Spatial Normalization Research Articles

Internally-consistent and fully-unbiased multimodal MRI brain template construction from UK Biobank: Oxford-MM

Abstract Anatomical MRI templates of the brain are essential to group-level analyses and image processing pipelines, as they provide a reference space for spatial normalisation. While it has become common for studies to acquire multimodal MRI data, many templates are still limited to one type of modality, usually either scalar or tensor-based. Aligning each modality in isolation does not take full advantage of the available complementary information, such as strong contrast between tissue types in structural images, or axonal organisation in the white matter in diffusion tensor images. Most existing strategies for multimodal template construction either do not use all modalities of interest to inform the template construction process, or do not use them in a unified framework. Here, we present multimodal, cross-sectional templates constructed from UK Biobank data: the OMM-1 template, and age-dependent templates for each year of life between 45 to 81. All templates are fully unbiased to represent the average shape of the populations they were constructed from, and internally consistent through jointly informing the template construction process with T1, T2-FLAIR and DTI data. The OMM-1 template was constructed with a multi-resolution, iterative approach using 240 individuals in the 50-55 year age range. The age-dependent templates were estimated using a Gaussian Process, which describes the change in average brain shape with age in 37,330 individuals. All templates show excellent contrast and alignment within and between modalities. The global brain shape and size is not preconditioned on existing templates, although maximal possible compatibility with MNI-152 space was maintained through rigid alignment. We showed benefits in registration accuracy across two datasets (UK Biobank and HCP), when using the OMM-1 as the template compared with FSL’s MNI-152 template, and found that the use of age-dependent templates further improved accuracy to a small but detectable extent. All templates are publicly available and can be used as a new reference space for uni- or multimodal spatial alignment.

Effects of temperature on Two-point statistical characteristics in turbulent jets

Abstract large eddy simulations are carried out for jets at Mach number 0.9, temperature ratio 2.0 and 3.4. Simulations are validated by comparing with experiments from literature. Two-point correlation- and coherence- functions are analyzed. Results show that the increment of jet temperature affects the decay rate of correlation functions. The correlation functions are analyzed both in space and time by employed a normalization method. The normalization coefficient is found changing with the temperature. Compared to spatial normalization coefficients, the time normalization coefficient is found more sensitive to the temperature. The coherence function is found asymmetric in the upstream and downstream of the reference point. The present attempt to construct an empirical model to normalize the coherence function, so as to provide reference for the study of acoustic field.

OSBA: An Open Neonatal Neuroimaging Atlas and Template for Spina Bifida Aperta

We present the Open Spina Bifida Aperta (OSBA) atlas, an open atlas and set of neuroimaging templates for spina bifida aperta (SBA). Traditional brain atlases may not adequately capture anatomical variations present in pediatric or disease-specific cohorts. The OSBA atlas fills this gap by representing the computationally averaged anatomy of the neonatal brain with SBA after fetal surgical repair. The OSBA atlas was constructed using structural T2-weighted and diffusion tensor MRIs of 28 newborns with SBA who underwent prenatal surgical correction. The corrected gestational age at MRI was 38.1 ± 1.1 weeks (mean ± SD). The OSBA atlas consists of T2-weighted and fractional anisotropy templates, along with nine tissue prior maps and region of interest (ROI) delineations. The OSBA atlas offers a standardized reference space for spatial normalization and anatomical ROI definition. Our image segmentation and cortical ribbon definition are based on a human-in-the-loop approach, which includes manual segmentation. The precise alignment of the ROIs was achieved by a combination of manual image alignment and automated, non-linear image registration. From the clinical and neuroimaging perspective, the OSBA atlas enables more accurate spatial standardization and ROI-based analyses and supports advanced analyses such as diffusion tractography and connectomic studies in newborns affected by this condition.

A dataset of soil water storage of typical ecosystems in arid and semi-arid areas in China, 2005-2017

Soil water is one of the important components of the earth's water resources, and also the main source of water directly consumed by vegetation and crops. Therefore, the storage, recharge, consumption, renewal and balance of soil water are of great significance to agriculture, animal husbandry, forestry, ecological environment and water resources balance, especially for the arid and semi-arid region of China. We collect and sort out the long-term observation data of soil moisture from eight ecosystem observation and research stations of the Chinese Ecosystem Research Network (CERN) located in the arid and semi-arid region of China during 2005 - 2017, and generates the corresponding soil water storage dataset through quality control, spatial and temporal normalization, statistical calculation, in order to provide long-term data support for understanding distribution and change of soil water storage in typical ecosystems in arid and semi-arid areas of China.

Improving 18F-FDG PET Quantification Through a Spatial Normalization Method.

Quantification of 18F-FDG PET images is useful for accurate diagnosis and evaluation of various brain diseases, including brain tumors, epilepsy, dementia, and Parkinson disease. However, accurate quantification of 18F-FDG PET images requires matched 3-dimensional T1 MRI scans of the same individuals to provide detailed information on brain anatomy. In this paper, we propose a transfer learning approach to adapt a pretrained deep neural network model from amyloid PET to spatially normalize 18F-FDG PET images without the need for 3-dimensional MRI. Methods: The proposed method is based on a deep learning model for automatic spatial normalization of 18F-FDG brain PET images, which was developed by fine-tuning a pretrained model for amyloid PET using only 103 18F-FDG PET and MR images. After training, the algorithm was tested on 65 internal and 78 external test sets. All T1 MR images with a 1-mm isotropic voxel size were processed with FreeSurfer software to provide cortical segmentation maps used to extract a ground-truth regional SUV ratio using cerebellar gray matter as a reference region. These values were compared with those from spatial normalization-based quantification methods using the proposed method and statistical parametric mapping software. Results: The proposed method showed superior spatial normalization compared with statistical parametric mapping, as evidenced by increased normalized mutual information and better size and shape matching in PET images. Quantitative evaluation revealed a consistently higher SUV ratio correlation and intraclass correlation coefficients for the proposed method across various brain regions in both internal and external datasets. The remarkably good correlation and intraclass correlation coefficient values of the proposed method for the external dataset are noteworthy, considering the dataset's different ethnic distribution and the use of different PET scanners and image reconstruction algorithms. Conclusion: This study successfully applied transfer learning to a deep neural network for 18F-FDG PET spatial normalization, demonstrating its resource efficiency and improved performance. This highlights the efficacy of transfer learning, which requires a smaller number of datasets than does the original network training, thus increasing the potential for broader use of deep learning-based brain PET spatial normalization techniques for various clinical and research radiotracers.

Effects of connectivity hyperalignment (CHA) on estimated brain network properties: from coarse-scale to fine-scale.

Recent gains in functional magnetic resonance imaging (fMRI) studies have been driven by increasingly sophisticated statistical and computational techniques and the ability to capture brain data at finer spatial and temporal resolution. These advances allow researchers to develop population-level models of the functional brain representations underlying behavior, performance, clinical status, and prognosis. However, even following conventional preprocessing pipelines, considerable inter-individual disparities in functional localization persist, posing a hurdle to performing compelling population-level inference. Persistent misalignment in functional topography after registration and spatial normalization will reduce power in developing predictive models and biomarkers, reduce the specificity of estimated brain responses and patterns, and provide misleading results on local neural representations and individual differences. This study aims to determine how connectivity hyperalignment (CHA)-an analytic approach for handling functional misalignment-can change estimated functional brain network topologies at various spatial scales from the coarsest set of parcels down to the vertex-level scale. The findings highlight the role of CHA in improving inter-subject similarities, while retaining individual-specific information and idiosyncrasies at finer spatial granularities. This highlights the potential for fine-grained connectivity analysis using this approach to reveal previously unexplored facets of brain structure and function.

CT- and MR-based image-based data mining are consistent in the brain

PurposeImage-based data mining (IBDM) is a voxel-based analysis technique to investigate dose–response. Most often, IBDM uses radiotherapy planning CTs because of their broad accessibility, however, it was unknown whether CT provided sufficient soft tissue contrast for brain IBDM. This study evaluates whether MR-based IBDM improves upon CT-based IBDM for studies of children with brain tumours. MethodsWe compared IBDM pipelines using either CT- or MRI-based spatial normalisation in 128 children (ages 3.3–19.7 years) who received photon radiotherapy for primary brain tumours at a single institution. We quantified spatial-normalisation accuracy using contour comparison measures (centre-of-mass separation, average contour distance-to-agreement (DTavg), and Hausdorff distance) at multiple anatomic loci. We performed an end-to-end test of CT- and MRI-IBDM using modified clinical dose distributions and simulated effect labels to detect associations in pre-defined anatomic loci. Accuracy was assessed via sensitivity and specificity. ResultsSpatial normalisation accuracy was comparable for both modalities, with a significant but small improvement for MRI compared to CT in all structures except the brainstem. The median (range) difference between the DTavg for the two pipelines was 0.37 (0.00–2.91) mm. The end-to-end test revealed no significant difference in sensitivity of the IBDM-identified regions for the two pipelines. Specificity slightly improved for MR-IBDM at the 99% significance level. ConclusionCT-based IBDM was comparable to MR-based IBDM, despite a small advantage in spatial normalisation accuracy with MRI. The use of CT-IBDM over MR-IBDM is useful for multi-institutional retrospective IBDM studies, where the availability of standardised MRI data can be limited.

Spatial coefficient of variation of arterial spin labeling magnetic resonance imaging can predict decreased cerebrovascular reactivity measured by acetazolamide challenge single-photon emission tomography.

The aim of this study was to investigate whether the spatial coefficient of variation of arterial spin labeling (ASL-CoV) acquired in clinical settings can be used to estimate decreased cerebrovascular reactivity (CVR) measured with single-photon emission computed tomography (SPECT) and acetazolamide challenge in patients with atherosclerotic stenosis of intra- or extracranial arteries. We evaluated the data of 27 atherosclerotic stenosis patients who underwent pseudocontinuous ASL and SPECT. After spatial normalization, regional values were measured using the distributed middle cerebral artery territorial atlas of each patient. We performed comparisons, correlations, and receiver operating characteristic (ROC) curve analyses between ASL-cerebral blood blow (CBF), ASL-CoV, SPECT-CBF and SPECT-CVR. Although the ASL-CBF values were positively correlated with SPECT-CBF values (r = 0.48, 95% confidence interval (CI) = 0.28-0.64), no significant difference in ASL-CBF values was detected between regions with and without decreased CVR. However, regions with decreased CVR had significantly greater ASL-CoV values than regions without decreased CVR. SPECT-CVR was negatively correlated with ASL-CoV (ρ = -0.29, 95% CI = -0.49 - -0.06). The area under the ROC curve of ASL-CoV in predicting decreased CVR (0.66, 95% CI = 0.51-0.81) was greater than that of ASL-CBF (0.51, 95% CI = 0.34-0.68). An ASL-CoV threshold value of 42% achieved a high specificity of 0.93 (sensitivity = 0.42, positive predictive value = 0.77, and negative predictive value = 0.75). ASL-CoV acquired by single postlabeling delay without an acetazolamide challenge may aid in the identification of patients with decreased CVR on SPECT.

Accurate Automated Quantification of Dopamine Transporter PET Without MRI Using Deep Learning-based Spatial Normalization.

Dopamine transporter imaging is crucial for assessing presynaptic dopaminergic neurons in Parkinson's disease (PD) and related parkinsonian disorders. While 18F-FP-CIT PET offers advantages in spatial resolution and sensitivity over 123I-β-CIT or 123I-FP-CIT SPECT imaging, accurate quantification remains essential. This study presents a novel automatic quantification method for 18F-FP-CIT PET images, utilizing an artificial intelligence (AI)-based robust PET spatial normalization (SN) technology that eliminates the need for anatomical images. The proposed SN engine consists of convolutional neural networks, trained using 213 paired datasets of 18F-FP-CIT PET and 3D structural MRI. Remarkably, only PET images are required as input during inference. A cyclic training strategy enables backward deformation from template to individual space. An additional 89 paired 18F-FP-CIT PET and 3D MRI datasets were used to evaluate the accuracy of striatal activity quantification. MRI-based PET quantification using FIRST software was also conducted for comparison. The proposed method was also validated using 135 external datasets. The proposed AI-based method successfully generated spatially normalized 18F-FP-CIT PET images, obviating the need for CT or MRI. The striatal PET activity determined by proposed PET-only method and MRI-based PET quantification using FIRST algorithm were highly correlated, with R 2 and slope ranging 0.96-0.99 and 0.98-1.02 in both internal and external datasets. Our AI-based SN method enables accurate automatic quantification of striatal activity in 18F-FP-CIT brain PET images without MRI support. This approach holds promise for evaluating presynaptic dopaminergic function in PD and related parkinsonian disorders.

A three-step, "brute-force" approach toward optimized affine spatial normalization.

The first step in spatial normalization of magnetic resonance (MR) images commonly is an affine transformation, which may be vulnerable to image imperfections (such as inhomogeneities or "unusual" heads). Additionally, common software solutions use internal starting estimates to allow for a more efficient computation, which may pose a problem in datasets not conforming to these assumptions (such as those from children). In this technical note, three main questions were addressed: one, does the affine spatial normalization step implemented in SPM12 benefit from an initial inhomogeneity correction. Two, does using a complexity-reduced image version improve robustness when matching "unusual" images. And three, can a blind "brute-force" application of a wide range of parameter combinations improve the affine fit for unusual datasets in particular. A large database of 2081 image datasets was used, covering the full age range from birth to old age. All analyses were performed in Matlab. Results demonstrate that an initial removal of image inhomogeneities improved the affine fit particularly when more inhomogeneity was present. Further, using a complexity-reduced input image also improved the affine fit and was beneficial in younger children in particular. Finally, blindly exploring a very wide parameter space resulted in a better fit for the vast majority of subjects, but again particularly so in infants and young children. In summary, the suggested modifications were shown to improve the affine transformation in the large majority of datasets in general, and in children in particular. The changes can easily be implemented into SPM12.

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.

Constructing the KOR152 Korean Young Adult Brain Atlas Utilizing the State-of-the-Art Method for the Age-Specific Population.

Spatial normalization is an essential process for comparative analyses that heavily depends on the standard brain template used. Brain morphological differences are observed in different populations due to genetic and environmental factors, causing mismatches in regions when the data are normalized to different population templates. Recent studies have indicated differences between Caucasian and East Asian populations as well as within East Asian populations, suggesting the necessity of population-specific brain templates. Thus, this study aimed to construct a Korean young adult age-specific brain template utilizing an advanced method of template construction to update the currently available Korean template. The KOR152 template was constructed via affine and nonlinear iterative procedures based on prior studies. We compared the morphological features of different population templates (MNI152, Indian_157, and CN200). The distance and volumetric changes before and after registering the data to these templates were calculated for registration accuracy. The KOR152 global brain features revealed a shorter overall length than the other population templates. The registration accuracy by distance and volumetric change was significantly lower than that of the other population templates, implying that the KOR152 was more accurate than other templates for the young adult Korean population. This study provided evidence for the need for a population-specific template that may be more appropriate for structural and functional studies in Korean populations.

Automated segmentation of ventricular volumes and subarachnoid hemorrhage from computed tomography images: Evaluation of a rule-based pipeline approach.

Changes in ventricular size, related to brain edema and hydrocephalus, as well as the extent of hemorrhage are associated with adverse outcomes in patients with subarachnoid hemorrhage (SAH). Frequently, these are measured manually using consecutive non-contrast computed tomography scans. Here, we developed a rule-based approach which incorporates both intensity and spatial normalization and utilizes user-defined thresholds and anatomical templates to segment both lateral ventricle (LV) and SAH blood volumes automatically from CT images. The algorithmic segmentations were evaluated against two expert neuroradiologists on representative slices from 20 admission scans from aneurysmal SAH patients. Previous methods have been developed to automate this time-consuming task, but they lack user feedback and are hard to implement due to large-scale data and complex design processes. Our results using automatic ventricular segmentation aligned well with expert reviewers with a median Dice coefficient of 0.81, AUC of 0.91, sensitivity of 81%, and precision of 84%. Automatic segmentation of SAH blood was most reliable near the base of the brain with a median Dice coefficient of 0.51, an AUC of 0.75, precision of 68%, and sensitivity of 50%. Ultimately, we developed a rule-based method that is easily adaptable through user feedback, generates spatially normalized segmentations that are comparable regardless of brain morphology or acquisition conditions, and automatically segments LV with good overall reliability and basal SAH blood with good precision. Our approach could benefit longitudinal studies in patients with SAH by streamlining assessment of edema and hydrocephalus progression, as well as blood resorption.

Clinical Performance Evaluation of an Artificial Intelligence-Powered Amyloid Brain PET Quantification Method

PurposeThis study assesses the clinical performance of BTXBrain-Amyloid, an artificial intelligence-powered software for quantifying amyloid uptake in brain PET images.Methods150 amyloid brain PET images were visually assessed by experts and categorized as negative and positive. Standardized uptake value ratio (SUVR) was calculated with cerebellum grey matter as the reference region, and receiver operating characteristic (ROC) and precision-recall (PR) analysis for BTXBrain-Amyloid were conducted. For comparison, same image processing and analysis was performed using Statistical Parametric Mapping (SPM) program. In addition, to evaluate the spatial normalization (SN) performance, mutual information (MI) between MRI template and spatially normalized PET images was calculated and SPM group analysis was conducted.ResultsBoth BTXBrain and SPM methods discriminated between negative and positive groups. However, BTXBrain exhibited lower SUVR standard deviation (0.06 and 0.21 for negative and positive, respectively) than SPM method (0.11 and 0.25). In ROC analysis, BTXBrain had an AUC of 0.979, compared to 0.959 for SPM, while PR curves showed an AUC of 0.983 for BTXBrain and 0.949 for SPM. At the optimal cut-off, the sensitivity and specificity were 0.983 and 0.921 for BTXBrain and 0.917 and 0.921 for SPM12, respectively. MI evaluation also favored BTXBrain (0.848 vs. 0.823), indicating improved SN. In SPM group analysis, BTXBrain exhibited higher sensitivity in detecting basal ganglia differences between negative and positive groups.ConclusionBTXBrain-Amyloid outperformed SPM in clinical performance evaluation, also demonstrating superior SN and improved detection of deep brain differences. These results suggest the potential of BTXBrain-Amyloid as a valuable tool for clinical amyloid PET image evaluation.

CT-guided spatial normalization of nuclear hybrid imaging adapted to enlarged ventricles: Impact on striatal uptake quantification

IntroductionSpatial normalization is a prerequisite step for the quantitative analysis of SPECT or PET brain images using volume-of-interest (VOI) template or voxel-based analysis. MRI-guided spatial normalization is the gold standard, but the wide use of PET/CT or SPECT/CT in routine clinical practice makes CT-guided spatial normalization a necessary alternative. Ventricular enlargement is observed with aging, and it hampers the spatial normalization of the lateral ventricles and striatal regions, limiting their analysis. The aim of the present study was to propose a robust spatial normalization method based on CT scans that takes into account features of the aging brain to reduce bias in the CT-guided striatal analysis of SPECT images. MethodsWe propose an enhanced CT-guided spatial normalization pipeline based on SPM12. Performance of the proposed pipeline was assessed on visually normal [123I]-FP-CIT SPECT/CT images. SPM12 default CT-guided spatial normalization was used as reference method. The metrics assessed were the overlap between the spatially normalized lateral ventricles and caudate/putamen VOIs, and the computation of caudate and putamen specific binding ratios (SBR). ResultsIn total 231 subjects (mean age ± SD = 61.9 ± 15.5 years) were included in the statistical analysis. The mean overlap between the spatially normalized lateral ventricles of subjects and the caudate VOI and the mean SBR of caudate were respectively 38.40 % (± SD = 19.48 %) of the VOI and 1.77 (± 0.79) when performing SPM12 default spatial normalization. The mean overlap decreased to 9.13 % (± SD = 1.41 %, P < 0.001) of the VOI and the SBR of caudate increased to 2.38 (± 0.51, P < 0.0001) when performing the proposed pipeline. Spatially normalized lateral ventricles did not overlap with putamen VOI using either method. The mean putamen SBR value derived from the proposed spatial normalization (2.75 ± 0.54) was not significantly different from that derived from the default SPM12 spatial normalization (2.83 ± 0.52, P > 0.05). ConclusionThe automatic CT-guided spatial normalization used herein led to a less biased spatial normalization of SPECT images, hence an improved semi-quantitative analysis. The proposed pipeline could be implemented in clinical routine to perform a more robust SBR computation using hybrid imaging.

SAN: Mitigating spatial covariance heterogeneity in cortical thickness data collected from multiple scanners or sites.

In neuroimaging studies, combining data collected from multiple study sites or scanners is becoming common to increase the reproducibility of scientific discoveries. At the same time, unwanted variations arise by using different scanners (inter-scanner biases), which need to be corrected before downstream analyses to facilitate replicable research and prevent spurious findings. While statistical harmonization methods such as ComBat have become popular in mitigating inter-scanner biases in neuroimaging, recent methodological advances have shown that harmonizing heterogeneous covariances results in higher data quality. In vertex-level cortical thickness data, heterogeneity in spatial autocorrelation is a critical factor that affects covariance heterogeneity. Our work proposes a new statistical harmonization method called spatial autocorrelation normalization (SAN) that preserves homogeneous covariance vertex-level cortical thickness data across different scanners. We use an explicit Gaussian process to characterize scanner-invariant and scanner-specific variations to reconstruct spatially homogeneous data across scanners. SAN is computationally feasible, and it easily allows the integration of existing harmonization methods. We demonstrate the utility of the proposed method using cortical thickness data from the Social Processes Initiative in the Neurobiology of the Schizophrenia(s) (SPINS) study. SAN is publicly available as an R package.

MRI atlas of the pituitary gland in young female adults.

The probabilistic topography and inter-individual variability of the pituitary gland (PG) remain undetermined. The absence of a standardized reference atlas hinders research on PG volumetrics. In this study, we aimed at creating maximum probability maps for the anterior and posterior PG in young female adults. We manually delineated the anterior and posterior parts of the pituitary glands in 26 healthy subjects using high-resolution MRI T1 images. A three-step procedure and a cost function-masking approach were employed to optimize spatial normalization for the PG. We generated probabilistic atlases and maximum probability maps, which were subsequently coregistered back to the subjects' space and compared to manual delineations. Manual measurements led to a total pituitary volume of 705 ± 88mm³, with the anterior and posterior volumes measuring 614 ± 82mm³ and 91 ± 20mm³, respectively. The mean relative volume difference between manual and atlas-based estimations was 1.3%. The global pituitary atlas exhibited an 80% (± 9%) overlap for the DICE index and 67% (± 11%) for the Jaccard index. Similarly, these values were 77% (± 13%) and 64% (± 14%) for the anterior pituitary atlas and 62% (± 21%) and 47% (± 17%) for the posterior PG atlas, respectively. We observed a substantial concordance and a significant correlation between the volume estimations of the manual and atlas-based methods for the global pituitary and anterior volumes. The maximum probability maps of the anterior and posterior PG lay the groundwork for automatic atlas-based segmentation methods and the standardized analysis of large PG datasets.

Qualitative and Visual Along-Tract Analysis of Diffusion-Based Parameters in Patients with Diffuse Gliomas.

Grade 2-3 diffuse gliomas (DGs) show extensive infiltration through white matter (WM) tracts. Along-tract analysis of WM tracts based on diffusion tensor tractography (DTI) can been performed to assess the microstructural integrity of WM tracts. The clinical implication of these DTI-related findings is still under debate, especially in tumor patients. The aim of this study was to analyze and compare diffusion-based parameters along WM tracts and variables specific to WM -tumor interactions in DGs and correlate them with preoperative neuropsychological assessment. Fourteen patients with IDH-mutated grade 2-3 DGs were included. Tumor volumes were manually segmented on 3D-FLAIR images after spatial normalisation to MNI space. DTI was acquired using a single-shot echo-planar sequence on a 3T with 48 sampling directions. DTI data were reconstructed within the MNI space using q-space diffeomorphic reconstruction (QSDR) in DSI studio. Five bilateral sets of WM tracts were reconstructed based on the HCP-1065 template. All WM tracts were stretched to the same length of 100 indices, and for each index diffusion-based parameters fractional anisotropy (FA), radial diffusivity (RD), axial diffusivity (AD), mean diffusivity (MD) and quantitative anisotropy (QA) were sampled. Tumor-related parameters (TRP); tumor volume (Tv), maximum tumor presence (MTP) and the number of sequential indices in which a tumor is present (Te) were derived based on the along-tract analysis. Normal data were constructed by calculating the average and standard deviations of contralateral and not-affected WM tracts for each diffusion-based parameter, respectively. Affected WM tracts were individually compared to normal data using a z-test. Preoperative neuropsychological assessment was performed in all subjects and correlated to results from the along-tract analysis using correlation and logistic regression models. Abnormalities in diffusion-based parameters were detected in WM tracts. Topographical and quantitative information were presented within the same graph. AD and MD displayed the highest linear correlation with the TRPs. Abnormal QA showed a linear correlation with Tv per WM tract. Neuropsychological impairment was correlated with all the TRPs and with abnormal FA (p < 0.05) and abnormal QA (p < 0.01). Abnormal QA was the only independent variable able to predict the presence of neuropsychological impairment in the patients based on the linear regression analysis. Graphical presentation of the along-tract analysis presented in this study shows that it may be a sensitive and robust method to acquire and display topographical and qualitative information regarding WM tracts in close proximity to DGs. Further studies and refinements to the methods presented herein may advance current clinical methods for evaluating displacement and infiltrations and further aid the efforts of pre-planning surgical interventions with the goal to maximise EOR and tailor oncological treatment.

Sketch-guided spatial adaptive normalization and high-level feature constraints based GAN image synthesis for steel strip defect detection data augmentation

Deep learning methods have made remarkable strides in surface defect detection. But, they heavily rely on large amount of training data, which can be a costly endeavor, especially for specific applications like steel strip surface defect detection, where acquiring and labeling large-scale data is impractical due to the rarity of certain defective categories in production environment. Hence, realistic defect image synthesis can greatly alleviate this issue. However, training image generation networks also demand substantial data, making image data augmentation merely an auxiliary effort. In this work, we propose a Generative Adversarial Network (GAN)-based image synthesis framework. We selectively extract the defect edges of the original image as well as the background texture information, and use them as network input through the spatially-adaptive (de)normalization (SPADE) module. This enriches the input information, thus significantly reducing the amount of training data for GAN network in image generation, and enhancing the background details as well as the defect boundaries in the generated images. Additionally, we introduce a novel generator loss term that balances the similarity and perceptual fidelity between synthetic and real images by constraining high-level features at different feature levels. This provides more valuable information for data augmentation in training object detection models using synthetic images. Our experimental results demonstrate the sophistication of the proposed image synthesis method and its effectiveness in data augmentation for steel strip surface defect detection tasks.

Spatial normalization for voxel-based lesion symptom mapping: impact of registration approaches.

Voxel-based lesion symptom mapping (VLSM) assesses the relation of lesion location at a voxel level with a specific clinical or functional outcome measure at a population level. Spatial normalization, that is, mapping the patient images into an atlas coordinate system, is an essential pre-processing step of VLSM. However, no consensus exists on the optimal registration approach to compute the transformation nor are downstream effects on VLSM statistics explored. In this work, we evaluate four registration approaches commonly used in VLSM pipelines: affine (AR), nonlinear (NLR), nonlinear with cost function masking (CFM), and enantiomorphic registration (ENR). The evaluation is based on a standard VLSM scenario: the analysis of statistical relations of brain voxels and regions in imaging data acquired early after stroke onset with follow-up modified Rankin Scale (mRS) values. Fluid-attenuated inversion recovery (FLAIR) MRI data from 122 acute ischemic stroke patients acquired between 2 and 3 days after stroke onset and corresponding lesion segmentations, and 30 days mRS values from a European multicenter stroke imaging study (I-KNOW) were available and used in this study. The relation of the voxel location with follow-up mRS was assessed by uni- as well as multi-variate statistical testing based on the lesion segmentations registered using the four different methods (AR, NLR, CFM, ENR; implementation based on the ANTs toolkit). The brain areas evaluated as important for follow-up mRS were largely consistent across the registration approaches. However, NLR, CFM, and ENR led to distortions in the patient images after the corresponding nonlinear transformations were applied. In addition, local structures (for instance the lateral ventricles) and adjacent brain areas remained insufficiently aligned with corresponding atlas structures even after nonlinear registration. For VLSM study designs and imaging data similar to the present work, an additional benefit of nonlinear registration variants for spatial normalization seems questionable. Related distortions in the normalized images lead to uncertainties in the VLSM analyses and may offset the theoretical benefits of nonlinear registration.