Applications of Computed Tomography Angiography (CTA) in Neuroimaging

All answers are in the images: A review of deep learning for cerebrovascular segmentation

Cerebrovascular imaging is a common examination. Its accurate cerebrovascular segmentation become an important auxiliary method for the diagnosis and treatment of cerebrovascular diseases, which has received extensive attention from researchers. Deep learning is a heuristic method that encourages researchers to derive answers from the images by driving datasets. With the continuous development of datasets and deep learning theory, it has achieved important success for cerebrovascular segmentation. Detailed survey is an important reference for researchers. To comprehensively analyze the newest cerebrovascular segmentation, we have organized and discussed researches centered on deep learning. This survey comprehensively reviews deep learning for cerebrovascular segmentation since 2015, it mainly includes sliding window based models, U-Net based models, other CNNs based models, small-sample based models, semi-supervised or unsupervised models, fusion based models, Transformer based models, and graphics based models. We organize the structures, improvement, and important parameters of these models, as well as analyze development trends and quantitative assessment. Finally, we have discussed the challenges and opportunities of possible research directions, hoping that our survey can provide researchers with convenient reference.

Clinical Perspective on Primary Angiitis of the Central Nervous System in Childhood (cPACNS)

Non-arteriosclerotic arteriopathies have emerged as important underlying pathomechanism in pediatric arterial ischemic stroke (AIS). The pathogenesis and classification of cerebral arteriopathies in childhood are heterogeneous. Different classifications base on (i) the anatomic site; (ii) the distribution and size of the affected vessel; (iii) the time course, for example, transient vs. progressive, monophasic vs. recurrent; (iv) the putative pathogenesis; (v) the magnetic resonance imaging morphology of the vasculopathies. Inflammation affecting the cerebral vessels is increasingly recognized as common cause of pediatric AIS. Primary cerebral vasculitis or primary angiitis of the central nervous system (CNS) in childhood (cPACNS) is an important differential diagnosis in pediatric AIS. Primary angiitis of the CNS is a rare disorder, and the pathogenesis is poorly understood so far. The current classification of cPACNS is based on the affected cerebral vessel size, the disease course, and angiographic pattern. Two large subtypes are currently recognized comprising large- and medium-sized vessel CNS vasculitis referred to as angiography-positive cPACNS and angiography-negative small vessel cPACNS. As the clinical manifestations of cPACNS are rather diverse, precise diagnosis can be challenging for the treating pediatrician because of the lack of vital laboratory tests or imaging features. Initial misdiagnosis is common because of overlapping phenotypes and pediatric AIS mimics. As untreated cPACNS is associated with a high morbidity and mortality, timely diagnosis, and induction of immunomodulatory and symptomatic therapy are essential. Survival and neurological outcome depend on early diagnosis and prompt therapy. Primary angiitis of the central nervous system in childhood differs in several aspects from primary cerebral angiitis in adults. The aim of this article is to give a brief comprehensive summary on pediatric primary cerebral vasculitis focusing on the clinical perspective regarding the classification, the putative pathogenesis, the disease course, the diagnostic tools, and emerging treatment options. A modified terminology for clinical practice is discussed.

Multimodal CT techniques for cerebrovascular and hemodynamic evaluation of ischemic stroke: occlusion, collaterals, and perfusion

Neuroimaging of cerebrovascular status and hemodynamics has vastly improved our understanding of stroke mechanisms and provided information for therapeutic decision-making. CT techniques are the most commonly used techniques due to wide availability, rapid acquisition and acceptable tolerance. Numerous multimodal CT techniques have been developed in the last few years. We summarize and explain the various multimodal CT acquisition techniques within three categories based on the scanning mode, namely static mode (single-phase CTA), multiple static mode (multi-phase CTA) and continuous mode (CT perfusion and dynamic CTA). Post-processing methods based on different acquisition modes are also introduced in an easy manner by focusing on the information extracted and products generated. We also describe the applications for these techniques along with their advantages and disadvantages.