath, V, Yeh, F-C, Girard, G, Barakovic, M, Rafael-Patino, J, Yu, T, Fischi-Gomez, E, Pizzolato, M, Ocampo-Pineda, M, Schiavi, S, Canales-Rodriguez, EJ, Daducci, A, Granziera, C, Innocenti, G, Thiran, J-P, Mancini, L, Wastling, S, Cocozza, S, Petracca, M, Pontillo, G, Mancini, M, Vos, SB, Vakharia, VN, Duncan, JS, Melero, H, Manzanedo, L, Sanz-Morales, E, Pena-Melian, A, Calamante, F, Attye, A, Cabeen, RP, Korobova, L, Toga, AW, Vijayakumari, AA, Parker, D, Verma, R, Radwan, A, Sunaert, S, Emsell, L, De Luca, A, Leemans, A, Bajada, CJ, Haroon, H, Azadbakht, H, Chamberland, M, Genc, S, Tax, CMW, Yeh, P-H, Srikanchana, R, Mcknight, CD, Yang, JY-M, Chen, J, Kelly, CE, Yeh, C-H, Cochereau, J, Maller, JJ, Welton, T, Almairac, F, Seunarine, KK, Clark, CA, Zhang, F, Makris, N, Golby, A, Rathi, Y, O'Donnell, LJ, Xia, Y, Aydogan, DB, Shi, Y, Fernandes, FG, Raemaekers, M, Warrington, S, Michielse, S, Ramirez-Manzanares, A, Concha, L, Aranda, R, Meraz, MR, Lerma-Usabiaga, G, Roitman, L, Fekonja, LS, Calarco, N, Joseph, M, Nakua, H, Voineskos, AN, Karan, P, Grenier, G, Legarreta, JH, Adluru, N, Nair, VA, Prabhakaran, V, Alexander, AL, Kamagata, K, Saito, Y, Uchida, W, Andica, C, Abe, M, Bayrak, RG, Wheeler-Kingshott, CAMG, D'Angelo, E, Palesi, F, Savini, G, Rolandi, N, Guevara, P, Houenou, J, Lopez-Lopez, N, Mangin, J-F, Poupon, C, Roman, C, Vazquez, A, Maffei, C, Arantes, M, Andrade, JP, Silva, SM, Calhoun, VD, Caverzasi, E, Sacco, S, Lauricella, M, Pestilli, F, Bullock, D, Zhan, Y, Brignoni-Perez, E, Lebel, C, Reynolds, JE, Nestrasil, I, Labounek, R, Lenglet, C, Paulson, A, Aulicka, S, Heilbronner, SR, Heuer, K, Chandio, BQ, Guaje, J, Tang, W, Garyfallidis, E, Raja, R, Anderson, AW, Landman, BA, Descoteaux, M, Schilling, KG, Rheault, F, Petit, L, Hansen, CB, Nath, V, Yeh, F-C, Girard, G, Barakovic, M, Rafael-Patino, J, Yu, T, Fischi-Gomez, E, Pizzolato, M, Ocampo-Pineda, M, Schiavi, S, Canales-Rodriguez, EJ, Daducci, A, Granziera, C, Innocenti, G, Thiran, J-P, Mancini, L, Wastling, S, Cocozza, S, Petracca, M, Pontillo, G, Mancini, M, Vos, SB, Vakharia, VN, Duncan, JS, Melero, H, Manzanedo, L, Sanz-Morales, E, Pena-Melian, A, Calamante, F, Attye, A, Cabeen, RP, Korobova, L, Toga, AW, Vijayakumari, AA, Parker, D, Verma, R, Radwan, A, Sunaert, S, Emsell, L, De Luca, A, Leemans, A, Bajada, CJ, Haroon, H, Azadbakht, H, Chamberland, M, Genc, S, Tax, CMW, Yeh, P-H, Srikanchana, R, Mcknight, CD, Yang, JY-M, Chen, J, Kelly, CE, Yeh, C-H, Cochereau, J, Maller, JJ, Welton, T, Almairac, F, Seunarine, KK, Clark, CA, Zhang, F, Makris, N, Golby, A, Rathi, Y, O'Donnell, LJ, Xia, Y, Aydogan, DB, Shi, Y, Fernandes, FG, Raemaekers, M, Warrington, S, Michielse, S, Ramirez-Manzanares, A, Concha, L, Aranda, R, Meraz, MR, Lerma-Usabiaga, G, Roitman, L, Fekonja, LS, Calarco, N, Joseph, M, Nakua, H, Voineskos, AN, Karan, P, Grenier, G, Legarreta, JH, Adluru, N, Nair, VA, Prabhakaran, V, Alexander, AL, Kamagata, K, Saito, Y, Uchida, W, Andica, C, Abe, M, Bayrak, RG, Wheeler-Kingshott, CAMG, D'Angelo, E, Palesi, F, Savini, G, Rolandi, N, Guevara, P, Houenou, J, Lopez-Lopez, N, Mangin, J-F, Poupon, C, Roman, C, Vazquez, A, Maffei, C, Arantes, M, Andrade, JP, Silva, SM, Calhoun, VD, Caverzasi, E, Sacco, S, Lauricella, M, Pestilli, F, Bullock, D, Zhan, Y, Brignoni-Perez, E, Lebel, C, Reynolds, JE, Nestrasil, I, Labounek, R, Lenglet, C, Paulson, A, Aulicka, S, Heilbronner, SR, Heuer, K, Chandio, BQ, Guaje, J, Tang, W, Garyfallidis, E, Raja, R, Anderson, AW, Landman, BA, and Descoteaux, M
Abstract: White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process.
Published: 2021

15. Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset?

Author: Schilling, Kurt G, Schilling, Kurt G, Rheault, François, Petit, Laurent, Hansen, Colin B, Nath, Vishwesh, Yeh, Fang-Cheng, Girard, Gabriel, Barakovic, Muhamed, Rafael-Patino, Jonathan, Yu, Thomas, Fischi-Gomez, Elda, Pizzolato, Marco, Ocampo-Pineda, Mario, Schiavi, Simona, Canales-Rodríguez, Erick J, Daducci, Alessandro, Granziera, Cristina, Innocenti, Giorgio, Thiran, Jean-Philippe, Mancini, Laura, Wastling, Stephen, Cocozza, Sirio, Petracca, Maria, Pontillo, Giuseppe, Mancini, Matteo, Vos, Sjoerd B, Vakharia, Vejay N, Duncan, John S, Melero, Helena, Manzanedo, Lidia, Sanz-Morales, Emilio, Peña-Melián, Ángel, Calamante, Fernando, Attyé, Arnaud, Cabeen, Ryan P, Korobova, Laura, Toga, Arthur W, Vijayakumari, Anupa Ambili, Parker, Drew, Verma, Ragini, Radwan, Ahmed, Sunaert, Stefan, Emsell, Louise, De Luca, Alberto, Leemans, Alexander, Bajada, Claude J, Haroon, Hamied, Azadbakht, Hojjatollah, Chamberland, Maxime, Genc, Sila, Tax, Chantal MW, Yeh, Ping-Hong, Srikanchana, Rujirutana, Mcknight, Colin D, Yang, Joseph Yuan-Mou, Chen, Jian, Kelly, Claire E, Yeh, Chun-Hung, Cochereau, Jerome, Maller, Jerome J, Welton, Thomas, Almairac, Fabien, Seunarine, Kiran K, Clark, Chris A, Zhang, Fan, Makris, Nikos, Golby, Alexandra, Rathi, Yogesh, O'Donnell, Lauren J, Xia, Yihao, Aydogan, Dogu Baran, Shi, Yonggang, Fernandes, Francisco Guerreiro, Raemaekers, Mathijs, Warrington, Shaun, Michielse, Stijn, Ramírez-Manzanares, Alonso, Concha, Luis, Aranda, Ramón, Meraz, Mariano Rivera, Lerma-Usabiaga, Garikoitz, Roitman, Lucas, Fekonja, Lucius S, Calarco, Navona, Joseph, Michael, Nakua, Hajer, Voineskos, Aristotle N, Karan, Philippe, Grenier, Gabrielle, Legarreta, Jon Haitz, Adluru, Nagesh, Nair, Veena A, Prabhakaran, Vivek, Alexander, Andrew L, Kamagata, Koji, Saito, Yuya, Uchida, Wataru, Andica, Christina, Abe, Masahiro, Bayrak, Roza G, Schilling, Kurt G, Schilling, Kurt G, Rheault, François, Petit, Laurent, Hansen, Colin B, Nath, Vishwesh, Yeh, Fang-Cheng, Girard, Gabriel, Barakovic, Muhamed, Rafael-Patino, Jonathan, Yu, Thomas, Fischi-Gomez, Elda, Pizzolato, Marco, Ocampo-Pineda, Mario, Schiavi, Simona, Canales-Rodríguez, Erick J, Daducci, Alessandro, Granziera, Cristina, Innocenti, Giorgio, Thiran, Jean-Philippe, Mancini, Laura, Wastling, Stephen, Cocozza, Sirio, Petracca, Maria, Pontillo, Giuseppe, Mancini, Matteo, Vos, Sjoerd B, Vakharia, Vejay N, Duncan, John S, Melero, Helena, Manzanedo, Lidia, Sanz-Morales, Emilio, Peña-Melián, Ángel, Calamante, Fernando, Attyé, Arnaud, Cabeen, Ryan P, Korobova, Laura, Toga, Arthur W, Vijayakumari, Anupa Ambili, Parker, Drew, Verma, Ragini, Radwan, Ahmed, Sunaert, Stefan, Emsell, Louise, De Luca, Alberto, Leemans, Alexander, Bajada, Claude J, Haroon, Hamied, Azadbakht, Hojjatollah, Chamberland, Maxime, Genc, Sila, Tax, Chantal MW, Yeh, Ping-Hong, Srikanchana, Rujirutana, Mcknight, Colin D, Yang, Joseph Yuan-Mou, Chen, Jian, Kelly, Claire E, Yeh, Chun-Hung, Cochereau, Jerome, Maller, Jerome J, Welton, Thomas, Almairac, Fabien, Seunarine, Kiran K, Clark, Chris A, Zhang, Fan, Makris, Nikos, Golby, Alexandra, Rathi, Yogesh, O'Donnell, Lauren J, Xia, Yihao, Aydogan, Dogu Baran, Shi, Yonggang, Fernandes, Francisco Guerreiro, Raemaekers, Mathijs, Warrington, Shaun, Michielse, Stijn, Ramírez-Manzanares, Alonso, Concha, Luis, Aranda, Ramón, Meraz, Mariano Rivera, Lerma-Usabiaga, Garikoitz, Roitman, Lucas, Fekonja, Lucius S, Calarco, Navona, Joseph, Michael, Nakua, Hajer, Voineskos, Aristotle N, Karan, Philippe, Grenier, Gabrielle, Legarreta, Jon Haitz, Adluru, Nagesh, Nair, Veena A, Prabhakaran, Vivek, Alexander, Andrew L, Kamagata, Koji, Saito, Yuya, Uchida, Wataru, Andica, Christina, Abe, Masahiro, and Bayrak, Roza G
Abstract: White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process.
Published: 2021

16. Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset?

Author: Schilling, Kurt G., Rheault, François, Petit, Laurent, Hansen, Colin B., Nath, Vishwesh, Yeh, Fang Cheng, Girard, Gabriel, Barakovic, Muhamed, Rafael-Patino, Jonathan, Yu, Thomas, Fischi-Gomez, Elda, Pizzolato, Marco, Ocampo-Pineda, Mario, Schiavi, Simona, Canales-Rodríguez, Erick J., Daducci, Alessandro, Granziera, Cristina, Innocenti, Giorgio, Thiran, Jean Philippe, Mancini, Laura, Wastling, Stephen, Cocozza, Sirio, Petracca, Maria, Pontillo, Giuseppe, Mancini, Matteo, Vos, Sjoerd B., Vakharia, Vejay N., Duncan, John S., Melero, Helena, Manzanedo, Lidia, Sanz-Morales, Emilio, Peña-Melián, Ángel, Calamante, Fernando, Attyé, Arnaud, Cabeen, Ryan P., Korobova, Laura, Toga, Arthur W., Vijayakumari, Anupa Ambili, Parker, Drew, Verma, Ragini, Radwan, Ahmed, Sunaert, Stefan, Emsell, Louise, De Luca, Alberto, Leemans, Alexander, Bajada, Claude J., Haroon, Hamied, Azadbakht, Hojjatollah, Chamberland, Maxime, Genc, Sila, Tax, Chantal M.W., Yeh, Ping Hong, Srikanchana, Rujirutana, Mcknight, Colin D., Yang, Joseph Yuan Mou, Chen, Jian, Kelly, Claire E., Yeh, Chun Hung, Cochereau, Jerome, Maller, Jerome J., Welton, Thomas, Almairac, Fabien, Seunarine, Kiran K., Clark, Chris A., Zhang, Fan, Makris, Nikos, Golby, Alexandra, Rathi, Yogesh, O'Donnell, Lauren J., Xia, Yihao, Aydogan, Dogu Baran, Shi, Yonggang, Fernandes, Francisco Guerreiro, Raemaekers, Mathijs, Warrington, Shaun, Michielse, Stijn, Ramírez-Manzanares, Alonso, Concha, Luis, Aranda, Ramón, Meraz, Mariano Rivera, Lerma-Usabiaga, Garikoitz, Roitman, Lucas, Fekonja, Lucius S., Calarco, Navona, Joseph, Michael, Nakua, Hajer, Voineskos, Aristotle N., Karan, Philippe, Grenier, Gabrielle, Legarreta, Jon Haitz, Adluru, Nagesh, Nair, Veena A., Prabhakaran, Vivek, Alexander, Andrew L., Kamagata, Koji, Saito, Yuya, Uchida, Wataru, Andica, Christina, Abe, Masahiro, Bayrak, Roza G., Wheeler-Kingshott, Claudia A.M.Gandini, D'Angelo, Egidio, Palesi, Fulvia, Savini, Giovanni, Rolandi, Nicolò, Guevara, Pamela, Houenou, Josselin, López-López, Narciso, Mangin, Jean François, Poupon, Cyril, Román, Claudio, Vázquez, Andrea, Maffei, Chiara, Arantes, Mavilde, Andrade, José Paulo, Silva, Susana Maria, Calhoun, Vince D., Caverzasi, Eduardo, Sacco, Simone, Lauricella, Michael, Pestilli, Franco, Bullock, Daniel, Zhan, Yang, Brignoni-Perez, Edith, Lebel, Catherine, Reynolds, Jess E., Nestrasil, Igor, Labounek, René, Lenglet, Christophe, Paulson, Amy, Aulicka, Stefania, Heilbronner, Sarah R., Heuer, Katja, Chandio, Bramsh Qamar, Guaje, Javier, Tang, Wei, Garyfallidis, Eleftherios, Raja, Rajikha, Anderson, Adam W., Landman, Bennett A., Descoteaux, Maxime, Schilling, Kurt G., Rheault, François, Petit, Laurent, Hansen, Colin B., Nath, Vishwesh, Yeh, Fang Cheng, Girard, Gabriel, Barakovic, Muhamed, Rafael-Patino, Jonathan, Yu, Thomas, Fischi-Gomez, Elda, Pizzolato, Marco, Ocampo-Pineda, Mario, Schiavi, Simona, Canales-Rodríguez, Erick J., Daducci, Alessandro, Granziera, Cristina, Innocenti, Giorgio, Thiran, Jean Philippe, Mancini, Laura, Wastling, Stephen, Cocozza, Sirio, Petracca, Maria, Pontillo, Giuseppe, Mancini, Matteo, Vos, Sjoerd B., Vakharia, Vejay N., Duncan, John S., Melero, Helena, Manzanedo, Lidia, Sanz-Morales, Emilio, Peña-Melián, Ángel, Calamante, Fernando, Attyé, Arnaud, Cabeen, Ryan P., Korobova, Laura, Toga, Arthur W., Vijayakumari, Anupa Ambili, Parker, Drew, Verma, Ragini, Radwan, Ahmed, Sunaert, Stefan, Emsell, Louise, De Luca, Alberto, Leemans, Alexander, Bajada, Claude J., Haroon, Hamied, Azadbakht, Hojjatollah, Chamberland, Maxime, Genc, Sila, Tax, Chantal M.W., Yeh, Ping Hong, Srikanchana, Rujirutana, Mcknight, Colin D., Yang, Joseph Yuan Mou, Chen, Jian, Kelly, Claire E., Yeh, Chun Hung, Cochereau, Jerome, Maller, Jerome J., Welton, Thomas, Almairac, Fabien, Seunarine, Kiran K., Clark, Chris A., Zhang, Fan, Makris, Nikos, Golby, Alexandra, Rathi, Yogesh, O'Donnell, Lauren J., Xia, Yihao, Aydogan, Dogu Baran, Shi, Yonggang, Fernandes, Francisco Guerreiro, Raemaekers, Mathijs, Warrington, Shaun, Michielse, Stijn, Ramírez-Manzanares, Alonso, Concha, Luis, Aranda, Ramón, Meraz, Mariano Rivera, Lerma-Usabiaga, Garikoitz, Roitman, Lucas, Fekonja, Lucius S., Calarco, Navona, Joseph, Michael, Nakua, Hajer, Voineskos, Aristotle N., Karan, Philippe, Grenier, Gabrielle, Legarreta, Jon Haitz, Adluru, Nagesh, Nair, Veena A., Prabhakaran, Vivek, Alexander, Andrew L., Kamagata, Koji, Saito, Yuya, Uchida, Wataru, Andica, Christina, Abe, Masahiro, Bayrak, Roza G., Wheeler-Kingshott, Claudia A.M.Gandini, D'Angelo, Egidio, Palesi, Fulvia, Savini, Giovanni, Rolandi, Nicolò, Guevara, Pamela, Houenou, Josselin, López-López, Narciso, Mangin, Jean François, Poupon, Cyril, Román, Claudio, Vázquez, Andrea, Maffei, Chiara, Arantes, Mavilde, Andrade, José Paulo, Silva, Susana Maria, Calhoun, Vince D., Caverzasi, Eduardo, Sacco, Simone, Lauricella, Michael, Pestilli, Franco, Bullock, Daniel, Zhan, Yang, Brignoni-Perez, Edith, Lebel, Catherine, Reynolds, Jess E., Nestrasil, Igor, Labounek, René, Lenglet, Christophe, Paulson, Amy, Aulicka, Stefania, Heilbronner, Sarah R., Heuer, Katja, Chandio, Bramsh Qamar, Guaje, Javier, Tang, Wei, Garyfallidis, Eleftherios, Raja, Rajikha, Anderson, Adam W., Landman, Bennett A., and Descoteaux, Maxime
Abstract: White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process.
Published: 2021

17. Drug Utilization Evaluation of High Alert Medications in Intensive Care Units of Tertiary Care Teaching Hospital

Author: Subbaiah, Meda Venkata, Babu, K Leela Prasad, Manohar, Dudekula, Sumalatha, Adluru, Mohammed, Pinjari, Mahitha, Budigireddy, Subbaiah, Meda Venkata, Babu, K Leela Prasad, Manohar, Dudekula, Sumalatha, Adluru, Mohammed, Pinjari, and Mahitha, Budigireddy
Abstract: Introduction: The utilization of HAMs is crucial in emergency and intensive care departments, as they can cause a significant amount of damage to the patient and health care members if we could not follow the standard treatment guidelines. Drug utilization evaluation/review involves a comprehensive review of the patient’s prescription and medication data before, during, and after dispensing to ensure appropriate medication decision making and positive patient outcomes. Objective: This study was taken up given finding the utilization patterns and rectifying the issues with the usage of high alert medications (HAMs) and improving their utilization. Methodology: A cross-sectional study was conducted for 6 months at a south Indian tertiary care hospital. Treatment guidelines were prepared to compare the actual drug use. Data were collected both retrospectively and prospectively by patients and care taker’s interview, medication chart review, and discussion with prescribers and applied WHO DUE indicators to evaluate utilization patterns. Results: Of 362 cases, 57.73 % were males/ and the majority geriatrics. Among all HAMs Insulin is frequently prescribed (34.5 %) and the costly drug is Enoxaparin. Generic names were used in writing prescriptions and parenteral formulations were mostly used. Around 9 ADRs were identified and managed, and a total of 133 moderate to severe Drug-Drug Interactions were found, of them, only 2 were actual. Conclusion: With this study, we conclude that the use of HAMs was found to be appropriate as per the guidelines as we observed very few DRPs with the study drugs. Keywords: HAM, DRP’s, DUE, ICU, DDD
Published: 2021

18. Drug Utilization Evaluation of High Alert Medications in Intensive Care Units of Tertiary Care Teaching Hospital

Author: Subbaiah, Meda Venkata, Babu, K Leela Prasad, Manohar, Dudekula, Sumalatha, Adluru, Mohammed, Pinjari, Mahitha, Budigireddy, Subbaiah, Meda Venkata, Babu, K Leela Prasad, Manohar, Dudekula, Sumalatha, Adluru, Mohammed, Pinjari, and Mahitha, Budigireddy
Abstract: Introduction: The utilization of HAMs is crucial in emergency and intensive care departments, as they can cause a significant amount of damage to the patient and health care members if we could not follow the standard treatment guidelines. Drug utilization evaluation/review involves a comprehensive review of the patient’s prescription and medication data before, during, and after dispensing to ensure appropriate medication decision making and positive patient outcomes. Objective: This study was taken up given finding the utilization patterns and rectifying the issues with the usage of high alert medications (HAMs) and improving their utilization. Methodology: A cross-sectional study was conducted for 6 months at a south Indian tertiary care hospital. Treatment guidelines were prepared to compare the actual drug use. Data were collected both retrospectively and prospectively by patients and care taker’s interview, medication chart review, and discussion with prescribers and applied WHO DUE indicators to evaluate utilization patterns. Results: Of 362 cases, 57.73 % were males/ and the majority geriatrics. Among all HAMs Insulin is frequently prescribed (34.5 %) and the costly drug is Enoxaparin. Generic names were used in writing prescriptions and parenteral formulations were mostly used. Around 9 ADRs were identified and managed, and a total of 133 moderate to severe Drug-Drug Interactions were found, of them, only 2 were actual. Conclusion: With this study, we conclude that the use of HAMs was found to be appropriate as per the guidelines as we observed very few DRPs with the study drugs. Keywords: HAM, DRP’s, DUE, ICU, DDD
Published: 2021

19. Accurate Automatic Segmentation of Amygdala Subnuclei and Modeling of Uncertainty via Bayesian Fully Convolutional Neural Network

Author: Liu, Yilin, Zhao, Gengyan, Nacewicz, Brendon M., Adluru, Nagesh, Kirk, Gregory R., Ferrazzano, Peter A, Styner, Martin, Alexander, Andrew L., Liu, Yilin, Zhao, Gengyan, Nacewicz, Brendon M., Adluru, Nagesh, Kirk, Gregory R., Ferrazzano, Peter A, Styner, Martin, and Alexander, Andrew L.
Abstract: Recent advances in deep learning have improved the segmentation accuracy of subcortical brain structures, which would be useful in neuroimaging studies of many neurological disorders. However, most of the previous deep learning work does not investigate the specific difficulties that exist in segmenting extremely small but important brain regions such as the amygdala and its subregions. To tackle this challenging task, a novel 3D Bayesian fully convolutional neural network was developed to apply a dilated dualpathway approach that retains fine details and utilizes both local and more global contextual information to automatically segment the amygdala and its subregions at high precision. The proposed method provides insights on network design and sampling strategy that target segmentations of small 3D structures. In particular, this study confirms that a large context, enabled by a large field of view, is beneficial for segmenting small objects; furthermore, precise contextual information enabled by dilated convolutions allows for better boundary localization, which is critical for examining the morphology of the structure. In addition, it is demonstrated that the uncertainty information estimated from our network may be leveraged to identify atypicality in data. Our method was compared with two state-of-the-art deep learning models and a traditional multi-atlas approach, and exhibited excellent performance as measured both by Dice overlap as well as average symmetric surface distance. To the best of our knowledge, this work is the first deep learning-based approach that targets the subregions of the amygdala.
Published: 2019

20. A novel hip implant using 3d woven composite material - Design and analysis

Author: Adluru, Hari Kishore and Adluru, Hari Kishore
Abstract: The present research focuses on analyzing the possibility of implementing three dimensional woven composite (3DWC) materials in hip implants. The integration of 3DWCs in hip implants has the possibility to both extend the life-time and improve patient outcomes; by spatially varying mechanical properties to meet both biological needs as well as required mechanical loading. In this study, the bulk material properties of 3DWCs were varied based on woven composite architecture and determined using physics based models, which reflect the realistic geometries of fibers in compaction and preform. The multi-digital chain method combined with Extended Finite Elemental Analysis (XFEA) are adopted in this micro-analysis for composite design. Four different woven architectures with a combination of different existing biocompatible fiber and resins are considered in this study. The main objective is to assess the mechanical response of these biocompatible materials in the design of 3D woven architectures and determine their ability to match the required modulus at different regions of a hip implant. Results obtained show 3DWCs are viable candidates for this application. Multiple architectures and materials chosen, were able to achieve the desired mechanical response. Additional studies can use these results as a starting point and framework for further mechanical and biological testing.
Published: 2015

21. A novel hip implant using 3d woven composite material - Design and analysis

Author: Adluru, Hari Kishore and Adluru, Hari Kishore
Abstract: The present research focuses on analyzing the possibility of implementing three dimensional woven composite (3DWC) materials in hip implants. The integration of 3DWCs in hip implants has the possibility to both extend the life-time and improve patient outcomes; by spatially varying mechanical properties to meet both biological needs as well as required mechanical loading. In this study, the bulk material properties of 3DWCs were varied based on woven composite architecture and determined using physics based models, which reflect the realistic geometries of fibers in compaction and preform. The multi-digital chain method combined with Extended Finite Elemental Analysis (XFEA) are adopted in this micro-analysis for composite design. Four different woven architectures with a combination of different existing biocompatible fiber and resins are considered in this study. The main objective is to assess the mechanical response of these biocompatible materials in the design of 3D woven architectures and determine their ability to match the required modulus at different regions of a hip implant. Results obtained show 3DWCs are viable candidates for this application. Multiple architectures and materials chosen, were able to achieve the desired mechanical response. Additional studies can use these results as a starting point and framework for further mechanical and biological testing.
Published: 2015

22. A Novel Hip Implant Using 3D Woven Composite Material – Design and Analysis

Author: Adluru, Hari Kishore and Adluru, Hari Kishore
Abstract: The present research focuses on analyzing the possibility of implementing three dimensional woven composite (3DWC) materials in hip implants. The integration of 3DWCs in hip implants has the possibility to both extend the life-time and improve patient outcomes; by spatially varying mechanical properties to meet both biological needs as well as required mechanical loading. In this study, the bulk material properties of 3DWCs were varied based on woven composite architecture and determined using physics based models, which reflect the realistic geometries of fibers in compaction and preform. The multi-digital chain method combined with Extended Finite Elemental Analysis (XFEA) are adopted in this micro-analysis for composite design. Four different woven architectures with a combination of different existing biocompatible fiber and resins are considered in this study. The main objective is to assess the mechanical response of these biocompatible materials in the design of 3D woven architectures and determine their ability to match the required modulus at different regions of a hip implant. Results obtained show 3DWCs are viable candidates for this application. Multiple architectures and materials chosen, were able to achieve the desired mechanical response. Additional studies can use these results as a starting point and framework for further mechanical and biological testing.
Published: 2015

23. Design and analysis of micro-channel heat-exchanger embedded in Low Temperature Co-fire Ceramic (LTCC)

Author: Adluru, Hari Kishore and Adluru, Hari Kishore
Abstract: Increased device density, switching speeds of integrated circuits and decrease in package size is placing new demands for high power thermal-management. The convectional method of forced air cooling with passive heat sink can handle heat fluxes up-to 3-5W/cm2; however current microprocessors are operating at levels of 100W/cm2, This demands the usage of novel thermal-management systems. In this work, water-cooling systems with active heat sink are embedded in the substrate. The research involved fabricating LTCC substrates of various configurations - an open-duct substrate, the second with thermal vias and the third with thermal vias and free-standing metal columns and metal foil. Thermal testing was performed experimentally and these results are compared with CFD results. An overall thermal resistance for the base substrate is demonstrated to be 3.4oC/W-cm2. Addition of thermal vias reduces the effective resistance of the system by 7times and further addition of free standing columns reduced it by 20times.
Published: 2004

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23 results on '"Adluru A"'

1. Tractography passes the test:Results from the diffusion-simulated connectivity (disco) challenge

2. Vision Backbone Enhancement via Multi-Stage Cross-Scale Attention

3. Age-related differences in white matter microstructure measured by advanced diffusion MRI in healthy older adults at risk for Alzheimer’s disease

4. Age-related differences in white matter microstructure measured by advanced diffusion MRI in healthy older adults at risk for Alzheimer’s disease

5. Biomedical image analysis competitions: The state of current participation practice

6. Age-related differences in white matter microstructure measured by advanced diffusion MRI in healthy older adults at risk for Alzheimer’s disease

7. Age-related differences in white matter microstructure measured by advanced diffusion MRI in healthy older adults at risk for Alzheimer’s disease

8. Insights from the IronTract challenge:Optimal methods for mapping brain pathways from multi-shell diffusion MRI

9. Insights from the IronTract challenge: Optimal methods for mapping brain pathways from multi-shell diffusion MRI

10. Age-related differences in white matter microstructure measured by advanced diffusion MRI in healthy older adults at risk for Alzheimer’s disease

11. Insights from the IronTract challenge: Optimal methods for mapping brain pathways from multi-shell diffusion MRI

12. Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset?

13. Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset?

14. Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset?

15. Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset?

16. Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset?

17. Drug Utilization Evaluation of High Alert Medications in Intensive Care Units of Tertiary Care Teaching Hospital

18. Drug Utilization Evaluation of High Alert Medications in Intensive Care Units of Tertiary Care Teaching Hospital

19. Accurate Automatic Segmentation of Amygdala Subnuclei and Modeling of Uncertainty via Bayesian Fully Convolutional Neural Network

20. A novel hip implant using 3d woven composite material - Design and analysis

21. A novel hip implant using 3d woven composite material - Design and analysis

22. A Novel Hip Implant Using 3D Woven Composite Material – Design and Analysis

23. Design and analysis of micro-channel heat-exchanger embedded in Low Temperature Co-fire Ceramic (LTCC)

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23 results on '"Adluru A"'

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