Direct inverse deformation field approach to pelvic-area symmetric image registration

This paper presents a novel technique for a consistent symmetric deformable image registration based on an accurate method for a direct inversion of a large motion model deformation field. The proposed image registration algorithm maintains one-to-one mapping between registered images by symmetrically warping them to another image. This makes the final estimation of forward and backward deformation fields anatomically plausible and applicable to adaptive prostate radiotherapy. The quantitative validation of the method is performed on magnetic resonance data obtained for pelvis area. The experiments demonstrate the improved robustness in terms of inverse consistency error and estimation accuracy of prostate position in comparison to the previously proposed methods

Finite element surface registration incorporating curvature, volume preservation, and statistical model information

We present a novel method for nonrigid registration of 3D surfaces and images. The method can be used to register surfaces by means of their distance images, or to register medical images directly. It is formulated as a minimization problem of a sum of several terms representing the desired properties of a registration result: smoothness, volume preservation, matching of the surface, its curvature, and possible other feature images, as well as consistency with previous registration results of similar objects, represented by a statistical deformation model. While most of these concepts are already known, we present a coherent continuous formulation of these constraints, including the statistical deformation model. This continuous formulation renders the registration method independent of its discretization. The finite element discretization we present is, while independent of the registration functional, the second main contribution of this paper. The local discontinuous Galerkin method has not previously been used in image registration, and it provides an efficient and general framework to discretize each of the terms of our functional. Computational efficiency and modest memory consumption are achieved thanks to parallelization and locally adaptive mesh refinement. This allows for the first time the use of otherwise prohibitively large 3D statistical deformation models

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

Adaptive Stochastic Conjugate Gradient optimization for temporal medical image registration

We propose an Adaptive Stochastic Conjugate Gradient (ASCG) optimization algorithm for temporal medical image registration. This method combines the advantages of Conjugate Gradient (CG) method and Adaptive Stochastic Gradient Descent (ASGD) method. The main idea is that the search direction of ASGD is replaced by stochastic approximations of the conjugate gradient of the cost function. In addition, the step size of ASCG is based on the approximation of the Lipschitz constant of the stochastic gradient function. Thus, this algorithm could maintain the good properties of the conjugate gradient method, meanwhile it uses less gradient computation time per iteration and adjusts the step size adaptively as the ASGD method. As a result, this algorithm takes less CPU time than the previous ASGD method. We demonstrate the efficiency of our algorithm on the public available 4D Lung CT data and our clinical Lung/Tumor CT data using the general 4D image registration model. We compare the ASCG with several existing iterative optimization strategies: steepest gradient descent method, conjugate gradient method, Quasi-Newton method (LBFGS) and adaptive stochastic gradient descent method. Our preliminary results indicate that our ASCG algorithm achieves 22% higher accuracy on the POPI dataset and it also performs better than existing methods on other datasets(DIR-Lab dataset and our clinical dataset). Furthermore, we demonstrate that compared with other methods, our ASCG algorithm is more robust to image noises

Stratified decision forests for accurate anatomical landmark localization in cardiac images

Accurate localization of anatomical landmarks is an important step in medical imaging, as it provides useful prior information for subsequent image analysis and acquisition methods. It is particularly useful for initialization of automatic image analysis tools (e.g. segmentation and registration) and detection of scan planes for automated image acquisition. Landmark localization has been commonly performed using learning based approaches, such as classifier and/or regressor models. However, trained models may not generalize well in heterogeneous datasets when the images contain large differences due to size, pose and shape variations of organs. To learn more data-adaptive and patient specific models, we propose a novel stratification based training model, and demonstrate its use in a decision forest. The proposed approach does not require any additional training information compared to the standard model training procedure and can be easily integrated into any decision tree framework. The proposed method is evaluated on 1080 3D highresolution and 90 multi-stack 2D cardiac cine MR images. The experiments show that the proposed method achieves state-of-theart landmark localization accuracy and outperforms standard regression and classification based approaches. Additionally, the proposed method is used in a multi-atlas segmentation to create a fully automatic segmentation pipeline, and the results show that it achieves state-of-the-art segmentation accuracy

Adaptive Physics-Based Non-Rigid Registration for Immersive Image-Guided Neuronavigation Systems

Objective: In image-guided neurosurgery, co-registered preoperative anatomical, functional, and diffusion tensor imaging can be used to facilitate a safe resection of brain tumors in eloquent areas of the brain. However, the brain deforms during surgery, particularly in the presence of tumor resection. Non-Rigid Registration (NRR) of the preoperative image data can be used to create a registered image that captures the deformation in the intraoperative image while maintaining the quality of the preoperative image. Using clinical data, this paper reports the results of a comparison of the accuracy and performance among several non-rigid registration methods for handling brain deformation. A new adaptive method that automatically removes mesh elements in the area of the resected tumor, thereby handling deformation in the presence of resection is presented. To improve the user experience, we also present a new way of using mixed reality with ultrasound, MRI, and CT. Materials and methods: This study focuses on 30 glioma surgeries performed at two different hospitals, many of which involved the resection of significant tumor volumes. An Adaptive Physics-Based Non-Rigid Registration method (A-PBNRR) registers preoperative and intraoperative MRI for each patient. The results are compared with three other readily available registration methods: a rigid registration implemented in 3D Slicer v4.4.0; a B-Spline non-rigid registration implemented in 3D Slicer v4.4.0; and PBNRR implemented in ITKv4.7.0, upon which A-PBNRR was based. Three measures were employed to facilitate a comprehensive evaluation of the registration accuracy: (i) visual assessment, (ii) a Hausdorff Distance-based metric, and (iii) a landmark-based approach using anatomical points identified by a neurosurgeon. Results: The A-PBNRR using multi-tissue mesh adaptation improved the accuracy of deformable registration by more than five times compared to rigid and traditional physics based non-rigid registration, and four times compared to B-Spline interpolation methods which are part of ITK and 3D Slicer. Performance analysis showed that A-PBNRR could be applied, on average, in \u3c2 min, achieving desirable speed for use in a clinical setting. Conclusions: The A-PBNRR method performed significantly better than other readily available registration methods at modeling deformation in the presence of resection. Both the registration accuracy and performance proved sufficient to be of clinical value in the operating room. A-PBNRR, coupled with the mixed reality system, presents a powerful and affordable solution compared to current neuronavigation systems

Adaptive stochastic gradient descent optimisation for image registration.

Abstract We present a stochastic gradient descent optimisation method for image registration with adaptive step size prediction. The method is based on the theoretical work by Plakhov and Cruz (J. Math. Sci. 120(1): [964][965][966][967][968][969][970][971][972][973] 2004). Our main methodological contribution is the derivation of an image-driven mechanism to select proper values for the most important free parameters of the method. The selection mechanism employs general characteristics of the cost functions that commonly occur in intensity-based image registration. Also, the theoretical convergence conditions of the optimisation method are taken into account. The proposed adaptive stochastic gradient descent (ASGD) method is compared to a standard, non-adaptive RobbinsMonro (RM) algorithm. Both ASGD and RM employ a stochastic subsampling technique to accelerate the optimisation process. Registration experiments were performed on 3D CT and MR data of the head, lungs, and prostate, using various similarity measures and transformation models. The results indicate that ASGD is robust to these variations in the registration framework and is less sensitive to the settings of the user-defined parameters than RM. The main disadvantage of RM is the need for a predetermined step size function. The ASGD method provides a solution for that issue

On Adaptive Image Segmentation of Remotely Sensed Imagery

A critical step in object-oriented geospatial analysis (OBIA) is image segmentation. A single set of parameters is often not effective segmenting an image. To solve this problem, an adaptive approach to image segmentation has been proposed, which utilizes segments determined from a lower-spatial resolution image as the context to analyse a corresponding image at a higher-spatial resolution to create multiple sets of segmentation parameters to address the needs of different parts of the image. However, due to inherent differences in perceptions of a scene at different spatial resolutions and co-registration, segment boundaries from the low spatial resolution image need to be adjusted before they are applied to the high-spatial resolution image. This is a non-trivial task due to considerations such as noise, image complexity, and determining appropriate boundaries. Accordingly, an innovative method was developed. Adjustments were executed for each boundary pixel based on the minimization of an energy function characterizing local homogeneity. Adjustments are based on a structure which rewarded movement towards edges, and superior changes towards homogeneity. The adjusted segments act as the basis for the determination of segmentation parameters through a variogram based method. The developed method was tested on a set of Quickbird, and ASTER images, from a study area in Ontario, Canada. Results showed that the adjusted segmentation boundaries obtained from the lower resolution imagery were aligned well with the features in the Quickbird imagery, and segmentation maps determined using the adaptive segmentation method were superior to those created by a non-adaptive approach. This work will allow users to more easily and quickly segment large high resolution images

Nonrigid Registration of Monomodal MRI Using Linear Viscoelastic Model

This paper describes a method for nonrigid registration of monomodal MRI based on physical laws. The proposed method assumes that the properties of image deformations are like those of viscoelastic matter, which exhibits the properties of both an elastic solid and a viscous fluid. Therefore, the deformation fields of the deformed image are constrained by both sets of properties. After global registration, the local shape variations are assumed to have the properties of the Maxwell model of linear viscoelasticity, and the deformation fields are constrained by the corresponding partial differential equations. To speed up the registration, an adaptive force is introduced according to the maximum displacement of each iteration. Both synthetic datasets and real datasets are used to evaluate the proposed method. We compare the results of the linear viscoelastic model with those of the fluid model on the basis of both the standard and adaptive forces. The results demonstrate that the adaptive force increases in both models and that the linear viscoelastic model improves the registration accuracy