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Treatment coronary arteries endovascular involves catheter navigation through patient vasculature. The projective angiography guidance is limited in the case of chronic total occlusion where occluded vessel can not be seen. Integrating standard preoperative CT angiography information with live fluoroscopic images addresses this limitation but requires alignment of both modalities. This article proposes a structure-based registration method that intrinsically preserves both the geometrical and topological coherencies of the vascular centrelines to be registered, by the means of a dedicated curve-to-curve distance pairs of closest curves are identified, while pairing their points. Preliminary experiments demonstrate that the proposed approach performs better than the standard Iterative Closest Point method giving a wider attraction basin and improved accuracy.

Radiotherapy planning requires accurate delineations of the critical structures. To avoid manual contouring, atlas-based segmentation can be used to get automatic delineations. However, the results strongly depend on the chosen atlas, especially for the head and neck region where the anatomical variability is high. To address this problem, atlases adapted to the patient's anatomy may allow for a better registration, and already showed an improvement in segmentation accuracy. However, building such atlases requires the definition of a criterion to select among a database the images that are the most similar to the patient. Moreover, the inter-expert variability of manual contouring may be high, and therefore bias the segmentation if selecting only one image for each region. To tackle these issues, we present an original method to design a piecewise most similar atlas. Given a query image, we propose an efficient criterion to select for each anatomical region the K most similar images among a database by considering local volume variations possibly induced by the tumor. Then, we present a new approach to combine the K images selected for each region into a piecewise most similar template. Our results obtained with 105 CT images of the head and neck show that our method reduces the over-segmentation seen with an average atlas while being robust to inter-expert manual segmentation variability.

Conformal radiotherapy planning needs accurate delineations of the critical structures. Atlas-based segmentation has been shown to be very efficient to delineate brain structures. It would therefore be very interesting to develop an atlas for the head and neck region where 7% of the cancers arise. However, the construction of an atlas in this region is very difficult due to the high variability of the anatomies. This can generate segmentation errors and over-segmented structures in the atlas. To overcome this drawback, we present an alternative method to build a template locally adapted to the patient's anatomy. This is done first by selecting in a database the images that are the most similar to the patient on predefined regions of interest, using on a distance between transformations. The first major contribution is that we do not compute every patient-to-image registration to find the most similar image, but only the registration of the patient towards an average image. This method is therefore computationally very efficient. The second major contribution is a novel method to use the selected images and the predefined regions to build a "Frankenstein's creature" for segmentation. We present a qualitative and quantitative comparison between the proposed method and a classical atlas-based segmentation method. This evaluation is performed on a subset of 58 patients among a database of 105 head and neck CT images and shows a great improvement of the specificity of the results.

Radiotherapy planning needs accurate delineations of the critical structures. Atlas-based segmentation has been shown to be very efficient to delineate brain structures. However, the construction of an atlas from a dataset of images, particularly for the head and neck region, is very difficult due to the high variability of the images and can generate over-segmented structures in the atlas. To overcome this drawback, we present in this paper an alternative method to select as a template the image in a database that is the most similar to the patient to be segmented. This similarity is based on a distance between transformations. A major contribution is that we do not compute every patient-to-sample registration to find the most similar template, but only the registration of the patient towards an average image. This method has therefore the advantage of being computationally very efficient. We present a qualitative and quantitative comparison between the proposed method and a classical atlas-based segmentation method. This evaluation is performed on a subset of 45 patients using a Leave-One-Out method and shows a great improvement of the specificity of the results.

In this paper we propose a new two step method to register the liver from two acquisitions. This registration helps experts to make an intra-patient follow-up for hepatic tumors. Firstly, an original and efficient tree matching is applied on different segmentations of the vascular system of a single patient. These vascular systems are segmented from CT-scan images acquired (every six months) during disease treatement, and then modeled as trees. Our method matches common bifurcations and vessels. Secondly, an estimation of liver deformation is computed from the results of the first step. This approach is validated on a large synthetic database containing cases with various deformation and segmentation problems. In each case, after the registration process, the liver recovery is very accurate (around 95%) and the mean localization error for 3D landmarks in liver is small (around 4 mm).

Deforming a digital atlas towards a patient image allows the simultaneous segmentation of several structures. Such an intersubject registration is difficult as the deformations to recover are highly inhomogeneous. A priori information about the local amount of deformation to expect is precious, since it allows to optimally balance the quality of the matching versus the regularity of the deformation. However, intersubject variability makes it hard to heuristically estimate the degree of deformation. Indeed, the sizes and shapes of various structures differ greatly and their relative positions vary in a rather complex manner. In this article, we perform a statistical study of the deformations yielded by the registration of an image database with an anatomical atlas, and we propose methods to re-inject this information into the registration. We show that this provides more accurate segmentations of brain structures.

In functional neurosurgery, there is a growing need for accurate localization of the functional targets. Since deep brain stimulation (DBS) of the Vim thalamic nucleus has been proposed for the treatment of Parkinson's disease, the target has evolved toward the globus pallidus and subthalamic nucleus (STN) and the therapeutic indications have enlarged to include psychiatric disorders such as Tourette syndrome or obsessive compulsive disorders. In these pathologies, the target has been restrained to smaller functional subterritories of the basal ganglia, requiring more refined techniques to localize smaller and smallerbrain regions, often invisible in routine clinical MRI. Different strategies have been developed to identify such deep brain targets. Direct methods can identify structures in the MRI itself, but only the larger ones. Indirect methods are based on the use of anatomical atlases. The present strategy comprised a 3D histological atlas and the MRI of the same brain specimen, and deformation methodology developped to fit the atlas toward the brain of any given patient. In this paper, this method is evaluated in the aim of being applied to further studies of anatomo-clinical correlation. The accuracy of the method is first discussed, followed by the study of short series of Parkinsonian patients treated by DBS, allowing to compare the deformed atlas with various per- and post-operative data.

In Emission Tomography imaging, respiratory motion causes artifacts in lungs and cardiac reconstructed images, which lead to misinterpretations and imprecise diagnosis. Solutions like respiratory gating, correlated dynamic PET techniques, list-mode data based techniques and others have been tested with improvements over the spatial activity distribution in lungs lesions, but with the disadvantages of requiring additional instrumentation or discarding part of the projection data used for reconstruction. The objective of this study is to incorporate respiratory motion correction directly into the image reconstruction process, without any additional acquisition protocol consideration. To this end, we propose an extension to the Maximum Likelihood Expectation Maximization (MLEM) algorithm that includes a respiratory motion model, which takes into account the displacements and volume deformations produced by the respiratory motion during the data acquisition process. We present results from synthetic simulations incorporating real respiratory motion as well as from phantom and patient data.