Voxel-based Approach Research Articles

The mediation role of gray matter volume in the relationship between childhood maltreatment and psychological resilience in adolescents with first-episode major depressive disorder

Previous studies have revealed morphologic alterations in patients with major depressive disorder (MDD) with experiences of childhood trauma. However, the underlying neural mechanisms remain largely unknown. This study aims to explore the brain structural changes and their possible mediation role in the relationship between childhood maltreatment and psychological resilience in drug-naïve adolescents with first-episode MDD. A total of 57 adolescents with first-episode MDD and 36 healthy controls (HCs) completed the T1-weighted magnetic resonance imaging scan. The adverse childhood experiences and current psychological resilience were assessed using the Childhood Trauma Questionnaire-Short Form and the Connor Davidson Resilience Scale, respectively. The voxel-based morphometry approach was applied to examine changes in the gray matter volume (GMV). Compared with the HCs, adolescents with MDD had significantly reduced GMV volumes in the left fusiform gyrus, right orbitofrontal gyrus, right superior temporal gyrus, right calcarine cortex, right middle frontal gyrus, left angular gyrus, right precuneus, right posterior cingulate gyrus, and right posterior central gyrus, as well as significantly increased GMV volumes in the left lenticular putamen and right lenticular pallidum. The GMV of the right calcarine cortex was found to be negatively correlated with the severity of emotional abuse and positively correlated with the level of psychological resilience. Moreover, the GMV of the right calcarine cortex might partially mediate the relationship between childhood maltreatment and psychological resilience. The present study provided further evidence for structural impairments in adolescents with MDD. Our findings also confirmed the important role of depression-related GMV changes in childhood growth experiences and psychological resilience characteristics during adolescent brain maturation.

3D Data Integration in the Voxel Domain

Abstract. The interest in 3D data integration is growing as the concept of Spatial Digital Twins gains traction among local governments and stakeholders. Spatial data is typically organized into standardised themes such as buildings, transport, and vegetation, or as ad hoc data products in either 2D or 3D dimensions. However, integrating these data sets into a cohesive 3D model presents several challenges. The foundational data sets may vary in terms of accuracy, resolution, representation, and actuality due to differences in creation standards and procedures. Consequently, inconsistencies often arise within the 3D models, making them difficult to identify and rectify. Many of these data integration issues can be traced back to the 3D vector representation. Vector 3D models exhibit significant geometric diversity, which complicates the computational operations required for examining and validating the models. In this paper, we propose a novel voxel-based approach for integrating 3D data. Voxels, analogous to pixels in 2D raster images, inherit several key properties. We employ a dedicated voxel data structure and a set of operations tailored for this purpose. First, we voxelise vector datasets, converting continuous geometric information into discrete voxel representations. Next, we merge voxel layers using a specialized voxel overlay operation. The resulting 3D model adheres to most validity and integrity requirements. To highlight the flexibility of our voxel data structure, we transformed it into point clouds for visualisation in ArcGIS Pro, enabling the addition of 3D base maps and GIS layers. We then published these datasets to Scene Services, granting public access. This allows users to easily explore the 3D scenes through a web browser, making complex geographic data more accessible. By leveraging voxels, our approach facilitates efficient and accurate 3D data integration, making it a valuable contribution to the field.

Perceptual and semantic deficits in face recognition in semantic dementia

State of the artSemantic dementia (SD) patients including semantic variant primary progressive aphasia (svPPA) and semantic behavioral variant frontotemporal dementia (sbvFTD) patients show semantic difficulties identifying faces and known people related to right anterior temporal lobe (ATL) atrophy. However, it remains unclear whether they also have perceptual deficits in face recognition. MethodologyWe selected 74 SD patients (54 with svPPA and predominant left ATL atrophy and 20 with sbvFTD and predominant right ATL atrophy) and 36 cognitively healthy controls (HC) from UCSF Memory and Aging Center. They underwent a perceptual face processing test (Benton facial recognition test-short version; BFRT-S), and semantic face processing tests (UCSF Famous people battery – Recognition, Naming, Semantic associations – pictures and words subtests), as well as structural magnetic resonance imaging (MRI). Neural correlates with the task’s performance were conducted with a Voxel-based morphometry approach using CAT12. ResultssvPPA and sbvFTD patients were impaired on all semantic face processing tests, with sbvFTD patients performing significantly lower on the famous faces’ recognition task in comparison to svPPA, and svPPA performing significantly lower on the naming task in comparison to sbvFTD. These tasks predominantly correlated with gray matter (GM) volumes in the right and left ATL, respectively. Compared to HC, both svPPA and sbvFTD patients showed preserved performance on the perceptual face processing test (BFRT-S), and performance on the BFRT-S negatively correlated with GM volume in the right posterior superior temporal sulcus (pSTS). ConclusionOur results suggest that early in the disease, with the atrophy mostly restricted to the anterior temporal regions, SD patients do not present with perceptual deficits. However, more severe SD cases with atrophy in right posterior temporal regions might show lower performance on face perception tests, in addition to the semantic face processing deficits. Early sparing of face perceptual deficits in SD patients, regardless of hemispheric lateralization, furthers our understanding of clinical phenomenology and therapeutical approaches of this complex disease.

Skin Tau Quantification as a Novel Biomarker in Huntington's Disease.

Emerging research implicates tau protein dysregulation in the pathophysiology of Huntington's disease. This study investigated skin tau quantification as a potential biomarker for Huntington's disease and its correlation with disease burden outcomes. In this cross-sectional study, we measured skin tau levels using enzyme-linked immunosorbent assay in 23 Huntington's disease mutations carriers and eight control subjects, examining group discrimination, correlations with genetic markers, clinical assessments, and neuroimaging data. Brain atrophy was quantified by both volumetric measurements from brain segmentation and a voxel-based morphometry approach. Our findings showed elevated skin tau levels in manifest Huntington's disease compared with premanifest and healthy controls. These levels correlated with CAG repeat length, CAG-Age-Product score, composite Unified Huntington's Disease Rating Scale Total Motor Score, cognitive assessments, and disease-related cortical and subcortical volumes, all independent of age and gender. Using skin tau levels in cluster analysis along with genetic and clinical measures led to improved subject stratification, providing enhanced distinction and validity of clusters. This study not only confirms the feasibility of skin tau quantification in Huntington's disease but also establishes its potential as a biomarker for enhancing group classification and assessing disease severity across the Huntington's disease spectrum, opening new directions in biomarker research. © 2024 International Parkinson and Movement Disorder Society.

Perceptual and semantic deficits in face recognition in semantic dementia.

Semantic dementia (SD) patients including semantic variant primary progressive aphasia (svPPA) and semantic behavioral variant frontotemporal dementia (sbvFTD) patients show semantic difficulties identifying faces and known people related to right anterior temporal lobe (ATL) atrophy. However, it remains unclear whether they also have perceptual deficits in face recognition. We selected 74 SD patients (54 with svPPA and predominant left ATL atrophy and 20 with sbvFTD and predominant right ATL atrophy) and 36 cognitively healthy controls (HC) from UCSF Memory and Aging Center. They underwent a perceptual face processing test (Benton facial recognition test-short version; BFRT-S), and semantic face processing tests (UCSF Famous people battery - Recognition, Naming, Semantic associations - pictures and words subtests), as well as structural magnetic resonance imaging (MRI). Neural correlates with the task's performance were conducted with a Voxel-based morphometry approach using CAT12. svPPA and sbvFTD patients were impaired on all semantic face processing tests, with sbvFTD patients performing significantly lower on the famous faces' recognition task in comparison to svPPA, and svPPA performing significantly lower on the naming task in comparison to sbvFTD. These tasks predominantly correlated with gray matter (GM) volumes in the right and left ATL, respectively. Compared to HC, both svPPA and sbvFTD patients showed preserved performance on the perceptual face processing test (BFRT-S), and performance on the BFRT-S negatively correlated with GM volume in the right posterior superior temporal sulcus (pSTS). Our results suggest that early in the disease, with the atrophy mostly restricted to the anterior temporal regions, SD patients do not present with perceptual deficits. However, more severe SD cases with atrophy in right posterior temporal regions might show lower performance on face perception tests, in addition to the semantic face processing deficits. Early sparing of face perceptual deficits in SD patients, regardless of hemispheric lateralization, furthers our understanding of clinical phenomenology and therapeutical approaches of this complex disease.

Characterizing the voxel-based approaches in radioembolization dosimetry with reDoseMC.

Yttrium-90 ( ) represents the primary radioisotope used in radioembolization procedures, while holmium-166 ( ) is hypothesized to serve as a viable substitute for due to its comparable therapeutic potential and improved quantitative imaging. Voxel-based dosimetry for these radioisotopes relies on activity images obtained through PET or SPECT and dosimetry methods, including the voxel S-value (VSV) and the local deposition method (LDM). However, the evaluation of the accuracy of absorbed dose calculations has been limited by the use of non-ideal reference standards and investigations restricted to the liver. The objective of this study was to expand upon these dosimetry characterizations by investigating the impact of image resolutions, voxel sizes, target volumes, and tissue materials on the accuracy of and dosimetry techniques. A specialized radiopharmaceutical dosimetry software called reDoseMC was developed using the Geant4 Monte Carlo toolkit and validated by benchmarking the generated kernels with published data. The decay spectra of both and were also compared. Multiple VSV kernels were generated for the liver, lungs, soft tissue, and bone for isotropic voxel sizes of 1 mm, 2 mm, and 4 mm. Three theoretical phantom setups were created with 20 or 40mm activity and mass density inserts for the same three voxel sizes. To replicate the limited spatial resolutions present in PET and SPECT images, image resolutions were modeled using a 3D Gaussian kernel with a Full Width at Half Maximum (FWHM) ranging from 0 to 16mm and with no added noise. The VSV and LDM dosimetry methods were evaluated by characterizing their respective kernels and analyzing their absorbed dose estimates calculated on theoretical phantoms. The ground truth for these estimations was calculated usingreDoseMC. The decay spectra obtained through reDoseMC showed less than a 1% difference when compared to previously published experimental data for energies below 1.9MeV in the case of and less than 1% for energies below 1.5MeV for . Additionally, the validation kernels for VSV exhibited results similar to those found in published Monte Carlo codes, with source dose depositions having less than a 3% error margin. Resolution thresholds ( ), defined as resolutions that resulted in similar dose estimates between the LDM and VSV methods, were observed for . They were 1.5mm for bone, 2.5 mm for soft tissue and liver, and 8.5 mm for lungs. For , the accuracy of absorbed dose deposition was found to be dependent on the contributions of absorbed dose from photons. Volume errors due to variations in voxel size impacted the final dose estimates. Larger target volumes yielded more accurate mean doses than smaller volumes. For both radioisotopes, the radial dose profiles for the VSV and LDM approximated but never matched the referencestandard. reDoseMC was developed and validated for radiopharmaceutical dosimetry. The accuracy of voxel-based dosimetry was found to vary widely with changes in image resolutions, voxel sizes, chosen target volumes, and tissue material; hence, the standardization of dosimetry protocols was found to be of great importance for comparable dosimetryanalysis.

Brain structural alterations associated with impulsiveness in male violent patients with schizophrenia

BackgroundViolence in schizophrenia (SCZ) is a phenomenon associated with neurobiological factors. However, the neural mechanisms of violence in patients with SCZ are not yet sufficiently understood. Thus, this study aimed to explore the structural changes associated with the high risk of violence and its association with impulsiveness in patients with SCZ to reveal the possible neurobiological basis.MethodThe voxel-based morphometry approach and whole-brain analyses were used to measure the alteration of gray matter volume (GMV) for 45 schizophrenia patients with violence (VSC), 45 schizophrenia patients without violence (NSC), and 53 healthy controls (HC). Correlation analyses were used to examine the association of impulsiveness and brain regions associated with violence.ResultsThe results demonstrated reduced GMV in the right insula within the VSC group compared with the NSC group, and decreased GMV in the right temporal pole and left orbital part of superior frontal gyrus only in the VSC group compared to the HC group. Spearman correlation analyses further revealed a positive correlation between impulsiveness and GMV of the left superior temporal gyrus, bilateral insula and left medial orbital part of the superior frontal gyrus in the VSC group.ConclusionOur findings have provided further evidence for structural alterations in patients with SCZ who had engaged in severe violence, as well as the relationship between the specific brain alterations and impulsiveness. This work provides neural biomarkers and improves our insight into the neural underpinnings of violence in patients with SCZ.

Generation of 3D water vapour tomography using voxel-based approach in the Himalayan region

Generation of 3D water vapour tomography using voxel-based approach in the Himalayan region

Comparison of Three Approaches for Estimating Understory Biomass in Yanshan Mountains

Aboveground biomass (AGB) of shrubs and low-statured trees constitutes a substantial portion of the total carbon pool in temperate forest ecosystems, contributing much to local biodiversity, altering tree-regeneration growth rates, and determining above- and belowground food webs. Accurate quantification of AGB at the shrub layer is crucial for ecological modeling and still remains a challenge. Several methods for estimating understory biomass, including inventory and remote sensing-based methods, need to be evaluated against measured datasets. In this study, we acquired 158 individual terrestrial laser scans (TLS) across 45 sites in the Yanshan Mountains and generated metrics including leaf area and stem volume from TLS data using voxel- and non-voxel-based approaches in both leaf-on and leaf-off scenarios. Allometric equations were applied using field-measured parameters as an inventory approach. The results indicated that allometric equations using crown area and height yielded results with higher accuracy than other inventory approach parameters (R2 and RMSE ranging from 0.47 to 0.91 and 12.38 to 38.11 g, respectively). The voxel-based approach using TLS data provided results with R2 and RMSE ranging from 0.86 to 0.96 and 6.43 to 21.03 g. Additionally, the non-voxel-based approach provided similar or slightly better results compared to the voxel-based approach (R2 and RMSE ranging from 0.93 to 0.96 and 4.23 to 11.27 g, respectively) while avoiding the complexity of selecting the optimal voxel size that arises during voxelization.

Neurodegeneration within the rostral spinal cord is associated with brain gray matter volume atrophy in the early stage of cervical spondylotic myelopathy.

Case-control study. Investigating the association between neurodegeneration within rostral spinal cord and brain gray matter volume (GMV) and assessing the relationship between remote neurodegenerative changes and clinical outcomes at the early phase of Cervical Spondylotic Myelopathy (CSM). University/hospital. Using Spinal Cord Toolbox, spinal cord morphometrics (cross-sectional area [CSA], gray matter area [GMA], white matter area [WMA]) of 40 patients with CSM and 28 healthy controls (HCs) were computed and compared using two-sample t test. Brain GMV of the two groups was analyzed using voxel-based morphometry approach. Pearson's correlation between spinal cord morphometrics and altered brain GMV and Spearman's relationship between remote neurodegenerations and clinical outcomes were conducted in CSM group. Compared to HCs, CSA and WMA at C2/3 and GMV in right postcentral gyrus (PoCG.R) and left supplementary motor area (SMA.L) were significantly decreased in patients with CSM. CSA and WMA at C2/3 were associated with GMV in SMA.L and MCG.R in patients with CSM. CSA at C2/3 and GMV in PoCG.R were related to modified Japanese Orthopedic Association score in patients with CSM. The associations between CSA and WMA at C2/3 and GMV in SMA.L and MCG.R suggest a concordant change pattern and adaptive mechanisms for neuronal plasticity underlying remote neurodegeneration in early CSM. The atrophy of CSA at C2/3 and GMV loss in PoCG.R can serve as potential neuroimaging biomarkers of early structural changes within spinal cord and brain preceding marked clinical disabilities in patients with CSM.

Associations of plasma proteomics and age-related outcomes with brain age in a diverse cohort

Machine learning models are increasingly being used to estimate “brain age” from neuroimaging data. The gap between chronological age and the estimated brain age gap (BAG) is potentially a measure of accelerated and resilient brain aging. Brain age calculated in this fashion has been shown to be associated with mortality, measures of physical function, health, and disease. Here, we estimate the BAG using a voxel-based elastic net regression approach, and then, we investigate its associations with mortality, cognitive status, and measures of health and disease in participants from Atherosclerosis Risk in Communities (ARIC) study who had a brain MRI at visit 5 of the study. Finally, we used the SOMAscan assay containing 4877 proteins to examine the proteomic associations with the MRI-defined BAG. Among N = 1849 participants (age, 76.4 (SD 5.6)), we found that increased values of BAG were strongly associated with increased mortality and increased severity of the cognitive status. Strong associations with mortality persisted when the analyses were performed in cognitively normal participants. In addition, it was strongly associated with BMI, diabetes, measures of physical function, hypertension, prevalent heart disease, and stroke. Finally, we found 33 proteins associated with BAG after a correction for multiple comparisons. The top proteins with positive associations to brain age were growth/differentiation factor 15 (GDF-15), Sushi, von Willebrand factor type A, EGF, and pentraxin domain-containing protein 1 (SEVP 1), matrilysin (MMP7), ADAMTS-like protein 2 (ADAMTS), and heat shock 70 kDa protein 1B (HSPA1B) while EGF-receptor (EGFR), mast/stem-cell-growth-factor-receptor (KIT), coagulation-factor-VII, and cGMP-dependent-protein-kinase-1 (PRKG1) were negatively associated to brain age. Several of these proteins were previously associated with dementia in ARIC. These results suggest that circulating proteins implicated in biological aging, cellular senescence, angiogenesis, and coagulation are associated with a neuroimaging measure of brain aging.

Impact of radiation dose distribution on nutritional supplementation needs in head and neck cancer radiotherapy: a voxel-based machine learning approach.

To investigate the relationship between nutritional supplementation and radiation dose to the pharyngeal constrictor muscles and larynx for head and neck (HN) cancer patients undergoing radiotherapy. We retrospectively analyzed radiotherapy (RT) dose for 231 HN cancer patients, focusing on the pharyngeal constrictors and larynx. We defined nutritional supplementation as feeding tube utilization or >10% weight loss from baseline within 90 days after radiotherapy completion. Using deformable image registration (DIR), we mapped each patient's anatomical structures to a reference coordinate system, and corresponding deformations were applied to dose matrices. Voxel doses were utilized as features for ridge logistic regression models, optimized through 5-fold cross-validation. Model performance was assessed with area under the curve of a receiver operating curve (AUC) and F1 score. We built and compared models using 1) pharyngeal constrictor voxels, 2) larynx voxels, 3) clinical factors and mean regional dose metrics, and 4) clinical factors and dose-volume histogram metrics. Test set AUCs were compared among the models, and feature importance was evaluated. DIR of the pharyngeal constrictors and larynx yielded mean Dice coefficients of 0.80 and 0.84, respectively. Pharyngeal constrictors voxels and larynx voxel models had AUC of 0.88 and 0.82, respectively. Voxel-based dose modeling identified the superior to middle regions of the pharyngeal constrictors and the superior region of larynx as most predictive of feeding tube use/weight loss. Univariate analysis found treatment setting, treatment laterality, chemotherapy, baseline dysphagia, weight, and socioeconomic status predictive of outcome. An aggregated model using mean doses of pharyngeal constrictors and larynx subregions had an AUC of 0.87 and the model using conventional DVH metrics had an AUC of 0.85 with p-value of 0.04. Feature importance calculations from the regional dose model indicated that mean doses to the superior-middle pharyngeal constrictor muscles followed by mean dose to the superior larynx were most predictive of nutritional supplementation. Machine learning modeling of voxel-level doses enables identification of subregions within organs that correlate with toxicity. For HN radiotherapy, doses to the superior-middle pharyngeal constrictors are most predictive of feeding tube use/weight loss followed by the doses to superior portion of the larynx.

Achieving the ideal balance between biological and mechanical requirements in composite bone scaffolds through a voxel-based approach

Achieving successful bone regeneration necessitates the design of scaffolds that meet diverse biological and mechanical requirements, often leading to conflicts in the design parameters. A key conflict arises between scaffold porosity and stiffness. Increasing porosity facilitates cell infiltration and nutrient exchange, promoting bone regeneration. However, higher porosity compromises scaffold stiffness, which is crucial for providing structural support in the defective region. Furthermore, appropriate scaffold stiffness is crucial for preventing stress shielding. Conventional geometry-based design methods utilizing single-phase materials have limited flexibility in resolving such conflicts. To address this challenge, we propose a voxel-based method for designing composite scaffolds composed of hydroxyapatite (HA) and polylactic acid (PLA). Our strategy involves first satisfying primary biological requirements by selecting appropriate porosity, pore shape, and size. Subsequently, scaffold stiffness requirements are met by selecting suitable phase materials and tuning their contents. The study demonstrates that the voxel-based approach effectively balances both biological and mechanical requirements in scaffold design. This method addresses the limitations of traditional designs by achieving an optimal balance between porosity and stiffness, which is crucial for scaffold performance in biomedical applications. Moreover, the scaffolds designed using this method can be manufactured using voxel-based 3D printing technology, which is emerging in the field. Future advancements in voxel-based 3D printing technology will further enhance the feasibility and practicality of this approach for bone tissue engineering applications.

Diffusion tensor-based analysis of white matter in dogs with idiopathic epilepsy.

The understanding of epileptic seizure pathogenesis has evolved over time, and it is now generally accepted that not only are cortical and subcortical areas involved but also the connection of these regions in the white matter (WM). Recent human neuroimaging studies confirmed the involvement of the WM in several epilepsy syndromes. Neuroimaging studies investigating WM integrity with diffusion tensor imaging (DTI) in canine idiopathic epilepsy are lacking. This study aimed to test the hypothesis that WM diffusion changes can be found in dogs affected by idiopathic epilepsy. Twenty-six dogs with idiopathic epilepsy (15 Border Collies and 11 Greater Swiss Mountain dogs) and 24 healthy controls (11 Beagle dogs, 5 Border Collies, and 8 Greater Swiss Mountain dogs) were prospectively enrolled. Most dogs with idiopathic epilepsy (17/26) were enrolled within 3 months after seizure onset. Diffusion tensor imaging of the brain with 32 diffusion directions (low b value = 0 s/mm2; maximal b value = 800 s/mm2) was performed in a 3 Tesla scanner. Tract-based spatial statistics (TBSS), a voxel-based approach, was used to investigate changes in fractional anisotropy (FA) and mean diffusivity (MD) in the idiopathic epilepsy group compared to the healthy control group. Additionally, FA and MD were investigated in the region of corpus callosum and cingulate white matter in both groups. We observed subtle changes in WM DTI between the idiopathic epilepsy group and the healthy control group limited to cingulate WM, with a significantly lower FA in the idiopathic epilepsy group compared to the healthy control group in the region of interest (ROI) approach (p = 0.027). No significant changes were found between the idiopathic epilepsy group and the healthy control group in the TBSS analysis and in the corpus callosum in the ROI approach. This study supports the cingulate area as a target structure in canine epilepsy. The subtle changes only might be explained by the short duration of epilepsy, small sample sizes, and the higher variability in canine brain anatomy. Furthermore, all included dogs showed generalized tonic-clonic seizures, possibly affected by generalized epilepsy syndrome, which are also associated with less pronounced DTI changes in humans than focal epilepsy syndromes.

A Motion-Based Conceptual Space Model to Support 3D Evacuation Simulation in Indoor Environments

Three-dimensional (3D) indoor models are a crucial component to simulate pedestrian evacuations realistically in indoor environments. However, existing 3D indoor models cannot fully represent realistic indoor environments to enable the simulation of 3D pedestrian motions in evacuations because spaces above/below some physical components (e.g., desks, chairs) have been largely overlooked. Thus, this paper introduces a conceptual space model to advance a space identification and classification scheme that can fully capture 3D pedestrian motions. This paper first proposes the definition and parameterisation of different 3D pedestrian motions. Then, the definition and specifications of three categories of space components are elaborated on based on the motions. Finally, a voxel-based approach is introduced to identify and classify the space components, which are demonstrated by an illustrative example. This work contributes to advancing 3D indoor modelling to enable a more realistic simulation of 3D pedestrian motions.

Differential cortical gray matter changes in early- and late-onset patients with amyotrophic lateral sclerosis.

Age at onset may be an important feature associated with distinct subtypes of amyotrophic lateral sclerosis (ALS). Little is known about the neuropathological mechanism of early-onset ALS (EO-ALS) and late-onset ALS (LO-ALS). Ninety ALS patients were divided into EO-ALS and LO-ALS group, and 128 healthy controls were matched into young controls(YCs) and old controls (OCs). A voxel-based morphometry approach was employed to investigate differences in gray matter volume (GMV). Significant age at onset-by-diagnosis interactions were found in the left parietal operculum, left precentral gyrus, bilateral postcentral gyrus, right occipital gyrus, and right orbitofrontal cortex. Post hoc analysis revealed a significant decrease in GMV in all affected regions of EO-ALS patients compared with YCs, with increased GMV in 5 of the 6 brain regions, except for the right orbitofrontal cortex, in LO-ALS patients compared with OCs. LO-ALS patients had a significantly increased GMV than EO-ALS patients after removing the aging effect. Correspondingly, GMV of the left postcentral gyrus correlated with disease severity in the 2 ALS groups. Our findings suggested that the pathological mechanisms in ALS patients with different ages at onset might differ. These findings provide unique insight into the clinical and biological heterogeneity of the 2 ALS subtypes.

AUTOMATIC GENERATION OF ROUTING GRAPHS FOR INDOOR-OUTDOOR TRANSITIONAL SPACE TO SUPPORT SEAMLESS NAVIGATION

Abstract. With the fast development of urbanization, the complexity of built environments has dramatically increased, driving a need for assistance in seamless indoor-outdoor navigation. This requires integration of spatial information of indoor and outdoor environments from heterogeneous data sources. While outdoor road network data is largely available from many sources (such as OpenStreetMap), indoor spatial information is either inexistent or is inconsistently represented using several different standards. Among these standards, IndoorGML is a well-developed standard with the focus on indoor location-based services. This standard has already been accepted by Open Geospatial Consortium (OGC) and is now under active development. Although in IndoorGML some mechanisms have been defined to enable integration of indoor and outdoor networks, there is still a lack of concrete guidelines for determination of indoor-outdoor connections. It also lacks solid scientific foundations and efficient tools to extract the connecting nodes and edges that link indoor and outdoor spaces. To address this gap, in this study we focus on the connection of indoor and outdoor spaces and aim to provide a tool, which can automatically construct navigation graphs of the indoor-outdoor transitional space to support seamless integration of indoor-outdoor navigation. To this end, voxel-based modeling approaches are used to model the connecting space between indoor and outdoor environments. Based on Python, we develop the intended tool, which can generate voxel models from point clouds, identify navigable space by taking into account the characteristics of agents (such as pedestrians, wheelchairs, and vehicles), and automatically build navigation graphs linking IndoorGML networks with outdoor street networks. It is expected that the methodology and tools developed from this project will benefit the IndoorGML ecosystem and greatly advance the capability of IndoorGML in representing navigable space to support location-based services.

Voxel-based structural monitoring model for building structures using terrestrial laser scanning

This study presents a structural monitoring model for building structures using Terrestrial Laser Scanning (TLS). The model includes three phases for data collection, processing, and structural assessment, integrating a voxel-based data processing approach with polynomial regression to estimate structural responses. To investigate the influence of voxel size on estimating responses, several voxel structures were generated, each utilizing a uniform voxel size with voxel sizes ranging from 10 mm to 40 mm. The effectiveness of this proposed model was examined on a large-scale, two-story steel building under gravity and lateral loads. The estimated results demonstrated reasonable agreement with reference data, particularly at higher drift levels. Despite environmental challenges, such as obstacles and blind spots, the regression method effectively mitigated data gaps. This research indicates the viability of TLS as an efficient and effective contactless alternative for structural assessment in terms of speed and accuracy. Future advancements in laser scanning technology could further enhance data density and precision, thus extending the applicability of the model to larger-scale projects.

Optimal Pre-processing of Laser Scanning Data for Indoor Scene Analysis and 3D Reconstruction of Building Models

The large-scale point cloud data representing buildings should be prudently pre-processed and down-sampled prior to post-data processing activities, such as data segmentation or classification and indoor scene analysis, and visualization of the processed results as three-dimensional (3D) digital models. However, current pre-processing tasks and down-sampling procedures encountered the challenge of preserving the geometric and semantic features of the scanned object and contributing to accurate 3D reconstruction due to the loss of significant features on buildings. Therefore, this paper proposed a framework that pre-processes unstructured laser scanning data to optimize the efficiency of data processing activities and the accuracy of the produced output, i.e., reconstructed 3D building models. The pre-processing framework includes building the topology of the unstructured dataset, down-sampling the raw data through an improved voxel-based approach, and performing indoor scene analysis such as normal estimation, point alignment, and data classification. The experimental results verified that the proposed framework efficiently pre-processes the input data and preserves the geometric characteristics of the digital models with high accuracy, thereby supporting reliable 3D reconstruction for various applications.

The effects of various penalty parameter values in Q.Clear algorithm for rectal cancer detection on 18F-FDG images using a BGO-based PET/CT scanner: a phantom and clinical study

BackgroundThe Q.Clear algorithm is a fully convergent iterative image reconstruction technique. We hypothesize that different PET/CT scanners with distinct crystal properties will require different optimal settings for the Q.Clear algorithm. Many studies have investigated the improvement of the Q.Clear reconstruction algorithm on PET/CT scanner with LYSO crystals and SiPM detectors. We propose an optimum penalization factor (β) for the detection of rectal cancer and its metastases using a BGO-based detector PET/CT system which obtained via accurate and comprehensive phantom and clinical studies.Methods18F-FDG PET-CT scans were acquired from NEMA phantom with lesion-to-background ratio (LBR) of 2:1, 4:1, 8:1, and 15 patients with rectal cancer. Clinical lesions were classified into two size groups. OSEM and Q.Clear (β value of 100–500) reconstruction was applied. In Q.Clear, background variability (BV), contrast recovery (CR), signal-to-noise ratio (SNR), SUVmax, and signal-to-background ratio (SBR) were evaluated and compared to OSEM.ResultsOSEM had 11.5–18.6% higher BV than Q.Clear using β value of 500. Conversely, RC from OSEM to Q.Clear using β value of 500 decreased by 3.3–7.7% for a sphere with a diameter of 10 mm and 2.5–5.1% for a sphere with a diameter of 37 mm. Furthermore, the increment of contrast using a β value of 500 was 5.2–8.1% in the smallest spheres compared to OSEM. When the β value was increased from 100 to 500, the SNR increased by 49.1% and 30.8% in the smallest and largest spheres at LBR 2:1, respectively. At LBR of 8:1, the relative difference of SNR between β value of 100 and 500 was 43.7% and 44.0% in the smallest and largest spheres, respectively. In the clinical study, as β increased from 100 to 500, the SUVmax decreased by 47.7% in small and 31.1% in large lesions. OSEM demonstrated the least SUVmax, SBR, and contrast. The decrement of SBR and contrast using OSEM were 13.6% and 12.9% in small and 4.2% and 3.4%, respectively, in large lesions.ConclusionsImplementing Q.Clear enhances quantitative accuracies through a fully convergent voxel-based image approach, employing a penalization factor. In the BGO-based scanner, the optimal β value for small lesions ranges from 200 for LBR 2:1 to 300 for LBR 8:1. For large lesions, the optimal β value is between 400 for LBR 2:1 and 500 for LBR 8:1. We recommended β value of 300 for small lesions and β value of 500 for large lesions in clinical study.