Abstract #1142

BoneMRI of the cervical spine: Deep learning-based radiodensity contrast generation for selective visualization of osseous structures

Marijn van Stralen^1,2, Britt YM van der Kolk³, Frank Zijlstra¹, Mateusz C Florkow¹, Elco Oost⁴, Jorik Slotman⁵, Jochen AC van Osch⁶, Martin Podlogar⁷, Jeroen Hendrikse⁸, Pim de Jong⁸, Rene M Castelein⁹, Max A Viergever¹, Mario Maas¹⁰, Martijn F Boomsma¹¹, and Peter R Seevinck^1,2

¹Center for Image Sciences, UMC Utrecht, Utrecht, Netherlands, ²MRIguidance BV, Utrecht, Netherlands, ³Dept. of Emergency Medicine, Isala, Zwolle, Netherlands, ⁴Image Sciences & Linguistics, MRIguidance BV, Utrecht, Netherlands, ⁵University of Twente, Enschede, Netherlands, ⁶Dept. of Medical Physics, Isala, Zwolle, Netherlands, ⁷Dept. of Neurosurgery, Isala, Zwolle, Netherlands, ⁸Dept of Radiology, UMC Utrecht, Utrecht, Netherlands, ⁹Dept of Orthopedics, UMC Utrecht, Utrecht, Netherlands, ¹⁰Dept of Radiology and Nuclear Medicine, Academic Medical Center, Amsterdam, Netherlands, ¹¹Dept of Radiology and Nuclear Medicine, Isala, Zwolle, Netherlands

In this work BoneMRI is presented, a deep learning approach aiming at visualisation of radiodensity contrast by learning a mapping from MRI to CT data from 25 patients. Normal as well as pathological osseous structures in the cervical spine were clearly depicted. Quantitatively, radiodensity contrast similarity and high geometrical accuracy of vertebral dimensions was demonstrated. As BoneMRI is a 3D method it facilitates multiplanar reformatting in any desired direction. As such, BoneMRI is a promising tool for efficient morphological assessment of osseous structures without the need for ionizing radiation, simultaneously providing soft tissue contrasts in a single examination.

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