Retrospective evaluation of a topology preserving non-rigid registration method

Which is the most appropriate strategy for conducting multivariate voxel-based group studies on diffusion tensors?

There is a real need in the neuroscience community for efficient tools to compare Diffusion Tensor Magnetic Resonance Imaging across cohorts of subjects. Most studies focus on the comparison of scalar images such as fractional anisotropy or mean diffusivity. Although different statistical frameworks have been proposed to compare the whole diffusion tensor information, they are still seldom used in neuroimaging studies. In this paper, we investigate on both simulated and real data whether there is a real added value of considering the whole tensor information for conducting voxel-based group studies. Then, we compare two statistical tests dedicated to tensor, namely the Cramér test and a tensor-based extension of the General Linear Model (GLM), the latter presenting the advantage to account for covariates. We also evaluate the impact of different metrics (Euclidean, Log-Euclidean and affine-invariant Riemannian metrics) for estimating the GLM parameters. Finally, we address the problem of interpreting the change detection maps obtained by tensor-based methods by proposing a way to characterize each of the detected clusters according to several scalar indices. Our study suggests that if there is no prior assumption about the nature of the expected changes, it is really preferable to use tensor-based rather than scalar-based statistical analysis. The Cramér test can advantageously be used when no confounding variable hampers the group comparison, otherwise the GLM should be considered. Finally, the different metrics show similar performance in the real scenario, with a significant computational overhead for the Riemannian framework.

Optimal number of atlases and label fusion for automatic multi-atlas-based brachial plexus contouring in radiotherapy treatment planning

BACKGROUND

The present study aimed to define the optimal number of atlases for automatic multi-atlas-based brachial plexus (BP) segmentation and to compare Simultaneous Truth and Performance Level Estimation (STAPLE) label fusion with Patch label fusion using the ADMIRE® software. The accuracy of the autosegmentations was measured by comparing all of the generated autosegmentations with the anatomically validated gold standard segmentations that were developed using cadavers.

MATERIALS AND METHODS

Twelve cadaver computed tomography (CT) atlases were used for automatic multi-atlas-based segmentation. To determine the optimal number of atlases, one atlas was selected as a patient and the 11 remaining atlases were registered onto this patient using a deformable image registration algorithm. Next, label fusion was performed by using every possible combination of 2 to 11 atlases, once using STAPLE and once using Patch. This procedure was repeated for every atlas as a patient. The similarity of the generated automatic BP segmentations and the gold standard segmentation was measured by calculating the average Dice similarity (DSC), Jaccard (JI) and True positive rate (TPR) for each number of atlases. These similarity indices were compared for the different number of atlases using an equivalence trial and for the two label fusion groups using an independent sample-t test.

RESULTS

DSC's and JI's were highest when using nine atlases with both STAPLE (average DSC = 0,532; JI = 0,369) and Patch (average DSC = 0,530; JI = 0,370). When comparing both label fusion algorithms using 9 atlases for both, DSC and JI values were not significantly different. However, significantly higher TPR values were achieved in favour of STAPLE (p < 0,001). When fewer than four atlases were used, STAPLE produced significantly lower DSC, JI and TPR values than did Patch (p = 0,0048).

CONCLUSIONS

Using 9 atlases with STAPLE label fusion resulted in the most accurate BP autosegmentations (average DSC = 0,532; JI = 0,369 and TPR = 0,760). Only when using fewer than four atlases did the Patch label fusion results in a significantly more accurate autosegmentation than STAPLE.

Dosimetric comparison of patient setup strategies in stereotactic body radiation therapy for lung cancer

PURPOSE

In this work, the authors retrospectively compared the accumulated dose over the treatment course for stereotactic body radiation therapy (SBRT) of lung cancer for three patient setup strategies.

METHODS

Ten patients who underwent lung SBRT were selected for this study. At each fraction, patients were immobilized using a vacuum cushion and were CT scanned. Treatment plans were performed on the simulation CT. The planning target volume (PTV) was created by adding a 5-mm uniform margin to the internal target volume derived from the 4DCT. All plans were normalized such that 99% of the PTV received 60 Gy. The plan parameters were copied onto the daily CT images for dose recalculation under three setup scenarios: skin marker, bony structure, and soft tissue based alignments. The accumulated dose was calculated by summing the dose at each fraction along the trajectory of a voxel over the treatment course through deformable image registration of each CT with the planning CT. The accumulated doses were analyzed for the comparison of setup accuracy.

RESULTS

The tumor volume receiving 60 Gy was 91.7 ± 17.9%, 74.1 ± 39.1%, and 99.6 ± 1.3% for setup using skin marks, bony structures, and soft tissue, respectively. The isodose line covering 100% of the GTV was 55.5 ± 7.1, 42.1 ± 16.0, and 64.3 ± 7.1 Gy, respectively. The corresponding average biologically effective dose of the tumor was 237.3 ± 29.4, 207.4 ± 61.2, and 258.3 ± 17.7 Gy, respectively. The differences in lung biologically effective dose, mean dose, and V20 between the setup scenarios were insignificant.

CONCLUSIONS

The authors' results suggest that skin marks and bony structure are insufficient for aligning patients in lung SBRT. Soft tissue based alignment is needed to match the prescribed dose delivered to the tumors.

Deformable medical image registration: a survey

Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: 1) multi-modality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; 2) longitudinal studies, where temporal structural or anatomical changes are investigated; and 3) population modeling and statistical atlases used to study normal anatomical variability. In this paper, we attempt to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain. Additional emphasis has been given to techniques applied to medical images. In order to study image registration methods in depth, their main components are identified and studied independently. The most recent techniques are presented in a systematic fashion. The contribution of this paper is to provide an extensive account of registration techniques in a systematic manner.

Nonrigid 3D medical image registration and fusion based on deformable models

For coregistration of medical images, rigid methods often fail to provide enough freedom, while reliable elastic methods are available clinically for special applications only. The number of degrees of freedom of elastic models must be reduced for use in the clinical setting to archive a reliable result. We propose a novel geometry-based method of nonrigid 3D medical image registration and fusion. The proposed method uses a 3D surface-based deformable model as guidance. In our twofold approach, the deformable mesh from one of the images is first applied to the boundary of the object to be registered. Thereafter, the non-rigid volume deformation vector field needed for registration and fusion inside of the region of interest (ROI) described by the active surface is inferred from the displacement of the surface mesh points. The method was validated using clinical images of a quasirigid organ (kidney) and of an elastic organ (liver). The reduction in standard deviation of the image intensity difference between reference image and model was used as a measure of performance. Landmarks placed at vessel bifurcations in the liver were used as a gold standard for evaluating registration results for the elastic liver. Our registration method was compared with affine registration using mutual information applied to the quasi-rigid kidney. The new method achieved 15.11% better quality with a high confidence level of 99% for rigid registration. However, when applied to the quasi-elastic liver, the method has an averaged landmark dislocation of 4.32 mm. In contrast, affine registration of extracted livers yields a significantly (P = 0.000001) smaller dislocation of 3.26 mm. In conclusion, our validation shows that the novel approach is applicable in cases where internal deformation is not crucial, but it has limitations in cases where internal displacement must also be taken into account.

Longitudinal change detection: inference on the diffusion tensor along white matter pathways

Diffusion weighted magnetic resonance imaging (DW-MRI) makes it possible to probe brain connections in vivo. This paper presents a change detection framework that relies on white matter pathways with application to neuromyelitis optica (NMO). The objective is to detect local or global fiber diffusion property modifications between two longitudinal DW-MRI acquisitions of a patient. To this end, we develop two frameworks based on statistical tests on tensor eigenvalues to detect local or global changes along the white matter pathways: a pointwise test that compares tensor populations extracted in bundles cross sections and a fiberwise test that compares paired tensors along all the fiber bundles. Experiments on both synthetic and real data highlight the benefit of considering fiber based statistical tests compared to standard voxelwise strategies.

The role of regularization in deformable image registration for head and neck adaptive radiotherapy

Deformable image registration provides a robust mathematical framework to quantify morphological changes that occur along the course of external beam radiotherapy treatments. As clinical reliability of deformable image registration is not always guaranteed, algorithm regularization is commonly introduced to prevent sharp discontinuities in the quantified deformation and achieve anatomically consistent results. In this work we analyzed the influence of regularization on two different registration methods, i.e. B-Splines and Log Domain Diffeomorphic Demons, implemented in an open-source platform. We retrospectively analyzed the simulation computed tomography (CTsim) and the corresponding re-planning computed tomography (CTrepl) scans in 30 head and neck cancer patients. First, we investigated the influence of regularization levels on hounsfield units (HU) information in 10 test patients for each considered method. Then, we compared the registration results of the open-source implementation at selected best performing regularization levels with a clinical commercial software on the remaining 20 patients in terms of mean volume overlap, surface and center of mass distances between manual outlines and propagated structures. The regularized B-Splines method was not statistically different from the commercial software. The tuning of the regularization parameters allowed open-source algorithms to achieve better results in deformable image registration for head and neck patients, with the additional benefit of a framework where regularization can be tuned on a patient specific basis.

Longitudinal change detection in diffusion MRI using multivariate statistical testing on tensors

This paper presents a longitudinal change detection framework for detecting relevant modifications in diffusion MRI, with application to neuromyelitis optica (NMO) and multiple sclerosis (MS). The core problem is to identify image regions that are significantly different between two scans. The proposed method is based on multivariate statistical testing which was initially introduced for tensor population comparison. We use this method in the context of longitudinal change detection by considering several strategies to build sets of tensors characterizing the variability of each voxel. These strategies make use of the variability existing in the diffusion weighted images (thanks to a bootstrap procedure), or in the spatial neighborhood of the considered voxel, or a combination of both. Results on synthetic evolutions and on real data are presented. Interestingly, experiments on NMO patients highlight the ability of the proposed approach to detect changes in the normal-appearing white matter (according to conventional MRI) that are related with physical status outcome. Experiments on MS patients highlight the ability of the proposed approach to detect changes in evolving and non-evolving lesions (according to conventional MRI). These findings might open promising prospects for the follow-up of NMO and MS pathologies.

Retrospective evaluation of PET-MRI registration algorithms

The purpose of this study is to evaluate the accuracy of registration positron emission tomography (PET) head images to the MRI-based brain atlas. The [(18)F]fluoro-2-deoxyglucose PET images were normalized to the MRI-based brain atlas using nine registration algorithms including objective functions of ratio image uniformity (RIU), normalized mutual information (NMI), and normalized cross correlation (CC) and transformation models of rigid-body, linear, affine, and nonlinear transformations. The accuracy of normalization was evaluated by visual inspection and quantified by the gray matter (GM) concordance between normalized PET images and the brain atlas. The linear and affine registration based on the RIU provided the best GM concordance (average similarity index of 0.71 for both). We also observed that the GM concordances of linear and affine registration were higher than those of the rigid and nonlinear registration among the methods evaluated.

Can injection CT scan assess the residual femoral head vascularity after acute neck fracture?

INTRODUCTION

Femoral neck fracture jeopardizes the vital prognosis of the elderly subject and the functional prognosis of the young subject. The vascular consequence is important, with the risk of osteonecrosis of the femoral head. In young patients, predicting the risk of necrosis at the acute stage seems warranted so as to optimize the choice of therapy. CT with injection could be useful to study the residual bone vascularity after an acute fracture of the femoral neck.

HYPOTHESIS

The CT scan with injection can diagnose ischemia of the femoral head after neck fracture by demonstrating hypoperfusion and thus estimating the risk for osteonecrosis.

PATIENTS AND METHOD

A CT scan with injection was performed prospectively in 20 adult patients who had given informed consent after verification of the inclusion and exclusion criteria. Ten presented femoral neck fracture and 10 a pertrochanteric fracture, the latter making up the control group. The second control group was the healthy side of patients presenting a femoral neck fracture. The images were analyzed after delineating a region of interest as a volume at the center of the femoral head. The results were analyzed after modeling based on the physical principle of diffusion.

RESULTS

No differences were found between the "healthy hip," "fractured hip," "femoral neck fracture," and "trochanteric region fracture" groups. The only statistically significant correlation was found between the "fractured hip" and "healthy hip" of the same patient independently of the type of fracture.

DISCUSSION

The results do not confirm the working hypothesis. This study was mainly limited by the small number of patients included, but this did not substantially effect the study's conclusions. According to the results, it seems that this study provided a CT evaluation of bone mineral density. At the end of the study, it seems that CT with injection is not well adapted in assessing residual femoral head vascularity or estimating the risk of progression towards avascular necrosis. According to the literature, only dynamic MRI with injection seems to be effective in this assessment and estimation.

LEVEL OF EVIDENCE

Level III prospective comparative diagnostic.

A reproducible evaluation of ANTs similarity metric performance in brain image registration

The United States National Institutes of Health (NIH) commit significant support to open-source data and software resources in order to foment reproducibility in the biomedical imaging sciences. Here, we report and evaluate a recent product of this commitment: Advanced Neuroimaging Tools (ANTs), which is approaching its 2.0 release. The ANTs open source software library consists of a suite of state-of-the-art image registration, segmentation and template building tools for quantitative morphometric analysis. In this work, we use ANTs to quantify, for the first time, the impact of similarity metrics on the affine and deformable components of a template-based normalization study. We detail the ANTs implementation of three similarity metrics: squared intensity difference, a new and faster cross-correlation, and voxel-wise mutual information. We then use two-fold cross-validation to compare their performance on openly available, manually labeled, T1-weighted MRI brain image data of 40 subjects (UCLA's LPBA40 dataset). We report evaluation results on cortical and whole brain labels for both the affine and deformable components of the registration. Results indicate that the best ANTs methods are competitive with existing brain extraction results (Jaccard=0.958) and cortical labeling approaches. Mutual information affine mapping combined with cross-correlation diffeomorphic mapping gave the best cortical labeling results (Jaccard=0.669±0.022). Furthermore, our two-fold cross-validation allows us to quantify the similarity of templates derived from different subgroups. Our open code, data and evaluation scripts set performance benchmark parameters for this state-of-the-art toolkit. This is the first study to use a consistent transformation framework to provide a reproducible evaluation of the isolated effect of the similarity metric on optimal template construction and brain labeling.

Change detection in diffusion MRI using multivariate statistical testing on tensors

This paper presents a longitudinal change detection framework for detecting relevant modifications in diffusion MRI, with application to Multiple Sclerosis (MS). The proposed method is based on multivariate statistical testings which were initially introduced for tensor population comparison. We use these methods in the context of longitudinal change detection by considering several strategies to build sets of tensors characterizing the variability of each voxel. These testing tools have been considered either for the comparison of tensor eigenvalues or eigenvectors, thus enabling to differentiate orientation and diffusivity changes. Results on simulated MS lesion evolutions and on real data are presented. Interestingly, experiments on an MS patient highlight the ability of the proposed approach to detect changes in non evolving lesions (according to conventional MRI) and around lesions (in the normal appearing white matter), which might open promising perspectives for the follow-up of the MS pathology.

Use of simulated atrophy for performance analysis of brain atrophy estimation approaches

In this paper, we study the performance of popular brain atrophy estimation algorithms using a simulated gold standard. The availability of a gold standard facilitates a sound evaluation of the measures of atrophy estimation, which is otherwise complicated. Firstly, we propose an approach for the construction of a gold standard. It involves the simulation of a realistic brain tissue loss based on the estimation of a topology preserving B-spline based deformation fields. Using this gold standard, we present an evaluation of three standard brain atrophy estimation methods (SIENA, SIENAX and BSI) in the presence of bias field inhomogeneity and noise. The effect of brain lesion load on the measured atrophy is also evaluated. Our experiments demonstrate that SIENA, SIENAX and BSI show a deterioration in their performance in the presence of bias field inhomogeneity and noise. The observed mean absolute errors in the measured Percentage of Brain Volume Change (PBVC) are 0.35% +/- 0.38, 2.03% +/- 1.46 and 0.91% +/- 0.80 for SIENA, SIENAX and BSI, respectively, for simulated whole brain atrophies in the range 0-1%.

Spectral-based automatic labeling and refining of human cortical sulcal curves using expert-provided examples

We present a spectral-based method for automatically labeling and refining major sulcal curves of a human cerebral cortex. Given a set of input (unlabeled) sulcal curves automatically extracted from a cortical surface and a collection of expert-provided examples (labeled sulcal curves), our objective is to identify the input major sulcal curves and assign their neuroanatomical labels, and then refines these curves based on the expert-provided example data, without employing any atlas-based registration scheme as preprocessing. In order to construct the example data, neuroanatomists manually labeled a set of 24 major sulcal curves (12 each for the left and right hemispheres) for each individual subject according to a precise protocol. We collected 30 sets of such curves from 30 subjects. Given the raw input sulcal curve set of a subject, we choose the most similar example curve to each input curve in the set to label and refine the latter according to the former. We adapt a spectral matching algorithm to choose the example curve by exploiting the sulcal curve features and their relationship. The high dimensionality of sulcal curve data in spectral matching is addressed by using their multi-resolution representations, which greatly reduces time and space complexities. Our method provides consistent labeling and refining results even under high variability of cortical sulci across the subjects. Through experiments we show that the results are comparable in accuracy to those done manually. Most output curves exhibited accuracy values higher than 80%, and the mean accuracy values of the curves in the left and the right hemispheres were 84.69% and 84.58%, respectively.

Non-rigid registration based segmentation of brain subcortical structures using a priori knowledge

Segmentation of the brain internal structures is an important and a challenging task due to their complex shapes, partial volume effects, low contrasts and anatomical variability between subjects. In this paper we propose a new non-rigid registration method that automatically segments the deep brain internal structures from brain MRI images. An atlas of the structures is used as a priori knowledge, which is modeled as a shape representation. By integrating the shape knowledge into a classical intensity based non-rigid registration algorithm, the proposed segmentation method allows to ameliorate the results in the case of low contrast on the boundaries of the structures. The shape model is based on distance representation obtained from the atlas. The segmentation of brain subcortical structures is performed on real MRI images and the obtained results are very encouraging.

Symmetric nonrigid image registration: application to average brain templates construction

Image registration aims at estimating a consistent mapping between two images. Common techniques consist in choosing arbitrarily one image as a reference image and the other one as a floating image, thus leading to the estimation of inconsistent mappings. We present a symmetric formulation of the registration problem that maps the two images in a common coordinate system halfway between them. This framework has been considered to devise an efficient strategy for mapping a large set of images in a common coordinate system. Some results are presented in the context of 3-D nonrigid brain MR image registration for the construction of average brain templates.

Validation of non-rigid registration between functional and anatomical magnetic resonance brain images

This paper presents a set of validation procedures for nonrigid registration of functional EPI to anatomical MRI brain images. Although various registration techniques have been developed and validated for high-resolution anatomical MRI images, due to a lack of quantitative and qualitative validation procedures, the use of nonrigid registration between functional EPI and anatomical MRI images has not yet been deployed in neuroimaging studies. In this paper, the performance of a robust formulation of a nonrigid registration technique is evaluated in a quantitative manner based on simulated data and is further evaluated in a quantitative and qualitative manner based on in vivo data as compared to the commonly used rigid and affine registration techniques in the neuroimaging software packages. The nonrigid registration technique is formulated as a second-order constrained optimization problem using a free-form deformation model and mutual information similarity measure. Bound constraints, resolution level and cross-validation issues have been discussed to show the degree of accuracy and effectiveness of the nonrigid registration technique. The analyses performed reveal that the nonrigid approach provides a more accurate registration, in particular when the functional regions of interest lie in regions distorted by susceptibility artifacts.

A fast nonrigid image registration with constraints on the Jacobian using large scale constrained optimization

This paper presents a new nonrigid monomodality image registration algorithm based on B-splines. The deformation is described by a cubic B-spline field and found by minimizing the energy between a reference image and a deformed version of a floating image. To penalize noninvertible transformation, we propose two different constraints on the Jacobian of the transformation and its derivatives. The problem is modeled by an inequality constrained optimization problem which is efficiently solved by a combination of the multipliers method and the L-BFGS algorithm to handle the large number of variables and constraints of the registration of 3-D images. Numerical experiments are presented on magnetic resonance images using synthetic deformations and atlas based segmentation.

Nonrigid coregistration of diffusion tensor images using a viscous fluid model and mutual information

In this paper, a nonrigid coregistration algorithm based on a viscous fluid model is proposed that has been optimized for diffusion tensor images (DTI), in which image correspondence is measured by the mutual information criterion. Several coregistration strategies are introduced and evaluated both on simulated data and on brain intersubject DTI data. Two tensor reorientation methods have been incorporated and quantitatively evaluated. Simulation as well as experimental results show that the proposed viscous fluid model can provide a high coregistration accuracy, although the tensor reorientation was observed to be highly sensitive to the local deformation field. Nevertheless, this coregistration method has demonstrated to significantly improve spatial alignment compared to affine image matching.

Brain functional localization: a survey of image registration techniques

Functional localization is a concept which involves the application of a sequence of geometrical and statistical image processing operations in order to define the location of brain activity or to produce functional/parametric maps with respect to the brain structure or anatomy. Considering that functional brain images do not normally convey detailed structural information and, thus, do not present an anatomically specific localization of functional activity, various image registration techniques are introduced in the literature for the purpose of mapping functional activity into an anatomical image or a brain atlas. The problems addressed by these techniques differ depending on the application and the type of analysis, i.e., single-subject versus group analysis. Functional to anatomical brain image registration is the core part of functional localization in most applications and is accompanied by intersubject and subject-to-atlas registration for group analysis studies. Cortical surface registration and automatic brain labeling are some of the other tools towards establishing a fully automatic functional localization procedure. While several previous survey papers have reviewed and classified general-purpose medical image registration techniques, this paper provides an overview of brain functional localization along with a survey and classification of the image registration techniques related to this problem.