SAMCOR: A robust and precise co-registration algorithm for brain CT and MR imaging

Abstract

ObjectiveCurrent methods for co-registration of computed tomography (CT) and magnetic resonance (MR) brain imaging employ optimization-based cost functions of advanced statistical metrics to align datasets (e.g. mutual information, MI). Yet the inherent differences between these modalities still lead to occasional failures of registration. To address these limitations, we developed a novel approached termed sequence adaptive multimodal co-registration (SAMCOR), which is robust to intensity, feature, and scanner-based differences. MethodsSAMCOR was validated using 152 CT and MRI datasets from subjects implanted with intracranial electrodes for evaluation of medically-refractory epilepsy. Alignment outcomes were compared against five current open-source co-registration algorithms and a widely used commercial neurosurgical navigation system and classified as aligned/not-aligned relative to manually co-registered datasets. ResultsSAMCOR was the only method to align all 152 datasets (100%), while success rates for existing algorithms ranged from 3% to 82%. Given the time-intensive nature of manual verification, we further developed a metric to automatically quantify co-registration accuracy using Dice Similarity Coefficients (DSC). We applied a binary classification analysis to evaluate this DSC metric performance against traditional mutual information (MI) metrics for predicting alignment outcomes. The DSC metric demonstrated significantly higher positive and negative predictive values (0.93 and 0.97, respectively) than MI (0.79 and 0.87, respectively). The DSC classifier accuracy was 94.7% (vs 83.2% for MI). ConclusionsOur novel approaches for CT-MRI co-registration and quantitative alignment outcome analysis demonstrate significant advancements of the state-of-the-art for intermodal image co-registration with clinical relevance in patients with implanted intracranial devices.