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Spatio-Temporal Regularization for Longitudinal Registration to Subject-Specific 3d Template.

Guizard N, Fonov VS, García-Lorenzo D, Nakamura K, Aubert-Broche B, Collins DL - PLoS ONE (2015)

Bottom Line: Noise due to MR scanners and other physiological effects may also introduce variability in the measurement.We propose to use 4D non-linear registration with spatio-temporal regularization to correct for potential longitudinal inconsistencies in the context of structure segmentation.The major contribution of this article is the use of individual template creation with spatio-temporal regularization of the deformation fields for each subject.

View Article: PubMed Central - PubMed

Affiliation: Montreal Neurological Institute, McGill University, Montréal, Canada.

ABSTRACT
Neurodegenerative diseases such as Alzheimer's disease present subtle anatomical brain changes before the appearance of clinical symptoms. Manual structure segmentation is long and tedious and although automatic methods exist, they are often performed in a cross-sectional manner where each time-point is analyzed independently. With such analysis methods, bias, error and longitudinal noise may be introduced. Noise due to MR scanners and other physiological effects may also introduce variability in the measurement. We propose to use 4D non-linear registration with spatio-temporal regularization to correct for potential longitudinal inconsistencies in the context of structure segmentation. The major contribution of this article is the use of individual template creation with spatio-temporal regularization of the deformation fields for each subject. We validate our method with different sets of real MRI data, compare it to available longitudinal methods such as FreeSurfer, SPM12, QUARC, TBM, and KNBSI, and demonstrate that spatially local temporal regularization yields more consistent rates of change of global structures resulting in better statistical power to detect significant changes over time and between populations.

No MeSH data available.


Related in: MedlinePlus

Longitudinal registration and template creation methods.Each vignette (a, b, c and d) represents different strategies proposed to overcome longitudinal MRI data analysis. The x-axis represents the time and the y-axis represents the anatomical variability of the image. Each subject’s time-points are connected by a colored line (blue, green and red) and the black line represents a longitudinal (4D) model. The square boxes represent the population 3D template in black and individual 3D templates in blue, green and red.
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pone.0133352.g001: Longitudinal registration and template creation methods.Each vignette (a, b, c and d) represents different strategies proposed to overcome longitudinal MRI data analysis. The x-axis represents the time and the y-axis represents the anatomical variability of the image. Each subject’s time-points are connected by a colored line (blue, green and red) and the black line represents a longitudinal (4D) model. The square boxes represent the population 3D template in black and individual 3D templates in blue, green and red.

Mentions: Image processing in MRI-based neuro-anatomical studies is often performed in a cross-sectional manner where each time-point is evaluated independently. Typically, brain morphometry comparisons can be done by matching paired images (template-to-subject or subject-to-subject), where the deformation field is used to map atlas regions or to compute voxel-wise comparisons of anatomical changes as in deformation-based morphometry (DBM). However, in the context of longitudinal datasets, the robust estimation of anatomical changes is still challenging [6]. Indeed, in the case of neurodegeneration occurring in a short period of time (2–3 years), if we assume that longitudinal changes are smoothly varying, spatially local, and temporally monotonic processes, considering individual time-points independently can generate unnecessarily noisy longitudinal measurements due to the intrinsic noise associated with each visit. Different studies have shown the impact of the MRI acquisition protocol on structural measurements [7] and cortical thickness [8]. Therefore, methods that integrate constraints from the temporal dimension (i.e., 4D methods) should produce more accurate, robust and stable measures of the longitudinal anatomical changes resulting in a more realistic estimation of temporal evolution. Different approaches have been proposed to overcome the complexity of anatomical 4D longitudinal data image analysis. We classify these methods in 2 major groups: “4D” and “longitudinal 3D”. The 4D approaches treat the individual and/or group-wise longitudinal data as an ensemble and provide longitudinal models or measurements. They are mathematically sophisticated approaches that have been proposed in the context of modeling larger anatomical changes over time (i.e., growth over the span of childhood). For example, a 4D population model creation using Gaussian kernel regression has been suggested by Davis et al. [9] where each image is registered independently to a moving average, avoiding the creation of an explicit parameterized model of the longitudinal changes (Fig 1A). Kernel regression has also been used in the framework of the Large Deformation Diffeomorphic Metric Mapping (LDDMM) for brain shapes [10] (Fig 1B) and images [10–12]. Regarding intra-subject 4D registration, Lorenzi et al. [13] have proposed 4D non-linear registration via a global 4D deformation optimization scheme in the Demons registration framework. Finally, Wu et al. [14] introduced an implicit mean-shape of the population which could be used for individuals. Their approach maximizes the spatio-temporal correspondence and continuity from a set of temporal fibre bundles (Fig 1C).


Spatio-Temporal Regularization for Longitudinal Registration to Subject-Specific 3d Template.

Guizard N, Fonov VS, García-Lorenzo D, Nakamura K, Aubert-Broche B, Collins DL - PLoS ONE (2015)

Longitudinal registration and template creation methods.Each vignette (a, b, c and d) represents different strategies proposed to overcome longitudinal MRI data analysis. The x-axis represents the time and the y-axis represents the anatomical variability of the image. Each subject’s time-points are connected by a colored line (blue, green and red) and the black line represents a longitudinal (4D) model. The square boxes represent the population 3D template in black and individual 3D templates in blue, green and red.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4547713&req=5

pone.0133352.g001: Longitudinal registration and template creation methods.Each vignette (a, b, c and d) represents different strategies proposed to overcome longitudinal MRI data analysis. The x-axis represents the time and the y-axis represents the anatomical variability of the image. Each subject’s time-points are connected by a colored line (blue, green and red) and the black line represents a longitudinal (4D) model. The square boxes represent the population 3D template in black and individual 3D templates in blue, green and red.
Mentions: Image processing in MRI-based neuro-anatomical studies is often performed in a cross-sectional manner where each time-point is evaluated independently. Typically, brain morphometry comparisons can be done by matching paired images (template-to-subject or subject-to-subject), where the deformation field is used to map atlas regions or to compute voxel-wise comparisons of anatomical changes as in deformation-based morphometry (DBM). However, in the context of longitudinal datasets, the robust estimation of anatomical changes is still challenging [6]. Indeed, in the case of neurodegeneration occurring in a short period of time (2–3 years), if we assume that longitudinal changes are smoothly varying, spatially local, and temporally monotonic processes, considering individual time-points independently can generate unnecessarily noisy longitudinal measurements due to the intrinsic noise associated with each visit. Different studies have shown the impact of the MRI acquisition protocol on structural measurements [7] and cortical thickness [8]. Therefore, methods that integrate constraints from the temporal dimension (i.e., 4D methods) should produce more accurate, robust and stable measures of the longitudinal anatomical changes resulting in a more realistic estimation of temporal evolution. Different approaches have been proposed to overcome the complexity of anatomical 4D longitudinal data image analysis. We classify these methods in 2 major groups: “4D” and “longitudinal 3D”. The 4D approaches treat the individual and/or group-wise longitudinal data as an ensemble and provide longitudinal models or measurements. They are mathematically sophisticated approaches that have been proposed in the context of modeling larger anatomical changes over time (i.e., growth over the span of childhood). For example, a 4D population model creation using Gaussian kernel regression has been suggested by Davis et al. [9] where each image is registered independently to a moving average, avoiding the creation of an explicit parameterized model of the longitudinal changes (Fig 1A). Kernel regression has also been used in the framework of the Large Deformation Diffeomorphic Metric Mapping (LDDMM) for brain shapes [10] (Fig 1B) and images [10–12]. Regarding intra-subject 4D registration, Lorenzi et al. [13] have proposed 4D non-linear registration via a global 4D deformation optimization scheme in the Demons registration framework. Finally, Wu et al. [14] introduced an implicit mean-shape of the population which could be used for individuals. Their approach maximizes the spatio-temporal correspondence and continuity from a set of temporal fibre bundles (Fig 1C).

Bottom Line: Noise due to MR scanners and other physiological effects may also introduce variability in the measurement.We propose to use 4D non-linear registration with spatio-temporal regularization to correct for potential longitudinal inconsistencies in the context of structure segmentation.The major contribution of this article is the use of individual template creation with spatio-temporal regularization of the deformation fields for each subject.

View Article: PubMed Central - PubMed

Affiliation: Montreal Neurological Institute, McGill University, Montréal, Canada.

ABSTRACT
Neurodegenerative diseases such as Alzheimer's disease present subtle anatomical brain changes before the appearance of clinical symptoms. Manual structure segmentation is long and tedious and although automatic methods exist, they are often performed in a cross-sectional manner where each time-point is analyzed independently. With such analysis methods, bias, error and longitudinal noise may be introduced. Noise due to MR scanners and other physiological effects may also introduce variability in the measurement. We propose to use 4D non-linear registration with spatio-temporal regularization to correct for potential longitudinal inconsistencies in the context of structure segmentation. The major contribution of this article is the use of individual template creation with spatio-temporal regularization of the deformation fields for each subject. We validate our method with different sets of real MRI data, compare it to available longitudinal methods such as FreeSurfer, SPM12, QUARC, TBM, and KNBSI, and demonstrate that spatially local temporal regularization yields more consistent rates of change of global structures resulting in better statistical power to detect significant changes over time and between populations.

No MeSH data available.


Related in: MedlinePlus