MRI based objective ischemic core-penumbra quantification in adult clinical stroke

Neuroprotection during Thrombectomy for Acute Ischemic Stroke: A Review of Future Therapies

Stroke is a major cause of death and disability worldwide. Endovascular thrombectomy has been impactful in decreasing mortality. However, many clinical results continue to show suboptimal functional outcomes despite high recanalization rates. This gap in recanalization and symptomatic improvement suggests a need for adjunctive therapies in post-thrombectomy care. With greater insight into ischemia-reperfusion injury, recent preclinical testing of neuroprotective agents has shifted towards preventing oxidative stress through upregulation of antioxidants and downstream effectors, with positive results. Advances in multiple neuroprotective therapies, including uric acid, activated protein C, nerinetide, otaplimastat, imatinib, verapamil, butylphthalide, edaravone, nelonemdaz, ApTOLL, regional hypothermia, remote ischemic conditioning, normobaric oxygen, and especially nuclear factor erythroid 2-related factor 2, have promising evidence for improving stroke care. Sedation and blood pressure management in endovascular thrombectomy also play crucial roles in improved stroke outcomes. A hand-in-hand approach with both endovascular therapy and neuroprotection may be the key to targeting disability due to stroke.

Intelligent Algorithm-Based MRI Image Features for Evaluating the Effect of Nursing on Recovery of the Neurological Function of Patients with Acute Stroke

The aim of this study is to analyze the application of early rehabilitation nursing in nursing intervention of neurological impairment among patients with acute ischemic stroke. 116 patients with acute ischemic stroke were selected as the research subjects in this paper. The patients were divided into 58 experimental (early rehabilitation care) and 58 control (routine rehabilitation care) groups according to the difference of care protocols, all of which were performed magnetic resonance imaging on. An image resolution reconstruction algorithm on the basis of deep convolutional neural network is proposed for MRI image processing. The results show that peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) of the included algorithm were remarkably greater than those of compressed sensing (CS) algorithm and nonlocal similarity and block low rank prior-based NSBL algorithm. Running time was shorter than that of the latter two algorithms (P < 0.05). The neurological impairment scores of patients in the experimental group 3 and 5 weeks after treatment were obviously lower than those of patients in the control group (P < 0.05). The Barthel indexes of patients in the experimental group 3 and 5 weeks after treatment were obviously higher than those of patients in the control group (P < 0.05). FugI-Meyer assessment (FMA) and Disability of Arm-Shoulder-Hand (DASH) scores of patients in the experimental group 3 and 5 weeks after treatment were obviously lower than those of patients in control group (P < 0.05). The results show that the deep learning algorithm for MRI image processing performance is better than the traditional algorithm. It not only improves the image quality but also improves the processing efficiency. Early rehabilitation nursing and routine rehabilitation nursing can effectively improve the neurological deficit symptoms, limb motor function, and daily living ability of patients with acute ischemic stroke, and the effect of early rehabilitation nursing is the best.

Multi-view iterative random walker for automated salvageable tissue delineation in ischemic stroke from multi-sequence MRI

BACKGROUND AND OBJECTIVE

Non-invasive and robust identification of salvageable tissue (penumbra) is crucial for interventional stroke therapy. Besides identifying stroke injury as a whole, the ability to automatically differentiate core and penumbra tissues, using both diffusion and perfusion magnetic resonance imaging (MRI) sequences is essential for ischemic stroke treatment.

METHOD

A fully automated and novel one-shot multi-view iterative random walker (MIRW) method with an automated injury seed point detection is developed for lesion delineation. MIRW utilizes the heirarchical decomposition of multi-sequence MRI physical properties of the underlying tissue within the lesion to maximize the inter-class variations of the volumetric histogram to estimate the probable seed points. These estimates are further utilized to conglomerate the lesion estimations iteratively from axial, coronal and sagittal MRI volumes for a computationally efficient segmentation and quantification of salvageable and necrotic tissues from multi-sequence MRI.

RESULTS

Comprehensive experimental analysis of MIRW is performed on three challenging adult(sub-)acute ischemic stroke datasets using performance measures like precision, sensitivity, specificity and Dice similarity score (DSC), which are computed with respect to the manual ground-truth.

COMPARISON WITH EXISTING METHODS

MIRW method resulted in a high DSC of 83.5% in a very less computational time of 98.23 s/volume, which is a significant improvement on the ISLES benchmark dataset for penumbra detection, compared to the state-of-the-art techniques.

CONCLUSION

Quantitative measures demonstrate the promising potential of MIRW for computational analysis of adult stroke and quantifying penumbra in stroke patients which is essential for selecting the good candidates for recanalization.

Symmetry determined superpixels for efficient lesion segmentation of ischemic stroke from MRI

Non-invasive, quantitative and robust identification of ischemic stroke and estimation of injury extent is essential for assisting neuroradiologists. Manual Iesion delineation techniques are susceptible to subjective errors and therefore computer aided preliminary screening of necrosis is warranted. Superpixel based segmentation has gained importance in the recent past by reducing the computational complexity and preserving the characteristics of a group of pixels with similar properties. Axial and coronal MR images of brain exhibit the important feature of symmetry which was integrated with superpixels for segmenting ischemic Iesion. This method was evaluated on a challenging 10 patient data set along with MICCAI challenge data of 28 patients yielding promising results. Proposed symmetry determined superpixel based method demonstrated accuracy close to manual Iesion demarcation with high performance indices with average sensitivity of 82.32%, specificity of 93.7% and Dice similarity score of81.14%.