Graph-Based Slice-to-Volume Deformable Registration

SymReg-GAN: Symmetric Image Registration With Generative Adversarial Networks

Symmetric image registration estimates bi-directional spatial transformations between images while enforcing an inverse-consistency. Its capability of eliminating bias introduced inevitably by generic single-directional image registration allows more precise analysis in different interdisciplinary applications of image registration, e.g., computational anatomy and shape analysis. However, most existing symmetric registration techniques especially for multimodal images are limited by low speed from the commonly-used iterative optimization, hardship in exploring inter-modality relations or high labor cost for labeling data. We propose SymReg-GAN to shatter these limits, which is a novel generative adversarial networks (GAN) based approach to symmetric image registration. We formulate symmetric registration of unimodal/multimodal images as a conditional GAN and train it with a semi-supervised strategy. The registration symmetry is realized by introducing a loss for encouraging that the cycle composed of the geometric transformation from one image to another and its reverse should bring an image back. The semi-supervised learning enables both the precious labeled data and large amounts of unlabeled data to be fully exploited. Experimental results from six public brain magnetic resonance imaging (MRI) datasets and 1 our own computed tomography (CT) and MRI dataset demonstrate the superiority of SymReg-GAN to several existing state-of-the-art methods.

Deformable Slice-to-Volume Registration for Motion Correction of Fetal Body and Placenta MRI

In in-utero MRI, motion correction for fetal body and placenta poses a particular challenge due to the presence of local non-rigid transformations of organs caused by bending and stretching. The existing slice-to-volume registration (SVR) reconstruction methods are widely employed for motion correction of fetal brain that undergoes only rigid transformation. However, for reconstruction of fetal body and placenta, rigid registration cannot resolve the issue of misregistrations due to deformable motion, resulting in degradation of features in the reconstructed volume. We propose a Deformable SVR (DSVR), a novel approach for non-rigid motion correction of fetal MRI based on a hierarchical deformable SVR scheme to allow high resolution reconstruction of the fetal body and placenta. Additionally, a robust scheme for structure-based rejection of outliers minimises the impact of registration errors. The improved performance of DSVR in comparison to SVR and patch-to-volume registration (PVR) methods is quantitatively demonstrated in simulated experiments and 20 fetal MRI datasets from 28-31 weeks gestational age (GA) range with varying degree of motion corruption. In addition, we present qualitative evaluation of 100 fetal body cases from 20-34 weeks GA range.

Deformable multimodal registration for navigation in beating-heart cardiac surgery

PURPOSE

Minimally invasive beating-heart surgery is currently performed using endoscopes and without navigation. Registration of intraoperative ultrasound to a preoperative cardiac CT scan is a valuable step toward image-guided navigation.

METHODS

The registration was achieved by first extracting a representative point set from each ultrasound image in the sequence using a deformable registration. A template shape representing the cardiac chambers was deformed through a hierarchy of affine transformations to match each ultrasound image using a generalized expectation maximization algorithm. These extracted point sets were matched to the CT by exhaustively searching over a large number of precomputed slices of 3D geometry. The result is a similarity transformation mapping the intraoperative ultrasound to preoperative CT.

RESULTS

Complete data sets were acquired for four patients. Transesophageal echocardiography ultrasound sequences were deformably registered to a model of oriented points with a mean error of 2.3 mm. Ultrasound and CT scans were registered to a mean of 3 mm, which is comparable to the error of 2.8 mm expected by merging ultrasound registration with uncertainty of cardiac CT.

CONCLUSION

The proposed algorithm registered 3D CT with dynamic 2D intraoperative imaging. The algorithm aligned the images in both space and time, needing neither dynamic CT imaging nor intraoperative electrocardiograms. The accuracy was sufficient for navigation in thoracoscopically guided beating-heart surgery.