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SU‐G‐BRA‐01: A Real‐Time Tumor Localization and Guidance Platform for Radiotherapy Using US and MRI
Author(s) -
Bednarz B,
Culberson W,
Bassetti M,
McMillan A,
Matrosic C,
Shepard A,
Zagzebski J,
Smith S,
Lee W,
Mills D,
Cao K,
Wang B,
Fiveland E,
Darrow R,
Foo T
Publication year - 2016
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4956925
Subject(s) - ultrasound , computer science , computer vision , radiation therapy , real time mri , magnetic resonance imaging , artificial intelligence , medical imaging , medicine , radiology
Purpose: To develop and validate a real‐time motion management platform for radiotherapy that directly tracks tumor motion using ultrasound and MRI. This will be a cost‐effective and non‐invasive real‐time platform combining the excellent temporal resolution of ultrasound with the excellent soft‐tissue contrast of MRI. Methods: A 4D planar ultrasound acquisition during the treatment that is coupled to a pre‐treatment calibration training image set consisting of a simultaneous 4D ultrasound and 4D MRI acquisition. The image sets will be rapidly matched using advanced image and signal processing algorithms, allowing the display of virtual MR images of the tumor/organ motion in real‐time from an ultrasound acquisition. Results: The completion of this work will result in several innovations including: a (2D) patch‐like, MR and LINAC compatible 4D planar ultrasound transducer that is electronically steerable for hands‐free operation to provide real‐time virtual MR and ultrasound imaging for motion management during radiation therapy; a multi‐ modal tumor localization strategy that uses ultrasound and MRI; and fast and accurate image processing algorithms that provide real‐time information about the motion and location of tumor or related soft‐tissue structures within the patient. Conclusion: If successful, the proposed approach will provide real‐time guidance for radiation therapy without degrading image or treatment plan quality. The approach would be equally suitable for image‐guided proton beam or heavy ion‐beam therapy. This work is partially funded by NIH grant R01CA190298