Image Data Research Articles

GroupFormer for hyperspectral image classification through group attention

Hyperspectral image (HSI) data has a wide range of valuable spectral information for numerous tasks. HSI data encounters challenges such as small training samples, scarcity, and redundant information. Researchers have introduced various research works to address these challenges. Convolution Neural Network (CNN) has gained significant success in the field of HSI classification. CNN’s primary focus is to extract low-level features from HSI data, and it has a limited ability to detect long-range dependencies due to the confined filter size. In contrast, vision transformers exhibit great success in the HSI classification field due to the use of attention mechanisms to learn the long-range dependencies. As mentioned earlier, the primary issue with these models is that they require sufficient labeled training data. To address this challenge, we proposed a spectral-spatial feature extractor group attention transformer that consists of a multiscale feature extractor to extract low-level or shallow features. For high-level semantic feature extraction, we proposed a group attention mechanism. Our proposed model is evaluated using four publicly available HSI datasets, which are Indian Pines, Pavia University, Salinas, and the KSC dataset. Our proposed approach achieved the best classification results in terms of overall accuracy (OA), average accuracy (AA), and Kappa coefficient. As mentioned earlier, the proposed approach utilized only 5%, 1%, 1%, and 10% of the training samples from the publicly available four datasets.

Ankle syndesmotic ligaments avulsion fractures: incidence in adult population

BackgroundDistal tibiofibular syndesmosis injury is often associated with ankle fractures in adults. Injuries to the anterior/posterior inferior tibiofibular ligament (AITFL/PITFL) may present as a mid-substance tear or as an avulsion at insertion. Tibial and fibular avulsion of the AITFL is known as Tillaux fracture and Wagstaffe fracture, respectively. Tibial avulsion of the PITFL is referred to as a Volkmann fracture, and fibular avulsion of the PITFL is still undefined and has not been reported yet. The aim of this study is to summarize the incidence of these four avulsions, that is, tibial and fibular avulsions of the AITFL and PITFL.MethodRadiography and computed tomography (CT) imaging data of all adult patients with ankle fractures treated at our hospital between November 2010 and March 2023 were retrospectively analyzed. All ankle fractures were classified according to the Weber-AO and Lauge-Hansen classification systems by two experienced radiologists and two surgeons. The incidence of the four avulsions of the AITFL/PITFL was determined.ResultsIn total, 1,770 ankle fractures in 1,758 patients were included in this study. The total incidence of avulsions at the four insertions of the AITFL/PITFL (occurring at one, two, or three insertions) was found to be 26.3% (465/1,770). Volkmann fracture had the highest incidence (19.9%, 353/1,770), and it was followed by Tillaux fracture (5.3%, 93/1,770), Wagstaffe fracture (3.3%, 59/1,770), and fibular avulsion of the PITFL (0.5%, 8/1,770). It is noteworthy that fibular avulsion of the PITFL has been reported here for the first time. The incidence of avulsion at one insertion was 23.6% (418/1770) and 2.7% (47/1770) at multiple insertions.ConclusionIn adult ankle fractures, avulsion occurs at the four insertions of the AITFL/PITFL in more than 25% of patients. The tibial insertion of the PITFL had the highest incidence of avulsion among the four insertions, while the fibular insertion of the PITFL had the lowest. The four types of avulsions can be isolated or in association with other avulsions. Future research studies on these four types of avulsion fractures would help in accurate diagnosis, decision-making and treatment of ankle Syndesmosis injuries.Level of evidenceLevel III, retrospective cohort study.

Enhancing urban landscape analysis through combined LiDAR and visual image data preprocessing

Enhancing urban landscape analysis through combined LiDAR and visual image data preprocessing

Ultrasonography-guided canal decompression combined with vertebroplasty and cement-augmented pedicle screw fixation for stage III Kümmell’s disease with neurological deficits: a retrospective cohort study

BackgroundPercutaneous vertebroplasty or kyphoplasty is the preferred procedure for stage I and II Kümmell’s diseases (KDs), but there exist controversies on the operative option of stage III KD. This study aimed at exploring the safety and efficacy of ultrasonography-guided canal decompression (UG-CD) combined with vertebroplasty and cement-augmented pedicle screw fixation (CA-PSF) for treating stage III KD with neurological deficit (ND).MethodsBetween September 2017 and December 2023, all patients who received the UG-CD combined with vertebroplasty and CA-PSF for managing stage III KD with NDs were reviewed retrospectively with their demographic and operation data, and complications recorded. Besides, the scores of Visual Analogue Scale (VAS) and Oswestry Disability Index (ODI), together with imaging data including the kyphotic Cobb angle (KCA), wedge angle (WA), spinal canal area (SCA) at the narrowest level as well as anterior (AHR) and middle (MHR) height ratios were measured and compared between pre- and post-operation.ResultsA total of eleven patients with a mean age of 70.09 ± 2.98 years old were included in our study with their surgical time, hospitalization length, blood loss, and follow-up time being 150.91 ± 17.94 min, 202.09 ± 39.95 ml, 8.18 ± 1.17 days, and 16.91 ± 4.09 months, respectively. During the final follow-up, the KCA, WA, VAS scores, ODI scores, AHR, MHR, and SCA were significantly improved (P < 0.01). Intraoperatively, one case suffered a transient decrease in the motor evoked potential. Another case experienced a cerebrospinal fluid leakage postoperatively that was then successfully treated.ConclusionUG-CD combined with vertebroplasty and CA-PSF could be a feasible procedure for safely and effectively handling stage III KD with NDs.

Development and validation of a clinical-radiomics nomogram for the early prediction of Klebsiella pneumoniae liver abscess

Background and aim Pyogenic liver abscess (PLA) is a devastating and potentially life-threatening disease globally, with Klebsiella pneumoniae liver abscess (KPLA) being the most prevalent in Asia. This study aims to develop an effective and comprehensive nomogram combining clinical and radiomics features for early prediction of KPLA. Methods 255 patients with PLA from 2013 to 2023 were enrolled and randomly divided into the training and validation cohorts at a 7:3 ratio. The differences between the two cohorts of patients were assessed via univariate analysis. The radiomics features were extracted from imaging data from enhanced CT of liver abscesses. The optimal radiomics features were filtered using the independent sample t-test and least absolute shrinkage and selection operator, and a radiomics score (Rad-score) was calculated by weighting their respective coefficients. Clinically independent predictors were identified from the clinical data and combined with the Rad-score to develop a nomogram by multivariate logistic regression. The predictive performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curve, and clinical decision curve. Results The nomogram incorporated four clinical features of diabetes mellitus, cryptogenic liver abscess, C-reactive protein level, and splenomegaly, and the Rad-score that was constructed based on seven optimal radiomics features. It had an AUC of 0.929 (95% CI, 0.894-0.964) and 0.923 (95% CI, 0.864-0.981) in the training and validation cohorts, respectively. The calibration and decision curves showed that the nomogram had good agreement and clinical applicability. Conclusions The clinical-radiomics nomogram performed well in predicting KPLA, hopefully serving as a reference for early diagnosis of KPLA.

Supercooled Water Cloud Detection From Polarized Multi‐Angle Imager Data Using 1.37 μm Water Vapor Polarized Channel

AbstractDetecting supercooled water clouds (SWCs) is essential for enhancing artificial rainfall, preventing aircraft ice accretion, and developing a better understanding of radiative energy balance. The 1.37 μm channel, known as strong water vapor absorbing, was made polarized in the polarized multi‐angle imager (PMAI) onboard FengYun‐3G satellite. The infight data shown that the new 1.37 μm polarized channel could be used to detect SWCs. The cloudbow is observed around the 140° scattering angle in the 1.37 μm polarization image, with a maximum polarization reflectance of approximately 0.04–0.06. The indicated water clouds with spherical particles in the high‐level altitude could be SWCs. Then, the SWCs detected by 1.37 μm polarized channel is verified using polarized reflectance of other channels, the reflectance difference of channels, and thermal infrared bright temperature. The presence of cloudbow in 1.03 and 1.64 μm channels indicate liquid water cloud. The reflectance difference between 1.03 and 1.64 μm of SWCs agree with characteristic of water cloud. The thermal infrared channels from the imager on the same platform indicate cold cloud with the brightness temperature far below 273.16 K. Therefore, the only use of 1.37 μm polarized channel could perform the identification of SWCs. PMAI provides a powerful tool for monitoring supercooled water clouds.

Comparison of the predictive value of multiparametric MRI and prostate-specific membrane antigen PET/CT for pelvic lymph node metastasis in prostate cancer

Objective: To compare the diagnostic performance of multiparametric magnetic resonance imaging (mpMRI) and prostate-specific membrane antigen (PSMA) PET/CT in detecting pelvic lymph node metastasis in prostate cancer. Methods: This is a retrospective case series study. A retrospective analysis was conducted on the data of 115 prostate cancer patients who underwent both mpMRI and PSMA PET/CT before undergoing radical prostatectomy and extended pelvic lymph node dissection at the Department of Urology, Xiangya Hospital, Central South University, between March 2020 and September 2023. The age (M(IQR)) was 67(10) years (range: 45 to 84 years), and the body mass index was 24(4) kg/m2 (range: 18 to 30 kg/m2). Pathological and imaging data were obtained from the patients. Lymph node pathology results were used as the gold standard to evaluate the diagnostic performance of mpMRI and PSMA PET/CT for detecting pelvic lymph node metastasis in PCa through diagnostic evaluation tests. Comparisons between groups were performed using independent samples t-test, Mann-Whitney U test, or χ2 test. Results: The positive rate for detecting pelvic lymph node metastasis was 18.3% (21/115) with mpMRI and 25.2% (29/115) with PSMA PET/CT. The pathological positive rate for lymph nodes was 28.7% (33/115). In patient-based analysis, the diagnostic sensitivity of PSMA PET/CT was significantly higher than that of mpMRI (63.6% vs. 30.3%, χ2=7.36, P=0.007). In lesion-based analysis, both the sensitivity and positive predictive value of PSMA PET/CT were significantly higher than those of mpMRI (sensitivity: 68.0% vs. 21.6%, χ2=42.20, P<0.01; positive predictive value: 50.0% vs. 23.1%, χ2=7.54, P=0.006). Conclusions: PSMA PET/CT and mpMRI both demonstrates good specificity in predicting pelvic lymph node metastasis in prostate cancer. However, PSMA PET/CT is significantly superior to mpMRI in terms of sensitivity and the detection rate of pathologically positive lymph nodes.

Prediction of stroke severity: systematic evaluation of lesion representations.

To systematically evaluate which lesion-based imaging features and methods allow for the best statistical prediction of poststroke deficits across independent datasets. We utilized imaging and clinical data from three independent datasets of patients experiencing acute stroke (N1 = 109, N2 = 638, N3 = 794) to statistically predict acute stroke severity (NIHSS) based on lesion volume, lesion location, and structural and functional disconnection with the lesion location using normative connectomes. We found that prediction models trained on small single-center datasets could perform well using within-dataset cross-validation, but results did not generalize to independent datasets (median R2 N1 = 0.2%). Performance across independent datasets improved using large single-center training data (R2 N2 = 15.8%) and improved further using multicenter training data (R2 N3 = 24.4%). These results were consistent across lesion attributes and prediction models. Including either structural or functional disconnection in the models outperformed prediction based on volume or location alone (P < 0.001, FDR-corrected). We conclude that (1) prediction performance in independent datasets of patients with acute stroke cannot be inferred from cross-validated results within a dataset, as performance results obtained via these two methods differed consistently, (2) prediction performance can be improved by training on large and, importantly, multicenter datasets, and (3) structural and functional disconnection allow for improved prediction of acute stroke severity.

Evaluating diagnostic accuracy and agreement of TI-RADS scoring in thyroid nodules: A comparative analysis between sonographers and radiologists.

The Thyroid Imaging Reporting and Data System (TI-RADS) is an essential tool for assessing thyroid nodules, primarily used by radiologists. This study aimed to compare the agreement of TI-RADS scores between sonographers and radiologists and to assess the diagnostic performance of these scores against histological findings in suspicious thyroid nodules. In a retrospective analysis, 168 patients with suspicious thyroid nodules classified as TR3 and above by the radiologists were included. Both sonographers and radiologists independently assigned the American College of Radiologists (ACR) TI-RADS scores, which were then compared for inter-reader agreement using Cohen's Kappa statistic. The scores were also evaluated for diagnostic performance against histological results based on the Bethesda system. The study revealed a moderate overall agreement between sonographers and radiologists in TI-RADS scoring (κ = 0.504; 95% CI: 0.409-0.599), with poor agreement noted specifically for nodule margin scores (κ = 0.102; 95% CI: -1.430-0.301). In terms of diagnostic performance against histological outcomes, sonographers' TI-RADS scores showed a sensitivity of 100% and a specificity of 44.6%, while radiologists' scores showed a sensitivity of 100% but a lower specificity of 29.3%. The findings indicate moderate agreement in TI-RADS scoring between sonographers and radiologists, with reproducibility challenges especially in scoring nodule margins. The marginally superior diagnostic performance of sonographers' scores suggests potential efficiency benefits in involving sonographers in preliminary assessments. Future research should aim to encompass a wider range of TI-RADS categories and focus on minimizing scoring variability to enhance the system's clinical utility.

Comprehensive Review of Post-treatment Imaging in Head and Neck Cancers: From expected to unexpected and beyond.

Head and neck cancer management requires multidisciplinary approach in which radical surgery with or without flap reconstructions and neck dissection, along with radiotherapy (RT)/chemoradiotherapy (CRT) serve as the key components. Neoadjuvant chemotherapy (NACT) and immunotherapy are used in selected cases based on the institutional preference. Knowledge of expected posttreatment changes on imaging is essential to differentiate it from recurrence. In addition, awareness of various posttreatment complications is imperative for their early detection on imaging. Distorted anatomy after treatment poses diagnostic challenge, hence, proper choice of imaging modality and appropriate timing of scan is pertinent for accurate posttreatment evaluation. In this article, we have provided a comprehensive review on expected imaging appearances post RT/CRT and after major types of head and neck squamous cell carcinoma (HNSCC) surgeries. In addition, we have extensively reviewed the imaging appearances of various posttreatment complications, and recurrences at the primary and lymph nodal sites. We have also delved into the patterns of recurrence in human papillomavirus (HPV) positive HNSCC in this article. Furthermore, we have provided flowcharts and discussed recommendations on the site-specific and treatment related imaging modalities to be used along with their appropriate timing, for adequate evaluation of HNSCC after treatment. We have also reviewed posttreatment reporting of imaging findings using Neck Imaging Reporting and Data Systems (NIRADS) and Hopkins criteria. In addition, we have also touched upon the role of advanced imaging techniques for posttreatment HNSCC evaluation.

A color image encryption algorithm based on a novel 4D hyperchaotic system and bit-level diffusion

As digital images are widely used in social media, medical, military and other fields, ensuring the privacy and security of image data has become a critical concern. Firstly, we propose a novel four-dimensional hyperchaotic system and validate that it exhibits a broad chaotic range, as demonstrated by bifurcation diagrams and Lyapunov exponent experiments. Additionally, simulated circuit diagrams verify the hardware feasibility of the proposed system. Secondly, we design a dynamic iterative scrambling (DIS) scheme that dynamically divides the image into multiple matrices for spatially indexed scrambling. Excellent substitution performance can be ensured by multiple iterations. In the diffusion stage, a multidirectional bit-level L-shaped (MDBL) scheme is proposed. Diffusion is conducted on the bit plane using a designed cross-multiplanar selection algorithm, which fuses the high and low bit planes, thereby enhancing the diffusion performance of MDBL. Thirdly, Based on the above concepts, a novel four-dimensional hyperchaotic system and an encryption algorithm based on bit-level diffusion are proposed. Finally, experimental results and security analyses demonstrate the effectiveness of the novel 4D hyperchaotic system and image encryption scheme. The proposed encryption scheme exhibits robust anti-interference capabilities and effectively safeguards image security.

High-throughput image data analysis shows the genetic diversity in Tartary buckwheat (Fagopyrum tataricum Gaertn.)

High-throughput image data analysis shows the genetic diversity in Tartary buckwheat (Fagopyrum tataricum Gaertn.)

Predicting Rock Hardness and Abrasivity Using Hyperspectral Imaging Data and Random Forest Regressor Model

This study aimed to develop predictive models for rock hardness and abrasivity based on hyperspectral imaging data, providing valuable information without interrupting the mining processes. The data collection stage first involved scanning 159 rock samples collected from 6 different blasted rock piles using visible and near-infrared (VNIR) and short-wave infrared (SWIR) sensors. The hardness and abrasivity of the samples were then determined through Leeb rebound hardness (LRH) and Cerchar abrasivity index (CAI) tests, respectively. The data preprocessing involved radiometric correction, background removal, and staking VNIR and SWIR images. An integrated approach based on K-means clustering and the band ratio concept was employed for feature extraction, resulting in 28 band-ratio-based features. Afterward, the random forest regressor (RFR) algorithm was employed to develop predictive models for rock hardness and abrasivity separately. The performance assessment showed that the developed models can estimate rock hardness and abrasivity of unseen data with R2 scores of 0.74 and 0.79, respectively, with the most influential features located mainly within the SWIR region. The results indicate that integrated hyperspectral data and RFR technique have strong potential for practical and efficient rock hardness and abrasivity characterization during mining processes.

Association between allostatic load and accelerated white matter brain aging: findings from the UK biobank.

White matter (WM) brain age, a neuroimaging-derived biomarker indicating WM microstructural changes, helps predict dementia and neurodegenerative disorder risks. The cumulative effect of chronic stress on WM brain aging remains unknown. In this study, we assessed cumulative stress using a multi-system composite allostatic load (AL) index based on inflammatory, anthropometric, respiratory, lipidemia, and glucose metabolism measures, and investigated its association with WM brain age gap (BAG), computed from diffusion tensor imaging data using a machine learning model, among 22 951 European ancestries aged 40 to 69 (51.40% women) from UK Biobank. Linear regression, Mendelian randomization, along with inverse probability weighting and doubly robust methods, were used to evaluate the impact of AL on WM BAG adjusting for age, sex, socioeconomic, and lifestyle behaviors. We found increasing one AL score unit significantly increased WM BAG by 0.29years in association analysis and by 0.33years in Mendelian analysis. The age- and sex-stratified analysis showed consistent results among participants 45-54 and 55-64years old, with no significant sex difference. This study demonstrated that higher chronic stress was significantly associated with accelerated brain aging, highlighting the importance of stress management in reducing dementia and neurodegenerative disease risks.

The potential of federated learning for self-configuring medical object detection in heterogeneous data distributions

Medical Object Detection (MOD) is a clinically relevant image processing method that locates structures of interest in radiological image data at object-level using bounding boxes. High-performing MOD models necessitate large datasets accurately reflecting the feature distribution of the corresponding problem domain. However, strict privacy regulations protecting patient data often hinder data consolidation, negatively affecting the performance and generalization of MOD models. Federated Learning (FL) offers a solution by enabling model training while the data remain at its original source institution. While existing FL solutions for medical image classification and segmentation demonstrate promising performance, FL for MOD remains largely unexplored. Motivated by this lack of technical solutions, we present an open-source, self-configuring and task-agnostic federated MOD framework. It integrates the FL framework Flower with nnDetection, a state-of-the-art MOD framework and provides several FL aggregation strategies. Furthermore, we evaluate model performance by creating simulated Independent Identically Distributed (IID) and non-IID scenarios, utilizing the publicly available datasets. Additionally, a detailed analysis of the distributions and characteristics of these datasets offers insights into how they can impact performance. Our framework’s implementation demonstrates the feasibility of federated self-configuring MOD in non-IID scenarios and facilitates the development of MOD models trained on large distributed databases.

High-Density Lipoprotein Mimetic Peptide 4F Reduces Toxic Amyloid-Beta Exposure to the Blood-Brain Barrier Endothelium in Alzheimer's Disease Transgenic Mice.

Aβ accumulation in the blood-brain barrier (BBB) endothelium, which lines the cerebrovascular lumen, is a significant contributor to cerebrovascular dysfunction in Alzheimer's disease (AD). Reduced high-density lipoprotein (HDL) levels are associated with increased AD risk, and the HDL mimetic peptide 4F has been developed as a promising therapeutic agent to improve cerebrovascular health in AD. In this study, we evaluated the impact of 4F on 125I-Aβ42 blood-to-brain distribution using dynamic SPECT/CT imaging in both wild-type and APP/PS1 transgenic mice. Graphical analysis of the imaging data demonstrated that 4F significantly reduced the blood-to-brain influx rate in wild-type mice and the distribution of 125I-Aβ42 in the BBB endothelium in APP/PS1 mice. To elucidate the molecular mechanisms underlying the effect of 4F, we evaluated its impact on the p38 pathway and its role in mediating Aβ42 trafficking in human BBB endothelial cell monolayers. Treatment with 4F significantly decreased Aβ42 induced p38 activation in BBB endothelial cells. Furthermore, inhibition of p38 kinase significantly reduced endothelial accumulation of fluorescence-labeled Aβ42 and luminal-to-abluminal permeability across the cell monolayer. While our previous publication has hinted at the potential of 4F to reduce Aβ accumulation in the brain parenchyma, the current findings demonstrated the protective effect of 4F in reducing Aβ42 accumulation in the BBB endothelium of AD transgenic mice. These findings revealed the impact of a clinically tested agent, the HDL mimetic peptide 4F, on Aβ exposure to the BBB endothelium and offer novel mechanistic insights into potential therapeutic strategies to treat cerebrovascular dysfunction in AD.

Uterine layers hypervascularization in atypical placentation: the comparison of magnetic resonance imaging with vascular pathomorphology

Rationale: One in five women worldwide delivers through cesarean section, which is associated to a high rate of placenta previa and abnormal placenta attachment to the uterine wall. The grade of placenta accreta spectrum (PAS) correlates with blood loss volume, which could be related to vascularization characteristics. Up to now, there have been no studies on the diagnostic value of magnetic resonance criteria for hypervascularization of the uterine wall and their correlation with an assumed grade of PAS according to the International Federation of Gynecology and Obstetrics (FIGO) classification. Also, abnormal vessels location and their wall structure have not been studied with state-of-the-art pathomorphological methods. Aim: To develop a classification for various types of the uterine wall hypervascularization depending on the depth of placental invasion assessed by magnetic resonance imaging (MRI). Methods: We retrospectively analyzed medical files of 66 patients aged 18 to 45 years, who were assumed to have PAS based on their sonography results. To clarify the extent and localization of the placental invasion, all patients had MRI performed at 24 to 32 weeks of gestation. In all patients, the PAS grade was verified pathologically; in 30 of them (45,5%), a detailed pathological examination was additionally performed. The PAS grades were classified according to Morbidly Adherent Placenta Imaging Reporting and Data System (MAPI-RADS). The MRI data were correlated with the PAS (FIGO) grade estimated from intraoperative data and with intraoperative blood loss. At the final study step, the MRI data was correlated with abnormal pathomorphology, with the assessment of the degree of vascularization and the visual picture of abnormal uteroplacental vasculature. Results: We have proposed a classification of MRI signs of hypervascularization in the lower uterine segment correlating with the depth of placenta accreta. Type 1. Single small vessels in the myometrium, medium caliber arteries in the projection of the uterine serosa (normal variation for the 3rd trimester of gestation). Type 2. Numerous small confluent vessels, mainly in the uteroplacental region, and retroplacental hematomas (typical for placenta accreta, or FIGO PAS 1). Type 3. Intrauterine hypervascularization with a deficient myometrium thickness, predominantly vessels of the middle layer (typical for placenta increta, or FIGO PAS 2). Type 4. Thinning of the lower uterine segment due to significant bulging of the placenta in the presence of curved anastomosing vessels of vast caliber over 4 mm (typical for placenta increta – FIGO PAS 2, as well as placenta percreta – FIGO PAS 3a). Type 5. Hypervascularization outside the uterus, over its surface and in the parametrial tissues: at the border with the bladder, uterine isthmus, including signs of fibrosis and growing into the posterior bladder wall (typical for various grades of placenta percreta – FIGO PAS 3b, 3c). Conclusion: The proposed classification of uterine wall hypervascularization in accordance with the grades of placenta accreta spectrum (FIGO PAS) could be useful for its practical implementation by radiologists.

Advanced imaging reveals enhanced malignancy in glioblastomas involving the subventricular zone: evidence of increased infiltrative growth and perfusion.

Glioblastoma's infiltrative growth and heterogeneity are influenced by neural, molecular, genetic, and immunological factors, with the precise origin of these tumors remaining elusive. Neurogenic zones might serve as the tumor stem cells' nest, with tumors in contact with these zones exhibiting worse outcomes and more aggressive growth patterns. This study aimed to determine if these characteristics are reflected in advanced imaging, specifically diffusion and perfusion data. In this monocentric retrospective study, 137 glioblastoma therapy-naive patients (IDH-wildtype, grade 4) with advanced preoperative MRI, including perfusion and diffusion imaging, were analyzed. Tumors and neurogenic zones were automatically segmented. Advanced imaging metrics, including cerebral blood volume (CBV) from perfusion imaging, tissue volume mask (TVM), and free water corrected fractional anisotropy (FA-FWE) from diffusion imaging, were extracted. SVZ infiltration positively correlated with CBV, indicating higher perfusion in tumors. Significant CBV differences were noted between high and low SVZ infiltration cases at specific percentiles. Negative correlation was observed with TVM and positive correlation with FA-FWE, suggesting more infiltrative tumor growth. Significant differences in TVM and FA-FWE values were found between high and low SVZ infiltration cases. Glioblastomas with SVZ infiltration exhibit distinct imaging characteristics, including higher perfusion and lower cell density per voxel, indicating a more infiltrative growth and higher vascularization. Stem cell-like characteristics in SVZ-infiltrating cells could explain the increased infiltration and aggressive behavior. Understanding these imaging and biological correlations could enhance the understanding of glioblastoma evolution.

Office-Based Digital Workflow for Managing Old Mandibular Fracture Malunion: Use of Short-Segment Drilling and Osteotomy Guides With Predictive Holes.

Treating old mandibular fractures with malunion is challenging due to the need for osteotomy and interference between bone segments, complicating occlusal restoration. This technical note introduces a novel office-based digital workflow utilizing surgical guides. Preoperative computed tomography (CT) data were imported into MIMICS software for virtual surgical planning to precisely realign bone fragments based on anatomical and occlusal relationships. A pre-bent reconstruction plate was adapted to a 3-dimensional-printed mandible model, and cone beam CT scans captured detailed screw path data. This information was used in 3-matic software to design short-segment drilling guides (SSDGs), and osteotomy guides with predictive holes. These guides facilitated accurate bone osteotomy and realignment during surgery. This workflow addresses the limitations of previous SSDGs, offering a reliable solution for managing malunion in old mandibular fractures. The approach reduces surgical complexity, enhances precision, and streamlines treatment, providing an innovative method for challenging mandibular fractures.

The effect of midline shift on survival time in dogs with structural brain disease diagnosed on MRI.

The effect of midline shift identified on brain MRI on survival time in dogs with structural brain disease is relatively unknown. This retrospective single-centered cohort study reviewed medical and imaging data of 77 dogs with structural brain lesions evident on MRI. Images were reviewed for the presence of midline shift, brain edema, foramen magnum herniation, and ventriculomegaly. Kaplan-Meier method and Cox regression analysis were undertaken to compare survival between dogs with and without midline shift. Midline shift was present in 40 of 77 (52%) dogs and absent in 37 of 77 (48%). Univariate analysis revealed that dogs with midline shift had a median survival time of 34.5 days (95% CI, 4-108 days) compared with 241 days (95% CI, 133,- days) in dogs without midline shift (hazard ratio=2.67, 95% CI, 1.5-4.49). Multivariate Cox regression analysis revealed a hazard ratio of 3.6 (95% CI, 1.7-7.6; P-value<.001) for dogs with midline shift. Shorter median survival times remained significant in all groups after segregation based on etiological diagnosis. The significantly shorter survival times observed herein for dogs with midline shifts, regardless of etiologic cause, provide further evidence that midline shift holds value as a negative prognostic factor in diagnostic imaging.