title: Hybrid MRI-Ultrasound acquisitions, and scannerless real-time imaging.
abstract: To combine MRI, ultrasound, and computer science methodologies toward generating MRI contrast at the high frame rates of ultrasound, inside and even outside the MRI bore.
  
  
  
  A small transducer, held onto the abdomen with an adhesive bandage, collected ultrasound signals during MRI. Based on these ultrasound signals and their correlations with MRI, a machine-learning algorithm created synthetic MR images at frame rates up to 100 per second. In one particular implementation, volunteers were taken out of the MRI bore with the ultrasound sensor still in place, and MR images were generated on the basis of ultrasound signal and learned correlations alone in a "scannerless" manner.
  
  
  
  Hybrid ultrasound-MRI data were acquired in eight separate imaging sessions. Locations of liver features, in synthetic images, were compared with those from acquired images: The mean error was 1.0 pixel (2.1 mm), with best case 0.4 and worst case 4.1 pixels (in the presence of heavy coughing). For results from outside the bore, qualitative validation involved optically tracked ultrasound imaging with/without coughing.
  
  
  
  The proposed setup can generate an accurate stream of high-speed MR images, up to 100 frames per second, inside or even outside the MR bore. Magn Reson Med 78:897-908, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
authors.affiliation: Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
authors.forename: Frank
authors.identifier: 0000-0002-1785-9351
authors.initials: F
authors.lastname: Preiswerk
authors.affiliation: The Laboratory for Imagery, Vision and Artificial Intelligence, École de Technologie Supérieure, Montréal, QC, Canada.
authors.forename: Matthew
authors.initials: M
authors.lastname: Toews
authors.affiliation: Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
authors.forename: Cheng-Chieh
authors.initials: CC
authors.lastname: Cheng
authors.affiliation: Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
authors.forename: Jr-Yuan George
authors.initials: JG
authors.lastname: Chiou
authors.affiliation: Department of Physics, Soochow University, Taipei, Taiwan.
authors.forename: Chang-Sheng
authors.initials: CS
authors.lastname: Mei
authors.affiliation: Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
authors.forename: Lena F
authors.initials: LF
authors.lastname: Schaefer
authors.affiliation: Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
authors.forename: W Scott
authors.initials: WS
authors.lastname: Hoge
authors.affiliation: Google Inc, New York, New York, USA.
authors.forename: Benjamin M
authors.initials: BM
authors.lastname: Schwartz
authors.affiliation: Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
authors.forename: Lawrence P
authors.initials: LP
authors.lastname: Panych
authors.affiliation: Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
authors.forename: Bruno
authors.initials: B
authors.lastname: Madore
mesh_terms: D000465:Algorithms; D004867:Equipment Design; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation / Q000379:methods*; D008099:Liver / Q000000981:diagnostic imaging; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009068:Movement / Q000502:physiology; D014159:Transducers; D014463:Ultrasonography / Q000379:methods*
keywords: MR-ultrasound imaging; hybrid imaging; image-guided therapy; machine learning; motion tracking