Zhuozhao Zheng

Purpose The cystic rhomboid lip (CRL), a diverticular expansion of the fourth ventricular lateral recess at the foramen of Luschka, is familiar to posterior fossa surgeons but remains incompletely characterized on Magnetic Resonance (MR) imaging. We aimed to determine its MR imaging detection frequency and morphologic features in a nonoperative referral population and to evaluate a practical recognition framework against surgically confirmed cystic cerebellopontine angle (CPA) mimics. Methods In this retrospective 2-center study, 662 consecutive patients who underwent thin-section skull base MR imaging for suspected posterior fossa cranial nerve disorders (2022–2024) were independently reviewed by 2 neuroradiologists. Based on anatomic and surgical literature, CRL was defined a priori by two required features at the foramen of Luschka: anterior displacement of CN IX–X and posterior displacement of the choroid plexus (the CN–choroid plexus separation sign), with a CSF-equivalent signal in the intervening space. Ipsilateral lateral recess dilatation was considered a supportive but nonmandatory feature. Extension pattern was classified with a radiologic adaptation of the Akiyama framework. The same features were then assessed in 54 surgically and histopathologically confirmed cystic or solid-cystic lesions at the level of the lower cranial nerves. Results CRL was detected in 42/662 patients (6.3%; 95% CI, 4.7–8.5%), comprising 48 lesions (6 bilateral). Mean age was 55.8 ± 15.8 years; 61.9% were women. Mean maximum diameter was 15.7 ± 2.8 mm. A jugular foramen extension pattern (JFT) was present in 43/48 lesions (89.6%) and a lateral extension pattern (LET) in 5/48 (10.4%); JFT lesions were larger than LET lesions. Lateral recess dilatation was present in 32/48 lesions (66.7%). The CN–choroid plexus separation sign was present in 48/48 CRLs and in 0/54 comparison lesions. CSF-equivalent signal was present in 14/54 comparison lesions (25.9%), all arachnoid cysts accompanying a solid tumor, none of which showed the separation sign. Interobserver agreement was perfect for CRL detection (κ = 1.00) and extension pattern (κ = 1.00) and excellent for diameter measurements (ICC, 0.91–0.93). Conclusion CRL is a relatively common finding on thin-section skull base MR imaging (6.3%). The CN–choroid plexus separation sign with CSF-equivalent signal reliably distinguishes it from cystic CPA mimics.

Wrist magnetic resonance imaging (MRI) is essential for evaluating soft‐tissue injury, bone marrow edema, and small‐joint abnormalities, yet high‐quality imaging still relies largely on dedicated wrist coils and often requires the uncomfortable prone “superman” position. Here, we present a clinically adaptive gradient metamaterial for supine wrist MRI. The conformal structure is built from split elliptical‐ring resonators and integrates a parallel inductor‐capacitor trap‐mediated nonlinear response with gradient‐capacitance shimming, enabling transmit‐phase detuning, receive‐phase resonance, and axial field homogenization within a compact architecture. On a 1.5 T clinical MRI system, simulations, phantom experiments, and in vivo studies in 23 participants (including one special case) are demonstrated. When integrated with a flex coil, the metamaterial‐assisted configuration achieved about 1.5 times in vivo signal‐to‐noise ratio and contrast‐to‐noise ratio gains compared with a dedicated 12‐channel wrist coil. When operated with a body coil, the same metamaterial provided a portable, low‐complexity imaging configuration with comparable or superior image quality in several clinical assessments. Both configurations improved patient comfort and showed diagnostic agreement with standard wrist‐coil imaging. This work establishes a materials‐design‐driven strategy for translating metamaterials into patient‐centered clinical MRI.

The accuracy of quantitative magnetic resonance imaging (qMRI) largely depends on the image signal‐to‐noise ratio (SNR). In recent years, studies on metamaterials in MRI have shown that they can significantly improve image quality through weak coupling with the body coil or receive coil. However, it is still unclear whether such coupling would affect the results of quantitative MRI. In this study, a metamaterial‐inspired wireless resonator is designed, and common clinical quantitative imaging sequences, including T1 mapping, T2 mapping, apparent diffusion coefficient (ADC), and proton density fat fraction (PDFF), are implemented on a 1.5 T MRI system. The linearity, bias, and repeatability of the quantitative results are systematically evaluated. The findings provide experimental evidence and support for the application of metamaterial‐inspired wireless resonators in quantitative MRI.

Background: Knee MRI image quality depends largely on the radiofrequency coil; metasurfaces offer a new approach to improving coil performance. Purpose: To evaluate the imaging performance and diagnostic capability of a metasurface coil for knee MRI. Study Type: Prospective. Phantom/Population: A cylindrical phantom, 60 healthy volunteers (36 females; median age, 33), and 52 patients with knee injuries (28 females; median age, 47). Field Strength/Sequences: T1 Weighted Spin Echo (T1W SE), Fat‐Saturated Proton Density‐Weighted Fast Spin‐Echo (FS PDW FSE), T1‐Weighted Gradient Recalled Echo (T1W GRE) at 1.5 T. Assessment: All subjects underwent paired scanning with a wireless metasurface coil (plus built‐in spine array) and a commercial 12‐channel knee coil. Phantom metrics included signal‐to‐noise ratio (SNR), percent image uniformity (PIU), ghost‐to‐signal ratio, and B1 mapping. Volunteers were scored for image quality (5‐point), SNR, contrast‐to‐noise ratio (CNR), and handling convenience. Patients were assessed for ACL, meniscal, and cartilage injuries against arthroscopy. Statistical Tests: T ‐test and Wilcoxon signed‐rank test for quantitative and semi‐quantitative data; McNemar test for diagnostic efficacy. Kendall's coefficient of concordance and kappa, with p < 0.05 considered significant. Results: In the phantom, SNR was 196.14 versus 183.34, PIU 92.47% versus 71.30%, and ghost‐to‐signal ratio 0.0093 versus 0.0145 (metasurface vs. commercial coil). Measured flip angle was 44.7° ± 1.31° (0.67% deviation from nominal 45°); B1 field uniformity was 91.14%. In volunteers, subjective image quality scores were slightly higher for the 12‐channel coil on FS PDW FSE ( p < 0.05), whereas scores were comparable on T1W SE and 3D FS T1W GRE ( p = 0.06, 0.38). Convenience was markedly better for the metasurface (5.0 vs. 4.0, p < 0.05). In patients, diagnostic sensitivity (72.7%–83.3% vs. 72.7%–81.5%), specificity (86.7%–96.4% vs. 85.6%–96.4%), and accuracy (82.1%–90.4% vs. 81.4%–88.5%) were comparable between coils, inter‐coil kappa was 0.884–1.000. “Data” Conclusion: The wireless metasurface coil was more convenient and provided image quality and diagnostic performance comparable to the 12‐channel commercial knee coil at 1.5 T. Evidence Level: 2. Technical Efficacy: Stage 3.

Magnetic resonance imaging (MRI) is crucial in global healthcare, but the traditional receive coils, as a core component of MRI, signal‐to‐noise ratio enhancement is limited due to the optimization of channel number and magnetic field strength faces high cost and complexity challenges. Here, this work demonstrates the use of a topological material to enhance MRI signal reception. Designed with a stack of weak couplings, this material forms quasi‐two‐dimensional dual topological boundary states. High properties are achieved through low‐loss signal transmission via these topological states, as well as only enhanced local magnetic fields and an increased number of channels. Initial tests demonstrate superior performance and accessibility compared with commercial coils, suggesting significant potential. This concept introduces a transformative paradigm for all MRI coil designs.

Objective This study aimed to systematically evaluate the longitudinal relationship between generalized anxiety disorder (GAD) and cognitive function changes. Specifically, we examined whether GAD is an independent risk factor for sustained cognitive decline and explored the potential effects of cognitive behavioral therapy (CBT) on cognitive and emotional outcomes.Methods We conducted a systematic search of PubMed, Embase, Web of Science, and the Cochrane Library for eligible longitudinal studies and randomized controlled trials published up to January 20, 2025. Meta-analyses were performed using RevMan 5.4.1. Effect sizes were estimated using mean differences (MD) and 95% confidence intervals. Heterogeneity was assessed with the I2 statistic. Sensitivity analyses and publication bias assessments were also conducted.Results A total of 13 studies were included. The results showed no significant differences between GAD patients and controls on memory and attention subscores of the dementia rating scale (p>0.05). Total error scores also showed no significant difference. Subgroup analyses indicated that GAD patients receiving CBT demonstrated greater improvement in clinical symptoms than those receiving non-cognitive interventions (MD=-4.39, p=0.0004). Beck Depression Inventory score differences were statistically significant, while Penn State Worry Questionnaire score differences were not.Conclusion This meta-analysis suggests that while GAD does not show consistent evidence of sustained cognitive decline, CBT may be associated with improvement in emotional symptoms. By integrating longitudinal data with treatment outcomes, this study offers preliminary insights into the potential indirect cognitive benefits of psychotherapy in GAD.

Objective. Motion artifacts remain a major obstacle in dynamic computed tomography (CT) reconstruction, particularly for nonperiodic rapid motion such as cardiac imaging in patients with fast or irregular heart rates, where ECG-gated approaches are unreliable. The extreme limited-angle problem arising from insufficient angular coverage within a single cardiac phase makes accurate reconstruction of fine cardiac structures especially challenging. This work aims to develop a self-supervised framework that recovers high-resolution dynamic CT images from nonperiodic motion scenarios without external training datasets. Approach. We propose BIRD, an implicit neural representation (INR) framework for nonperiodic dynamic CT reconstruction, with three components: (1) a dual-feature representation decomposing the dynamic scene into topology-preserving and free-form features, enabling modeling of both deformable motion and residual variations such as contrast kinetics; (2) a backward-warping deformation model enabling direct ray-based training, overcoming the resolution limitations of prior forward-warping INR approaches at clinically relevant sub-millimeter resolution; and (3) a diffeomorphism-based regularization enforcing anatomically plausible deformation vector fields through bidirectional inverse consistency, without restricting representational capacity. Main results. BIRD was validated on digital and physical cardiac phantoms and retrospective patient data. In the XCAT phantom study under severe limited-angle conditions (0.5 s rot ⁻¹ , 120 bpm), BIRD improved peak signal-to-noise ratio over the next-best method by 1.43 dB on the right coronary artery and 1.21 dB on the right ventricle. Curved planar reformation confirmed improved vessel continuity and boundary sharpness. On real projection data from a physical cardiac phantom and a retrospective patient scan, BIRD reduced motion artifacts and improved depiction of cardiac and vascular structures compared with conventional reconstructions. Significance. The proposed framework performs self-supervised reconstruction of nonperiodic dynamic CT images solely from projection data. It offers potential clinical applications including non-ECG-gated cardiac imaging for patients with arrhythmia, cinematic image sequences, and retrospective motion artifact correction in conventional CT scans.

Background: Artificial intelligence (AI), particularly deep learning, has shown promise in enhancing medical image interpretation and improving radiologists’ efficiency. In China, growing imaging demand and workforce shortages have placed increasing pressure on radiology services. However, evidence on the operational impact of AI on reporting efficiency remains limited. Objective: This study aimed to evaluate the effect of an AI system on radiologists’ reporting efficiency by examining changes in report-drafting time for lung nodule diagnosis in chest computed tomography (CT) images. Methods: We analyzed 185,044 chest CT reports from Beijing Anzhen and Tsinghua Changgung Hospitals (2018-2023) using a difference-in-differences design with nonequivalent comparison groups. Report-drafting time before, immediately after, and up to 2 years following AI implementation was compared, adjusting for radiologist gender, seniority, and years of working experience. Results: The pooled analysis showed a modest overall increase of 0.86 minutes (95% CI 0.14 to 1.57). However, this masked substantial heterogeneity between hospitals due to differing implementation timelines. In the first year after AI deployment, Tsinghua Changgung Hospital experienced a nonsignificant increase of 0.90 minutes (95% CI –0.28 to 2.08). In contrast, at Beijing Anzhen Hospital, the AI-assisted group exhibited an absolute reduction of 0.76 minutes by the first year and a further 1.83-minute reduction by the second year (an approximate 28% time saved vs baseline), while the control group remained stable over time. Using a difference-in-differences framework, this corresponded to a 2.66-minute relative improvement compared with the counterfactual trend (P<.001). Conclusions: AI-assisted lung nodule diagnosis may initially increase report-drafting time due to adaptation and workflow adjustment. Sustained, meaningful efficiency gains were heterogeneous and observed at only 1 of the 2 study sites, indicating that long-term impacts are strongly contingent on site-specific implementation dynamics, learning curves, and local context.

Background: This study aims to systematically review the association between social relationships and cognitive impairment in the elderly, and to identify risk and protective factors through quantitative meta-analysis, providing evidence to inform cognitive health intervention strategies. Methods: PubMed, Embase, Web of Science, CNKI, and other databases were systematically searched for studies published before November 1, 2024. Eligible studies included cross-sectional and cohort studies involving adults aged ≥60 years. Separate meta-analyses were conducted for ratio-based effect sizes (OR/HR/RR) and regression coefficients (β) to synthesize the associations between social relationships and cognitive impairment. Results: From 1013 retrieved records, 11 studies were included. Meta-analysis showed that reduced social networks and social isolation were significantly associated with an increased risk of cognitive impairment (e.g., loneliness and mild cognitive impairment: OR = 2.89, 95% CI = 1.19–7.02). In contrast, larger social networks and frequent social participation were associated with a substantially lower risk of cognitive impairment, with participation in ≥1 social activity linked to an approximately 64% risk reduction. β-based meta-analysis further indicated positive associations between social support and cognitive performance, while specific activities such as volunteering conferred additional protective effects. Moderate heterogeneity was observed across social relationship dimensions. Conclusion: Social relationships play an important role in maintaining cognitive health in older adults. This meta-analysis provides quantitative evidence supporting both the risk and protective effects of social relationships on cognitive impairment, underscoring the importance of promoting social engagement to support cognitive health in aging populations.