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21 results on '"Bashyam A"'

1. Safe Step Act: does it undermine step therapy?

Author

Bashyam, Arjun M, Bashyam, Arjun M, Williford, Phillip M, Feldman, Steven R, Bashyam, Arjun M, Bashyam, Arjun M, Williford, Phillip M, and Feldman, Steven R

Abstract

Drug expenditure in the United States has continued to increase unsustainably; the specialty of dermatology has been particularly affected. Resources are limited - someone has to make decisions about what treatments will be covered and how they will be reimbursed. Step therapy is a cost-control method used by insurers to encourage the use of the most cost-effective treatments before more expensive options are attempted. However, a rigid step therapy policy can be problematic when protocols are out of date, or delay necessary treatment leading to unnecessary suffering, increased morbidity, and overall cost. To address some of these concerns, the proposed Safe Step Act (S. 2546 and H.R. 2279) attempts to create a requirement that insurers provide a transparent, expeditious exceptions process for step therapy protocols. Increased flexibility in this process will allow for the unique circumstances of individual patients and improve access to expensive drugs for special cases. However, this bill may be exploited, further weakening insurers' ability to negotiate on cost. We should be cautious about measures that reduce the effectiveness of this tool, particularly if we, as a society, aim to expand access to basic care to all Americans.

Published
2020

2. Safe Step Act: does it undermine step therapy?

Author

Bashyam, Arjun M, Bashyam, Arjun M, Williford, Phillip M, Feldman, Steven R, Bashyam, Arjun M, Bashyam, Arjun M, Williford, Phillip M, and Feldman, Steven R

Abstract

Drug expenditure in the United States has continued to increase unsustainably; the specialty of dermatology has been particularly affected. Resources are limited - someone has to make decisions about what treatments will be covered and how they will be reimbursed. Step therapy is a cost-control method used by insurers to encourage the use of the most cost-effective treatments before more expensive options are attempted. However, a rigid step therapy policy can be problematic when protocols are out of date, or delay necessary treatment leading to unnecessary suffering, increased morbidity, and overall cost. To address some of these concerns, the proposed Safe Step Act (S. 2546 and H.R. 2279) attempts to create a requirement that insurers provide a transparent, expeditious exceptions process for step therapy protocols. Increased flexibility in this process will allow for the unique circumstances of individual patients and improve access to expensive drugs for special cases. However, this bill may be exploited, further weakening insurers' ability to negotiate on cost. We should be cautious about measures that reduce the effectiveness of this tool, particularly if we, as a society, aim to expand access to basic care to all Americans.

Published
2020

3. Deep Generative Medical Image Harmonization for Improving Cross-Site Generalization in Deep Learning Predictors

Author

Bashyam, VM, Doshi, J, Erus, G, Srinivasan, D, Abdulkadir, A, Singh, A, Habes, M, Fan, Y, Masters, CL, Maruff, P, Zhuo, C, Voelzke, H, Johnson, SC, Fripp, J, Koutsouleris, N, Satterthwaite, TD, Wolf, DH, Gur, RE, Gur, RC, Morris, JC, Albert, MS, Grabe, HJ, Resnick, SM, Bryan, NR, Wittfeld, K, Bulow, R, Wolk, DA, Shou, H, Nasrallah, IM, Davatzikos, C, Bashyam, VM, Doshi, J, Erus, G, Srinivasan, D, Abdulkadir, A, Singh, A, Habes, M, Fan, Y, Masters, CL, Maruff, P, Zhuo, C, Voelzke, H, Johnson, SC, Fripp, J, Koutsouleris, N, Satterthwaite, TD, Wolf, DH, Gur, RE, Gur, RC, Morris, JC, Albert, MS, Grabe, HJ, Resnick, SM, Bryan, NR, Wittfeld, K, Bulow, R, Wolk, DA, Shou, H, Nasrallah, IM, and Davatzikos, C

Abstract

BACKGROUND: In the medical imaging domain, deep learning-based methods have yet to see widespread clinical adoption, in part due to limited generalization performance across different imaging devices and acquisition protocols. The deviation between estimated brain age and biological age is an established biomarker of brain health and such models may benefit from increased cross-site generalizability. PURPOSE: To develop and evaluate a deep learning-based image harmonization method to improve cross-site generalizability of deep learning age prediction. STUDY TYPE: Retrospective. POPULATION: Eight thousand eight hundred and seventy-six subjects from six sites. Harmonization models were trained using all subjects. Age prediction models were trained using 2739 subjects from a single site and tested using the remaining 6137 subjects from various other sites. FIELD STRENGTH/SEQUENCE: Brain imaging with magnetization prepared rapid acquisition with gradient echo or spoiled gradient echo sequences at 1.5 T and 3 T. ASSESSMENT: StarGAN v2, was used to perform a canonical mapping from diverse datasets to a reference domain to reduce site-based variation while preserving semantic information. Generalization performance of deep learning age prediction was evaluated using harmonized, histogram matched, and unharmonized data. STATISTICAL TESTS: Mean absolute error (MAE) and Pearson correlation between estimated age and biological age quantified the performance of the age prediction model. RESULTS: Our results indicated a substantial improvement in age prediction in out-of-sample data, with the overall MAE improving from 15.81 (±0.21) years to 11.86 (±0.11) with histogram matching to 7.21 (±0.22) years with generative adversarial network (GAN)-based harmonization. In the multisite case, across the 5 out-of-sample sites, MAE improved from 9.78 (±6.69) years to 7.74 (±3.03) years with histogram normalization to 5.32 (±4.07) years with GAN-based harmonization. DATA CONCLUSION: While

Published
2022

4. Machine-learning aided in situ drug sensitivity screening predicts treatment outcomes in ovarian PDX tumors

Author

Koch Institute for Integrative Cancer Research at MIT, Harvard University--MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology. Department of Materials Science and Engineering, Cotler, Max J, Ramadi, Khalil B, Hou, Xiaonan, Christodoulopoulos, Elena, Ahn, Sebastian, Bashyam, Ashvin, Ding, Huiming, Larson, Melissa, Oberg, Ann L, Whittaker, Charles, Jonas, Oliver, Kaufmann, Scott H, Weroha, S John, Cima, Michael J, Koch Institute for Integrative Cancer Research at MIT, Harvard University--MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology. Department of Materials Science and Engineering, Cotler, Max J, Ramadi, Khalil B, Hou, Xiaonan, Christodoulopoulos, Elena, Ahn, Sebastian, Bashyam, Ashvin, Ding, Huiming, Larson, Melissa, Oberg, Ann L, Whittaker, Charles, Jonas, Oliver, Kaufmann, Scott H, Weroha, S John, and Cima, Michael J

Abstract

Long-term treatment outcomes for patients with high grade ovarian cancers have not changed despite innovations in therapies. There is no recommended assay for predicting patient response to second-line therapy, thus clinicians must make treatment decisions based on each individual patient. Patient-derived xenograft (PDX) tumors have been shown to predict drug sensitivity in ovarian cancer patients, but the time frame for intraperitoneal (IP) tumor generation, expansion, and drug screening is beyond that for tumor recurrence and platinum resistance to occur, thus results do not have clinical utility. We describe a drug sensitivity screening assay using a drug delivery microdevice implanted for 24 h in subcutaneous (SQ) ovarian PDX tumors to predict treatment outcomes in matched IP PDX tumors in a clinically relevant time frame. The SQ tumor response to local microdose drug exposure was found to be predictive of the growth of matched IP tumors after multi-week systemic therapy using significantly fewer animals (10 SQ vs 206 IP). Multiplexed immunofluorescence image analysis of phenotypic tumor response combined with a machine learning classifier could predict IP treatment outcomes against three second-line cytotoxic therapies with an average AUC of 0.91.

Published
2022

5. Dehydration assessment via a portable, single sided magnetic resonance sensor

Author

Koch Institute for Integrative Cancer Research at MIT, Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Harvard University--MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology. Department of Materials Science and Engineering, Bashyam, Ashvin, Frangieh, Chris J., Li, Matthew, Cima, Michael J., Koch Institute for Integrative Cancer Research at MIT, Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Harvard University--MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology. Department of Materials Science and Engineering, Bashyam, Ashvin, Frangieh, Chris J., Li, Matthew, and Cima, Michael J.

Published
2022

6. Applications of Generative Adversarial Networks in Neuroimaging and Clinical Neuroscience

Author

Wang, Rongguang, Bashyam, Vishnu, Yang, Zhijian, Yu, Fanyang, Tassopoulou, Vasiliki, Chintapalli, Sai Spandana, Skampardoni, Ioanna, Sreepada, Lasya P., Sahoo, Dushyant, Nikita, Konstantina, Abdulkadir, Ahmed, Wen, Junhao, Davatzikos, Christos, Wang, Rongguang, Bashyam, Vishnu, Yang, Zhijian, Yu, Fanyang, Tassopoulou, Vasiliki, Chintapalli, Sai Spandana, Skampardoni, Ioanna, Sreepada, Lasya P., Sahoo, Dushyant, Nikita, Konstantina, Abdulkadir, Ahmed, Wen, Junhao, and Davatzikos, Christos

Abstract

Generative adversarial networks (GANs) are one powerful type of deep learning models that have been successfully utilized in numerous fields. They belong to a broader family called generative methods, which generate new data with a probabilistic model by learning sample distribution from real examples. In the clinical context, GANs have shown enhanced capabilities in capturing spatially complex, nonlinear, and potentially subtle disease effects compared to traditional generative methods. This review appraises the existing literature on the applications of GANs in imaging studies of various neurological conditions, including Alzheimer's disease, brain tumors, brain aging, and multiple sclerosis. We provide an intuitive explanation of various GAN methods for each application and further discuss the main challenges, open questions, and promising future directions of leveraging GANs in neuroimaging. We aim to bridge the gap between advanced deep learning methods and neurology research by highlighting how GANs can be leveraged to support clinical decision making and contribute to a better understanding of the structural and functional patterns of brain diseases.

Published
2022
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7. SARS-CoV-2 B.1.617.2 Delta variant replication and immune evasion

Author

Mlcochova, Petra, Kemp, Steven A., Dhar, Mahesh Shanker, Papa, Guido, Meng, Bo, Ferreira, Isabella A. T. M., Datir, Rawlings, Collier, Dami A., Albecka, Anna, Singh, Sujeet, Pandey, Rajesh, Brown, Jonathan, Zhou, Jie, Goonawardane, Niluka, Mishra, Swapnil, Whittaker, Charles, Mellan, Thomas, Marwal, Robin, Datta, Meena, Sengupta, Shantanu, Ponnusamy, Kalaiarasan, Radhakrishnan, Venkatraman Srinivasan, Abdullahi, Adam, Charles, Oscar, Chattopadhyay, Partha, Devi, Priti, Caputo, Daniela, Peacock, Tom, Wattal, Chand, Goel, Neeraj, Satwik, Ambrish, Vaishya, Raju, Agarwal, Meenakshi, Chauhan, Himanshu, Dikid, Tanzin, Gogia, Hema, Lall, Hemlata, Verma, Kaptan, Singh, Manoj K., Soni, Namita, Meena, Namonarayan, Madan, Preeti, Singh, Priyanka, Sharma, Ramesh, Sharma, Rajeev, Kabra, Sandhya, Kumar, Sattender, Kumari, Swati, Sharma, Uma, Chaudhary, Urmila, Sivasubbu, Sridhar, Scaria, Vinod, Oberoi, J. K., Raveendran, Reena, Datta, S., Das, Saumitra, Maitra, Arindam, Chinnaswamy, Sreedhar, Biswas, Nidhan Kumar, Parida, Ajay, Raghav, Sunil K., Prasad, Punit, Sarin, Apurva, Mayor, Satyajit, Ramakrishnan, Uma, Palakodeti, Dasaradhi, Seshasayee, Aswin Sai Narain, Thangaraj, K., Bashyam, Murali Dharan, Dalal, Ashwin, Bhat, Manoj, Shouche, Yogesh, Pillai, Ajay, Abraham, Priya, Potdar, Varsha Atul, Cherian, Sarah S., Desai, Anita Sudhir, Pattabiraman, Chitra, Manjunatha, M. V., Mani, Reeta S., Udupi, Gautam Arunachal, Nandicoori, Vinay, Tallapaka, Karthik Bharadwaj, Sowpati, Divya Tej, Kawabata, Ryoko, Morizako, Nanami, Sadamasu, Kenji, Asakura, Hiroyuki, Nagashima, Mami, Yoshimura, Kazuhisa, Ito, Jumpei, Kimura, Izumi, Uriu, Keiya, Kosugi, Yusuke, Suganami, Mai, Oide, Akiko, Yokoyama, Miyabishara, Chiba, Mika, Saito, Akatsuki, Butlertanaka, Erika P., Tanaka, Yuri L., Ikeda, Terumasa, Motozono, Chihiro, Nasser, Hesham, Shimizu, Ryo, Yuan, Yue, Kitazato, Kazuko, Hasebe, Haruyo, Nakagawa, So, Wu, Jiaqi, Takahashi, Miyoko, Fukuhara, Takasuke, Shimizu, Kenta, Tsushima, Kana, Kubo, Haruko, Shirakawa, Kotaro, Kazuma, Yasuhiro, Nomura, Ryosuke, Horisawa, Yoshihito, Takaori-Kondo, Akifumi, Tokunaga, Kenzo, Ozono, Seiya, Baker, Stephen, Dougan, Gordon, Hess, Christoph, Kingston, Nathalie, Lehner, Paul J., Lyons, Paul A., Matheson, Nicholas J., Owehand, Willem H., Saunders, Caroline, Summers, Charlotte, Thaventhiran, James E. D., Toshner, Mark, Weekes, Michael P., Maxwell, Patrick, Shaw, Ashley, Bucke, Ashlea, Calder, Jo, Canna, Laura, Domingo, Jason, Elmer, Anne, Fuller, Stewart, Harris, Julie, Hewitt, Sarah, Kennet, Jane, Jose, Sherly, Kourampa, Jenny, Meadows, Anne, O'Brien, Criona, Price, Jane, Publico, Cherry, Rastall, Rebecca, Ribeiro, Carla, Rowlands, Jane, Ruffolo, Valentina, Tordesillas, Hugo, Bullman, Ben, Dunmore, Benjamin J., Fawke, Stuart, Graf, Stefan, Hodgson, Josh, Huang, Christopher, Hunter, Kelvin, Jones, Emma, Legchenko, Ekaterina, Matara, Cecilia, Martin, Jennifer, Mescia, Federica, O'Donnell, Ciara, Pointon, Linda, Pond, Nicole, Shih, Joy, Sutcliffe, Rachel, Tilly, Tobias, Treacy, Carmen, Tong, Zhen, Wood, Jennifer, Wylot, Marta, Bergamaschi, Laura, Betancourt, Ariana, Bower, Georgie, Cossetti, Chiara, De Sa, Aloka, Epping, Madeline, Gleadall, Nick, Grenfell, Richard, Hinch, Andrew, Huhn, Oisin, Jackson, Sarah, Jarvis, Isobel, Krishna, Ben, Lewis, Daniel, Marsden, Joe, Nice, Francesca, Okecha, Georgina, Omarjee, Ommar, Perera, Marianne, Potts, Martin, Richoz, Nathan, Romashova, Veronika, Yarkoni, Natalia Savinykh, Sharma, Rahul, Stefanucci, Luca, Stephens, Jonathan, Strezlecki, Mateusz, Turner, Lori, De Bie, Eckart M. D. D., Bunclark, Katherine, Josipovic, Masa, Mackay, Michael, Rossi, Sabrina, Selvan, Mayurun, Spencer, Sarah, Yong, Cissy, Allison, John, Butcher, Helen, Clapham-Riley, Debbie, Dewhurst, Eleanor, Furlong, Anita, Graves, Barbara, Gray, Jennifer, Ivers, Tasmin, Kasanicki, Mary, Le Gresley, Emma, Linger, Rachel, Meloy, Sarah, Muldoon, Francesca, Ovington, Nigel, Papadia, Sofia, Phelan, Isabel, Stark, Hannah, Stirrups, Kathleen E., Townsend, Paul, Walker, Neil, Webster, Jennifer, Scholtes, Ingrid, Hein, Sabine, King, Rebecca, Mavousian, Antranik, Lee, Joo Hyeon, Bassi, Jessica, Silacci-Fegni, Chiara, Saliba, Christian, Pinto, Dora, Irie, Takashi, Yoshida, Isao, Hamilton, William L., Sato, Kei, Bhatt, Samir, Flaxman, Seth, James, Leo C., Corti, Davide, Piccoli, Luca, Barclay, Wendy S., Rakshit, Partha, Agrawal, Anurag, Gupta, Ravindra K., Mlcochova, Petra, Kemp, Steven A., Dhar, Mahesh Shanker, Papa, Guido, Meng, Bo, Ferreira, Isabella A. T. M., Datir, Rawlings, Collier, Dami A., Albecka, Anna, Singh, Sujeet, Pandey, Rajesh, Brown, Jonathan, Zhou, Jie, Goonawardane, Niluka, Mishra, Swapnil, Whittaker, Charles, Mellan, Thomas, Marwal, Robin, Datta, Meena, Sengupta, Shantanu, Ponnusamy, Kalaiarasan, Radhakrishnan, Venkatraman Srinivasan, Abdullahi, Adam, Charles, Oscar, Chattopadhyay, Partha, Devi, Priti, Caputo, Daniela, Peacock, Tom, Wattal, Chand, Goel, Neeraj, Satwik, Ambrish, Vaishya, Raju, Agarwal, Meenakshi, Chauhan, Himanshu, Dikid, Tanzin, Gogia, Hema, Lall, Hemlata, Verma, Kaptan, Singh, Manoj K., Soni, Namita, Meena, Namonarayan, Madan, Preeti, Singh, Priyanka, Sharma, Ramesh, Sharma, Rajeev, Kabra, Sandhya, Kumar, Sattender, Kumari, Swati, Sharma, Uma, Chaudhary, Urmila, Sivasubbu, Sridhar, Scaria, Vinod, Oberoi, J. K., Raveendran, Reena, Datta, S., Das, Saumitra, Maitra, Arindam, Chinnaswamy, Sreedhar, Biswas, Nidhan Kumar, Parida, Ajay, Raghav, Sunil K., Prasad, Punit, Sarin, Apurva, Mayor, Satyajit, Ramakrishnan, Uma, Palakodeti, Dasaradhi, Seshasayee, Aswin Sai Narain, Thangaraj, K., Bashyam, Murali Dharan, Dalal, Ashwin, Bhat, Manoj, Shouche, Yogesh, Pillai, Ajay, Abraham, Priya, Potdar, Varsha Atul, Cherian, Sarah S., Desai, Anita Sudhir, Pattabiraman, Chitra, Manjunatha, M. V., Mani, Reeta S., Udupi, Gautam Arunachal, Nandicoori, Vinay, Tallapaka, Karthik Bharadwaj, Sowpati, Divya Tej, Kawabata, Ryoko, Morizako, Nanami, Sadamasu, Kenji, Asakura, Hiroyuki, Nagashima, Mami, Yoshimura, Kazuhisa, Ito, Jumpei, Kimura, Izumi, Uriu, Keiya, Kosugi, Yusuke, Suganami, Mai, Oide, Akiko, Yokoyama, Miyabishara, Chiba, Mika, Saito, Akatsuki, Butlertanaka, Erika P., Tanaka, Yuri L., Ikeda, Terumasa, Motozono, Chihiro, Nasser, Hesham, Shimizu, Ryo, Yuan, Yue, Kitazato, Kazuko, Hasebe, Haruyo, Nakagawa, So, Wu, Jiaqi, Takahashi, Miyoko, Fukuhara, Takasuke, Shimizu, Kenta, Tsushima, Kana, Kubo, Haruko, Shirakawa, Kotaro, Kazuma, Yasuhiro, Nomura, Ryosuke, Horisawa, Yoshihito, Takaori-Kondo, Akifumi, Tokunaga, Kenzo, Ozono, Seiya, Baker, Stephen, Dougan, Gordon, Hess, Christoph, Kingston, Nathalie, Lehner, Paul J., Lyons, Paul A., Matheson, Nicholas J., Owehand, Willem H., Saunders, Caroline, Summers, Charlotte, Thaventhiran, James E. D., Toshner, Mark, Weekes, Michael P., Maxwell, Patrick, Shaw, Ashley, Bucke, Ashlea, Calder, Jo, Canna, Laura, Domingo, Jason, Elmer, Anne, Fuller, Stewart, Harris, Julie, Hewitt, Sarah, Kennet, Jane, Jose, Sherly, Kourampa, Jenny, Meadows, Anne, O'Brien, Criona, Price, Jane, Publico, Cherry, Rastall, Rebecca, Ribeiro, Carla, Rowlands, Jane, Ruffolo, Valentina, Tordesillas, Hugo, Bullman, Ben, Dunmore, Benjamin J., Fawke, Stuart, Graf, Stefan, Hodgson, Josh, Huang, Christopher, Hunter, Kelvin, Jones, Emma, Legchenko, Ekaterina, Matara, Cecilia, Martin, Jennifer, Mescia, Federica, O'Donnell, Ciara, Pointon, Linda, Pond, Nicole, Shih, Joy, Sutcliffe, Rachel, Tilly, Tobias, Treacy, Carmen, Tong, Zhen, Wood, Jennifer, Wylot, Marta, Bergamaschi, Laura, Betancourt, Ariana, Bower, Georgie, Cossetti, Chiara, De Sa, Aloka, Epping, Madeline, Gleadall, Nick, Grenfell, Richard, Hinch, Andrew, Huhn, Oisin, Jackson, Sarah, Jarvis, Isobel, Krishna, Ben, Lewis, Daniel, Marsden, Joe, Nice, Francesca, Okecha, Georgina, Omarjee, Ommar, Perera, Marianne, Potts, Martin, Richoz, Nathan, Romashova, Veronika, Yarkoni, Natalia Savinykh, Sharma, Rahul, Stefanucci, Luca, Stephens, Jonathan, Strezlecki, Mateusz, Turner, Lori, De Bie, Eckart M. D. D., Bunclark, Katherine, Josipovic, Masa, Mackay, Michael, Rossi, Sabrina, Selvan, Mayurun, Spencer, Sarah, Yong, Cissy, Allison, John, Butcher, Helen, Clapham-Riley, Debbie, Dewhurst, Eleanor, Furlong, Anita, Graves, Barbara, Gray, Jennifer, Ivers, Tasmin, Kasanicki, Mary, Le Gresley, Emma, Linger, Rachel, Meloy, Sarah, Muldoon, Francesca, Ovington, Nigel, Papadia, Sofia, Phelan, Isabel, Stark, Hannah, Stirrups, Kathleen E., Townsend, Paul, Walker, Neil, Webster, Jennifer, Scholtes, Ingrid, Hein, Sabine, King, Rebecca, Mavousian, Antranik, Lee, Joo Hyeon, Bassi, Jessica, Silacci-Fegni, Chiara, Saliba, Christian, Pinto, Dora, Irie, Takashi, Yoshida, Isao, Hamilton, William L., Sato, Kei, Bhatt, Samir, Flaxman, Seth, James, Leo C., Corti, Davide, Piccoli, Luca, Barclay, Wendy S., Rakshit, Partha, Agrawal, Anurag, and Gupta, Ravindra K.

Published
2021

8. Food Insecurity, Climate Change and Role of Physicians

Author

Bhagratie, Joe, Iyengar, Bashyam, Bhagratie, Joe, and Iyengar, Bashyam

Abstract

Food insecurity is a growing concern both locally and globally. Food insecurity has a number of causative factors. The food delivery system is laden with many inequities and fragilities. We will outline a framework of the food delivery system and the areas where inequities exist. Climate change is an equally expansive topic. Changes to our climate have a myriad of impacts on our food delivery systems and in turn can exacerbate our already tenuous delivery system. Continued impacts on our environment creates even more imbalance and can subsequently exacerbate food inequities further. The role climate change has is significant when it comes to food insecurity. Physicians are intimately aware of the role that proper nutrition plays in our health, equally we are aware that poor access to nutritious foods has deleterious effects on a person’s health and wellbeing. Physicians are at the forefront of this interface and are thus uniquely poised to advocate for changes in food delivery, as well as advocate for better resources to combat climate change.

Published
2021

9. SARS-CoV-2 B.1.617.2 Delta variant replication and immune evasion

Author

Mlcochova, Petra, Kemp, Steven A., Dhar, Mahesh Shanker, Papa, Guido, Meng, Bo, Ferreira, Isabella A. T. M., Datir, Rawlings, Collier, Dami A., Albecka, Anna, Singh, Sujeet, Pandey, Rajesh, Brown, Jonathan, Zhou, Jie, Goonawardane, Niluka, Mishra, Swapnil, Whittaker, Charles, Mellan, Thomas, Marwal, Robin, Datta, Meena, Sengupta, Shantanu, Ponnusamy, Kalaiarasan, Radhakrishnan, Venkatraman Srinivasan, Abdullahi, Adam, Charles, Oscar, Chattopadhyay, Partha, Devi, Priti, Caputo, Daniela, Peacock, Tom, Wattal, Chand, Goel, Neeraj, Satwik, Ambrish, Vaishya, Raju, Agarwal, Meenakshi, Chauhan, Himanshu, Dikid, Tanzin, Gogia, Hema, Lall, Hemlata, Verma, Kaptan, Singh, Manoj K., Soni, Namita, Meena, Namonarayan, Madan, Preeti, Singh, Priyanka, Sharma, Ramesh, Sharma, Rajeev, Kabra, Sandhya, Kumar, Sattender, Kumari, Swati, Sharma, Uma, Chaudhary, Urmila, Sivasubbu, Sridhar, Scaria, Vinod, Oberoi, J. K., Raveendran, Reena, Datta, S., Das, Saumitra, Maitra, Arindam, Chinnaswamy, Sreedhar, Biswas, Nidhan Kumar, Parida, Ajay, Raghav, Sunil K., Prasad, Punit, Sarin, Apurva, Mayor, Satyajit, Ramakrishnan, Uma, Palakodeti, Dasaradhi, Seshasayee, Aswin Sai Narain, Thangaraj, K., Bashyam, Murali Dharan, Dalal, Ashwin, Bhat, Manoj, Shouche, Yogesh, Pillai, Ajay, Abraham, Priya, Potdar, Varsha Atul, Cherian, Sarah S., Desai, Anita Sudhir, Pattabiraman, Chitra, Manjunatha, M. V., Mani, Reeta S., Udupi, Gautam Arunachal, Nandicoori, Vinay, Tallapaka, Karthik Bharadwaj, Sowpati, Divya Tej, Kawabata, Ryoko, Morizako, Nanami, Sadamasu, Kenji, Asakura, Hiroyuki, Nagashima, Mami, Yoshimura, Kazuhisa, Ito, Jumpei, Kimura, Izumi, Uriu, Keiya, Kosugi, Yusuke, Suganami, Mai, Oide, Akiko, Yokoyama, Miyabishara, Chiba, Mika, Saito, Akatsuki, Butlertanaka, Erika P., Tanaka, Yuri L., Ikeda, Terumasa, Motozono, Chihiro, Nasser, Hesham, Shimizu, Ryo, Yuan, Yue, Kitazato, Kazuko, Hasebe, Haruyo, Nakagawa, So, Wu, Jiaqi, Takahashi, Miyoko, Fukuhara, Takasuke, Shimizu, Kenta, Tsushima, Kana, Kubo, Haruko, Shirakawa, Kotaro, Kazuma, Yasuhiro, Nomura, Ryosuke, Horisawa, Yoshihito, Takaori-Kondo, Akifumi, Tokunaga, Kenzo, Ozono, Seiya, Baker, Stephen, Dougan, Gordon, Hess, Christoph, Kingston, Nathalie, Lehner, Paul J., Lyons, Paul A., Matheson, Nicholas J., Owehand, Willem H., Saunders, Caroline, Summers, Charlotte, Thaventhiran, James E. D., Toshner, Mark, Weekes, Michael P., Maxwell, Patrick, Shaw, Ashley, Bucke, Ashlea, Calder, Jo, Canna, Laura, Domingo, Jason, Elmer, Anne, Fuller, Stewart, Harris, Julie, Hewitt, Sarah, Kennet, Jane, Jose, Sherly, Kourampa, Jenny, Meadows, Anne, O'Brien, Criona, Price, Jane, Publico, Cherry, Rastall, Rebecca, Ribeiro, Carla, Rowlands, Jane, Ruffolo, Valentina, Tordesillas, Hugo, Bullman, Ben, Dunmore, Benjamin J., Fawke, Stuart, Graf, Stefan, Hodgson, Josh, Huang, Christopher, Hunter, Kelvin, Jones, Emma, Legchenko, Ekaterina, Matara, Cecilia, Martin, Jennifer, Mescia, Federica, O'Donnell, Ciara, Pointon, Linda, Pond, Nicole, Shih, Joy, Sutcliffe, Rachel, Tilly, Tobias, Treacy, Carmen, Tong, Zhen, Wood, Jennifer, Wylot, Marta, Bergamaschi, Laura, Betancourt, Ariana, Bower, Georgie, Cossetti, Chiara, De Sa, Aloka, Epping, Madeline, Gleadall, Nick, Grenfell, Richard, Hinch, Andrew, Huhn, Oisin, Jackson, Sarah, Jarvis, Isobel, Krishna, Ben, Lewis, Daniel, Marsden, Joe, Nice, Francesca, Okecha, Georgina, Omarjee, Ommar, Perera, Marianne, Potts, Martin, Richoz, Nathan, Romashova, Veronika, Yarkoni, Natalia Savinykh, Sharma, Rahul, Stefanucci, Luca, Stephens, Jonathan, Strezlecki, Mateusz, Turner, Lori, De Bie, Eckart M. D. D., Bunclark, Katherine, Josipovic, Masa, Mackay, Michael, Rossi, Sabrina, Selvan, Mayurun, Spencer, Sarah, Yong, Cissy, Allison, John, Butcher, Helen, Clapham-Riley, Debbie, Dewhurst, Eleanor, Furlong, Anita, Graves, Barbara, Gray, Jennifer, Ivers, Tasmin, Kasanicki, Mary, Le Gresley, Emma, Linger, Rachel, Meloy, Sarah, Muldoon, Francesca, Ovington, Nigel, Papadia, Sofia, Phelan, Isabel, Stark, Hannah, Stirrups, Kathleen E., Townsend, Paul, Walker, Neil, Webster, Jennifer, Scholtes, Ingrid, Hein, Sabine, King, Rebecca, Mavousian, Antranik, Lee, Joo Hyeon, Bassi, Jessica, Silacci-Fegni, Chiara, Saliba, Christian, Pinto, Dora, Irie, Takashi, Yoshida, Isao, Hamilton, William L., Sato, Kei, Bhatt, Samir, Flaxman, Seth, James, Leo C., Corti, Davide, Piccoli, Luca, Barclay, Wendy S., Rakshit, Partha, Agrawal, Anurag, Gupta, Ravindra K., Mlcochova, Petra, Kemp, Steven A., Dhar, Mahesh Shanker, Papa, Guido, Meng, Bo, Ferreira, Isabella A. T. M., Datir, Rawlings, Collier, Dami A., Albecka, Anna, Singh, Sujeet, Pandey, Rajesh, Brown, Jonathan, Zhou, Jie, Goonawardane, Niluka, Mishra, Swapnil, Whittaker, Charles, Mellan, Thomas, Marwal, Robin, Datta, Meena, Sengupta, Shantanu, Ponnusamy, Kalaiarasan, Radhakrishnan, Venkatraman Srinivasan, Abdullahi, Adam, Charles, Oscar, Chattopadhyay, Partha, Devi, Priti, Caputo, Daniela, Peacock, Tom, Wattal, Chand, Goel, Neeraj, Satwik, Ambrish, Vaishya, Raju, Agarwal, Meenakshi, Chauhan, Himanshu, Dikid, Tanzin, Gogia, Hema, Lall, Hemlata, Verma, Kaptan, Singh, Manoj K., Soni, Namita, Meena, Namonarayan, Madan, Preeti, Singh, Priyanka, Sharma, Ramesh, Sharma, Rajeev, Kabra, Sandhya, Kumar, Sattender, Kumari, Swati, Sharma, Uma, Chaudhary, Urmila, Sivasubbu, Sridhar, Scaria, Vinod, Oberoi, J. K., Raveendran, Reena, Datta, S., Das, Saumitra, Maitra, Arindam, Chinnaswamy, Sreedhar, Biswas, Nidhan Kumar, Parida, Ajay, Raghav, Sunil K., Prasad, Punit, Sarin, Apurva, Mayor, Satyajit, Ramakrishnan, Uma, Palakodeti, Dasaradhi, Seshasayee, Aswin Sai Narain, Thangaraj, K., Bashyam, Murali Dharan, Dalal, Ashwin, Bhat, Manoj, Shouche, Yogesh, Pillai, Ajay, Abraham, Priya, Potdar, Varsha Atul, Cherian, Sarah S., Desai, Anita Sudhir, Pattabiraman, Chitra, Manjunatha, M. V., Mani, Reeta S., Udupi, Gautam Arunachal, Nandicoori, Vinay, Tallapaka, Karthik Bharadwaj, Sowpati, Divya Tej, Kawabata, Ryoko, Morizako, Nanami, Sadamasu, Kenji, Asakura, Hiroyuki, Nagashima, Mami, Yoshimura, Kazuhisa, Ito, Jumpei, Kimura, Izumi, Uriu, Keiya, Kosugi, Yusuke, Suganami, Mai, Oide, Akiko, Yokoyama, Miyabishara, Chiba, Mika, Saito, Akatsuki, Butlertanaka, Erika P., Tanaka, Yuri L., Ikeda, Terumasa, Motozono, Chihiro, Nasser, Hesham, Shimizu, Ryo, Yuan, Yue, Kitazato, Kazuko, Hasebe, Haruyo, Nakagawa, So, Wu, Jiaqi, Takahashi, Miyoko, Fukuhara, Takasuke, Shimizu, Kenta, Tsushima, Kana, Kubo, Haruko, Shirakawa, Kotaro, Kazuma, Yasuhiro, Nomura, Ryosuke, Horisawa, Yoshihito, Takaori-Kondo, Akifumi, Tokunaga, Kenzo, Ozono, Seiya, Baker, Stephen, Dougan, Gordon, Hess, Christoph, Kingston, Nathalie, Lehner, Paul J., Lyons, Paul A., Matheson, Nicholas J., Owehand, Willem H., Saunders, Caroline, Summers, Charlotte, Thaventhiran, James E. D., Toshner, Mark, Weekes, Michael P., Maxwell, Patrick, Shaw, Ashley, Bucke, Ashlea, Calder, Jo, Canna, Laura, Domingo, Jason, Elmer, Anne, Fuller, Stewart, Harris, Julie, Hewitt, Sarah, Kennet, Jane, Jose, Sherly, Kourampa, Jenny, Meadows, Anne, O'Brien, Criona, Price, Jane, Publico, Cherry, Rastall, Rebecca, Ribeiro, Carla, Rowlands, Jane, Ruffolo, Valentina, Tordesillas, Hugo, Bullman, Ben, Dunmore, Benjamin J., Fawke, Stuart, Graf, Stefan, Hodgson, Josh, Huang, Christopher, Hunter, Kelvin, Jones, Emma, Legchenko, Ekaterina, Matara, Cecilia, Martin, Jennifer, Mescia, Federica, O'Donnell, Ciara, Pointon, Linda, Pond, Nicole, Shih, Joy, Sutcliffe, Rachel, Tilly, Tobias, Treacy, Carmen, Tong, Zhen, Wood, Jennifer, Wylot, Marta, Bergamaschi, Laura, Betancourt, Ariana, Bower, Georgie, Cossetti, Chiara, De Sa, Aloka, Epping, Madeline, Gleadall, Nick, Grenfell, Richard, Hinch, Andrew, Huhn, Oisin, Jackson, Sarah, Jarvis, Isobel, Krishna, Ben, Lewis, Daniel, Marsden, Joe, Nice, Francesca, Okecha, Georgina, Omarjee, Ommar, Perera, Marianne, Potts, Martin, Richoz, Nathan, Romashova, Veronika, Yarkoni, Natalia Savinykh, Sharma, Rahul, Stefanucci, Luca, Stephens, Jonathan, Strezlecki, Mateusz, Turner, Lori, De Bie, Eckart M. D. D., Bunclark, Katherine, Josipovic, Masa, Mackay, Michael, Rossi, Sabrina, Selvan, Mayurun, Spencer, Sarah, Yong, Cissy, Allison, John, Butcher, Helen, Clapham-Riley, Debbie, Dewhurst, Eleanor, Furlong, Anita, Graves, Barbara, Gray, Jennifer, Ivers, Tasmin, Kasanicki, Mary, Le Gresley, Emma, Linger, Rachel, Meloy, Sarah, Muldoon, Francesca, Ovington, Nigel, Papadia, Sofia, Phelan, Isabel, Stark, Hannah, Stirrups, Kathleen E., Townsend, Paul, Walker, Neil, Webster, Jennifer, Scholtes, Ingrid, Hein, Sabine, King, Rebecca, Mavousian, Antranik, Lee, Joo Hyeon, Bassi, Jessica, Silacci-Fegni, Chiara, Saliba, Christian, Pinto, Dora, Irie, Takashi, Yoshida, Isao, Hamilton, William L., Sato, Kei, Bhatt, Samir, Flaxman, Seth, James, Leo C., Corti, Davide, Piccoli, Luca, Barclay, Wendy S., Rakshit, Partha, Agrawal, Anurag, and Gupta, Ravindra K.

Published
2021

10. GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging

Author

Pati, Sarthak, Thakur, Siddhesh P., Hamamcı, İbrahim Ethem, Baid, Ujjwal, Baheti, Bhakti, Bhalerao, Megh, Güley, Orhun, Mouchtaris, Sofia, Lang, David, Thermos, Spyridon, Gotkowski, Karol, González, Camila, Grenko, Caleb, Getka, Alexander, Edwards, Brandon, Sheller, Micah, Wu, Junwen, Karkada, Deepthi, Panchumarthy, Ravi, Ahluwalia, Vinayak, Zou, Chunrui, Bashyam, Vishnu, Li, Yuemeng, Haghighi, Babak, Chitalia, Rhea, Abousamra, Shahira, Kurc, Tahsin M., Gastounioti, Aimilia, Er, Sezgin, Bergman, Mark, Saltz, Joel H., Fan, Yong, Shah, Prashant, Mukhopadhyay, Anirban, Tsaftaris, Sotirios A., Menze, Bjoern, Davatzikos, Christos, Kontos, Despina, Karargyris, Alexandros, Umeton, Renato, Mattson, Peter, Bakas, Spyridon, Pati, Sarthak, Thakur, Siddhesh P., Hamamcı, İbrahim Ethem, Baid, Ujjwal, Baheti, Bhakti, Bhalerao, Megh, Güley, Orhun, Mouchtaris, Sofia, Lang, David, Thermos, Spyridon, Gotkowski, Karol, González, Camila, Grenko, Caleb, Getka, Alexander, Edwards, Brandon, Sheller, Micah, Wu, Junwen, Karkada, Deepthi, Panchumarthy, Ravi, Ahluwalia, Vinayak, Zou, Chunrui, Bashyam, Vishnu, Li, Yuemeng, Haghighi, Babak, Chitalia, Rhea, Abousamra, Shahira, Kurc, Tahsin M., Gastounioti, Aimilia, Er, Sezgin, Bergman, Mark, Saltz, Joel H., Fan, Yong, Shah, Prashant, Mukhopadhyay, Anirban, Tsaftaris, Sotirios A., Menze, Bjoern, Davatzikos, Christos, Kontos, Despina, Karargyris, Alexandros, Umeton, Renato, Mattson, Peter, and Bakas, Spyridon

Abstract

Deep Learning (DL) has the potential to optimize machine learning in both the scientific and clinical communities. However, greater expertise is required to develop DL algorithms, and the variability of implementations hinders their reproducibility, translation, and deployment. Here we present the community-driven Generally Nuanced Deep Learning Framework (GaNDLF), with the goal of lowering these barriers. GaNDLF makes the mechanism of DL development, training, and inference more stable, reproducible, interpretable, and scalable, without requiring an extensive technical background. GaNDLF aims to provide an end-to-end solution for all DL-related tasks in computational precision medicine. We demonstrate the ability of GaNDLF to analyze both radiology and histology images, with built-in support for k-fold cross-validation, data augmentation, multiple modalities and output classes. Our quantitative performance evaluation on numerous use cases, anatomies, and computational tasks supports GaNDLF as a robust application framework for deployment in clinical workflows., Comment: Deep Learning, Framework, Segmentation, Regression, Classification, Cross-validation, Data augmentation, Deployment, Clinical, Workflows

Published
2021
Full Text
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11. GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging

Author

Pati, Sarthak, Thakur, Siddhesh, Bhalerao, Megh, Thermos, Spyridon, Baid, Ujjwal, Gotkowski, Karol, Gonzalez, Camila, Güley, Orhun, Hamamci, Ibrahim Ethem, Er, Sezgin, Grenko, Caleb, Brandon, Edwards, Sheller, Micah, Agraz, Jose, Baheti, Bhakti, Bashyam, Vishnu, Sharma, Parth, Haghighi, Babak, Gastounioti, Aimilia, Bergman, Mark, Anirban, Mukhopadhyay, Sotirios A., Tsaftaris, Menze, Bjoern; https://orcid.org/0000-0003-4136-5690, Kontos, Despina, Davatzikos, Christos; https://orcid.org/0000-0002-1025-8561, Bakas, Spyridon; https://orcid.org/0000-0001-8734-6482, Pati, Sarthak, Thakur, Siddhesh, Bhalerao, Megh, Thermos, Spyridon, Baid, Ujjwal, Gotkowski, Karol, Gonzalez, Camila, Güley, Orhun, Hamamci, Ibrahim Ethem, Er, Sezgin, Grenko, Caleb, Brandon, Edwards, Sheller, Micah, Agraz, Jose, Baheti, Bhakti, Bashyam, Vishnu, Sharma, Parth, Haghighi, Babak, Gastounioti, Aimilia, Bergman, Mark, Anirban, Mukhopadhyay, Sotirios A., Tsaftaris, Menze, Bjoern; https://orcid.org/0000-0003-4136-5690, Kontos, Despina, Davatzikos, Christos; https://orcid.org/0000-0002-1025-8561, and Bakas, Spyridon; https://orcid.org/0000-0001-8734-6482

Abstract

Deep Learning (DL) has greatly highlighted the potential impact of optimized machine learning in both the scientific and clinical communities. The advent of open-source DL libraries from major industrial entities, such as TensorFlow (Google), PyTorch (Facebook), and MXNet (Apache), further contributes to DL promises on the democratization of computational analytics. However, increased technical and specialized background is required to develop DL algorithms, and the variability of implementation details hinders their reproducibility. Towards lowering the barrier and making the mechanism of DL development, training, and inference more stable, reproducible, and scalable, without requiring an extensive technical background, this manuscript proposes the Generally Nuanced Deep Learning Framework (GaNDLF). With built-in support for k-fold cross-validation, data augmentation, multiple modalities and output classes, and multi-GPU training, as well as the ability to work with both radiographic and histologic imaging, GaNDLF aims to provide an end-to-end solution for all DL-related tasks, to tackle problems in medical imaging and provide a robust application framework for deployment in clinical workflows.

Published
2021

12. Medical Image Harmonization Using Deep Learning Based Canonical Mapping: Toward Robust and Generalizable Learning in Imaging

Author

Bashyam, Vishnu M., Doshi, Jimit, Erus, Guray, Srinivasan, Dhivya, Abdulkadir, Ahmed, Habes, Mohamad, Fan, Yong, Masters, Colin L., Maruff, Paul, Zhuo, Chuanjun, Völzke, Henry, Johnson, Sterling C., Fripp, Jurgen, Koutsouleris, Nikolaos, Satterthwaite, Theodore D., Wolf, Daniel H., Gur, Raquel E., Gur, Ruben C., Morris, John C., Albert, Marilyn S., Grabe, Hans J., Resnick, Susan M., Bryan, R. Nick, Wolk, David A., Shou, Haochang, Nasrallah, Ilya M., Davatzikos, Christos, Bashyam, Vishnu M., Doshi, Jimit, Erus, Guray, Srinivasan, Dhivya, Abdulkadir, Ahmed, Habes, Mohamad, Fan, Yong, Masters, Colin L., Maruff, Paul, Zhuo, Chuanjun, Völzke, Henry, Johnson, Sterling C., Fripp, Jurgen, Koutsouleris, Nikolaos, Satterthwaite, Theodore D., Wolf, Daniel H., Gur, Raquel E., Gur, Ruben C., Morris, John C., Albert, Marilyn S., Grabe, Hans J., Resnick, Susan M., Bryan, R. Nick, Wolk, David A., Shou, Haochang, Nasrallah, Ilya M., and Davatzikos, Christos

Abstract

Conventional and deep learning-based methods have shown great potential in the medical imaging domain, as means for deriving diagnostic, prognostic, and predictive biomarkers, and by contributing to precision medicine. However, these methods have yet to see widespread clinical adoption, in part due to limited generalization performance across various imaging devices, acquisition protocols, and patient populations. In this work, we propose a new paradigm in which data from a diverse range of acquisition conditions are "harmonized" to a common reference domain, where accurate model learning and prediction can take place. By learning an unsupervised image to image canonical mapping from diverse datasets to a reference domain using generative deep learning models, we aim to reduce confounding data variation while preserving semantic information, thereby rendering the learning task easier in the reference domain. We test this approach on two example problems, namely MRI-based brain age prediction and classification of schizophrenia, leveraging pooled cohorts of neuroimaging MRI data spanning 9 sites and 9701 subjects. Our results indicate a substantial improvement in these tasks in out-of-sample data, even when training is restricted to a single site.

Published
2020

13. Computationally Guided Intracerebral Drug Delivery via Chronically Implanted Microdevices

Author

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, McGovern Institute for Brain Research at MIT, Massachusetts Institute of Technology. Department of Materials Science and Engineering, Massachusetts Institute of Technology. Department of Mechanical Engineering, Koch Institute for Integrative Cancer Research at MIT, Ramadi, Khalil, Bashyam, Ashvin, Frangieh, Chris J., Rousseau, Erin Byrne, Cotler, Max Joseph, Langer, Robert S, Graybiel, Ann M, Cima, Michael J., Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, McGovern Institute for Brain Research at MIT, Massachusetts Institute of Technology. Department of Materials Science and Engineering, Massachusetts Institute of Technology. Department of Mechanical Engineering, Koch Institute for Integrative Cancer Research at MIT, Ramadi, Khalil, Bashyam, Ashvin, Frangieh, Chris J., Rousseau, Erin Byrne, Cotler, Max Joseph, Langer, Robert S, Graybiel, Ann M, and Cima, Michael J.

Abstract

Treatments for neurologic diseases are often limited in efficacy due to poor spatial and temporal control over their delivery. Intracerebral delivery partially overcomes this by directly infusing therapeutics to the brain. Brain structures, however, are nonuniform and irregularly shaped, precluding complete target coverage by a single bolus without significant off-target effects and possible toxicity. Nearly complete coverage is crucial for effective modulation of these structures. We present a framework with computational mapping algorithms for neural drug delivery (COMMAND) to guide multi-bolus targeting of brain structures that maximizes coverage and minimizes off-target leakage. Custom-fabricated chronic neural implants leverage rational fluidic design to achieve multi-bolus delivery in rodents through a single infusion of radioactive tracer (Cu-64). The resulting spatial distributions replicate computed spatial coverage with 5% error in vivo, as detected by positron emission tomography. COMMAND potentially enables accurate, efficacious targeting of discrete brain regions., National Institute of Biomedical Imaging and Bioengineering (U.S.) (Grant R01 EB016101), National Cancer Institute (U.S.) (Grant P30-CA14051)

Published
2020

17. Quantifying [18F]fluorodeoxyglucose uptake in the arterial wall: the effects of dual time-point imaging and partial volume effect correction

Author

Heelkunde assistenten niet opleiding, Blomberg, Björn A., Bashyam, Arjun, Ramachandran, Abhinay, Gholami, Saeid, Houshmand, Sina, Salavati, Ali, Werner, Tom, Zaidi, Habib, Alavi, Abass, Heelkunde assistenten niet opleiding, Blomberg, Björn A., Bashyam, Arjun, Ramachandran, Abhinay, Gholami, Saeid, Houshmand, Sina, Salavati, Ali, Werner, Tom, Zaidi, Habib, and Alavi, Abass

Published
2015

18. Integrated Product and Process Design Environment Tool for Manufacturing T/R Modules

Author

Karne, Ramesh K., ISR, CSHCN, Karne, Ramesh K., Lai, Hsing-Tsu, Dandekar, Swati V., Baras, John S., Ball, Michael O., Bashyam, Sridhar, Trichur, Vinai S., Karir, Manish, Kebede, Abraham, Williams, Jim, Karne, Ramesh K., ISR, CSHCN, Karne, Ramesh K., Lai, Hsing-Tsu, Dandekar, Swati V., Baras, John S., Ball, Michael O., Bashyam, Sridhar, Trichur, Vinai S., Karir, Manish, Kebede, Abraham, and Williams, Jim

Abstract

We present a decision making assistant tool for integrated product and process design environment for manufacturing applications. Specifically, we target microwave modules which use Electro-mechanical components and require optimal solutions to reduce cost, improve quality, and gain leverage in time to market the product. This tool will assist the product and process designer to improve their productivity and also enable to cooperate and coordinate their designs through a common design interface. We consider a multiobjective optimization model that determines components and processes for a given conceptual designs for microwave modules. This model outputs a set of solutions that are Pareto optimal with respect to cost, quality, and other metrics. In addition, we identify system integration issues for manufacturing applications, and propose an architecture which will serve as a building block to our continuing research in virtual manufacturing applications.

Published
1997

19. On the Selection of Parts and Processes during Design of Printed Circuit Board Assemblies

Author

Ball, Michael O., ISR, Ball, Michael O., Baras, John S., Bashyam, Sridhar, Karne, Ramesh K., Trichur, Vinai S., Ball, Michael O., ISR, Ball, Michael O., Baras, John S., Bashyam, Sridhar, Karne, Ramesh K., and Trichur, Vinai S.

Abstract

We consider a multiobjective optimization model that determines components and processes for given conceptual designs of printed circuit board assemblies. Specifically, out model outputs a set of solutions that are Pareto optimal with respect to a cost and a quality metric. The discussion here broadly outlines an integer programming based solution strategy, and represents in-progress work being carried out in collaboration with a manufacturing firm.

Published
1995

20. Engine Hybrid Ceramic Bearings.

Author

GE AIRCRAFT ENGINES CINCINNATI OH, Rhoads, M., Bashyam, M., GE AIRCRAFT ENGINES CINCINNATI OH, Rhoads, M., and Bashyam, M.

Abstract

The objective of this program was the development of additional performance data and condition-monitoring techniques for silicon nitride (Si3N4) bearings. Induced-defect testing was performed to assess the ability of silicon nitride balls to withstand the high shock loads experienced during the growth of a race spall. The defect was placed in the outer ring of a hybrid bearing. Conclusions were made as to the survivability of ceramic balls under such severe conditions. High-speed thermal performance was compared between an all-steel bearing, a hybrid with the same internal geometry, and a hybrid with tightened internal geometry to lower the hertzian contact stress. Conclusions were made as to whether the hybrid bearings offer any improvement in thermal performance over an all-steel bearing. A condition-monitoring methodology was developed for early detection of an impending bearing failure. A technique was developed to find both metallic and ceramic debris from bearings. A monitoring device was developed, constructed, and demonstrated.

Published
1994

21. Internal Structural Characterization

Author

GENERAL ELECTRIC CO CINCINNATI OH AIRCRAFT ENGINE TECHNICAL DIV, Klaassen, R., Bashyam, M., GENERAL ELECTRIC CO CINCINNATI OH AIRCRAFT ENGINE TECHNICAL DIV, Klaassen, R., and Bashyam, M.

Abstract

The nondestructive evaluation (NDE) techniques most appropriate for the detection of a variety of defects in metal-and ceramic matrix composites (MMC and CMC's) are described. NDE techniques which were investigated for application to these materials include acoustic microscopy, ultrasonic pulse- echo, ultrasonic through-transmission, ultrasonic velocity, ultrasonic backscatter, ultrasonic surface wave velocity, ultrasonic surface wave attenuation, film radiography, microtomographic radiography, and dual-energy microtomographic radiography. Types of defects for which optimized techniques are summarized include delaminations, broken fibers, fiber spacing, fiber orientation, fiber/matrix interface, matrix cracks and voids/porosity. Further NDE development is needed for these materials in the areas of basic development for other NDE modalities, advanced development for nondestructive material characterization, and the scale-up of proven NDE techniques for full scale components, to accommodate cost and geometry limitations. Ultrasonics, Radiography, Cracks, Characterization, Metal Matrix Composite, Nondestructive Evaluation(NDE), Defect, Ceramic Matrix Composite, Computed Tomography Detection

Published
1992

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