title: Intelligent and automatic in vivo detection and quantification of transplanted cells in MRI.
abstract: Magnetic resonance imaging (MRI)-based cell tracking has emerged as a useful tool for identifying the location of transplanted cells, and even their migration. Magnetically labeled cells appear as dark contrast in T2*-weighted MRI, with sensitivities of individual cells. One key hurdle to the widespread use of MRI-based cell tracking is the inability to determine the number of transplanted cells based on this contrast feature. In the case of single cell detection, manual enumeration of spots in three-dimensional (3D) MRI in principle is possible; however, it is a tedious and time-consuming task that is prone to subjectivity and inaccuracy on a large scale. This research presents the first comprehensive study on how a computer-based intelligent, automatic, and accurate cell quantification approach can be designed for spot detection in MRI scans.
  
  
  
  Magnetically labeled mesenchymal stem cells (MSCs) were transplanted into rats using an intracardiac injection, accomplishing single cell seeding in the brain. T2*-weighted MRI of these rat brains were performed where labeled MSCs appeared as spots. Using machine learning and computer vision paradigms, approaches were designed to systematically explore the possibility of automatic detection of these spots in MRI. Experiments were validated against known in vitro scenarios.
  
  
  
  Using the proposed deep convolutional neural network (CNN) architecture, an in vivo accuracy up to 97.3% and in vitro accuracy of up to 99.8% was achieved for automated spot detection in MRI data.
  
  
  
  The proposed approach for automatic quantification of MRI-based cell tracking will facilitate the use of MRI in large-scale cell therapy studies. Magn Reson Med 78:1991-2002, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
authors.affiliation: Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, USA.
authors.forename: Muhammad Jamal
authors.initials: MJ
authors.lastname: Afridi
authors.affiliation: Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, USA.
authors.forename: Arun
authors.initials: A
authors.lastname: Ross
authors.affiliation: Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, USA.
authors.forename: Xiaoming
authors.initials: X
authors.lastname: Liu
authors.affiliation: Vascular Medicine Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
authors.forename: Margaret F
authors.initials: MF
authors.lastname: Bennewitz
authors.affiliation: Department of Radiology, Michigan State University, East Lansing, Michigan, USA.
authors.forename: Dorela D
authors.initials: DD
authors.lastname: Shuboni
authors.affiliation: Department of Radiology, Michigan State University, East Lansing, Michigan, USA.
authors.forename: Erik M
authors.initials: EM
authors.lastname: Shapiro
mesh_terms: D000465:Algorithms; D000818:Animals; D001921:Brain / Q000166:cytology / Q000000981:diagnostic imaging; D058948:Cell Tracking / Q000379:methods*; D007091:Image Processing, Computer-Assisted; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods*; D045164:Mesenchymal Stem Cell Transplantation; D059630:Mesenchymal Stem Cells / Q000166:cytology*; D010363:Pattern Recognition, Automated; D051381:Rats
keywords: MRI; Machine learning; cell therapy; iron oxide