Search

Your search keyword '"Boss, Michael A."' showing total 287 results
Start Over Author "Boss, Michael A."
287 results on '"Boss, Michael A."'

1. Breast Multiparametric MRI for Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer: The BMMR2 Challenge.

Author

Partridge, Savannah, Newitt, David, Steingrimsson, Jon, Marques, Helga, Bolan, Patrick, Hirano, Michael, Bearce, Benjamin, Kalpathy-Cramer, Jayashree, Boss, Michael, Teng, Xinzhi, Zhang, Jiang, Cai, Jing, Kontos, Despina, Cohen, Eric, Mankowski, Walter, Liu, Michael, Ha, Richard, Pellicer-Valero, Oscar, Maier-Hein, Klaus, Rabinovici-Cohen, Simona, Tlusty, Tal, Ozery-Flato, Michal, Parekh, Vishwa, Jacobs, Michael, Sung, Kyunghyun, Kazerouni, Anum, DiCarlo, Julie, Yankeelov, Thomas, Chenevert, Thomas, Hylton, Nola, Yan, Ran, and Li, Wen

Subjects
Breast, MRI, Tumor Response, Female, Humans, Middle Aged, Artificial Intelligence, Breast Neoplasms, Magnetic Resonance Imaging, Multiparametric Magnetic Resonance Imaging, Neoadjuvant Therapy, Pathologic Complete Response, Adult
Abstract

Purpose To describe the design, conduct, and results of the Breast Multiparametric MRI for prediction of neoadjuvant chemotherapy Response (BMMR2) challenge. Materials and Methods The BMMR2 computational challenge opened on May 28, 2021, and closed on December 21, 2021. The goal of the challenge was to identify image-based markers derived from multiparametric breast MRI, including diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) MRI, along with clinical data for predicting pathologic complete response (pCR) following neoadjuvant treatment. Data included 573 breast MRI studies from 191 women (mean age [±SD], 48.9 years ± 10.56) in the I-SPY 2/American College of Radiology Imaging Network (ACRIN) 6698 trial (ClinicalTrials.gov: NCT01042379). The challenge cohort was split into training (60%) and test (40%) sets, with teams blinded to test set pCR outcomes. Prediction performance was evaluated by area under the receiver operating characteristic curve (AUC) and compared with the benchmark established from the ACRIN 6698 primary analysis. Results Eight teams submitted final predictions. Entries from three teams had point estimators of AUC that were higher than the benchmark performance (AUC, 0.782 [95% CI: 0.670, 0.893], with AUCs of 0.803 [95% CI: 0.702, 0.904], 0.838 [95% CI: 0.748, 0.928], and 0.840 [95% CI: 0.748, 0.932]). A variety of approaches were used, ranging from extraction of individual features to deep learning and artificial intelligence methods, incorporating DCE and DWI alone or in combination. Conclusion The BMMR2 challenge identified several models with high predictive performance, which may further expand the value of multiparametric breast MRI as an early marker of treatment response. Clinical trial registration no. NCT01042379 Keywords: MRI, Breast, Tumor Response Supplemental material is available for this article. © RSNA, 2024.

Published
2024

2. Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Author

Pati, Sarthak, Baid, Ujjwal, Edwards, Brandon, Sheller, Micah, Wang, Shih-Han, Reina, G Anthony, Foley, Patrick, Gruzdev, Alexey, Karkada, Deepthi, Davatzikos, Christos, Sako, Chiharu, Ghodasara, Satyam, Bilello, Michel, Mohan, Suyash, Vollmuth, Philipp, Brugnara, Gianluca, Preetha, Chandrakanth J, Sahm, Felix, Maier-Hein, Klaus, Zenk, Maximilian, Bendszus, Martin, Wick, Wolfgang, Calabrese, Evan, Rudie, Jeffrey, Villanueva-Meyer, Javier, Cha, Soonmee, Ingalhalikar, Madhura, Jadhav, Manali, Pandey, Umang, Saini, Jitender, Garrett, John, Larson, Matthew, Jeraj, Robert, Currie, Stuart, Frood, Russell, Fatania, Kavi, Huang, Raymond Y, Chang, Ken, Balana, Carmen, Capellades, Jaume, Puig, Josep, Trenkler, Johannes, Pichler, Josef, Necker, Georg, Haunschmidt, Andreas, Meckel, Stephan, Shukla, Gaurav, Liem, Spencer, Alexander, Gregory S, Lombardo, Joseph, Palmer, Joshua D, Flanders, Adam E, Dicker, Adam P, Sair, Haris I, Jones, Craig K, Venkataraman, Archana, Jiang, Meirui, So, Tiffany Y, Chen, Cheng, Heng, Pheng Ann, Dou, Qi, Kozubek, Michal, Lux, Filip, Michálek, Jan, Matula, Petr, Keřkovský, Miloš, Kopřivová, Tereza, Dostál, Marek, Vybíhal, Václav, Vogelbaum, Michael A, Mitchell, J Ross, Farinhas, Joaquim, Maldjian, Joseph A, Yogananda, Chandan Ganesh Bangalore, Pinho, Marco C, Reddy, Divya, Holcomb, James, Wagner, Benjamin C, Ellingson, Benjamin M, Cloughesy, Timothy F, Raymond, Catalina, Oughourlian, Talia, Hagiwara, Akifumi, Wang, Chencai, To, Minh-Son, Bhardwaj, Sargam, Chong, Chee, Agzarian, Marc, Falcão, Alexandre Xavier, Martins, Samuel B, Teixeira, Bernardo C A, Sprenger, Flávia, Menotti, David, Lucio, Diego R, LaMontagne, Pamela, Marcus, Daniel, Wiestler, Benedikt, Kofler, Florian, Ezhov, Ivan, Metz, Marie, Jain, Rajan, Lee, Matthew, Lui, Yvonne W, McKinley, Richard, Slotboom, Johannes, Radojewski, Piotr, Meier, Raphael, Wiest, Roland, Murcia, Derrick, Fu, Eric, Haas, Rourke, Thompson, John, Ormond, David Ryan, Badve, Chaitra, Sloan, Andrew E, Vadmal, Vachan, Waite, Kristin, Colen, Rivka R, Pei, Linmin, Ak, Murat, Srinivasan, Ashok, Bapuraj, J Rajiv, Rao, Arvind, Wang, Nicholas, Yoshiaki, Ota, Moritani, Toshio, Turk, Sevcan, Lee, Joonsang, Prabhudesai, Snehal, Morón, Fanny, Mandel, Jacob, Kamnitsas, Konstantinos, Glocker, Ben, Dixon, Luke V M, Williams, Matthew, Zampakis, Peter, Panagiotopoulos, Vasileios, Tsiganos, Panagiotis, Alexiou, Sotiris, Haliassos, Ilias, Zacharaki, Evangelia I, Moustakas, Konstantinos, Kalogeropoulou, Christina, Kardamakis, Dimitrios M, Choi, Yoon Seong, Lee, Seung-Koo, Chang, Jong Hee, Ahn, Sung Soo, Luo, Bing, Poisson, Laila, Wen, Ning, Tiwari, Pallavi, Verma, Ruchika, Bareja, Rohan, Yadav, Ipsa, Chen, Jonathan, Kumar, Neeraj, Smits, Marion, van der Voort, Sebastian R, Alafandi, Ahmed, Incekara, Fatih, Wijnenga, Maarten MJ, Kapsas, Georgios, Gahrmann, Renske, Schouten, Joost W, Dubbink, Hendrikus J, Vincent, Arnaud JPE, Bent, Martin J van den, French, Pim J, Klein, Stefan, Yuan, Yading, Sharma, Sonam, Tseng, Tzu-Chi, Adabi, Saba, Niclou, Simone P, Keunen, Olivier, Hau, Ann-Christin, Vallières, Martin, Fortin, David, Lepage, Martin, Landman, Bennett, Ramadass, Karthik, Xu, Kaiwen, Chotai, Silky, Chambless, Lola B, Mistry, Akshitkumar, Thompson, Reid C, Gusev, Yuriy, Bhuvaneshwar, Krithika, Sayah, Anousheh, Bencheqroun, Camelia, Belouali, Anas, Madhavan, Subha, Booth, Thomas C, Chelliah, Alysha, Modat, Marc, Shuaib, Haris, Dragos, Carmen, Abayazeed, Aly, Kolodziej, Kenneth, Hill, Michael, Abbassy, Ahmed, Gamal, Shady, Mekhaimar, Mahmoud, Qayati, Mohamed, Reyes, Mauricio, Park, Ji Eun, Yun, Jihye, Kim, Ho Sung, Mahajan, Abhishek, Muzi, Mark, Benson, Sean, Beets-Tan, Regina G H, Teuwen, Jonas, Herrera-Trujillo, Alejandro, Trujillo, Maria, Escobar, William, Abello, Ana, Bernal, Jose, Gómez, Jhon, Choi, Joseph, Baek, Stephen, Kim, Yusung, Ismael, Heba, Allen, Bryan, Buatti, John M, Kotrotsou, Aikaterini, Li, Hongwei, Weiss, Tobias, Weller, Michael, Bink, Andrea, Pouymayou, Bertrand, Shaykh, Hassan F, Saltz, Joel, Prasanna, Prateek, Shrestha, Sampurna, Mani, Kartik M, Payne, David, Kurc, Tahsin, Pelaez, Enrique, Franco-Maldonado, Heydy, Loayza, Francis, Quevedo, Sebastian, Guevara, Pamela, Torche, Esteban, Mendoza, Cristobal, Vera, Franco, Ríos, Elvis, López, Eduardo, Velastin, Sergio A, Ogbole, Godwin, Oyekunle, Dotun, Odafe-Oyibotha, Olubunmi, Osobu, Babatunde, Shu'aibu, Mustapha, Dorcas, Adeleye, Soneye, Mayowa, Dako, Farouk, Simpson, Amber L, Hamghalam, Mohammad, Peoples, Jacob J, Hu, Ricky, Tran, Anh, Cutler, Danielle, Moraes, Fabio Y, Boss, Michael A, Gimpel, James, Veettil, Deepak Kattil, Schmidt, Kendall, Bialecki, Brian, Marella, Sailaja, Price, Cynthia, Cimino, Lisa, Apgar, Charles, Shah, Prashant, Menze, Bjoern, Barnholtz-Sloan, Jill S, Martin, Jason, and Bakas, Spyridon

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multiple sites. However, such centralization is challenging to scale (or even not feasible) due to various limitations. Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here we present findings from the largest FL study to-date, involving data from 71 healthcare institutions across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, utilizing the largest dataset of such patients ever used in the literature (25,256 MRI scans from 6,314 patients). We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent. We anticipate our study to: 1) enable more studies in healthcare informed by large and diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further quantitative analyses for glioblastoma via performance optimization of our consensus model for eventual public release, and 3) demonstrate the effectiveness of FL at such scale and task complexity as a paradigm shift for multi-site collaborations, alleviating the need for data sharing., Comment: federated learning, deep learning, convolutional neural network, segmentation, brain tumor, glioma, glioblastoma, FeTS, BraTS

Published
2022
Full Text
View/download PDF

3. Author Correction: Federated learning enables big data for rare cancer boundary detection

Author

Pati, Sarthak, Baid, Ujjwal, Edwards, Brandon, Sheller, Micah, Wang, Shih-Han, Reina, G. Anthony, Foley, Patrick, Gruzdev, Alexey, Karkada, Deepthi, Davatzikos, Christos, Sako, Chiharu, Ghodasara, Satyam, Bilello, Michel, Mohan, Suyash, Vollmuth, Philipp, Brugnara, Gianluca, Preetha, Chandrakanth J., Sahm, Felix, Maier-Hein, Klaus, Zenk, Maximilian, Bendszus, Martin, Wick, Wolfgang, Calabrese, Evan, Rudie, Jeffrey, Villanueva-Meyer, Javier, Cha, Soonmee, Ingalhalikar, Madhura, Jadhav, Manali, Pandey, Umang, Saini, Jitender, Garrett, John, Larson, Matthew, Jeraj, Robert, Currie, Stuart, Frood, Russell, Fatania, Kavi, Huang, Raymond Y., Chang, Ken, Balaña, Carmen, Capellades, Jaume, Puig, Josep, Trenkler, Johannes, Pichler, Josef, Necker, Georg, Haunschmidt, Andreas, Meckel, Stephan, Shukla, Gaurav, Liem, Spencer, Alexander, Gregory S., Lombardo, Joseph, Palmer, Joshua D., Flanders, Adam E., Dicker, Adam P., Sair, Haris I., Jones, Craig K., Venkataraman, Archana, Jiang, Meirui, So, Tiffany Y., Chen, Cheng, Heng, Pheng Ann, Dou, Qi, Kozubek, Michal, Lux, Filip, Michálek, Jan, Matula, Petr, Keřkovský, Miloš, Kopřivová, Tereza, Dostál, Marek, Vybíhal, Václav, Vogelbaum, Michael A., Mitchell, J. Ross, Farinhas, Joaquim, Maldjian, Joseph A., Yogananda, Chandan Ganesh Bangalore, Pinho, Marco C., Reddy, Divya, Holcomb, James, Wagner, Benjamin C., Ellingson, Benjamin M., Cloughesy, Timothy F., Raymond, Catalina, Oughourlian, Talia, Hagiwara, Akifumi, Wang, Chencai, To, Minh-Son, Bhardwaj, Sargam, Chong, Chee, Agzarian, Marc, Falcão, Alexandre Xavier, Martins, Samuel B., Teixeira, Bernardo C. A., Sprenger, Flávia, Menotti, David, Lucio, Diego R., LaMontagne, Pamela, Marcus, Daniel, Wiestler, Benedikt, Kofler, Florian, Ezhov, Ivan, Metz, Marie, Jain, Rajan, Lee, Matthew, Lui, Yvonne W., McKinley, Richard, Slotboom, Johannes, Radojewski, Piotr, Meier, Raphael, Wiest, Roland, Murcia, Derrick, Fu, Eric, Haas, Rourke, Thompson, John, Ormond, David Ryan, Badve, Chaitra, Sloan, Andrew E., Vadmal, Vachan, Waite, Kristin, Colen, Rivka R., Pei, Linmin, Ak, Murat, Srinivasan, Ashok, Bapuraj, J. Rajiv, Rao, Arvind, Wang, Nicholas, Yoshiaki, Ota, Moritani, Toshio, Turk, Sevcan, Lee, Joonsang, Prabhudesai, Snehal, Morón, Fanny, Mandel, Jacob, Kamnitsas, Konstantinos, Glocker, Ben, Dixon, Luke V. M., Williams, Matthew, Zampakis, Peter, Panagiotopoulos, Vasileios, Tsiganos, Panagiotis, Alexiou, Sotiris, Haliassos, Ilias, Zacharaki, Evangelia I., Moustakas, Konstantinos, Kalogeropoulou, Christina, Kardamakis, Dimitrios M., Choi, Yoon Seong, Lee, Seung-Koo, Chang, Jong Hee, Ahn, Sung Soo, Luo, Bing, Poisson, Laila, Wen, Ning, Tiwari, Pallavi, Verma, Ruchika, Bareja, Rohan, Yadav, Ipsa, Chen, Jonathan, Kumar, Neeraj, Smits, Marion, van der Voort, Sebastian R., Alafandi, Ahmed, Incekara, Fatih, Wijnenga, Maarten M. J., Kapsas, Georgios, Gahrmann, Renske, Schouten, Joost W., Dubbink, Hendrikus J., Vincent, Arnaud J. P. E., van den Bent, Martin J., French, Pim J., Klein, Stefan, Yuan, Yading, Sharma, Sonam, Tseng, Tzu-Chi, Adabi, Saba, Niclou, Simone P., Keunen, Olivier, Hau, Ann-Christin, Vallières, Martin, Fortin, David, Lepage, Martin, Landman, Bennett, Ramadass, Karthik, Xu, Kaiwen, Chotai, Silky, Chambless, Lola B., Mistry, Akshitkumar, Thompson, Reid C., Gusev, Yuriy, Bhuvaneshwar, Krithika, Sayah, Anousheh, Bencheqroun, Camelia, Belouali, Anas, Madhavan, Subha, Booth, Thomas C., Chelliah, Alysha, Modat, Marc, Shuaib, Haris, Dragos, Carmen, Abayazeed, Aly, Kolodziej, Kenneth, Hill, Michael, Abbassy, Ahmed, Gamal, Shady, Mekhaimar, Mahmoud, Qayati, Mohamed, Reyes, Mauricio, Park, Ji Eun, Yun, Jihye, Kim, Ho Sung, Mahajan, Abhishek, Muzi, Mark, Benson, Sean, Beets-Tan, Regina G. H., Teuwen, Jonas, Herrera-Trujillo, Alejandro, Trujillo, Maria, Escobar, William, Abello, Ana, Bernal, Jose, Gómez, Jhon, Choi, Joseph, Baek, Stephen, Kim, Yusung, Ismael, Heba, Allen, Bryan, Buatti, John M., Kotrotsou, Aikaterini, Li, Hongwei, Weiss, Tobias, Weller, Michael, Bink, Andrea, Pouymayou, Bertrand, Shaykh, Hassan F., Saltz, Joel, Prasanna, Prateek, Shrestha, Sampurna, Mani, Kartik M., Payne, David, Kurc, Tahsin, Pelaez, Enrique, Franco-Maldonado, Heydy, Loayza, Francis, Quevedo, Sebastian, Guevara, Pamela, Torche, Esteban, Mendoza, Cristobal, Vera, Franco, Ríos, Elvis, López, Eduardo, Velastin, Sergio A., Ogbole, Godwin, Soneye, Mayowa, Oyekunle, Dotun, Odafe-Oyibotha, Olubunmi, Osobu, Babatunde, Shu’aibu, Mustapha, Dorcas, Adeleye, Dako, Farouk, Simpson, Amber L., Hamghalam, Mohammad, Peoples, Jacob J., Hu, Ricky, Tran, Anh, Cutler, Danielle, Moraes, Fabio Y., Boss, Michael A., Gimpel, James, Veettil, Deepak Kattil, Schmidt, Kendall, Bialecki, Brian, Marella, Sailaja, Price, Cynthia, Cimino, Lisa, Apgar, Charles, Shah, Prashant, Menze, Bjoern, Barnholtz-Sloan, Jill S., Martin, Jason, and Bakas, Spyridon

Published
2023
Full Text
View/download PDF

4. Impact of Alternate b-Value Combinations and Metrics on the Predictive Performance and Repeatability of Diffusion-Weighted MRI in Breast Cancer Treatment: Results from the ECOG-ACRIN A6698 Trial

Author

Partridge, Savannah C, Steingrimsson, Jon, Newitt, David C, Gibbs, Jessica E, Marques, Helga S, Bolan, Patrick J, Boss, Michael A, Chenevert, Thomas L, Rosen, Mark A, and Hylton, Nola M

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Cancer, Clinical Research, Biomedical Imaging, Breast Cancer, Evaluation of treatments and therapeutic interventions, 6.1 Pharmaceuticals, Benchmarking, Breast Neoplasms, Diffusion Magnetic Resonance Imaging, Female, Humans, Neoadjuvant Therapy, ROC Curve, Tumor Microenvironment, breast cancer, diffusion-weighted MRI, apparent diffusion coefficient, treatment response, repeatability, reproducibility, quantitative imaging biomarker alliance
Abstract

In diffusion-weighted MRI (DW-MRI), choice of b-value influences apparent diffusion coefficient (ADC) values by probing different aspects of the tissue microenvironment. As a secondary analysis of the multicenter ECOG-ACRIN A6698 trial, the purpose of this study was to investigate the impact of alternate b-value combinations on the performance and repeatability of tumor ADC as a predictive marker of breast cancer treatment response. The final analysis included 210 women who underwent standardized 4-b-value DW-MRI (b = 0/100/600/800 s/mm2) at multiple timepoints during neoadjuvant chemotherapy treatment and a subset (n = 71) who underwent test−retest scans. Centralized tumor ADC and perfusion fraction (fp) measures were performed using variable b-value combinations. Prediction of pathologic complete response (pCR) based on the mid-treatment/12-week percent change in each metric was estimated by area under the receiver operating characteristic curve (AUC). Repeatability was estimated by within-subject coefficient of variation (wCV). Results show that two-b-value ADC calculations provided non-inferior predictive value to four-b-value ADC calculations overall (AUCs = 0.60−0.61 versus AUC = 0.60) and for HR+/HER2− cancers where ADC was most predictive (AUCs = 0.75−0.78 versus AUC = 0.76), p < 0.05. Using two b-values (0/600 or 0/800 s/mm2) did not reduce ADC repeatability over the four-b-value calculation (wCVs = 4.9−5.2% versus 5.4%). The alternate metrics ADCfast (b ≤ 100 s/mm2), ADCslow (b ≥ 100 s/mm2), and fp did not improve predictive performance (AUCs = 0.54−0.60, p = 0.08−0.81), and ADCfast and fp demonstrated the lowest repeatability (wCVs = 6.71% and 12.4%, respectively). In conclusion, breast tumor ADC calculated using a simple two-b-value approach can provide comparable predictive value and repeatability to full four-b-value measurements as a marker of treatment response.

Published
2022

5. Prospective evaluation of in vivo and phantom repeatability and reproducibility of diffusion-weighted MRI sequences on 1.5 T MRI-linear accelerator (MR-Linac) and MR simulator devices for head and neck cancers

Author

McDonald, Brigid A., Salzillo, Travis, Mulder, Samuel, Ahmed, Sara, Dresner, Alex, Preston, Kathryn, He, Renjie, Christodouleas, John, Mohamed, Abdallah S.R., Philippens, Marielle, van Houdt, Petra, Thorwarth, Daniela, Wang, Jihong, Shukla Dave, Amita, Boss, Michael, and Fuller, Clifton D.

Published
2023
Full Text
View/download PDF

6. Radiofrequency transmit calibration: A multi‐center evaluation of vendor‐provided radiofrequency transmit mapping methods

Author

Bliesener, Yannick, Zhong, Xinran, Guo, Yi, Boss, Michael, Bosca, Ryan, Laue, Hendrik, Chung, Caroline, Sung, Kyunghyun, and Nayak, Krishna S

Subjects
Clinical Research, Bioengineering, Biomedical Imaging, 4.2 Evaluation of markers and technologies, Detection, screening and diagnosis, Calibration, Magnetic Resonance Imaging, Phantoms, Imaging, Radio Waves, Reproducibility of Results, DCE-MRI, RF transmit, RF inhomogeneity, high field, quantitative MRI, Other Physical Sciences, Biomedical Engineering, Oncology and Carcinogenesis, Nuclear Medicine & Medical Imaging
Abstract

PurposeTo determine the accuracy and test-retest repeatability of fast radiofrequency (RF) transmit measurement approaches used in Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI). Spatial variation in the transmitted RF field introduces bias and increased variance in quantitative DCE-MRI metrics including tracer kinetic parameter maps. If unaccounted for, these errors can dominate all other sources of bias and variance. The amount and pattern of variation depend on scanner-specific hardware and software.MethodsHuman tissue mimicking torso and brain phantoms were constructed. RF transmit maps were measured and compared across eight different commercial scanners, from three major vendors, and three clinical sites. Vendor-recommended rapid methods for RF mapping were compared to a slower reference method. Imaging was repeated at all sites after 2 months. Ranges and magnitude of RF inhomogeneity were compared scanner-wise at two time points. Limits of Agreement of vendor-recommended methods and double-angle reference method were assessed.ResultsAt 3 T, B1 + inhomogeneity spans across 35% in the head and 120% in the torso. Fast vendor provided methods are within 30% agreement with the reference double angle method for both the head and the torso phantom.ConclusionsIf unaccounted for, B1 + inhomogeneity can severely impact tracer-kinetic parameter estimation. Depending on the scanner, fast vendor provided B1 + mapping sequences allow unbiased and reproducible measurements of B1 + inhomogeneity to correct for this source of bias.

Published
2019

7. Recommendations towards standards for quantitative MRI (qMRI) and outstanding needs

Author

Keenan, Kathryn E, Biller, Joshua R, Delfino, Jana G, Boss, Michael A, Does, Mark D, Evelhoch, Jeffrey L, Griswold, Mark A, Gunter, Jeffrey L, Hinks, R Scott, Hoffman, Stuart W, Kim, Geena, Lattanzi, Riccardo, Li, Xiaojuan, Marinelli, Luca, Metzger, Gregory J, Mukherjee, Pratik, Nordstrom, Robert J, Peskin, Adele P, Perez, Elena, Russek, Stephen E, Sahiner, Berkman, Serkova, Natalie, Shukla‐Dave, Amita, Steckner, Michael, Stupic, Karl F, Wilmes, Lisa J, Wu, Holden H, Zhang, Huiming, Jackson, Edward F, and Sullivan, Daniel C

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Anthropometry, Breast, Decision Making, Deep Learning, Equipment Design, Female, Humans, Image Interpretation, Computer-Assisted, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Phantoms, Imaging, Precision Medicine, Radiology, Interventional, Reference Standards, Reference Values, Reproducibility of Results, Robotics, Software, quantitative MRI, reference objects, phantom, standards, validation, Physical Sciences, Engineering, Medical and Health Sciences, Nuclear Medicine & Medical Imaging, Clinical sciences
Abstract

Level of evidence5 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2019.

Published
2019

8. Accuracy, repeatability, and interplatform reproducibility of T1 quantification methods used for DCE‐MRI: Results from a multicenter phantom study

Author

Bane, Octavia, Hectors, Stefanie J, Wagner, Mathilde, Arlinghaus, Lori L, Aryal, Madhava P, Cao, Yue, Chenevert, Thomas L, Fennessy, Fiona, Huang, Wei, Hylton, Nola M, Kalpathy‐Cramer, Jayashree, Keenan, Kathryn E, Malyarenko, Dariya I, Mulkern, Robert V, Newitt, David C, Russek, Stephen E, Stupic, Karl F, Tudorica, Alina, Wilmes, Lisa J, Yankeelov, Thomas E, Yen, Yi‐Fei, Boss, Michael A, and Taouli, Bachir

Subjects
Engineering, Biomedical Engineering, Patient Safety, Biomedical Imaging, Brain, Breast, Contrast Media, Female, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Neoplasms, Phantoms, Imaging, Prostate, Reproducibility of Results, Signal Processing, Computer-Assisted, T-1 mapping, DCE-MRI, phantom, multicenter, T1 mapping, Nuclear Medicine & Medical Imaging, Biomedical engineering
Abstract

PurposeTo determine the in vitro accuracy, test-retest repeatability, and interplatform reproducibility of T1 quantification protocols used for dynamic contrast-enhanced MRI at 1.5 and 3 T.MethodsA T1 phantom with 14 samples was imaged at eight centers with a common inversion-recovery spin-echo (IR-SE) protocol and a variable flip angle (VFA) protocol using seven flip angles, as well as site-specific protocols (VFA with different flip angles, variable repetition time, proton density, and Look-Locker inversion recovery). Factors influencing the accuracy (deviation from reference NMR T1 measurements) and repeatability were assessed using general linear mixed models. Interplatform reproducibility was assessed using coefficients of variation.ResultsFor the common IR-SE protocol, accuracy (median error across platforms = 1.4-5.5%) was influenced predominantly by T1 sample (P

Published
2018

9. Multisite concordance of apparent diffusion coefficient measurements across the NCI Quantitative Imaging Network

Author

Newitt, David C, Malyarenko, Dariya, Chenevert, Thomas L, Quarles, C Chad, Bell, Laura, Fedorov, Andriy, Fennessy, Fiona, Jacobs, Michael A, Solaiyappan, Meiyappan, Hectors, Stefanie, Taouli, Bachir, Muzi, Mark, Kinahan, Paul E, Schmainda, Kathleen M, Prah, Melissa A, Taber, Erin N, Kroenke, Christopher, Huang, Wei, Arlinghaus, Lori R, Yankeelov, Thomas E, Cao, Yue, Aryal, Madhava, Yen, Yi-Fen, Kalpathy-Cramer, Jayashree, Shukla-Dave, Amita, Fung, Maggie, Liang, Jiachao, Boss, Michael, and Hylton, Nola

Subjects
Biomedical and Clinical Sciences, Engineering, Clinical Sciences, Biomedical Engineering, Biomedical Imaging, Cancer, Clinical Research, apparent diffusion coefficient, reproducibility, breast MRI, Clinical sciences, Biomedical engineering
Abstract

Diffusion weighted MRI has become ubiquitous in many areas of medicine, including cancer diagnosis and treatment response monitoring. Reproducibility of diffusion metrics is essential for their acceptance as quantitative biomarkers in these areas. We examined the variability in the apparent diffusion coefficient (ADC) obtained from both postprocessing software implementations utilized by the NCI Quantitative Imaging Network and online scan time-generated ADC maps. Phantom and in vivo breast studies were evaluated for two ([Formula: see text]) and four ([Formula: see text]) [Formula: see text]-value diffusion metrics. Concordance of the majority of implementations was excellent for both phantom ADC measures and in vivo [Formula: see text], with relative biases [Formula: see text] ([Formula: see text]) and [Formula: see text] (phantom [Formula: see text]) but with higher deviations in ADC at the lowest phantom ADC values. In vivo [Formula: see text] concordance was good, with typical biases of [Formula: see text] to 3% but higher for online maps. Multiple b-value ADC implementations were separated into two groups determined by the fitting algorithm. Intergroup mean ADC differences ranged from negligible for phantom data to 2.8% for [Formula: see text] in vivo data. Some higher deviations were found for individual implementations and online parametric maps. Despite generally good concordance, implementation biases in ADC measures are sometimes significant and may be large enough to be of concern in multisite studies.

Published
2018

10. Persistent opioid and benzodiazepine use after radical cystectomy in enhanced recovery after surgery (ERAS) patients

Author

Beuhler, Michael, Boss, Michael J., Gibbs, Michael, Griggs, Christopher, Jarrett, Steven, Leas, Daniel, Odum, Susan, Runyon, Michael, Saha, Animita, Sullivan, D. Matthew, Yu, Ziqing, Wally, Meghan, Watling, Brad, Ivan, Samuel J., Holck, Hailey W., Robinson, Myra M., Shea, Reilly E., Wallander, Michelle L., Parker, Blair, Matulay, Justin T., Gaston, Kris E., Clark, Peter E., Seymour, Rachel, Hsu, Joseph R., and Riggs, Stephen B.

Published
2023
Full Text
View/download PDF

12. Breast Multiparametric MRI for Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer: The BMMR2 Challenge

Author

Li, Wen, primary, Partridge, Savannah C., additional, Newitt, David C., additional, Steingrimsson, Jon, additional, Marques, Helga S., additional, Bolan, Patrick J., additional, Hirano, Michael, additional, Bearce, Benjamin Aaron, additional, Kalpathy-Cramer, Jayashree, additional, Boss, Michael A., additional, Teng, Xinzhi, additional, Zhang, Jiang, additional, Cai, Jing, additional, Kontos, Despina, additional, Cohen, Eric A., additional, Mankowski, Walter C., additional, Liu, Michael, additional, Ha, Richard, additional, Pellicer-Valero, Oscar J., additional, Maier-Hein, Klaus, additional, Rabinovici-Cohen, Simona, additional, Tlusty, Tal, additional, Ozery-Flato, Michal, additional, Parekh, Vishwa S., additional, Jacobs, Michael A., additional, Yan, Ran, additional, Sung, Kyunghyun, additional, Kazerouni, Anum S., additional, DiCarlo, Julie C., additional, Yankeelov, Thomas E., additional, Chenevert, Thomas L., additional, and Hylton, Nola M., additional

Published
2024
Full Text
View/download PDF

13. Design of a breast phantom for quantitative MRI

Author

Keenan, Kathryn E, Wilmes, Lisa J, Aliu, Sheye O, Newitt, David C, Jones, Ella F, Boss, Michael A, Stupic, Karl F, Russek, Stephen E, and Hylton, Nola M

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Cancer, Breast Cancer, Biomedical Imaging, Biomimetic Materials, Breast, Equipment Design, Equipment Failure Analysis, Female, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Phantoms, Imaging, Reproducibility of Results, Sensitivity and Specificity, breast MRI, apparent diffusion coefficient, relaxation, tissue mimic, phantoms, quality control, Physical Sciences, Engineering, Medical and Health Sciences, Nuclear Medicine & Medical Imaging, Clinical sciences
Abstract

PurposeWe present a breast phantom designed to enable quantitative assessment of measurements of T1 relaxation time, apparent diffusion coefficient (ADC), and other attributes of breast tissue, with long-term support from a national metrology institute.Materials and methodsA breast phantom was created with two independent, interchangeable units for diffusion and T1 /T2 relaxation, each with flexible outer shells. The T1 unit was filled with corn syrup solution and grapeseed oil to mimic the relaxation behavior of fibroglandular and fatty tissues, respectively. The diffusion unit contains plastic tubes filled with aqueous solutions of polyvinylpyrrolidone (PVP) to modulate the ADC. The phantom was imaged at 1.5T and 3.0T using magnetic resonance imaging (MRI) scanners and common breast coils from multiple manufacturers to assess T1 and T2 relaxation time and ADC values.ResultsThe fibroglandular mimic exhibited target T1 values on 1.5T and 3.0T clinical systems (25-75 percentile range: 1289 to 1400 msec and 1533 to 1845 msec, respectively) across all bore temperatures. PVP solutions mimicked the range of ADC values from malignant tumors to normal breast tissue (40% PVP median: 633 × 10(-6) mm(2) /s to 0% PVP median: 2231 × 10(-6) mm(2) /s) at temperatures of 17-24°C. The interchangeable phantom units allowed both the diffusion and T1 /T2 units to be tested on the left and right sides of the coil to assess any variation.ConclusionThis phantom enables T1 and ADC measurements, fits in a variety of clinical breast coils, and can serve as a quality control tool to facilitate the standardization of quantitative measurements for breast MRI. J. Magn. Reson. Imaging 2016;44:610-619.

Published
2016

14. Persistent opioid and benzodiazepine use after radical cystectomy in enhanced recovery after surgery (ERAS) patients

Author

Ivan, Samuel J., primary, Holck, Hailey W., additional, Robinson, Myra M., additional, Shea, Reilly E., additional, Wallander, Michelle L., additional, Parker, Blair, additional, Matulay, Justin T., additional, Gaston, Kris E., additional, Clark, Peter E., additional, Seymour, Rachel, additional, Hsu, Joseph R., additional, Riggs, Stephen B., additional, Beuhler, Michael, additional, Boss, Michael J., additional, Gibbs, Michael, additional, Griggs, Christopher, additional, Jarrett, Steven, additional, Leas, Daniel, additional, Odum, Susan, additional, Runyon, Michael, additional, Saha, Animita, additional, Sullivan, D. Matthew, additional, Yu, Ziqing, additional, Wally, Meghan, additional, and Watling, Brad, additional

Published
2023
Full Text
View/download PDF

16. Contagion Flow Through Banking Networks

Author

Boss, Michael, Summer, Martin, and Thurner, Stefan

Subjects
Condensed Matter - Other Condensed Matter, Quantitative Finance - General Finance
Abstract

Based on an empirical analysis of the network structure of the Austrian inter-bank market, we study the flow of funds through the banking network following exogenous shocks to the system. These shocks are implemented by stochastic changes in variables like interest rates, exchange rates, etc. We demonstrate that the system is relatively stable in the sence that defaults of individual banks are unlikely to spread over the entire network. We study the contagion impact of all individual banks, meaning the number of banks which are driven into insolvency as a result of a single bank's default. We show that the vertex betweenness of individual banks is linearly related to their contagion impact., Comment: 8 pages, 2 figures, contribution to iccs 2004

Published
2004

17. The Network Topology of the Interbank Market

Author

Boss, Michael, Elsinger, Helmut, Summer, Martin, and Thurner, Stefan

Subjects
Condensed Matter
Abstract

We provide an empirical analysis of the network structure of the Austrian interbank market based on a unique data set of the Oesterreichische Nationalbank (OeNB). We show that the contract size distribution follows a power law over more than 3 decades. By using a novel ''dissimilarity'' measure we find that the interbank network shows a community structure that exactly mirrors the regional and sectoral organization of the actual Austrian banking system. The degree distribution of the interbank network shows two different power law exponents which are one-to-one related to two sub-network structures, differing in the degree of hierarchical organization. The banking network moreover shares typical structural features known in numerous complex real world networks: a low clustering coefficient and a relatively short average shortest path length. These empirical findings are in marked contrast to interbank networks that have been analyzed in the theoretical economic and econo-physics literature., Comment: 5 pages 3 eps figures

Published
2003

18. Prospective Evaluation of In Vivo and Phantom Repeatability and Reproducibility of Diffusion-Weighted MRI Sequences on 1.5T MRI-Linear Accelerator (MR-Linac) and MR Simulator Devices for Head and Neck Cancers

Author

McDonald, Brigid A., primary, Salzillo, Travis, additional, Mulder, Samuel, additional, Ahmed, Sara, additional, Dresner, Alex, additional, Preston, Kathryn, additional, He, Renjie, additional, Christodouleas, John, additional, S. R. Mohamed, Abdallah, additional, Philippens, Marielle, additional, van Houdt, Petra, additional, Thorwarth, Daniela, additional, Wang, Jihong, additional, Shukla Dave, Amita, additional, Boss, Michael, additional, and Fuller, Clifton D., additional

Published
2023
Full Text
View/download PDF

20. Prospective Evaluation of In Vivo and Phantom Repeatability and Reproducibility of Diffusion-Weighted MRI Sequences on 1.5T MRI-Linear Accelerator (MR-Linac) and MR Simulator Devices for Head and Neck Cancers

Author

Klinische Fysica RT, Cancer, McDonald, Brigid A, Salzillo, Travis, Mulder, Samuel, Ahmed, Sara, Dresner, Alex, Preston, Kathryn, He, Renjie, Christodouleas, John, S R Mohamed, Abdallah, Philippens, Marielle, van Houdt, Petra, Thorwarth, Daniela, Wang, Jihong, Shukla Dave, Amita, Boss, Michael, Fuller, Clifton D, Klinische Fysica RT, Cancer, McDonald, Brigid A, Salzillo, Travis, Mulder, Samuel, Ahmed, Sara, Dresner, Alex, Preston, Kathryn, He, Renjie, Christodouleas, John, S R Mohamed, Abdallah, Philippens, Marielle, van Houdt, Petra, Thorwarth, Daniela, Wang, Jihong, Shukla Dave, Amita, Boss, Michael, and Fuller, Clifton D

Published
2023

21. Federated learning enables big data for rare cancer boundary detection

Author

Pati, Sarthak, primary, Baid, Ujjwal, additional, Edwards, Brandon, additional, Sheller, Micah, additional, Wang, Shih-Han, additional, Reina, G. Anthony, additional, Foley, Patrick, additional, Gruzdev, Alexey, additional, Karkada, Deepthi, additional, Davatzikos, Christos, additional, Sako, Chiharu, additional, Ghodasara, Satyam, additional, Bilello, Michel, additional, Mohan, Suyash, additional, Vollmuth, Philipp, additional, Brugnara, Gianluca, additional, Preetha, Chandrakanth J., additional, Sahm, Felix, additional, Maier-Hein, Klaus, additional, Zenk, Maximilian, additional, Bendszus, Martin, additional, Wick, Wolfgang, additional, Calabrese, Evan, additional, Rudie, Jeffrey, additional, Villanueva-Meyer, Javier, additional, Cha, Soonmee, additional, Ingalhalikar, Madhura, additional, Jadhav, Manali, additional, Pandey, Umang, additional, Saini, Jitender, additional, Garrett, John, additional, Larson, Matthew, additional, Jeraj, Robert, additional, Currie, Stuart, additional, Frood, Russell, additional, Fatania, Kavi, additional, Huang, Raymond Y., additional, Chang, Ken, additional, Balaña, Carmen, additional, Capellades, Jaume, additional, Puig, Josep, additional, Trenkler, Johannes, additional, Pichler, Josef, additional, Necker, Georg, additional, Haunschmidt, Andreas, additional, Meckel, Stephan, additional, Shukla, Gaurav, additional, Liem, Spencer, additional, Alexander, Gregory S., additional, Lombardo, Joseph, additional, Palmer, Joshua D., additional, Flanders, Adam E., additional, Dicker, Adam P., additional, Sair, Haris I., additional, Jones, Craig K., additional, Venkataraman, Archana, additional, Jiang, Meirui, additional, So, Tiffany Y., additional, Chen, Cheng, additional, Heng, Pheng Ann, additional, Dou, Qi, additional, Kozubek, Michal, additional, Lux, Filip, additional, Michálek, Jan, additional, Matula, Petr, additional, Keřkovský, Miloš, additional, Kopřivová, Tereza, additional, Dostál, Marek, additional, Vybíhal, Václav, additional, Vogelbaum, Michael A., additional, Mitchell, J. Ross, additional, Farinhas, Joaquim, additional, Maldjian, Joseph A., additional, Yogananda, Chandan Ganesh Bangalore, additional, Pinho, Marco C., additional, Reddy, Divya, additional, Holcomb, James, additional, Wagner, Benjamin C., additional, Ellingson, Benjamin M., additional, Cloughesy, Timothy F., additional, Raymond, Catalina, additional, Oughourlian, Talia, additional, Hagiwara, Akifumi, additional, Wang, Chencai, additional, To, Minh-Son, additional, Bhardwaj, Sargam, additional, Chong, Chee, additional, Agzarian, Marc, additional, Falcão, Alexandre Xavier, additional, Martins, Samuel B., additional, Teixeira, Bernardo C. A., additional, Sprenger, Flávia, additional, Menotti, David, additional, Lucio, Diego R., additional, LaMontagne, Pamela, additional, Marcus, Daniel, additional, Wiestler, Benedikt, additional, Kofler, Florian, additional, Ezhov, Ivan, additional, Metz, Marie, additional, Jain, Rajan, additional, Lee, Matthew, additional, Lui, Yvonne W., additional, McKinley, Richard, additional, Slotboom, Johannes, additional, Radojewski, Piotr, additional, Meier, Raphael, additional, Wiest, Roland, additional, Murcia, Derrick, additional, Fu, Eric, additional, Haas, Rourke, additional, Thompson, John, additional, Ormond, David Ryan, additional, Badve, Chaitra, additional, Sloan, Andrew E., additional, Vadmal, Vachan, additional, Waite, Kristin, additional, Colen, Rivka R., additional, Pei, Linmin, additional, Ak, Murat, additional, Srinivasan, Ashok, additional, Bapuraj, J. Rajiv, additional, Rao, Arvind, additional, Wang, Nicholas, additional, Yoshiaki, Ota, additional, Moritani, Toshio, additional, Turk, Sevcan, additional, Lee, Joonsang, additional, Prabhudesai, Snehal, additional, Morón, Fanny, additional, Mandel, Jacob, additional, Kamnitsas, Konstantinos, additional, Glocker, Ben, additional, Dixon, Luke V. M., additional, Williams, Matthew, additional, Zampakis, Peter, additional, Panagiotopoulos, Vasileios, additional, Tsiganos, Panagiotis, additional, Alexiou, Sotiris, additional, Haliassos, Ilias, additional, Zacharaki, Evangelia I., additional, Moustakas, Konstantinos, additional, Kalogeropoulou, Christina, additional, Kardamakis, Dimitrios M., additional, Choi, Yoon Seong, additional, Lee, Seung-Koo, additional, Chang, Jong Hee, additional, Ahn, Sung Soo, additional, Luo, Bing, additional, Poisson, Laila, additional, Wen, Ning, additional, Tiwari, Pallavi, additional, Verma, Ruchika, additional, Bareja, Rohan, additional, Yadav, Ipsa, additional, Chen, Jonathan, additional, Kumar, Neeraj, additional, Smits, Marion, additional, van der Voort, Sebastian R., additional, Alafandi, Ahmed, additional, Incekara, Fatih, additional, Wijnenga, Maarten M. J., additional, Kapsas, Georgios, additional, Gahrmann, Renske, additional, Schouten, Joost W., additional, Dubbink, Hendrikus J., additional, Vincent, Arnaud J. P. E., additional, van den Bent, Martin J., additional, French, Pim J., additional, Klein, Stefan, additional, Yuan, Yading, additional, Sharma, Sonam, additional, Tseng, Tzu-Chi, additional, Adabi, Saba, additional, Niclou, Simone P., additional, Keunen, Olivier, additional, Hau, Ann-Christin, additional, Vallières, Martin, additional, Fortin, David, additional, Lepage, Martin, additional, Landman, Bennett, additional, Ramadass, Karthik, additional, Xu, Kaiwen, additional, Chotai, Silky, additional, Chambless, Lola B., additional, Mistry, Akshitkumar, additional, Thompson, Reid C., additional, Gusev, Yuriy, additional, Bhuvaneshwar, Krithika, additional, Sayah, Anousheh, additional, Bencheqroun, Camelia, additional, Belouali, Anas, additional, Madhavan, Subha, additional, Booth, Thomas C., additional, Chelliah, Alysha, additional, Modat, Marc, additional, Shuaib, Haris, additional, Dragos, Carmen, additional, Abayazeed, Aly, additional, Kolodziej, Kenneth, additional, Hill, Michael, additional, Abbassy, Ahmed, additional, Gamal, Shady, additional, Mekhaimar, Mahmoud, additional, Qayati, Mohamed, additional, Reyes, Mauricio, additional, Park, Ji Eun, additional, Yun, Jihye, additional, Kim, Ho Sung, additional, Mahajan, Abhishek, additional, Muzi, Mark, additional, Benson, Sean, additional, Beets-Tan, Regina G. H., additional, Teuwen, Jonas, additional, Herrera-Trujillo, Alejandro, additional, Trujillo, Maria, additional, Escobar, William, additional, Abello, Ana, additional, Bernal, Jose, additional, Gómez, Jhon, additional, Choi, Joseph, additional, Baek, Stephen, additional, Kim, Yusung, additional, Ismael, Heba, additional, Allen, Bryan, additional, Buatti, John M., additional, Kotrotsou, Aikaterini, additional, Li, Hongwei, additional, Weiss, Tobias, additional, Weller, Michael, additional, Bink, Andrea, additional, Pouymayou, Bertrand, additional, Shaykh, Hassan F., additional, Saltz, Joel, additional, Prasanna, Prateek, additional, Shrestha, Sampurna, additional, Mani, Kartik M., additional, Payne, David, additional, Kurc, Tahsin, additional, Pelaez, Enrique, additional, Franco-Maldonado, Heydy, additional, Loayza, Francis, additional, Quevedo, Sebastian, additional, Guevara, Pamela, additional, Torche, Esteban, additional, Mendoza, Cristobal, additional, Vera, Franco, additional, Ríos, Elvis, additional, López, Eduardo, additional, Velastin, Sergio A., additional, Ogbole, Godwin, additional, Soneye, Mayowa, additional, Oyekunle, Dotun, additional, Odafe-Oyibotha, Olubunmi, additional, Osobu, Babatunde, additional, Shu’aibu, Mustapha, additional, Dorcas, Adeleye, additional, Dako, Farouk, additional, Simpson, Amber L., additional, Hamghalam, Mohammad, additional, Peoples, Jacob J., additional, Hu, Ricky, additional, Tran, Anh, additional, Cutler, Danielle, additional, Moraes, Fabio Y., additional, Boss, Michael A., additional, Gimpel, James, additional, Veettil, Deepak Kattil, additional, Schmidt, Kendall, additional, Bialecki, Brian, additional, Marella, Sailaja, additional, Price, Cynthia, additional, Cimino, Lisa, additional, Apgar, Charles, additional, Shah, Prashant, additional, Menze, Bjoern, additional, Barnholtz-Sloan, Jill S., additional, Martin, Jason, additional, and Bakas, Spyridon, additional

Published
2022
Full Text
View/download PDF

22. Quantitative magnetic resonance imaging phantoms: A review and the need for a system phantom

Author

Keenan, Kathryn E., Ainslie, Maureen, Barker, Alex J., Boss, Michael A., Cecil, Kim M., Charles, Cecil, Chenevert, Thomas L., Clarke, Larry, Evelhoch, Jeffrey L., Finn, Paul, Gembris, Daniel, Gunter, Jeffrey L., Hill, Derek L.G., Jack, Clifford R., Jr., Jackson, Edward F., Liu, Guoying, Russek, Stephen E., Sharma, Samir D., Steckner, Michael, Stupic, Karl F., Trzasko, Joshua D., Yuan, Chun, and Zheng, Jie

Published
2018
Full Text
View/download PDF

26. In Vivo and Phantom Repeatability of Diffusion-Weighted MRI Sequences on 1.5T MRI-Linear Accelerator (MR-Linac) and MR Simulator Devices for Head and Neck Cancers: Results from a Prospective R-IDEAL Stage 2a Evaluation of Tumor and Normal Tissue Apparent Diffusion Coefficients as Quantitative Imaging Biomarkers

Author

McDonald, Brigid A, primary, Salzillo, Travis, additional, Mulder, Samuel, additional, Ahmed, Sara, additional, Dresner, Alex, additional, Preston, Kathryn, additional, He, Renjie, additional, Christodouleas, John, additional, Mohamed, Abdallah Sherif Radwan, additional, Philippens, Marielle, additional, van Houdt, Petra, additional, Thorwarth, Daniela, additional, Wang, Jihong, additional, Shukla-Dave, Amita, additional, Boss, Michael, additional, and Fuller, Clifton David, additional

Published
2022
Full Text
View/download PDF

27. Repeatability and Reproducibility Assessment of the Apparent Diffusion Coefficient in the Prostate: A Trial of the ECOG-ACRIN Research Group (ACRIN 6701)

Author

Boss, Michael A., Snyder, Bradley S., Kim, Eunhee, Flamini, Dena, Englander, Sarah, Sundaram, Karthik M., Gumpeni, Naveen, Palmer, Suzanne L., Choi, Haesun, Froemming, Adam T., Persigehl, Thorsten, Davenport, Matthew S., Malyarenko, Dariya, Chenevert, Thomas L., Rosen, Mark A., Boss, Michael A., Snyder, Bradley S., Kim, Eunhee, Flamini, Dena, Englander, Sarah, Sundaram, Karthik M., Gumpeni, Naveen, Palmer, Suzanne L., Choi, Haesun, Froemming, Adam T., Persigehl, Thorsten, Davenport, Matthew S., Malyarenko, Dariya, Chenevert, Thomas L., and Rosen, Mark A.

Abstract

Background Uncertainty regarding the reproducibility of the apparent diffusion coefficient (ADC) hampers the use of quantitative diffusion-weighted imaging (DWI) in evaluation of the prostate with magnetic resonance imaging MRI. The quantitative imaging biomarkers alliance (QIBA) profile for quantitative DWI claims a within-subject coefficient of variation (wCV) for prostate lesion ADC of 0.17. Improved understanding of ADC reproducibility would aid the use of quantitative diffusion in prostate MRI evaluation. Purpose Evaluation of the repeatability (same-day) and reproducibility (multi-day) of whole-prostate and focal-lesion ADC assessment in a multi-site setting. Study Type Prospective multi-institutional. Subjects Twenty-nine males, ages 53 to 80 (median 63) years, following diagnosis of prostate cancer, 10 with focal lesions. Field Strength/Sequence 3T, single-shot spin-echo diffusion-weighted echo-planar sequence with four b-values. Assessment Sites qualified for the study using an ice-water phantom with known ADC. Readers performed DWI analyses at visit 1 (V1) and visit 2 (V2, 2-14 days after V1), where V2 comprised scans before (V2pre) and after (V2post) a coffee-break interval with subject removal and repositioning. A single reader segmented the whole prostate. Two readers separately placed region-of-interests for focal lesions. Statistical Tests Reproducibility and repeatability coefficients for whole prostate and focal lesions derived from median pixel ADC. We estimated the wCV and 95% confidence interval using a variance stabilizing transformation and assessed interreader reliability of focal lesion ADC using the intraclass correlation coefficient (ICC). Results The ADC biases from b(0)-b(600) and b(0)-b(800) phantom scans averaged 1.32% and 1.44%, respectively; mean b-value dependence was 0.188%. Repeatability and reproducibility of whole prostate median pixel ADC both yielded wCVs of 0.033 (N = 29). In 10 subjects with an evaluable focal lesion, the ind

Published
2022

31. Repeatability and Reproducibility Assessment of the Apparent Diffusion Coefficient in the Prostate: A Trial of the ECOG‐ACRIN Research Group ( ACRIN 6701)

Author

Boss, Michael A., primary, Snyder, Bradley S., additional, Kim, Eunhee, additional, Flamini, Dena, additional, Englander, Sarah, additional, Sundaram, Karthik M., additional, Gumpeni, Naveen, additional, Palmer, Suzanne L., additional, Choi, Haesun, additional, Froemming, Adam T., additional, Persigehl, Thorsten, additional, Davenport, Matthew S., additional, Malyarenko, Dariya, additional, Chenevert, Thomas L., additional, and Rosen, Mark A., additional

Published
2022
Full Text
View/download PDF

33. The Supply and Cost of Education and the Vote: A Political-Economic Theory of School Finance Elections.

Author

Oregon Univ., Eugene. Center for Advanced Study of Educational Administration. and Boss, Michael

Abstract

The recent marked increase in voter-taxpayer rejection of school budget and school bond issues at polls across the United States -- a phenomenon popularly called the "taxpayers' revolt" -- has given rise to the widespread claim that public school finance is in a state of crisis. This paper develops a simplified model of a political marketplace, linking together individual demand for a public good and the vote in fiscal elections to set the supply and the tax-cost of such a good, where both the system of elections and the system of taxation are taken as givens. Data from school budget elections in Oregon are applied to the model and the demand for education under varying tax-cost conditions and overtime in Oregon is estimated. The resultant analysis leads to a markedly different interpretation of the increase in rejection of school finance proposals than that of a "taxpayers' revolt." Moreover, the analysis traces directly the linkage between demand for education and the tax-cost of education and indirectly the linkage between demand for education and the quality or valuation of education. Such findings have major implications in assessing the present state of public school finance and in formulating public school finance policy. (Author)

Published
1973

40. Multi-site, multi-platform comparison of MRI T1 measurement using the system phantom

Author

Keenan, Kathryn E., primary, Gimbutas, Zydrunas, additional, Dienstfrey, Andrew, additional, Stupic, Karl F., additional, Boss, Michael A., additional, Russek, Stephen E., additional, Chenevert, Thomas L., additional, Prasad, P. V., additional, Guo, Junyu, additional, Reddick, Wilburn E., additional, Cecil, Kim M., additional, Shukla-Dave, Amita, additional, Aramburu Nunez, David, additional, Shridhar Konar, Amaresh, additional, Liu, Michael Z., additional, Jambawalikar, Sachin R., additional, Schwartz, Lawrence H., additional, Zheng, Jie, additional, Hu, Peng, additional, and Jackson, Edward F., additional

Published
2021
Full Text
View/download PDF

41. A standard system phantom for magnetic resonance imaging

Author

Stupic, Karl F., primary, Ainslie, Maureen, additional, Boss, Michael A., additional, Charles, Cecil, additional, Dienstfrey, Andrew M., additional, Evelhoch, Jeffrey L., additional, Finn, Paul, additional, Gimbutas, Zydrunas, additional, Gunter, Jeffrey L., additional, Hill, Derek L. G., additional, Jack, Clifford R., additional, Jackson, Edward F., additional, Karaulanov, Todor, additional, Keenan, Kathryn E., additional, Liu, Guoying, additional, Martin, Michele N., additional, Prasad, Pottumarthi V., additional, Rentz, Nikki S., additional, Yuan, Chun, additional, and Russek, Stephen E., additional

Published
2021
Full Text
View/download PDF

42. Development, validation, qualification, and dissemination of quantitative MR methods: Overview and recommendations by the ISMRM quantitative MR study group.

Author

Weingärtner, Sebastian, Desmond, Kimberly L., Obuchowski, Nancy A., Baessler, Bettina, Zhang, Yuxin, Biondetti, Emma, Ma, Dan, Golay, Xavier, Boss, Michael A., Gunter, Jeffrey L., Keenan, Kathryn E., and Hernando, Diego

Subjects
QUANTITATIVE research, MAGNETIC resonance, RESEARCH institutes, RESEARCH methodology
Abstract

On behalf of the International Society for Magnetic Resonance in Medicine (ISMRM) Quantitative MR Study Group, this article provides an overview of considerations for the development, validation, qualification, and dissemination of quantitative MR (qMR) methods. This process is framed in terms of two central technical performance properties, i.e., bias and precision. Although qMR is confounded by undesired effects, methods with low bias and high precision can be iteratively developed and validated. For illustration, two distinct qMR methods are discussed throughout the manuscript: quantification of liver proton‐density fat fraction, and cardiac T1. These examples demonstrate the expansion of qMR methods from research centers toward widespread clinical dissemination. The overall goal of this article is to provide trainees, researchers, and clinicians with essential guidelines for the development and validation of qMR methods, as well as an understanding of necessary steps and potential pitfalls for the dissemination of quantitative MR in research and in the clinic. [ABSTRACT FROM AUTHOR]

Published
2022
Full Text
View/download PDF

45. A founder mutation in presenilin 1 causing early-onset Alzheimer disease in unrelated Caribbean Hispanic families

Author

Athan, Eleni S., Williamson, Jennifer, Ciappa, Alejandra, Santana, Vincent, Romas, Stavra N., Lee, Joseph H., Rondon, Haydee, Lantigua, Rafael A., Medrano, Martin, Torres, Mayobanex, Arawaka, Shigeki, Rogaeva, Ekaterina, Song, You-Qiang, Sato, Christine, Kawarai, Toshitaka, Fafel, Kimberley C., Boss, Michael A., Seltzer, William K., Stern, Yaakov, Ceorge-Hyslop, Peter St, Tycko, Benjamin, and Mayeux, Richard

Subjects
Alzheimer's disease -- Genetic aspects, Hispanic Americans -- Diseases, Caribbean Americans -- Diseases
Abstract

A mutation in the presenilin 1 gene was found in eight out of 19 Caribbean Hispanic families with a family history of Alzheimer's disease that occurred before the age of 55. The families were not related to each other, indicating that they all had a common ancestor. The rate of Alzheimer's disease is higher in Caribbean Hispanics than in non-Hispanic whites.

Published
2001

48. Multi-site, multi-platform comparison of MRI T1 measurement using the system phantom.

Author

Keenan, Kathryn E., Gimbutas, Zydrunas, Dienstfrey, Andrew, Stupic, Karl F., Boss, Michael A., Russek, Stephen E., Chenevert, Thomas L., Prasad, P. V., Guo, Junyu, Reddick, Wilburn E., Cecil, Kim M., Shukla-Dave, Amita, Aramburu Nunez, David, Shridhar Konar, Amaresh, Liu, Michael Z., Jambawalikar, Sachin R., Schwartz, Lawrence H., Zheng, Jie, Hu, Peng, and Jackson, Edward F.

Subjects
MAGNETIC resonance imaging, DETECTION limit, MAGNETIC resonance, NUMBER systems, REFERENCE values
Abstract

Recent innovations in quantitative magnetic resonance imaging (MRI) measurement methods have led to improvements in accuracy, repeatability, and acquisition speed, and have prompted renewed interest to reevaluate the medical value of quantitative T1. The purpose of this study was to determine the bias and reproducibility of T1 measurements in a variety of MRI systems with an eye toward assessing the feasibility of applying diagnostic threshold T1 measurement across multiple clinical sites. We used the International Society of Magnetic Resonance in Medicine/National Institute of Standards and Technology (ISMRM/NIST) system phantom to assess variations of T1 measurements, using a slow, reference standard inversion recovery sequence and a rapid, commonly-available variable flip angle sequence, across MRI systems at 1.5 tesla (T) (two vendors, with number of MRI systems n = 9) and 3 T (three vendors, n = 18). We compared the T1 measurements from inversion recovery and variable flip angle scans to ISMRM/NIST phantom reference values using Analysis of Variance (ANOVA) to test for statistical differences between T1 measurements grouped according to MRI scanner manufacturers and/or static field strengths. The inversion recovery method had minor over- and under-estimations compared to the NMR-measured T1 values at both 1.5 T and 3 T. Variable flip angle measurements had substantially greater deviations from the NMR-measured T1 values than the inversion recovery measurements. At 3 T, the measured variable flip angle T1 for one vendor is significantly different than the other two vendors for most of the samples throughout the clinically relevant range of T1. There was no consistent pattern of discrepancy between vendors. We suggest establishing rigorous quality control procedures for validating quantitative MRI methods to promote confidence and stability in associated measurement techniques and to enable translation of diagnostic threshold from the research center to the entire clinical community. [ABSTRACT FROM AUTHOR]

Published
2021
Full Text
View/download PDF

49. Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE‐MRI derived biomarkers in multicenter oncology trials

Author

Shukla‐Dave, Amita, primary, Obuchowski, Nancy A., additional, Chenevert, Thomas L., additional, Jambawalikar, Sachin, additional, Schwartz, Lawrence H., additional, Malyarenko, Dariya, additional, Huang, Wei, additional, Noworolski, Susan M., additional, Young, Robert J., additional, Shiroishi, Mark S., additional, Kim, Harrison, additional, Coolens, Catherine, additional, Laue, Hendrik, additional, Chung, Caroline, additional, Rosen, Mark, additional, Boss, Michael, additional, and Jackson, Edward F., additional

Published
2019
Full Text
View/download PDF

50. Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE‐MRI derived biomarkers in multicenter oncology trials

Author

Shukla‐Dave, Amita, primary, Obuchowski, Nancy A., additional, Chenevert, Thomas L., additional, Jambawalikar, Sachin, additional, Schwartz, Lawrence H., additional, Malyarenko, Dariya, additional, Huang, Wei, additional, Noworolski, Susan M., additional, Young, Robert J., additional, Shiroishi, Mark S., additional, Kim, Harrison, additional, Coolens, Catherine, additional, Laue, Hendrik, additional, Chung, Caroline, additional, Rosen, Mark, additional, Boss, Michael, additional, and Jackson, Edward F., additional

Published
2018
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results