Accuracy Of Spatial Normalization Research Articles

Construction and validation of a brain magnetic resonance imaging template for normal older Koreans

BackgroundSpatial normalization to a standardized brain template is a crucial step in magnetic resonance imaging (MRI) studies. Brain templates made from sufficient sample size have low brain variability, improving the accuracy of spatial normalization. Using population-specific template improves accuracy of spatial normalization because brain morphology varies according to ethnicity and age.MethodsWe constructed a brain template of normal Korean elderly (KNE200) using MRI scans 100 male and 100 female aged over 60 years old with normal cognition. We compared the deformation after spatial normalization of the KNE200 template to that of the KNE96, constructed from 96 cognitively normal elderly Koreans and to that of the brain template (OCF), constructed from 434 non-demented older Caucasians to examine the effect of sample size and ethnicity on the accuracy of brain template, respectively. We spatially normalized the MRI scans of elderly Koreans and quantified the amount of deformations associated with spatial normalization using the magnitude of displacement and volumetric changes of voxels.ResultsThe KNE200 yielded significantly less displacement and volumetric change in the parahippocampal gyrus, medial and posterior orbital gyrus, fusiform gyrus, gyrus rectus, cerebellum and vermis than the KNE96. The KNE200 also yielded much less displacement in the cerebellum, vermis, hippocampus, parahippocampal gyrus and thalamus and much less volumetric change in the cerebellum, vermis, hippocampus and parahippocampal gyrus than the OCF.ConclusionKNE200 had the better accuracy than the KNE96 due to the larger sample size and was far accurate than the template constructed from elderly Caucasians in elderly Koreans.

Development and comprehensive evaluation of high‐resolution 0.5 mm isotropic T2‐weighted, PD‐weighted, T2 map and FLAIR templates of the MIITRA atlas.

AbstractBackgroundVoxel‐wise, disease‐monitoring and other MRI investigations involving T2‐weighted(T2w), PD‐weighted(PDw), T2 map and FLAIR templates typically utilize young adult templates such as those of the ICBM atlas. Moreover, a comprehensive assessment of how such available standardized templates perform on aging population has not yet been performed. Here, a high‐resolution older‐adult population based T2w, PDw T2 map and FLAIR templates of the MIITRA atlas was developed using theories of super‐resolution and sparse‐representation and compared to other available standardized templates in terms of image quality, spatial normalization accuracy and representativeness of the older adult brain.MethodT1‐weighted MPRAGE of isotropic 1mm resolution, five spin‐echo(0.5×0.5×0.5 mm3,TE = 20,40,60,80,100ms) and FLAIR data (0.5×0.5×4 mm3) were collected on 3T MRI scanners for 400 non‐demented older adults (50% male; 65‐95 years of age, 54% white and 43% black) participating in the construction of the MIITRA atlas. The subject T2 maps and PD‐weighted data were computed by fitting the T2 decay equation. The template construction technique involved upsampling the images to 0.5×0.5×0.5 mm3 using non‐local upsampling, and converting the T2‐weighted(TE = 60ms) to synthetic T1‐weighted, followed by linear registration to upsampled T1‐weighted data using ANTs. Next, the resulting transformations concatenated with the combined iterative T1w and DTI driven MIITRA atlas deformations were used to forward map each participant to exact physical locations in the MIITRA space. Finally, sparse representation data fusion technique was utilized on the transformed signals to construct the templates at isotropic 0.5mm resolution (Figure.1). MIITRA third echo T2‐weighted and FLAIR template was compared to available standardized templates (Figure. 2) in terms of inter‐subject spatial normalization accuracy when used for spatial normalization of 400 ADNI T2‐weighted and FLAIR participants respectively.ResultThe T2‐weighted and FLAIR templates of the MIITRA atlas exhibited higher image sharpness exemplified by higher spatial frequency content of the normalized power spectra(Figure. 2). MIITRA templates demonstrated higher inter‐subject spatial normalization accuracy and required the least deformations for spatial normalization of ADNI data(Figure. 3)ConclusionThe new T2w, PDw, and FLAIR templates of the MIITRA atlas exhibited superior image quality, required lower deformations, facilitating higher spatial normalization accuracy for use in studies on older adults such as the ADNI datasets.

Construction of the MIITRA T1‐weighted and DTI templates of the older adult brain in a common space at 0.5mm resolution

AbstractBackgroundHigh resolution multimodal MRI templates of the older adult brain have the potential to facilitate template‐based studies of aging. As part of the Multichannel Illinois Institute of Technology & Rush university Aging (MIITRA) atlas, the present work developed high quality 0.5mm isotropic resolution T1‐weighted (T1w) and diffusion tensor imaging (DTI) templates in common space using data from a large, diverse, community cohort of non‐demented older adults.Method Data T1w and DTI data were collected using two 3T MRI scanners on 400 non‐demented older adults (50% male;64.9‐98.9 yrs;54% white,43% black) (Bennett et al.,2018; Barnes et al.,2012). Template construction Step1: An unbiased initial T1w template was created by pairwise affine registration (Ridwan et al.,2021).Step2: Spatial normalization was driven by T1w and DTI data alternatively (Wu et al., 2019). All transformations were combined to minimize interpolations and applied to both modalities. A patch‐based tissue‐guided sparse‐representation approach was employed to compute the template (Ridwan et al.,2021).Step3: High spatial resolution templates were created based on principles of super‐resolution (Niaz et al.,2022)The resulting templates are referred to as MIITRA_0.5mm templates. Evaluation T1w and DTI data from 202 ADNI3 non‐demented older adults were used for evaluation.The MIITRA_0.5mm templates were compared to other high resolution templates and the templates including older adults in terms of image sharpness and spatial normalization accuracy of ADNI3 data when each template was used as a reference for normalization.ResultMIITRA_0.5mm templates exhibited higher image sharpness (Fig.1,2). MIITRA_0.5mm templates allowed the highest inter‐subject spatial normalization accuracy across spatially normalized ADNI3 data and required less deformation than other high‐resolution templates (Fig.3,4). It should be noted that 1mm templates require on average less deformation than high‐resolution templates because larger voxels limit local deformations. Finally, the MIITRA_0.5mm templates allowed higher inter‐modality spatial matching of normalized older adult data (Fig.5).ConclusionThis work developed high resolution T1w and DTI multimodal templates of the older adult brain for the MIITRA atlas. The new templates exhibited higher image quality, were more representative of the older adult brain, and allowed higher spatial normalization accuracy compared to other templates. The new templates will be available for download at www.nitrc.org/projects/miitra.

Development and systematic evaluation of high resolution PD‐weighted, T2‐weighted, and T2 map templates of the MIITRA atlas for use in studies on older adults

AbstractBackgroundAtlas based MRI investigations involving PD‐weighted (PDw), T2‐weighted (T2w) MRI sequences on older adults typically utilized younger adult templates such as those of the ICBM. Additionally, a thorough quantitative assessment of how available standardized PDw and T2w templates perform on aging population has not yet been performed. Here a new standardized high resolution PDw, T2w and T2 map templates of the MIITRA atlas dedicated for studies on older adults was developed using concepts of super‐resolution and sparse‐representation and compared to other available standardized templates in terms of image quality, inter‐subject spatial normalization accuracy and representativeness of the older adult brain.MethodT2‐weighted multi‐echo spin‐echo brain MRI (0.5mmx0.5mmx3mm isotropic, TE=20,40,60,80,100ms) and its corresponding T1‐weighted (isotropic 1mm) data obtained from the 400 non‐demented older adults (65‐95 age range, male:female=1:1) participating in the construction of the MIITRA atlas were used in this work. Additionally, subject T2 maps were constructed by fitting the T2 decay equation. The template construction involved reconstruction of high resolution voxels of T2‐weighted data utilizing nonlocal super‐resolution upsampling technique, mapping of each of the five echo (TE=20,40,60,80,100ms) separately using combined iterative T1‐weighted and DTI spatial normalization based transformations on a super‐resolution grid followed by patch‐based sparse representation to construct our T2‐weighted templates of the MIITRA atlas (Fig.1).The first echo PD‐weighted and the third echo T2‐weighted templates were compared to other available templates in terms of image sharpness and inter‐subject spatial normalization accuracy achieved when used as references for normalization of 30 ADNI PD‐weighted and T2‐weighted participants.ResultThe new PD and T2 weighted templates of the MIITRA atlas demonstrated higher image sharpness compared to the other templates, as exhibited by larger high spatial frequency content in the normalized power spectra (Fig. 2A,B,C,D). Inter‐subject spatial normalization was higher when using the new templates compared to the other templates (Fig.3,4). The new templates required the least spatial deformation for normalizing ADNI data (Fig.5A,B).ConclusionWe conclude that the new PD‐weighted and T2‐weighted templates of the MIITRA atlas are characterized by superior image quality, require lower deformation and allow for higher spatial normalization accuracy for use in studies on older adults such as the ADNI datasets.

Fast and accurate amyloid brain PET quantification without MRI using deep neural networks

AbstractBackgroundWe propose a novel method for automatic quantification of amyloid PET using a deep learning‐based spatial normalization (SN) of PET images, which does not require corresponding MRI or CT image of the same patient. In this study, the accuracy of the proposed method was evaluated for three different amyloid PET radiotracers by comparison with MRI parcellation‐based PET quantification using FreeSurfer, which is more accurate than SN‐based method but requires significantly longer computation time.MethodDeep neural network model used for SN of amyloid PET images was trained using 994 multicenter amyloid PET images, as well as corresponding 3D MRIs of the patients with Alzheimer’s disease and mild cognitive impairments and cognitive normal subjects. The accuracy of SN and SUVR quantification accuracy relative to FreeSurfer‐based estimation was evaluated using other 148 PET images. Additional external validation was also performed using independent data set (30 18F‐Flutemetamol, 67 18F‐Florbetaben, and 39 18F‐Florbetapir). For the comparison, PET SN was also conducted using SPM12 program. Then, the quantification results using SN methods were compared with SUVR values obtained using on FreeSurfer in individual brain space. Reference region was cerebellar grey matter and AAL3 atlas was used to extract the regional SUVR values.ResultsThe quantification results using the proposed method showed stronger correlations with FreeSurfer estimates than SPM SN using MRI did. For example, the slope, y‐intercept and R2 value between SPM and FreeSurfer for global cortex were 0.869, 0.113 and 0.946, respectively. On the other hand, those for proposed method were 1.019, ‐0.016 and 0.986. The external validation study also demonstrated the better performance of proposed method without MR images than SPM with MRI. In most brain regions, it outperformed the SPM SN in terms of the linear regression parameters and intraclass correlation coefficients.ConclusionIn this study, we evaluated a novel deep learning‐based SN method, which allows quantitative analysis of amyloid brain PET images without structural MRI. The proposed quantification results showed strong correlation with MRI‐parcellation‐based amyloid PET quantification using FreeSurfer in all clinical amyloid radiotracers. Therefore, the proposed method will be useful for investigating Alzheimer’s disease and related brain disorders using amyloid PET scans.

Effects of variability in manually contoured spinal cord masks on fMRI co-registration and interpretation.

Functional magnetic resonance imaging (fMRI) of the human spinal cord (SC) is a unique non-invasive method for characterizing neurovascular responses to stimuli. Group-analysis of SC fMRI data involves co-registration of subject-level data to standard space, which requires manual masking of the cord and may result in bias of group-level SC fMRI results. To test this, we examined variability in SC masks drawn in fMRI data from 21 healthy participants from a completed study mapping responses to sensory stimuli of the C7 dermatome. Masks were drawn on temporal mean functional image by eight raters with varying levels of neuroimaging experience, and the rater from the original study acted as a reference. Spatial agreement between rater and reference masks was measured using the Dice Similarity Coefficient, and the influence of rater and dataset was examined using ANOVA. Each rater's masks were used to register functional data to the PAM50 template. Gray matter-white matter signal contrast of registered functional data was used to evaluate the spatial normalization accuracy across raters. Subject- and group-level analyses of activation during left- and right-sided sensory stimuli were performed for each rater's co-registered data. Agreement with the reference SC mask was associated with both rater (F(7, 140) = 32.12, P < 2 × 10-16, η2 = 0.29) and dataset (F(20, 140) = 20.58, P < 2 × 10-16, η2 = 0.53). Dataset variations may reflect image quality metrics: the ratio between the signal intensity of spinal cord voxels and surrounding cerebrospinal fluid was correlated with DSC results (p < 0.001). As predicted, variability in the manually-drawn masks influenced spatial normalization, and GM:WM contrast in the registered data showed significant effects of rater and dataset (rater: F(8, 160) = 23.57, P < 2 × 10-16, η2 = 0.24; dataset: F(20, 160) = 22.00, P < 2 × 10-16, η2 = 0.56). Registration differences propagated into subject-level activation maps which showed rater-dependent agreement with the reference. Although group-level activation maps differed between raters, no systematic bias was identified. Increasing consistency in manual contouring of spinal cord fMRI data improved co-registration and inter-rater agreement in activation mapping, however our results suggest that improvements in image acquisition and post-processing are also critical to address.

The SACT Template: A Human Brain Diffusion Tensor Template for School-age Children.

School-age children are in a specific development stage corresponding to juvenility, when the white matter of the brain experiences ongoing maturation. Diffusion-weighted magnetic resonance imaging (DWI), especially diffusion tensor imaging (DTI), is extensively used to characterize the maturation by assessing white matter properties in vivo. In the analysis of DWI data, spatial normalization is crucial for conducting inter-subject analyses or linking the individual space with the reference space. Using tensor-based registration with an appropriate diffusion tensor template presents high accuracy regarding spatial normalization. However, there is a lack of a standardized diffusion tensor template dedicated to school-age children with ongoing brain development. Here, we established the school-age children diffusion tensor (SACT) template by optimizing tensor reorientation on high-quality DTI data from a large sample of cognitively normal participants aged 6-12 years. With an age-balanced design, the SACT template represented the entire age range well by showing high similarity to the age-specific templates. Compared with the tensor template of adults, the SACT template revealed significantly higher spatial normalization accuracy and inter-subject coherence upon evaluation of subjects in two different datasets of school-age children. A practical application regarding the age associations with the normalized DTI-derived data was conducted to further compare the SACT template and the adult template. Although similar spatial patterns were found, the SACT template showed significant effects on the distributions of the statistical results, which may be related to the performance of spatial normalization. Looking forward, the SACT template could contribute to future studies of white matter development in both healthy and clinical populations. The SACT template is publicly available now ( https://figshare.com/articles/dataset/SACT_template/14071283 ).

Development and evaluation of an unbiased, high‐resolution and detail‐preserving structural T1‐weighted template for use in studies on older adults

AbstractBackgroundConstruction of an optimal T1‐weighted template for older adults often requires selection of a target anatomy for nonlinear‐registrations, however a poorly chosen template may introduce bias due to large structural discrepancies between template and subjects used for template construction resulting in misregistration and thereby loss of important anatomical details. Moreover, large structural brain differences across older adults may introduce registration errors. Here, a minimum‐deformation T1‐weighted template specifically for studies on older‐adults was developed using a sparse, patch‐based, detail‐preserving template construction method that minimizes biases.MethodThis work used T1‐weighted brain MRI data obtained from 400 non‐demented older adults (65‐95 years of age, male:female=1:1) participating in longitudinal cohort studies of aging. The template construction scheme involved a reference‐free groupwise normalization utilizing affine transformations followed by ANTs SyGN groupwise normalization utilizing a weighted average scheme in every iteration. Finally a patch‐based tissue‐guided sparse‐representation approach was employed to generate the new template (Fig. 1). The new template was compared to the weighted average template and other 0.5mm templates through visual inspection, in terms of image sharpness by means of the normalized power spectral density and also inter‐subject spatial normalization accuracy achieved when used as references for normalization of T1‐weighted data from 202 ADNI3 participants. Normalization accuracy was assessed for each template by means of the average pairwise normalized cross correlation, average pairwise Jaccard index and average log‐Jacobian determinant of ADNI3 participants.ResultThe new template maintained anatomical consistency and exhibited higher image sharpness as demonstrated by larger high spatial frequency content in the normalized power spectra compared to other templates (Fig. 2 &amp; Fig. 3A,C). Detailed cortical features were better resolved with the new template compared to MCALT_0.5mm (Fig. 3A,B). Visual inspection revealed atypical anatomical structures in the cortex of MCALT_0.5mm (red circles) (Fig. 3B), absent in other templates. Inter‐subject spatial normalization accuracy was higher when using the new template compared to the other templates (Fig. 4A,B,C). The new template required lower deformation for spatially normalizing ADNI3 dataset (Fig. 5).ConclusionThe new template is an unbiased high‐resolution detail‐preserving structural template of the older adult brain that requires lower deformation and provides higher spatial normalization accuracy for older adult data than other templates.

Development and evaluation of MIITRA T1w and DTI brain templates at 0.5 mm resolution

AbstractBackgroundAn MRI multimodal older adult brain atlas with high spatial resolution is in high demand. As part of the Multichannel Illinois Institute of Technology &amp; Rush university Aging (MIITRA) atlas project, this work aimed to develop high quality 0.5mm resolution T1‐weighted (T1w) and diffusion tensor imaging (DTI) multimodal templates using data from a large, diverse, community cohort of non‐demented older adults and quantitatively evaluate the new templates.MethodTemplate construction: T1w and DTI data were collected on 400 non‐demented older adults (50% male;64.9‐98.9 yrs;54% white,43% black) participating in longitudinal cohort studies of aging (Bennett et al.,2018; Barnes et al.,2012) with two 3T MRI scanners.First, a recently introduced approach for the development of high quality multimodal templates (Wu et al., 2019) was conducted in a 0.5mm isotropic resolution space to maximize the quality and anatomical consistency of the templates. Then an approach for the development of high spatial resolution templates based on principles of super‐resolution (Niaz et al., 2019) was used to eliminate interpolations in conventional template building methods. The resulting templates are named MIITRA templates. Evaluation: T1w and DTI data on 202 non‐demented older adults from ADNI3 were used for evaluation. The MIITRA templates were compared to other standardized templates in terms of the accuracy of inter‐subject spatial normalization and inter‐modality spatial matching of ADNI3 data achieved when each template was used as a reference for normalization.ResultVisual inspection shows that the MIITRA T1w and DTI templates with 0.5mm isotropic voxels exhibit high sharpness and are free of artifacts (Fig. 1, 2). Figures 3 and 4 demonstrate that the MIITRA templates provided higher inter‐subject spatial normalization accuracy of older adult data and required less deformation than other templates. Finally, the MIITRA T1w and DTI templates exhibited high spatial matching to each other and allowed higher inter‐modality spatial matching of normalized older adult data (Fig. 5).ConclusionThis work developed high quality 0.5mm resolution T1w and DTI templates for the MIITRA atlas using data from a large, diverse, community cohort of non‐demented older adults. The new templates allowed higher inter‐subject and inter‐modality spatial normalization of older adult data compared to other templates.

Development and evaluation of a high performance T1-weighted brain template for use in studies on older adults.

Τhe accuracy of template‐based neuroimaging investigations depends on the template's image quality and representativeness of the individuals under study. Yet a thorough, quantitative investigation of how available standardized and study‐specific T1‐weighted templates perform in studies on older adults has not been conducted. The purpose of this work was to construct a high‐quality standardized T1‐weighted template specifically designed for the older adult brain, and systematically compare the new template to several other standardized and study‐specific templates in terms of image quality, performance in spatial normalization of older adult data and detection of small inter‐group morphometric differences, and representativeness of the older adult brain. The new template was constructed with state‐of‐the‐art spatial normalization of high‐quality data from 222 older adults. It was shown that the new template (a) exhibited high image sharpness, (b) provided higher inter‐subject spatial normalization accuracy and (c) allowed detection of smaller inter‐group morphometric differences compared to other standardized templates, (d) had similar performance to that of study‐specific templates constructed with the same methodology, and (e) was highly representative of the older adult brain.

Learning 4D Infant Cortical Surface Atlas with Unsupervised Spherical Networks.

Spatiotemporal (4D) cortical surface atlas during infancy plays an important role for surface-based visualization, normalization and analysis of the dynamic early brain development. Conventional atlas construction methods typically rely on classical group-wise registration on sub-populations and ignore longitudinal constraints, thus having three main issues: 1) constructing templates at discrete time points; 2) resulting in longitudinal inconsistency among different age's atlases; and 3) taking extremely long runtime. To address these issues, in this paper, we propose a fast unsupervised learning-based surface atlas construction framework incorporating longitudinal constraints to enforce the within-subject temporal correspondence in the atlas space. To well handle the difficulties of learning large deformations, we propose a multi-level multimodal spherical registration network to perform cortical surface registration in a coarse-to-fine manner. Thus, only small deformations need to be estimated at each resolution level using the registration network, which further improves registration accuracy and atlas quality. Our constructed 4D infant cortical surface atlas based on 625 longitudinal scans from 291 infants is temporally continuous, in contrast to the state-of-the-art UNC 4D Infant Surface Atlas, which only provides the atlases at a few discrete sparse time points. By evaluating the intra- and inter-subject spatial normalization accuracy after alignment onto the atlas, our atlas demonstrates more detailed and fine-grained cortical patterns, thus leading to higher accuracy in surface registration.

Multi‐channel IIT and Rush University Aging (MIITRA) Atlas: Development and evaluation of multimodal templates of the older adult brain

AbstractBackgroundRecent years have seen an increasing demand for multimodal MRI templates of the older adult brain. As part of the Multi‐channel IIT and Rush university Aging (MIITRA) atlas, this work aimed to develop high quality multi‐modal T1‐weighted (T1w) and diffusion tensor imaging (DTI) templates of the older adult brain. This was accomplished through a proposed iterative multimodal template construction strategy.MethodTemplate construction: T1w and DTI data on 202 non‐demented older adults (50% male,65.2‐94.9 yrs) from Rush MAP were used. DTI data were aligned to the raw T1w images. In the proposed approach, each iteration includes two steps(Fig.1). In step1, a temporary T1w template is generated and the resulting transformations are also applied to the DTI data. In step2, a temporary DTI template is generated and the resulting transformations are also applied to the T1w data. The template quality at each iteration was assessed through the spatial matching of deformed data. The T1w and DTI template spatial matching was assessed by the overlap between the white matter tissue probability map and the FA map. Comparison to other templates: For the evaluation, T1w and DTI data on 202 non‐demented older adults from ADNI3 were used. The new MIITRA templates generated through the proposed approach were compared with study‐specific template(constructed using evaluation data) and other standardized templates including older adults in terms of inter‐subject spatial normalization accuracy and inter‐modality spatial matching when used as reference for normalization of evaluation data.ResultsFigure 2 demonstrates that the spatial normalization of the T1w and DTI data used to construct the templates improves(p&lt;10‐10) and the spatial matching between the T1w and DTI templates also improves(p&lt;10‐10), suggesting better quality of the templates with more iterations. Figure 4 to 6 show that the new MIITRA templates allow higher spatial normalization accuracy of data from older adults compared to other available templates(p&lt;10‐10).ConclusionThis work proposed a new multimodal templates construction approach and generated new MIITRA T1w and DTI templates of the older adult brain in the same space, which allow better spatial normalization of T1w and DTI data from older adults compared to other available templates.

Development and evaluation of 0.5 mm isotropic resolution T1‐weighted and DTI templates of the older adult brain

AbstractBackgroundHuman brain MRI templates with low spatial resolution lack important fine details due to partial volume effects. The purpose of this work was to introduce a novel approach for high‐resolution template construction based on principles of super‐resolution, and using this technique, to develop high‐resolution T1‐weighted and DTI templates of the older adult brain.MethodT1‐weighted (222 images,1mm isotropic) and DTI (202 images,2mm isotropic) brain MRI data from non‐demented older adults (65‐95 age‐range, male:female=1:1) participating in the Memory and Aging Project were used in this work (Fig.1). The raw images were spatially normalized in a 0.5mm isotropic resolution space using conventional template building methods. The resulting non‐linear deformations were utilized to map the signals from raw space to exact physical locations in the 0.5mm template space, minimizing interpolations that occur in conventional template‐building method. The final signal in a 0.5mm isotropic voxel in template space was calculated as the weighted average of all signals included in that voxel using a Gaussian kernel. The new T1‐weighted template, MIITRA_0.5mm_T1, was compared to MCALT_0.5mm, ICBM2009b_0.5mm, Colin27_0.5mm, and MIITRA_1mm_T1 (Fig.2A). The new DTI template, MIITRA_0.5mm_DTI, was compared to ICBM81, NTU, IIT_v5.0, EVE, and IXI (Fig.4A). The templates were evaluated in terms of image quality and spatial normalization accuracy achieved when used as references for alignment of MRI data from a large number of older adults.ResultFine structures were resolved in MIITRA_0.5mm_T1 in contrast to MCALT_0.5mm and MIITRA_1mm_T1 (Fig.2B). Visual inspection revealed atypical brain features in the cortex of MCALT_0.5mm (red circles). MIITRA_0.5mm_T1 has higher image sharpness (Fig.3A) and allowed higher inter‐subject spatial normalization accuracy of older adult T1‐weighted data (Fig.3B,C,D) compared to other templates. Fine white matter structures such as the anterior commissure, posterior commissure, inter‐thalamic adhesion and optic chiasm were clearly resolved in MIITRA_0.5mm_DTI (Fig.4B), in contrast to other DTI templates. MIITRA_0.5mm_DTI exhibits higher image sharpness (Fig.5A) and allowed higher inter‐subject spatial normalization accuracy of older adult DTI data (Fig.5B,C,D,E,F) compared to other templates.ConclusionMIITRA_0.5mm_T1 and MIITRA_0.5mm_DTI are high‐quality, high‐resolution templates of the older adult brain and allow higher spatial normalization accuracy of older adult data compared to other available templates.

Spatial normalization of multiple sclerosis brain MRI data depends on analysis method and software package

BackgroundSpatially normalizing brain MRI data to a template is commonly performed to facilitate comparisons between individuals or groups. However, the presence of multiple sclerosis (MS) lesions and other MS-related brain pathologies may compromise the performance of automated spatial normalization procedures. We therefore aimed to systematically compare five commonly used spatial normalization methods for brain MRI – including linear (affine), and nonlinear MRIStudio (LDDMM), FSL (FNIRT), ANTs (SyN), and SPM (CAT12) algorithms – to evaluate their performance in the presence of MS-related pathologies. Methods3 Tesla MRI images (T1-weighted and T2-FLAIR) were obtained for 20 participants with MS from an ongoing cohort study (used to assess a real dataset) and 1 healthy control participant (used to create a simulated lesion dataset). Both raw and lesion-filled versions of each participant's T1-weighted brain images were warped to the Montreal Neurological Institute (MNI) template using all five normalization approaches for the real dataset, and the same procedure was then repeated using the simulated lesion dataset (i.e., total of 400 spatial normalizations). As an additional quality-assurance check, the resulting deformations were also applied to the corresponding lesion masks to evaluate how each processing pipeline handled focal white matter lesions. For each normalization approach, inter-subject variability (across normalized T1-weighted images) was quantified using both mutual information (MI) and coefficient of variation (COV), and the corresponding normalized lesion volumes were evaluated using paired-sample t-tests. ResultsAll four nonlinear warping methods outperformed conventional linear normalization, with SPM (CAT12) yielding the highest MI values, lowest COV values, and proportionately-scaled lesion volumes. Although lesion-filling improved spatial normalization accuracy for each of the methods tested, these effects were small compared to differences between normalization algorithms. ConclusionsSPM (CAT12) warping, ideally combined with lesion-filling, is recommended for use in future MS brain imaging studies requiring spatial normalization.

Evaluation of standardized and study-specific diffusion tensor imaging templates of the adult human brain: Template characteristics, spatial normalization accuracy, and detection of small inter-group FA differences

Digital diffusion tensor imaging (DTI) templates of the adult human brain are commonly used in neuroimaging research, and their characteristics influence the accuracy of the application. However, a systematic evaluation of the characteristics and performance of standardized and study-specific DTI templates has not been conducted. The purpose of this work was to compare eight available standardized DTI templates to each other (ICBM81, ENIGMA, FMRIB58, SRI24, IIT2, NTU-DSI-122-DTI, IIT v.3.0, Eve), as well as to study-specific templates, in terms of template characteristics (image sharpness, ability to identify small brain structures, artifacts, mean values, noise properties) and performance in spatial normalization and detection of small inter-group FA differences. The IIT v.3.0 template was shown to combine a number of desirable characteristics: includes full-tensor information, is population-based, has high image sharpness, shows no visible artifacts, has low noise levels, has diffusion tensor properties and spatial features representative of data from the average individual adult brain. Furthermore, the IIT v.3.0 template was shown to allow higher inter-subject DTI spatial normalization accuracy, and detection of smaller inter-group FA differences, compared to all other templates, including study-specific templates. These findings were consistent when evaluating the templates in younger as well as older adult cohorts.

Cerebellum-specific 18F-FDG PET analysis for the detection of subregional glucose metabolism changes in spinocerebellar ataxia.

The cerebellum (CB) consists of complex anatomical and functional subregions. To better investigate the complicated functional anatomy, a detailed subregional analysis and/or a precise spatial normalization of the fluorine-18 fluorodeoxyglucose (F-FDG) PET imaging data are essential. Here, the 28 MRIcron CB volumes of interests (VOIs) template merged into eight cerebellar subregional VOIs (bilateral anterior, superior, and inferior posterior lobes of the CB cortex, and the superior and inferior vermis) on mean F-FDG PET templates. We also developed a new spatial normalization method using a study-specific and CB-specific template (CBSST) to better localize the VOIs and to minimize interparticipant differences for the locations of whole and subregional CB VOIs, as well as to increase the accuracy of the subregional mean F-FDG uptake. Using VOIs of individual F-FDG PET images normalized to the F-FDG template, we analyzed subregional cerebellar glucose metabolism in patients with spinocerebellar ataxia, a representative disease involving the spinocerebellum, and compared them with age-matched and sex-matched healthy normal controls. We achieved significant improvement over the Montreal Neurological Institute template in spatial normalization accuracy using our CBSST approach for CB VOI location agreement increases (79 vs. 90%) and VOI uptake error decreases in many CB subregions. We also found significant decreases in the anterior/posterior ratio of F-FDG uptake in patients with spinocerebellar ataxia (0.45) compared with those in normal controls (0.73) only using our CBSST approach. Therefore, we established an accurate CB subregional VOI analysis framework, and this may be useful for understanding and differentiating many of the cerebellar ataxia diseases.

A Standardized [18F]-FDG-PET Template for Spatial Normalization in Statistical Parametric Mapping of Dementia

[18F]-fluorodeoxyglucose (FDG) Positron Emission Tomography (PET) is a widely used diagnostic tool that can detect and quantify pathophysiology, as assessed through changes in cerebral glucose metabolism. [18F]-FDG PET scans can be analyzed using voxel-based statistical methods such as Statistical Parametric Mapping (SPM) that provide statistical maps of brain abnormalities in single patients. In order to perform SPM, a "spatial normalization" of an individual's PET scan is required to match a reference PET template. The PET template currently used for SPM normalization is based on [15O]-H2O images and does not resemble either the specific metabolic features of [18F]-FDG brain scans or the specific morphological characteristics of individual brains affected by neurodegeneration. Thus, our aim was to create a new [18F]-FDG PET aging and dementia-specific template for spatial normalization, based on images derived from both age-matched controls and patients. We hypothesized that this template would increase spatial normalization accuracy and thereby preserve crucial information for research and diagnostic purposes. We investigated the statistical sensitivity and registration accuracy of normalization procedures based on the standard and new template-at the single-subject and group level-independently for subjects with Mild Cognitive Impairment (MCI), probable Alzheimer's Disease (AD), Frontotemporal lobar degeneration (FTLD) and dementia with Lewy bodies (DLB). We found a significant statistical effect of the population-specific FDG template-based normalisation in key anatomical regions for each dementia subtype, suggesting that spatial normalization with the new template provides more accurate estimates of metabolic abnormalities for single-subject and group analysis, and therefore, a more effective diagnostic measure.

Role of standardized and study‐specific human brain diffusion tensor templates in inter‐subject spatial normalization

To investigate the effect of standardized and study-specific human brain diffusion tensor templates on the accuracy of spatial normalization, without ignoring the important roles of data quality and registration algorithm effectiveness. Two groups of diffusion tensor imaging (DTI) datasets, with and without visible artifacts, were normalized to two standardized diffusion tensor templates (IIT2, ICBM81) as well as study-specific templates, using three registration approaches. The accuracy of inter-subject spatial normalization was compared across templates, using the most effective registration technique for each template and group of data. It was demonstrated that, for DTI data with visible artifacts, the study-specific template resulted in significantly higher spatial normalization accuracy than standardized templates. However, for data without visible artifacts, the study-specific template and the standardized template of higher quality (IIT2) resulted in similar normalization accuracy. For DTI data with visible artifacts, a carefully constructed study-specific template may achieve higher normalization accuracy than that of standardized templates. However, as DTI data quality improves, a high-quality standardized template may be more advantageous than a study-specific template, because in addition to high normalization accuracy, it provides a standard reference across studies, as well as automated localization/segmentation when accompanied by anatomical labels.

Subject-matched templates for spatial normalization.

Spatial normalization of images from multiple subjects is a common problem in group comparison studies, such as voxel-based and deformation-based morphometric analyses. Use of a study-specific template for normalization may improve normalization accuracy over a study-independent standard template (Good et al., NeuroImage, 14(1):21-36, 2001). Here, we develop this approach further by introducing the concept of subject-matched templates. Rather than using a single template for the entire population, a different template is used for every subject, with the template matched to the subject in terms of age, sex, and potentially other parameters (e.g., disease). All subject-matched templates are created from a single generative regression model of atlas appearance, thus providing a priori template-to-template correspondence without registration. We demonstrate that such an approach is technically feasible and significantly improves spatial normalization accuracy over using a single template.

Construction of periventricular white matter hyperintensity maps by spatial normalization of the lateral ventricles

Subcortical and periventricular white matter hyperintensities (WMHs) may have different associations with cognition and pathophysiology. The aim of the present study is to develop an automated method for construction of periventricular WMH maps that enables the analysis of between-group differences in WMH location and characteristics in the periventricular region without the requirement of prior boundary definition. To avoid influence of WMHs on spatial normalization, a reference image of the lateral ventricles was constructed based on images of 24 subjects. Construction was not biased to a single subject. WMHs were segmented by k-nearest neighbor-based classification of magnetic resonance inversion recovery and fluid attenuated inversion recovery images. Cerebrospinal fluid segmentations of individual subjects were nonrigidly mapped to the reference image of the lateral ventricles. The subject's WMHs were transformed to the reference space accordingly. Spatial normalization accuracy was validated using measures of overlap and of displacement relative to the boundary of the lateral ventricles. After spatial normalization, the boundaries of the lateral ventricles closely matched the reference image and in an area of approximately 1 cm around the lateral ventricles the relative displacement was less than 1 mm. To illustrate the method, it was applied to 61 patients with Type 2 diabetes and 26 control subjects, whereupon periventricular WMH maps were constructed and compared. The proposed method is particularly suited to analyze WMH distribution differences at the level of the lateral ventricles between large groups of patients.