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Background: This study aimed to explore the coordinated and independent actions of lung lobes during respiration using quantitative computed tomography (CT) in order to increase our in vivo understanding of pulmonary anatomy., Methods: Cases for whom test results showed normal pulmonary function tests (PFTs) results, and normal paired inspiratory-expiratory chest CT findings, as assessed by 2 radiologists, were retrospectively included in this study. From the chest CT results, we measured quantitative indices of lung volume (LV) and mean lung density (MLD) for the total lung (TL), left lung (LL), right lung (RL), and 5 lobes in inspiratory and expiratory phases. The differences of these measures between bilateral lungs and among the lobes were evaluated to study whether they were consistent or different during respiration., Results: A total of 70 cases were included {median age of 49.5 [interquartile range (IQR), 38.0 to 60.3] years; 32 males; 38 females}. Overall, the inspiratory and expiratory volumes of the LL were smaller than those of the RL (both P<0.001). For the ventilation workload ( λ , which indicates the ratio of lobar volume to total LV), the end-expiratory volume ratio ( λ ex ) of the LL was 0.44 (IQR, 0.43 to 0.46), while the end-inspiratory volume ratio ( λ in ) had risen to 0.46 (IQR, 0.45 to 0.47) (P<0.001). Comparing the 5 lobes, not all lobes shared the same LV. However, the left lower lobe (LLL) and right lower lobe (RLL) showed some similarities. The λ in-LLL and λ in-RLL was higher than λ ex-LLL and λ ex-RLL , respectively (both P<0.001), while the ratios of the other lobes reduced. The pairwise mean absolute difference (PMAD) on λ in and λ ex of the bilateral lower lobes was low in inspiration (0.0288) and expiration (0.0346). The MLD of bilateral lower lobes showed consistency in inspiration or in expiration (inspiration: P>0.999; expiration: P=0.975). In addition, the PMADs between the right middle lobe (RML) and other lobes were significantly larger than the PMAD between other pairs of lobes in both inspiration and expiration. Beyond that, the expiratory MLD of RML [-789.6 (IQR, -814 to -762.05) HU] was the lowest among the 5 lobes., Conclusions: We found that the LL assumes a higher workload during ventilation than it does during respiration. The 5 normal lobes were non-synchronous during respiration and contributed differently to ventilation. The bilateral lower lobes showed similarities and had a high-ventilation function, while and the LV and MLD of the RML showed the least changes within a respiration cycle., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://dx.doi.org/10.21037/qims-21-348). JW was employed by Philips Healthcare. The other authors have no conflicts of interest to declare., (2022 Quantitative Imaging in Medicine and Surgery. All rights reserved.)

This study aimed to explore the associations between cerebral white matter (WM) alterations, mental health status, and metabolism in recovered COVID-19 patients. We included 28 recovered COVID-19 patients and 27 healthy controls between April 2020 and June 2020. Demographic data, the mental health scores, diffusion-tensor imaging (DTI) data, and plasma metabolomics were collected and compared between the two groups. Tract-based spatial statistics and graph theory approaches were used for DTI data analysis. Untargeted metabolomics analysis of the plasma was performed. Correlation analyses were performed between these characteristics. Recovered COVID-19 patients showed decreased fractional anisotropy, increased mean diffusivity and radial diffusivity values in widespread brain regions, and significantly lower global efficiency, longer shortest path length, and less nodal local efficiency in superior occipital gyrus (all, P < 0.05, Bonferroni corrected). Our results also demonstrated significantly different plasma metabolic profiling in recovered COVID-19 patients even at 3 months after their hospital discharge, which was mainly related to purine pathways, amino acids, lipids, and amine metabolism. Certain regions with cerebral WM alterations in the recovered patients showed significant correlations with different metabolites and the mental health scores. We observed multiple alterations in both WM integrity and plasma metabolomics that may explain the deteriorated mental health of recovered COVID-19 patients. These findings may provide potential biomarkers for the mental health evaluation for the recovered COVID-19 patients and potential targets for novel therapeutics.

Patients with spontaneous isolated superior mesenteric artery (SMA) dissection (SISMAD) usually present with acute or chronic abdominal pain and are admitted to the emergency or digestive diseases department to undergo auxiliary examinations, typically abdominal plain CT or contrast-enhanced CT (CECT). Plain CT is the most crucial examination in emergency radiology. An enlarged SMA diameter and perivascular fat stranding (PFS) on plain CT, though non-specific, may be the only indications for SISMAD. These results may be easily overlooked and the diagnosis of SISMAD may be missed. However, PFS around the SMA on CT may be the only indicator of the possible presence of SISMAD, particularly during the early stage when there are no massive changes in the vascular wall. The present study aimed to determine whether PFS surrounding the SMA on CT may help with the diagnosis of SISMAD by indicating the requirement for further examination. The data of 161 consecutive patients with SMA dissection who underwent abdominal CECT or underwent SMA CT angiography (CTA) after abdominal plain CT between February 2015 and February 2018 were retrospectively reviewed. SMA diameter, classification, PFS, complications, comorbidities and treatments were analyzed. The results demonstrated that SISMAD with PFS was significantly associated with admission type (emergency), clinical manifestations (abdominal pain), diagnostic modality and dissection subtype. On plain CT, PFS surrounding the SMA may be a marker for SISMAD, particularly in the emergency setting, and indicates the requirement for CTA examination., (Copyright © 2020, Spandidos Publications.)

Cirrhosis is a major public health concern. However, little is known about the neurobiological mechanisms underlying brain microstructure alterations in cirrhotic patients. The purpose of this prospective study was to investigate brain microstructural alterations in cirrhosis with or without minimal hepatic encephalopathy (MHE) and their relationship with patients' neurocognitive performance and disease duration using voxel-based analysis of diffusion kurtosis imaging (DKI). DKI data were acquired from 30 cirrhotic patients with MHE, 31 patients without MHE (NMHE) and 59 healthy controls. All DKI-derived parametric maps were compared across the three groups to investigate their group differences. Correlation analyses were further performed to assess relationships between altered imaging parameters and clinical data. Voxel-based analysis of DKI data results showed that MHE/NMHE patients had increased radial diffusivity, axial diffusivity (AD) and mean diffusivity in addition to decreased axial kurtosis (AK) and fractional anisotropy of kurtosis in several regions. Compared to controls, these regions were primarily the cingulum, temporal and frontal cortices. The DKI metrics (i.e., AK and AD) were correlated with clinical variables in the two patient groups. In conclusion, DKI is useful for detecting brain microstructural abnormalities in MHE and NMHE patients. Abnormal DKI parameters suggest alterations in brain microstructural complexity in cirrhotic patients, which may contribute to the neurobiological basis of neurocognitive impairment. These results may provide additional information on the pathophysiology of cirrhosis.

Alzheimer's disease significantly affects the quality of life of patients. This paper proposes an approach to identify Alzheimer's disease based on transfer learning using functional MRI images, which is especially useful when the training dataset is small. Transfer learning improves the performance of the classifier with the help of an auxiliary dataset, which may be obtained from a different population group and/or machine. First, we used the joint distribution adaptation method to project the source and target domain samples into a new feature space, and then we built a classifier that works well in both the source and target domains but emphasizes the target domain. In the classifier, we assigned larger weights to the target domain samples and minimized the weighted loss in classifying the samples in both domains. Experimental results verify the effectiveness of our proposed approach and, with the help of the auxiliary samples, the classification accuracy of our target dataset has been greatly improved.