Acute Stroke Lesions Research Articles

Investigations of Acute Ischemic Stroke Detection Techniques from CT Images Using Machine Learning Approaches

Stroke is a kind of cerebrovascular accident (CVA) that primarily affects individuals over the age of 50, while it can afflict people of all ages. Stroke patients suffer from a chronic condition that leaves them physically unable till their death. To identify the onset of this illness, numerous studies have been carried out. Common signs of a stroke include stiffness, a shift in posture, and tremors in the arms and legs. Functional and emotional mediation are linked to a significant area of the brain. The symptoms of many brain disorders are similar in the early stages because of disruptions to dopamine and other regulating mechanisms. This is a crucial step in how stroke lesions develop. The CAD system is the most efficient method for identifying a stroke. By speeding up the analysis of the required aberration, the CAD system improves the process of illness diagnosis. The goal of the proposed study is to create a compact system that can lessen processing errors, false positive rates, and complexity—three major problems with the current system. The proposed study uses a machine learning model to interpret and classify a CT brain image that shows an Acute Ischaemic Stroke (AIS) lesion. To distinguish between normal and AIS stroke lesions, the pre-processed image is segmented and classed using a Random Forest classifier. Performance criteria like Peak signal ratio, average gradient, accuracy, specificity, sensitivity, and dice index are used to evaluate the experimental output of the proposed work. The suggested model has a straightforward processing structure and the highest efficiency.

Optical coherence tomography enables longitudinal evaluation of cell graft-directed remodeling in stroke lesions.

Stem cell grafting can promote glial repair of adult stroke injuries during the subacute wound healing phase, but graft survival and glial repair outcomes are perturbed by lesion severity and mode of injury. To better understand how stroke lesion environments alter the functions of cell grafts, we employed optical coherence tomography (OCT) to longitudinally image mouse cortical photothrombotic ischemic strokes treated with allogeneic neural progenitor cell (NPC) grafts. OCT angiography, signal intensity, and signal decay resulting from optical scattering were assessed at multiple timepoints across two weeks in mice receiving an NPC graft or an injection of saline at two days after stroke. OCT scattering information revealed pronounced axial lesion contraction that naturally occurred throughout the subacute wound healing phase that was not modified by either NPC or saline treatment. By analyzing OCT signal intensity along the coronal plane, we observed dramatic contraction of the cortex away from the imaging window in the first week after stroke which impaired conventional OCT angiography but which enabled the detection of NPC graft-induced glial repair. There was moderate, but variable, NPC graft survival at photothrombotic strokes at two weeks which was inversely correlated with acute stroke lesion sizes as measured by OCT prior to treatment, suggesting a prognostic role for OCT imaging and reinforcing the dominant effect of lesion size and severity on graft outcome. Overall, our findings demonstrate the utility of OCT imaging for both tracking and predicting natural and treatment-directed changes in ischemic stroke lesion cores.

Segmentation of acute ischemic stroke lesions based on deep feature fusion

Acute ischemic stroke (AIS) is a common brain disease worldwide, and diagnosing AIS requires effectively utilizing information from multiple Computed Tomography Perfusion (CTP) maps. As far as we know, most methods independently process each CTP map or fail to fully utilize medical prior information when integrating the information from CTP maps. Considering the characteristics of AIS lesions, we propose a method for efficient information fusion of CTP maps to achieve accurate segmentation results. We propose Window Multi-Head Cross-Attention Net (WMHCA-Net), which employs a multi-path U-shaped architecture for encoding and decoding. After encoding, multiple independent windowed cross-attentions are used to deeply integrate information from different maps. During the decoding phase, a Channel Cross-Attention (CCA) module is utilized to enhance information recovery during upsampling. We also added a segmentation optimization module to optimize low-resolution segmentation results, improving the overall performance. Finally, experimental results demonstrate that our proposed method exhibits strong balance and excels across multiple metrics. It can provide more accurate AIS lesion segmentation results to assist doctors in evaluating patient conditions. Our code are available at https://github.com/MTVLab/WMHCA-Net.

Predictors of symptomatic intracerebral hemorrhage after endovascular treatment for acute ischemic stroke due to tandem lesion in anterior circulation

BackgroundEndovascular treatment (EVT) of tandem lesion (TL) in the anterior circulation acute ischemic stroke (IS) usually requires periprocedural antithrombotic treatment and early initiation of dual antiplatelet therapy (DAPT) after carotid stenting. However, it may contribute to an occurrence of symptomatic intracerebral hemorrhage (SICH) in some cases. We investigated factors influencing the SICH occurrence and assessed the possible predictors of SICH after EVT. MethodsIS patients with TL in the anterior circulation treated with EVT were enrolled in the multicenter retrospective ASCENT study. A good three-month clinical outcome was scored as 0–2 points in modified Rankin Scale (mRS) and recanalization using the TICI scale. SICH was assessed using the SITS-MOST criteria. Logistic regression analysis was used for the assessment of possible predictors of SICH with adjustment for potential confounders. ResultsIn total, 300 (68.7 % males, mean age 67.3 ± 10.2 years) patients with median of admission NIHSS 17 were analyzed. Recanalization (TICI 2b-3) was achieved in 290 (96.7 %) patients and 176 (58.7 %) had mRS 0–2. SICH occurred in 25 (8.3 %) patients. Patients with SICH did not differ from those without SICH in the rate of periprocedural antithrombotic treatment (64 vs. 57.5 %, p = 0.526) and in the rate of DAPT started within the first 12 h after EVT (20 vs. 42.2 %, p = 0.087). After adjustment, admission NIHSS and admission glycemia were found as the only predictors of SICH after EVT. ConclusionAdmission NIHSS and glycemia were found as the only predictors of SICH after EVT for TL. No associations between periprocedural antithrombotic treatment, early start of DAPT after EVT and SICH occurrence were found.

Hybrid CNN-Transformer Network With Circular Feature Interaction for Acute Ischemic Stroke Lesion Segmentation on Non-Contrast CT Scans.

Lesion segmentation is a fundamental step for the diagnosis of acute ischemic stroke (AIS). Non-contrast CT (NCCT) is still a mainstream imaging modality for AIS lesion measurement. However, AIS lesion segmentation on NCCT is challenging due to low contrast, noise and artifacts. To achieve accurate AIS lesion segmentation on NCCT, this study proposes a hybrid convolutional neural network (CNN) and Transformer network with circular feature interaction and bilateral difference learning. It consists of parallel CNN and Transformer encoders, a circular feature interaction module, and a shared CNN decoder with a bilateral difference learning module. A new Transformer block is particularly designed to solve the weak inductive bias problem of the traditional Transformer. To effectively combine features from CNN and Transformer encoders, we first design a multi-level feature aggregation module to combine multi-scale features in each encoder and then propose a novel feature interaction module containing circular CNN-to-Transformer and Transformer-to-CNN interaction blocks. Besides, a bilateral difference learning module is proposed at the bottom level of the decoder to learn the different information between the ischemic and contralateral sides of the brain. The proposed method is evaluated on three AIS datasets: the public AISD, a private dataset and an external dataset. Experimental results show that the proposed method achieves Dices of 61.39% and 46.74% on the AISD and the private dataset, respectively, outperforming 17 state-of-the-art segmentation methods. Besides, volumetric analysis on segmented lesions and external validation results imply that the proposed method is potential to provide support information for AIS diagnosis.

Automated diagnosis of acute cerebral ischemic stroke lesions from diffusion-weighted magnetic resonance images in assessment with modified Rankin Scale

Purpose: Automated diagnosis of acute cerebral ischemic stroke lesions (ACISL) is an evolving science. Early detection and exact delineation of ACISL automatically from diffusion-weighted magnetic resonance (DWMR) images are crucial for initiating prompt treatment. Thus, this work aims to determine the appropriate slice out of 60 pieces using multi-fractal analysis (MFA) and to segment the lesions in DWMR images using a hybrid optimization method. Features extracted from the segmented images were clinically correlated with the modified Rankin Scale (mRS). Methods: Thirty-one real-time stroke patients’ images were collected from Rajiv Gandhi Government General Hospital, Chennai, India. Multiple MRI slices were taken from each patient and filtered using an anisotropic diffusion filter (ADF). These filtered images were skull-stripped automatically by the maximum entropy thresholding technique incorporating mathematical morphological operations (MEM). The multi-fractal analysis (MFA) identifies the prominent slice with the significant infarct lesion. An isodata algorithm that integrated differential evolution with the particle swarm optimization method based on Kapur’s (IDPK) and Otsu’s (IDPO) approaches was attempted to segment the ACISL. Finally, the geometric and moment features extracted from the segmented lesions categorized the stroke severity and were correlated with the mRS. Results: The findings of the experimental work confirm that the suggested IDPK approach achieved usual normalized values for image similarity indices such as Sokal-Michener Coefficient (98.51%), Roger-Tanimoto Coefficient (90.16%), Sokel-Sneath-2 (91.04%), and Sorenson Index (90.04%) are superior to IDPO. Statistical significance proved that the segmented lesions’ area (r = 0.820, p < 0.0001) and perimeter (r = 0.928, p < 0.0001) were strongly correlated with the mild and moderate criteria of mRS. Conclusion: The proposed work effectively detected ischemic stroke lesions and their severity within the studied image groups. It could be a promising and potential tool to aid radiologists in validating their diagnosis.

Abstract TP150: Magnetic Resonance Imaging Predictors of Atrial Fibrillation in Cryptogenic Stroke Cases

Introduction: Ischemic stroke is a leading cause of death and disability in the US, with 20-40% of cases, classified as cryptogenic or with an unexplained cause. Detection of one cause, paroxysmal atrial fibrillation (AF), is critical to ensuring optimal treatment with direct oral anticoagulant (DOAC). Currently, the most reliable AF detection strategy is use of an insertable cardiac monitor (ICM). However, earlier detection of occult cardioembolic patterns using MRI may promote earlier decisions for DOAC use. The overall goal of this study is to determine if MRI lesion patterns are predictive of AF detection by ICM. Methodology: Cases of consecutive patients (1/1/2015 - 12/31/2017) with MRI-confirmed stroke performed 48h from time last known well and prior to endovascular treatment were retrospectively analyzed. The primary outcome, presence of occult atrial fibrillation, was detected by ICM placement within 90 days and follow-up within 180 days from stroke. Four imaging patterns were tested as predictors of AF: i) acute stroke lesion involving multiple vascular territories (MVT, i.e. right or left carotid and/or posterior circulation), ii) MVT plus wedge-shaped cortical infarct or chronic stroke on FLAIR, iii) MVT involving 3 territories, and iv) MVT in 3 territories plus chronic FLAIR lesion. Adjustment variables were based on univariate logistic regression predictors of AF at P ≤0.1000. Results: Of the 101 cases in this analysis, the median age was 63 years and 49.5% male. Stroke in multiple vascular territories MVT was present in 22/101 (22%) at baseline. The total AF 6-month detection rate was 36/101 (36%). The imaging pattern most predictive of AF was pattern ii, MVT plus chronic FLAIR with an unadjusted odds ratio (OR) of 3.47, 95% CI of 0.3442-2.1731, P=.0073. The adjusted OR (age ≥ 55, history of stroke, and history of TIA,) was 3.26, 95% CI: 1.0358-11.1860, P =.0480. Conclusion: The presence of acute lesions in MVT and a chronic FLAIR lesion may be a biomarker of occult cardioembolic source that could be used early after the onset of stroke to determine optimal DOAC use for secondary prevention and potential risk reduction of stroke recurrence from suspected but unproven cardioembolic source. Funding: Lone Star Stroke Research Consortium

Ischemic and hemorrhagic stroke lesion environments differentially alter the glia repair potential of neural progenitor cell and immature astrocyte grafts

Using cell grafting to direct glia-based repair mechanisms in adult CNS injuries represents a potential therapeutic strategy for supporting functional neural parenchymal repair. However, glia repair directed by neural progenitor cell (NPC) grafts is dramatically altered by increasing lesion size, severity, and mode of injury. To address this, we studied the interplay between astrocyte differentiation and cell proliferation of NPC in vitro to generate proliferating immature astrocytes (ImA) using hysteretic conditioning. ImA maintain proliferation rates at comparable levels to NPC but showed robust immature astrocyte marker expression including Gfap and Vimentin. ImA demonstrated enhanced resistance to myofibroblast-like phenotypic transformations upon exposure to serum enriched environments in vitro compared to NPC and were more effective at scratch wound closure in vitro compared to quiescent astrocytes. Glia repair directed by ImA at acute ischemic striatal stroke lesions was equivalent to NPC but better than quiescent astrocyte grafts. While ischemic injury environments supported enhanced survival of grafts compared to healthy striatum, hemorrhagic lesions were hostile towards both NPC and ImA grafts leading to poor survival and ineffective modulation of natural wound repair processes. Our findings demonstrate that lesion environments, rather than transcriptional pre-graft states, determine the survival, cell-fate, and glia repair competency of cell grafts applied to acute CNS injuries.

DCTP-Net: Dual-Branch CLIP-Enhance Textual Prompt-Aware Network for Acute Ischemic Stroke Lesion Segmentation From CT Image.

Detecting early ischemic lesions (EIL) in computed tomography (CT) images is crucial for reducing diagnostic time and minimizing neuron loss due to oxygen deprivation. This paper introduces DCTP-Net, a dual-branch network for segmenting acute ischemic stroke lesions in CT images, consisting of a segmentation branch and a prompt-aware branch. The segmentation branch uses an encoder-decoder network as the backbone to identify lesions, where the encoder fuses CT image features with prompt features from the prompt-aware branch. To enhance semantic feature extraction and reduce the impact of cerebral structural details, we introduce a cross-collaboration dynamic connection (CCDC) module to link the encoder and decoder. The prompt-aware branch includes a learnable prompt (LP) block to incorporate cerebral prior knowledge, and the prompt-aware encoder (PAE) combines the LP block with multi-level features from the segmentation branch for more precise representation. Additionally, we propose a CLIP-enhance textual prompt (CETP) module that utilizes the CLIP text encoder to generate specialized convolutional parameters for the segmentation head. These parameters are tailored to the unique characteristics of each input image, improving segmentation performance. Qualitative and quantitative studies reveal that DCTP-Net outperforms the current state-of-the-art, IS-Net, with Dice score increases of 3.9% on AISD and 3.8% on ISLES2018, demonstrating its superiority in EIL segmentation.

Vessel Wall MRI in the Diagnosis and Follow-Up of Nonstenosing Intracranial Atherosclerotic Lesions in Acute Stroke.

Vessel Wall MRI in the Diagnosis and Follow-Up of Nonstenosing Intracranial Atherosclerotic Lesions in Acute Stroke.

Brain connectivity networks underlying resting heart rate variability in acute ischemic stroke

Acute strokes can affect heart rate variability (HRV), the mechanisms how are not well understood. We included 42 acute stroke patients (2–7 days after ischemic stroke, mean age 66 years, 16 women). For analysis of HRV, 20 matched controls (mean age 60.7, 10 women) were recruited. HRV was assessed at rest, in a supine position and individual breathing rhythmus for 5 min. The coefficient of variation (VC), the root mean square of successive differences (RMSSD), the powers of low (LF, 0.04–0.14 Hz) and high (HF, 0.15–0.50 Hz) frequency bands were extracted. HRV parameters were z-transformed related to age- and sex-matched normal subjects. Z-values < -1 indicate reduced HRV. Acute stroke lesions were marked on diffusion-weighted images employing MRIcroN and co-registered to a T1-weighted structural volume-dataset. Using independent component analysis (ICA), stroke lesions were related to HRV. Subsequently, we used the ICA-derived lesion pattern as a seed and estimated the connectivity between these brain regions and seven common functional networks, which were obtained from 50 age-matched healthy subjects (mean age 68.9, 27 women). Especially, LF and VC were frequently reduced in patients. ICA revealed one covarying lesion pattern for LF and one similar for VC, predominantly affecting the right hemisphere. Activity in brain areas corresponding to these lesions mainly impact on limbic (r = 0.55 ± 0.08) and salience ventral attention networks (0.61 ± 0.10) in the group with reduced LF power (z-score < -1), but on control and default mode networks in the group with physiological LF power (z-score > -1). No different connectivity could be found for the respective VC groups. Our results suggest that HRV alteration after acute stroke might be due to affecting resting-state brain networks.

Acute intracranial stenting in acute ischemic stroke with underlying atherosclerosis: A two-center retrospective study

Objective In patients with large vessel occlusion, successful reperfusion is a predictor of favorable outcome. However, patients with underlying intracranial atherosclerotic disease (ICAD) show higher rates of immediate reocclusion, frequently requiring rescue stenting. We aimed to investigate the safety, efficacy, and outcome of acute stenting in acute stroke patients with underlying ICAD. Methods Prospective databases from two centers between 2012 and 2021 were reviewed. Adult patients who underwent ICAD-related acute intracranial stenting, after mechanical thrombectomy (MT) were included. Clinical and procedural data, technical success measured by Modified Thrombolysis in Cerebral Infarction score, periprocedural complications, stent restenosis, and follow up mRS at three months were evaluated. Results During a 10-year period, a total of 96 patients with acute stroke and intracranial atherosclerotic steno-occlusive lesions underwent stenting after MT. Mean age was 63 (SD 14) and 54 patients were male (56.3%). Successful reperfusion (TICI ≥ 2b) was achieved in 86 patients (89.6%) following rescue stenting; favorable clinical outcome (mRS &lt; 3) was observed in 32 patients (37.6%). Symptomatic intracranial hemorrhage and mortality were observed in 9.5% and 22.4% of patients, respectively. Multivariate regression yielded older age (OR: 0.477, 95% CI: 0.933–0.999, p=0.046) as an independent negative predictor of favorable functional outcome after acute intracranial stenting. Conclusion Our results suggest that acute intracranial stenting is safe and effective as a rescue treatment after MT in acute stroke patients with underlying ICAD. Younger age was found as an independent predictor of good outcome. Nevertheless, ICAD still carries a high mortality and poor functional clinical outcome.

Nogo-A is secreted in extracellular vesicles, occurs in blood and can influence vascular permeability

Nogo-A is a transmembrane protein with multiple functions in the central nervous system (CNS), including restriction of neurite growth and synaptic plasticity. Thus far, Nogo-A has been predominantly considered a cell contact-dependent ligand signaling via cell surface receptors. Here, we show that Nogo-A can be secreted by cultured cells of neuronal and glial origin in association with extracellular vesicles (EVs). Neuron- and oligodendrocyte-derived Nogo-A containing EVs inhibited fibroblast spreading, and this effect was partially reversed by Nogo-A receptor S1PR2 blockage. EVs purified from HEK cells only inhibited fibroblast spreading upon Nogo-A over-expression. Nogo-A-containing EVs were found in vivo in the blood of healthy mice and rats, as well as in human plasma. Blood Nogo-A concentrations were elevated after acute stroke lesions in mice and rats. Nogo-A active peptides decreased barrier integrity in an in vitro blood-brain barrier model. Stroked mice showed increased dye permeability in peripheral organs when tested 2 weeks after injury. In the Miles assay, an in vivo test to assess leakage of the skin vasculature, a Nogo-A active peptide increased dye permeability. These findings suggest that blood borne, possibly EV-associated Nogo-A could exert long-range regulatory actions on vascular permeability.

Utilizing deep learning via the 3D U-net neural network for the delineation of brain stroke lesions in MRI image

The segmentation of acute stroke lesions plays a vital role in healthcare by assisting doctors in making prompt and well-informed treatment choices. Although Magnetic Resonance Imaging (MRI) is a time-intensive procedure, it produces high-fidelity images widely regarded as the most reliable diagnostic tool available. Employing deep learning techniques for automated stroke lesion segmentation can offer valuable insights into the precise location and extent of affected tissue, enabling medical professionals to effectively evaluate treatment risks and make informed assessments. In this research, a deep learning approach is introduced for segmenting acute and sub-acute stroke lesions from MRI images. To enhance feature learning through brain hemisphere symmetry, pre-processing techniques are applied to the data. To tackle the class imbalance challenge, we employed a strategy of using small patches with balanced sampling during training, along with a dynamically weighted loss function that incorporates f1-score and IOU-score (Intersection over Union). Furthermore, the 3D U-Net architecture is used to generate predictions for complete patches, employing a high degree of overlap between patches to minimize the requirement for subsequent post-processing steps. The 3D U-Net model, utilizing ResnetV2 as the pre-trained encoder for IOU-score and Seresnext101 for f1-score, stands as the leading state-of-the-art (SOTA) model for segmentation tasks. However, recent research has introduced a novel model that surpasses these metrics and demonstrates superior performance compared to other backbone architectures. The f1-score and IOU-score were computed for various backbones, with Seresnext101 achieving the highest f1-score and ResnetV2 performing the highest IOU-score. These calculations were conducted using a threshold value of 0.5. This research proposes a valuable model based on transfer learning for the classification of brain diseases in MRI scans. The achieved f1-score using the recommended classifiers demonstrates the effectiveness of the approach employed in this study. The findings indicate that Seresnext101 attains the highest f1-score of 0.94226, while ResnetV2 achieves the best IOU-score of 0.88342, making it the preferred architecture for segmentation methods. Furthermore, the study presents experimental results of the 3D U-Net model applied to brain stroke lesion segmentation, suggesting prospects for researchers interested in segmenting brain strokes and enhancing 3D U-Net models.

Acute ischemic stroke lesion segmentation in non-contrast CT images using 3D convolutional neural networks

In this paper, an automatic algorithm aimed at volumetric segmentation of acute ischemic stroke lesion in non-contrast computed tomography brain 3D images is proposed. Our deep-learning approach is based on the popular 3D U-Net convolutional neural network architecture, which was modified by adding the squeeze-and-excitation blocks and residual connections. Robust pre-processing methods were implemented to improve the segmentation accuracy. Moreover, a special patches sampling strategy was used to address the large size of medical images and class imbalance and to stabilize neural network training. All experiments were performed using five-fold cross-validation on the dataset containing non-contrast computed tomography volumetric brain scans of 81 patients diagnosed with acute ischemic stroke. Two radiology experts manually segmented images independently and then verified the labeling results for inconsistencies. The quantitative results of the proposed algorithm and obtained segmentation were measured by the Dice similarity coefficient, sensitivity, specificity and precision metrics. The suggested pipeline provides a Dice improvement of 12.0 %, sensitivity of 10.2 % and precision 10.0 % over the baseline and achieves an average Dice of 62.8 ± 3.3 %, sensitivity of 69.9 ± 3.9 %, specificity of 99.7 ± 0.2 % and precision of 61.9 ± 3.6 %, showing promising segmentation results.

MSA-YOLOv5: Multi-scale attention-based YOLOv5 for automatic detection of acute ischemic stroke from multi-modality MRI images

Background and objectiveAcute ischemic stroke (AIS) is a common neurological disorder characterized by the sudden onset of cerebral ischemia, leading to functional impairments. Swift and precise detection of AIS lesions is crucial for stroke diagnosis and treatment but poses a significant challenge. This study aims to leverage multimodal fusion technology to combine complementary information from various modalities, thereby enhancing the detection performance of AIS target detection models. MethodsIn this retrospective study of AIS, we collected data from 316 AIS patients and created a multi-modality magnetic resonance imaging (MRI) dataset. We propose a Multi-Scale Attention-based YOLOv5 (MSA-YOLOv5), targeting challenges such as small lesion size and blurred borders at low resolutions. Specifically, we augment YOLOv5 with a prediction head to detect objects at various scales. Next, we replace the original prediction head with a Multi-Scale Swin Transformer Prediction Head (MS-STPH), which reduces computational complexity to linear levels and enhances the ability to detect small lesions. We incorporate a Second-Order channel attention (SOCA) module to adaptively rescale channel features by employing second-order feature statistics for more discriminative representations. Finally, we further validate the effectiveness of our method using the ISLES 2022 dataset. ResultsOn our in-house AIS dataset, MSA-YOLOv5 achieves a 79.0% mAP0.5, substantially surpassing other single-stage models. Compared to two-stage models, it maintains a comparable performance level while significantly reducing the number of parameters and resolution. On the ISLES 2022 dataset, MSA-YOLOv5 attains an 80.0% mAP0.5, outperforming other network models by a considerable margin. MS-STPH and SOCA modules can significantly increase mAP0.5 by 2.7% and 1.9%, respectively. Visualization interpretability results show that the proposed MSA-YOLOv5 restricts high attention in the small regions of AIS lesions. ConclusionsThe proposed MSA-YOLOv5 is capable of automatically and effectively detecting acute ischemic stroke lesions in multimodal images, particularly for small lesions and artifacts. Our enhanced model reduces the number of parameters while improving detection accuracy. This model can potentially assist radiologists in providing more accurate diagnosis, and enable clinicians to develop better treatment plans.

Automated acute ischemic stroke lesion delineation based on apparent diffusion coefficient thresholds.

Automated lesion segmentation is increasingly used in acute ischemic stroke magnetic resonance imaging (MRI). We explored in detail the performance of apparent diffusion coefficient (ADC) thresholding for delineating baseline diffusion-weighted imaging (DWI) lesions. Retrospective, exploratory analysis of the prospective observational single-center 1000Plus study from September 2008 to June 2013 (clinicaltrials.org; NCT00715533). We built a fully automated lesion segmentation algorithm using a fixed ADC threshold (≤620 × 10-6 mm2/s) to delineate the baseline DWI lesion and analyzed its performance compared to manual assessments. Diagnostic capabilities of best possible ADC thresholds were investigated using receiver operating characteristic curves. Influential patient factors on ADC thresholding techniques' performance were studied by conducting multiple linear regression. 108 acute ischemic stroke patients were selected for analysis. The median Dice coefficient for the algorithm was 0.43 (IQR 0.20-0.64). Mean ADC values in the DWI lesion (β = -0.68, p < 0.001) and DWI lesion volumes (β = 0.29, p < 0.001) predicted performance. Optimal individual ADC thresholds differed between subjects with a median of ≤691 × 10-6 mm2/s (IQR ≤660-750 × 10-6 mm2/s). Mean ADC values in the DWI lesion (β = -0.96, p < 0.001) and mean ADC values in the brain parenchyma (β = 0.24, p < 0.001) were associated with the performance of individual thresholds. The performance of ADC thresholds for delineating acute stroke lesions varies substantially between patients. It is influenced by factors such as lesion size as well as lesion and parenchymal ADC values. Considering the inherent noisiness of ADC maps, ADC threshold-based automated delineation of very small lesions is not reliable.

Balloon‐Expandable Stenting as a Bridging Therapy in Patients With Acute Stroke and Tandem Occlusions

Background Stenting extracranial internal carotid artery (ICA) lesions in acute ischemic stroke with tandem lesions is technically challenging. Its safety is highly debated because of the requirement of dual‐antiplatelet therapy. The optimal stenting device, timing, and periprocedural antiplatelet therapy for extracranial ICA stenting in the setting of acute tandem occlusion are still unclear. Methods We performed a retrospective study of patients with acute ischemic stroke attributable to tandem lesions who underwent endovascular treatment during a 5‐year period receiving either conventional self‐expanding carotid stents (SX) or balloon‐expandable carotid stent (BX). BX stents were restented with an SX in the subacute phase. Primary outcomes of interest were extracranial ICA patency at follow‐up and symptomatic intracranial hemorrhage. Results A total of 112 patients admitted from April 2016 to April 2021 were included. Dual‐antiplatelet therapy immediately following endovascular treatment was more frequently administered in the SX group (35/39 [89.7%]) compared with the BX group (20/73 [27.4%]) ( P &lt;0.001). Patients in the BX stent group (3/73 [4.1%]) developed a lower rate of symptomatic intracranial hemorrhage compared with patients in the SX stent group (7/39 [17.9%]) ( P =0.031). No differences in extracranial ICA high‐grade restenosis or reocclusion were found between groups at 24 hours after procedure (BX: 20/73 [27.4%]; SX: 9/39 [23.1%]; P =0.673). Conclusions In patients with acute ischemic stroke and tandem occlusions, a bridging therapy including BX stents with less‐aggressive antiplatelet therapy and subsequent definitive SX stenting to treat extracranial ICA lesions resulted in a lower rate of symptomatic hemorrhagic transformation and no differences in stent patency.

Preliminary research of the classification of the brain acute stroke lesions by the Diffusion Kurtosis Imaging (DKI) and Diffusion Weighted Imaging (DWI) parameters.

Diffusion-weighted magnetic resonance imaging (DWI) is a mature scanning technique. With high sensitivity in detecting cerebral infractions, it has become an essential part of the clinical evaluation of acute stroke. However, with the update in medical ideals and treatment, clinicians are now focusing on distinguishing between reversible and irreversible brain tissue damage rather than detecting ischaemic lesions alone. We supposed that Diffusion Kurtosis Imaging (DKI) could classify heterogeneous DWI lesions, deepening the understanding of tissue injury. We systematically studied the different parameters of DKI in acute stroke patients in the literature. We collected 41 patients (26 male, 15 female), including different infarctions with acute cerebral infarction in different brain regions. Of all patients, 20 were single-infarction, while others were multi-infarctions. In this paper, we categorized acute cerebral infarction lesions into two types according to the parametric characteristics of both DKI and DWI. Type I means the DKI and DWI were matched, and Type II means the DKI and DWI were mismatched. Based on each parametric map, the region of interest (ROI) is outlined in each most severe lesion area (as large as possible in the center of the lesion). In the control group, ROIs of the same size are located in the corresponding regions of the contralateral hemisphere. In both Type I and Type II, all parameters conform to a normal distribution. An independent sample T-test was used to compare the differences between each group. In Type I, we found the FA, MD, Da, Dr, MK and Ka values were statistically different (P< 0.05), while in Type II, only the MK and Ka values were statistically different (P< 0.05). DKI, compared to DWI, can provide more imaging information about intracranial ischemic infarction, which can deepen the understanding of the mechanism of ischemic tissue damage. Our classification of the brain acute stroke lesions by DKI parameters and DWI may help us rediscover the real core of infraction.

Localizing Delirium through Automated Lesion Segmentation and Neural Network Mapping (N1.001)

<h3>Objective:</h3> To determine whether delirium in acute ischemic stroke is localizable to specific neural networks. <h3>Background:</h3> Delirium is estimated to occur in one-fourth of patients with acute stroke. Despite its associated morbidity and mortality, prediction and pathophysiological understanding of delirium remains challenging. A recent meta-analysis of patients with delirium and stroke found increased risks of delirium in supratentorial, cortical, and anterior circulation strokes, raising the possibility of an underlying neural network for delirium. <h3>Design/Methods:</h3> We performed a retrospective cohort study of patients admitted to a comprehensive stroke center with acute ischemic stroke from January 2016 to April 2019. Patients were assessed for delirium by trained clinical nursing staff using the Confusion Assessment Method (CAM) framework. Acute stroke lesions from MRI diffusion weighted images (DWI) lesions were automatically segmented using a machine learning algorithm. Lesions were registered to a 2mm Montreal Neurological Institute template (MNI-152). We then used lesion network mapping to identify potential unifying brain networks for delirium in patients with acute stroke. <h3>Results:</h3> 907 scans from unique patients were analyzed. 274 patients had delirium (30.2%), 47.5% were women (431), and the mean age was 69.6 years old. In comparison to a general stroke cohort, patients with delirium had lesions connected to the right and left inferior frontal gyri and left middle temporal gyrus and left temporo-parietal junction (one-sample t-test, family-wise error p&lt;0.05). Sensitivity analysis showed that delirium was not associated with parietal, occipital, brainstem, and cerebellar lesions. <h3>Conclusions:</h3> Delirium in stroke localizes to a network connected to frontotemporal regions. These results have implications for prediction of delirium in patients with acute stroke, with the potential for targeting multimodal prevention. Future work will refine their specificity, however, the results suggest that frontotemporal networks may be particularly important for cardinal symptoms of delirium, a disorder of impaired attention and awareness <b>Disclosure:</b> Dr. Rhee has stock in NTAP. Dr. Rhee has stock in TSLA. Dr. Rhee has stock in BABA. Dr. Rhee has stock in ZM. Dr. Rhee has stock in GM. Dr. Rhee has stock in PFF. Dr. Rhee has stock in AMZN. Dr. Rhee has stock in META. Dr. Rhee has stock in GE. Dr. Rhee has received publishing royalties from a publication relating to health care. Dr. Rhee has received personal compensation in the range of $0-$499 for serving as a Director of the Hippocratic Forum with Abigail Adams Institute. Dr. Ferguson has nothing to disclose. Dr. Bonkhoff has received personal compensation in the range of $500-$4,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for NeuroImage Clinical (Elsevier). Mr. Patel has nothing to disclose. Dr. Bretzner has nothing to disclose. Dr. Hong has nothing to disclose. Dr. Ryan has nothing to disclose. Dr. Westover has stock in Beacon Biosignals. The institution of Dr. Westover has received research support from NIH. Dr. Westover has received publishing royalties from a publication relating to health care. Dr. Westover has a non-compensated relationship as a cofounder with Beacon Biosignals that is relevant to AAN interests or activities. Dr. Fox has received personal compensation in the range of $500-$4,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for Wiley. Dr. Rost has received personal compensation in the range of $5,000-$9,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for Stroke - AHA/ASA Journal. The institution of Dr. Rost has received research support from NIH. Dr. Rost has received publishing royalties from a publication relating to health care. The institution of Dr. Kimchi has received research support from NIH. The institution of an immediate family member of Dr. Kimchi has received research support from NIH.