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Abstract #0033

Hybrid MRI-ultrasound acquisitions, and scannerless real-time imaging

Frank Preiswerk1, Matthew Toews2, Cheng-Chieh Cheng1, Jr-yuan George Chiou1, Chang-Sheng Mei3, Lena F. Schaefer1, W. Scott Hoge1, Benjamin M. Schwartz4, Lawrence P. Panych1, and Bruno Madore1

1Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States, 2The Laboratory for Imagery, Vision and Artificial Intelligence, École de Technologie Supérieure, Montréal, QC, Canada, 3Department of Physics, Soochow University, Taipei, Taiwan, 4Google Inc, New York, NY, United States

The goal of this project was to combine MRI, ultrasound (US) and computer science methodologies toward generating MRI at high frame rates, inside and even outside the bore. A small US transducer, fixed to the abdomen, collected signals during MRI. Based on these signals and correlations with MRI, a machine-learning algorithm created synthetic MR images at up to 100 frames per second. In one particular implementation volunteers were taken out of the MRI bore with US sensor still in place, and MR images were generated on the basis of ultrasound signal and learned correlations alone, in a 'scannerless' manner.

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