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Direct validation of atlas‐based red nucleus identification for functional radiosurgery
Author(s) -
Stancanello Joseph,
Romanelli Pantaleo,
Sebastiano Fabio,
Modugno Nicola,
Muacevic Alexander,
Cerveri Pietro,
Esposito Vincenzo,
Ferrigno Giancarlo,
Uggeri Fulvio,
Cantore Giampaolo
Publication year - 2007
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 - 10.1118/1.2750971
Subject(s) - subthalamic nucleus , atlas (anatomy) , deep brain stimulation , radiosurgery , magnetic resonance imaging , stereotaxis , computer science , brain atlas , movement disorders , nuclear medicine , thalamus , artificial intelligence , medicine , radiology , anatomy , pathology , parkinson's disease , disease , radiation therapy
Treatment targets in functional neurosurgery usually consist of selected structures within the thalamus and basal ganglia, which can be stimulated in order to affect specific brain pathways. Chronic electrical stimulation of these structures is a widely used approach for selected patients with advanced movement disorders. An alternative therapeutic solution consists of producing a lesion in the target nucleus, for example by means of radiosurgery, a noninvasive procedure, and this prevents the use of intraoperative microelectrode recording as a method for accurate target definition. The need to have accurate noninvasive localization of the target motivated our previous work on atlas‐based identification; the aim of this present work is to provide additional validation of this approach based on the identification of the red nuclei (RN), which are located near the subthalamic nucleus (STN). Coordinates of RN were obtained from the Talairach and Tournoux (TT) atlas and transformed into the coordinates of the Montreal Neurological Institute (MNI) atlas, creating a mask representation of RN. The MNI atlas volume was nonrigidly registered onto the patient magnetic resonance imaging (MRI). This deformation field was then applied to the RN mask, providing its location on the patient MRI. Because RN are easily identifiable on 1.5 T T2‐MRI images, they were manually delineated; the coordinates of the centers of mass of the manually and automatically identified structures were compared. Additionally, volumetric overlapping indices were calculated. Ten patients were examined by this technique. All indices indicated a high level of agreement between manually and automatically identified structures. These results not only confirm the accuracy of the method but also allow fine tuning of the automatic identification method to be performed.