Chapter title |
Hybrid Utrasound and MRI Acquisitions for High-Speed Imaging of Respiratory Organ Motion.
|
---|---|
Chapter number | 39 |
Book title |
Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015
|
Published in |
Lecture notes in computer science, November 2015
|
DOI | 10.1007/978-3-319-24553-9_39 |
Pubmed ID | |
Book ISBNs |
978-3-31-924552-2, 978-3-31-924553-9
|
Authors |
Frank Preiswerk, Matthew Toews, W. Scott Hoge, Jr-yuan George Chiou, Lawrence P. Panych, William M. Wells III, Bruno Madore, William M. Wells, William M. WellsIII |
Editors |
Nassir Navab, Joachim Hornegger, William M. Wells, Alejandro F. Frangi |
Abstract |
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 offine 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. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 10 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Other | 2 | 20% |
Student > Doctoral Student | 1 | 10% |
Professor | 1 | 10% |
Student > Ph. D. Student | 1 | 10% |
Student > Master | 1 | 10% |
Other | 2 | 20% |
Unknown | 2 | 20% |
Readers by discipline | Count | As % |
---|---|---|
Engineering | 3 | 30% |
Medicine and Dentistry | 2 | 20% |
Computer Science | 1 | 10% |
Neuroscience | 1 | 10% |
Agricultural and Biological Sciences | 1 | 10% |
Other | 0 | 0% |
Unknown | 2 | 20% |