The computed tomography angiography (CTA) postprocessing manually recognized by technologists is extremely labor intensive and error prone. We propose an artificial intelligence reconstruction system supported by an optimized physiological anatomical-based 3D convolutional neural network that can automatically achieve CTA reconstruction in healthcare services. This system is trained and tested with 18,766 head and neck CTA scans from 5 tertiary hospitals in China collected between June 2017 and November 2018. The overall reconstruction accuracy of the independent testing dataset is 0.931. It is clinically applicable due to its consistency with manually processed images, which achieves a qualification rate of 92.1%. This system reduces the time consumed from 14.22 ± 3.64 min to 4.94 ± 0.36 min, the number of clicks from 115.87 ± 25.9 to 4 and the labor force from 3 to 1 technologist after five months application. Thus, the system facilitates clinical workflows and provides an opportunity for clinical technologists to improve humanistic patient care.

Functional MRI (fMRI) studies have traditionally relied on intersubject normalization based on global brain morphology, which cannot establish proper functional correspondence between subjects due to substantial intersubject variability in functional organization. Here, we reliably identified a set of discrete, homologous functional regions in individuals to improve intersubject alignment of fMRI data. These functional regions demonstrated marked intersubject variability in size, position, and connectivity. We found that previously reported intersubject variability in functional connectivity maps could be partially explained by variability in size and position of the functional regions. Importantly, individual differences in network topography are associated with individual differences in task-evoked activations, suggesting that these individually specified regions may serve as the “localizer” to improve the alignment of task-fMRI data. We demonstrated that aligning task-fMRI data using the regions derived from resting state fMRI may lead to increased statistical power of task-fMRI analyses. In addition, resting state functional connectivity among these homologous regions is able to capture the idiosyncrasies of subjects and better predict fluid intelligence (gF) than connectivity measures derived from group-level brain atlases. Critically, we showed that not only the connectivity but also the size and position of functional regions are related to human behavior. Collectively, these findings suggest that identifying homologous functional regions across individuals can benefit a wide range of studies in the investigation of connectivity, task activation, and brain-behavior associations. No two individuals are alike. The size, shape, position, and connectivity patterns of brain functional regions can vary drastically between individuals. While interindividual differences in functional organization are well recognized, to date, standard procedures for functional neuroimaging research still rely on aligning different subjects’ data to a nominal “average” brain based on global brain morphology. We developed an approach to reliably identify homologous functional regions in each individual and demonstrated that aligning data based on these homologous functional regions can significantly improve the study of resting state functional connectivity, task-fMRI activations, and brain-behavior associations. Moreover, we showed that individual differences in size, position, and connectivity of brain functional regions are dissociable, and each can provide nonredundant information in explaining human behavior.

Acupuncture has been used in the therapy of Alzheimer disease (AD); however, its neural mechanisms are still unclear. The aim of this study is to examine the effect of acupuncture on the functional connectivity in AD by using resting-state functional magnetic resonance imaging (rs-fMRI). Twenty-eight subjects (14 AD and 14 normal controls) participated in this study. The rs-fMRI data were acquired before and after acupuncture stimulation at the acupoints of Tai chong (Liv3) and Hegu (LI4). During the baseline resting state, by using the amplitude of low-frequency fluctuations (ALFF), we found a significantly decreased or increased ALFF in the AD patients relative to the controls. These regions were located in the right superior frontal gyrus (SFG), left postcentral gyrus, subgenual cingulate cortex (SCC), right middle cingulate cortex (MCC), right inferior frontal gyrus (IFG), right hippocampus and the right inferior temporal gyrus (ITG). Then, we selected these brain regions as seeds to investigate whether regional activity and functional connectivity could be modulated by acupuncture in the AD patients. When compared to the pre-acupuncture stage, several of the above regions showed an increased or decreased ALFF after acupuncture in the AD patients. In addition, the functional connectivity between the hippocampus and the precentral gyrus showed enhancement after acupuncture in the AD patients. Finally, there were close correlations between the functional activity, connectivity and clinical performance in the AD patients. The current study confirmed that acupuncture at Tai chong (Liv3) and He gu (LI4) can modulate functional activity and connectivity of specific cognition-related regions in AD patients.

Purpose: Glioma treatment planning requires precise tumor delineation, which is typically performed with contrast-enhanced (CE) MRI. However, CE MRI fails to reflect the entire extent of glioma. O-(2-¹⁸F-fluoroethyl)-L-tyrosine (¹⁸F-FET) PET may detect tumor volumes missed by CE MRI. We investigated the clinical value of simultaneous FET-PET and CE MRI in delineating tumor extent before treatment planning. Guided stereotactic biopsy was used to validate the findings. Methods: Conventional MRI and ¹⁸F-FET PET were performed simultaneously on a hybrid PET/MR in 33 patients with histopathologically confirmed glioma. Tumor volumes were quantified using a tumor-to-brain ratio ≥ 1.6 (V_PET) and a visual threshold (V_CE). We visually assessed abnormal areas on FLAIR images and calculated Dice’s coefficient (DSC), overlap volume (OV), discrepancy-PET, and discrepancy-CE. Additionally, several stereotactic biopsy samples were taken from “matched” or “mismatched” FET-PET and CE MRI regions. Results: Among 31 patients (93.94%), FET-PET delineated significantly larger tumor volumes than CE MRI (77.84 ± 51.74 cm³ vs. 34.59 ± 27.07 cm³, P < 0.05). Of the 21 biopsy samples obtained from regions with increased FET uptake, all were histopathologically confirmed as glioma tissue or tumor infiltration, whereas only 13 showed enhancement on CE MRI. Among all patients, the spatial similarity between V_PET and V_CE was low (average DSC 0.56 ± 0.22), while the overlap was high (average OV 0.95 ± 0.08). The discrepancy-CE and discrepancy-PET were lower than 10% in 28 and 0 patients, respectively. Eleven patients showed V_PET partially beyond abnormal signal areas on FLAIR images. Conclusion: The metabolically active biodistribution of gliomas delineated with FET-PET significantly exceeds tumor volume on CE MRI, and histopathology confirms these findings. Our preliminary results indicate that combining the anatomic and molecular information obtained from conventional MRI and FET-PET would reveal a more accurate glioma extent, which is critical for individualized treatment planning.

Electronic skin (e-skin) is attracting huge attention due to its promising applications in diverse fields, including biomimetic machines, artificial intelligence and smart robotics. However, most e-skin circuits involve complicated fabrication processes and high costs. Here, a simple, fast fabrication method for highly conductive (6 × 10⁶ S m⁻¹) and stretchable (above 100%) e-skin based on the adhesion selectivity of a semi-liquid metal (Cu–EGaIn) on laser printed toner and a polymethacrylate (PMA) coating is developed. The stretchable e-skin circuits are shown to transfer to human skin and 3D substrates as wearable electronics for a variety of applications, such as temperature monitoring, interactive devices, and flexible displays. Different from other liquid metal patterning techniques that require expensive equipment or rigid metal traces such as silver nanoparticles, this approach can be used to directly print Cu–EGaIn on a stretchable substrate with a commercial laser printer in seconds. Further, by virtue of the advantage of the high radiological imaging capability of Cu–EGaIn, the e-skin is patterned as a computed tomography (CT) assistant localization marker, which is demonstrated to be very helpful for doctors and surgical robots to efficiently localize a biopsy, which is a core issue in clinics. The present study holds promise for future health care, surgical guidance and personalized entertainment.