Associations of Cerebral Small Vessel Disease and Chronic Kidney Disease in Patients With Acute Intracerebral Hemorrhage: A Cross-Sectional Study.

BACKGROUND AND OBJECTIVES: Chronic kidney disease (CKD) may be associated with the pathogenesis and phenotype of cerebral small vessel disease (SVD), which is the commonest cause of intracerebral hemorrhage (ICH). The purpose of this study was to investigate the associations of CKD with ICH neuroimaging phenotype, volume, and location, total burden of small vessel disease, and its individual components. METHODS: In 2 cohorts of consecutive patients with ICH evaluated with MRI, we investigated the frequency and severity of CKD based on established Kidney Disease Improving Global Outcomes criteria, requiring estimated glomerular filtration rate (eGFR) measurements <60 mL/min/1.732 ≥ 3 months apart to define CKD. MRI scans were rated for ICH neuroimaging phenotype (arteriolosclerosis, cerebral amyloid angiopathy, mixed location SVD, or cryptogenic ICH) and the presence of markers of SVD (white matter hyperintensities [WMHs], cerebral microbleeds [CMBs], lacunes, and enlarged perivascular spaces, defined according to the STandards for ReportIng Vascular changes on nEuroimaging criteria). We used multinomial, binomial logistic, and ordinal logistic regression models adjusted for age, sex, hypertension, and diabetes to account for possible confounding caused by shared risk factors of CKD and SVD. RESULTS: Of 875 patients (mean age 66 years, 42% female), 146 (16.7%) had CKD. After adjusting for age, sex, and comorbidities, patients with CKD had higher rates of mixed SVD than those with eGFR >60 (relative risk ratio 2.39, 95% CI 1.16-4.94, p = 0.019). Severe WMHs, deep microbleeds, and lacunes were more frequent in patients with CKD, as was a higher overall SVD burden score (odds ratio 1.83 for each point on the ordinal scale, 95% CI 1.31-2.56, p < 0.001). Patients with eGFR ≤30 had more CMBs (median 7 [interquartile range 1-23] vs 2 [0-8] for those with eGFR >30, p = 0.007). DISCUSSION: In patients with ICH, CKD was associated with SVD burden, a mixed SVD phenotype, and markers of arteriolosclerosis. Our findings indicate that CKD might independently contribute to the pathogenesis of arteriolosclerosis and mixed SVD, although we could not definitively account for the severity of shared risk factors. Longitudinal and experimental studies are, therefore, needed to investigate causal associations. Nevertheless, stroke clinicians should be aware of CKD as a potentially independent and modifiable risk factor of SVD.

Hematoma expansion prediction based on SMOTE and XGBoost algorithm.

Hematoma expansion (HE) is a high risky symptom with high rate of occurrence for patients who have undergone spontaneous intracerebral hemorrhage (ICH) after a major accident or illness. Correct prediction of the occurrence of HE in advance is critical to help the doctors to determine the next step medical treatment. Most existing studies focus only on the occurrence of HE within 6 h after the occurrence of ICH, while in reality a considerable number of patients have HE after the first 6 h but within 24 h. In this study, based on the medical doctors recommendation, we focus on prediction of the occurrence of HE within 24 h, as well as the occurrence of HE every 6 h within 24 h. Based on the demographics and computer tomography (CT) image extraction information, we used the XGBoost method to predict the occurrence of HE within 24 h. In this study, to solve the issue of highly imbalanced data set, which is a frequent case in medical data analysis, we used the SMOTE algorithm for data augmentation. To evaluate our method, we used a data set consisting of 582 patients records, and compared the results of proposed method as well as few machine learning methods. Our experiments show that XGBoost achieved the best prediction performance on the balanced dataset processed by the SMOTE algorithm with an accuracy of 0.82 and F1-score of 0.82. Moreover, our proposed method predicts the occurrence of HE within 6, 12, 18 and 24 h at the accuracy of 0.89, 0.82, 0.87 and 0.94, indicating that the HE occurrence within 24 h can be predicted accurately by the proposed method.

Necrotizing enterocolitis in a term newborn after spontaneous cerebral parenchymal hemorrhage: a case report.

BACKGROUND: Necrotizing enterocolitis (NEC) and intracranial hemorrhage are severe emergencies in the neonatal period. The two do not appear to be correlated. However, our report suggests that parenchymal brain hemorrhage in full-term newborns may put patients at risk for NEC by altering intestinal function through the brain-gut axis. CASE PRESENTATION: We present a case of spontaneous parenchymal cerebral hemorrhage in a full-term newborn who developed early-stage NEC on Day 15. CONCLUSIONS: It is possible to consider brain parenchymal hemorrhage as a risk factor for the appearance of NEC. Clinicians should be highly cautious about NEC in infants who have experienced parenchymal hemorrhage. This article is the first to discuss the relationship between parenchymal hemorrhage and NEC in full-term newborns.

HE-Mind: A model for automatically predicting hematoma expansion after spontaneous intracerebral hemorrhage.

PURPOSE: To develop and validate an end-to-end model for automatically predicting hematoma expansion (HE) after spontaneous intracerebral hemorrhage (sICH) using a novel deep learning framework. METHODS: This multicenter retrospective study collected cranial noncontrast computed tomography (NCCT) images of 490 patients with sICH at admission for model training (n = 236), internal testing (n = 60), and external testing (n = 194). A HE-Mind model was designed to predict HE, which consists of a densely connected U-net for segmentation process, a multi-instance learning strategy for resolving label ambiguity and a Siamese network for classification process. Two radiomics models based on support vector machine or logistic regression and two deep learning models based on residual network or Swin transformer were developed for performance comparison. Reader experiments including physician diagnosis mode and artificial intelligence mode were conducted for efficiency comparison. RESULTS: The HE-Mind model showed better performance compared to the comparative models in predicting HE, with areas under the curve of 0.849 and 0.809 in the internal and external test sets respectively. With the assistance of the HE-Mind model, the predictive accuracy and work efficiency of the emergency physician, junior radiologist, and senior radiologist were significantly improved, with accuracies of 0.768, 0.789, and 0.809 respectively, and reporting times of 7.26 s, 5.08 s, and 3.99 s respectively. CONCLUSIONS: The HE-Mind model could rapidly and automatically process the NCCT data and predict HE after sICH within three seconds, indicating its potential to assist physicians in the clinical diagnosis workflow of HE.

Lesion-Filling Index from Quantitative DSA Correlates with Hemorrhage of Cerebral AVM.

BACKGROUND AND PURPOSE: Rupture is the most life-threatening manifestation of cerebral AVMs. This study aimed to explore the hemodynamic mechanism of AVM rupture. We introduced a new quantitative DSA parameter that can reflect the degree of intranidal blood stasis, called the lesion-filling index. MATERIALS AND METHODS: This study examined patients with AVMs who had undergone both DSA and MR imaging between 2013 and 2014. Clinical presentations, angioarchitecture, and hemodynamic parameters generated from quantitative DSA were analyzed using univariate and multivariable logistic regression. The lesion-filling index was defined as the arterial diagnostic window divided by the volume of the AVM. To assess the correlation between the lesion-filling index and rupture, we incorporated the lesion-filling index into 2 published prediction models widely recognized for predicting AVM rupture risk, R2eD and VALE. The DeLong test was used to examine whether the addition of the lesion-filling index improved predictive efficacy. RESULTS: A total of 180 patients with AVMs were included. The mean lesion-filling index values in the ruptured group were higher compared with the unruptured group (390.27 [SD, 919.81] versus 49.40 [SD, 98.25]), P < .001). A higher lesion-filling index was significantly correlated with AVM rupture in 3 different multivariable logistic models, adjusting for angioarchitecture factors (OR = 1.004, P = .02); hemodynamic factors (OR = 1.005, P = .009); and combined factors (OR = 1.004, P = .03). Both R2eD (area under the curve, 0.601 versus 0.624; P = .15) and VALE (area under the curve, 0.603 versus 0.706; P < .001) predictive models showed improved predictive performance after incorporating the lesion-filling index and conducting 10-fold cross-validation. CONCLUSIONS: The lesion-filling index showed a strong correlation with AVM rupture, suggesting that overperfusion is the hemodynamic mechanism leading to AVM rupture.

Swirl sign score system: a novel and practical tool for predicting hematoma expansion risk after spontaneous intracerebral haemorrhage.

OBJECTIVE: To methodically analyse the swirl sign and construct a scoring system to predict the risk of hematoma expansion (HE) after spontaneous intracerebral haemorrhage (sICH). METHODS: We analysed 231 of 683 sICH patients with swirl signs on baseline noncontrast CT (NCCT) images. The characteristics of the swirl sign were analysed, including the number, maximum diameter, shape, boundary, minimum CT value of the swirl sign, and the minimum distance from the swirl sign to the edge of the hematoma. In the development cohort, univariate and multivariate analyses were used to identify independent predictors of HE, and logistic regression analysis was used to construct the swirl sign score system. The swirl sign score system was verified in the validation cohort. RESULTS: The number and the minimum CT value of the swirl sign were independent predictors of HE. The swirl sign score system was constructed (2 points for the number of swirl signs >1 and 1 point for the minimum CT value ≤41 Hounsfield units). The area under the curve of the swirl sign score system in predicting HE was 0.773 and 0.770 in the development and validation groups, respectively. CONCLUSIONS: The swirl sign score system is an easy-to-use radiological grading scale that requires only baseline NCCT images to effectively identify subjects at high risk of HE. ADVANCES IN KNOWLEDGE: Our newly developed semiquantitative swirl sign score system greatly improves the ability of swirl sign to predict HE.

Predicting hematoma expansion using machine learning: An exploratory analysis of the ATACH 2 trial.

INTRODUCTION: Hematoma expansion (HE) in patients with intracerebral hemorrhage (ICH) is a key predictor of poor prognosis and potentially amenable to treatment. This study aimed to build a classification model to predict HE in patients with ICH using deep learning algorithms without using advanced radiological features. METHODS: Data from the ATACH-2 trial (Antihypertensive Treatment of Acute Cerebral Hemorrhage) was utilized. Variables included in the models were chosen as per literature consensus on salient variables associated with HE. HE was defined as increase in either >33% or 6 mL in hematoma volume in the first 24 h. Multiple machine learning algorithms were employed using iterative feature selection and outcome balancing methods. 70% of patients were used for training and 30% for internal validation. We compared the ML models to a logistic regression model and calculated AUC, accuracy, sensitivity and specificity for the internal validation models respective models. RESULTS: Among 1000 patients included in the ATACH-2 trial, 924 had the complete parameters which were included in the analytical cohort. The median [interquartile range (IQR)] initial hematoma volume was 9.93.mm3 [5.03-18.17] and 25.2% had HE. The best performing model across all feature selection groups and sampling cohorts was using an artificial neural network (ANN) for HE in the testing cohort with AUC 0.702 [95% CI, 0.631-0.774] with 8 hidden layer nodes The traditional logistic regression yielded AUC 0.658 [95% CI, 0.641-0.675]. All other models performed with less accuracy and lower AUC. Initial hematoma volume, time to initial CT head, and initial SBP emerged as most relevant variables across all best performing models. CONCLUSION: We developed multiple ML algorithms to predict HE with the ANN classifying the best without advanced radiographic features, although the AUC was only modestly better than other models. A larger, more heterogenous dataset is needed to further build and better generalize the models.

Timing of Spot Sign Appearance, Spot Sign Volume, and Leakage Rate among Phases of Multiphase CTA Predict Intracerebral Hemorrhage Growth.

BACKGROUND AND PURPOSE: The presence of spot sign is associated with a high risk of hematoma growth. Our aim was to investigate the timing of the appearance, volume, and leakage rate of the spot sign for predicting hematoma growth in acute intracerebral hemorrhage using multiphase CTA. MATERIALS AND METHODS: In this single-center retrospective study, multiphase CTA in 3 phases was performed in acute intracerebral hemorrhage (defined as intraparenchymal ± intraventricular hemorrhages). Phases of the spot sign first appearance, spot sign volumes (microliter), and leakage rates among phases (microliter/second) were measured. Associations between baseline clinical and imaging variables including spot sign volume parameters (volume and leakage rate divided by median) and hematoma growth (>6 mL) were investigated using regression models. Receiver operating characteristic analysis was used as appropriate. RESULTS: Two hundred seventeen patients (131 men; median age, 70 years) were included. The spot sign was detected in 21.7%, 30.0%, and 29.0% in the first, second, and third phases, respectively, with median volumes of 19.7, 31.4, and 34.8 µl in these phases. Hematoma growth was seen in 44 patients (20.3%). By means of modeling, the following variables, namely the spot sign appearing in the first phase, first phase spot sign volume, spot sign appearing in the second or third phase, and spot sign positive and negative leakage rates, were associated with hematoma growth. Among patients with a spot sign, the absolute leakage rate accounting for both positive and negative leakage rates was also associated with hematoma growth (per 1-µl/s increase; OR, 1.26; 95% CI, 1.04-1.52). Other hematoma growth predictors were stroke history, baseline NIHSS score, onset-to-imaging time, and baseline hematoma volume (all P values < .05). CONCLUSIONS: The timing of the appearance of the spot sign, volume, and leakage rate were all associated with hematoma growth. Development of automated software to generate these spot sign volumetric parameters would be an important next step to maximize the potential of temporal intracerebral hemorrhage imaging such as multiphase CTA for identifying those most at risk of hematoma growth.

Abnormal dynamic functional connectivity in young nondisabling intracerebral hemorrhage patients.

OBJECTIVE: Previous resting-state functional magnetic resonance imaging studies on intracerebral hemorrhage patients have focused more on the static characteristics of brain activity, while the time-varying effects during scanning have received less attention. Therefore, the current study aimed to explore the dynamic functional network connectivity changes of intracerebral hemorrhage patients. METHODS: Using independent component analysis, the sliding window approach, and the k-means clustering analysis method, different dynamic functional network connectivity states were detected from resting-state functional magnetic resonance imaging data of 37 intracerebral hemorrhage patients and 44 healthy controls. The inter-group differences in dynamic functional network connectivity patterns and temporal properties were investigated, followed by correlation analyses between clinical scales and abnormal functional indexes. RESULTS: Ten resting-state networks were identified, and the dynamic functional network connectivity matrices were clustered into four different states. The transition numbers were decreased in the intracerebral hemorrhage patients compared with healthy controls, which was associated with trail making test scores in patients. The cerebellar network and executive control network connectivity in State 1 was reduced in patients, and this abnormal dynamic functional connectivity was positively correlated with the animal fluency test scores of patients. INTERPRETATION: The current study demonstrated the characteristics of dynamic functional network connectivity in intracerebral hemorrhage patients and revealed that abnormal temporal properties and functional connectivity may be related to the performance of different cognitive domains after ictus. These results may provide new insights into exploring the neurocognitive mechanisms of intracerebral hemorrhage.

Boston Criteria v2.0 for Cerebral Amyloid Angiopathy Without Hemorrhage: An MRI-Neuropathologic Validation Study.

BACKGROUND AND OBJECTIVES: Updated criteria for the clinical-MRI diagnosis of cerebral amyloid angiopathy (CAA) have recently been proposed. However, their performance in individuals without symptomatic intracerebral hemorrhage (ICH) presentations is less defined. We aimed to assess the diagnostic performance of the Boston criteria version 2.0 for CAA diagnosis in a cohort of individuals ranging from cognitively normal to dementia in the community and memory clinic settings. METHODS: Fifty-four participants from the Mayo Clinic Study of Aging or Alzheimer's Disease Research Center were included if they had an antemortem MRI with gradient-recall echo sequences and a brain autopsy with CAA evaluation. Performance of the Boston criteria v2.0 was compared with v1.5 using histopathologically verified CAA as the reference standard. RESULTS: The median age at MRI was 75 years (interquartile range 65-80) with 28/54 participants having histopathologically verified CAA (i.e., moderate-to-severe CAA in at least 1 lobar region). The sensitivity and specificity of the Boston criteria v2.0 were 28.6% (95% CI 13.2%-48.7%) and 65.3% (95% CI 44.3%-82.8%) for probable CAA diagnosis (area under the receiver operating characteristic curve [AUC] 0.47) and 75.0% (55.1-89.3) and 38.5% (20.2-59.4) for any CAA diagnosis (possible + probable; AUC 0.57), respectively. The v2.0 Boston criteria were not superior in performance compared with the prior v1.5 criteria for either CAA diagnostic category. DISCUSSION: The Boston criteria v2.0 have low accuracy in patients who are asymptomatic or only have cognitive symptoms. Additional biomarkers need to be explored to optimize CAA diagnosis in this population.

Red cell distribution width to lymphocyte ratio could serve as a new inflammatory biomarker for predicting hematoma expansion in patients with intracerebral hemorrhage.

BACKGROUND: Hematoma expansion is a critical factor associated with increased mortality and adverse outcomes in patients with intracerebral hemorrhage (ICH). Identifying and preventing hematoma expansion early on is crucial for effective therapeutic intervention. This study aimed to investigate the potential association between the Red cell distribution width to lymphocyte ratio (RDWLR) and hematoma expansion in ICH patients. METHODS: We conducted a retrospective analysis of clinical data from 303 ICH patients treated at our department between May 2018 and May 2023. Demographic, clinical, radiological, and laboratory data, including RDWLR upon admission, were assessed. Binary logistic regression analysis was employed to determine independent associations between various variables and hematoma expansion. RESULTS: The study included 303 ICH patients, comprising 167 (55.1%) males and 136 (44.9%) females, with a mean age of 65.25 ± 7.32 years at admission. Hematoma expansion occurred in 73 (24.1%) cases. Multivariate analysis revealed correlations between hematoma volume at baseline (OR, 2.73; 95% CI: 1.45 -4,78; P < 0.001), admission systolic blood pressure (OR, 2.98 ; 95% CI: 1.54-4.98; P < 0.001), Glasgow Coma Scale (GCS) (OR, 1.58; 95% CI: 1.25-2.46; P = 0.017), and RDWLR (OR, 1.58; 95% CI: 1.13-2.85; P = 0.022) and hematoma expansion in these patients. CONCLUSIONS: Our findings suggest that RDWLR could serve as a new inflammatory biomarker for hematoma expansion in ICH patients. This cost-effective and readily available biomarker has the potential for early prediction of hematoma expansion in these patients.

Real-time diagnosis of intracerebral hemorrhage by generating dual-energy CT from single-energy CT.

Real-time diagnosis of intracerebral hemorrhage after thrombectomy is crucial for follow-up treatment. However, this is difficult to achieve with standard single-energy CT (SECT) due to similar CT values of blood and contrast agents under a single energy spectrum. In contrast, dual-energy CT (DECT) scanners employ two different energy spectra, which allows for real-time differentiation between hemorrhage and contrast extravasation based on energy-related attenuation characteristics. Unfortunately, DECT scanners are not as widely used as SECT scanners due to their high costs. To address this dilemma, in this paper, we generate pseudo DECT images from a SECT image for real-time diagnosis of hemorrhage. More specifically, we propose a SECT-to-DECT Transformer-based Generative Adversarial Network (SDTGAN), which is a 3D transformer-based multi-task learning framework equipped with a shared attention mechanism. In this way, SDTGAN can be guided to focus more on high-density areas (crucial for hemorrhage diagnosis) during the generation. Meanwhile, the introduced multi-task learning strategy and the shared attention mechanism also enable SDTGAN to model dependencies between interconnected generation tasks, improving generation performance while significantly reducing model parameters and computational complexity. In the experiments, we approximate real SECT images using mixed 120kV images from DECT data to address the issue of not being able to obtain the true paired DECT and SECT data. Extensive experiments demonstrate that SDTGAN can generate DECT images better than state-of-the-art methods. The code of our implementation is available at https://github.com/jiang-cw/SDTGAN.

Biplane Enhancement Coil for Magnetic Induction Tomography of Cerebral Hemorrhage.

Magnetic Induction Tomography (MIT) is a non-invasive imaging technique used for dynamic monitoring and early screening of cerebral hemorrhage. Currently, there is a significant challenge in cerebral hemorrhage MIT due to weak detection signals, which seriously affects the accuracy of the detection results. To address this issue, a dual-plane enhanced coil was proposed by combining the target field method with consideration of the spatial magnetic field attenuation pattern within the imaging target region. Simulated detection models were constructed using the proposed coil and cylindrical coil as excitation coils, respectively, and simulation imaging tests were conducted using the detection results. The simulation results indicate that compared to the cylindrical coil, the proposed coil enhances the linearity of the magnetic field within the imaging target region by 60.43%. Additionally, it effectively enhances the detection voltage and phase values. The simulation results of hemorrhage detection show that the proposed coil improves the accuracy of hemorrhage detection by 18.26%. It provides more precise detection results, offering a more reliable solution for cerebral hemorrhage localization and detection.

Machine learning models for predicting early hemorrhage progression in traumatic brain injury.

This study explores the progression of intracerebral hemorrhage (ICH) in patients with mild to moderate traumatic brain injury (TBI). It aims to predict the risk of ICH progression using initial CT scans and identify clinical factors associated with this progression. A retrospective analysis of TBI patients between January 2010 and December 2021 was performed, focusing on initial CT evaluations and demographic, comorbid, and medical history data. ICH was categorized into intraparenchymal hemorrhage (IPH), petechial hemorrhage (PH), and subarachnoid hemorrhage (SAH). Within our study cohort, we identified a 22.2% progression rate of ICH among 650 TBI patients. The Random Forest algorithm identified variables such as petechial hemorrhage (PH) and countercoup injury as significant predictors of ICH progression. The XGBoost algorithm, incorporating key variables identified through SHAP values, demonstrated robust performance, achieving an AUC of 0.9. Additionally, an individual risk assessment diagram, utilizing significant SHAP values, visually represented the impact of each variable on the risk of ICH progression, providing personalized risk profiles. This approach, highlighted by an AUC of 0.913, underscores the model's precision in predicting ICH progression, marking a significant step towards enhancing TBI patient management through early identification of ICH progression risks.

Improved differentiation of cavernous malformation and acute intraparenchymal hemorrhage on CT using an AI algorithm.

This study aimed to evaluate the utility of an artificial intelligence (AI) algorithm in differentiating between cerebral cavernous malformation (CCM) and acute intraparenchymal hemorrhage (AIH) on brain computed tomography (CT). A retrospective, multireader, randomized study was conducted to validate the performance of an AI algorithm in differentiating AIH from CCM on brain CT. CT images of CM and AIH (< 3 cm) were identified from the database. Six blinded reviewers, including two neuroradiologists, two radiology residents, and two emergency department physicians, evaluated CT images from 288 patients (CCM, n = 173; AIH, n = 115) with and without AI assistance, comparing diagnostic performance. Brain CT interpretation with AI assistance resulted in significantly higher diagnostic accuracy than without (86.92% vs. 79.86%, p < 0.001). Radiology residents and emergency department physicians showed significantly improved accuracy of CT interpretation with AI assistance than without (84.21% vs. 75.35%, 80.73% vs. 72.57%; respectively, p < 0.05). Neuroradiologists showed a trend of higher accuracy with AI assistance in the interpretation but lacked statistical significance (95.83% vs. 91.67%, p = 0.56). The use of an AI algorithm can enhance the differentiation of AIH from CCM in brain CT interpretation, particularly for nonexperts in neuroradiology.

Ultrasound combined with urokinase under key-shaped bone window enhances blood clot lysis in an in vitro model of spontaneous intracerebral hemorrhage.

OBJECTIVE: Minimally invasive surgery for spontaneous intracerebral hemorrhage is impeded by inadequate lysis of the target blood clot. Ultrasound is thought to expedite intravascular thrombolysis, thereby facilitating vascular recanalization. However, the impact of ultrasound on intracerebral blood clot lysis remains uncertain. This study aimed to explore the feasibility of combining ultrasound with urokinase to enhance blood clot lysis in an in vitro model of spontaneous intracerebral hemorrhage. METHODS: The blood clots were divided into four groups: control group, ultrasound group, urokinase group, and ultrasound + urokinase group. Using our experimental setup, which included a key-shaped bone window, we simulated a minimally invasive puncture and drainage procedure for spontaneous intracerebral hemorrhage. The blood clot was then irradiated using ultrasound. Blood clot lysis was assessed by weighing the blood clot before and after the experiment. Potential adverse effects were evaluated by measuring the temperature variation around the blood clot in the ultrasound + urokinase group. RESULTS: A total of 40 blood clots were observed, with 10 in each experimental group. The blood clot lysis rate in the ultrasound group, urokinase group, and ultrasound + urokinase group (24.83 ± 4.67%, 47.85 ± 7.09%, 61.13 ± 4.06%) was significantly higher than that in the control group (16.11 ± 3.42%) (p = 0.02, p < 0.001, p < 0.001). The blood clot lysis rate in the ultrasound + urokinase group (61.13 ± 4.06%) was significantly higher than that in the ultrasound group (24.83 ± 4.67%) (p < 0.001) or urokinase group (47.85 ± 7.09%) (p < 0.001). In the ultrasound + urokinase group, the mean increase in temperature around the blood clot was 0.26 ± 0.15°C, with a maximum increase of 0.38 ± 0.09°C. There was no significant difference in the increase in temperature regarding the main effect of time interval (F = 0.705, p = 0.620), the main effect of distance (F = 0.788, p = 0.563), or the multiplication interaction between time interval and distance (F = 1.100, p = 0.342). CONCLUSIONS: Our study provides evidence supporting the enhancement of blood clot lysis in an in vitro model of spontaneous intracerebral hemorrhage through the combined use of ultrasound and urokinase. Further animal experiments are necessary to validate the experimental methods and results.

Neurodevelopmental outcome in preterm infants with intraventricular hemorrhages: the potential of quantitative brainstem MRI.

OBJECTIVES: This retrospective study aimed to identify quantitative magnetic resonance imaging markers in the brainstem of preterm neonates with intraventricular hemorrhages. It delves into the intricate associations between quantitative brainstem magnetic resonance imaging metrics and neurodevelopmental outcomes in preterm infants with intraventricular hemorrhage, aiming to elucidate potential relationships and their clinical implications. MATERIALS AND METHODS: Neuroimaging was performed on preterm neonates with intraventricular hemorrhage using a multi-dynamic multi-echo sequence to determine T1 relaxation time, T2 relaxation time, and proton density in specific brainstem regions. Neonatal outcome scores were collected using the Bayley Scales of Infant and Toddler Development. Statistical analysis aimed to explore potential correlations between magnetic resonance imaging metrics and neurodevelopmental outcomes. RESULTS: Sixty preterm neonates (mean gestational age at birth 26.26 ± 2.69 wk; n = 24 [40%] females) were included. The T2 relaxation time of the midbrain exhibited significant positive correlations with cognitive (r = 0.538, P < 0.0001, Pearson's correlation), motor (r = 0.530, P < 0.0001), and language (r = 0.449, P = 0.0008) composite scores at 1 yr of age. CONCLUSION: Quantitative magnetic resonance imaging can provide valuable insights into neurodevelopmental outcomes after intraventricular hemorrhage, potentially aiding in identifying at-risk neonates. Multi-dynamic multi-echo sequence sequences hold promise as an adjunct to conventional sequences, enhancing the sensitivity of neonatal magnetic resonance neuroimaging and supporting clinical decision-making for these vulnerable patients.