EVolution: an edge-based variational method for non-rigid multi-modal image registration

Abstract

Image registration is part of a large variety of medical applications including diagnosis, monitoring disease progression and/or treatment effectiveness and, more recently, therapy guidance. Such applications usually involve several imaging modalities such as ultrasound, computed tomography, positron emission tomography, x-ray or magnetic resonance imaging, either separately or combined. In the current work, we propose a non-rigid multi-modal registration method (namely EVolution: an edge-based variational method for non-rigid multi-modal image registration) that aims at maximizing edge alignment between the images being registered. The proposed algorithm requires only contrasts between physiological tissues, preferably present in both image modalities, and assumes deformable/elastic tissues. Given both is shown to be well suitable for non-rigid co-registration across different image types/contrasts (T1/T2) as well as different modalities (CT/MRI). This is achieved using a variational scheme that provides a fast algorithm with a low number of control parameters. Results obtained on an annotated CT data set were comparable to the ones provided by state-of-the-art multi-modal image registration algorithms, for all tested experimental conditions (image pre-filtering, image intensity variation, noise perturbation). Moreover, we demonstrate that, compared to existing approaches, our method possesses increased robustness to transient structures (i.e. that are only present in some of the images).

Influence of different isoflurane anesthesia protocols on murine cerebral hemodynamics measured with pseudo‐continuous arterial spin labeling

“…In each dataset, the in‐plane spatial transformation between the anatomical T2‐weighted magnitude image and the first frame of the pCASL magnitude image (ie the reference image chosen previously) was then automatically estimated as follows: an edge‐based variational method for nonrigid multimodal registration was employed to circumvent potential geometrical distortions induced by the EPI readout. For this purpose, the data fidelity term in Equation (left part of the integral) was replaced by a multimodal similarity metric which favors the alignment of edges/gradients that are present in both images, as described in Chen et al The obtained motion‐vector field was subsequently employed to adjust the position of the masks of each brain region.…”

Numerical modelling challenges for clinical electroporation ablation technique of liver tumors

“…This is particularly true when needles are inserted: the insertion imposes a deformation to the liver, and therefore a rigid registration lead to non relevant results. In [20] we propose to use the EVolution algorithm proposed by Denis de Senneville et al [14]. Roughly speaking, the velocity field between the two gradient of the images is regularized by a diffusion term.…”

Numerical workflow of irreversible electroporation for deep-seated tumor

“…It is therefore the computational reference framework. The ROIs determined on the preoperative imaging are then registered on the CBCT with needles thanks to the non-rigid registration algorithm EVolution of De Senneville et al [7], which is dedicated to multi-modal images registration. Similarly, the ROI of the treatment area observed on the postoperative MRI are registered on the CBCT, which is thus the unique reference framework.…”

“…Registration of the pretreatment ROIs on the CBCT. In order to obtain the geometrical framework of the procedure, we performed the registration of the preoperative image on the CBCT using the EVolution algorithm [7]. This non rigid registration algorithm has been validated on clinical data and the clinical relevance of the registration has been verified by radiologists of the University Hospital J. Verdier.…”

“…The modeling assumptions for the registration are not discussed here. For more details, we refer the reader to [7].…”