Premium
Hybrid MRI ‐Ultrasound acquisitions, and scannerless real‐time imaging
Author(s) -
Preiswerk Frank,
Toews Matthew,
Cheng ChengChieh,
Chiou Jryuan George,
Mei ChangSheng,
Schaefer Lena F.,
Hoge W. Scott,
Schwartz Benjamin M.,
Panych Lawrence P.,
Madore Bruno
Publication year - 2017
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.26467
Subject(s) - ultrasound , magnetic resonance imaging , computer science , frame rate , pixel , frame (networking) , artificial intelligence , biomedical engineering , mri scan , computer vision , medicine , radiology , nuclear medicine , telecommunications
Purpose 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. Methods 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. Results 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. Conclusion 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.