Deformable MRI-ultrasound registration using 3D convolutional neural network

Abstract

Precise tracking of intra-operative tissue shift is important for accurate resection of brain tumor. Alignment of pre-interventional magnetic resonance imaging (MRI) to intra-operative ultrasound (iUS) is required to access tissue shift and enable guided surgery. However, accurate and robust image registration needed to relate pre-interventional MRI to iUS images is difficult due to the very different nature of image intensity between modalities. Here we present a framework that can perform non-rigid MRI-ultrasound registration using 3D convolutional neural network (CNN). The framework is composed of three components: feature extractor, deformation field generator and spatial sampler. Our automatic registration framework adopts unsupervised learning approach, allows accurate end-to-end deformable MRI-ultrasound registration. Our proposed method avoids the downfall of intensity-based methods by considering both image intensity and gradient. It achieves competitive registration accuracy on RESECT dataset. In addition, our method takes only about one second to register each image pair, enabling applications such as real time registration.