Subcortical, cerebellar, and magnetic resonance based consistent brain image registration

Quantifying deep grey matter atrophy using automated segmentation approaches: A systematic review of structural MRI studies

The deep grey matter (DGM) nuclei of the brain play a crucial role in learning, behaviour, cognition, movement and memory. Although automated segmentation strategies can provide insight into the impact of multiple neurological conditions affecting these structures, such as Multiple Sclerosis (MS), Huntington's disease (HD), Alzheimer's disease (AD), Parkinson's disease (PD) and Cerebral Palsy (CP), there are a number of technical challenges limiting an accurate automated segmentation of the DGM. Namely, the insufficient contrast of T1 sequences to completely identify the boundaries of these structures, as well as the presence of iso-intense white matter lesions or extensive tissue loss caused by brain injury. Therefore in this systematic review, 269 eligible studies were analysed and compared to determine the optimal approaches for addressing these technical challenges. The automated approaches used among the reviewed studies fall into three broad categories, atlas-based approaches focusing on the accurate alignment of atlas priors, algorithmic approaches which utilise intensity information to a greater extent, and learning-based approaches that require an annotated training set. Studies that utilise freely available software packages such as FIRST, FreeSurfer and LesionTOADS were also eligible, and their performance compared. Overall, deep learning approaches achieved the best overall performance, however these strategies are currently hampered by the lack of large-scale annotated data. Improving model generalisability to new datasets could be achieved in future studies with data augmentation and transfer learning. Multi-atlas approaches provided the second-best performance overall, and may be utilised to construct a "silver standard" annotated training set for deep learning. To address the technical challenges, providing robustness to injury can be improved by using multiple channels, highly elastic diffeomorphic transformations such as LDDMM, and by following atlas-based approaches with an intensity driven refinement of the segmentation, which has been done with the Expectation Maximisation (EM) and level sets methods. Accounting for potential lesions should be achieved with a separate lesion segmentation approach, as in LesionTOADS. Finally, to address the issue of limited contrast, R2*, T2* and QSM sequences could be used to better highlight the DGM due to its higher iron content. Future studies could look to additionally acquire these sequences by retaining the phase information from standard structural scans, or alternatively acquiring these sequences for only a training set, allowing models to learn the "improved" segmentation from T1-sequences alone.

Gray Matter Atrophy in the Cerebellum-Evidence of Increased Vulnerability of the Crus and Vermis with Advancing Age

This study examined patterns of cerebellar volumetric gray matter (GM) loss across the adult lifespan in a large cross-sectional sample. Four hundred and seventy-nine healthy participants (age range: 7-86 years) were drawn from the Brain Resource International Database who provided T1-weighted MRI scans. The spatially unbiased infratentorial template (SUIT) toolbox in SPM8 was used for normalisation of the cerebellum structures. Global volumetric and voxel-based morphometry analyses were performed to evaluate age-associated trends and gender-specific age-patterns. Global cerebellar GM shows a cross-sectional reduction with advancing age of 2.5 % per decade-approximately half the rate seen in the whole brain. The male cerebellum is larger with a lower percentage of GM, however, after controlling for total brain volume, no gender difference was detected. Analysis of age-related changes in GM volume revealed large bilateral clusters involving the vermis and cerebellar crus where regional loss occurred at nearly twice the average cerebellar rate. No gender-specific patterns were detected. These data confirm that regionally specific GM loss occurs in the cerebellum with age, and form a solid base for further investigation to find functional correlates for this global and focal loss.

Robust automated constellation-based landmark detection in human brain imaging

A robust fully automated algorithm for identifying an arbitrary number of landmark points in the human brain is described and validated. The proposed method combines statistical shape models with trained brain morphometric measures to estimate midbrain landmark positions reliably and accurately. Gross morphometric constraints provided by automatically identified eye centers and the center of the head mass are shown to provide robust initialization in the presence of large rotations in the initial head orientation. Detection of primary midbrain landmarks are used as the foundation from which extended detection of an arbitrary set of secondary landmarks in different brain regions by applying a linear model estimation and principle component analysis. This estimation model sequentially uses the knowledge of each additional detected landmark as an improved foundation for improved prediction of the next landmark location. The accuracy and robustness of the presented method was evaluated by comparing the automatically generated results to two manual raters on 30 identified landmark points extracted from each of 30 T1-weighted magnetic resonance images. For the landmarks with unambiguous anatomical definitions, the average discrepancy between the algorithm results and each human observer differed by less than 1 mm from the average inter-observer variability when the algorithm was evaluated on imaging data collected from the same site as the model building data. Similar results were obtained when the same model was applied to a set of heterogeneous image volumes from seven different collection sites representing 3 scanner manufacturers. This method is reliable for general application in large-scale multi-site studies that consist of a variety of imaging data with different orientations, spacings, origins, and field strengths.

A fast algorithm to estimate inverse consistent image transformation based on corresponding landmarks

Inverse consistency is an important feature for non-rigid image transformation in medical imaging analysis. In this paper, a simple and efficient inverse consistent image transformation estimation algorithm is proposed to preserve correspondence of landmarks and accelerate convergence. The proposed algorithm estimates both the forward and backward transformations simultaneously in the way that they are inverse to each other based on the correspondence of landmarks. Instead of computing the inverse functions and the inverse consistent transformations, respectively, we combine them together, which can improve computation efficiency significantly. Moreover, radial basis functions (RBFs) based transformation is adopted in our algorithm, which can handle deformation with local or global support. Our algorithm maps one landmark to its corresponding position exactly using the forward and backward transformations. Moreover, our algorithm is employed to estimate the forward and backward transformations in robust point matching, as well to demonstrate the application of our algorithm in image registration. The experiment results of uniform grids and test images indicate the improvement of the proposed algorithm in the aspect of inverse consistency of transformations and the reduction of the computation time of the forward and the backward transformations. The performance of our algorithm applying to robust point matching is evaluated using both brain slices and lung slices. Our experiments show that by combing robust point matching with our algorithm, the registration accuracy can be improved and the smoothness of transformations can be preserved.

Relapse duration, treatment intensity, and brain tissue loss in schizophrenia: a prospective longitudinal MRI study

OBJECTIVE

Longitudinal structural MRI studies have shown that patients with schizophrenia have progressive brain tissue loss after onset. Recurrent relapses are believed to play a role in this loss, but the relationship between relapse and structural MRI measures has not been rigorously assessed. The authors analyzed longitudinal data to examine this question.

METHODS

The authors studied data from 202 patients drawn from the Iowa Longitudinal Study of first-episode schizophrenia for whom adequate structural MRI data were available (N=659 scans) from scans obtained at regular intervals over an average of 7 years. Because clinical follow-up data were obtained at 6-month intervals, the authors were able to compute measures of relapse number and duration and relate them to structural MRI measures. Because higher treatment intensity has been associated with smaller brain tissue volumes, the authors also examined this countereffect in terms of dose-years.

RESULTS

Relapse duration was related to significant decreases in both general (e.g., total cerebral volume) and regional (e.g., frontal) brain measures. Number of relapses was unrelated to brain measures. Significant effects were also observed for treatment intensity.

CONCLUSIONS

Extended periods of relapse may have a negative effect on brain integrity in schizophrenia, suggesting the importance of implementing proactive measures that may prevent relapse and improve treatment adherence. By examining the relative balance of effects, that is, relapse duration versus antipsychotic treatment intensity, this study sheds light on a troublesome dilemma that clinicians face. Relapse prevention is important, but it should be sustained using the lowest possible medication dosages that will control symptoms.

Smaller intracranial volume in prodromal Huntington's disease: evidence for abnormal neurodevelopment

Huntington's disease is an autosomal dominant brain disease. Although conceptualized as a neurodegenerative disease of the striatum, a growing number of studies challenge this classic concept of Huntington's disease aetiology. Intracranial volume is the tissue and fluid within the calvarium and is a representation of the maximal brain growth obtained during development. The current study reports intracranial volume obtained from an magnetic resonance imaging brain scan in a sample of subjects (n = 707) who have undergone presymptomatic gene testing. Participants who are gene-expanded but not yet manifesting the disease (prodromal Huntington's disease) are compared with subjects who are non-gene expanded. The prodromal males had significantly smaller intracranial volume measures with a mean volume that was 4% lower compared with controls. Although the prodromal females had smaller intracranial volume measures compared with their controls, this was not significant. The current findings suggest that mutant huntingtin can cause abnormal development, which may contribute to the pathogenesis of Huntington's disease.

Elevated cerebral blood flow and volume in systemic lupus measured by dynamic susceptibility contrast magnetic resonance imaging

OBJECTIVE

Studies that have examined abnormalities in cerebral blood flow (CBF) in patients with systemic lupus erythematosus (SLE) reported CBF relative to a region assumed to be normal in the brain. We examined the absolute differences in both regional CBF and cerebral blood volume (CBV) between patients with SLE and healthy controls.

METHODS

CBF and CBV were measured with dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI), a technique that provides an alternative to radionuclide perfusion studies and permits quantitative anatomic, CBF, and CBV imaging in a single scanning session. CBF and CBV were measured in lesions and in normal-appearing tissue in the major cerebral and subcortical brain regions. Unlike most perfusion studies in SLE, CBF and CBV values were not normalized to a region of the brain assumed to be healthy.

RESULTS

CBF and CBV within MRI-visible lesions were markedly reduced relative to surrounding normal-appearing white matter. CBF and CBV in normal-appearing tissue were both higher in SLE patient groups, with or without lesions, relative to the control group.

CONCLUSION

DSC MRI, without normalization to a region presumed to be healthy, revealed that CBF and CBV in normal-appearing tissue in patients with SLE was higher than CBF and CBV in controls. Since this finding was made in subgroups of patients with and without lesions, the higher CBF and CBV appear to precede lesion pathology.

Robust atlas-based brain segmentation using multi-structure confidence-weighted registration

We present a robust and accurate atlas-based brain segmentation method which uses multiple initial structure segmentations to simultaneously drive the image registration and achieve anatomically constrained correspondence. We also derive segmentation confidence maps (SCMs) from a given manually segmented training set; these characterize the accuracy of a given set of segmentations as compared to manual segmentations. We incorporate these in our cost term to weight the influence of initial segmentations in the multi-structure registration, such that low confidence regions are given lower weight in the registration. To account for correspondence errors in the underlying registration, we use a supervised atlas correction technique and present a method for correcting the atlas segmentation to account for possible errors in the underlying registration. We applied our multi-structure atlas-based segmentation and supervised atlas correction to segment the amygdala in a set of 23 autistic patients and controls using leave-one-out cross validation, achieving a Dice overlap score of 0.84. We also applied our method to eight subcortical structures in MRI from the Internet Brain Segmentation Repository, with results better or comparable to competing methods.

Multi-structure whole brain registration and population average

We present here a novel method for whole brain magnetic resonance (MR) image registration that explicitly penalizes the mismatch of cortical and subcortical regions by simultaneously utilizing anatomic segmentation information from multiple cortical and subcortical structures, represented as volumetric images, with given T1-weighted MR image for registration. The registration is computed via variational optimization in the space of smooth velocity fields in the large deformation diffeomorphic metric matching (LDDMM) framework. We tested our method using a set of 10 manually labeled brains, and found quantitatively that subcortical and cortical alignment is improved over traditional single-channel MRI registration. We use this new method to generate a volumetric and cortical surface-based population average. The average grayscale image is found to be crisp, and allows the reconstruction and labeling of the cortical surface.

Motor abnormalities in premanifest persons with Huntington's disease: the PREDICT-HD study

The PREDICT-HD study seeks to identify clinical and biological markers of Huntington's disease in premanifest individuals who have undergone predictive genetic testing. We compared baseline motor data between gene-expansion carriers (cases) and nongene-expansion carriers (controls) using t-tests and Chi-square. Cases were categorized as near, mid, or far from diagnosis using a CAG-based formula. Striatal volumes were calculated using volumetric magnetic resonance imaging measurements. Multiple linear regression associated total motor score, motor domains, and individual motor items with estimated diagnosis and striatal volumes. Elevated total motor scores at baseline were associated with higher genetic probability of disease diagnosis in the near future (partial R(2) 0.14, P < 0.0001) and smaller striatal volumes (partial R(2) 0.15, P < 0.0001). Nearly all motor domain scores showed greater abnormality with increasing proximity to diagnosis, although bradykinesia and chorea were most highly associated with diagnostic immediacy. Among individual motor items, worse scores on finger tapping, tandem gait, Luria, saccade initiation, and chorea show unique association with diagnosis probability. Even in this premanifest population, subtle motor abnormalities were associated with a higher probability of disease diagnosis and smaller striatal volumes. Longitudinal assessment will help inform whether motor items will be useful measures in preventive clinical trials.

Stereotactic atlas-based depth electrode localization in the human amygdala

BACKGROUND

The efficacy of stereotactic neurosurgery procedures is critically dependent on the accuracy of the device placement procedure. The first step in this process involves correctly identifying the target location in three-dimensional brain space. In some clinical applications, this targeting process cannot be accomplished using MRI images of gross anatomical structures alone. The amygdala complex is a case in point, in that it consists of multiple histologically defined subnuclei with different functional characteristics.

METHODS

In this report, we describe an elastic atlas brain-morphing method that projects amygdala subnuclear anatomical information onto the MRI volumes of individual subjects.

RESULTS

The accuracy of this method was tested in 5 representative subjects using quantitative image-matching analytical techniques. The results demonstrate a high degree of intersubject variability in medial temporal lobe anatomy, and markedly superior anatomical matching performance by the elastic morphing method compared to Affine transformation.

CONCLUSION

Nonlinear elastic morphing technique provides superior performance on fitting atlas templates to individual brain. The strengths and limitations of this and other atlas morphing methods are discussed in the context of emerging functional neurosurgery applications.

Application of Kohonen network for automatic point correspondence in 2D medical images

In this paper, a generalized application of Kohonen Network for automatic point correspondence of unimodal medical images is presented. Given a pair of two-dimensional medical images of the same anatomical region and a set of interest points in one of the images, the algorithm detects effectively the set of corresponding points in the second image, by exploiting the properties of the Kohonen self organizing maps (SOMs) and embedding them in a stochastic optimization framework. The correspondences are established by determining the parameters of local transformations that map the interest points of the first image to their corresponding points in the second image. The parameters of each transformation are computed in an iterative way, using a modification of the competitive learning, as implemented by SOMs. The proposed algorithm was tested on medical imaging data from three different modalities (CT, MR and red-free retinal images) subject to known and unknown transformations. The quantitative results in all cases exhibited sub-pixel accuracy. The algorithm also proved to work efficiently in the case of noise corrupted data. Finally, in comparison to a previously published algorithm that was also based on SOMs, as well as two widely used techniques for detection of point correspondences (template matching and iterative closest point), the proposed algorithm exhibits an improved performance in terms of accuracy and robustness.

A modified probabilistic neural network for partial volume segmentation in brain MR image

A modified probabilistic neural network (PNN) for brain tissue segmentation with magnetic resonance imaging (MRI) is proposed. In this approach, covariance matrices are used to replace the singular smoothing factor in the PNN's kernel function, and weighting factors are added in the pattern of summation layer. This weighted probabilistic neural network (WPNN) classifier can account for partial volume effects, which exist commonly in MRI, not only in the final result stage, but also in the modeling process. It adopts the self-organizing map (SOM) neural network to overly segment the input MR image, and yield reference vectors necessary for probabilistic density function (pdf) estimation. A supervised "soft" labeling mechanism based on Bayesian rule is developed, so that weighting factors can be generated along with corresponding SOM reference vectors. Tissue classification results from various algorithms are compared, and the effectiveness and robustness of the proposed approach are demonstrated.

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.

Registration of in vivo magnetic resonance T1-weighted brain images to triphenyltetrazolium chloride stained sections in small animals

Signal changes observed in high-resolution in vivo magnetic resonance (MR) images acquired during cerebral ischemia in small animal models must be correlated to molecular indicators of tissue damage obtained from digitized histological brain sections. An effective image registration technique that incorporates both a linear and non-linear thin plate spline transform was developed to compensate the distortions that occur in the brain during the extraction, fixation, and staining process. Features in different layers of the brain were utilized in conjunction with a radial guideline-assisted landmark selection method to register tissue layers with few distinguishing characteristics. Quantitative analysis using simulated data demonstrated average registration error of 400 microm (corresponding to approximately 2.5 pixels in the MR images) when > or =50 landmark points are used. Visual agreement was obtained between T(1)-weighted MR images and 2,3,5-triphenyltetrazolium chloride stained histology. These methods will allow accurate registration of in vivo images with histology to correlate in vivo surrogate markers of tissue damage with specific histological indicators of disease.

A hybrid tissue segmentation approach for brain MR images

A novel hybrid algorithm for the tissue segmentation of brain magnetic resonance images is proposed. The core of the algorithm is a probabilistic neural network (PNN) in which weighting factors are added to the summation layer, such that partial volume effects can be taken into account in the modeling process. The mean vectors for the probability density function estimation and the corresponding weighting factors are generated by a hierarchical scheme involving a self-organizing map neural network and an expectation maximization algorithm. Unlike conventional PNN, this approach circumvents the need for training sets. Tissue segmentation results from various algorithms are compared and the effectiveness and robustness of the proposed approach are demonstrated.

Unbiased diffeomorphic atlas construction for computational anatomy

Construction of population atlases is a key issue in medical image analysis, and particularly in brain mapping. Large sets of images are mapped into a common coordinate system to study intra-population variability and inter-population differences, to provide voxel-wise mapping of functional sites, and help tissue and object segmentation via registration of anatomical labels. Common techniques often include the choice of a template image, which inherently introduces a bias. This paper describes a new method for unbiased construction of atlases in the large deformation diffeomorphic setting. A child neuroimaging autism study serves as a driving application. There is lack of normative data that explains average brain shape and variability at this early stage of development. We present work in progress toward constructing an unbiased MRI atlas of 2 years of children and the building of a probabilistic atlas of anatomical structures, here the caudate nucleus. Further, we demonstrate the segmentation of new subjects via atlas mapping. Validation of the methodology is performed by comparing the deformed probabilistic atlas with existing manual segmentations.

Mindboggle: a scatterbrained approach to automate brain labeling

Mindboggle (http://www.binarybottle.com/mindboggle.html) is a fully automated, feature matching approach to label cortical structures and activity anatomically in human brain MRI data. This approach does not assume that the existence of component structures and their relative spatial relationship is preserved from brain to brain, but instead disassembles a labeled atlas and reassembles its pieces to match corresponding pieces in an unlabeled subject brain before labeling. Mindboggle: (1) converts linearly coregistered subject and atlas MRI data into sulcus pieces, (2) matches each atlas piece with a combination of subject pieces by minimizing a cost function, (3) transforms atlas label boundaries to the matching subject pieces, (4) warps atlas labels to their transformed boundaries, and (5) propagates labels to fill remaining gaps in a mask derived from the subject brain. We compared Mindboggle with four registration methods: linear registration, and nonlinear registration using SPM2, AIR, and ANIMAL. Automated labeling by all of the nonlinear methods was found to be at least comparable with linear registration. Mindboggle outperformed every other method, as measured by the agreement between overlapping atlas labels and manually assigned subject labels, with respect to the union or the intersection of voxels. After applying the same procedure that Mindboggle uses to fill a subject's segmented gray matter mask with labels (step 5), the results of the other methods improved. However, after performing a one-way ANOVA (and Tukey's honestly significant difference criterion) in a multiple comparison between the results obtained by the different methods, Mindboggle was still found to be the only nonlinear method whose labeling performance was significantly better than that of linear registration or SPM2. Further advantages to Mindboggle include a high degree of robustness against image artifacts, poor image quality, and incomplete brain data. We tested the latter hypothesis by conducting all of the tests again, this time registering the atlas to an artificially lesioned version of itself, and found that Mindboggle was the only method whose performance did not degrade significantly as the lesion size increased.

Capturing intraoperative deformations: research experience at Brigham and Women's Hospital

During neurosurgical procedures the objective of the neurosurgeon is to achieve the resection of as much diseased tissue as possible while achieving the preservation of healthy brain tissue. The restricted capacity of the conventional operating room to enable the surgeon to visualize critical healthy brain structures and tumor margin has lead, over the past decade, to the development of sophisticated intraoperative imaging techniques to enhance visualization. However, both rigid motion due to patient placement and nonrigid deformations occurring as a consequence of the surgical intervention disrupt the correspondence between preoperative data used to plan surgery and the intraoperative configuration of the patient's brain. Similar challenges are faced in other interventional therapies, such as in cryoablation of the liver, or biopsy of the prostate. We have developed algorithms to model the motion of key anatomical structures and system implementations that enable us to estimate the deformation of the critical anatomy from sequences of volumetric images and to prepare updated fused visualizations of preoperative and intraoperative images at a rate compatible with surgical decision making. This paper reviews the experience at Brigham and Women's Hospital through the process of developing and applying novel algorithms for capturing intraoperative deformations in support of image guided therapy.