Image Data Research Articles

Imaging characteristics and treatment strategies for carotid artery occlusion caused by skull base fracture: Three case reports

Imaging characteristics and treatment strategies for carotid artery occlusion caused by skull base fracture: Three case reports

Artificial Intelligence and Machine Learning in Hepatocellular Carcinoma Screening, Diagnosis and Treatment - A Comprehensive Systematic Review

Background: Medical image analysis plays a crucial role in the screening, monitoring, diagnosis, and prognosis of diseases. Hepatocellular Carcinoma (HCC) often remain asymptomatic in their early stages. Timely identification and intervention are crucial to prevent the progression to decompensated liver diseases and advanced-stage HCC, minimizing morbidity and mortality. Methodology: This study examined twenty relevant research articles that met the inclusion criteria. Utilizing the PRISMA criteria, a systematic search was conducted on PubMed/MEDLINE and Google Scholar for studies on HCC screening employing Artificial Intelligence (AI) and Machine Learning (ML). The search focused on the keywords "artificial intelligence" and "hepatocellular carcinoma," with inclusion criteria specifying studies in the English language published in and after 2020. Exclusions were made for histology, animal research, and investigations conducted before 2020. Titles and abstracts were thoroughly reviewed, and any discrepancies were discussed. Results: The comprehensive review reveals the transformative impact of AI and ML on HCC screening, diagnosis, and therapy. ML models demonstrated effectiveness in early HCC diagnosis, distinguishing hepatic lesions, predicting treatment responses, and assessing recurrence risks across various techniques. Integration of mass spectrometry-based technologies, advanced imaging, and real-world data significantly improved diagnostic accuracy and clinical decision support. AI models exhibited diagnostic expertise with potential applications in therapy suggestions, personalized surveillance, and prognostic evaluations. However, further validation and seamless integration into clinical practice are essential for realizing their full potential. Conclusion: This systematic study underscores the progress made in the application of AI and ML in HCC screening, diagnosis, and treatment. Numerous studies highlight the capability of AI and ML systems to enhance hepatocellular carcinoma diagnosis, predict outcomes, and optimize therapy. The enduring and versatile nature of these technologies points towards a revolutionary future in personalized and efficient HCC management.

Imaging characteristics and diagnosis of primary middle mediastinal tumors

Objectives: To investigate the proportion of different histological types and CT enhanced imaging features of primary middle mediastinal lesions in order to improve the understanding of these tumors and the accuracy of preoperative diagnosis. Methods: Retrospective analysis was conducted on 84 patients with primary middle mediastinal lesions and clear histological classifications diagnosed and treated at the Cancer Hospital, Chinese Academy of Medical Sciences from January 2012 to December 2022. Clinical, imaging, and pathological data were collected and classified according to tumor histological classifications. CT imaging manifestations such as tumor location, size, morphology, edge, boundary, internal components, enhancement characteristics, and surrounding tissue invasion were evaluated and recorded. Results: The histological types of the primary middle mediastinal lesions from the 84 patients included mesenchymal tumors, anterior intestinal cysts, giant lymph node hyperplasia, substernal goiter, neuroendocrine carcinoma, lymphohematopoietic system tumors, and mesothelioma, accounting for 28.6%, 27.4%, 14.3%, 3.6%, 11.9%, 9.5%, and 4.8%, respectively. Mesenchymal tumors included peripheral nerve sheath tumors, vascular tumors, adipogenic tumors, solitary fibrous tumors, and synovial sarcoma, accounting for 54.2%, 20.8%, 12.5%, 8.3%, and 4.2%, respectively. The above tumors had diverse imaging manifestations and specific imaging features. Mature fat were found in 3 cases of liposarcoma; Calcification was observed in 2 cases of thyroid nodules and 7 cases of giant lymph node hyperplasia; Enhanced scanning showed significant enhancement in 2 cases of solitary fibrous tumors, 3 cases of thyroid nodules, and 11 cases of giant lymph node hyperplasia; Mediastinal large lymph nodes was observed in 6 cases of lymphoma and 3 cases of mesothelioma; High invasiveness was observed in 4 cases of mesothelioma and 9 cases of neuroendocrine carcinoma. Conclusion: Mediastinal tumors have low incidence rate and rich histological types, and their imaging manifestations are diverse. Preoperative differential diagnosis can be made according to their specific imaging characteristics.

Estimation of population affinity using proximal femoral measurements based on computed tomographic images in the Japanese and western Australian populations.

The present study analyzes morphological differences femora of contemporary Japanese and Western Australian individuals and investigates the feasibility of population affinity estimation based on computed tomographic (CT) data. The latter is deemed to be of practical importance because most anthropological methods rely on the assessment of aspects of skull morphology, which when damaged and/or unavailable, often hampers attempts to estimate population affinity. The study sample comprised CT scans of 297 (146 females; 151 males) Japanese and 330 (145 females; 185 males) Western Australian adult individuals. A total of 10 measurements were acquired in two-dimensional CT images of the left and right femora; two machine learning methods (random forest modeling [RFM]) and support vector machine [SVM]) were then applied for population affinity classification. The accuracy of the two-way (sex-specific and sex-mixed) model was between 71.38 and 82.07% and 76.09-86.09% for RFM and SVM, respectively. Sex-specific (female and male) models were slightly more accurate compared to the sex-mixed models; there were no considerable differences in the correct classification rates between the female- and male-specific models. All the classification accuracies were higher in the Western Australian population, except for the male model using SVM. The four-way sex and population affinity model had an overall classification accuracy of 74.96% and 79.11% for RFM and SVM, respectively. The Western Australian females had the lowest correct classification rate followed by the Japanese males. Our data indicate that femoral measurements may be particularly useful for classification of Japanese and Western Australian individuals.

MRIO: the Magnetic Resonance Imaging Acquisition and Analysis Ontology.

Magnetic resonance imaging of the brain is a useful tool in both the clinic and research settings, aiding in the diagnosis and treatments of neurological disease and expanding our knowledge of the brain. However, there are many challenges inherent in managing and analyzing MRI data, due in large part to the heterogeneity of data acquisition. To address this, we have developed MRIO, the Magnetic Resonance Imaging Acquisition and Analysis Ontology. MRIO provides well-reasoned classes and logical axioms for the acquisition of several MRI acquisition types and well-known, peer-reviewed analysis software, facilitating the use of MRI data. These classes provide a common language for the neuroimaging research process and help standardize the organization and analysis of MRI data for reproducible datasets. We also provide queries for automated assignment of analyses for given MRI types. MRIO aids researchers in managing neuroimaging studies by helping organize and annotate MRI data and integrating with existing standards such as Digital Imaging and Communications in Medicine and the Brain Imaging Data Structure, enhancing reproducibility and interoperability. MRIO was constructed according to Open Biomedical Ontologies Foundry principles and has contributed several classes to the Ontology for Biomedical Investigations to help bridge neuroimaging data to other domains. MRIO addresses the need for a "common language" for MRI that can help manage the neuroimaging research, by enabling researchers to identify appropriate analyses for sets of scans and facilitating data organization and reporting.

AI-enhanced integration of genetic and medical imaging data for risk assessment of Type 2 diabetes.

Type 2 diabetes (T2D) presents a formidable global health challenge, highlighted by its escalating prevalence, underscoring the critical need for precision health strategies and early detection initiatives. Leveraging artificial intelligence, particularly eXtreme Gradient Boosting (XGBoost), we devise robust risk assessment models for T2D. Drawing upon comprehensive genetic and medical imaging datasets from 68,911 individuals in the Taiwan Biobank, our models integrate Polygenic Risk Scores (PRS), Multi-image Risk Scores (MRS), and demographic variables, such as age, sex, and T2D family history. Here, we show that our model achieves an Area Under the Receiver Operating Curve (AUC) of 0.94, effectively identifying high-risk T2D subgroups. A streamlined model featuring eight key variables also maintains a high AUC of 0.939. This high accuracy for T2D risk assessment promises to catalyze early detection and preventive strategies. Moreover, we introduce an accessible online risk assessment tool for T2D, facilitating broader applicability and dissemination of our findings.

Characterization of spring and durum wheat using non-destructive synchrotron phase contrast X-ray microtomography during storage.

Post-harvest losses during cereal grain storage are a big concern in both developing and developed countries, where spring and durum wheat are staple food grains. Varieties under these classes behave differently under storage, which affects their end storage life. High resolution imaging data of dry as well as spoiled seed are not available for any class of wheat; therefore, an attempt was made to generate 3D data for better understanding of seed structure and changes due to spoilage. Six wheat varieties (3 varieties for each class of wheat) were stored for 5 week at 17% moisture content (wb) before scanning. Seeds were also stored in a freezer (-18 °C) for further scanning to determine if any changes occur in the structure of seeds due to freezing. Spring varieties of wheat performed better than durum varieties and freezing did not affect seed structure. Data could also help plant breeders to develop varieties that do not easily spoil, adjust grain processing techniques, and develop post-harvest recommendations for other wheat varieties.

Fast computational approach with prior dimension reduction for three-dimensional chemical component analysis using CT data of spectral imaging.

Spectral image (SI) measurement techniques, such as X-ray absorption fine structure (XAFS) imaging and scanning transmission electron microscopy (STEM) with energy-dispersive X-ray spectroscopy (EDS) or electron energy loss spectroscopy (EELS), are useful for identifying chemical structures in composite materials. Machine-learning techniques have been developed for automatic analysis of SI data, and their usefulness has been proven. Recently, an extended measurement technique combining SI with a computed tomography (CT) technique (CT-SI), such as CT-XAFS and STEM-EDS/EELS tomography, was developed to identify the three-dimensional (3D) structures of chemical components. CT-SI analysis can be conducted by combining CT reconstruction algorithms and chemical component analysis based on machine learning techniques. However, this analysis incurs high computational costs owing to the size of the CT-SI datasets. To address this problem, this study proposed a fast computational approach for 3D chemical component analysis in an unsupervised learning setting. The primary idea for reducing the computational cost involved compressing the CT-SI data prior to CT computation and performing 3D reconstruction and chemical component analysis on the compressed data. The proposed approach significantly reduced the computational cost without losing information about the 3D structure and chemical components. We experimentally evaluated the proposed approach using synthetic and real CT-XAFS data, which demonstrated that our approach achieved a significantly faster computational speed than the conventional approach while maintaining analysis performance. As the proposed procedure can be implemented with any CT algorithm, it is expected to accelerate 3D analyses with sparse regularized CT algorithms in noisy and sparse CT-SI datasets.

Absorption Correction for 3D Elemental Distributions of Dental Composite Materials Using Laboratory Confocal Micro-X-ray Fluorescence Spectroscopy.

Confocal micro-X-ray fluorescence (micro-XRF) spectroscopy facilitates three-dimensional (3D) elemental imaging of heterogeneous samples in the micrometer range. Laboratory setups using X-ray tube excitation render the method accessible for diverse research fields but interpretation of results and quantification remain challenging. The attenuation of X-rays in composites depends on the photon energy as well as on the composition and density of the material. For confocal micro-XRF, attenuation severely impacts elemental distribution information, as the signal from deeper layers is distorted by superficial layers. Absorption correction and quantification of fluorescence measurements in heterogeneous composite samples have so far not been reported. Here, an absorption correction approach for confocal micro-XRF combining density information from microcomputed tomography (micro-CT) data with laboratory X-ray absorption spectroscopy (XAS) and synchrotron transmission measurements is presented. The energy dependency of the probing volume is considered during the correction. The methodology is demonstrated on a model composite sample consisting of a bovine tooth with a clinically used restoration material.

Evaluation of optical coherence tomography findings in patients with multiple sclerosis and glaucoma.

Glaucoma and multiple sclerosis (MS) can cause optic disc pathology and, in this way, affect optical coherence tomography (OCT) data. In this context, the objective of this study is to investigate the changes in the mean, quadrant, and sector data measured by OCT in glaucoma and MS patients. The sample of this prospective cohort study consisted of 42 MS patients (84 eyes), 34 Primary open-angle glaucomas patients (67 eyes), and 24 healthy control subjects (48 eyes). The MS group was divided into two groups according to the presence of a history of optic neuritis. Accordingly, those with a history of optic neuritis were included in the MS ON group, and those without a history of optic neuritis were included in the MS NON group. The differences between these groups in the mean, quadrant, and sector data related to the retinal nerve fiber layer (RNFL) and ganglion cell complex (GCC) were evaluated. Superior nasal (SN), superior temporal (ST), inferior nasal (IN), and superior quadrant (SUP) values were significantly lower in the glaucoma group than in the MS group (p < 0.05). The mean superior GCC (GCC SUP) value was significantly lower in the MS ON group than in the glaucoma group (p < 0.05). On the other hand, SN, ST, inferior temporal (IT), IN, average RNFL (AVE RNFL), semi-average superior RNFL (SUP AVE RNFL), semi-average inferior RNFL (INF AVE RNFL), SUP, and inferior quadrant RNFL (INF) values were significantly lower in the glaucoma group than in the MS NON group (p < 0.05). RNFL and GCC parameters get thinner in MS and glaucoma patients. While the inferior and superior RNFL quadrants are more frequently affected in glaucoma patients, the affected quadrants vary according to the presence of a history of optic neuritis in MS patients. It is noteworthy that the GCC superior quadrant was thin in MS ON patients. The findings of this study indicate that OCT data may be valuable in the differential diagnosis of glaucoma and MS.

Cochlear implantation for rare Streptococcus suis meningitis with hearing loss.

This study aimed to explore the diagnostic sensitivity of 3D heavily weighted T2-weighted MRI (T2MRI) and high-resolution computed tomography (HRCT) in patients with cochlear fibrosis associated with Streptococcus suis (S. suis) meningitis and the practicality of Cochlear implantation (CI) treatments. Between January 2020 and December 2022, we enrolled four patients with rare cochlear S. suis meningitis with associated hearing loss despite aggressive or non-aggressive follow-up antibiotic treatment. Clinical imaging data, surgical performances and post-surgical-electrode impedance were evaluated. Combined with HRCT and T2MRI, the cochlea had varying degrees of fibrosis and ossification in different cases. However, the electrodes were successfully and wholly inserted after intraoperative removal of the ossified and fibrotic foci. Post-surgical electrode impedance values of MP1 + 2 mode were normal in all 4 cases at initial activation. In patients with S. suis meningitis and associated cochlear fibrosis, T2MRI examination of the inner ear was more sensitive than HRCT. This research highlights the feasibility of CI treatment in S. suis meningitis patients with severe cochlear fibrosis.

In vivo demonstration of a real-time temporal SNR acoustic output adjustment method.

This work proposes a novel method of temporal signal-to-noise ratio (SNR) guided adaptive acoustic output adjustment and demonstrates this approach during in vivo fetal imaging. Acoustic output adjustment is currently the responsibility of sonographers, but ultrasound safety studies show recommended ALARA (As Low As Reasonably Achievable) practices are inconsistently followed. This study explores an automated ALARA method that adjusts the Mechanical Index (MI) output, targeting imaging conditions matching the temporal noise perception threshold. A 28 dB threshold SNR is used as the target SNR, following prior work showing relevant noise quantities are imperceptible once this image data quality level is reached. After implementing adaptive output adjustment on a clinical system, the average MI required to achieve 28 dB SNR in an eleven-volunteer fetal abdomen imaging test ranged from 0.17 to 0.26. The higher MI levels were required when imaging at higher frequencies. During tests with 20-second MI adjustment imaging periods, the degree of motion impacted the adaptive performance. For stationary imaging views, target SNR levels were maintained in 90% of SNR evaluations. When scanning between targets the imaging conditions were more variable, but the target SNR was still maintained in 71% of the evaluations. Given the relatively low MI recommended when performing MI adjustment and the successful adjustment of MI in response to changing imaging conditions, these results encourage adoption of adaptive acoustic output approaches guided by temporal SNR.

Development of a CT-Based comprehensive model combining clinical, radiomics with deep learning for differentiating pulmonary metastases from noncalcified pulmonary hamartomas, a retrospective cohort study.

Clinical differentiation between pulmonary metastases and noncalcified pulmonary hamartomas (NCPH) often presents challenges, leading to potential misdiagnosis. However, the efficacy of a comprehensive model that integrates clinical features, radiomics, and deep learning (CRDL) for differential diagnosis of these two diseases remains uncertain. This study evaluated the diagnostic efficacy of a Clinical Features, Radiomics, and Deep Learning (CRDL) model in differentiating pulmonary metastases from noncalcified pulmonary hamartomas (NCPH). We retrospectively analyzed the clinical and imaging data of 256 patients from Hospital A and 85 patients from Hospital B, who were pathologically confirmed pulmonary hamartomas or pulmonary metastases after thoracic surgery. Employing Python 3.7 software suites, we extracted radiomic features and deep learning attributes from patient datasets. The cohort was divided into training set, internal validation set, and external validation set. The diagnostic performance of the constructed models was evaluated using receiver operating characteristic (ROC) curve analysis to determine their effectiveness in differentiating between pulmonary metastases and NCPH. Clinical features such as white blood cell count (WBC), platelet count (PLT), history of cancer, carcinoembryonic antigen (CEA) level, tumor marker status, lesion margin characteristics (smooth or blurred) and maximum diameter were found to have diagnostic value in differentiating between the two diseases. In the domains of radiomics and deep learning. Of the 1,130 radiomics features and 512 deep learning features, 24 and 7, respectively, were selected for model development. The area under the ROC curve (AUC) values for the four groups were 0.980, 0.979, 0.999, and 0.985 in the training set, 0.947, 0.816, 0.934, and 0.952 in the internal validation set, and 0.890, 0.904, 0.923, and 0.938 in the external validation set. This demonstrated that the CRDL model showed the greatest efficacy. The comprehensive model incorporating clinical features, radiomics, and deep learning shows promise for aiding in the differentiation between pulmonary metastases and hamartomas.

Imaging and blood flow characteristics of cerebrovascular fenestration malformation and its relationship with the occurrence of ischemic cerebrovascular disease.

To explore the imaging and transcranial Doppler cerebral blood flow characteristics of cerebrovascular fenestration malformation and its relationship with the occurrence of ischemic cerebrovascular disease. A retrospective analysis was conducted on the imaging data of 194 patients with cerebrovascular fenestration malformation who visited the Heyuan People's Hospital from July 2021 to July 2023. The location and morphology of the fenestration malformation blood vessels as well as the presence of other cerebrovascular diseases were analyzed. Transcranial Doppler cerebral blood flow detection data of patients with cerebral infarction and those with basilar artery fenestration malformation were also analyzed. A total of 194 patients with cerebral vascular fenestration malformation were found. Among the artery fenestration malformation, basilar artery fenestration was the most common, accounting for 46.08% (94/194). 61 patients (31.44%) had other vascular malformations, 97 patients (50%) had cerebral infarction, of which 30 were cerebral infarction in the fenestrated artery supply area. 28 patients with cerebral infarction in the fenestrated artery supply area received standardized antiplatelet, lipid-lowering and plaque-stabilizing medication treatment. During the follow-up period, these patients did not experience any symptoms of cerebral infarction or transient ischemic attack again. There were no differences in peak systolic flow velocity and end diastolic flow velocity, pulsatility index and resistance index between the ischemic stroke group and the no ischemic stroke group in patients with basal artery fenestration malformation (P > 0.05). Cerebrovascular fenestration malformation is most common in the basilar artery. Cerebrovascular fenestration malformation may also be associated with other cerebrovascular malformations. Standardized antiplatelet and statin lipid-lowering and plaque-stabilizing drugs are suitable for patients with cerebral infarction complicated with fenestration malformation. The relationship between cerebral blood flow changes in basilar artery fenestration malformation and the occurrence of ischemic stroke may not be significant.

New Tools for Data Harmonization and Their Potential Applications in Organ Transplantation.

In organ transplantation, accurate analysis of clinical outcomes requires large, high-quality data sets. Not only are outcomes influenced by a multitude of factors such as donor, recipient, and transplant characteristics and posttransplant events but they may also change over time. Although large data sets already exist and are continually expanding in transplant registries and health institutions, these data are rarely combined for analysis because of a lack of harmonization. Promoted by the digitalization of the healthcare sector, effective data harmonization tools became available, with potential applications also for organ transplantation. We discuss herein the present problems in the harmonization of organ transplant data and offer solutions to enhance its accuracy through the use of emerging new tools. To overcome the problem of inadequate representation of transplantation-specific terms, ontologies and common data models particular to this field could be created and supported by a consortium of related stakeholders to ensure their broad acceptance. Adopting clear data-sharing policies can diminish administrative barriers that impede collaboration between organizations. Secure multiparty computation frameworks and the artificial intelligence (AI) approach federated learning can facilitate decentralized and harmonized analysis of data sets, without sharing sensitive data and compromising patient privacy. A common image data model built upon a standardized format would be beneficial to AI-based analysis of pathology images. Implementation of these promising new tools and measures, ideally with the involvement and support of transplant societies, is expected to produce improved integration and harmonization of transplant data and greater accuracy in clinical decision-making, enabling improved patient outcomes.

Oblique projections in the analysis of multislice computed tomography data in gunshot wounds

Oblique projections in the analysis of multislice computed tomography data in gunshot wounds

The Spanish Familial Pancreatic Cancer Registry (PANGENFAM): a decade follow-up of individuals at high-risk for pancreatic cancer.

The Spanish Familial Pancreatic Cancer Registry (PANGENFAM) was established in 2009 and aims to characterize the genotype and phenotype of familial pancreatic cancer (FPC). Furthermore, an early detection screening program for pancreatic ductal adenocarcinoma (PDAC) is provided to healthy high-risk individuals from FPC and hereditary pancreatic cancer families (first-degree relatives). This article describes our experience over the last 10years in high-risk screening. Hereditary and familial pancreatic cancer families were identified through the oncology and gastroenterology units. High-risk individuals underwent annual screening with endoscopic ultrasound (EUS) and magnetic resonance (MRI) from age 40 or 10years younger than the youngest affected family member. Results: PANGENFAM has enrolled 290 individuals from 143 families, including 52 PDAC cases and 238 high-risk individuals. All high-risk individuals eligible for screening were offered to enter the surveillance program, with 143 currently participating. Pancreatic abnormalities were detected in 94 individuals (median age 53years (29-83), with common findings including cystic lesions and inhomogeneous parenchyma. Imaging test concordance was 66%. Surgical intervention was performed in 4 high-risk individuals following highly suspicious lesions detected by imaging. PANGENFAM is a valuable resource for science innovation, suchas biobanking, withclinical and imaging data available for analysis. For high-risk families, it may offer a potential for early diagnosis. Collaboration with other national and internationalregistries is needed to increase our understanding of the disease biology and to standardize criteria for inclusion and follow-up, optimizing cost-effectiveness and efficacy.

Diagnostic performance of regional systematic biopsy for prostate cancer stratified by PI-RADS and histologic zones.

To explore the diagnostic performance of targeted biopsy (TB) combined with regional systematic biopsy (RSB) in patients with different Prostate Imaging Reporting and Data System (PI-RADS) and histologic zones for prostate lesions. This retrospective study included 1301 patients who underwent multiparametric MRI followed by combined MRI/US fusion-guided TB+systematic biopsy (SB) between January 2019 and October 2022. RSB was defined as the four perilesional SB cores adjacent to an MRI-positive lesion. Cancer detection rates were calculated for TB + SB, TB, SB, and TB + RSB, while the McNemar test was utilized for multiple comparisons among them. Subgroup analyses were performed based on different Pl-RADS and histologic zones. Of 1301 included participants (median age, 68 years; interquartile range, 63-74 years), 16,104 total biopsy cores were performed. TB + RSB detected clinically significant prostate cancer in 70.9% (922/1301) of patients, which was significantly higher than TB (67.4%, p < 0.001) or SB (67.5%, p < 0.001) but similar to TB + SB (71.0%, p = 0.50). Compared with TB + SB, TB + RSB required fewer median biopsy cores (6.3 vs. 12.4, p < 0.001) and had a higher proportion of positive cores (56.3% vs. 39.0%, p < 0.001). Subgroup analysis showed that TB had outstanding sensitivity for detecting PI-RADS 5 lesions in the PZ. Compared with TB + SB, TB + RSB achieved a similar clinically significant prostate cancer detection rate while requiring fewer biopsy cores and exhibiting higher diagnostic efficiency. For MRI-positive prostate lesions, targeted biopsy combined with regional systematic biopsy could serve as an alternative diagnostic approach to targeted biopsy combined with systematic biopsy. The scheme of prostate biopsy needs to be optimized. Regional systematic biopsy decreases the total number of cores taken. Targeted biopsies combined with regional systematic biopsies improve prostate diagnostic efficiency.

Modeling brain sex in the limbic system as phenotype for female-prevalent mental disorders

BackgroundSex differences exist in the prevalence and clinical manifestation of several mental disorders, suggesting that sex-specific brain phenotypes may play key roles. Previous research used machine learning models to classify sex from imaging data of the whole brain and studied the association of class probabilities with mental health, potentially overlooking regional specific characteristics.MethodsWe here investigated if a regionally constrained model of brain volumetric imaging data may provide estimates that are more sensitive to mental health than whole brain-based estimates. Given its known role in emotional processing and mood disorders, we focused on the limbic system. Using two different cohorts of healthy subjects, the Human Connectome Project and the Queensland Twin IMaging, we investigated sex differences and heritability of brain volumes of limbic structures compared to non-limbic structures, and subsequently applied regionally constrained machine learning models trained solely on limbic or non-limbic features. To investigate the biological underpinnings of such models, we assessed the heritability of the obtained sex class probability estimates, and we investigated the association with major depression diagnosis in an independent clinical sample. All analyses were performed both with and without controlling for estimated total intracranial volume (eTIV).ResultsLimbic structures show greater sex differences and are more heritable compared to non-limbic structures in both analyses, with and without eTIV control. Consequently, machine learning models performed well at classifying sex based solely on limbic structures and achieved performance as high as those on non-limbic or whole brain data, despite the much smaller number of features in the limbic system. The resulting class probabilities were heritable, suggesting potentially meaningful underlying biological information. Applied to an independent population with major depressive disorder, we found that depression is associated with male–female class probabilities, with largest effects obtained using the limbic model. This association was significant for models not controlling for eTIV whereas in those controlling for eTIV the associations did not pass significance correction.ConclusionsOverall, our results highlight the potential utility of regionally constrained models of brain sex to better understand the link between sex differences in the brain and mental disorders.

Addressing incomplete tile phenomena in image tiling: Introducing the grid six-intersection model

Abstract With the continuous development of Earth Observation technology, resolution of imagery and gridded data has significantly increased, leading to a rapid increase in data volume. To efficiently acquire and analyze these vast amounts of imagery and gridded data, image tiling technology has been developed to effectively access data of interested areas. Tiling technology divides large-scale image data into smaller tiles, providing fast, accurate, and efficient access support for imagery and gridded data. The spatial grid model, as the foundational framework of the new generation of geographic spatial information, plays a critical role in the retrieval, integration, services, and applications of imagery and gridded data resources. When tiling image data based on the spatial grid model, it always generates both complete and incomplete tiles. Particularly, when conducting image tile retrieval using the same rule-based grid in the retrieval area, incomplete tile phenomena along the boundary regions of the retrieved images often occur, resulting in gaps within the retrieval area. To tackle this issue, in this study, we present a new topological model called the Grid Six-Intersection Model (G-6IM), specifically designed for regular rectangular grids, to accurately represent boundary issues in image tiling. Through a practical case study, we demonstrate the effectiveness and practical application potential of the proposed G-6IM model, providing new insights and guidance for the improvement and optimization of imagery and gridded data tiling technology.