The Role of Vascular Imaging in the Initial Assessment of Patients with Acute Ischemic Stroke

Stroke rehabilitation: from diagnosis to therapy

Stroke remains a significant global health burden, necessitating comprehensive and innovative approaches in rehabilitation to optimize recovery outcomes. This paper provides a thorough exploration of rehabilitation strategies in stroke management, focusing on diagnostic methods, acute management, and diverse modalities encompassing physical, occupational, speech, and cognitive therapies. Emphasizing the importance of early identification of rehabilitation needs and leveraging technological advancements, including neurostimulation techniques and assistive technologies, this manuscript highlights the challenges and opportunities in stroke rehabilitation. Additionally, it discusses future directions, such as personalized rehabilitation approaches, neuroplasticity concepts, and advancements in assistive technologies, which hold promise in reshaping the landscape of stroke rehabilitation. By delineating these multifaceted aspects, this manuscript aims to provide insights and directions for optimizing stroke rehabilitation practices and enhancing the quality of life for stroke survivors.

Advances in imaging acute ischemic stroke: evaluation before thrombectomy

Recent advances in neuroimaging have demonstrated significant assessment benefits and appropriate triage of patients based on specific clinical and radiological features in the acute stroke setting. Endovascular thrombectomy is arguably the most important aspect of acute stroke management with an extended time window. Imaging-based physiological information may potentially shift the treatment paradigm from a rigid time-based model to a more flexible and individualized, tissue-based approach, increasing the proportion of patients amenable to treatment. Various imaging modalities are routinely used in the diagnosis and management of acute ischemic stroke, including multimodal computed tomography (CT) and magnetic resonance imaging (MRI). Therefore, these imaging methods should provide information beyond the presence or absence of intracranial hemorrhage as well as the presence and extent of the ischemic core, collateral circulation and penumbra in patients with neurological symptoms. Target mismatch may optimize selection of patients with late or unknown symptom onset who would potentially be eligible for revascularization therapy. The purpose of this study was to provide a comprehensive review of the current evidence about efficacy and theoretical basis of present imaging modalities, and explores future directions for imaging in the management of acute ischemic stroke.

Data-efficient deep learning of radiological image data for outcome prediction after endovascular treatment of patients with acute ischemic stroke

Treatment selection is becoming increasingly more important in acute ischemic stroke patient care. Clinical variables and radiological image biomarkers (old age, pre-stroke mRS, NIHSS, occlusion location, ASPECTS, among others) have an important role in treatment selection and prognosis. Radiological biomarkers require expert annotation and are subject to inter-observer variability. Recently, Deep Learning has been introduced to reproduce these radiological image biomarkers. Instead of reproducing these biomarkers, in this work, we investigated Deep Learning techniques for building models to directly predict good reperfusion after endovascular treatment (EVT) and good functional outcome using CT angiography images. These models do not require image annotation and are fast to compute. We compare the Deep Learning models to Machine Learning models using traditional radiological image biomarkers. We explored Residual Neural Network (ResNet) architectures, adapted them with Structured Receptive Fields (RFNN) and auto-encoders (AE) for network weight initialization. We further included model visualization techniques to provide insight into the network's decision-making process. We applied the methods on the MR CLEAN Registry dataset with 1301 patients. The Deep Learning models outperformed the models using traditional radiological image biomarkers in three out of four cross-validation folds for functional outcome (average AUC of 0.71) and for all folds for reperfusion (average AUC of 0.65). Model visualization showed that the arteries were relevant features for functional outcome prediction. The best results were obtained for the ResNet models with RFNN. Auto-encoder initialization often improved the results. We concluded that, in our dataset, automated image analysis with Deep Learning methods outperforms radiological image biomarkers for stroke outcome prediction and has the potential to improve treatment selection.

Efficacy of Thrombus Density on Noninvasive Computed Tomography Neuroimaging for Predicting Thrombus Pathology and Patient Outcome after Mechanical Thrombectomy in Acute Ischemic Stroke

Background and Purpose:

The aim of this study was to investigate the efficacy of thrombus density on noninvasive computed tomography (CT) neuroimaging for predicting thrombus pathology and patient outcome after mechanical thrombectomy in acute ischemic stroke.

Materials and Methods:

This retrospective chart and imaging review included patients that were treated by mechanical thrombectomy at Siriraj Hospital according to the American Heart Association/American Stroke Association guidelines for the early management of patients with acute ischemic stroke from March 2010 to February 2015 study period. Preintervention noncontrast CT (NCCT), CT angiography (CTA), and/or contrast-enhanced CT (CECT) images were interpreted using CT densitometry. Pathology results were classified as white, red, or mixed thrombi. The result of treatment was evaluated by the modified Rankin Scale at 90 days after treatment.

Results:

From 97 included patients – 97 NCCT images, 48 CTA images, 48 CECT images, and 54 pathologic results of cerebral thrombi were included in the final analysis. Mean clot Hounsfield unit values on NCCT, CTA, and CECT were significantly different between red and white thrombus (P = 0.001 on NCCT, P = 0.03 on CTA, and P = 0.001 on CECT), and between red and mixed thrombus (P = 0.043 on NCCT and P = 0.002 on CTA). However, no significant difference was observed between white thrombus and mixed thrombus (P = 0.09 on NCCT, P = 1.00 on CTA, and P = 0.054 on CECT). There was no significant correlation between type of cerebral thrombus or clot density and the result of treatment.

Conclusion:

Thrombus density on CT was found to be a significant predictor of thrombus pathology; however, no significant association was observed between thrombus type or clot density and patient outcome after mechanical thrombectomy.

Investigating Muscle Function After Stroke Rehabilitation with 31P-MRS: A Preliminary Study

BACKGROUND New evidence reveals significant metabolic changes in skeletal muscle after stroke. However, it is unknown if 31P magnetic resonance spectroscopy (31P-MRS) can evaluate these metabolic changes. Our objective here was to investigate: (a) if muscle energy metabolism changes in the affected side; (b) if muscle energy metabolism changes after rehabilitation; and (c) if energy metabolism measured by 31P-MRS can reflect changes in the Modified Modified Ashworth Scale (MMAS) and Fugl-Meyer assessment-lower extremity (FMA-LE) scores after rehabilitation. MATERIAL AND METHODS We enrolled 13 patients with stroke symptoms and hemiplegia. Lower-limb motor status on the affected side was evaluated by FMA-LE and MMAS. The 31P-MRS measures included phosphocreatine (PCr), inorganic phosphate (Pi), PCr/Pi, and pH. We statistically compared these measures in the affected and unaffected lower leg muscles before rehabilitation and after rehabilitation on the affected side. Spearman correlational analyses was performed to determine correlations between change in energy metabolism and change in FMA-LE score and MMAS score after rehabilitation. RESULTS PCr and PCr/Pi were significantly lower in the affected muscle compared to the unaffected muscle; however, there were no significant differences in Pi or pH. After rehabilitation, PCr, Pi, PCr/Pi, and pH did not significantly change. However, FMA-LE and MMAS score improved significantly after rehabilitation. Changes in energy metabolism measured by 31P-MRS had no correlation with FMA-LE change after rehabilitation. However, changes in PCr and PCr/Pi were correlated with change in MMAS score after rehabilitation. CONCLUSIONS 31P-MRS can evaluate changes in muscle energy metabolism in patients with stroke. PCr measured by 31P-MRS can reflect changes in MMAS after rehabilitation.

Deep learning guided stroke management: a review of clinical applications

Stroke is a leading cause of long-term disability, and outcome is directly related to timely intervention. Not all patients benefit from rapid intervention, however. Thus a significant amount of attention has been paid to using neuroimaging to assess potential benefit by identifying areas of ischemia that have not yet experienced cellular death. The perfusion-diffusion mismatch, is used as a simple metric for potential benefit with timely intervention, yet penumbral patterns provide an inaccurate predictor of clinical outcome. Machine learning research in the form of deep learning (artificial intelligence) techniques using deep neural networks (DNNs) excel at working with complex inputs. The key areas where deep learning may be imminently applied to stroke management are image segmentation, automated featurization (radiomics), and multimodal prognostication. The application of convolutional neural networks, the family of DNN architectures designed to work with images, to stroke imaging data is a perfect match between a mature deep learning technique and a data type that is naturally suited to benefit from deep learning's strengths. These powerful tools have opened up exciting opportunities for data-driven stroke management for acute intervention and for guiding prognosis. Deep learning techniques are useful for the speed and power of results they can deliver and will become an increasingly standard tool in the modern stroke specialist's arsenal for delivering personalized medicine to patients with ischemic stroke.

Sex Differences in the Cerebral Collateral Circulation

Premenopausal women and intact female rodents sustain smaller cerebral infarctions than males. Several sex-dependent differences have been identified as potential contributors, but many questions remain unanswered. Mice exhibit wide variation in native collateral number and diameter (collateral extent) that is dependent on differences in genetic background, aging, and other comorbidities and that contributes to their also-wide differences in infarct volume. Likewise, variation in infarct volume correlates with differences in collateral-dependent blood flow in patients with acute ischemic stroke. We examined whether extent of pial collateral arterioles and posterior communicating collateral arteries (PComAs) differ depending on sex in young, aged, obese, hypertensive, and genetically different mice. We combined new data with meta-analysis of our previously published data. Females of C57BL/6J (B6) and BALB/cByJ (BC) strains sustained smaller infarctions than males after permanent MCA occlusion. This protection was unchanged in BC mice after introgression of the B6 allele of Dce1, the major genetic determinant of variation in pial collaterals among mouse strains. Consistent with this, collateral extent in these and other strains did not differ with sex. Extent of PComAs and primary cerebral arteries also did not vary with sex. No dimorphism was evident for loss of pial collateral number and/or diameter (collateral rarefaction) caused by aging, obesity, and hypertension, nor for collateral remodeling after pMCAO. However, rarefaction was greater in females with long-standing hypertension. We conclude that smaller infarct volume in female mice is not due to greater collateral extent, greater remodeling, or less rarefaction caused by aging, obesity, or hypertension.

Sigmoid Sinus Characteristics Correlate with Early Clinical and Imaging Surrogates in Anterior Circulation Ischemic Stroke

Cerebral venous outflow may play a decisive role in acute ischemic stroke. Here, we assessed the relation of cerebral sinus vein characteristics with clinical and imaging surrogates of early outcome in acute ischemic stroke. We evaluated cerebral vein characteristics in 212 patients with the middle cerebral artery (MCA) occlusive stroke confirmed by CT angiography CTA within 6 h from symptom onset. Readout parameters included volume and density of the sigmoid sinus (SS) and density of the superior sagittal sinus (SupSagS). These were correlated with early clinical outcome defined as hospital death (HD), final infarct volume (FIV), and National Institute of Health Stroke Scale (NIHSS) at discharge. We found a correlation for the volume of the right SS and the FIV when the M1 segment of the MCA of either side was occluded (p = 0.002, Rho = 0.206, n = 134). A decrease in SS density was more pronounced in the subgroup with unfavorable outcome (NIHSS > 15 + HD) but only when the left hemisphere was affected (p = 0.026, n = 101). On stepwise logistic regression analysis, adjusted for on-admission NIHSS, age at presentation, and FIV, smaller SS volume was independently associated with lower odds for hospital death (n = 183, OR 0.13, 95 % CI 0.02–0.94, p = 0.043). A larger right SS and a decrease in density increase the risk of unfavorable early clinical and imaging outcome in AIS. This finding of an outflow pattern independent of the stroke site implicates an involvement of the cerebral venous drainage system in the pathophysiology of ischemic stroke.