CT for Treatment Selection in Acute Ischemic Stroke: A Code Stroke Primer

Brain remodeling in stroke patients: A comprehensive review of mechanistic and neuroimaging studies

Stroke-induced brain remodeling involves a complex interplay of neurovascular components, including endothelial cells, microglia, astrocytes, and pericytes, which collectively contribute to the restoration of brain function. These processes are crucial for repairing the blood-brain barrier, regulating inflammation, and promoting neurogenesis. This review examines the mechanisms underlying brain remodeling and the role of advanced neuroimaging techniques-such as functional MRI (fMRI), positron emission tomography (PET), functional near-infrared spectroscopy (fNIRS), and functional ultrasound (fUS)-in assessing these changes. We also discuss various therapeutic approaches aimed at enhancing brain remodeling, including pharmacological agents, stem cell therapy, and rehabilitation strategies that target neurovascular repair and functional recovery. Despite significant progress, challenges remain in translating imaging insights into effective treatments. Future research should focus on integrating multiple imaging modalities to provide a comprehensive view of neurovascular changes and refining therapeutic interventions to optimize recovery and functional outcomes in stroke patients.

Clinical uncertainty in large vessel occlusion ischemic stroke: does automated perfusion imaging make a difference? An intra-rater and inter-rater agreement study

BACKGROUND

Limited research exists regarding the impact of neuroimaging on endovascular thrombectomy (EVT) decisions for late-window cases of large vessel occlusion (LVO) stroke.

OBJECTIVE

T0 assess whether perfusion CT imaging: (1) alters the proportion of recommendations for EVT, and (2) enhances the reliability of EVT decision-making compared with non-contrast CT and CT angiography.

METHODS

We conducted a survey using 30 patients drawn from an institutional database of 3144 acute stroke cases. These were presented to 29 Canadian physicians with and without perfusion imaging. We used non-overlapping 95% confidence intervals and difference in agreement classification as criteria to suggest a difference between the Gwet AC1 statistics (κG).

RESULTS

The percentage of EVT recommendations differed by 1.1% with or without perfusion imaging. Individual decisions changed in 21.4% of cases (11.3% against EVT and 10.1% in favor). Inter-rater agreement (κG) among the 29 raters was similar between non-perfusion and perfusion CT neuroimaging (κG=0.487; 95% CI 0.327 to 0.647 and κG=0.552; 95% CI 0.430 to 0.675). The 95% CIs overlapped with moderate agreement in both. Intra-rater agreement exhibited overlapping 95% CIs for all 28 raters. κG was either substantial or excellent (0.81-1) for 71.4% (20/28) of raters in both groups.

CONCLUSIONS

Despite the minimal difference in overall EVT recommendations with either neuroimaging protocol one in five decisions changed with perfusion imaging. Regarding agreement we found that the use of automated CT perfusion images does not significantly impact the reliability of EVT decisions for patients with late-window LVO.

Association between atherosclerosis and the development of multi-organ pathologies

Atherosclerosis is a chronic inflammatory disease affecting the vascular system, characterised by the accumulation of modified lipoproteins, immune cell aggregation and the development of fibrous tissue within blood vessel walls. As atherosclerosis impacts blood vessels, its adverse effects may manifest across various tissues and organs. In this review, we examine the association of atherosclerosis with Alzheimer's disease, stroke, pancreatic and thyroid dysfunction, kidney stones and chronic kidney diseases. In several cases, the reciprocal causative effect of these diseases on the progression of atherosclerosis is also discussed. Particular attention is given to common risk factors, biomarkers and identified molecular mechanisms linking the pathophysiology of atherosclerosis to the dysfunction of multiple tissues and organs. Understanding the role of atherosclerosis and its associated microenvironmental conditions in the pathology of multi-organ disorders may unveil novel therapeutic avenues for the prevention and treatment of cardiovascular and associated diseases.

An atypical presentation of Wernicke-Korsakoff encephalopathy mimicking a left hemispheric stroke: case report

Background

Wernicke-Korsakoff encephalopathy is a metabolic disease caused by vitamin B1 deficiency that predominantly affects alcoholic patients. Its clinical picture is characterized mainly by altered mental status with memory deficits, ophthalmoparesis, and ataxia, although other clinical manifestations may also be present. The current case presents certain clinical difficulties regarding the diagnosis when confronting an atypical presentation of a classical disease in an acute setting when a decision to administer an intravenous thrombolytic agent needs to be made.

Case presentation

This case involves a young male patient, with a history of chronic alcohol abuse, malnourished, and in poor general health, who presented with right-sided hemiparesis and language disturbance of acute onset, suggesting a left hemispheric stroke. The psychological examination was difficult due to a mix of confusion and aphasia, the latter being challenging to assess as the patient had a dismissive and highly deflective attitude toward the examiner. The initial cerebral computed tomography scan was unremarkable, in line with early imaging in ischemic stroke patients. On subsequent magnetic resonance imaging, lesions were observed in the medullo-pontine tegmentum, around the aqueduct of Sylvius, in the mamillary bodies, in the medial thalami, but also extensive bilateral cortical involvement in the frontal lobes. After receiving intravenous vitamin B1 treatment, the patient made a slow, but full, recovery, after eight weeks of hospitalization, and was subsequently transferred to a psychiatry clinic for treatment of his addiction.

Conclusion

Atypical Wernicke-Korsakoff encephalopathy can closely mimic stroke, usually akin to a vertebro-basilar lesion. Our case is the first report we are aware of Wernicke-Korsakoff encephalopathy feigning a left hemispheric stroke with aphasia and right hemiparesis. This has implications for the emergency medicine doctor, neurologist and the stroke specialist when considering an emergency differential diagnosis for a patient with an initial normal computed tomography scan, especially in regard to deciding acute therapy.

DeepSAP: A Novel Brain Image-Based Deep Learning Model for Predicting Stroke-Associated Pneumonia From Spontaneous Intracerebral Hemorrhage

RATIONALE AND OBJECTIVE

Stroke-associated pneumonia (SAP) often appears as a complication following intracerebral hemorrhage (ICH), leading to poor prognosis and increased mortality rates. Previous studies have typically developed prediction models based on clinical data alone, without considering that ICH patients often undergo CT scans immediately upon admission. As a result, these models are subjective and lack real-time applicability, with low accuracy that does not meet clinical needs. Therefore, there is an urgent need for a quick and reliable model to timely predict SAP.

METHODS

In this retrospective study, we developed an image-based model (DeepSAP) using brain CT scans from 244 ICH patients to classify the presence and severity of SAP. First, DeepSAP employs MRI-template-based image registration technology to eliminate structural differences between samples, achieving statistical quantification and spatial standardization of cerebral hemorrhage. Subsequently, the processed images and filtered clinical data were simultaneously input into a deep-learning neural network for training and analysis. The model was tested on a test set to evaluate diagnostic performance, including accuracy, specificity, and sensitivity.

RESULTS

Brain CT scans from 244 ICH patients (mean age, 60.24; 66 female) were divided into a training set (n = 170) and a test set (n = 74). The cohort included 143 SAP patients, accounting for 58.6% of the total, with 66 cases classified as moderate or above, representing 27% of the total. Experimental results showed an AUC of 0.93, an accuracy of 0.84, a sensitivity of 0.79, and a precision of 0.95 for classifying the presence of SAP. In comparison, the model relying solely on clinical data showed an AUC of only 0.76, while the radiomics method had an AUC of 0.74. Additionally, DeepSAP achieved an optimal AUC of 0.84 for the SAP grading task.

CONCLUSION

DeepSAP's accuracy in predicting SAP stems from its spatial normalization and statistical quantification of the ICH region. DeepSAP is expected to be an effective tool for predicting and grading SAP in clinic.

APIS: a paired CT-MRI dataset for ischemic stroke segmentation - methods and challenges

Stroke, the second leading cause of mortality globally, predominantly results from ischemic conditions. Immediate attention and diagnosis, related to the characterization of brain lesions, play a crucial role in patient prognosis. Standard stroke protocols include an initial evaluation from a non-contrast CT to discriminate between hemorrhage and ischemia. However, non-contrast CTs lack sensitivity in detecting subtle ischemic changes in this phase. Alternatively, diffusion-weighted MRI studies provide enhanced capabilities, yet are constrained by limited availability and higher costs. Hence, we idealize new approaches that integrate ADC stroke lesion findings into CT, to enhance the analysis and accelerate stroke patient management. This study details a public challenge where scientists applied top computational strategies to delineate stroke lesions on CT scans, utilizing paired ADC information. Also, it constitutes the first effort to build a paired dataset with NCCT and ADC studies of acute ischemic stroke patients. Submitted algorithms were validated with respect to the references of two expert radiologists. The best achieved Dice score was 0.2 over a test study with 36 patient studies. Despite all the teams employing specialized deep learning tools, results reveal limitations of computational approaches to support the segmentation of small lesions with heterogeneous density.

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

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Implementation of training level-specific self-study guides for diagnostic radiology residents

INTRODUCTION

Currently, there is no universally accepted diagnostic radiology curriculum for self-studying, so diagnostic radiology residents often have a challenging time knowing where to begin their independent studying away from work. In an effort to meet the needs of today's residents, we attempted to provide structured, comprehensive self-studying suggestions in a digestible and personalized format.

MATERIALS AND METHODS

Each radiology division attempted to create an optimal learning resource form for the residents to use for self-studying while on each rotation. Each self-study guide included hyperlinks to easily accessible online resources.

RESULTS

Training level-specific week-by-week recommendations were provided based on the expected types of cases a resident would typically encounter during that timeframe. All of the neuroradiology rotation forms and several of the other subspecialty forms were originally made available to the residents and faculty electronically on July 1, 2022. The forms are now distributed to newly rotating residents on a monthly basis.

DISCUSSION

There is a fine balance between providing residents with comprehensive review material and promoting realistic expectations. In addition, educators must keep in mind financial limitations of their residents and institutions. Learning resources must be affordable to be accessible to all residents. As radiology and technology continuously advance, there will undoubtedly be more and more excellent resources for trainees to learn from. To optimize self-studying, retention of information, and wellness, it is imperative to provide our residents with a structured, personalized, manageable curriculum including easily accessible high-yield resources.

Biochemical, biomechanical and imaging biomarkers of ischemic stroke: Time for integrative thinking

Stroke is one of the leading causes of adult disability affecting millions of people worldwide. Post-stroke cognitive and motor impairments diminish quality of life and functional independence. There is an increased risk of having a second stroke and developing secondary conditions with long-term social and economic impacts. With increasing number of stroke incidents, shortage of medical professionals and limited budgets, health services are struggling to provide a care that can break the vicious cycle of stroke. Effective post-stroke recovery hinges on holistic, integrative and personalized care starting from improved diagnosis and treatment in clinics to continuous rehabilitation and support in the community. To improve stroke care pathways, there have been growing efforts in discovering biomarkers that can provide valuable insights into the neural, physiological and biomechanical consequences of stroke and how patients respond to new interventions. In this review paper, we aim to summarize recent biomarker discovery research focusing on three modalities (brain imaging, blood sampling and gait assessments), look at some established and forthcoming biomarkers, and discuss their usefulness and complementarity within the context of comprehensive stroke care. We also emphasize the importance of biomarker guided personalized interventions to enhance stroke treatment and post-stroke recovery.

Clinical Uncertainty in Large Vessel Occlusion ischemic stroke (CULVO): Does automated perfusion scanning make a difference? Protocol of an intrarater and interrater agreement study

BACKGROUND

Guidelines recommend the use of perfusion computed tomography (CT) to identify emergent large vessel ischemic stroke (ELVIS) patients who are likely to benefit from endovascular thrombectomy (EVT) if they present within 6-24 hour (late window) of stroke onset. We aim to determine if the interrater and intrarater reliability among physicians when recommending EVT is significantly different when perfusion CT or non-perfusion CT is reviewed.

METHODS

A total of 30 non-consecutive patients will be selected from our institutional database comprising 3144 cranial CT scans performed for acute stroke symptoms January 2018 to August 2022. The clinical and radiologic data of the 30 patients will be presented in random order to a group of 29 physicians in two separate sessions at least three weeks apart. In each session, the physicians will evaluate each patient once with automated perfusion images and once without. We will use non-overlapping 95% confidence intervals and difference in agreement classification as criteria to suggest a difference between the Gwet AC1 statistics (κG).

DISCUSSION

The results obtained from this study, combined with the clinical outcomes data of patients categorized through the two imaging techniques and a cost-effectiveness analysis, will offer a comprehensive evaluation of the clinical utility of perfusion CT neuroimaging. Should there be no significant disparity in the reliability of decisions made by clinicians using the two neuroimaging protocols, it may be necessary to revise existing recommendations regarding neuroimaging in the later time window to align with these findings.

Focused view CT angiography for selective visualization of stroke related arteries: technical feasibility

OBJECTIVES

This study investigated the technical feasibility of focused view CTA for the selective visualization of stroke related arteries.

METHODS

A total of 141 CTA examinations for acute ischemic stroke evaluation were divided into a set of 100 cases to train a deep learning algorithm (dubbed "focused view CTA") that selectively extracts brain (including intracranial arteries) and extracranial arteries, and a test set of 41 cases. The visibility of anatomic structures at focused view and unmodified CTA was assessed using the following scoring system: 5 = completely visible, diagnostically sufficient; 4 = nearly completely visible, diagnostically sufficient; 3 = incompletely visible, barely diagnostically sufficient; 2 = hardly visible, diagnostically insufficient; 1 = not visible, diagnostically insufficient.

RESULTS

At focused view CTA, median scores for the aortic arch, subclavian arteries, common carotid arteries, C1, C6, and C7 segments of the internal carotid arteries, V4 segment of the vertebral arteries, basilar artery, cerebellum including cerebellar arteries, cerebrum including cerebral arteries, and dural venous sinuses, were all 4. Median scores for the C2 to C5 segments of the internal carotid arteries, and V1 to V3 segments of the vertebral arteries ranged between 3 and 2. At unmodified CTA, median score for all above-mentioned anatomic structures was 5, which was significantly higher (p < 0.0001) than that at focused view CTA.

CONCLUSION

Focused view CTA shows promise for the selective visualization of stroke-related arteries. Further improvements should focus on more accurately visualizing the smaller and tortuous internal carotid and vertebral artery segments close to bone.

CLINICAL RELEVANCE

Focused view CTA may speed up image interpretation time for LVO detection and may potentially be used as a tool to study the clinical relevance of incidental findings in future prospective long-term follow-up studies.

KEY POINTS

• A deep learning-based algorithm ("focused view CTA") was developed to selectively visualize relevant structures for acute ischemic stroke evaluation at CTA. • The elimination of unrequested anatomic background information was complete in all cases. • Focused view CTA may be used to study the clinical relevance of incidental findings.

Performance of RAPID noncontrast CT stroke platform in large vessel occlusion and intracranial hemorrhage detection

Background

Noncontrast CT (NCCT) is used to evaluate for intracerebral hemorrhage (ICH) and ischemia in acute ischemic stroke (AIS). Large vessel occlusions (LVOs) are a major cause of AIS, but challenging to detect on NCCT.

Aims

The purpose of this study is to evaluate an AI software called RAPID NCCT Stroke (RAPID, iSchemaView, Menlo Park, CA) for ICH and LVO detection compared to expert readers.

Methods

In this IRB approved retrospective, multicenter study, stand-alone performance of the software was assessed based on the consensus of 3 neuroradiologists and sensitivity and specificity were determined. The platform's performance was then compared to interpretation by readers comprised of eight general radiologists (GR) and three neuroradiologists (NR) in detecting ICH and hyperdense vessel sign (HVS) indicating LVO.

Results

A total of 244 cases were included. Of the 244, 115 were LVOs and 26 were ICHs. One hundred three cases did not have LVO nor ICH. Stand-alone performance of the software demonstrated sensitivities and specificities of 96.2 and 99.5% for ICH and 63.5 and 95.1% for LVO detection. Compared to all 11 readers and eight GR readers only respectively, the software demonstrated superiority, achieving significantly higher sensitivities (63.5% versus 43.6%, p < 0.0001 and 63.5% versus 40.9%, p = 0.001).

Conclusion

The RAPID NCCT Stroke platform demonstrates superior performance to radiologists for detecting LVO from a NCCT. Use of this software platform could lead to earlier LVO detection and expedited transfer of these patients to a thrombectomy capable center.

Neuroimaging Modalities Used for Ischemic Stroke Diagnosis and Monitoring

Strokes are one of the global leading causes of physical or mental impairment and fatality, classified into hemorrhagic and ischemic strokes. Ischemic strokes happen when a thrombus blocks or plugs an artery and interrupts or reduces blood supply to the brain tissue. Deciding on the imaging modality which will be used for stroke detection depends on the expertise and availability of staff and the infrastructure of hospitals. Magnetic resonance imaging provides valuable information, and its sensitivity for smaller infarcts is greater, while computed tomography is more extensively used, since it can promptly exclude acute cerebral hemorrhages and is more favorable speed-wise. The aim of this article was to give information about the neuroimaging modalities used for the diagnosis and monitoring of ischemic strokes. We reviewed the available literature and presented the use of computed tomography, CT angiography, CT perfusion, magnetic resonance imaging, MR angiography and MR perfusion for the detection of ischemic strokes and their monitoring in different phases of stroke development.

Design and Evaluation of Wearable Multimodal RF Sensing System for Vascular Dementia Detection

Vascular dementia is the second most common form of dementia and a leading cause of death. Brain stroke and brain atrophy are the major degenerative pathologies associated with vascular dementia. Timely detection of these progressive pathologies is critical to avoid brain damage. Brain imaging is an important diagnostic tool and determines future treatment options available to the patient. Traditional medical technologies are expensive, require extensive supervision and are not easily accessible. This article presents a novel concept of low- complexity wearable sensing system for the detection of brain stroke and brain atrophy using RF sensors. This multimodal RF sensing system provides a first-of-its-kind RF sensing solution for the detection of cerebral blood density variations and blood clots at an initial stage of neurodegeneration. A customized microwave imaging algorithm is presented for the reconstruction of images in affected areas of the brain. Designs are validated using software simulations and hardware modeling. Fabricated sensors are experimentally validated and can effectively detect blood density variation (1050 ± 50 Kg/m3), artificial stroke targets with a volume of 27 mm3 and density of 1025-1050 Kg/m3, and brain atrophy with a cavity of 58 mm3 within a realistic brain phantom. The safety of the proposed wearable RF sensing system is studied through the evaluation of the Specific Absorption Rate (SAR < 1.4 W/Kg, 100 mW) and thermal conductivity of the brain (<0.152 °C). The results indicate that the device is viable as an efficient, portable, and low-cost substitute for vascular dementia detection.

Prognostic Neuroimaging Biomarkers in Acute Vascular Brain Injury and Traumatic Brain Injury

Prognostic imaging biomarkers after acute brain injury inform treatment decisions, track the progression of intracranial injury, and can be used in shared decision-making processes with families. Herein, key established biomarkers and prognostic scoring systems are surveyed in the literature, and their applications in clinical practice and clinical trials are discussed. Biomarkers in acute ischemic stroke include computed tomography (CT) hypodensity scoring, diffusion-weighted lesion volume, and core infarct size on perfusion imaging. Intracerebral hemorrhage biomarkers include hemorrhage volume, expansion, and location. Aneurysmal subarachnoid biomarkers include hemorrhage grading, presence of diffusion-restricting lesions, and acute hydrocephalus. Traumatic brain injury CT scoring systems, contusion expansion, and diffuse axonal injury grading are reviewed. Emerging biomarkers including white matter disease scoring, diffusion tensor imaging, and the automated calculation of scoring systems and volumetrics are discussed.

The essential role of neuroimaging in diagnosing and managing cerebrovascular disease in Africa: a review

BACKGROUND

Cerebrovascular disease is a significant cause of morbidity and mortality in Africa, and using neuroimaging techniques has improved the diagnosis and management of this disease. However, there is a lack of comprehensive reviews of the role and effectiveness of neuroimaging techniques in the African context.

METHODS

We reviewed the literature to evaluate the role of neuroimaging in diagnosing and managing cerebrovascular disease in Africa. Our search included electronic databases such as PubMed, Scopus, and Google Scholar from 2000 to April 2023. We included peer-reviewed studies written in English that reported on the use of neuroimaging in diagnosing and managing cerebrovascular disease in African populations. We excluded non-peer-reviewed articles, letters, editorials, and studies unrelated to cerebrovascular disease, neuroimaging, or Africa. A total of 102 potential articles were identified; after applying our exclusion criteria and removing duplicated articles, 51 articles were reviewed.

RESULTS

Our findings suggest that neuroimaging techniques such as CT, MRI, and Skull x-ray play a crucial role in diagnosing and managing cerebrovascular disease in Africa. CT and MRI were the most commonly used techniques, with CT being more widely available and less expensive than MRI. However, challenges to using neuroimaging in Africa include the high cost of equipment and maintenance, lack of trained personnel, and inadequate infrastructure. These challenges limit the widespread use of neuroimaging in diagnosing and managing cerebrovascular disease in Africa.

CONCLUSION

Neuroimaging techniques are essential for diagnosing and managing cerebrovascular disease in Africa, but challenges to their use must be addressed to improve healthcare outcomes. Our policy recommendations can help improve the availability and accessibility of neuroimaging services in Africa.

Combining convolutional attention mechanism and residual deformable Transformer for infarct segmentation from CT scans of acute ischemic stroke patients

Background

Segmentation and evaluation of infarcts on medical images are essential for diagnosis and prognosis of acute ischemic stroke (AIS). Computed tomography (CT) is the first-choice examination for patients with AIS.

Methods

To accurately segment infarcts from the CT images of patients with AIS, we proposed an automated segmentation method combining the convolutional attention mechanism and residual Deformable Transformer in this article. The method used the encoder-decoder structure, where the encoders were employed for downsampling to obtain the feature of the images and the decoder was used for upsampling and segmentation. In addition, we further applied the convolutional attention mechanism and residual network structure to improve the effectiveness of feature extraction. Our code is available at: https://github.com/XZhiXiang/AIS-segmentation/tree/master.

Results

The proposed method was assessed on a public dataset containing 397 non-contrast CT (NCCT) images of AIS patients (AISD dataset). The symptom onset to CT time was less than 24 h. The experimental results illustrate that this work had a Dice coefficient (DC) of 58.66% for AIS infarct segmentation, which outperforms several existing methods. Furthermore, volumetric analysis of infarcts indicated a strong correlation (Pearson correlation coefficient = 0.948) between the AIS infarct volume obtained by the proposed method and manual segmentation.

Conclusion

The strong correlation between the infarct segmentation obtained via our method and the ground truth allows us to conclude that our method could accurately segment infarcts from NCCT images.

Diagnosis and treatment of Watershed strokes: a narrative review

Watershed strokes have been described previously as ischemic strokes located in vulnerable border zones between brain tissue supplied by the anterior, posterior, and middle cerebral arteries in the distal junction between two non-anastomotic arterial territories. Ischemic strokes in border zones are well-recognized entities and well-described in terms of imaging features, but the pathophysiological mechanism of brain injury production is not fully defined. Border zone ischemia is caused by cerebral hypoperfusion through decreased cerebral blood flow and arterial embolism in unstable atheroma plaque. It is often difficult to say which mechanisms are fully responsible for producing cerebral ischemic lesions. This review aimed to highlight the imaging aspect of watershed strokes and to correlate the clinical characteristics of this type of stroke with the diagnostic algorithm for optimal therapeutic management. Neurologists should promptly recognize this type of stroke and investigate its etiology in the shortest possible time.

Automated LVO detection and collateral scoring on CTA using a 3D self-configuring object detection network: a multi-center study

The use of deep learning (DL) techniques for automated diagnosis of large vessel occlusion (LVO) and collateral scoring on computed tomography angiography (CTA) is gaining attention. In this study, a state-of-the-art self-configuring object detection network called nnDetection was used to detect LVO and assess collateralization on CTA scans using a multi-task 3D object detection approach. The model was trained on single-phase CTA scans of 2425 patients at five centers, and its performance was evaluated on an external test set of 345 patients from another center. Ground-truth labels for the presence of LVO and collateral scores were provided by three radiologists. The nnDetection model achieved a diagnostic accuracy of 98.26% (95% CI 96.25-99.36%) in identifying LVO, correctly classifying 339 out of 345 CTA scans in the external test set. The DL-based collateral scores had a kappa of 0.80, indicating good agreement with the consensus of the radiologists. These results demonstrate that the self-configuring 3D nnDetection model can accurately detect LVO on single-phase CTA scans and provide semi-quantitative collateral scores, offering a comprehensive approach for automated stroke diagnostics in patients with LVO.

Advanced Imaging for Acute Stroke Treatment Selection: CT, CTA, CT Perfusion, and MR Imaging

There is constant evolution in the diagnosis and treatment of acute ischemic stroke due to advances in treatments, imaging, and outreach. Two major revolutions were the advent of intravenous thrombolysis in the 1990s and endovascular thrombectomy in 2010s. Neuroimaging approaches have also evolved with key goals-detect hemorrhage, augment thrombolysis treatment selection, detect arterial occlusion, estimate infarct core, estimate viable penumbra, and augment thrombectomy treatment selection. The ideal approach to diagnosis and treatment may differ depending on the system of care and available resources. Future directions include expanding indications for these treatments, including a shift from time-based to tissue-based selection.

Recommendations on the use of computed tomography in the stroke code: Consensus document SENR, SERAU, GEECV-SEN, SERAM

The Spanish Society of Emergency Radiology (SERAU), the Spanish Society of Neuroradiology (SENR), the Spanish Society of Neurology through its Cerebrovascular Diseases Study Group (GEECV-SEN) and the Spanish Society of Medical Radiology (SERAM) have met to draft this consensus document that will review the use of computed tomography in the stroke code patients, focusing on its indications, the technique for its correct acquisition and the possible interpretation mistakes.

Deep reinforcement learning for cerebral anterior vessel tree extraction from 3D CTA images

Extracting the cerebral anterior vessel tree of patients with an intracranial large vessel occlusion (LVO) is relevant to investigate potential biomarkers that can contribute to treatment decision making. The purpose of our work is to develop a method that can achieve this from routinely acquired computed tomography angiography (CTA) and computed tomography perfusion (CTP) images. To this end, we regard the anterior vessel tree as a set of bifurcations and connected centerlines. The method consists of a proximal policy optimization (PPO) based deep reinforcement learning (DRL) approach for tracking centerlines, a convolutional neural network based bifurcation detector, and a breadth-first vessel tree construction approach taking the tracking and bifurcation detection results as input. We experimentally determine the added values of various components of the tracker. Both DRL vessel tracking and CNN bifurcation detection were assessed in a cross validation experiment using 115 subjects. The anterior vessel tree formation was evaluated on an independent test set of 25 subjects, and compared to interobserver variation on a small subset of images. The DRL tracking result achieves a median overlapping rate until the first error (1.8 mm off the reference standard) of 100, [46, 100] % on 8032 vessels over 115 subjects. The bifurcation detector reaches an average recall and precision of 76% and 87% respectively during the vessel tree formation process. The final vessel tree formation achieves a median recall of 68% and precision of 70%, which is in line with the interobserver agreement.

Imaging of Central Nervous System Ischemia

OBJECTIVE

This article describes imaging modalities used in the evaluation of patients presenting with symptoms of acute ischemic stroke.

LATEST DEVELOPMENTS

The year 2015 marked the beginning of a new era in acute stroke care with the widespread adoption of mechanical thrombectomy. Subsequent randomized controlled trials in 2017 and 2018 brought the stroke community even further into this new territory with the expansion of the eligibility window for thrombectomy using imaging-based patient selection, which led to an increase in the use of perfusion imaging. Now, after several years of routine use, the debate is ongoing as to when this additional imaging is truly required and when it results in unnecessary delays in time-sensitive stroke care. At this time, more than ever, a robust understanding of neuroimaging techniques, applications, and interpretation is essential for the practicing neurologist.

ESSENTIAL POINTS

CT-based imaging is the first step in most centers for the evaluation of patients presenting with symptoms of acute stroke because of its wide availability, speed, and safety. Noncontrast head CT alone is sufficient for IV thrombolysis decision making. CT angiography is very sensitive for the detection of large-vessel occlusion and can be used reliably to make this determination. Advanced imaging including multiphase CT angiography, CT perfusion, MRI, and MR perfusion can provide additional information useful for therapeutic decision making in specific clinical scenarios. In all cases, it is essential that neuroimaging be performed and interpreted rapidly to allow for timely reperfusion therapy.

Pictorial Review on Imaging Findings in Cerebral CTP in Patients with Acute Stroke and Its Mimics: A Primer for General Radiologists

The imaging evaluation of computed tomography (CT), CT angiography (CTA), and CT perfusion (CTP) is of crucial importance in the setting of each emergency department for suspected cerebrovascular impairment. A fast and clear assignment of characteristic imaging findings of acute stroke and its differential diagnoses is essential for every radiologist. Different entities can mimic clinical signs of an acute stroke, thus the knowledge and fast identification of stroke mimics is important. A fast and clear assignment is necessary for a correct diagnosis and a rapid initiation of appropriate therapy. This pictorial review describes the most common imaging findings in CTP with clinical signs for acute stroke or other acute neurological disorders. The knowledge of these pictograms is therefore essential and should also be addressed in training and further education of radiologists.

Multiphase computed tomography angiography (mCTA) derived source images in acute ischemic stroke: Beyond collaterals. Can it obviate the need for computed tomography perfusion (CTP)?

BACKGROUND AND PURPOSE

To compare Multiphase CT Angiography derived source images (mCTA-SI) in acute ischemic stroke (AIS) with CT Perfusion (CTP) derived automated color maps of cerebral blood flow (CBF) and cerebral blood volume (CBV) and to assess the comparability of mCTA-SI with CTP in the prediction of final radiological and clinical outcome.

METHODS

This prospective single-centre observational study comprised of patients with AIS of the anterior circulation, presenting within 24 h and undergoing neuroimaging under stroke protocol with follow-up. Non-contrast computed tomography (NCCT), mCTA, and CTP were acquired with follow-up NCCT at 24 h and modified Rankin score (mRS) at 3 months. mCTA-SI and CTP color maps were scored by the ASPECTS (Alberta Stroke program early CT score) method and compared amongst each other and with the outcome. ROC (Receiver operating characteristic) curves were plotted considering mRS 0-2 and FIV≤ 28 ml as favourable clinical and radiological outcomes respectively.

RESULTS

The study included 55 patients. The 1st and 2nd phase of mCTA-SI correlated significantly with CBF maps (r = 0.845, p < 0.01, r = 0.842, p < 0.01 respectively). 3rd phase of mCTA-SI correlated significantly with CBV maps (r = 0.904, p < 0.01). A favourable functional and radiological outcome was best predicted by the 1st (AUC 0.8, 95%CI 0.671-0.896) and 2nd ( AUC 0.895, 95% CI 0.783-0.962⁾ phase of mCTA-SI respectively.

CONCLUSIONS

The 1st and 2nd phases of mCTA-SI produces results congruent to CBF color maps and the 3rd phase of mCTA-SI simulate CBV color maps. In addition to predicting radiological and functional outcomes, mCTA can predict the salvageable and non-salvageable tissue in AIS and is non-inferior to CTP.

A lightweight asymmetric U-Net framework for acute ischemic stroke lesion segmentation in CT and CTP images

BACKGROUND AND OBJECTIVES

This paper has introduced a patch-based, residual, asymmetric, encoder-decoder CNN that solves two major problems in acute ischemic stroke lesion segmentation from CT and CT perfusion data using deep neural networks. First, the class imbalance is encountered since the lesion core size covers less than 5% of the volume of the entire brain. Second, deeper neural networks face the drawback of vanishing gradients, and this degrades the learning ability of the network.

METHODS

The neural network architecture has been designed for better convergence and faster inference time without compromising performance to address these difficulties. It uses a training strategy combining Focal Tversky and Binary cross-entropy loss functions to overcome the class imbalance issue. The model comprises only four resolution steps with a total of 11 convolutional layers. A base filter of 8, used for the residual connection with two convolutional blocks at the encoder side, is doubled after each resolution step. Simultaneously, the decoder consists of residual blocks with one convolutional layer and a constant number of 8 filters in each resolution step. This proposition allows for a lighter build with fewer trainable parameters as well as aids in avoiding overfitting by allowing the decoder to decode only necessary information.

RESULTS

The presented method has been evaluated through submission on the publicly accessible platform of the Ischemic Stroke Lesion Segmentation (ISLES) 2018 medical image segmentation challenge achieving the second-highest testing dice similarity coefficient (DSC). The experimental results demonstrate that the proposed model achieves comparable performance to other submitted strategies in terms of DSC Precision, Recall, and Absolute Volume Difference (AVD).

CONCLUSIONS

Through the proposed approach, the two major research gaps are coherently addressed while achieving high challenge scores by solving the mentioned problems. Our model can serve as a tool for clinicians and radiologists to hasten decision-making and detect strokes efficiently.

Drivers of Ischemic Stroke Hospital Cost Trends Among Older Adults in the United States

PURPOSE

The increased use of neuroimaging and innovations in ischemic stroke (IS) treatment have improved outcomes, but the impact on median hospital costs is not well understood.

METHODS

A retrospective study was conducted using Medicare 5% claims data for 75,525 consecutive index IS hospitalizations for patients aged ≥65 years from 2012 to 2019 (values in 2019 dollars). IS episode cost was calculated in each year for trend analysis and stratified by cost components, including neuroimaging (CT angiography [CTA], CT perfusion [CTP], MRI, and MR angiography [MRA]), treatment (endovascular thrombectomy [EVT] and/or intravenous thrombolysis), and patient sociodemographic factors. Logistic regression was performed to analyze the drivers of high-cost episodes and median regression to assess drivers of median costs.

RESULTS

The median IS episode cost increased by 4.9% from $9,509 in 2012 to $9,973 in 2019 (P = .0021). Treatment with EVT resulted in the greatest odds of having a high-cost (>$20,000) hospitalization (odds ratio [OR], 71.86; 95% confidence interval [CI], 54.62-94.55), as did intravenous thrombolysis treatment (OR, 3.19; 95% CI, 2.90-3.52). Controlling for other factors, neuroimaging with CTA (OR, 1.72; 95% CI, 1.58-1.87), CTP (OR, 1.32; 95% CI, 1.14-1.52), and/or MRA (OR, 1.26; 95% CI, 1.15-1.38) had greater odds of having high-cost episodes than those without CTA, CTP, and MRA. Length of stay > 4 days (OR, 4.34; 95% CI, 3.99-4.72) and in-hospital mortality (OR, 1.85; 95% CI, 1.63-2.10) were also associated with high-cost episodes.

CONCLUSIONS

From 2012 to 2019, the median IS episode cost increased by 4.9%, with EVT as the main cost driver. However, the increasing treatment cost trends have been partially offset by decreases in median length of stay and in-hospital mortality.

Toward automatic reformation at the orbitomeatal line in head computed tomography using object detection algorithm

Consistent cross-sectional imaging is desirable to accurately detect lesions and facilitate follow-up in head computed tomography (CT). However, manual reformation causes image variations among technologists and requires additional time. We therefore developed a system that reformats head CT images at the orbitomeatal (OM) line and evaluated the system performance using real-world clinical data. Retrospective data were obtained for 681 consecutive patients who underwent non-contrast head CT. The datasets were randomly divided into one of three sets for training, validation, or testing. Four landmarks (bilateral eyes and external auditory canal) were detected with the trained You Look Only Once (YOLO)v5 model, and the head CT images were reformatted at the OM line. The precision, recall, and mean average precision at the intersection over union threshold of 0.5 were computed in the validation sets. The reformation quality in testing sets was evaluated by three radiological technologists on a qualitative 4-point scale. The precision, recall, and mean average precision of the trained YOLOv5 model for all categories were 0.688, 0.949, and 0.827, respectively. In our environment, the mean implementation time was 23.5 ± 2.4 s for each case. The qualitative evaluation in the testing sets showed that post-processed images of automatic reformation had clinically useful quality with scores 3 and 4 in 86.8%, 91.2%, and 94.1% for observers 1, 2, and 3, respectively. Our system demonstrated acceptable quality in reformatting the head CT images at the OM line using an object detection algorithm and was highly time efficient.

Performance of Automated RAPID Intracranial Hemorrhage Detection in Real-World Practice: A Single-Institution Experience

BACKGROUND AND PURPOSE

Intracranial hemorrhage (ICH) is a common finding in patients presenting to the emergency department with acute neurological symptoms. Noncontrast head computed tomography (NCCT) is the primary modality for assessment and detection of ICH in the acute setting. RAPID ICH software aims to automatically detect ICH on NCCT and was previously shown to have high accuracy when applied to a curated test data set. Here, we measured the test performance characteristics of RAPID ICH software in detecting ICH on NCCT performed in patients undergoing emergency stroke evaluation at a tertiary academic comprehensive stroke center.

MATERIALS AND METHODS

This retrospective study assessed consecutive patients over a 6-month period who presented with acute neurological symptoms suspicious for stroke and underwent NCCT with RAPID ICH postprocessing. RAPID ICH detection was compared with the interpretation of a reference standard comprising a board-certified or board-eligible neuroradiologist, or in cases of discrepancy, adjudicated by a consensus panel of 3 neuroradiologists. Accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of RAPID ICH for ICH detection were determined.

RESULTS

Three hundred seven NCCT scans were included in the study. RAPID ICH correctly identified 34 of 37 cases with ICH and 228 of 270 without ICH. RAPID ICH had a sensitivity of 91.9% (78.1%-98.3%), specificity of 84.4% (79.6%-88.6%), NPV of 98.7% (96.3%-99.6%), PPV of 44.7% (37.6%-52.1%), and overall accuracy of 85.3% (80.9%-89.1%).

CONCLUSIONS

In a real-world scenario, RAPID ICH software demonstrated high NPV but low PPV for the presence of ICH when evaluating possible stroke patients.

Predictive value of Tmax perfusion maps on final core in acute ischemic stroke: an observational single-center study

PURPOSE

To assess utility of computed tomography perfusion (CTP) protocols for selection of patients with acute ischemic stroke (AIS) for reperfusive treatments and compare the diagnostic accuracy (ACC) in predicting follow-up infarction, using time-to-maximum (Tmax) maps.

METHODS

We retrospectively reviewed consecutive AIS patients evaluated for reperfusive treatments at comprehensive stroke center, employing a multimodal computed tomography. To assess prognostic accuracy of CTP summary maps in predicting final infarct area (FIA) in AIS patients, we assumed the best correlation between non-viable tissue (NVT) and FIA in early and fully recanalized patients and/or in patients with favorable clinical response (FCR). On the other hand, the tissue at risk (TAR) should better correlate with FIA in untreated patients and in treatment failure.

RESULTS

We enrolled 158 patients, for which CTP maps with Tmax thresholds of 9.5 s and 16 s, presented sensitivity of 82.5%, specificity of 74.6%, and ACC of 75.9%. In patients selected for perfusion deficit in anterior circulation territory, CTP-Tmax > 16 s has proven relatively reliable to identify NVT in FCR patients, with a tendency to overestimate NVT. Similarly, CTP-Tmax > 9.5 s was reliable for TAR, but it was overestimated comparing to FIA, in patients with unfavorable outcomes.

CONCLUSIONS

In our experience, Tmax thresholds have proven sufficiently reliable to identify global hypoperfusion, with tendency to overestimate both NVT and TAR, not yielding satisfactory differentiation between true penumbra and benign oligoemia. In particular, the overestimation of NVT could have serious consequences in not selecting potential candidates for a reperfusion treatment.

Evolving Radiology Trainee Neuroimaging Workloads: A National Medicare Claims-based Analysis

RATIONALE AND OBJECTIVES

While radiology training programs aim to prepare trainees for clinical practice, the relationship between trainee, and national radiology workforce demands is unclear. This study assesses changing radiology trainee neuroimaging workloads nationwide for neuroimaging studies.

MATERIALS AND METHODS

Using aggregate Medicare claims files from 2002 to 2018, we identified all computed tomography (CT) and magnetic resonance (MR) examinations of the brain, head and neck, and spine (hereafter "neuroimaging") in Medicare fee-for-service beneficiaries nationwide. Using separate Medicare files, we calculated population utilization rates, and work relative value unit (wRVU) weights of all diagnostic neuroradiology services. Using claims modifiers, we identified services rendered by radiology trainees. Using separate national trainee enrollment files, we calculated mean annual per trainee wRVUs.

RESULTS

Between 2002 and 2018, total Medicare neuroimaging claims increased for both radiologists overall (86.1%) and trainees (162.5%), including increases in both CT (102.9% vs 196.8%), and MR (59.9% vs 106.6%). The national percentage of all radiologist neuroimaging wRVUs rendered by trainees increased 46.1% (3.8% of all wRVUs nationally in 2002 to 5.6% in 2018). National trainee increases were present across all neuroimaging services but greatest for head and neck CT (+86.5%). Mean annual per radiology trainee neuroimaging Medicare wRVUs increased +174.9% (42.1 per trainee in 2002 to 115.70 in 2018). Mean per trainee wRVU increases were greatest for spine CT (+394.2%) but present across all neuroimaging services.

CONCLUSION

As neuroimaging utilization in Medicare beneficiaries has grown, radiology trainee neuroimaging workloads have increased disproportionately.

Overview of Noninterpretive Artificial Intelligence Models for Safety, Quality, Workflow, and Education Applications in Radiology Practice

Artificial intelligence has become a ubiquitous term in radiology over the past several years, and much attention has been given to applications that aid radiologists in the detection of abnormalities and diagnosis of diseases. However, there are many potential applications related to radiologic image quality, safety, and workflow improvements that present equal, if not greater, value propositions to radiology practices, insurance companies, and hospital systems. This review focuses on six major categories for artificial intelligence applications: study selection and protocoling, image acquisition, worklist prioritization, study reporting, business applications, and resident education. All of these categories can substantially affect different aspects of radiology practices and workflows. Each of these categories has different value propositions in terms of whether they could be used to increase efficiency, improve patient safety, increase revenue, or save costs. Each application is covered in depth in the context of both current and future areas of work. Keywords: Use of AI in Education, Application Domain, Supervised Learning, Safety © RSNA, 2022.

ASPECTS-Based Attenuation Changes on CT Angiography as an Imaging Biomarker to Predict Hemorrhagic Transformation in Acute Ischemic Stroke

INTRODUCTION

Imaging-based early warning indicators and feasible stratification of acute ischemic stroke (AIS) patients with hemorrhagic transformation (HT), especially high-risk patients with parenchymal hematoma (PH), are crucial in determining subsequent treatment strategies. This study combined automated ASPECTS software with noncontrast CT (NCCT) and CTA source image (CTASI) attenuation changes using Hounsfield unit (HU) values to predict HT and PH in patients with AIS.

MATERIALS AND METHODS

We retrospectively enrolled 172 consecutive patients with anterior circulation large-vessel occlusion between 2016 and 2020. Univariate and multivariate logistic regression and receiver operating characteristic (ROC) analyses were used to investigate the relationship between NCCT and CTASI-ASPECTS-HU, as well as other clinical and radiological parameters of HT and PH. Univariate and multivariate logistic regression analyses were performed to explore risk factors for HT or PH, and an ROC curve was used to evaluate their diagnostic values.

RESULTS

A multivariate analysis showed that CTASI-ASPECTS-HU and NIHSS score were independent predictors of HT (CTASI-ASPECTS-HU: odds ratio (OR), 2.22; 95% CI, 1.01-4.92; NIHSS: OR, 1.07; 95% CI, 1.02-1.13) and PH (CTASI-ASPECTS-HU: OR, 6.51; 95% CI, 2.29-18.50; NIHSS: OR, 1.07; 95% CI, 1.01-1.13). According to ROC analysis, CTASI-ASPECTS-HU >0.09 identified HT (area under the curve, 0.70; sensitivity, 70.15%; specificity, 61.90%), and CTASI-ASPECTS-HU >0.10 identified PH (area under the curve, 0.79; sensitivity, 76.19%; specificity, 73.33%). The area under the curve for predicting HT or PH increased when CTASI-ASPECTS-HU was combined with NIHSS score (HT: area under the curve, 0.74; sensitivity, 73.13%; specificity, 70.48%; PH: area under the curve, 0.81; sensitivity, 85.71%; specificity, 72.38%).

CONCLUSION

CTASI-ASPECTS-HU is a reliable radiological predictor of HT and PH in patients with AIS. Its predictive efficacy is moderately improved when combined with NIHSS score.

Contemporary Neuroprotection Strategies during Cardiac Surgery: State of the Art Review

Open-heart surgery is the leading cause of neuronal injury in the perioperative state, with some patients complicating with cerebrovascular accidents and delirium. Neurological fallout places an immense burden on the psychological well-being of the person affected, their family, and the healthcare system. Several randomised control trials (RCTs) have attempted to identify therapeutic and interventional strategies that reduce the morbidity and mortality rate in patients that experience perioperative neurological complications. However, there is still no consensus on the best strategy that yields improved patient outcomes, such that standardised neuroprotection protocols do not exist in a significant number of anaesthesia departments. This review aims to discuss contemporary evidence for preventing and managing risk factors for neuronal injury, mechanisms of injury, and neuroprotection interventions that lead to improved patient outcomes. Furthermore, a summary of existing RCTs and large observational studies are examined to determine which strategies are supported by science and which lack definitive evidence. We have established that the overall evidence for pharmacological neuroprotection is weak. Most neuroprotective strategies are based on animal studies, which cannot be fully extrapolated to the human population, and there is still no consensus on the optimal neuroprotective strategies for patients undergoing cardiac surgery. Large multicenter studies using universal standardised neurological fallout definitions are still required to evaluate the beneficial effects of the existing neuroprotective techniques.

Virtual monoenergetic dual-energy CT reconstructions at 80 keV are optimal non-contrast CT technique for early stroke detection

BACKGROUND

Virtual monoenergetic (VM) dual-energy computed tomography (DE-CT) enables grey-to-white matter contrast-to-noise ratio optimization, potentially increasing ischaemic brain oedema visibility. The aim of this study was to compare the diagnostic accuracy of VM and standard DE-CT reconstructions for early stroke detection.

METHODS

Consecutive patients with non-contrast DE-CT of the brain scanned within 12 h of stroke symptom onset were prospectively included in the study. Patients with other significant brain pathology were excluded. Two radiologists jointly evaluated standard and VM reconstructions (from 40 to 190 keV at increments of 10 keV) for early stroke signs on a four-point Likert scale: (a) stroke definitely present, (b) stroke probably present, (c) probably no stroke, and (d) definitely no stroke. Follow-up imaging and clinical data served as the standard of reference. Diagnostic accuracy was evaluated by receiver operating characteristic analysis.

RESULTS

Stroke incidence among 184 patients was 76%. In 64 patients follow-up imaging served as the standard of reference: ischemic brain oedema detection was significantly more accurate on VM reconstructions at 80 keV compared with standard DE-CT reconstructions (area under the curve (AUC) = 0.821 vs. AUC = 0.672, p = 0.002). The difference was most prominent within the first 3 h after symptom onset (at 11%, AUC = 0.819 vs. AUC = 0.709, p = 0.17) and in patients with National Institutes of Health Stroke Scale above 16 (at 37.5%, AUC = 1 vs. AUC = 0.625, p = 0.14).

CONCLUSION

VM DE-CT reconstructions at 80 keV appear to be the optimal non-contrast CT technique for diagnosing early ischaemic stroke, particularly within the first 3 h after symptom onset and in severely ill patients.

A Real-World Clinical Implementation of Automated Processing Using Intelligent Work Aid for Rapid Reformation at the Orbitomeatal Line in Head Computed Tomography

MATERIALS AND METHODS

We retrospectively reviewed 781 head CTs (median, 70 years; 441 men) collected by CT systems from 3 vendors. In addition to the orbitomeatal line image reformatted by a CT specialist as a reference, we obtained the fully automated orbitomeatal line image using the intelligent work aid. We calculated the offset angle from the reference of the automatically reformatted image. We defined the large offset angle groups as those with an offset angle greater than 3 degrees. Multivariate logistic regression was used to determine the independent factors for the large offset angle groups. We compared the postprocessing times measured using the intelligent work aid or by a CT specialist.

RESULTS

With the intelligent work aid, 99.7% of CTs were automatically reformatted to the orbitomeatal line without error. Furthermore, 88.1% of CTs were within the 3 degrees' offset angle when compared with the reference produced by a CT specialist. The median offset angle from the reference was 1.41 degrees. Multivariate analysis showed that the offset angle of the positioning plane was an independent factor (odds ratio, 1.045; P = 0.005) for predicting the large offset angle group. Furthermore, this technique was 4 times faster (6.4 ± 0.7 seconds) than a CT specialist (25.6 ± 6.4 seconds).

CONCLUSIONS

The intelligent work aid can generate a fast and precise head CT image aligned at the orbitomeatal line, even in real-world clinical CTs. However, precise positioning remains essential.

Study on Model Iterative Reconstruction Algorithm vs. Filter Back Projection Algorithm for Diagnosis of Acute Cerebral Infarction Using CT Images

The aim was to explore the application value of computed tomography (CT) perfusion (CTP) imaging based on the iterative model reconstruction (IMR) in the diagnosis of acute cerebral infarction (ACI). 80 patients with ACI, admitted to hospital, were selected as the research objects and divided randomly into a routine treatment group (group A) and a low-dose group (group B) (each group with 40 patients). Patients in group A were scanned at 80 kV–150 mAs, and the traditional filtered back projection (FBP) algorithm was employed to reconstruct the images; besides, 80 kV–30 mAs was adopted to scan the patients in group B, and the images were reconstructed by IMR1, IMR2, IMR3, iDose4 (a kind of hybrid iterative reconstruction technology), and FBP, respectively. The application values of different algorithms were evaluated by CTP based on the collected CTP images of patients and detecting indicators. The results showed that the gray and white matter CT value, SD value, SNR, CNR, and subjective image scores of patients in group B were basically consistent with those of group A (p > 0.05) after the IMR1 reconstruction, and the CT and SD of gray and white matter in patients from group B reduced steeply (p < 0.05), while SNR and CNR increased dramatically after IMR2 and IMR3 reconstruction in contrast to group A (p < 0.05). Furthermore, the cerebral blood volume (CBV), cerebral blood flow (CBF), mean transit time (MTT) of contrast agent, and time to peak (TTP) of contrast agent in patients from group B after iDose4 and IMR reconstruction were basically the same as those of group A (p > 0.05). Therefore, IMR combined with low-dose CTP could obtain high-quality CTP images of the brain with stable perfusion indicators and low radiation dose, which could be clinically applied in the diagnosis of ACI.

Emerging role of artificial intelligence in stroke imaging

Introduction: The recognition and therapy of patients with stroke is becoming progressively intricate as additional treatment choices become accessible and new associations between disease characteristics and treatment response are incessantly uncovered. Therefore, clinicians must regularly learn new skill, stay up to date with the literature and integrate advances into daily practice. The application of artificial intelligence (AI) to assist clinical decision making could diminish inter-rater variation in routine clinical practice and accelerate the mining of vital data that could expand recognition of patients with stroke, forecast of treatment responses and patient outcomes.Areas covered: In this review, the authors provide an up-to-date review of AI in stroke, analyzing the latest papers on this subject. These have been divided in two main groups: stroke diagnosis and outcome prediction.Expert opinion: The highest value of AI is its capability to merge, select and condense a large amount of clinical and imaging features of a single patient and to associate these with fitted models that have gone through robust assessment and optimization with large cohorts of data to support clinical decision making.

Automated Processing of Head CT Perfusion Imaging for Ischemic Stroke Triage: A Practical Guide to Quality Assurance and Interpretation

Recent successful trials of thrombectomy launched a shift to imaging-based patient selection for stroke intervention. Many centers have adopted CT perfusion imaging (CTP) as a routine part of stroke workflow, and the demand for emergent CTP interpretation is growing. Fully automated CTP postprocessing software that rapidly generates standardized color-coded CTP summary maps with minimal user input and with easy accessibility of the software output is increasingly being adopted. Such automated postprocessing greatly streamlines clinical workflow and CTP interpretation for radiologists and other frontline physicians. However, the straightforward interface overshadows the computational complexity of the underlying postprocessing workflow, which, if not carefully examined, predisposes the interpreting physician to diagnostic errors. Using case examples, this article aims to familiarize the general radiologist with interpreting automated CTP software data output in the context of contemporary stroke management, providing a discussion of CTP acquisition and postprocessing, a stepwise guide for CTP quality assurance and troubleshooting, and a framework for avoiding clinically significant CTP interpretative pitfalls in commonly encountered clinical scenarios. Interpreting radiologists should apply the outlined approach for quality assurance and develop a comprehensive search pattern for the identified pitfalls, to ensure accurate CTP interpretation and optimize patient selection for reperfusion.

Controversies in Imaging of Patients with Acute Ischemic Stroke: AJR Expert Panel Narrative Review

The development of reperfusion therapies has profoundly impacted stroke care, initially with the advent of IV thrombolytic (IVT) treatment and, more recently, with the development and refinement of endovascular treatment (EVT). Progress in neuroimaging has supported the paradigm shift of stroke care, and advanced neuroimaging now has a fundamental role in triaging patients for both IVT and EVT. As the standard of care for acute ischemic stroke (AIS) evolves, controversies remain in certain clinical scenarios. This article explores the use of multimodality imaging for treatment selection of AIS in the context of recent guidelines, highlighting controversial topics and providing guidance for clinical practice. Results of major randomized trials supporting EVT are reviewed. Advantages and disadvantages of CT, CTA, MRI, and MRA in stroke diagnosis are summarized, with attention to level 1 evidence supporting the role of vascular imaging and perfusion imaging. Patient selection is compared between approaches based on time thresholds and physiologic approaches based on infarct core measurement using imaging. Moreover, various imaging approaches to core measurement are described. As ongoing studies push treatment boundaries, advanced imaging is expected to help identify a widening range of patients who may benefit from therapy.

Computed Tomography Based Score of Early Ischemic Changes Predicts Malignant Infarction

Background and Purpose: Identification of ischemic stroke patients at high risk of developing life-threatening malignant infarction at an early stage is critical to consider more rigorous monitoring and further therapeutic measures. We hypothesized that a score consisting of simple measurements of visually evident ischemic changes in non-enhanced CT (NEMMI score) predicts malignant middle cerebral artery (MCA) infarctions (MMI) with similar diagnostic power compared to other baseline clinical and imaging parameters. Methods: One hundred and nine patients with acute proximal MCA occlusion were included. Fifteen (13.8%) patients developed MMI. NEMMI score was defined using the sum of the maximum diameter (anterior-posterior plus medio-lateral) of the hypoattenuated lesion in baseline-CT multiplied by a hypoattenuation factor (3-point visual grading in non-enhanced CT, no/subtle/clear hypoattenuation = 1/2/3). Receiver operating characteristic (ROC) curve analysis and multivariable logistic regression analysis were used to calculate the predictive values of the NEMMI score, baseline clinical and other imaging parameters. Results: The median NEMMI score at baseline was 13.6 (IQR: 11.6-31.1) for MMI patients, and 7.7 (IQR: 3.9-11.2) for patients with non-malignant infarctions (p < 0.0001). Based on ROC curve analysis, a NEMMI score >10.5 identified MMI with good discriminative power (AUC: 0.84, sensitivity/specificity: 93.3/70.7%), which was higher compared to age (AUC: 0.76), NIHSS (AUC: 0.61), or ischemic core volume (AUC: 0.80). In multivariable logistic regression analysis, NEMMI score was significantly and independently associated with MMI (OR: 1.33, 95%CI: 1.13-1.56, p < 0.001), adjusted for recanalization status. Conclusion: The NEMMI score is a quick and simple rating tool of early ischemic changes on CT and could serve as an important surrogate marker for developing malignant edema. Its diagnostic accuracy was similar to CTP and clinical parameters.

Acute reperfusion therapies for acute ischemic stroke patients with unknown time of symptom onset or in extended time windows: an individualized approach

Recent randomized controlled clinical trials (RCTs) have revolutionized acute ischemic stroke care by extending the use of intravenous thrombolysis and endovascular reperfusion therapies in time windows that have been originally considered futile or even unsafe. Both systemic and endovascular reperfusion therapies have been shown to improve outcome in patients with wake-up strokes or symptom onset beyond 4.5 h for intravenous thrombolysis and beyond 6 h for endovascular treatment; however, they require advanced neuroimaging to select stroke patients safely. Experts have proposed simpler imaging algorithms but high-quality data on safety and efficacy are currently missing. RCTs used diverse imaging and clinical inclusion criteria for patient selection during the dawn of this novel stroke treatment paradigm. After taking into consideration the dismal prognosis of nonrecanalized ischemic stroke patients and the substantial clinical benefit of reperfusion therapies in selected late presenters, we propose rescue reperfusion therapies for acute ischemic stroke patients not fulfilling all clinical and imaging inclusion criteria as an option in a subgroup of patients with clinical and radiological profiles suggesting low risk for complications, notably hemorrhagic transformation as well as local or remote parenchymal hemorrhage. Incorporating new data to treatment algorithms may seem perplexing to stroke physicians, since treatment and imaging capabilities of each stroke center may dictate diverse treatment pathways. This narrative review will summarize current data that will assist clinicians in the selection of those late presenters that will most likely benefit from acute reperfusion therapies. Different treatment algorithms are provided according to available neuroimaging and endovascular treatment capabilities.

The role of CT texture analysis in predicting the clinical outcomes of acute ischemic stroke patients undergoing mechanical thrombectomy

OBJECTIVES

To evaluate the performance of CT-based texture analysis (TA) for predicting clinical outcomes of mechanical thrombectomy (MT) in acute ischemic stroke (AIS).

METHODS

This single-center, retrospective study contained 64 consecutive patients with AIS who underwent MT for large anterior circulation occlusion between December 2016 and January 2020. Patients were divided into 2 groups according to the modified Rankin scale (mRS) scores at 3 months as good outcome (mRS ≤ 2) and bad outcome (mRS > 2). Two observers examined the early ischemic changes for TA on baseline non-contrast CT images independently. Demographic, clinical, periprocedural, and texture variables were compared between the groups and ROC curves were made. Logistic regression analysis was used and a model was created to determine the independent predictors of a bad outcome.

RESULTS

Sixty-four patients (32 female, 32 male; mean age 63.03 ± 14.42) were included in the study. Fourteen texture parameters were significantly different between patients with good and bad outcomes. The long-run high gray-level emphasis (LRHGE), which is a gray-level run-length matrix (GLRLM) feature, showed the highest sensitivity (80%) and specificity (70%) rates to predict disability. The GLRLM_LRHGE value of > 4885.0 and the time from onset to puncture of > 237.5 mi were found as independent predictors of the bad outcome. The diagnostic rate was 80.0% when using the combination of the GLRLM_LRHGE and the time from onset to puncture cutoff values.

CONCLUSION

CT-based TA might be a promising modality to predict clinical outcome after MT in patients with AIS.

KEY POINTS

• The gray-level run-length matrix parameters displayed higher diagnostic performance among the texture features. • The long-run high gray-level emphasis showed the highest sensitivity and specificity rates for predicting a bad outcome in stroke patients undergoing mechanical thrombectomy. • The gray-level run-length matrix_long-run high gray-level emphasis value of > 4885.0 (OR = 11.06; 95% CI = 2.51 - 48.77; p = 0.001) and the time from onset to puncture of > 237.5 min (OR = 8.55; 95% CI = 1.96 - 37.21; p = 0.004) were found as independent predictors of the bad outcome.

Diagnostic performance of single-phase CT angiography in detecting large vessel occlusion in ischemic stroke: A systematic review

PURPOSE

To systematically review the diagnostic performance of single-phase CT angiography (CTA) in detecting intracranial large vessel occlusion (LVO).

METHOD

MEDLINE and Embase were searched for studies investigating the diagnostic performance of single-phase CTA in detecting LVO. Study quality was assessed. Sensitivity and specificity were calculated and meta-analyzed with a bivariate random-effects model. Heterogeneity was assessed with a chi-squared test.

RESULTS

Eleven studies were included. High risk of bias with regard to "patient selection", "reference standard", and "flow and timing" was present in 4, 1, and 2 studies, respectively. In 7 studies, it was unclear whether reference tests were interpreted blinded to CTA readings. There was variability in types of vessel segments analyzed, resulting in heterogeneous sensitivity and specificity (P < 0.05). Two studies provided data for the proximal anterior circulation (distal intracranial carotid artery, A1-, A2-, M1- and M2-segments), with pooled sensitivity of 88.4 % (95 % CI: 62.2-97.2 %) and pooled specificity of 98.5 % (95 % CI: 33.2-100 %). One study suggested that multiphase CTA improved agreement between nonexperts and an expert in detecting A1-, A2-, M1-, M2-, and M3-segment occlusions compared to single-phase CTA (ĸ = 0.72-0.76 vs. ĸ = 0.32-0.45). No other included study reported added value of advanced CTA (CT perfusion, 4D-CTA, or multiphase CTA) compared to single-phase CTA in detecting proximal anterior circulation LVO.

CONCLUSION

There is lack of high-quality studies on the diagnostic performance of single-phase CTA for LVO detection in the proximal anterior circulation. The added value of advanced CTA techniques in detecting proximal anterior circulation LVO is not completely clear yet.

ColorViz, a New and Rapid Tool for Assessing Collateral Circulation during Stroke

Prognosis of patients with acute ischemic stroke is strictly related to the patency and prominence of the collateral leptomeningeal pathways distal to the arterial occlusion. The gold standard for assessment of collateral circulation is conventional angiography, but it is invasive and used in selected cases. To date, the most reliable technique is multiphase CTA; currently, the available classifications of collateral circles are often complex, time-consuming, and require a trained observer. The purpose of our work is to establish the effectiveness of a new semi-automatic post-processing software (ColorViz FastStroke, GE Healthcare, Milwaukee, Wisconsin) in evaluation of collateral circulation compared to the six-point classifications of multiphase CTA already validated in literature. We selected 86 patients with anterior ischemic stroke symptoms who underwent multiphasic CTA in our emergency department. Two radiologists separately evaluated the collateral leptomeningeal vessels, analyzing respectively, the multiphase CTA (using the six-point scale and its trichotomized form) and ColorViz (using a three-point scale). Then the results were matched. We found a good correlation between the two different analyses; the main advantage of ColorViz is that, while maintaining fast diagnostic times, it allows a simpler and more immediate evaluation of collateral circulation, especially for less experienced radiologists.

Artificial intelligence for decision support in acute stroke - current roles and potential

The identification and treatment of patients with stroke is becoming increasingly complex as more treatment options become available and new relationships between disease features and treatment response are continually discovered. Consequently, clinicians must constantly learn new skills (such as clinical evaluations or image interpretation), stay up to date with the literature and incorporate advances into everyday practice. The use of artificial intelligence (AI) to support clinical decision making could reduce inter-rater variation in routine clinical practice and facilitate the extraction of vital information that could improve identification of patients with stroke, prediction of treatment responses and patient outcomes. Such support systems would be ideal for centres that deal with few patients with stroke or for regional hubs, and could assist informed discussions with the patients and their families. Moreover, the use of AI for image processing and interpretation in stroke could provide any clinician with an imaging assessment equivalent to that of an expert. However, any AI-based decision support system should allow for expert clinician interaction to enable identification of errors (for example, in automated image processing). In this Review, we discuss the increasing importance of imaging in stroke management before exploring the potential and pitfalls of AI-assisted treatment decision support in acute stroke.

Usefulness of deep learning-assisted identification of hyperdense MCA sign in acute ischemic stroke: comparison with readers' performance

PURPOSE

To evaluate the usefulness of deep learning-assisted diagnosis for identifying hyperdense middle cerebral artery sign (HMCAS) on non-contrast computed tomography in comparison with the diagnostic performance of neuroradiologists.

MATERIALS AND METHODS

We obtained 46 HMCAS-positive and 52 HMCAS-negative test samples extracted using 50-pixel-diameter circular regions of interest. Five neuroradiologists undertook an initial diagnostic performance test by describing the HMCAS-positive prediction rate in each sample. Their diagnostic performance was compared with that of a deep convolutional neural network (DCNN) model that had been trained using another dataset in our previous study. In the second test, readers could reference the prediction rate of the DCNN model in each sample.

RESULTS

The diagnostic performance of the DCNN for HMCAS showed an accuracy of 81.6% and area under the receiver-operating characteristic curve (AUC) of 0.869, whereas the initial diagnostic performance of neuroradiologists showed an accuracy of 78.8% and AUC of 0.882. The second diagnostic test of neuroradiologists with reference to the results of the DCNN model showed an accuracy of 84.7% and AUC of 0.932. In all readers, AUC values were higher in the second test than the initial test.

CONCLUSION

The ability of DCNN to identify HMCAS is comparable with the diagnostic performance of neuroradiologists.

A Review of the Applications of Dual-Energy CT in Acute Neuroimaging

Dual-energy computed tomography (CT) is a promising tool with increasing availability and multiple emerging and established clinical applications in neuroradiology. With its ability to allow characterization of materials based on their differential attenuation when imaged at two different energy levels, dual-energy CT can help identify the composition of brain, neck, and spinal components. Virtual monoenergetic imaging allows a range of simulated single energy-level reconstructions to be created with postprocessing. Low-energy reconstructions can aid identification of edema, ischemia, and subtle lesions due to increased soft tissue contrast as well as increasing contrast-to-noise ratios on angiographic imaging. Higher energy reconstructions can reduce image artifact from dental amalgam, aneurysm clips and coils, spinal hardware, dense contrast, and dense bones. Differentiating iodine from hemorrhage may help guide management of patients after thrombectomy and aid diagnosis of enhancing tumors within parenchymal hemorrhages. Iodine quantification may predict hematoma expansion in aneurysmal bleeds and outcomes in traumatic brain injury. Calcium and bone subtraction can be used to distinguish hemorrhage from brain parenchymal mineralization as well as improving visualization of extra-axial lesions and vessels adjacent to dense plaque or skull. This article reviews the basics of dual-energy CT and highlights many of its clinical applications in the evaluation of acute neurological presentations.