  During neurosurgery, medical images of the brain are used to locate tumors
and critical structures, but brain tissue shifts make pre-operative images
unreliable for accurate removal of tumors. Intra-operative imaging can track
these deformations but is not a substitute for pre-operative data. To address
this, we use Dynamic Data-Driven Non-Rigid Registration (NRR), a complex and
time-consuming image processing operation that adjusts the pre-operative image
data to account for intra-operative brain shift. Our review explores a specific
NRR method for registering brain MRI during image-guided neurosurgery and
examines various strategies for improving the accuracy and speed of the NRR
method. We demonstrate that our implementation enables NRR results to be
delivered within clinical time constraints while leveraging Distributed
Computing and Machine Learning to enhance registration accuracy by identifying
optimal parameters for the NRR method. Additionally, we highlight challenges
associated with its use in the operating room.
