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1. Reproducible Machine Learning Methods for Lung Cancer Detection Using Computed Tomography Images: Algorithm Development and Validation

Author

Yu, Kun-Hsing, Lee, Tsung-Lu Michael, Yen, Ming-Hsuan, Kou, S C, Rosen, Bruce, Chiang, Jung-Hsien, and Kohane, Isaac S

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundChest computed tomography (CT) is crucial for the detection of lung cancer, and many automated CT evaluation methods have been proposed. Due to the divergent software dependencies of the reported approaches, the developed methods are rarely compared or reproduced. ObjectiveThe goal of the research was to generate reproducible machine learning modules for lung cancer detection and compare the approaches and performances of the award-winning algorithms developed in the Kaggle Data Science Bowl. MethodsWe obtained the source codes of all award-winning solutions of the Kaggle Data Science Bowl Challenge, where participants developed automated CT evaluation methods to detect lung cancer (training set n=1397, public test set n=198, final test set n=506). The performance of the algorithms was evaluated by the log-loss function, and the Spearman correlation coefficient of the performance in the public and final test sets was computed. ResultsMost solutions implemented distinct image preprocessing, segmentation, and classification modules. Variants of U-Net, VGGNet, and residual net were commonly used in nodule segmentation, and transfer learning was used in most of the classification algorithms. Substantial performance variations in the public and final test sets were observed (Spearman correlation coefficient = .39 among the top 10 teams). To ensure the reproducibility of results, we generated a Docker container for each of the top solutions. ConclusionsWe compared the award-winning algorithms for lung cancer detection and generated reproducible Docker images for the top solutions. Although convolutional neural networks achieved decent accuracy, there is plenty of room for improvement regarding model generalizability.

Published
2020
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2. Uncertainty Estimation and Out-of-Distribution Detection for Deep Learning-Based Image Reconstruction using the Local Lipschitz

Author

Bhutto, Danyal F., Zhu, Bo, Liu, Jeremiah Z., Koonjoo, Neha, Li, Hongwei B., Rosen, Bruce R., and Rosen, Matthew S.

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

Accurate image reconstruction is at the heart of diagnostics in medical imaging. Supervised deep learning-based approaches have been investigated for solving inverse problems including image reconstruction. However, these trained models encounter unseen data distributions that are widely shifted from training data during deployment. Therefore, it is essential to assess whether a given input falls within the training data distribution for diagnostic purposes. Uncertainty estimation approaches exist but focus on providing an uncertainty map to radiologists, rather than assessing the training distribution fit. In this work, we propose a method based on the local Lipschitz-based metric to distinguish out-of-distribution images from in-distribution with an area under the curve of 99.94%. Empirically, we demonstrate a very strong relationship between the local Lipschitz value and mean absolute error (MAE), supported by a high Spearman's rank correlation coefficient of 0.8475, which determines the uncertainty estimation threshold for optimal model performance. Through the identification of false positives, the local Lipschitz and MAE relationship was used to guide data augmentation and reduce model uncertainty. Our study was validated using the AUTOMAP architecture for sensor-to-image Magnetic Resonance Imaging (MRI) reconstruction. We compare our proposed approach with baseline methods: Monte-Carlo dropout and deep ensembles, and further analysis included MRI denoising and Computed Tomography (CT) sparse-to-full view reconstruction using UNET architectures. We show that our approach is applicable to various architectures and learned functions, especially in the realm of medical image reconstruction, where preserving the diagnostic accuracy of reconstructed images remains paramount.

Published
2023

5. Neuroimmune activation and increased brain aging in chronic pain patients after the COVID-19 pandemic onset

Author

Brusaferri, Ludovica, Alshelh, Zeynab, Schnieders, Jack H., Sandström, Angelica, Mohammadian, Mehrbod, Morrissey, Erin J., Kim, Minhae, Chane, Courtney A., Grmek, Grace C., Murphy, Jennifer P., Bialobrzewski, Julia, DiPietro, Alexa, Klinke, Julie, Zhang, Yi, Torrado-Carvajal, Angel, Mercaldo, Nathaniel, Akeju, Oluwaseun, Wu, Ona, Rosen, Bruce R., Napadow, Vitaly, Hadjikhani, Nouchine, and Loggia, Marco L.

Published
2024
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8. Connecting the Dots: Approaching a Standardized Nomenclature for Molecular Connectivity Combining Data and Literature

Author

Reed, Murray Bruce, primary, Cocchi, Luca, additional, Knudsen, Gitte Moos, additional, Sander, Christin, additional, Gryglewski, Gregor, additional, Chen, Jingyuan, additional, Volpi, Tommaso, additional, Fisher, Patrick, additional, Khattar, Nikkita, additional, Silberbauer, Leo R, additional, Murgas, Matej, additional, Godbersen, Godber Mathis, additional, Nics, Lukas, additional, Walter, Martin, additional, Hacker, Marcus, additional, Hammers, Alexander, additional, Ogden, Todd R, additional, Mann, Joseph J, additional, Biswal, Bharat, additional, Rosen, Bruce, additional, Carson, Richard, additional, Price, Julie, additional, Lanzenberger, Rupert, additional, and Hahn, Andreas, additional

Published
2024
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9. Consensus recommendations for a dynamic susceptibility contrast MRI protocol for use in high-grade gliomas

Author

Boxerman, Jerrold L, Quarles, Chad C, Hu, Leland S, Erickson, Bradley J, Gerstner, Elizabeth R, Smits, Marion, Kaufmann, Timothy J, Barboriak, Daniel P, Huang, Raymond H, Wick, Wolfgang, Weller, Michael, Galanis, Evanthia, Kalpathy-Cramer, Jayashree, Shankar, Lalitha, Jacobs, Paula, Chung, Caroline, van den Bent, Martin J, Chang, Susan, Al Yung, WK, Cloughesy, Timothy F, Wen, Patrick Y, Gilbert, Mark R, Rosen, Bruce R, Ellingson, Benjamin M, Schmainda, Kathleen M, Arons, David F, Kingston, Ann, Sandak, David, Wallace, Max, Musella, Al, and Haynes, Chas

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Brain Disorders, Biomedical Imaging, Neurosciences, Clinical Research, Clinical Trials and Supportive Activities, Cancer, Rare Diseases, Minority Health, Brain Cancer, Algorithms, Brain Neoplasms, Consensus, Contrast Media, Glioma, Humans, Magnetic Resonance Imaging, cerebral blood volume, clinical trial, consensus protocol, DSC-MRI, high-grade glioma, Jumpstarting Brain Tumor Drug Development Coalition Imaging Standardization Steering Committee, Oncology & Carcinogenesis, Oncology and carcinogenesis
Abstract

Despite the widespread clinical use of dynamic susceptibility contrast (DSC) MRI, DSC-MRI methodology has not been standardized, hindering its utilization for response assessment in multicenter trials. Recently, the DSC-MRI Standardization Subcommittee of the Jumpstarting Brain Tumor Drug Development Coalition issued an updated consensus DSC-MRI protocol compatible with the standardized brain tumor imaging protocol (BTIP) for high-grade gliomas that is increasingly used in the clinical setting and is the default MRI protocol for the National Clinical Trials Network. After reviewing the basis for controversy over DSC-MRI protocols, this paper provides evidence-based best practices for clinical DSC-MRI as determined by the Committee, including pulse sequence (gradient echo vs spin echo), BTIP-compliant contrast agent dosing (preload and bolus), flip angle (FA), echo time (TE), and post-processing leakage correction. In summary, full-dose preload, full-dose bolus dosing using intermediate (60°) FA and field strength-dependent TE (40-50 ms at 1.5 T, 20-35 ms at 3 T) provides overall best accuracy and precision for cerebral blood volume estimates. When single-dose contrast agent usage is desired, no-preload, full-dose bolus dosing using low FA (30°) and field strength-dependent TE provides excellent performance, with reduced contrast agent usage and elimination of potential systematic errors introduced by variations in preload dose and incubation time.

Published
2020

11. Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Author

Bakas, Spyridon, Reyes, Mauricio, Jakab, Andras, Bauer, Stefan, Rempfler, Markus, Crimi, Alessandro, Shinohara, Russell Takeshi, Berger, Christoph, Ha, Sung Min, Rozycki, Martin, Prastawa, Marcel, Alberts, Esther, Lipkova, Jana, Freymann, John, Kirby, Justin, Bilello, Michel, Fathallah-Shaykh, Hassan, Wiest, Roland, Kirschke, Jan, Wiestler, Benedikt, Colen, Rivka, Kotrotsou, Aikaterini, Lamontagne, Pamela, Marcus, Daniel, Milchenko, Mikhail, Nazeri, Arash, Weber, Marc-Andre, Mahajan, Abhishek, Baid, Ujjwal, Gerstner, Elizabeth, Kwon, Dongjin, Acharya, Gagan, Agarwal, Manu, Alam, Mahbubul, Albiol, Alberto, Albiol, Antonio, Albiol, Francisco J., Alex, Varghese, Allinson, Nigel, Amorim, Pedro H. A., Amrutkar, Abhijit, Anand, Ganesh, Andermatt, Simon, Arbel, Tal, Arbelaez, Pablo, Avery, Aaron, Azmat, Muneeza, B., Pranjal, Bai, W, Banerjee, Subhashis, Barth, Bill, Batchelder, Thomas, Batmanghelich, Kayhan, Battistella, Enzo, Beers, Andrew, Belyaev, Mikhail, Bendszus, Martin, Benson, Eze, Bernal, Jose, Bharath, Halandur Nagaraja, Biros, George, Bisdas, Sotirios, Brown, James, Cabezas, Mariano, Cao, Shilei, Cardoso, Jorge M., Carver, Eric N, Casamitjana, Adrià, Castillo, Laura Silvana, Catà, Marcel, Cattin, Philippe, Cerigues, Albert, Chagas, Vinicius S., Chandra, Siddhartha, Chang, Yi-Ju, Chang, Shiyu, Chang, Ken, Chazalon, Joseph, Chen, Shengcong, Chen, Wei, Chen, Jefferson W, Chen, Zhaolin, Cheng, Kun, Choudhury, Ahana Roy, Chylla, Roger, Clérigues, Albert, Colleman, Steven, Colmeiro, Ramiro German Rodriguez, Combalia, Marc, Costa, Anthony, Cui, Xiaomeng, Dai, Zhenzhen, Dai, Lutao, Daza, Laura Alexandra, Deutsch, Eric, Ding, Changxing, Dong, Chao, Dong, Shidu, Dudzik, Wojciech, Eaton-Rosen, Zach, Egan, Gary, Escudero, Guilherme, Estienne, Théo, Everson, Richard, Fabrizio, Jonathan, Fan, Yong, Fang, Longwei, Feng, Xue, Ferrante, Enzo, Fidon, Lucas, Fischer, Martin, French, Andrew P., Fridman, Naomi, Fu, Huan, Fuentes, David, Gao, Yaozong, Gates, Evan, Gering, David, Gholami, Amir, Gierke, Willi, Glocker, Ben, Gong, Mingming, González-Villá, Sandra, Grosges, T., Guan, Yuanfang, Guo, Sheng, Gupta, Sudeep, Han, Woo-Sup, Han, Il Song, Harmuth, Konstantin, He, Huiguang, Hernández-Sabaté, Aura, Herrmann, Evelyn, Himthani, Naveen, Hsu, Winston, Hsu, Cheyu, Hu, Xiaojun, Hu, Xiaobin, Hu, Yan, Hu, Yifan, Hua, Rui, Huang, Teng-Yi, Huang, Weilin, Van Huffel, Sabine, Huo, Quan, HV, Vivek, Iftekharuddin, Khan M., Isensee, Fabian, Islam, Mobarakol, Jackson, Aaron S., Jambawalikar, Sachin R., Jesson, Andrew, Jian, Weijian, Jin, Peter, Jose, V Jeya Maria, Jungo, Alain, Kainz, B, Kamnitsas, Konstantinos, Kao, Po-Yu, Karnawat, Ayush, Kellermeier, Thomas, Kermi, Adel, Keutzer, Kurt, Khadir, Mohamed Tarek, Khened, Mahendra, Kickingereder, Philipp, Kim, Geena, King, Nik, Knapp, Haley, Knecht, Urspeter, Kohli, Lisa, Kong, Deren, Kong, Xiangmao, Koppers, Simon, Kori, Avinash, Krishnamurthi, Ganapathy, Krivov, Egor, Kumar, Piyush, Kushibar, Kaisar, Lachinov, Dmitrii, Lambrou, Tryphon, Lee, Joon, Lee, Chengen, Lee, Yuehchou, Lee, M, Lefkovits, Szidonia, Lefkovits, Laszlo, Levitt, James, Li, Tengfei, Li, Hongwei, Li, Wenqi, Li, Hongyang, Li, Xiaochuan, Li, Yuexiang, Li, Heng, Li, Zhenye, Li, Xiaoyu, Li, Zeju, Li, XiaoGang, Lin, Zheng-Shen, Lin, Fengming, Lio, Pietro, Liu, Chang, Liu, Boqiang, Liu, Xiang, Liu, Mingyuan, Liu, Ju, Liu, Luyan, Llado, Xavier, Lopez, Marc Moreno, Lorenzo, Pablo Ribalta, Lu, Zhentai, Luo, Lin, Luo, Zhigang, Ma, Jun, Ma, Kai, Mackie, Thomas, Madabushi, Anant, Mahmoudi, Issam, Maier-Hein, Klaus H., Maji, Pradipta, Mammen, CP, Mang, Andreas, Manjunath, B. S., Marcinkiewicz, Michal, McDonagh, S, McKenna, Stephen, McKinley, Richard, Mehl, Miriam, Mehta, Sachin, Mehta, Raghav, Meier, Raphael, Meinel, Christoph, Merhof, Dorit, Meyer, Craig, Miller, Robert, Mitra, Sushmita, Moiyadi, Aliasgar, Molina-Garcia, David, Monteiro, Miguel A. B., Mrukwa, Grzegorz, Myronenko, Andriy, Nalepa, Jakub, Ngo, Thuyen, Nie, Dong, Ning, Holly, Niu, Chen, Nuechterlein, Nicholas K, Oermann, Eric, Oliveira, Arlindo, Oliveira, Diego D. C., Oliver, Arnau, Osman, Alexander F. I., Ou, Yu-Nian, Ourselin, Sebastien, Paragios, Nikos, Park, Moo Sung, Paschke, Brad, Pauloski, J. Gregory, Pawar, Kamlesh, Pawlowski, Nick, Pei, Linmin, Peng, Suting, Pereira, Silvio M., Perez-Beteta, Julian, Perez-Garcia, Victor M., Pezold, Simon, Pham, Bao, Phophalia, Ashish, Piella, Gemma, Pillai, G. N., Piraud, Marie, Pisov, Maxim, Popli, Anmol, Pound, Michael P., Pourreza, Reza, Prasanna, Prateek, Prkovska, Vesna, Pridmore, Tony P., Puch, Santi, Puybareau, Élodie, Qian, Buyue, Qiao, Xu, Rajchl, Martin, Rane, Swapnil, Rebsamen, Michael, Ren, Hongliang, Ren, Xuhua, Revanuru, Karthik, Rezaei, Mina, Rippel, Oliver, Rivera, Luis Carlos, Robert, Charlotte, Rosen, Bruce, Rueckert, Daniel, Safwan, Mohammed, Salem, Mostafa, Salvi, Joaquim, Sanchez, Irina, Sánchez, Irina, Santos, Heitor M., Sartor, Emmett, Schellingerhout, Dawid, Scheufele, Klaudius, Scott, Matthew R., Scussel, Artur A., Sedlar, Sara, Serrano-Rubio, Juan Pablo, Shah, N. Jon, Shah, Nameetha, Shaikh, Mazhar, Shankar, B. Uma, Shboul, Zeina, Shen, Haipeng, Shen, Dinggang, Shen, Linlin, Shen, Haocheng, Shenoy, Varun, Shi, Feng, Shin, Hyung Eun, Shu, Hai, Sima, Diana, Sinclair, M, Smedby, Orjan, Snyder, James M., Soltaninejad, Mohammadreza, Song, Guidong, Soni, Mehul, Stawiaski, Jean, Subramanian, Shashank, Sun, Li, Sun, Roger, Sun, Jiawei, Sun, Kay, Sun, Yu, Sun, Guoxia, Sun, Shuang, Suter, Yannick R, Szilagyi, Laszlo, Talbar, Sanjay, Tao, Dacheng, Teng, Zhongzhao, Thakur, Siddhesh, Thakur, Meenakshi H, Tharakan, Sameer, Tiwari, Pallavi, Tochon, Guillaume, Tran, Tuan, Tsai, Yuhsiang M., Tseng, Kuan-Lun, Tuan, Tran Anh, Turlapov, Vadim, Tustison, Nicholas, Vakalopoulou, Maria, Valverde, Sergi, Vanguri, Rami, Vasiliev, Evgeny, Ventura, Jonathan, Vera, Luis, Vercauteren, Tom, Verrastro, C. A., Vidyaratne, Lasitha, Vilaplana, Veronica, Vivekanandan, Ajeet, Wang, Guotai, Wang, Qian, Wang, Chiatse J., Wang, Weichung, Wang, Duo, Wang, Ruixuan, Wang, Yuanyuan, Wang, Chunliang, Wen, Ning, Wen, Xin, Weninger, Leon, Wick, Wolfgang, Wu, Shaocheng, Wu, Qiang, Wu, Yihong, Xia, Yong, Xu, Yanwu, Xu, Xiaowen, Xu, Peiyuan, Yang, Tsai-Ling, Yang, Xiaoping, Yang, Hao-Yu, Yang, Junlin, Yang, Haojin, Yang, Guang, Yao, Hongdou, Ye, Xujiong, Yin, Changchang, Young-Moxon, Brett, Yu, Jinhua, Yue, Xiangyu, Zhang, Songtao, Zhang, Angela, Zhang, Kun, Zhang, Xuejie, Zhang, Lichi, Zhang, Xiaoyue, Zhang, Yazhuo, Zhang, Lei, Zhang, Jianguo, Zhang, Xiang, Zhang, Tianhao, Zhao, Sicheng, Zhao, Yu, Zhao, Xiaomei, Zhao, Liang, Zheng, Yefeng, Zhong, Liming, Zhou, Chenhong, Zhou, Xiaobing, Zhou, Fan, Zhu, Hongtu, Zhu, Jin, Zhuge, Ying, Zong, Weiwei, Kalpathy-Cramer, Jayashree, Farahani, Keyvan, Davatzikos, Christos, van Leemput, Koen, and Menze, Bjoern

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset., Comment: The International Multimodal Brain Tumor Segmentation (BraTS) Challenge

Published
2018

12. DeepNeuro: an open-source deep learning toolbox for neuroimaging

Author

Beers, Andrew, Brown, James, Chang, Ken, Hoebel, Katharina, Gerstner, Elizabeth, Rosen, Bruce, and Kalpathy-Cramer, Jayashree

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Translating neural networks from theory to clinical practice has unique challenges, specifically in the field of neuroimaging. In this paper, we present DeepNeuro, a deep learning framework that is best-suited to putting deep learning algorithms for neuroimaging in practical usage with a minimum of friction. We show how this framework can be used to both design and train neural network architectures, as well as modify state-of-the-art architectures in a flexible and intuitive way. We display the pre- and postprocessing functions common in the medical imaging community that DeepNeuro offers to ensure consistent performance of networks across variable users, institutions, and scanners. And we show how pipelines created in DeepNeuro can be concisely packaged into shareable Docker containers and command-line interfaces using DeepNeuro's pipeline resources.

Published
2018

15. Institutionally Distributed Deep Learning Networks

Author

Chang, Ken, Balachandar, Niranjan, Lam, Carson K, Yi, Darvin, Brown, James M, Beers, Andrew, Rosen, Bruce R, Rubin, Daniel L, and Kalpathy-Cramer, Jayashree

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

Deep learning has become a promising approach for automated medical diagnoses. When medical data samples are limited, collaboration among multiple institutions is necessary to achieve high algorithm performance. However, sharing patient data often has limitations due to technical, legal, or ethical concerns. In such cases, sharing a deep learning model is a more attractive alternative. The best method of performing such a task is unclear, however. In this study, we simulate the dissemination of learning deep learning network models across four institutions using various heuristics and compare the results with a deep learning model trained on centrally hosted patient data. The heuristics investigated include ensembling single institution models, single weight transfer, and cyclical weight transfer. We evaluated these approaches for image classification in three independent image collections (retinal fundus photos, mammography, and ImageNet). We find that cyclical weight transfer resulted in a performance (testing accuracy = 77.3%) that was closest to that of centrally hosted patient data (testing accuracy = 78.7%). We also found that there is an improvement in the performance of cyclical weight transfer heuristic with high frequency of weight transfer.

Published
2017

16. Sequential 3D U-Nets for Biologically-Informed Brain Tumor Segmentation

Author

Beers, Andrew, Chang, Ken, Brown, James, Sartor, Emmett, Mammen, CP, Gerstner, Elizabeth, Rosen, Bruce, and Kalpathy-Cramer, Jayashree

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Deep learning has quickly become the weapon of choice for brain lesion segmentation. However, few existing algorithms pre-configure any biological context of their chosen segmentation tissues, and instead rely on the neural network's optimizer to develop such associations de novo. We present a novel method for applying deep neural networks to the problem of glioma tissue segmentation that takes into account the structured nature of gliomas - edematous tissue surrounding mutually-exclusive regions of enhancing and non-enhancing tumor. We trained multiple deep neural networks with a 3D U-Net architecture in a tree structure to create segmentations for edema, non-enhancing tumor, and enhancing tumor regions. Specifically, training was configured such that the whole tumor region including edema was predicted first, and its output segmentation was fed as input into separate models to predict enhancing and non-enhancing tumor. Our method was trained and evaluated on the publicly available BraTS dataset, achieving Dice scores of 0.882, 0.732, and 0.730 for whole tumor, enhancing tumor and tumor core respectively.

Published
2017

17. An Efficient Approach to Perform MR-Assisted PET Data Optimization in Simultaneous PET/MR Neuroimaging Studies

Author

Chen, Kevin T, Salcedo, Stephanie, Gong, Kuang, Chonde, Daniel B, Izquierdo-Garcia, David, Drzezga, Alexander, Rosen, Bruce, Qi, Jinyi, Dickerson, Bradford C, and Catana, Ciprian

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Bioengineering, Neurosciences, Brain Disorders, Biomedical Imaging, Clinical Research, Detection, screening and diagnosis, 4.1 Discovery and preclinical testing of markers and technologies, Neurological, PET/MRI, motion correction, anatomy-aided reconstruction, simultaneous imaging, Image Processing, Image Reconstruction, Nuclear Medicine & Medical Imaging, Clinical sciences
Abstract

A main advantage of PET is that it provides quantitative measures of the radiotracer concentration, but its accuracy is confounded by several factors, including attenuation, subject motion, and limited spatial resolution. Using the information from one simultaneously acquired morphological MR sequence with embedded navigators, we propose an efficient method called MR-assisted PET data optimization (MaPET) to perform attenuation correction (AC), motion correction, and anatomy-aided reconstruction. Methods: For attenuation correction, voxel-wise linear attenuation coefficient maps were generated using an SPM8-based approach method on the MR volume. The embedded navigators were used to derive head motion estimates for event-based PET motion correction. The anatomy provided by the MR volume was incorporated into the PET image reconstruction using a kernel-based method. Region-based analyses were carried out to assess the quality of images generated through various stages of PET data optimization. Results: The optimized PET images reconstructed with MaPET was superior in image quality compared to images reconstructed using only attenuation correction, with high SNR and low coefficient of variation (5.08 and 0.229 in a composite cortical region compared to 3.12 and 0.570). The optimized images were also shown using the Cohen's d metric to achieve a greater effect size in distinguishing cortical regions with hypometabolism from regions of preserved metabolism in each individual for different diagnosis groups. Conclusion: We have shown the spatiotemporally correlated data acquired using a single MR sequence can be used for PET attenuation, motion and partial volume effects corrections and the MaPET method may enable more accurate assessment of pathological changes in dementia and other brain disorders.

Published
2019

18. Mapping the human connectome using diffusion MRI at 300 mT/m gradient strength: Methodological advances and scientific impact

Author

Fan, Qiuyun, Eichner, Cornelius, Afzali, Maryam, Mueller, Lars, Tax, Chantal M.W., Davids, Mathias, Mahmutovic, Mirsad, Keil, Boris, Bilgic, Berkin, Setsompop, Kawin, Lee, Hong-Hsi, Tian, Qiyuan, Maffei, Chiara, Ramos-Llordén, Gabriel, Nummenmaa, Aapo, Witzel, Thomas, Yendiki, Anastasia, Song, Yi-Qiao, Huang, Chu-Chung, Lin, Ching-Po, Weiskopf, Nikolaus, Anwander, Alfred, Jones, Derek K., Rosen, Bruce R., Wald, Lawrence L., and Huang, Susie Y.

Published
2022
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19. The pandemic brain: Neuroinflammation in non-infected individuals during the COVID-19 pandemic

Author

Brusaferri, Ludovica, Alshelh, Zeynab, Martins, Daniel, Kim, Minhae, Weerasekera, Akila, Housman, Hope, Morrissey, Erin J., Knight, Paulina C., Castro-Blanco, Kelly A., Albrecht, Daniel S., Tseng, Chieh-En, Zürcher, Nicole R., Ratai, Eva-Maria, Akeju, Oluwaseun, Makary, Meena M., Catana, Ciprian, Mercaldo, Nathaniel D., Hadjikhani, Nouchine, Veronese, Mattia, Turkheimer, Federico, Rosen, Bruce R., Hooker, Jacob M., and Loggia, Marco L.

Published
2022
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20. Positron Emission Tomography/Magnetic Resonance Imaging (PET/MRI) Versus the Standard of Care Imaging in the Diagnosis of Peritoneal Carcinomatosis

Author

Furtado, Felipe S., Wu, Mark Z., Esfahani, Shadi A., Ferrone, Cristina R., Blaszkowsky, Lawrence S., Clark, Jeffrey W., Ryan, David P., Goyal, Lipika, Franses, Joseph W., Wo, Jennifer Y., Hong, Theodore S., Qadan, Motaz, Tanabe, Kenneth K., Weekes, Colin D., Cusack, James C., Crafa, Francesco, Mahmood, Umar, Anderson, Mark A., Mojtahed, Amirkasra, Hahn, Peter F., Caravan, Peter, Kilcoyne, Aoife, Vangel, Mark, Striar, Robin M., Rosen, Bruce R., and Catalano, Onofrio A.

Published
2023
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22. Segmentation, Survival Prediction, and Uncertainty Estimation of Gliomas from Multimodal 3D MRI Using Selective Kernel Networks

Author

Patel, Jay, Chang, Ken, Hoebel, Katharina, Gidwani, Mishka, Arun, Nishanth, Gupta, Sharut, Aggarwal, Mehak, Singh, Praveer, Rosen, Bruce R., Gerstner, Elizabeth R., Kalpathy-Cramer, Jayashree, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Crimi, Alessandro, editor, and Bakas, Spyridon, editor

Published
2021
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23. Image reconstruction by domain transform manifold learning

Author

Zhu, Bo, Liu, Jeremiah Z., Rosen, Bruce R., and Rosen, Matthew S.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Image reconstruction plays a critical role in the implementation of all contemporary imaging modalities across the physical and life sciences including optical, MRI, CT, PET, and radio astronomy. During an image acquisition, the sensor encodes an intermediate representation of an object in the sensor domain, which is subsequently reconstructed into an image by an inversion of the encoding function. Image reconstruction is challenging because analytic knowledge of the inverse transform may not exist a priori, especially in the presence of sensor non-idealities and noise. Thus, the standard reconstruction approach involves approximating the inverse function with multiple ad hoc stages in a signal processing chain whose composition depends on the details of each acquisition strategy, and often requires expert parameter tuning to optimize reconstruction performance. We present here a unified framework for image reconstruction, AUtomated TransfOrm by Manifold APproximation (AUTOMAP), which recasts image reconstruction as a data-driven, supervised learning task that allows a mapping between sensor and image domain to emerge from an appropriate corpus of training data. We implement AUTOMAP with a deep neural network and exhibit its flexibility in learning reconstruction transforms for a variety of MRI acquisition strategies, using the same network architecture and hyperparameters. We further demonstrate its efficiency in sparsely representing transforms along low-dimensional manifolds, resulting in superior immunity to noise and reconstruction artifacts compared with conventional handcrafted reconstruction methods. In addition to improving the reconstruction performance of existing acquisition methodologies, we anticipate accelerating the discovery of new acquisition strategies across modalities as the burden of reconstruction becomes lifted by AUTOMAP and learned-reconstruction approaches., Comment: 18 pages, 4 figures

Published
2017
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24. Impact of repeated blast exposure on active-duty United States Special Operations Forces.

Author

Gilmore, Natalie, Chieh-En J. Tseng, Maffei, Chiara, Tromly, Samantha L., Deary, Katryna B., McKinney, Isabella R., Kelemen, Jessica N., Healy, Brian C., Hu, Collin G., Ramos-Llordén, Gabriel, Masood, Maryam, Cali, Ryan J., Guo, Jennifer, Belanger, Heather G., Yao, Eveline F., Baxter, Timothy, Fischl, Bruce, Foulkes, Andrea S., Polimeni, Jonathan R., and Rosen, Bruce R.

Subjects
EXPOSURE therapy, POSITRON emission tomography, EXECUTIVE function, DIFFUSION magnetic resonance imaging, PREFRONTAL cortex, TRANSLOCATOR proteins
Abstract

United States (US) Special Operations Forces (SOF) are frequently exposed to explosive blasts in training and combat, but the effects of repeated blast exposure (RBE) on SOF brain health are incompletely understood. Furthermore, there is no diagnostic test to detect brain injury from RBE. As a result, SOF personnel may experience cognitive, physical, and psychological symptoms for which the cause is never identified, and they may return to training or combat during a period of brain vulnerability. In 30 active-duty US SOF, we assessed the relationship between cumulative blast exposure and cognitive performance, psychological health, physical symptoms, blood proteomics, and neuroimaging measures (Connectome structural and diffusion MRI, 7 Tesla functional MRI, [11C]PBR28 translocator protein [TSPO] positron emission tomography [PET]-MRI, and [18F]MK6240 tau PET-MRI), adjusting for age, combat exposure, and blunt head trauma. Higher blast exposure was associated with increased cortical thickness in the left rostral anterior cingulate cortex (rACC), a finding that remained significant after multiple comparison correction. In uncorrected analyses, higher blast exposure was associated with worse health-related quality of life, decreased functional connectivity in the executive control network, decreased TSPO signal in the right rACC, and increased cortical thickness in the right rACC, right insula, and right medial orbitofrontal cortex--nodes of the executive control, salience, and default mode networks. These observations suggest that the rACC may be susceptible to blast overpressure and that a multimodal, network-based diagnostic approach has the potential to detect brain injury associated with RBE in active-duty SOF. [ABSTRACT FROM AUTHOR]

Published
2024
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25. Connectome 2.0: Developing the next-generation ultra-high gradient strength human MRI scanner for bridging studies of the micro-, meso- and macro-connectome

Author

Huang, Susie Y., Witzel, Thomas, Keil, Boris, Scholz, Alina, Davids, Mathias, Dietz, Peter, Rummert, Elmar, Ramb, Rebecca, Kirsch, John E., Yendiki, Anastasia, Fan, Qiuyun, Tian, Qiyuan, Ramos-Llordén, Gabriel, Lee, Hong-Hsi, Nummenmaa, Aapo, Bilgic, Berkin, Setsompop, Kawin, Wang, Fuyixue, Avram, Alexandru V., Komlosh, Michal, Benjamini, Dan, Magdoom, Kulam Najmudeen, Pathak, Sudhir, Schneider, Walter, Novikov, Dmitry S., Fieremans, Els, Tounekti, Slimane, Mekkaoui, Choukri, Augustinack, Jean, Berger, Daniel, Shapson-Coe, Alexander, Lichtman, Jeff, Basser, Peter J., Wald, Lawrence L., and Rosen, Bruce R.

Published
2021
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28. Abnormal vascular structure and function within brain metastases is linked to pembrolizumab resistance

Author

Kim, Albert E, primary, Lou, Kevin W, additional, Giobbie-Hurder, Anita, additional, Chang, Ken, additional, Gidwani, Mishka, additional, Hoebel, Katharina, additional, Patel, Jay B, additional, Cleveland, Mason C, additional, Singh, Praveer, additional, Bridge, Christopher P, additional, Ahmed, Syed Rakin, additional, Bearce, Benjamin A, additional, Liu, William, additional, Fuster-Garcia, Elies, additional, Lee, Eudocia Q, additional, Lin, Nancy U, additional, Overmoyer, Beth, additional, Wen, Patrick Y, additional, Nayak, Lakshmi, additional, Cohen, Justine V, additional, Dietrich, Jorg, additional, Eichler, April, additional, Heist, Rebecca, additional, Krop, Ian, additional, Lawrence, Donald, additional, Ligibel, Jennifer, additional, Tolaney, Sara, additional, Mayer, Erica, additional, Winer, Eric, additional, Perrino, Carmen M, additional, Summers, Elizabeth J, additional, Mahar, Maura, additional, Oh, Kevin, additional, Shih, Helen A, additional, Cahill, Daniel P, additional, Rosen, Bruce R, additional, Yen, Yi-Fen, additional, Kalpathy-Cramer, Jayashree, additional, Martinez-Lage, Maria, additional, Sullivan, Ryan J, additional, Brastianos, Priscilla K, additional, Emblem, Kyrre E, additional, and Gerstner, Elizabeth R, additional

Published
2023
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30. Expert-centered Evaluation of Deep Learning Algorithms for Brain Tumor Segmentation

Author

Hoebel, Katharina V., primary, Bridge, Christopher P., additional, Ahmed, Sara, additional, Akintola, Oluwatosin, additional, Chung, Caroline, additional, Huang, Raymond, additional, Johnson, Jason M., additional, Kim, Albert, additional, Ly, K. Ina, additional, Chang, Ken, additional, Patel, Jay, additional, Pinho, Marco, additional, Batchelor, Tracy T., additional, Rosen, Bruce R., additional, Gerstner, Elizabeth R., additional, and Kalpathy-Cramer, Jayashree, additional

Published
2023
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31. A suite of neurophotonic tools to underpin the contribution of internal brain states in fMRI

Author

Mächler, Philipp, Broggini, Thomas, Mateo, Celine, Thunemann, Martin, Fomin-Thunemann, Natalie, Doran, Patrick R., Sencan, Ikbal, Kilic, Kivilcim, Desjardins, Michèle, Uhlirova, Hana, Yaseen, Mohammad A., Boas, David A., Linninger, Andreas A., Vergassola, Massimo, Yu, Xin, Lewis, Laura D., Polimeni, Jonathan R., Rosen, Bruce R., Sakadžić, Sava, Buxton, Richard B., Lauritzen, Martin, Kleinfeld, David, and Devor, Anna

Published
2021
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33. An international expert opinion statement on the utility of PET/MR for imaging of skeletal metastases

Author

Husseini, Jad S., Amorim, Bárbara Juarez, Torrado-Carvajal, Angel, Prabhu, Vinay, Groshar, David, Umutlu, Lale, Herrmann, Ken, Cañamaque, Lina García, Garzón, José Ramón García, Palmer, William E., Heidari, Pedram, Shih, Tiffany Ting-Fang, Sosna, Jacob, Matushita, Cristina, Cerci, Juliano, Queiroz, Marcelo, Muglia, Valdair Francisco, Nogueira-Barbosa, Marcello H., Borra, Ronald J. H., Kwee, Thomas C., Glaudemans, Andor W. J. M., Evangelista, Laura, Salvatore, Marco, Cuocolo, Alberto, Soricelli, Andrea, Herold, Christian, Laghi, Andrea, Mayerhoefer, Marius, Mahmood, Umar, Catana, Ciprian, Daldrup-Link, Heike E., Rosen, Bruce, and Catalano, Onofrio A.

Published
2021
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35. Positron Emission Tomography/Magnetic Resonance Imaging Versus the Standard of Care Imaging in the Diagnosis of Peritoneal Carcinomatosis

Author

Furtado, Felipe S., Wu, Mark Z., Esfahani, Shadi A., Ferrone, Cristina R., Blaszkowsky, Lawrence S., Clark, Jeffrey W., Ryan, David P., Goyal, Lipika, Franses, Joseph W., Wo, Jennifer Y., Hong, Theodore S., Qadan, Motaz, Tanabe, Kenneth K., Weekes, Colin D., Cusack, James C., Crafa, Francesco, Mahmood, Umar, Anderson, Mark A., Mojtahed, Amirkasra, Hahn, Peter F., Caravan, Peter, Kilcoyne, Aoife, Vangel, Mark, Striar, Robin R., Rosen, Bruce R., and Catalano, Onofrio A.

Published
2022
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38. Improving staging of rectal cancer in the pelvis: the role of PET/MRI

Author

Catalano, Onofrio A., Lee, Susanna I., Parente, Chiara, Cauley, Christy, Furtado, Felipe S., Striar, Robin, Soricelli, Andrea, Salvatore, Marco, Li, Yan, Umutlu, Lale, Cañamaque, Lina Garcia, Groshar, David, Mahmood, Umar, Blaszkowsky, Lawrence S., Ryan, David P., Clark, Jeffrey W., Wo, Jennifer, Hong, Theodore S., Kunitake, Hiroko, Bordeianou, Liliana, Berger, David, Ricciardi, Rocco, and Rosen, Bruce

Published
2021
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47. Multimodal Investigation of Neuroinflammation in Aviremic Patients With HIV on Antiretroviral Therapy and HIV Elite Controllers

Author

Sari, Hasan, Galbusera, Riccardo, Bonnier, Guillaume, Lin, Yang, Alshelh, Zeynab, Torrado-Carvajal, Angel, Mukerji, Shibani S., Ratai, Eva M., Gandhi, Rajesh T., Chu, Jacqueline T., Akeju, Oluwaseun, Orhurhu, Vwaire, Salvatore, Andrew N., Sherman, Janet, Kwon, Douglas S., Walker, Bruce, Rosen, Bruce, Price, Julie C., Pollak, Lauren E., Loggia, Marco, and Granziera, Cristina

Published
2022
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48. Individual differences in frontolimbic circuitry and anxiety emerge with adolescent changes in endocannabinoid signaling across species

Author

Gee, Dylan G, Fetcho, Robert N, Jing, Deqiang, Li, Anfei, Glatt, Charles E, Drysdale, Andrew T, Cohen, Alexandra O, Dellarco, Danielle V, Yang, Rui R, Dale, Anders M, Jernigan, Terry L, Lee, Francis S, Casey, BJ, San Diego, UC, McCabe, Connor, Chang, Linda, Hawaii, U, Akshoomoff, Natacha, Newman, Erik, Core, MRI Acquisition, Ernst, Thomas, Van Zijl, Peter, Kuperman, Joshua, Murray, Sarah, Bloss, Cinnamon, Schork, Nicholas J, Appelbaum, Mark, Gamst, Anthony, Thompson, Wesley, Bartsch, Hauke, Keating, Brian, Amaral, David, Sowell, Elizabeth, Kaufmann, Walter, Mostofsky, Stewart, Ruberry, Erika J, Powers, Alisa, Rosen, Bruce, Kenet, Tal, Frazier, Jean, Kennedy, David, University, Yale, and Gruen, Jeffrey

Subjects
Neurosciences, Brain Disorders, Mental Health, Behavioral and Social Science, Genetics, Pediatric, 2.1 Biological and endogenous factors, Aetiology, Mental health, Adolescent, Adult, Animals, Child, Child, Preschool, Endocannabinoids, Female, Frontal Lobe, Humans, Limbic Lobe, Male, Mice, Mice, Transgenic, Nerve Net, Signal Transduction, Species Specificity, FAAH, frontolimbic, gene x development, anxiety, cross-species, PING Consortium, gene × development
Abstract

Anxiety disorders peak in incidence during adolescence, a developmental window that is marked by dynamic changes in gene expression, endocannabinoid signaling, and frontolimbic circuitry. We tested whether genetic alterations in endocannabinoid signaling related to a common polymorphism in fatty acid amide hydrolase (FAAH), which alters endocannabinoid anandamide (AEA) levels, would impact the development of frontolimbic circuitry implicated in anxiety disorders. In a pediatric imaging sample of over 1,000 3- to 21-y-olds, we show effects of the FAAH genotype specific to frontolimbic connectivity that emerge by ∼12 y of age and are paralleled by changes in anxiety-related behavior. Using a knock-in mouse model of the FAAH polymorphism that controls for genetic and environmental backgrounds, we confirm phenotypic differences in frontoamygdala circuitry and anxiety-related behavior by postnatal day 45 (P45), when AEA levels begin to decrease, and also, at P75 but not before. These results, which converge across species and level of analysis, highlight the importance of underlying developmental neurobiology in the emergence of genetic effects on brain circuitry and function. Moreover, the results have important implications for the identification of risk for disease and precise targeting of treatments to the biological state of the developing brain as a function of developmental changes in gene expression and neural circuit maturation.

Published
2016

50. Stroke‐Related Visceral Alterations: a Voxel‐Based Neuroanatomic Localization Study

Author

Arsava, Ethem Murat, primary, Chang, Ken, additional, Tawakol, Ahmed, additional, Loggia, Marco L., additional, Goldstein, Joshua N., additional, Brown, James, additional, Park, Kwang‐Yeol, additional, Singhal, Aneesh B., additional, Kalpathy‐Cramer, Jayashree, additional, Sorensen, Alma Gregory, additional, Rosen, Bruce R., additional, Samuels, Martin Allen, additional, and Ay, Hakan, additional

Published
2023
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