Automatic pose correction for image-guided nonhuman primate brain surgery planning

Soheil Ghafurian; Antong Chen; Catherine Hines; Belma Dogdas; Ashleigh Bone; Kenneth Lodge; Stacey O'Malley; Christopher T. Winkelmann; Ansuman Bagchi; Laura S. Lubbers; Jason M. Uslaner; Colena Johnson; John Renger; Hatim A. Zariwala

doi:10.1117/12.2217534

18 March 2016 Automatic pose correction for image-guided nonhuman primate brain surgery planning

Soheil Ghafurian, Antong Chen, Catherine Hines, Belma Dogdas, Ashleigh Bone, Kenneth Lodge, Stacey O'Malley, Christopher T. Winkelmann, Ansuman Bagchi, Laura S. Lubbers, Jason M. Uslaner, Colena Johnson, John Renger, Hatim A. Zariwala

Author Affiliations +

Proceedings Volume 9786, Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling; 97860O (2016) https://doi.org/10.1117/12.2217534
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

Intracranial delivery of recombinant DNA and neurochemical analysis in nonhuman primate (NHP) requires precise targeting of various brain structures via imaging derived coordinates in stereotactic surgeries. To attain targeting precision, the surgical planning needs to be done on preoperative three dimensional (3D) CT and/or MR images, in which the animals head is fixed in a pose identical to the pose during the stereotactic surgery. The matching of the image to the pose in the stereotactic frame can be done manually by detecting key anatomical landmarks on the 3D MR and CT images such as ear canal and ear bar zero position. This is not only time intensive but also prone to error due to the varying initial poses in the images which affects both the landmark detection and rotation estimation. We have introduced a fast, reproducible, and semi-automatic method to detect the stereotactic coordinate system in the image and correct the pose. The method begins with a rigid registration of the subject images to an atlas and proceeds to detect the anatomical landmarks through a sequence of optimization, deformable and multimodal registration algorithms. The results showed similar precision (maximum difference of 1.71 in average in-plane rotation) to a manual pose correction.

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Soheil Ghafurian, Antong Chen, Catherine Hines, Belma Dogdas, Ashleigh Bone, Kenneth Lodge, Stacey O'Malley, Christopher T. Winkelmann, Ansuman Bagchi, Laura S. Lubbers, Jason M. Uslaner, Colena Johnson, John Renger, and Hatim A. Zariwala "Automatic pose correction for image-guided nonhuman primate brain surgery planning", Proc. SPIE 9786, Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling, 97860O (18 March 2016); https://doi.org/10.1117/12.2217534

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