Very High-Resolution Morphometry Using Mass-Preserving Deformations and HAMMER Elastic Registration

doi:10.1006/nimg.2002.1301

NeuroImage

Volume 18, Issue 1, January 2003, Pages 28-41

https://doi.org/10.1006/nimg.2002.1301 Get rights and content

Abstract

This article presents a very high-resolution voxel-based morphometric method, by using a mass-preserving deformation mechanism and a fully automated spatial normalization approach, referred to as HAMMER. By using a hierarchical attribute-based deformation strategy, HAMMER partly overcomes limitations of several existing spatial normalization methods, and it achieves a level of accuracy that makes possible morphometric measurements of spatial specificity close to the voxel dimensions. The proposed method is validated by a series of experiments, with both simulated and real brain images.

References (49)

J. Ashburner et al.
Voxel-based morphometry: The methods
NeuroImage
(2000)
C. Davatzikos
Spatial transformation and registration of brain images using elastically deformable models
Comput. Vis. Image Understanding
(1997)
C. Davatzikos
Measuring biological shape using geometry-based shape transformations
Image Vis. Comput.
(2001)
C. Davatzikos et al.
Voxel-based morphometry using the RAVENS maps: Methods and validation using simulation of longitudinal atrophy
NeuroImage
(2001)
G. Subsol et al.
A general scheme for automatically building 3D morphometric anatomical atlases: Application to a skull atlas
Med. Image Anal.
(1998)
Y. Wang et al.
Physical model-based non-rigid registration incorporating statistical shape information
Med. Image Anal.
(2000)
I.C. Wright et al.
A voxel-based method for the statistical analysis of gray and white matter density applied to schizophrenia
NeuroImage
(1995)
J. Ashburner et al.
Identifying global anatomical differences: Deformation-based morphometry
Hum. Brain Map.
(1998)
R. Bajcsy et al.
A computerized system for the elastic matching of deformed radiographic images to idealized atlas images
J. Comput. Assist. Tomogr.
(1983)
F.L. Bookstein
Principal warps: Thin-plate splines and the decomposition of deformations
IEEE Trans. Pattern Anal. Mach. Intell.
(1989)

Bookstein, F. L. Voxel-based morphometry should not be used with imperfectly registered images. NeuroImage14:...

M. Breijl et al.

Object localization and border detection criteria design in edge-based image segmentation: automated learning from examples

IEEE Trans. Med. Imag.

(2000)

L.L. Briquer et al.

Design of a statistical model for brain shape

Lect. Notes Comp. Sci.: Proc. Inform. Proc. Med. Imag.

(1997)

M. Chen et al.

3-D deformable registration of medical images using a statistical atlas

MICCAI

(1999)

G.E. Christensen

Consistent linear-elastic transformations for image matching

Information Processing in Medical Imaging

(1999)

G.E. Christensen et al.

Consistent image registration

IEEE Trans. Med. Imag.

(2001)

H. Chui et al.

A Unified feature registration method for brain mapping

Information Processing in Medical Imaging

(2001)

D.L. Collins et al.

ANIMAL+INSECT: Improved cortical structure segmentation

Information Processing In Medical Imaging: Proceedings Lecture Notes in Computer Science

(1999)

C. Davatzikos et al.

A computerized method for morphological analysis of the corpus callosum

J. Comput. Assist. Tomogr.

(1996)

C. Davatzikos et al.

Sex differences in interhemispheric connectivity: Correlations with cognition in women but not in men

Cereb. Cortex

(1998)

C. Davatzikos

Mapping of image data to stereotaxic spaces: Applications to brain mapping

Hum. Brain Map.

(1998)

A.C. Evans et al.

Warping of a computerized 3-D atlas to match brain image volumes for quantitative neuroanatomical and functional analysis

SPIE Proc. Image Process.

(1991)

P.A. Freeborough et al.

Modeling brain deformations in Alzheimer disease by fluid registration of serial MR images

J. Comput. Assist. Tomogr.

(1998)

Gee, J. C., Reivich, M., and Bajcsy, R.Elastically deforming 3D atlas to match anatomical brain images. J. Comput....

Cited by (148)

Longitudinal MRI analysis using a hybrid DenseNet-BiLSTM method for Alzheimer's disease prediction
2024, Behavioural Brain Research
Alzheimer's disease is a progressive neurological disorder characterized by brain atrophy and cell death, leading to cognitive decline and impaired functioning. Previous research has primarily focused on using cross-sectional data for Alzheimer's disease identification, but analyzing longitudinal sequential MR images is crucial for improved diagnostic accuracy and understanding disease progression. However, existing deep learning models face challenges in learning spatial and temporal features from such data. To address these challenges, this study presents a novel hybrid DenseNet-BiLSTM method for Alzheimer's disease prediction using longitudinal MRI analysis. The proposed framework combines Convolutional DenseNet for spatial information extraction and joined BiLSTM layers for capturing temporal characteristics and relationships between longitudinal images at different time points. This approach overcomes issues like overfitting, vanishing gradients, and incomplete patient data. We evaluated the model on 684 longitudinal MRI images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, including normal controls, individuals with mild cognitive impairment, and Alzheimer's disease patients. The results demonstrate high classification accuracy, with 95.28% for AD/CN, 88.19% for NC/MCI, 83.51% for sMCI/pMCI, and 92.14% for MCI/AD. These findings highlight the substantial improvement in Alzheimer's disease diagnosis achieved through the utilization of longitudinal MRI images. The contributions of this study lie in both the deep learning and medical domains. In the deep learning domain, our hybrid framework effectively learns spatial and temporal features from longitudinal data, addressing the challenges associated with multi-dimensional and sequential time series data. In the medical domain, our study emphasizes the importance of analyzing baseline and longitudinal MR images for accurate diagnosis and understanding disease progression.
Long range early diagnosis of Alzheimer's disease using longitudinal MR imaging data
2021, Medical Image Analysis
The enormous social and economic cost of Alzheimer's disease (AD) has driven a number of neuroimaging investigations for early detection and diagnosis. Towards this end, various computational approaches have been applied to longitudinal imaging data in subjects with Mild Cognitive Impairment (MCI), as serial brain imaging could increase sensitivity for detecting changes from baseline, and potentially serve as a diagnostic biomarker for AD. However, current state-of-the-art brain imaging diagnostic methods have limited utility in clinical practice due to the lack of robust predictive power. To address this limitation, we propose a flexible spatial-temporal solution to predict the risk of MCI conversion to AD prior to the onset of clinical symptoms by sequentially recognizing abnormal structural changes from longitudinal magnetic resonance (MR) image sequences. Firstly, our model is trained to sequentially recognize different length partial MR image sequences from different stages of AD. Secondly, our method is leveraged by the inexorably progressive nature of AD. To that end, a Temporally Structured Support Vector Machine (TS-SVM) model is proposed to constrain the partial MR image sequence's detection score to increase monotonically with AD progression. Furthermore, in order to select the best morphological features for enabling classifiers, we propose a joint feature selection and classification framework. We demonstrate that our early diagnosis method using only two follow-up MR scans is able to predict conversion to AD 12 months ahead of an AD clinical diagnosis with 81.75% accuracy.
RNN-based longitudinal analysis for diagnosis of Alzheimer's disease
2019, Computerized Medical Imaging and Graphics
Alzheimer's disease (AD) is an irreversible neurodegenerative disorder with progressive impairment of memory and other mental functions. Magnetic resonance images (MRI) have been widely used as an important imaging modality of brain for AD diagnosis and monitoring the disease progression. The longitudinal analysis of sequential MRIs is important to model and measure the progression of the disease along the time axis for more accurate diagnosis. Most existing methods extracted the features capturing the morphological abnormalities of brain and their longitudinal changes using MRIs and then designed a classifier to discriminate different groups. However, these methods have several limitations. First, since the feature extraction and classifier model are independent, the extracted features may not capture the full characteristics of brain abnormalities related to AD. Second, longitudinal MR images may be missing at some time points for some subjects, which results in difficulties for extraction of consistent features for longitudinal analysis. In this paper, we present a classification framework based on combination of convolutional and recurrent neural networks for longitudinal analysis of structural MR images in AD diagnosis. First, Convolutional Neural Networks (CNN) is constructed to learn the spatial features of MR images for the classification task. After that, recurrent Neural Networks (RNN) with cascaded three bidirectional gated recurrent units (BGRU) layers is constructed on the outputs of CNN at multiple time points for extracting the longitudinal features for AD classification. Instead of independently performing feature extraction and classifier training, the proposed method jointly learns the spatial and longitudinal features and disease classifier, which can achieve optimal performance. In addition, the proposed method can model the longitudinal analysis using RNN from the imaging data at various time points. Our method is evaluated with the longitudinal T1-weighted MR images of 830 participants including 198 AD, 403 mild cognitive impairment (MCI), and 229 normal controls (NC) subjects from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Experimental results show that the proposed method achieves classification accuracy of 91.33% for AD vs. NC and 71.71% for pMCI vs. sMCI, demonstrating the promising performance for longitudinal MR image analysis.
MIDAS: Regionally linear multivariate discriminative statistical mapping
2018, NeuroImage
Statistical parametric maps formed via voxel-wise mass-univariate tests, such as the general linear model, are commonly used to test hypotheses about regionally specific effects in neuroimaging cross-sectional studies where each subject is represented by a single image. Despite being informative, these techniques remain limited as they ignore multivariate relationships in the data. Most importantly, the commonly employed local Gaussian smoothing, which is important for accounting for registration errors and making the data follow Gaussian distributions, is usually chosen in an ad hoc fashion. Thus, it is often suboptimal for the task of detecting group differences and correlations with non-imaging variables. Information mapping techniques, such as searchlight, which use pattern classifiers to exploit multivariate information and obtain more powerful statistical maps, have become increasingly popular in recent years. However, existing methods may lead to important interpretation errors in practice (i.e., misidentifying a cluster as informative, or failing to detect truly informative voxels), while often being computationally expensive. To address these issues, we introduce a novel efficient multivariate statistical framework for cross-sectional studies, termed MIDAS, seeking highly sensitive and specific voxel-wise brain maps, while leveraging the power of regional discriminant analysis. In MIDAS, locally linear discriminative learning is applied to estimate the pattern that best discriminates between two groups, or predicts a variable of interest. This pattern is equivalent to local filtering by an optimal kernel whose coefficients are the weights of the linear discriminant. By composing information from all neighborhoods that contain a given voxel, MIDAS produces a statistic that collectively reflects the contribution of the voxel to the regional classifiers as well as the discriminative power of the classifiers. Critically, MIDAS efficiently assesses the statistical significance of the derived statistic by analytically approximating its null distribution without the need for computationally expensive permutation tests. The proposed framework was extensively validated using simulated atrophy in structural magnetic resonance imaging (MRI) and further tested using data from a task-based functional MRI study as well as a structural MRI study of cognitive performance. The performance of the proposed framework was evaluated against standard voxel-wise general linear models and other information mapping methods. The experimental results showed that MIDAS achieves relatively higher sensitivity and specificity in detecting group differences. Together, our results demonstrate the potential of the proposed approach to efficiently map effects of interest in both structural and functional data.
Classification of multi-site MR images in the presence of heterogeneity using multi-task learning
2018, NeuroImage: Clinical
Citation Excerpt :
In site C, all scans were obtained using a GE 3-T Signa scanner (GE Medical Systems, Milwaukee WI, USA) with the following protocol: slice thickness = 1 mm, TE = 3.2 ms, TR = 8.2 ms, flip angle = 12°, acquisition matrix = 256 × 256, FOV = 25.6 cm. The imaging data were preprocessed using the following steps as previously described (Davatzikos, 1998; Davatzikos et al., 2001; Goldszal et al., 1998; Shen and Davatzikos, 2003): (1) skull stripping, (2) bias field correction, (3) brain tissue segmentation into gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), (4) spatial registration to Montreal Neurological Institute (MNI) template, (5) generation of the Regional Analysis of Volumes Examined in Normalized Space (RAVENS) (Davatzikos et al., 2001; Goldszal et al., 1998) maps of GM, WM, and CSF using deformable registration (DRAMMS) (Ou et al., 2011), and (6) spatial smoothing of RAVENS maps using a 6-mm Full Width at Half Maximum (FWHM) Gaussian filter. In our study, we investigated the RAVENS value changes in the GM images, which indicated regional GM volumetric alterations in the brain.
With the advent of Big Data Imaging Analytics applied to neuroimaging, datasets from multiple sites need to be pooled into larger samples. However, heterogeneity across different scanners, protocols and populations, renders the task of finding underlying disease signatures challenging. The current work investigates the value of multi-task learning in finding disease signatures that generalize across studies and populations. Herein, we present a multi-task learning type of formulation, in which different tasks are from different studies and populations being pooled together. We test this approach in an MRI study of the neuroanatomy of schizophrenia (SCZ) by pooling data from 3 different sites and populations: Philadelphia, Sao Paulo and Tianjin (50 controls and 50 patients from each site), which posed integration challenges due to variability in disease chronicity, treatment exposure, and data collection. Some existing methods are also tested for comparison purposes. Experiments show that classification accuracy of multi-site data outperformed that of single-site data and pooled data using multi-task feature learning, and also outperformed other comparison methods. Several anatomical regions were identified to be common discriminant features across sites. These included prefrontal, superior temporal, insular, anterior cingulate cortex, temporo-limbic and striatal regions consistently implicated in the pathophysiology of schizophrenia, as well as the cerebellum, precuneus, and fusiform, middle temporal, inferior parietal, postcentral, angular, lingual and middle occipital gyri. These results indicate that the proposed multi-task learning method is robust in finding consistent and reliable structural brain abnormalities associated with SCZ across different sites, in the presence of multiple sources of heterogeneity.
Multitemplate-based multiview learning for Alzheimer's disease diagnosis
2016, Machine Learning and Medical Imaging
Multitemplate-based brain morphometric pattern analysis using magnetic resonance imaging has recently been proposed for automatic diagnosis of Alzheimer’s disease (AD) and its prodromal stage (ie, mild cognitive impairment or MCI). In such methods, multiview morphological patterns generated from multiple templates are used as feature representation for brain images. This chapter presents some of the latest advancements in multitemplate-based multiview learning for AD and MCI diagnosis. We will first present a multiview feature representation method by employing multiple templates. Then we will discuss how to make use of those multiview representations for effective diagnosis of AD and MCI. Specifically, we will introduce four multiview learning methods for AD/MCI classification, and demonstrate that these methods can further promote the disease diagnosis performance.

View all citing articles on Scopus

View full text

Regular ArticleVery High-Resolution Morphometry Using Mass-Preserving Deformations and HAMMER Elastic Registration

Abstract

NeuroImage

Comput. Vis. Image Understanding

Image Vis. Comput.

NeuroImage

Med. Image Anal.

Med. Image Anal.

NeuroImage

Identifying global anatomical differences: Deformation-based morphometry

Hum. Brain Map.

A computerized system for the elastic matching of deformed radiographic images to idealized atlas images

J. Comput. Assist. Tomogr.

Principal warps: Thin-plate splines and the decomposition of deformations

IEEE Trans. Pattern Anal. Mach. Intell.

Object localization and border detection criteria design in edge-based image segmentation: automated learning from examples

IEEE Trans. Med. Imag.

Design of a statistical model for brain shape

Lect. Notes Comp. Sci.: Proc. Inform. Proc. Med. Imag.

3-D deformable registration of medical images using a statistical atlas

MICCAI

Consistent linear-elastic transformations for image matching

Information Processing in Medical Imaging

Consistent image registration

IEEE Trans. Med. Imag.

A Unified feature registration method for brain mapping

Information Processing in Medical Imaging

ANIMAL+INSECT: Improved cortical structure segmentation

Information Processing In Medical Imaging: Proceedings Lecture Notes in Computer Science

A computerized method for morphological analysis of the corpus callosum

J. Comput. Assist. Tomogr.

Sex differences in interhemispheric connectivity: Correlations with cognition in women but not in men

Cereb. Cortex

Mapping of image data to stereotaxic spaces: Applications to brain mapping

Hum. Brain Map.

Warping of a computerized 3-D atlas to match brain image volumes for quantitative neuroanatomical and functional analysis

SPIE Proc. Image Process.

Modeling brain deformations in Alzheimer disease by fluid registration of serial MR images

J. Comput. Assist. Tomogr.

Regular Article
Very High-Resolution Morphometry Using Mass-Preserving Deformations and HAMMER Elastic Registration