BackgroundSpinal cord infarction (SCI) is rarely caused by vertebral artery dissection (VAD), which is an important cause of posterior circulation stroke in young and middle-aged patients. We report the case of a middle-aged patient without obvious risk factors for atherosclerosis who had SCI from right VAD.Case presentationAn otherwise healthy 40-year-old man presented with acute right-sided body weakness. Six days earlier, he had experienced posterior neck pain without obvious inducement. Neurologic examination revealed a right Brown-Séquard syndrome. Magnetic resonance imaging (MRI) of the head was normal. Further, cervical spine MRI showed spinal cord infarction (SCI) on the right at the C1-C3 level. Three-dimensional high-resolution MRI (3D HR-MRI) volumetric isotropic turbo spin echo acquisition (VISTA) scan showed evidence of vertebral artery dissection (VAD). The patient was significantly relieved of symptoms and demonstrated negative imaging findings after therapy with anticoagulation (AC) and antiplatelets (AP) for 3 months.ConclusionsThe possibility of vertebral artery dissection (VAD) should be considered in the case of young and middle-aged patients without obvious risk factors for atherosclerosis. Furthermore the VISTA black blood sequence plays an important role in the pathological diagnosis of vertebral artery stenosis. Early correct diagnosis and active therapy are crucial to the prognosis.
Recently, image-based diagnostic technology has made encouraging and astonishing development. Modern medical care and imaging technology are increasingly inseparable. However, the current diagnosis pattern of Signal-to-Image-to-Knowledge inevitably leads to information distortion and noise introduction in the procedure of image reconstruction (Signal-to-Image). Artificial intelligence (AI) technologies that can mine knowledge from vast amounts of data offer opportunities to disrupt established workflows. In this prospective study, for the first time, we developed an AI-based Signal-to-Knowledge diagnostic scheme for lung nodule classification directly from the CT rawdata (the signal). We found that the rawdata achieved almost comparable performance with CT indicating that we can diagnose diseases without reconstructing images. Meanwhile, the introduction of rawdata could greatly promote the performance of CT, demonstrating that rawdata contains some diagnostic information that CT does not have. Our results break new ground and demonstrate the potential for direct Signal-to-Knowledge domain analysis.
The habenula (Hb) is involved in many natural human behaviors, and the relevance of its alterations in size and neural activity to several psychiatric disorders and addictive behaviors has been presumed and investigated in recent years using magnetic resonance imaging (MRI). Although the Hb is small, an increasing number of studies have overcome the difficulties in MRI. Conventional structural‐based imaging also has great defects in observing the Hb contrast with adjacent structures. In addition, more and more attention should be paid to the Hb's functional, structural, and quantitative imaging studies. Several advanced MRI methods have recently been employed in clinical studies to explore the Hb and its involvement in psychiatric diseases. This review summarizes the anatomy and function of the human Hb; moreover, it focuses on exploring the human Hb with noninvasive MRI approaches, highlighting strategies to overcome the poor contrast with adjacent structures and the need for multiparametric MRI to develop imaging markers for diagnosis and treatment follow‐up.Level of Evidence3Technical Efficacy Stage2
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