Non-contrast Brain Research Articles

The Impact of Weighting Factors on Dual-Energy Computed Tomography Image Quality in Non-Contrast Head Examinations: Phantom and Patient Study

Background: This study aims to evaluate the impact of various weighting factors (WFs) on the quality of weighted average (WA) dual-energy computed tomography (DECT) non-contrast brain images and to determine the optimal WF value. Because they simulate standard CT images, 0.4-WA reconstructions are routinely used. Methods: In the initial phase of the research, quantitative and qualitative analyses of WA DECT images of an anthropomorphic head phantom, utilizing WFs ranging from 0 to 1 in 0.1 increments, were conducted. Based on the phantom study findings, WFs of 0.4, 0.6, and 0.8 were chosen for patient analyses, which were identically carried out on 85 patients who underwent non-contrast head DECT. Three radiologists performed subjective phantom and patient analyses. Results: Quantitative phantom image analysis revealed the best gray-to-white matter contrast-to-noise ratio (CNR) at the highest WFs and minimal noise artifacts at the lowest WF values. However, the WA reconstructions were deemed non-diagnostic by all three readers. Two readers found 0.6-WA patient reconstructions significantly superior to 0.4-WA images (p < 0.001), while reader 1 found them to be equally good (p = 0.871). All readers agreed that 0.8-WA images exhibited the lowest image quality. Conclusions: In conclusion, 0.6-WA reconstructions demonstrated superior image quality over 0.4-WA and are recommended for routine non-contrast brain DECT.

A General Neurologist's Practical Diagnostic Algorithm for Atypical Parkinsonian Disorders: A Consensus Statement.

The most common four neurodegenerative atypical parkinsonian disorders (APDs) are progressive supranuclear palsy (PSP), multiple system atrophy (MSA), corticobasal syndrome (CBS), and dementia with Lewy bodies (DLB). Their formal diagnostic criteria often require subspecialty experience to implement as designed and all require excluding competing diagnoses without clearly specifying how to do that. Validated diagnostic criteria are not available at all for many of the other common APDs, including normal pressure hydrocephalus (NPH), vascular parkinsonism (VP), or drug-induced parkinsonism (DIP). APDs also include conditions of structural, genetic, vascular, toxic/metabolic, infectious, and autoimmune origin. Their differential diagnosis can be challenging early in the course, if the presentation is atypical, or if a rare or non-neurodegenerative condition is present. This review equips community general neurologists to make an early provisional diagnosis before, or in place of, referral to a tertiary center. Early diagnosis would allay diagnostic uncertainty, allow prompt symptomatic management, provide disease-specific information and support resources, avoid further pointless testing and treatments, and create the possibility of trial referral. We address 64 APDs using one over-arching flow diagram and a series of detailed tables. Most instances of APDs can be diagnosed with a careful history and neurological exam, along with a non-contrast brain MRI. Additional diagnostic tests are rarely needed but are delineated where applicable. Our diagnostic algorithm encourages referral to a tertiary center whenever the general neurologist feels it would be in the patient's best interest. Our algorithm emphasizes that the diagnosis of APDs is an iterative process, refined with the appearance of new diagnostic features, availability of new technology, and advances in scientific understanding of the disorders. Clinicians' proposals for all diagnostic tests for the APDs, including repeat visits, should be discussed with patients and their families to ensure that the potential information to be gained aligns with their larger clinical goals. We designed this differential diagnostic algorithm for the APDs to enhance general neurologists' diagnostic skills and confidence and to help them address the less common or more ambiguous cases.

Discrimination of thrombus types in ischemic stroke using Dual-Energy CT

ObjectiveThis study explores the clinical utility of dual-energy computed tomography (DECT) in discriminating thrombus types in ischemic stroke. MethodsPatients with acute ischemic stroke who underwent brain DECT non-contrast scanning and brain CT perfusion (CTP) before thrombectomy were included, and the thrombus composition was analyzed by postoperative pathology. DECT data was conducted to reconstruct polychromatic images and effective atomic number images. Computed tomography (CT) values, effective atomic numbers, and spectral curve slopes of the thrombus were measured and calculated. Thrombus attenuation increase was obtained from CTP data. Parameters were compared between red blood cell (RBC)-dominant thrombi and fibrin/platelet (F/P)-dominant thrombi. Thresholds, sensitivity, specificity, and area under the curve (AUC) were analyzed to distinguish these thrombi. The associations between DECT parameters and proportion of RBCs were analyzed by Spearman’s correlation. ResultsPathological analysis of 42 enrolled patients revealed 24 cases of RBC-dominant thrombi and 18 cases of F/P-dominant thrombi. Effective atomic numbers, spectral curve slopes, and polychromatic images CT values were significantly higher in the RBC-dominant thrombi group compared with the F/P-dominant thrombi group. Although the average thrombus attenuation increase was greater in the F/P-dominant thrombi group, this difference was not statistically significant. Among the DECT parameters, polychromatic images CT values had the greatest AUC at 0.924 (0.848–0.999) for discriminating RBC-dominant and F/P-dominant thrombi, with a threshold of 59 HU, sensitivity of 79.2 %, and specificity of 94.4 %. The combined diagnostic AUC reached 0.938 (0.863–1.012), with 87.5 % sensitivity and 94.4 % specificity. DECT polychromatic images CT values, effective atomic numbers, and spectral curve slopes were significantly correlated with proportion of RBCs (r = 0.673, 0.574, and 0.571, all p < 0.01). ConclusionDECT non-contrast scan parameters are associated with thrombus composition, which could be effective in distinguishing between RBC-dominant and F/P-dominant thrombi.

An Elderly Lady with Tetany and Brain Calcification

We present the case of a 59-year-old Bangladeshi lady who presented in Intensive Care Unit with tetany and altered level of consciousness. Tetany resolved after giving IV calcium injection. Initial laboratory investigations showed hypocalcemia. Non-contrast CT brain showed irregular bilateral symmetrical calcifications in basal ganglia, thalami, cerebellum and corona radiata. Subsequent laboratory test revealed low parathyroid hormone level. So, she was diagnosed as Fahr’s syndrome due to secondary hypoparathyroidism. This is a rare neurological disorder characterized by metabolic, biochemical, neuroradiological and neuropsychiatric abnormalities due to symmetrical and bilateral intracranial calcifications. Bangladesh Crit Care J September 2024; 12 (2): 160-162

Symptomatic Cerebellar Thrombosed Developmental Venous Anomaly: A Case Report

Abstract Background With the rising prevalence of advanced neuroradiological imaging, detection of incidental developmental venous anomalies (DVAs) is more commonplace. DVAs are rarely symptomatic (&amp;lt;2%). Typically, symptoms arise due to flow-related complications or mechanical effects. Of particular concern is venous thrombosis, a frequent flow-related complication, which can cause venous ischemia or haemorrhage. Thus, early recognition and intervention is important. Methods We describe a case of a 54-year-old male, with background of hypertension, presenting three days after sudden onset headache, ataxia, and diplopia. Initial clinical examination revealed bidirectional horizontal gaze-evoked nystagmus, skew deviation, and decreased temperature sensation of the left face. Ophthalmological examination identified left inferior rectus limitation. Results Initial non-contrast CT brain identified hypoattenuation in the right pons, suggestive of sub-acute infarction. Secondary prevention was initiated. Subsequent MRI performed 5 days later revealed hyperattenuation on T2 and FLAIR in the pons, right middle cerebellar peduncle, and right cerebellar hemisphere without restricted diffusion. SWI sequences identified an abnormal venous structure in the right cerebellum, with increased signal within it, suggestive of thrombus. The patient was diagnosed with a thrombosed DVA with associated venous oedema. Symptoms had resolved on dual antiplatelet therapy, without anticoagulation. Conclusion This case highlights the potential for DVAs, generally considered a benign entity, to become symptomatic with associated parenchymal damage and a rare, but important, stroke mimic. Moreover, it underscores the pivotal role of MRI imaging; while a CT scan might have led to the assumption of a subacute infarct, MR imaging led to a complete re-evaluation of the diagnosis in this case.

Benefits of Brain Dual-Energy CT Imaging in Detecting Intracranial Hemorrhage in Non-Contrast Brain CT Scans

Benefits of Brain Dual-Energy CT Imaging in Detecting Intracranial Hemorrhage in Non-Contrast Brain CT Scans

ESR Essentials: imaging of suspected child abuse-practice recommendations by the European Society of Paediatric Radiology.

The goal of this paper is to provide a useful desktop reference for the imaging of suspected child abuse with clear, age-specific pathways for appropriate evidence-based imaging and follow-up. We aim to provide a road map for the imaging evaluation and follow-up of this important and vulnerable cohort of patients presenting with signs and symptoms concerning forinflicted injury. As the imaging recommendations differ for children of different ages, we provide a flowchart of the appropriate imaging pathway for infants, toddlers, and older children, which allows ease of selection of which children should undergo skeletal survey, non-contrast computed tomography (CT) brain with 3-dimensional (D) reformats, and magnetic resonance imaging (MRI) of the brain and whole spine. For ease of review, we include a table of the common intracranial and spinal patterns of injury in abusive head trauma. We summarise search patterns, areas of review, and key findings to include in the report. To exclude skeletal injury, infants and children under 2 years of age should undergo a full skeletal survey in accordance with national guidelines, with a limited follow-up skeletal survey performed 11-14 days later. For children over 2 years of age, the need for skeletal imaging should be decided on a case-by-case basis. All infants should undergo a non-contrast-enhanced CT brain with 3-D reformats. If this is normal with no abnormal neurology, then no further neuroimaging is required. If this is abnormal, then they should proceed to MRI brain and whole spine within 2-5 days. Children older than 1 year of age who have abnormal neurology and/or findings on skeletal survey that are suggestive of inflicted injury should undergo non-contrast CT brain with 3-D reformats and, depending on the findings, may also require MRI of the brain and whole spine. We hope that this will be a helpful contribution to the radiology literature, particularly for the general radiologist with low volumes of paediatrics in their practice, supporting them with managing these important cases when they arise in daily practice. KEY POINTS: The choice of initial imaging (skeletal survey and/or brain CT) depends on the age of the child in whom abuse is suspected. A follow-up skeletal survey is mandatory 11-14 days after the initial survey. If an MRI of the brain is performed, then an MRI of the whole spine should be performed concurrently.

Radiation Optic Neuropathy

Radiation optic neuropathy (RON) is a rapid-onset, severe, challanging optic neuropathy that can cause unilateral or bilateral vision loss, which may occur years after radiotherapy. RON mostly occurs with radiation therapy exceeding 50 Gy to the visual axis. The risk of RON occurring at radiation doses between 50-60 Gy is 5% within 10 years. Since RON can cause acute, irreversible, unilateral or bilateral profound vision loss, patients at risk need to be carefully monitored. Early diagnosis and treatment is very important to prevent serious vision loss. The first test to be performed in patients with suspected RON is fat-suppressed, non-contrast brain MRI, which is recommended to be performed in 3 mm and thinner sections. In addition; lumbar puncture and Optical coherence tomography angiography (OCT-A) are helpful tests in the differential diagnosis. Anti-VEGF molecules have been shown to be effective in treatment, which aim to reduce edema and retinal hemorrhages. Hyperbaric oxygen therapy is not a proven treatment for RON – but there are some studies about it’s speculated effect. Studies presenting the success rates of this treatment are quite limited in number. Systemic corticosteroids, heparin, warfarin and other anticoagulation treatments have not been proven successful in the treatment of RON. RON is one of the optic neuropathies that chronically damages visual function. The prognosis is generally poor. It is important for the patient and the physician to be aware of RON after radiotherapy and to minimize the possibility of permanent damage through preventive measures that can be taken. Since there are no definitive treatments for RON, preventive medicine is of great importance.

Evaluation and Management of Idiopathic Unilateral Footdrop.

We evaluated patients who were followed after acutely developing single-sided footdrop and improving with conservative management or spontaneously. In 2019 and 2020, ten patients were retrospectively evaluated for unilateral weakness of the lower extremity in the form of absent dorsiflexion at the ankle joint and were given a diagnosis of footdrop without etiologic cause. Patients were followed for 18 months. Patients were evaluated for acute footdrop of the affected extremity with electromyography, lumbar spine magnetic resonance imaging (MRI), knee MRI, peripheral MRI neurography, and noncontrast brain MRI. Each patient was evaluated for a history of COVID-19 infection during the past year. Patients with any identified cause were excluded. Initial evaluation of muscle strength revealed 0/5 by the Medical Research Council muscle testing scale. In two patients, muscle strength was 3/5 at month 6 and in eight patients it was 4/5. Muscle strength of all of the patients improved to 5/5 at 1 year. Six patients were dispensed an ankle-foot orthosis, and nine patients performed physical therapy. Electromyography identified significant neuropathy at the level of the common peroneal at the fibular head in all of the patients. Compared with peroneal nerve stimulation below and above the fibular head in the lateral popliteal fossa, a 50% reduction in sensory amplitude and motor conduction slowing greater than 10 m/sec were present. Knee MRI revealed no masses, edema, or anatomical variations at the level of the fibular head. Spontaneous resolution of unilateral acute footdrop without an etiologic cause can occur within 1 year.

Ensemble machine learning to predict futile recanalization after mechanical thrombectomy based on non-contrast CT imaging

ObjectivesDespite successful recanalization after Mechanical Thrombectomy (MT), approximately 25 % of patients with Acute Ischemic Stroke (AIS) due to Large Vessel Occlusion (LVO) show unfavorable clinical outcomes, namely Futile Recanalization (FR). We aimed to use a Machine Learning (ML) Non-Contrast brain CT (NCCT) imaging predictive model to identify FR in patients undergoing MT. Materials & methodsBetween July 2022 and December 2022, 70 consecutive patients with LVO undergoing a complete recanalization (eTICI 3) with MT within 8 h from onset at our Centre were analyzed. Two NCCT images per patient of middle cerebral artery vascular territory and patients' clinical characteristics were classified by the presence of ischemic features on 24 h NCCT after MT. Each slice was segmented with “Mazda” software ver.4.6 by placing a Region Of Interest (ROI) on the whole brain by two radiologists in consensus. A total of 381 features were extracted for each slice. The dataset was split into train and test set with a 70:30 ratio. ResultsEleven classification models were trained. An Ensemble Machine Learning (EML) model was obtained by averaging the predictions of models with accuracy on a test set >70 %, with and without patients' clinical characteristics.The EML model combined with clinical data showed an accuracy of 0.76, a sensitivity of 0.88, a specificity of 0.69 with a NPV of 0.90, a PPV of 0.64, with AUC of 0.84. ConclusionNCCT and ML analysis shows promise in predicting FR after complete recanalization following MT in AIS patients. Larger studies are required to confirm these preliminary results.

Revolutionizing Intracranial Hemorrhage Diagnosis: A Retrospective Analytical Study of Viz.ai ICH for Enhanced Diagnostic Accuracy.

Introduction Artificial intelligence (AI) alerts the radiologist to the presence of intracranial hemorrhage (ICH) as fast as 1-2 minutes from scan completion, leading to faster diagnosis and treatment. We wanted to validate a new AI application called Viz.ai ICH to improve the diagnosis of suspected ICH. Methods We performed a retrospective analysis of 4,203 consecutive non-contrast brain computed tomography (CT) reports in a single institution between September 1, 2021, and January 31, 2022. The reports were made by neuroradiologists who reviewed each case for the presence of ICH. Reports and identified cases with positive findings for ICH were reviewed. Positive cases were categorized based on subtype, timing, and size/volume. Viz.ai ICH output was reviewed for positive cases. This AI model was validated by assessing its performance with Viz.ai ICH as the index test compared to the neuroradiologists' interpretation as the gold standard. Results According to neuroradiologists, 9.2% of non-contrast brain CT reports were positive for ICH. The sensitivity of Viz.ai ICH was 85%, specificity was 98%, positive predictive value was 81%, and negative predictive value was 99%. Subgroup analysis was performed based on intraparenchymal, subarachnoid, subdural, and intraventricular subtypes. Sensitivities were 94%, 79%, 83%, and 44%, respectively. Further stratification revealed sensitivity improves with higher acuity and volume/size across subtypes. Conclusion Our analysis indicates that AI can accurately detect ICH's presence, particularly for large-volume/large-size ICH. The paper introduces a novel AI model for detecting ICH. This advancement contributes to the field by revolutionizing ICH detection and improving patient outcomes.

The association of temporalis muscle thickness with post-stroke dysphagia based on swallowing kinematic analysis

Background/purposePost-stroke dysphagia (PSD) is a common functional deficit after stroke. Temporal muscle thickness (TMT) had been proven to be an independent factor for PSD. However, the relationship between TMT and PSD based on quantitative swallowing kinematic analysis remains unexplored. We aimed to investigate the association between TMT and PSD using videofluoroscopic swallow study (VFSS). MethodWe retrospectively recruited stroke patients from May 2015 to March 2020 in the tertiary referral hospital. A total of 83 patients with dysphagia met all the enrollment criteria and were included in the study. TMT was measured by non-contrast brain computed tomography (CT) images. Parameters of VFSS were obtained, including penetration-aspiration scale (PAS), oral transit time (OTT), pharyngeal transit time (PTT) and swallowing trigger time (STT) in four standardized barium formulas respectively. The association between TMT and variables of VFSS were analyzed by adjusted linear and logistic multivariate regression models. Subgroup analysis based on age, sex, and premorbid modified Rankin Scale (mRS) stratification was conducted. ResultsTMT was significantly correlated with gender and premorbid mRS as the confounders. Univariate regression showed smaller TMT (p = 0.010) and poorer premorbid mRS (p = 0.018) was associated with prolonged PTT of the thick formula; lesser TMT was associated with prolonged PTT of the paste formula (p = 0.037). Multivariate analyses after confounder-adjustment demonstrated TMT was an independent indicator for PTT in the thick formula (p = 0.028). ConclusionsTMT was associated with swallowing kinematic changes in patients diagnosed with PSD. TMT is an independent indicator for delayed pharyngeal stage in the thick standardized formula during deglutition in PSD patients.

Feasibility and Potential Diagnostic Value of Noncontrast Brain MRI in Nonsedated Children With Sturge-Weber Syndrome and Healthy Siblings.

Postcontrast magnetic resonance imaging (MRI), obtained under anesthesia, is often used to evaluate brain parenchymal and vascular abnormalities in young children, including those with Sturge-Weber syndrome. However, anesthesia and contrast administration may carry risks. We explored the feasibility and potential diagnostic value of a noncontrast, nonsedate MRI acquisition in Sturge-Weber syndrome children and their siblings with a wide range of cognitive and behavioral functioning. Twenty children (10 with Sturge-Weber syndrome and 10 healthy siblings; age: 0.7-13.5 years) underwent nonsedate 3-tesla (T) brain MRI acquisition with noncontrast sequences (including susceptibility-weighted imaging) prospectively along with neuropsychology assessment. All images were evaluated for quality, and MRI abnormalities identified in the Sturge-Weber syndrome group were compared to those identified on previous clinical pre- and postcontrast MRI. Nineteen participants (95%) completed the MRI with good (n = 18) or adequate (n = 1) quality, including all children with Sturge-Weber syndrome and all 5 children ≤5 years of age. The Sturge-Weber syndrome group had lower cognitive functions than the controls, and both groups had several children with behavioral issues, without an apparent effect on the success and quality of the MR images. Susceptibility-weighted imaging detected key venous vascular abnormalities and calcifications and, along with the other noncontrast sequences, provided diagnostic information comparable to previous clinical MRI performed with contrast administration under anesthesia. This study demonstrates the feasibility and the potential diagnostic value of a nonsedate, noncontrast MRI acquisition protocol in young children including those with cognitive impairment and/or behavioral concerns. This approach can facilitate clinical trials in children where safe serial MRI is warranted.

Stroke Evaluation in the Interventional Suite Using Dual-Layer Detector Cone-Beam CT: a First-in-human Prospective Cohort Study (the Next Generation X-ray Imaging System Trial)

PurposeCone-beam CT in the interventional suite could be an alternative to CT to shorten door-to-thrombectomy time. However, image quality in cone-beam CT is limited by artifacts and poor differentiation between gray and white matter. This study compared non-contrast brain dual-layer cone-beam CT in the interventional suite to reference standard CT in stroke patients.MethodsA prospective single-center study enrolled consecutive participants with ischemic or hemorrhagic stroke. The hemorrhage detection accuracy, per-region ASPECTS accuracy and subjective image quality (Likert scales for gray-white matter differentiation, structure perception and artifacts) were assessed by three neuroradiologists blinded to clinical data on dual-layer cone-beam CT 75 keV monoenergetic images compared to CT. Objective image quality was assessed by region-of-interest metrics. Non-inferiority for hemorrhage detection and ASPECTS accuracy was determined by the exact binomial test with a one-sided lower performance boundary prospectively set to 80% (98.75% CI).Results27 participants were included (74 years ± 9; 19 female) in the hyperacute or acute stroke phase. One reader missed a small bleeding, but all hemorrhages were detected in the majority analysis (100% accuracy, CI lower boundary 86%, p = 0.002). ASPECTS majority analysis showed 90% accuracy (CI lower boundary 85%, p < 0.001). Sensitivity was 66% (individual readers 67%, 69%, and 76%), specificity was 97% (97%, 96%, 89%). Subjective and objective image quality were inferior to CT.ConclusionIn a small single-center cohort, dual-layer cone-beam CT showed non-inferior hemorrhage detection and ASPECTS accuracy to CT. Despite inferior image quality, the technique may be useful for stroke evaluation in the interventional suite.Trial Registration NumberNCT04571099 (clinicaltrials.gov). Prospectively registered 2020-09-04.

A 2.5D Self-Training Strategy for Carotid Artery Segmentation in T1-Weighted Brain Magnetic Resonance Images.

Precise annotations for large medical image datasets can be time-consuming. Additionally, when dealing with volumetric regions of interest, it is typical to apply segmentation techniques on 2D slices, compromising important information for accurately segmenting 3D structures. This study presents a deep learning pipeline that simultaneously tackles both challenges. Firstly, to streamline the annotation process, we employ a semi-automatic segmentation approach using bounding boxes as masks, which is less time-consuming than pixel-level delineation. Subsequently, recursive self-training is utilized to enhance annotation quality. Finally, a 2.5D segmentation technique is adopted, wherein a slice of a volumetric image is segmented using a pseudo-RGB image. The pipeline was applied to segment the carotid artery tree in T1-weighted brain magnetic resonance images. Utilizing 42 volumetric non-contrast T1-weighted brain scans from four datasets, we delineated bounding boxes around the carotid arteries in the axial slices. Pseudo-RGB images were generated from these slices, and recursive segmentation was conducted using a Res-Unet-based neural network architecture. The model's performance was tested on a separate dataset, with ground truth annotations provided by a radiologist. After recursive training, we achieved an Intersection over Union (IoU) score of (0.68 ± 0.08) on the unseen dataset, demonstrating commendable qualitative results.

Automated Prediction of Proximal Middle Cerebral Artery Occlusions in Noncontrast Brain Computed Tomography.

Early identification of large vessel occlusion (LVO) in patients with ischemic stroke is crucial for timely interventions. We propose a machine learning-based algorithm (JLK-CTL) that uses handcrafted features from noncontrast computed tomography to predict LVO. We included patients with ischemic stroke who underwent concurrent noncontrast computed tomography and computed tomography angiography in seven hospitals. Patients from 5 of these hospitals, admitted between May 2011 and March 2015, were randomly divided into training and internal validation (9:1 ratio). Those from the remaining 2 hospitals, admitted between March 2021 and September 2021, were designated for external validation. From each noncontrast computed tomography scan, we extracted differences in volume, tissue density, and Hounsfield unit distribution between bihemispheric regions (striatocapsular, insula, M1-M3, and M4-M6, modified from the Alberta Stroke Program Early Computed Tomography Score). A deep learning algorithm was used to incorporate clot signs as an additional feature. Machine learning models, including ExtraTrees, random forest, extreme gradient boosting, support vector machine, and multilayer perceptron, as well as a deep learning model, were trained and evaluated. Additionally, we assessed the models' performance after incorporating the National Institutes of Health Stroke Scale scores as an additional feature. Among 2919 patients, 83 were excluded. Across the training (n=2463), internal validation (n=275), and external validation (n=95) datasets, the mean ages were 68.5±12.4, 67.6±13.8, and 67.9±13.6 years, respectively. The proportions of men were 57%, 53%, and 59%, with LVO prevalences of 17.0%, 16.4%, and 26.3%, respectively. In the external validation, the ExtraTrees model achieved a robust area under the curve of 0.888 (95% CI, 0.850-0.925), with a sensitivity of 80.1% (95% CI, 72.0-88.1) and a specificity of 88.6% (95% CI, 84.7-92.5). Adding the National Institutes of Health Stroke Scale score to the ExtraTrees model increased sensitivity (from 80.1% to 92.1%) while maintaining specificity. Our algorithm provides reliable predictions of LVO using noncontrast computed tomography. By enabling early LVO identification, our algorithm has the potential to expedite the stroke workflow.

Dual-Energy Computed Tomography Virtual Noncalcium Imaging of Intracranial Arteries in Acute Ischemic Stroke: Differentiation Between Acute Thrombus and Calcification.

Hyperdense artery sign (HAS) on noncontrast brain computed tomography (CT) indicates an acute thrombus within the cerebral artery. It is a valuable imaging biomarker for diagnosing large-vessel occlusion; however, its identification may be challenging with the presence of vascular calcification. Dual-energy CT virtual noncalcium (VNCa) imaging using a 3-material decomposition algorithm is helpful for differentiating between calcification and hemorrhage. This study aimed to clarify the potential of VNCa imaging for differentiating HAS from vascular calcification. Patients with acute ischemic stroke and large-vessel occlusion identified on MR angiography, who also underwent noncontrast dual-energy CT, were included. The 80 kV/Sn 140 kV mixed images, with a weighting factor of 0.4, were considered 120 kVp-equivalent images. Postprocessing using a 3-material decomposition algorithm to differentiate between calcium (Ca), cerebrospinal fluid, and hemorrhage was performed via a commercially available 3-dimensional workstation. A mixed image, VNCa image, color-coded Ca image, and color-coded Ca image with VNCa image overlay (color-coded Ca-overlay image) were obtained, and axial reconstruction with a 1-mm slice thickness was performed for each image type. Two experienced neuroradiologists conducted imaging evaluations in consensus. Thirty-four patients (mean age, 76.0 years; 21 male and 13 female patients) were included. The mixed and VNCa images revealed an HAS (indicating an acute clot) corresponding to the large-vessel occlusion site in 30 patients. Among them, the VNCa and color-coded Ca-overlay images enabled clear differentiation between the acute thrombus and adjacent vessel wall calcification in 5 patients. Among the other 4 patients, the VNCa, Ca-overlay, and Ca images identified calcified cerebral emboli in the M1 segment in 1 patient. For the other 3 patients, no high attenuation corresponding to magnetic resonance angiography findings was observed in any of the mixed, VNCa, Ca-overlay, or Ca images. VNCa and color-coded Ca-overlay images obtained via dual-energy brain CT enabled differentiation of acute thrombus from vessel wall calcification and calcified cerebral emboli in patients with acute ischemic stroke.

Current State of Evidence for Neuroimaging Paradigms in Management of Acute Ischemic Stroke.

Stroke is the chief differential diagnosis in patient presenting to the emergency room with abrupt onset focal neurological deficits. Neuroimaging, including non-contrast computed tomography (CT), magnetic resonance imaging (MRI), vascular and perfusion imaging, is a cornerstone in the diagnosis and treatment decision-making. This review examines the current state of evidence behind the different imaging paradigms for acute ischemic stroke diagnosis and treatment, including current recommendations from the guidelines. Non-contrast CT brain, or in some centers MRI, can help differentiate ischemic stroke and intracerebral hemorrhage (ICH), a pivotal juncture in stroke diagnosis and treatment algorithm, especially for early window thrombolytics. Advanced imaging such as MRI or perfusion imaging can also assist making a diagnosis of ischemic stroke versus mimics such as migraine, Todd's paresis, or functional disorders. Identification of medium-large vessel occlusions with CT or MR angiography triggers consideration of endovascular thrombectomy (EVT), with additional perfusion imaging help identify salvageable brain tissue in patients who are likely to benefit from reperfusion therapies, particularly in the ≥6 h window. We also review recent advances in neuroimaging and ongoing trials in key therapeutic areas and their imaging selection criteria to inform the readers on potential future transitions into use of neuroimaging for stroke diagnosis and treatment decision making. ANN NEUROL 2024;95:1017-1034.

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.

Diagnostic Accuracy of Hounsfield Unit Value and Hounsfield Unit to Hematocrit Ratio in Predicting Cerebral Venous Sinus Thrombosis: A Retrospective Case-Control Study.

Non-contrast computed tomography (CT) of the brain is a primary neuroimagingmodalityin emergencypatients suspected of having cerebral venous sinus thrombosis (CVST). Theobjective of thestudy was to determine thediagnostic accuracy of Hounsfield unit (HU) values and the ratio of HU to hematocrit value (HU/Htc) inpredicting CVST in suspected patients. A retrospective, case-control study was done in a tertiary care institute which included 35 patients with CVST constituted as cases and 41 patients without CVST as controls on the basis of magnetic resonance venography (MRV). Non-contrast CT brain of all 76 subjects were assessed by two experienced radiologists independently. HU values of dural venous sinuses were calculated in both groups, and HU/Htc ratio was also determined.StatisticalPackage for Social Sciences (SPSS) version 25.0(SPSS© for Windows, IBM© Corp.) was used for statistical analysis. Independent samples t-test was applied to compare the means of continuous variables. The diagnostic values were computed using the Calculator 1 tool on clinical research calculators tab on vassarstats.net. The predictive values of HU and HU/Htc ratio were estimated by the receiver operating characteristic (ROC) curve analysis. In CVST group, the mean Hounsfield Unit (HU) value was "75.9±3.9 (mean±SD)", while in control group, it was57.78±4.65 (mean±SD), p < 0.001. The mean HU/Htc ratio was 1.98±0.42 (mean±SD) in the CVST group and 1.51±0.12 (mean±SD) in the control group (p < 0.001). Optimum cut-off HU value was calculated as 68, with 97% sensitivity and 100% specificity. For HU/Htc ratio, optimum cut-off was calculated as 1.69, yielding 71.4% sensitivity and 100% specificityon the basis of ROC curves. The difference was not statistically significant in hemoglobin and hematocrit (Htc) values between the cases and controls. Thequantitative measurementslike HU value and HU/Htc ratioprovidean easily obtainable metric inpatients with suspected CVST on non-contrast CT brain, thusenhancing the role ofnon-contrast CT brain in diagnosing CVST.