# Generative Adversarial CT Volume Extrapolation for Robust Small-to-Large Field of View Registration
###### Abstract  
Intraoperative Computer Tomography (iCT) provide near real time visualizations which can be registered with high-quality preoperative images to improve the confidence of surgical instrument navigation. However, intraoperative images have a small field of view making the registration process error prone due to the reduced amount of mutual information. We herein propose a method to extrapolate thin acquisitions as a prior step to registration, to increase the field of view of the intraoperative images, and hence also the robustness of the guiding system. The method is based on a deep neural network which is trained adversarially using self-supervision to extrapolate slices from the existing ones. Median landmark detection errors are reduced by approximately 40%, yielding a better initial alignment. Furthermore, the intensity-based registration is improved; the surface distance errors are reduced by an order of magnitude, from 5.66 mm to 0.57 mm (\(p\)-value = \(4.18\times 10^{-6}\)). The proposed extrapolation method increases the registration robustness, which plays a key role in guiding the surgical intervention confidently. generative adversarial networks; volume extrapolation; self-supervision; volume registration 202212944. 22221294. 222212294. 222212221222212  
slice-to-volume, projection-to-volume, volume-to-volume, etc. [4]. Herein we focus on the latter, aiming to boost the performance of two Computer Tomograph (CT) volumes rigid registration. Two volumes can be registered using a feature-based approach, an intensity-based approach or a combination of the two techniques. In feature-based registration, a set of corresponding features (e.g., landmarks, center of mass, etc.) are used to compute the transformation \(\varphi\) to register a volume (called the moving or template volume, \(T\)) to the space of the other volume (fixed or reference volume, \(R\)) [5]. The intensity-based approach can be formulated as an optimization problem, seeking the best set of parameters for the transformation \(\varphi\) to minimize a predefined distance measure: \(argmin_{\varphi}[D(R,T\circ\varphi)]\)[3,6]. However, this approach is not robust due to the potential presence of local minimums caused by image artifacts and sub-optimal distance metrics. Combinations of the two approaches might be used to improve registration accuracy and robustness (e.g., using intensity-based registration as a refinement step for the feature-based registration). To the best of our knowledge, registration of thin images has been overlooked so far. Since all the registration techniques are highly dependent on the amount of mutual information (common data presented by both images from different perspectives), analysis of thin images is very challenging due to their reduced field of view (FOV). However, during surgeries low-resolution thin CT slabs are acquired to mitigate the patient's exposure risk. In this context, despite performing an initial alignment based on center of mass or geometric center, intensity-based registration is prone to failure given to the distinct fields of view of the operands. To reliably retrieve the corresponding high resolution preoperative data, a feature-based approach must be considered. However landmark detection algorithms might also be affected by the thin volume quality thus yielding a poor registration performance. We therefore propose a method to extrapolate thin CT slabs, generating additional slices from the few existing ones, hence providing enhanced context information required by registration algorithms to work robustly. Artificial intelligence in medical applications has received significant attention from the scientific community over the past few years. Due to their potential to model complex problems based on large datasets of examples, deep learning algorithms are nowadays employed for solving a wide variety of tasks such as regression, classification, segmentation and image generation [7,8].