Abstract：
OBJECTIVE: The accurate and timely assessment of the collateral perfusion status is crucial in the diagnosis and treatment of patients with acute ischemic stroke. Previous works have shown that collateral imaging, derived from CT angiography, MR perfusion, and MR angiography, aids in evaluating the collateral status. However, such methods are time-consuming and/or sub-optimal due to the nature of manual processing and heuristics. Recently, deep learning approaches have shown to be promising for generating collateral imaging. These, however, suffer from the computational complexity and cost.
METHODS: In this study, we propose a mobile, lightweight deep regression neural network for collateral imaging in acute ischemic stroke, leveraging dynamic susceptibility contrast MR perfusion (DSC-MRP). Built based upon lightweight convolution and Transformer architectures, the proposed model manages the balance between the model complexity and performance.
RESULTS: We evaluated the performance of the proposed model in generating the five-phase collateral maps, including arterial, capillary, early venous, late venous, and delayed phases, using DSC-MRP from 952 patients. In comparison with various deep learning models, the proposed method was superior to the competitors with similar complexity and was comparable to the competitors of high complexity.
CONCLUSIONS: The results suggest that the proposed model is able to facilitate rapid and precise assessment of the collateral status of patients with acute ischemic stroke, leading to improved patient care and outcome.
METHODS: In this study, we propose a mobile, lightweight deep regression neural network for collateral imaging in acute ischemic stroke, leveraging dynamic susceptibility contrast MR perfusion (DSC-MRP). Built based upon lightweight convolution and Transformer architectures, the proposed model manages the balance between the model complexity and performance.
RESULTS: We evaluated the performance of the proposed model in generating the five-phase collateral maps, including arterial, capillary, early venous, late venous, and delayed phases, using DSC-MRP from 952 patients. In comparison with various deep learning models, the proposed method was superior to the competitors with similar complexity and was comparable to the competitors of high complexity.
CONCLUSIONS: The results suggest that the proposed model is able to facilitate rapid and precise assessment of the collateral status of patients with acute ischemic stroke, leading to improved patient care and outcome.
摘要：
目的：准确、及时地评估侧支灌注状态对急性缺血性卒中患者的诊断和治疗至关重要。以前的工作表明，抵押品成像，来源于CT血管造影,MR灌注,和MR血管造影,有助于评估抵押品状态。然而,由于手动处理和试探法的性质,这样的方法是耗时的和/或次优的。最近,深度学习方法已被证明是有前途的生成侧支成像。这些,然而,受到计算复杂性和成本的影响。
方法：在本研究中，我们提出了一个手机，轻量级深度回归神经网络用于急性缺血性卒中侧支成像,利用动态磁化率对比MR灌注（DSC-MRP）。基于轻量级卷积和Transformer架构构建，提出的模型管理模型复杂性和性能之间的平衡。
结果：我们评估了所提出的模型在生成五阶段抵押品图时的性能，包括动脉,毛细管,早期静脉，晚期静脉，和延迟阶段，使用952例患者的DSC-MRP。与各种深度学习模型相比，所提出的方法优于具有相似复杂性的竞争对手，并且与高复杂性的竞争对手相当。
结论：结果表明，所提出的模型能够促进对急性缺血性卒中患者侧支状态的快速和精确评估，改善患者护理和预后。
方法：在本研究中，我们提出了一个手机，轻量级深度回归神经网络用于急性缺血性卒中侧支成像,利用动态磁化率对比MR灌注（DSC-MRP）。基于轻量级卷积和Transformer架构构建，提出的模型管理模型复杂性和性能之间的平衡。
结果：我们评估了所提出的模型在生成五阶段抵押品图时的性能，包括动脉,毛细管,早期静脉，晚期静脉，和延迟阶段，使用952例患者的DSC-MRP。与各种深度学习模型相比，所提出的方法优于具有相似复杂性的竞争对手，并且与高复杂性的竞争对手相当。
结论：结果表明，所提出的模型能够促进对急性缺血性卒中患者侧支状态的快速和精确评估，改善患者护理和预后。
