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

Clinical Imaging of the Penumbra in Ischemic Stroke: From the Concept to the Era of Mechanical Thrombectomy

The ischemic penumbra is defined as the severely hypoperfused, functionally impaired, at-risk but not yet infarcted tissue that will be progressively recruited into the infarct core. Early reperfusion aims to save the ischemic penumbra by preventing infarct core expansion and is the mainstay of acute ischemic stroke therapy. Intravenous thrombolysis and mechanical thrombectomy for selected patients with large vessel occlusion has been shown to improve functional outcome. Given the varying speed of infarct core progression among individuals, a therapeutic window tailored to each patient has recently been proposed. Recent studies have demonstrated that reperfusion therapies are beneficial in patients with a persistent ischemic penumbra, beyond conventional time windows. As a result, mapping the penumbra has become crucial in emergency settings for guiding personalized therapy. The penumbra was first characterized as an area with a reduced cerebral blood flow, increased oxygen extraction fraction and preserved cerebral metabolic rate of oxygen using positron emission tomography (PET) with radiolabeled O₂. Because this imaging method is not feasible in an acute clinical setting, the magnetic resonance imaging (MRI) mismatch between perfusion-weighted imaging and diffusion-weighted imaging, as well as computed tomography perfusion have been proposed as surrogate markers to identify the penumbra in acute ischemic stroke patients. Transversal studies comparing PET and MRI or using longitudinal assessment of a limited sample of patients have been used to define perfusion thresholds. However, in the era of mechanical thrombectomy, these thresholds are debatable. Using various MRI methods, the original penumbra definition has recently gained a significant interest. The aim of this review is to provide an overview of the evolution of the ischemic penumbra imaging methods, including their respective strengths and limitations, as well as to map the current intellectual structure of the field using bibliometric analysis and explore future directions.

MCA-DN: Multi-path convolution leveraged attention deep network for salvageable tissue detection in ischemic stroke from multi-parametric MRI

BACKGROUND AND OBJECTIVE

Accurate and timely treatment of ischemic stroke can restore the blood flow in the affected area and reduce the risk of disability and death. Identification and localisation of both direct and collateral blood flow restriction from MRI using computational intelligence play a crucial role in assisting manual diagnosis decisions in stroke treatment.

METHOD

A novel multi-path convolution leveraged attention based deep network (MCA-DN) is proposed to address this challenge. MCA-DN combines multi-path convolution derived attention making different weighted filters in each attention convolution sub-path, with interactions on the same level of abstraction. This facilitates the network to focus on voxels with enhanced weighted activations, directing to a plausible lesion. Such a proposition of acquiring attention by embedding multiple filter paths, also prioritizes the selective activation of multi-parametric MRI sequences. The multi-path convolution assisted attention block allows the network layers to gain more insights on the input tensor, enabling the expansion of hypothesis search space with a controlled parameter count.

RESULTS

The algorithm is evaluated on 139 patients of 3 datasets with 4 sub-datasets, including 2 benchmarked challenge datasets of ISLES-2015, 2017. MCA-DN achieved parametric measures of Dice similarity coefficient: 77.3 %, sensitivity: 82.8 %, and specificity: 98.8 %, for stroke segmentation, outperforming the five state-of-the-art methods in the field with encouraging success.

CONCLUSION

Competitive performance of the MCA-DN demonstrates immense potential to assist patient-specific stroke treatment planning by estimating the benefit of reperfusion.