Junzhong Xu1,2,3,4, Xiaoyu Jiang1,2, Sean P Devan1, Lori R Arlinghaus1, Eliot T McKinley5, Jingping Xie1, Zhongliang Zu1,2, Qing Wang6, A Bapsi Chakravarthy7, Yong Wang8, John C Gore1,2,3,4. 1. Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA. 2. Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA. 3. Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA. 4. Department of Physics and Astronomy, Vanderbilt University, Nashville, TN, USA. 5. Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN, USA. 6. Department of Radiology, Washington University, St. Louis, MO, USA. 7. Department of Radiation Oncology, Vanderbilt University Medical Center, Nashville, TN, USA. 8. Department of Obstetrics and Gynecology, Washington University School of Medicine, St. Louis, MO, USA.
Abstract
PURPOSE: This report introduces and validates a new diffusion MRI-based method, termed MRI-cytometry, which can noninvasively map intravoxel, nonparametric cell size distributions in tissues. METHODS: MRI was used to acquire diffusion MRI signals with a range of diffusion times and gradient factors, and a model was fit to these data to derive estimates of cell size distributions. We implemented a 2-step fitting method to avoid noise-induced artificial peaks and provide reliable estimates of tumor cell size distributions. Computer simulations in silico, experimental measurements on cultured cells in vitro, and animal xenografts in vivo were used to validate the accuracy and precision of the method. Tumors in 7 patients with breast cancer were also imaged and analyzed using this MRI-cytometry approach on a clinical 3 Tesla MRI scanner. RESULTS: Simulations and experimental results confirm that MRI-cytometry can reliably map intravoxel, nonparametric cell size distributions and has the potential to discriminate smaller and larger cells. The application in breast cancer patients demonstrates the feasibility of direct translation of MRI-cytometry to clinical applications. CONCLUSION: The proposed MRI-cytometry method can characterize nonparametric cell size distributions in human tumors, which potentially provides a practical imaging approach to derive specific histopathological information on biological tissues.
PURPOSE: This report introduces and validates a new diffusion MRI-based method, termed MRI-cytometry, which can noninvasively map intravoxel, nonparametric cell size distributions in tissues. METHODS: MRI was used to acquire diffusion MRI signals with a range of diffusion times and gradient factors, and a model was fit to these data to derive estimates of cell size distributions. We implemented a 2-step fitting method to avoid noise-induced artificial peaks and provide reliable estimates of tumor cell size distributions. Computer simulations in silico, experimental measurements on cultured cells in vitro, and animal xenografts in vivo were used to validate the accuracy and precision of the method. Tumors in 7 patients with breast cancer were also imaged and analyzed using this MRI-cytometry approach on a clinical 3 Tesla MRI scanner. RESULTS: Simulations and experimental results confirm that MRI-cytometry can reliably map intravoxel, nonparametric cell size distributions and has the potential to discriminate smaller and larger cells. The application in breast cancer patients demonstrates the feasibility of direct translation of MRI-cytometry to clinical applications. CONCLUSION: The proposed MRI-cytometry method can characterize nonparametric cell size distributions in human tumors, which potentially provides a practical imaging approach to derive specific histopathological information on biological tissues.
Authors: Daniel C Alexander; Penny L Hubbard; Matt G Hall; Elizabeth A Moore; Maurice Ptito; Geoff J M Parker; Tim B Dyrby Journal: Neuroimage Date: 2010-05-23 Impact factor: 6.556
Authors: Isaac Daimiel Naranjo; Alexis Reymbaut; Patrik Brynolfsson; Roberto Lo Gullo; Karin Bryskhe; Daniel Topgaard; Dilip D Giri; Jeffrey S Reiner; Sunitha B Thakur; Katja Pinker-Domenig Journal: Cancers (Basel) Date: 2021-03-31 Impact factor: 6.639