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Purpose: We sought to evaluate the performance of a new "one-stop-shop" scan protocol combining brain computed tomography perfusion (CTP) and head-and-neck CT angiography (CTA) imaging for acute stroke patients using a 256-detector CT scanner.Method: From March to August 2020, 60 patients (30 men and 30 women) aged 22-88 years with suspected acute stroke were enrolled and randomly divided into 2 groups to undergo brain CTP and head-and-neck CTA with a 256-detector CT system. Group A used traditional scan protocol with a separate brain CTP and head-and-neck CT examination that included non-contrast-enhanced and contrast-enhanced acquisitions; group B used the new "one-stop-shop" scan protocol with head-and-neck CTA data inserted into brain CTP scans at the peak time (PT) of the arterial phase. The insertion point of the head-and-neck CTA data was determined by a test bolus. The examination time, contrast dose, radiation dose, and image quality were compared between the groups.Results: The total contrast dose was reduced by 40% in group B compared to group A (60 mL vs. 100 mL). The imaging time was 52.5 ± 2.6 s in group B and 74.9 ± 3.3 s in group A, showing a reduction of approximately 43% in group B. There was no significant difference in image quality both quantitatively and qualitatively between the groups (all P > 0.05). Group B had a slight reduction in dose length product (1139.0 ± 45.3 vs. 1211.6 ± 31.9 mGy·cm, P < 0.001).Conclusions: The proposed "one-stop-shop" scan protocol combining brain CTP and head-and-neck CTA on a 256-detector CT system can reduce imaging time and contrast dose, without affecting image quality or perfusion results, compared to the traditional protocol of separating the examinations. [ABSTRACT FROM AUTHOR]

[Display omitted] • The abnormal local and long-range FCD in MCI compared with HCs. • The reversed FCD was found in MCI after treatments of acupoint-related moxibustion. • After moxibustion treatment, the MoCA scores of MCI were improved. Mild cognitive impairment (MCI) is a general neurodegenerative disease. Moxibustion has been shown to have remarkable effect on cognitive improvement, however, less is known about the effect of moxibustion on MCI and its underlying neural mechanism. This study aimed to investigate the ameliorative brain network in MCI after treatments of acupoint-related moxibustion. Resting-state functional MRI were derived from 47 MCI patients and 30 healthy controls (HCs). Patients were randomized as Tiaoshen YiZhi (TSYZ, n = 27) and sham (SHAM, n = 20) acupoint moxibustion groups. Functional connectivity density (FCD) method and repeated-measures two-way analysis of variance (ANOVA) were performed to ascertain the interaction effects between groups (TSYZ and SHAM) and time (baseline and post-treatment). Abnormal FCD was examined between baseline and post-treatment in TSYZ and SHAM groups, respectively. Compared with HCs, MCI showed altered FCD in the middle frontal cortex (MFC), inferior frontal cortex, temporal pole, thalamus and middle cingulate cortex. After moxibustion treatment in MCI, 1) a significant time-by-groups interaction was observed in the medial prefrontal cortex (mPFC); 2) abnormal long-range FCD (lrFCD) in the mPFC and MFC were modulated in TSYZ group; 3) significantly improved clinical symptoms; 4) changed lrFCD in the MFC was significantly negatively correlated with the increased Montreal Cognitive Assessment scores in TSYZ group. These imaging findings suggest that treatments of acupoint-related moxibustion could improve lrFCD in certain regions related to self-related cognitive and decision making. Our study might promote understanding of MCI neural mechanisms and expand the clinical application of moxibustion in MCI. [ABSTRACT FROM AUTHOR]

• An new multi-task learning framework is devised for differential diagnosis of subjective cognitive decline and mild cognive impairment. • The proposed multi-task learning algorithm combines functional and structural brain information. • The proposed method can discover the most disease-related brain regions and their connectivity. • The extensive experiments demonstrate good classification performance against competing techniques. Early diagnosis and intervention of mild cognitive impairment (MCI) and its early stage (i.e., subjective cognitive decline (SCD)) is able to delay or reverse the disease progression. However, discrimination between SCD, MCI and healthy subjects accurately remains challenging. This paper proposes an auto-weighted centralised multi-task (AWCMT) learning framework for differential diagnosis of SCD and MCI. AWCMT is based on structural and functional connectivity information inferred from magnetic resonance imaging (MRI). To be specific, we devise a novel multi-task learning algorithm to combine neuroimaging functional and structural connective information. We construct a functional brain network through a sparse and low-rank machine learning method, and also a structural brain network via fibre bundle tracking. Those two networks are constructed separately and independently. Multi-task learning is then used to identify features integration of functional and structural connectivity. Hence, we can learn each task's significance automatically in a balanced way. By combining the functional and structural information, the most informative features of SCD and MCI are obtained for diagnosis. The extensive experiments on the public and self-collected datasets demonstrate that the proposed algorithm obtains better performance in classifying SCD, MCI and healthy people than traditional algorithms. The newly proposed method has good interpretability as it is able to discover the most disease-related brain regions and their connectivity. The results agree well with current clinical findings and provide new insights into early AD detection based on the multi-modal neuroimaging technique. [Display omitted] [ABSTRACT FROM AUTHOR]