Brain Computed Tomography Imaging Research Articles

The enhanced reliability of higher national institute of health stroke scale thresholds over the conventional 6-point scale

IntroductionIt is still uncertain if higher thresholds on National Institute of Health Stroke Scale (NIHSS) are better predictors of large infarctions than the conventional 6-point cutoff. MethodsWe used 6-point and higher NIHSS thresholds including 8, 9, and 10-point to predict relative infarct areas, expressed as percentage of the affected hemisphere on axial brain computed tomography images, beginning at 5% with 5% increments each time until reaching the 40% cutoff for large infarctions, or achieving 100% sensitivity. Results were compared using area under the receiver operating characteristic curves (AUROC). ResultsWe enrolled 151 patients of acute ischemic stroke (Mean age: 62.88 years ± 12.71; Female: 48.34%). 77 patients (50.99%) exhibited left hemisphere strokes, while 74 (49%) had right hemisphere involvement. Sensitivity values of the 6-point for infarcts measuring 5%, 10%, 20%, 30%, and 40% were 62%, 64%, 77%, 82%, and 100%, respectively. At 40% infarct-size, 8-point achieved comparable results (52%, 55%, 69%, 76%, 100%), closely aligning with the 9-point (50%, 53%, 69%, 76%, 100%). The10-point was slightly trailing behind in sensitivity at 40% infarct-core (96%). Moreover, higher thresholds exhibited improved false-positive rates (FPR). At 40% infarct size, the FPRs of 6, 8, 9, and 10 points were 39%, 27%, 27%, and 21% respectively. Higher thresholds had augmented AUROC values (0.86, 0.86, 0.89) as compared to the 6-point (0.80). Logistic regression identified 14-point as definitive cutoff for large infarctions. ConclusionHigher thresholds can better differentiate small and medium infarcts as true-negatives and substantially reduce false-positive referrals for mechanical thrombectomy.

Effect of Target Temperature Management on Optic Nerve Sheath Diameter in Post-Cardiac Arrest Patients.

Target Temperature Management (TTM) is a procedure used in post-cardiac arrest (CA) patients to reduce mortality and morbidity. The goal of this study was to investigate the link between intracranial pressure (ICP) and optic nerve sheath diameter (ONSD) in this patient group, which has a high mortality rate, despite TTM, and to see if ONSD may be used to predict mortality. The research was designed to be a retrospective observational study. The study comprised patients who were followed up on in a tertiary intensive care unit, had post-CA TTM, and had brain computed tomography (BCT) before and 0-6 hours after TTM. ONSD measurements were acquired from patients' BCT images recorded before and after TTM. The difference in pre-TTM ONSD and post-TTM ONSD measurements in all post-CA patients, as well as the difference in pre-TTM ONSD and post-TTM ONSD measurements in surviving and deceased patients, was compared. The study involved 33 participants. The patients' average age was 60.58-12.39 years, and 75.8% were male. Around 51.5% of the patients died. When the pre-TTM and post-TTM ONSDs of all patients were compared, there was no statistically significant difference (p = 0.856). When the percentage change (Δ) values between the post-TTM ONSD and pre-TTM ONSD and post-TTM ONSD measures of the surviving patients and the deceased patients were compared, a difference was observed (p < 0.01). Increased ICP in post-CA patients is a significant clinical issue associated with mortality and poor neurological prognosis. ONSD measurement may be useful in monitoring ICP, which may rise, despite TTM, and higher ONSD measurements may be used as an indicator for mortality in post-CA patients, who have received TTM.

Outcome prediction of cardiac arrest with automatically computed gray-white matter ratio on computed tomography images

BackgroundThis study aimed to develop an automated method to measure the gray-white matter ratio (GWR) from brain computed tomography (CT) scans of patients with out-of-hospital cardiac arrest (OHCA) and assess its significance in predicting early-stage neurological outcomes.MethodsPatients with OHCA who underwent brain CT imaging within 12 h of return of spontaneous circulation were enrolled in this retrospective study. The primary outcome endpoint measure was a favorable neurological outcome, defined as cerebral performance category 1 or 2 at hospital discharge. We proposed an automated method comprising image registration, K-means segmentation, segmentation refinement, and GWR calculation to measure the GWR for each CT scan. The K-means segmentation and segmentation refinement was employed to refine the segmentations within regions of interest (ROIs), consequently enhancing GWR calculation accuracy through more precise segmentations.ResultsOverall, 443 patients were divided into derivation N=265, 60% and validation N=178, 40% sets, based on age and sex. The ROI Hounsfield unit values derived from the automated method showed a strong correlation with those obtained from the manual method. Regarding outcome prediction, the automated method significantly outperformed the manual method in GWR calculation (AUC 0.79 vs. 0.70) across the entire dataset. The automated method also demonstrated superior performance across sensitivity, specificity, and positive and negative predictive values using the cutoff value determined from the derivation set. Moreover, GWR was an independent predictor of outcomes in logistic regression analysis. Incorporating the GWR with other clinical and resuscitation variables significantly enhanced the performance of prediction models compared to those without the GWR.ConclusionsAutomated measurement of the GWR from non-contrast brain CT images offers valuable insights for predicting neurological outcomes during the early post-cardiac arrest period.

Improving the quality of computed tomography brain images in the presence of cochlear implant induced metal artefacts through the additional use of tissue mimicking materials alongside metal artefact reduction software

IntroductionMetal artefact reduction software (MAR) can be used to improve Computed Tomography (CT) image quality in the presence of implanted metalwork; however, this software is not effective for superficial metallic structures such as cochlear implants (CI).This study aimed to investigate whether the effectiveness of MAR software could be improved for brain scans with CI present through the use of tissue mimicking materials (TMM) placed exteriorly to the implant. MethodsIn this two-part study, a CI was positioned on the surface of water and anthropomorphic phantoms and imaged using a helical CT brain protocol. Three TMM, Superflab, Sure Thermal heat packs, and Bart's Bolus, were utilised and images were acquired to assess the resulting artefact reduction in terms of CT numbers, noise and artefact index (Aind). Changes in CTDIvol were assessed for the anthropomorphic phantom scans. ResultsIn the water phantom, statistically significant reductions in CT number (p = 0.038) and noise (p = 0.033) were observed for Superflab, whilst the heat packs produced similar significant reductions in CT number (p < 0.001) and noise (p = 0.001) for the anthropomorphic phantom images. Aind values were significantly reduced through the use of Superflab (p = 0.009) and the heat packs (p < 0.001). No significant effects were observed for Bart's Bolus. CTDIvol increases of generally less than 5% were observed for scans with TMM in place. ConclusionThe additional use of TMM alongside MAR software yielded statistically significant reductions in CI induced metal artefacts on both water and anthropomorphic phantom scans with minimal dose increases. Implications for practiceThe extent of metal artefacts in clinical head scans with CI in place could be significantly reduced through combined use of TMM and MAR software, consequently providing greater diagnostic confidence in the images.

Diagnostic Utility of Glial Fibrillary Acidic Protein Beyond 12 Hours After Traumatic Brain Injury: A TRACK-TBI Study.

Blood levels of glial fibrillary acidic protein (GFAP) and ubiquitin carboxyl-terminal hydrolase-L1 (UCH-L1) within 12h of suspected traumatic brain injury (TBI) have been approved by the Food and Drug administration to aid in determining the need for a brain computed tomography (CT) scan. The current study aimed to determine whether this context of use can be expanded beyond 12h post-TBI in patients presenting with Glasgow Coma Scale (GCS) 13-15. The prospective, 18-center Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study enrolled TBI participants aged ≥17 years who presented to a United States Level 1 trauma center and received a clinically indicated brain CT scan within 24h post-injury, a blood draw within 24h and at 14 days for biomarker analysis. Data from participants with emergency department arrival GCS 13-15 and biomarker values at days 1 and 14 were extracted for the primary analysis. A subgroup of hospitalized participants with serial biomarkers at days 1, 3, 5, and 14 were analyzed, including plasma GFAP and UCH-L1, and serum neuron-specific enolase (NSE) and S100 calcium-binding protein B (S100B). The primary analysis compared biomarker values dichotomized by head CT results (CT+/CT-). Area under receiver-operating characteristic curve (AUC) was used to determine diagnostic accuracy. The overall cohort included 1142 participants with initial GCS 13-15, with mean age 39.8 years, 65% male, and 73% Caucasian. The GFAP provided good discrimination in the overall cohort at days 1 (AUC = 0.82) and 14 (AUC = 0.72), and in the hospitalized subgroup at days 1 (AUC = 0.84), 3 (AUC = 0.88), 5 (AUC = 0.82), and 14 (AUC = 0.74). The UCH-L1, NSE, and S100B did not perform well (AUC = 0.51-0.57 across time points). This study demonstrates the utility of GFAP to aid in decision-making for diagnostic brain CT imaging beyond the 12h time frame in patients with TBI who have a GCS 13-15.

Consideration of Brain CT Imaging Standard for Mild Head Injuries.

It has been reported that various clinical criteria indicate computed tomography (CT) examination for mild head injury (MHI). However, the decision to perform CT for MHI largely depends on the physician. Data on severe head injuries is available in sources such as the Japan Neurotrauma Data Bank, but only a few data has been collected on MHI. A total of 1688 patients with MHI (Glasgow Coma Scale 14 and 15) treated at our hospital from June 2017 to May 2019 were reviewed. CT was performed in 1237 patients (73.28%), and intracranial hemorrhage was detected in 50 patients. Three patients deteriorated, and all were surgically treated. Statistical analysis of the presence or absence of acute intracranial hemorrhage and "risk factors for complications of intracranial lesions in MHI" showed significant differences in unclear or ambiguous accident history (p = 0.022), continued post-traumatic amnesia (p < 0.01), trauma above the clavicles including clinical signs of skull fracture (skull base or depressed skull fracture) (p = 0.012), age <60 years (p < 0.01), coagulation disorders (p < 0.01), and alcohol or drug intoxication (p < 0.01). The 453 patients who did not satisfy these risk factors included only one patient with intracranial hemorrhage, so the negative predictive value was 99.78%. This study shows that the "risk factors for complications of intracranial lesions in MHI" are effective criteria for excluding acute intracranial hemorrhage and CT should be actively considered for patients with the above factors that showed significant differences.

Complex Combination of Pulmonary Embolism, Stroke, and ST-Segment Elevation Myocardial Infarction in Young Patient with Recent COVID-19 Infection and Newly Diagnosed Lung Carcinoma

BACKGROUND: Ischemic stroke, pulmonary embolism (PE), and acute ST-segment elevation myocardial infarction (STEMI) are very rarely all present in younger patients without classical risk factors or a family history of dyslipidemia or cardiovascular disease. They represent a medical emergency and necessitate fast diagnosis, treatment, and search for etiology, not explained purely by atherosclerosis. Thrombophilia testing should be considered in younger patients with several major thrombotic episodes, although there is much disagreement concerning the importance of homocysteine level testing in patients with arterial and venous thrombotic events, including PE. COVID-19 infection may trigger several thrombotic complications caused by unique immunothrombotic processes. CASE PRESENTATION: A 44-year-old patient complaining of chest pain was admitted at our clinic due to acute anterior STEMI. He has a previous medical history of ischemic stroke, COVID-19 infection, and PE in the past 2 months. Newly, diagnosed lung adenocarcinoma was confirmed by biopsy 10 days before STEMI admission, without surgical or previous hemotherapy. The patient was on regular anticoagulant therapy with rivaroxaban 20 mg OAD and antiplatelet therapy with Aspirin due to previous PE and ischemic stroke. Patient denied standard risk factors, family history for cardiovascular disease or any previous minor or major bleedings, history of anemia, liver, or renal dysfunction. Acute stroke was confirmed by brain computed tomography imaging. Thrombophilia panel testing revealed homozygote mutations for MTHFR 677 gene, heterozygote mutation for integrin subunit alpha 2, and fibrinogen B beta chain) genes. The patient was treated with low molecular weight heparin, aspirin, clopidogrel, and heart failure therapy in the acute phase. Clinical consultations were performed with a team of doctors which included a cardiologist, neurologist, and oncologist. Patients have a high bleeding risk, assessed by HAS-BLEED score of 4. CONCLUSION: Our patient is a rare case of serious multi-vascular thrombotic events with underlying thrombophilia, lung cancer, and past COVID-19 infection complicated by pulmonary embolism, ischemic stroke, and STEMI.

Determination of symmetry plane on human brain CT images

A method of automatic detection of human brain symmetry plain in computed tomography (CT) images is described. An algorithm consists of four steps: region of interest, symmetry curve detection, symmetry axis evaluation and calculation of symmetry plane. Symmetry plane are calculated using vertex slices of human brain CT images. There are approximately 40 which has symmetry curve `` ` Falx cerebri'' property. In the first, using this feature symmetry axis in slice detected. In second, coordinates of detected symmetry axis and least square method are used to calculate symmetry plane. In others images where symmetry property is not available symmetry axis is projection of calculated plane.

Weakly guided attention model with hierarchical interaction for brain CT report generation

Brain Computed Tomography (CT) report generation, which aims to assist radiologists in diagnosing cerebrovascular diseases efficiently, is challenging in feature representation for dozens of images and language descriptions with several sentences. Existing report generation methods have achieved significant achievement based on the encoder–decoder framework and attention mechanism. However, current research has limitations in solving the many-to-many alignment between the multi-images of Brain CT imaging and the multi-sentences of Brain CT report, and fails to attend to critical images and lesion areas, resulting in inaccurate descriptions. In this paper, we propose a novel Weakly Guided Attention Model with Hierarchical Interaction, named WGAM-HI, to improve Brain CT report generation. Specifically, WGAM-HI conducts many-to-many matching for multiple visual images and semantic sentences via a hierarchical interaction framework with a two-layer attention model and a two-layer report generator. In addition, two weakly guided mechanisms are proposed to facilitate the attention model to focus more on important images and lesion areas under the guidance of pathological events and Gradient-weighted Class Activation Mapping (Grad-CAM) respectively. The pathological event acts as a bridge between the essential serial images and the corresponding sentence, and the Grad-CAM bridges the lesion areas and pathology words. Therefore, under the hierarchical interaction with the weakly guided attention model, the report generator generates more accurate words and sentences. Experiments on the Brain CT dataset demonstrate the effectiveness of WGAM-HI in attending to important images and lesion areas gradually, and generating more accurate reports.

Posttraumatic hydrocephalus: Recent advances and new therapeutic strategies.

Hydrocephalus or ventriculomegaly is a condition brought on by an overabundance of cerebrospinal fluid (CSF) in the ventricular system. The major contributor to posttraumatic hydrocephalus (PTH) is traumatic brain injuries (TBIs), especially in individuals with occupations set in industrial settings. A variety of criteria have been employed for the diagnosis of PTH, including the combination of neurological symptoms like nerve deficits and headache, as well as an initial improvement followed by a worsened relapse of altered consciousness and neurological deterioration, which is detected by computed tomography-brain imaging that reveals gradual ventriculomegaly. In this article, we discuss and summarize briefly the current understandings and advancements in the management of PTH. The available literature for this review was searched on various bibliographic databases using an individually verified, prespecified approach. The level of evidence of the included studies was considered as per the Centre for Evidence-Based Medicine recommendations. The commonly practiced current treatment modality involves shunting CSF but is often associated with complications and recurrence. The lack of a definitive management strategy for PTH warrants the utilization of novel and innovative modalities such as stem cell transplantations and antioxidative stress therapies. One of the worst complications of a TBI is PTH, which has a high morbidity and mortality rate. Even though there hasn't been a successful method in stopping PTH from happening, hemorrhage-derived blood, and its metabolic by-products, like iron, hemoglobin, free radicals, thrombin, and red blood cells, may be potential targets for PTH hindrance and management. Also, using stem cell transplantations in animal models and antioxidative stress therapies in future studies can lower PTH occurrence and improve its outcome. Moreover, the integration of clinical trials and theoretical knowledge should be encouraged in future research projects to establish effective and updated management guidelines for PTH.

Reporting Diagnostic Reference Levels for Paediatric Patients Undergoing Brain Computed Tomography.

Brain computed tomography (CT) is a diagnostic imaging tool routinely used to assess all paediatric neurologic disorders and other head injuries. Despite the continuous development of paediatric CT imaging, radiation exposure remains a concern. Using diagnostic reference levels (DRLs) helps to manage the radiation dose delivered to patients, allowing one to identify an unusually high dose. In this paper, we propose DRLs for paediatric brain CT examinations in Saudi clinical practices and compare the findings with those of other reported DRL studies. Data including patient and scanning protocols were collected retrospectively from three medical cities for a total of 225 paediatric patients. DRLs were derived for four different age groupings. The resulting DRL values for the dose-length product (DLP) for the age groups of newborns (0-1 year), 1-y-old (1-5 years), 5-y-old (5-10 years) and 10-y-old (10-15 years) were 404 mGy cm, 560 mGy cm, 548 mGy cm, and 742 mGy cm, respectively. The DRLs for paediatric brain CT imaging are comparable to or slightly lower than other DRLs due to the current use of dose optimisation strategies. This study emphasises the need for an international standardisation for the use of weight group categories in DRL establishment for paediatric care in order to provide a more comparable measurement of dose quantities across different hospitals globally.

Intracranial hemorrhage detection in 3D computed tomography images using a bi-directional long short-term memory network-based modified genetic algorithm.

Intracranial hemorrhage detection in 3D Computed Tomography (CT) brain images has gained more attention in the research community. The major issue to deal with the 3D CT brain images is scarce and hard to obtain the labelled data with better recognition results. To overcome the aforementioned problem, a new model has been implemented in this research manuscript. After acquiring the images from the Radiological Society of North America (RSNA) 2019 database, the region of interest (RoI) was segmented by employing Otsu's thresholding method. Then, feature extraction was performed utilizing Tamura features: directionality, contrast, coarseness, and Gradient Local Ternary Pattern (GLTP) descriptors to extract vectors from the segmented RoI regions. The extracted vectors were dimensionally reduced by proposing a modified genetic algorithm, where the infinite feature selection technique was incorporated with the conventional genetic algorithm to further reduce the redundancy within the regularized vectors. The selected optimal vectors were finally fed to the Bi-directional Long Short Term Memory (Bi-LSTM) network to classify intracranial hemorrhage sub-types, such as subdural, intraparenchymal, subarachnoid, epidural, and intraventricular. The experimental investigation demonstrated that the Bi-LSTM based modified genetic algorithm obtained 99.40% sensitivity, 99.80% accuracy, and 99.48% specificity, which are higher compared to the existing machine learning models: Naïve Bayes, Random Forest, Support Vector Machine (SVM), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) network.

Misdiagnosis of acute basilar artery occlusion (BAOs) in the emergency department in vietnam: A single-center, retrospective, cross-sectional study

BackgroundIschemic stroke caused by basilar artery occlusions (BAOs) poses a risk for misdiagnosis, leading to an increase the rates of mortality and disability, particularly in low-, and middle-income countries. Methods: A retrospective and cross-sectional study evaluated 21 patients with an initial misdiagnosis of BAO among 110 BAO patients from 7 district hospitals who were subsequently admitted to the stroke center at the 103 Military Hospital from June 2021 to September 2022 in Ha Noi city, Vietnam. Patient data collected were age, sex, medical history, initial diagnosis, neuroradiologic findings, and treatment outcome at 1 month after hospital discharge. Results: The rate for misdiagnosis for BAOs in the initial emergency department (ED) was 19.1%. The average patient age was 68.9 (63–81) years. The most common symptom at the disease onset was dizziness, followed by altered consciousness, seizures, quadriplegia, hemiplegia, functional swallowing disorder, and breathing disorders. Vestibular disorder was the most common misdiagnosis of BAOs, followed by epilepsy, hypoglycemia, and delirium tremens, impaired consciousness with unknown cause, polyneuritis, and aspiration pneumonia. Cerebral infarction was not detected in all brain computed tomography images, but was detected in brain magnetic resonance images. The average score of the Modified Rankin Scale was 5.4 ± 0.5, and 47.6% of the patients who died after 1 month from the hospital discharge. Conclusion: Misdiagnosis of BAOs at the initial ED visit resulted in serious consequences. We highly recommend the use of brain magnetic resonance imaging as the preferred imaging method for patients with suspected BAOs who present to the ED.All authors certify that they have participated sufficiently in the work to take public responsibility for the content, including participation in the concept, design, analysis, writing, or revision of the manuscript. All authors have read and approved the final submitted revised manuscript.

Noise reduction performance of a deep learning-based reconstruction in brain computed tomography images acquired with organ-based tube current modulation.

We aimed to evaluate the image quality of brain computed tomography (CT) images reconstructed using deep learning-based reconstruction (DLR) in organ-based tube current modulation (OB-TCM) acquisition. An anthropomorphic head phantom and a cylindrical low-contrast phantom were scanned at the standard dose level for adult brain CT in axial volume acquisition without OB-TCM. Moreover, image acquisition with OB-TCM was performed. The radiation dose on the eye lens was measured using a scintillation fibre-optic dosimeter placed on the anthropomorphic phantom's eye surface. The task transfer function (TTF), contrast-to-noise ratio (CNR), and low-contrast object specific CNR obtained from low-contrast phantom images reconstructed with filtered back projection (FBP), hybrid iterative reconstruction (HIR), and two types of DLR (DLRCTA and DLRLCD) were compared. In result, OB-TCM achieved a 32.5% dose reduction in the eye lens. Although HIR, DLRCTA, and DLRLCD showed lower TTF than FBP, the difference in TTF at the highest contributing spatial frequency corresponding to the contrast rod diameter was < 10%. Despite the OB-TCM acquisition, DLRCTA and DLRLCD achieved significantly lower noise and a higher CNR than FBP without OB-TCM (p < 0.05). However, low-contrast object specific CNR was equivalent among all reconstruction methods for the objective diameter of 5mm and slightly improved in DLRLCD for the objective diameter of 7mm. DLR with OB-TCM acquisition enabled dose reduction for the eye lens and high CNR image appearance, whereas the low contrast detectability evaluated by low-contrast object specific CNR did not always improve.

Assessment of Unjustified Brain Ct Requests in The Emergency Room of The Public Health Care System in Palestine Case Study: Hebron Governmental Hospital

Computed tomography (CT), which gives doctors a precise cross-sectional image that clearly shows the features of the organs, is one of the most crucial pieces of medical imaging equipment. In contrast to other equipment, the CT scan may expose the patient to a very high dose of radiation. As a result of the hazards associated with imaging patients, the use of CT images must be restricted, especially when imaging the head. The aim of the study is to the evaluation of the percentage of unjustified brain CT requests at the emergency rooms of the governmental health sector in Palestine. The 1957-built Hebron Governmental Hospital (HGH) was chosen as a research study case with a clinical capacity of 237 beds and 596 staff members; it is one of the key Palestinian government hospitals currently operating in the Hebron Governorate of the West Bank-Palestine. Samples were taken from the hospital's emergency room between the first of the year and the end of the year in 2021. 6152 or so brain CT images were gathered. Following that, 500 instances were picked for the investigation. Radiologists were asked to assess 100 of these 500 cases to determine whether the patient needed a scan or not. According to the study, 78% of brain tomography images were for unjustifiable reasons. Additionally, it was revealed by the study's results that unnecessary head CT scans were more common than those from retrospective investigations. In particular, non-specialized doctors who request cross-sectional brain imaging for no apparent reason are to blame for this because of their lack of knowledge.

Prediction of Anemia From Cerebral Venous Sinus Attenuation on Deep-Learning Reconstructed Brain Computed Tomography Images.

The aim of the study is to evaluate whether the prediction of anemia is possible using quantitative analyses of unenhanced cranial computed tomography (CT) with deep learning reconstruction (DLR) compared with conventional methods. This cross-sectional retrospective study included 116 participants (76 males; mean age, 66.7) who had hemoglobin (Hb) levels obtained within 24 hours of unenhanced cranial CT, which included 2 reconstruction methods: DLR and hybrid iterative reconstruction. Regions of interest were the confluence of sinuses (CoS) and the right and left transverse sinuses. In addition, edge rise distance of cerebrospinal fluid and venous was measured. Spearman rank correlation coefficient demonstrated a positive association between Hb levels and sinus attenuation values. Among these, the CoS in DLR had the best correlation (r = 0.703, P < 0.001). For the prediction of anemia (Hb < 11 g/dL), the area under the curve of CoS in DLR (area under the curve = 0.874; 95% confidence interval, 0.798-0.949; P < 0.001) was the highest; however, there were no significant differences among reconstruction method and sinus. The attenuation values of DLR were significantly higher than those of hybrid iterative reconstruction (P < 0.001, paired t test), and the differences between the 2 methods were 4.1 (standard deviation [SD], 1.6) for CoS, 5.2 (SD, 2.2) for right transverse sinuses, and 5.8 (SD, 2.4) for left transverse sinuses. The signal-to-noise ratio (P < 0.001, paired t test) and edge rise distance (P < 0.001, Wilcoxon signed rank test) of DLR was significantly higher. Higher CT attenuation values should be considered for predicting anemia based on brain DLR images.

Assessment of Brain Computed Tomography as a Practical Way to Predict Neurological Outcomes in Post-Cardiac Arrest Survivors

Objective: To evaluate whether a visual inspection of ischemic changes in brain computed tomography (CT) images, which is very practical in clinical settings, could predict neurological outcomes in post-cardiac arrest survivors. Materials and Methods: The authors retrospectively reviewed medical records and brain CT images of 62 patients who remained comatose after surviving cardiac arrest and had undergone a CT scan within 24 hours to seven days. Neurological outcomes at one month were assessed using the Cerebral Performance Category (CPC). The CPC scores of 1-2 and 3-5 referred to good and poor neurological outcomes, respectively. Findings from the inspection of the images were graded into grade 1 for absence of acute ischemic change, grade 2 for decreased attenuation of the grey matter in some brain region(s), and grade 3 for diffused loss of grey-white matter differentiation or apparent brain swelling. An experienced neurologist and a consensus group of four pre-clerkship medical students, blinded to the neurological outcomes, evaluated the grade of ischemic changes in CT images. Results: Positive correlations were observed between CPC and CT grading by both the neurologist (ρ=0.76, 95% CI 0.63 to 0.90, p&lt;0.001) and medical students (ρ=0.57, 95% CI 0.38 to 0.77, p&lt;0.001). The CT grading of 2 or more by the neurologist could predict poor neurological outcomes with specificity of 1.00, sensitivity of 0.89, and receiver operating characteristic (ROC) AUC of 0.94 (95% CI 0.89 to 1.00). The evaluation by medical students showed an ROC AUC of 0.80 (95% CI 0.64 to 0.96). Conclusion: The simple visual inspection of ischemic changes in brain CT images showed a high diagnostic accuracy and could be a practical method for predicting neurological outcomes in post-cardiac arrest survivors. Keywords: Cardiac arrest; Post-cardiac arrest ischemic brain injury; Neurological outcomes; Computerized tomography

Ischemia and Hemorrhage detection in CT images with Hyper parameter optimization of classification models and Improved UNet Segmentation Model

Deep learning is a powerful technique that has been applied to the task of stroke detection using medical imaging. Stroke is a medical condition that occurs when the blood supply to the brain is interrupted, which can cause brain damage and other serious complications. Detection of stroke is important in order to minimize damage and improve patient outcomes. One of the most common imaging modalities used for stroke detection is CT(Computed Tomography). CT can provide detailed images of the brain and can be used to identify the presence and location of a stroke. Deep learning models, particularly convolutional neural networks (CNNs), have shown promise for the task of stroke detection using CT images. These models can learn to automatically identify patterns in the images that are indicative of a stroke, such as the presence of an infarct or hemorrhage. Some examples of deep learning models used for stroke detection in CT images are U-Net, which is commonly used for medical image segmentation tasks, and CNNs, which have been trained to classify brain CT images into normal or abnormal.&#x0D; The purpose of this study is to identify the type of stroke from brain CT images taken without the administration of a contrast agent, i.e. occlusive (ischemic) or hemorrhagic (hemorrhagic). Stroke images were collected and a dataset was constructed with medical specialists. Deep learning classification models were evaluated with hyperparameter optimization techniques. And the result segmented with improved Unet model to visualize the stroke in CT images. Classification models were compared and VGG16 achieved %94 success. Unet model was achieved %60 IOU and detected the ischemia and hemorrhage differences.

Deep learning based automatic detection algorithm for acute intracranial haemorrhage: a pivotal randomized clinical trial

Acute intracranial haemorrhage (AIH) is a potentially life-threatening emergency that requires prompt and accurate assessment and management. This study aims to develop and validate an artificial intelligence (AI) algorithm for diagnosing AIH using brain-computed tomography (CT) images. A retrospective, multi-reader, pivotal, crossover, randomised study was performed to validate the performance of an AI algorithm was trained using 104,666 slices from 3010 patients. Brain CT images (12,663 slices from 296 patients) were evaluated by nine reviewers belonging to one of the three subgroups (non-radiologist physicians, n = 3; board-certified radiologists, n = 3; and neuroradiologists, n = 3) with and without the aid of our AI algorithm. Sensitivity, specificity, and accuracy were compared between AI-unassisted and AI-assisted interpretations using the chi-square test. Brain CT interpretation with AI assistance results in significantly higher diagnostic accuracy than that without AI assistance (0.9703 vs. 0.9471, p < 0.0001, patient-wise). Among the three subgroups of reviewers, non-radiologist physicians demonstrate the greatest improvement in diagnostic accuracy for brain CT interpretation with AI assistance compared to that without AI assistance. For board-certified radiologists, the diagnostic accuracy for brain CT interpretation is significantly higher with AI assistance than without AI assistance. For neuroradiologists, although brain CT interpretation with AI assistance results in a trend for higher diagnostic accuracy compared to that without AI assistance, the difference does not reach statistical significance. For the detection of AIH, brain CT interpretation with AI assistance results in better diagnostic performance than that without AI assistance, with the most significant improvement observed for non-radiologist physicians.

Synthesizing human brain CT images from MRI with deep learning

Rapid development of artificial intelligence techniques significantly promotes important medical imaging applications such as disease diagnosis, medical image analysis and cross-modality synthesis. It is valuable to synthesize Computed Tomography (CT) scans of human brains from Magnetic Resonance Imaging (MRI) scans because CT images contain particular information for diseases such as malignant tumors, cerebrovascular malformations and aneurysms. Although there has been many studies trying to apply machine learning methods (e.g., fully convolutional neural networks and generative adversarial networks) to predict CT images based on MRI, there are still opportunities to increase the quality of synthesized CT images using the latest machine learning algorithms. In this study, we aim to synthesize high-credibility human brain CT images from MRI images using machine learning approaches. We build a dataset of both CT and MRI images of 41 patients from The Cancer Imaging Archive. A data pre-processing pipeline is designed to guarantee that the processed images are suitable as inputs to machine learning algorithms. The pipeline includes (1) pairing MRI and CT scans according to a certain time interval between CT and MRI exams of the same patient, (2) registering all MRI images to a standard MRI template, (3) registering all CT images to paired MRI images, (4) intensity normalization and (5) extracting 2D slices from 3D CT and MRI volumes. Three deep neural networks with encoder–decoder structure are selected and implemented including U-Net, U-Net++ and pix2pix to synthesize human brain CT images from MRI images. Experimental results illustrate that U-Net++ shows better performance than U-Net and pix2pix in synthesizing CT images from MRI by achieving a Mean Squared Error of 0.025, a Mean Absolute Error of 0.082 and a Peak Signal-to-Noise Ratio of 67.90, respectively. The trained machine learning models from this work can be applied in future human brain CT image prediction where CT images are absent and MRI images are available for specific patients, thereby supporting disease diagnosis based on CT images.