Imaging of acute stroke and transient ischaemic attack

Automated ASPECTS Segmentation and Scoring Tool: a Method Tailored for a Colombian Telestroke Network

To evaluate our two non-machine learning (non-ML)-based algorithmic approaches for detecting early ischemic infarcts on brain CT images of patients with acute ischemic stroke symptoms, tailored to our local population, to be incorporated in our telestroke software. One-hundred and thirteen acute stroke patients, excluding hemorrhagic, subacute, and chronic patients, with accessible brain CT images were divided into calibration and test sets. The gold standard was determined through consensus among three neuroradiologist. Four neuroradiologist independently reported Alberta Stroke Program Early CT Scores (ASPECTSs). ASPECTSs were also obtained using a commercial ML solution (CMLS), and our two methods, namely the Mean Hounsfield Unit (HU) relative difference (RELDIF) and the density distribution equivalence test (DDET), which used statistical analyze the of the HUs of each region and its contralateral side. Automated segmentation was perfect for cortical regions, while minimal adjustment was required for basal ganglia regions. For dichotomized-ASPECTSs (ASPECTS < 6) in the test set, the area under the receiver operating characteristic curve (AUC) was 0.85 for the DDET method, 0.84 for the RELDIF approach, 0.64 for the CMLS, and ranged from 0.71-0.89 for the neuroradiologist. The accuracy was 0.85 for the DDET method, 0.88 for the RELDIF approach, and was ranged from 0.83 - 0.96 for the neuroradiologist. Equivalence at a margin of 5% was documented among the DDET, RELDIF, and gold standard on mean ASPECTSs. Noninferiority tests of the AUC and accuracy of infarct detection revealed similarities between both DDET and RELDIF, and the CMLS, and with at least one neuroradiologist. The alignment of our methods with the evaluations of neuroradiologist and the CMLS indicates the potential of our methods to serve as supportive tools in clinical settings, facilitating prompt and accurate stroke diagnosis, especially in health care settings, such as Colombia, where neuroradiologist are limited.

Role of Nanotechnology in Ischemic Stroke: Advancements in Targeted Therapies and Diagnostics for Enhanced Clinical Outcomes

Each year, the number of cases of strokes and deaths due to this is increasing around the world. This could be due to work stress, lifestyles, unhealthy food habits, and several other reasons. Currently, there are several traditional methods like thrombolysis and mechanical thrombectomy for managing strokes. The current approach has several limitations, like delayed diagnosis, limited therapeutic delivery, and risks of secondary injuries. So, there is a need for some effective and reliable methods for the management of strokes, which could help in early diagnosis followed by the treatment of strokes. Nanotechnology has played an immense role in managing strokes, and recently, it has emerged as a transformative solution offering innovative diagnostic tools and therapeutic strategies. Nanoparticles (NPs) belonging to several classes, including metallic (metallic and metal oxide), organic (lipids, liposome), and carbon, can cross the blood-brain barrier and may exhibit immense potential for managing various strokes. Moreover, these NPs have exhibited promise in improving imaging specificity and therapeutic delivery by precise drug delivery and real-time monitoring of treatment efficacy. Nanomaterials like cerium oxide (CeO2) and liposome-encapsulated agents have neuroprotective properties that reduce oxidative stress and promote neuroregeneration. In the present article, the authors have emphasized the significant advancements in the nanomedicine management of stroke, including NPs-based drug delivery systems, neuroprotective and neuroregenerative therapies, and multimodal imaging advancements.

Artificially-reconstructed brain images with stroke lesions from non-imaging data: modeling in categorized patients based on lesion occurrence and sparsity

Brain imaging is necessary for understanding disease symptoms, including stroke. However, frequent imaging procedures encounter practical limitations. Estimating the brain information (e.g., lesions) without imaging sessions is beneficial for this scenario. Prospective estimating variables are non-imaging data collected from standard tests. Therefore, the current study aims to examine the variable feasibility for modelling lesion locations. Heterogeneous variables were employed in the multivariate logistic regression. Furthermore, patients were categorized (i.e., unsupervised clustering through k-means method) by the charasteristics of lesion occurrence (i.e., ratio between the lesioned and total regions) and sparsity (i.e., density measure of lesion occurrences across regions). Considering those charasteristics in models improved estimation performances. Lesions (116 regions in Automated Anatomical Labeling) were adequately predicted (sensitivity: 80.0-87.5% in median). We confirmed that the usability of models was extendable to different resolution levels in the brain region of interest (e.g., lobes, hemispheres). Patients' charateristics (i.e., occurrence and sparsity) might also be explained by the non-imaging data as well. Advantages of the current approach can be experienced by any patients (i.e., with or without imaging sessions) in any clinical facilities (i.e., with or without imaging instrumentation).

Multimodality Imaging in Acute Ischemic Stroke

Stroke is the most common cause of mortality and morbidity worldwide. The prognosis of stroke depends upon the area affected and its early treatment. Time is of the essence in the care of stroke patients as it is estimated that approximately 1.9 million neurons, 14 billion synapses, and 12 km myelinated nerve fibers are lost per minute. Therefore, early diagnosis and prompt treatment are necessary. The primary goal of imaging in acute stroke is to diagnose the underlying cause, estimate the area affected, predict response towards thrombolytic therapy and to exclude the conditions mimicking stroke. With advancements in radiology, multiple imaging modalities are available for diagnosis and predicting prognosis. None of them is considered alone to be perfect. In this era of multimodality imaging, the decision of choosing appropriate techniques depends upon purpose and availability. Non-Contrast Computed Tomography is time effective, and helps in excluding other causes, Trans Cranial Doppler is time-effective and cost-effective with wide availability, however, is operator dependent and less sensitive. It holds a great future in sonothrombolysis. Magnetic Resonance Imaging is so far considered to be the most superior one in terms of early diagnosis, planning for interventional treatment and predicting the response of treatment. However, it is limited due to high cost and lack of availability. The current review gives a detailed account of all imaging modalities available for imaging stroke and their associated pros and cons.

Statistical properties of cerebral CT perfusion imaging systems. Part I. Cerebral blood volume maps generated from nondeconvolution-based systems

PURPOSE

The development and clinical employment of a computed tomography (CT) imaging system benefit from a thorough understanding of the statistical properties of the output images; cerebral CT perfusion (CTP) imaging system is no exception. A series of articles will present statistical properties of CTP systems and the dependence of these properties on system parameters. This Part I paper focuses on the signal and noise properties of cerebral blood volume (CBV) maps calculated using a nondeconvolution-based method.

METHODS

The CBV imaging chain was decomposed into a cascade of subimaging stages, which facilitated the derivation of analytical models for the probability density function, mean value, and noise variance of CBV. These models directly take CTP source image acquisition, reconstruction, and postprocessing parameters as inputs. Both numerical simulations and in vivo canine experiments were performed to validate these models.

RESULTS

The noise variance of CBV is linearly related to the noise variance of source images and is strongly influenced by the noise variance of the baseline images. Uniformly partitioning the total radiation dose budget across all time frames was found to be suboptimal, and an optimal dose partition method was derived to minimize CBV noise. Results of the numerical simulation and animal studies validated the derived statistical properties of CBV.

CONCLUSIONS

The statistical properties of CBV imaging systems can be accurately modeled by extending the linear CT systems theory. Based on the statistical model, several key signal and noise characteristics of CBV were identified and an optimal dose partition method was developed to improve the image quality of CBV.

Potential Therapies by Stem Cell-Derived Exosomes in CNS Diseases: Focusing on the Neurogenic Niche

Neurodegenerative disorders are one of the leading causes of death and disability and one of the biggest burdens on health care systems. Novel approaches using various types of stem cells have been proposed to treat common neurodegenerative disorders such as Alzheimer's Disease, Parkinson's Disease, or stroke. Moreover, as the secretome of these cells appears to be of greater benefit compared to the cells themselves, the extracellular components responsible for its therapeutic benefit have been explored. Stem cells, as well as most cells, release extracellular vesicles such as exosomes, which are nanovesicles able to target specific cell types and thus to modify their function by delivering proteins, lipids, and nucleic acids. Exosomes have recently been tested in vivo and in vitro as therapeutic conveyors for the treatment of diseases. As such, they could be engineered to target specific populations of cells within the CNS. Considering the fact that many degenerative brain diseases have an impact on adult neurogenesis, we discuss how the modulation of the adult neurogenic niches may be a therapeutic target of stem cell-derived exosomes. These novel approaches should be examined in cellular and animal models to provide better, more effective, and specific therapeutic tools in the future.