Magnetic Resonance MR imaging provides excellent image quality at a high cost and low frame rate. Ultrasound US provides poor image quality at a low cost and high frame rate. We propose an instance-based learning system to obtain the best of both worlds: high quality MR images at high frame rates from a low cost single-element US sensor. Concurrent US and MRI pairs are acquired during a relatively brief offline learning phase involving the US transducer and MR scanner. High frame rate, high quality MR imaging of respiratory organ motion is then predicted from US measurements, even after stopping MRI acquisition, using a probabilistic kernel regression framework. Experimental results show predicted MR images to be highly representative of actual MR images.
['Frank Preiswerk', 'Matthew Toews', 'W. Scott Hoge', 'Jr‐yuan George Chiou', 'Lawrence P. Panych', 'William M. Wells', 'Bruno Madore']
Hybrid Utrasound and MRI Acquisitions for High-Speed Imaging of Respiratory Organ Motion