Deformable MR-CT image registration using an unsupervised, dual-channel network for neurosurgical guidance.

ABSTRACT Purpose . The accuracy of minimally invasive, intracranial neurosurgery can be challenged by deformation of brain tissue – e.g., up to 10 mm due to egress of cerebrospinal fluid during neuroendoscopic approach. We report an unsupervised, deep learning-based registration framework to resolve such deformations between preoperative MR and intraoperative CT with fast runtime for neurosurgical guidance. Method . The framework incorporates subnetworks for MR and CT image synthesis with a dual-channel registration subnetwork (with synthesis uncertainty providing spatially varying weights on the dual-channel loss) to estimate a diffeomorphic deformation field from both the MR and CT channels. An end-to-end training is proposed that jointly optimizes both the synthesis and registration subnetworks. The proposed framework was investigated using three datasets: (1) paired MR/CT with simulated deformations; (2) paired MR/CT with real deformations; and (3) a neurosurgery dataset with real deformation. Two state-of-the-art methods (Symmetric Normalization and VoxelMorph) were implemented as a basis of comparison, and variations in the proposed dual-channel network were investigated, including single-channel registration, fusion without uncertainty weighting, and conventional sequential training of the synthesis and registration subnetworks. Results . The proposed method achieved: (1) Dice coefficient = 0.82±0.07 and TRE = 1.2±0.6 mm on paired MR/CT with simulated deformations; (2) Dice coefficient = 0.83±0.07 and TRE = 1.4±0.7 mm on paired MR/CT with real deformations; and (3) Dice = 0.79±0.13 and TRE = 1.6±1.0 mm on the neurosurgery dataset with real deformations. The dual-channel registration with uncertainty weighting demonstrated superior performance (e.g., TRE = 1.2±0.6 mm) compared to single-channel registration (TRE = 1.6±1.0 mm, p Conclusion . The deformable registration framework based on dual-channel MR/CT registration with spatially varying weights and end-to-end training achieved geometric accuracy and runtime that was superior to state-of-the-art baseline methods and various ablations of the proposed network. The accuracy and runtime of the method may be compatible with the requirements of high-precision neurosurgery.