3D registration of MR and X-ray spine images using an articulated model

Présentation: Cet article a été publié dans le journal : Computerised medical imaging and graphics (CMIG). Le but de cet article est de recaler les vertèbres extraites à partir d’images RM avec des vertèbres extraites à partir d’images RX pour des patients scoliotiques, en tenant compte des déformations non-rigides due au changement de posture entre ces deux modalités. À ces fins, une méthode de recalage à l’aide d’un modèle articulé est proposée. Cette méthode a été comparée avec un recalage rigide en calculant l’erreur sur des points de repère, ainsi qu’en calculant la différence entre l’angle de Cobb avant et après recalage. Une validation additionelle de la méthode de recalage présentée ici se trouve dans l’annexe A. Ce travail servira de première étape dans la fusion des images RM, RX et TP du tronc complet. Donc, cet article vérifie l’hypothèse 1 décrite dans la section 3.2.1.Abstract This paper presents a magnetic resonance image (MRI)/X-ray spine registration method that compensates for the change in the curvature of the spine between standing and prone positions for scoliotic patients. MRIs in prone position and X-rays in standing position are acquired for 14 patients with scoliosis. The 3D reconstructions of the spine are then aligned using an articulated model which calculates intervertebral transformations. Results show significant decrease in regis- tration error when the proposed articulated model is compared with rigid registration. The method can be used as a basis for full body MRI/X-ray registration incorporating soft tissues for surgical simulation.Canadian Institute of Health Research (CIHR

A Survey on Deep Learning in Medical Image Registration: New Technologies, Uncertainty, Evaluation Metrics, and Beyond

Over the past decade, deep learning technologies have greatly advanced the field of medical image registration. The initial developments, such as ResNet-based and U-Net-based networks, laid the groundwork for deep learning-driven image registration. Subsequent progress has been made in various aspects of deep learning-based registration, including similarity measures, deformation regularizations, and uncertainty estimation. These advancements have not only enriched the field of deformable image registration but have also facilitated its application in a wide range of tasks, including atlas construction, multi-atlas segmentation, motion estimation, and 2D-3D registration. In this paper, we present a comprehensive overview of the most recent advancements in deep learning-based image registration. We begin with a concise introduction to the core concepts of deep learning-based image registration. Then, we delve into innovative network architectures, loss functions specific to registration, and methods for estimating registration uncertainty. Additionally, this paper explores appropriate evaluation metrics for assessing the performance of deep learning models in registration tasks. Finally, we highlight the practical applications of these novel techniques in medical imaging and discuss the future prospects of deep learning-based image registration

Three-dimensional quantitative evaluation method of nonrigid registration algorithms for adaptive radiotherapy

Purpose: Current radiotherapy is progressing to the concept of adaptive radiotherapy, which implies the adaptation of planning along the treatment course. Nonrigid registration is an essential image processing tool for adaptive radiotherapy and image guided radiotherapy, and the three-dimensional (3D) nature of the current radiotherapy techniques requires a 3D quantification of the registration error that existing evaluation methods do not cover appropriately. The authors present a method for 3D evaluation of nonrigid registration algorithms’ performance, based on organ delineations, capable of working with near-spherical volumes even in the presence of concavities. Methods: The evaluation method is composed by a volume shape description stage, developed using a new ad hoc volume reconstruction algorithm proposed by the authors, and an error quantification stage. The evaluation method is applied to the organ delineations of prostate and seminal vesicles, obtained by an automatic segmentation method over images of prostate cancer patients treated with intensity modulated radiation therapy. Results: The volume reconstruction algorithm proposed has been shown to accurately model complex 3D surfaces by the definition of clusters of control points. The quantification method, inspired by the Haussdorf–Chebysev distance, provides a measure of the largest registration error per control direction, defining a valid metric for concave-convex volumes. Summarizing, the proposed evaluation methodology presents accurate results with a high spatial resolution in a negligible computation time in comparison with the nonrigid registration time. Conclusions: Experimental results show that the metric selected for quantifying the registration error is of utmost importance in a quantitative evaluation based on measuring distances between volumes. The accuracy of the volume reconstruction algorithm is not so relevant as long as the reconstruction is tight enough on the actual volume of the organ. The new evaluation method provides a smooth and accurate volume reconstruction for both the reference and the registered organ, and a complete 3D description of nonrigid registration algorithms’ performance, resulting in a useful tool for study and comparison of registration algorithms for adaptive radiotherapy

MRI Superresolution Using Self-Similarity and Image Priors

In Magnetic Resonance Imaging typical clinical settings, both low- and high-resolution images of different types are routinarily acquired. In some cases, the acquired low-resolution images have to be upsampled to match with other high-resolution images for posterior analysis or postprocessing such as registration or multimodal segmentation. However, classical interpolation techniques are not able to recover the high-frequency information lost during the acquisition process. In the present paper, a new superresolution method is proposed to reconstruct high-resolution images from the low-resolution ones using information from coplanar high resolution images acquired of the same subject. Furthermore, the reconstruction process is constrained to be physically plausible with the MR acquisition model that allows a meaningful interpretation of the results. Experiments on synthetic and real data are supplied to show the effectiveness of the proposed approach. A comparison with classical state-of-the-art interpolation techniques is presented to demonstrate the improved performance of the proposed methodology

Motion compensated micro-CT reconstruction for in-situ analysis of dynamic processes

This work presents a framework to exploit the synergy between Digital Volume Correlation ( DVC) and iterative CT reconstruction to enhance the quality of high-resolution dynamic X-ray CT (4D-mu CT) and obtain quantitative results from the acquired dataset in the form of 3D strain maps which can be directly correlated to the material properties. Furthermore, we show that the developed framework is capable of strongly reducing motion artifacts even in a dataset containing a single 360 degrees rotation

Quicksilver: Fast Predictive Image Registration - a Deep Learning Approach

This paper introduces Quicksilver, a fast deformable image registration method. Quicksilver registration for image-pairs works by patch-wise prediction of a deformation model based directly on image appearance. A deep encoder-decoder network is used as the prediction model. While the prediction strategy is general, we focus on predictions for the Large Deformation Diffeomorphic Metric Mapping (LDDMM) model. Specifically, we predict the momentum-parameterization of LDDMM, which facilitates a patch-wise prediction strategy while maintaining the theoretical properties of LDDMM, such as guaranteed diffeomorphic mappings for sufficiently strong regularization. We also provide a probabilistic version of our prediction network which can be sampled during the testing time to calculate uncertainties in the predicted deformations. Finally, we introduce a new correction network which greatly increases the prediction accuracy of an already existing prediction network. We show experimental results for uni-modal atlas-to-image as well as uni- / multi- modal image-to-image registrations. These experiments demonstrate that our method accurately predicts registrations obtained by numerical optimization, is very fast, achieves state-of-the-art registration results on four standard validation datasets, and can jointly learn an image similarity measure. Quicksilver is freely available as an open-source software.Comment: Add new discussion

Mjolnir: Extending HAMMER Using a Diffusion Transformation Model and Histogram Equalization for Deformable Image Registration

Image registration is a crucial step in many medical image analysis procedures such as image fusion, surgical planning, segmentation and labeling, and shape comparison in population or longitudinal studies. A new approach to volumetric intersubject deformable image registration is presented. The method, called Mjolnir, is an extension of the highly successful method HAMMER. New image features in order to better localize points of correspondence between the two images are introduced as well as a novel approach to generate a dense displacement field based upon the weighted diffusion of automatically derived feature correspondences. An extensive validation of the algorithm was performed on T1-weighted SPGR MR brain images from the NIREP evaluation database. The results were compared with results generated by HAMMER and are shown to yield significant improvements in cortical alignment as well as reduced computation time

Assessing the Accuracy Factors in the Determination of Postoperative Acetabular Cup Orientation Using Hybrid 2D-3D Registration

Single standard anteroposterior radiograph-based methods for measuring cup orientation following total hip arthroplasty (THA) are subject to substantial errors if the individual pelvic orientation with respect to X-ray plate is not taken into consideration. Previously, we proposed to use a hybrid 2D-3D registration scheme to determine the postoperative acetabular cup orientation and developed an object-oriented cross-program called "HipMatch.” However, its accuracy and robustness have not been fully investigated. To assess the potential factors that may affect the accuracy and robustness of the hybrid 2D-3D registration scheme in determining the postoperative acetabular cup orientation, a comprehensive validation study using a cadaver pelvis was performed. Nine X-ray radiographs taken from different pelvic positions relative to the X-ray plate and two computed tomography volumes of the pelvis with one acquired before the cup implantation and the other acquired after the cup implantation were used in the validation study. Potential factors that may affect the accuracy and robustness of the hybrid 2D-3D registration scheme were experimentally determined. Our experimental results demonstrate that (1) the plain radiograph-based method is not accurate; (2) the hybrid 2D-3D registration scheme helps to improve the estimation accuracy; (3) the hybrid 2D-3D registration scheme can robustly and accurately estimate the cup orientation even when a big portion of the radiograph is occluded; and (4) image resolution has minor effect on the estimation accuracy. The hybrid 2D-3D registration scheme is an accurate and robust method to measure exact cup orientation in THA. It holds the promise to be a valuable tool for clinical routine usage for providing evidence-based informatio