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Abstract #3559

Stereotactic MRI-guided radiosurgery using AI resting state networks recognition

Luis Ancari1, Abril Vergne2,3, María Paula Del Popolo2,3,4, Guillermo Alvarez3, Clara Lisazo3, Federico Julián González1,3, Sebastián Moguilner5, Maximo Melchor2,3, Trinidad González3,6, and Daniel Fino1,3,4
1Instituto Balseiro, Universidad Nacional de Cuyo, Bariloche, Argentina, 2Universidad de Mendoza, Mendoza, Argentina, 3FundaciĆ³n Escuela de Medicina Nuclear, Mendoza, Argentina, 4Fundacion Argentina para el Desarollo en Salud, Mendoza, Argentina, 5Harvard Medical School, Boston, MA, United States, 6FundaciĆ³n Argentina para el Desarrollo en Salud, Mendoza, Argentina

Synopsis

Keywords: Tumors, RadiotherapyStereotactic radiosurgery (SRS) is a minimally invasive procedure that reduces tumor size without subjecting surrounding organs at risk (OAR) to harmful radiation levels. The delimitation of OAR in SRS planning, and the conservation of the prescribed dose in tumoral regions, can be improved by the implementation of independent component analysis to obtain resting-state fMRI networks (RSNs). In this aspect, AI plays an important role to decrease computational-processing times while increasing the method’s efficiency. One of the main solved challenges was the lack of compatibility between the Nifti protocol (RSNs format) and the treatment planning system’s protocol (coordinate system of CT-simulation).

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Keywords