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Luyao Shen

Publications and source records attributed to Luyao Shen.

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Liver Metastasis Detection at Reduced Radiation Dose: Diagnostic Evaluation of a Novel Organ-Level Tube Current Modulation Method

Objective: To evaluate retroOpt, a novel organ-level tube current modulation (TCM) method that minimizes effective radiation dose while preserving diagnostic image quality for liver metastasis detection. Methods: In this retrospective, IRB-approved study, 22 patients with 68 liver lesions (38 malignant, 30 benign) underwent portal venous phase contrast-enhanced chest-abdomen-pelvis CT. Using projection-domain noise emulation, five series were generated per patient: original full dose (Orig-100), uniform dose reduction to 40% (UD-40) and 60% (UD-60) of the original effective dose, and organ-level TCM at the same levels (Opt-40, Opt-60). Three abdominal radiologists independently detected and classified lesions and rated image quality (5-point Likert). Per-lesion sensitivity was compared by McNemar test. Malignant lesion-size thresholds for 50% and 90% sensitivity (x50, x90) were estimated by logistic regression. Image quality was compared by Wilcoxon signed-rank test. Results: At the 60% level, Opt-60 achieved mean malignant lesion sensitivity comparable to full-dose CT (82% vs 80%) and exceeded uniform reduction (67%). At the 40% level, Opt-40 improved mean sensitivity over UD-40 from 52% to 68%. Mean all-lesion sensitivity rose from 54% (UD-40) to 64% (Opt-40) and from 65% (UD-60) to 77% (Opt-60), remaining comparable to full dose (76%). All optimized-versus-uniform differences were significant (p <= 0.003). Opt-60 lesion-size thresholds closely matched Orig-100 (x50 3.3 vs 3.7 mm; x90 15.7 vs 16.8 mm). Median Opt-60 image quality ranged from 3 to 4.5 across readers. Conclusions: Organ-level TCM preserved liver metastasis detection at 60% of the original effective dose, outperforming uniform dose reduction. Task-specific organ-level dose optimization may enable greater CT dose reduction than uniform strategies for patients requiring repeated metastasis surveillance.

physics.med-ph

"It Became My Buddy, But I'm Not Afraid to Disagree": A Multi-Session Study of UX Evaluators Collaborating with Conversational AI Assistants

AI-assisted usability analysis can potentially reduce the time and effort of finding usability problems, yet little is known about how AI's perceived expertise influences evaluators' analytic strategies and perceptions over time. We ran a within-subjects, five-session study (six hours per participant) with 12 professional UX evaluators who worked with two conversational assistants designed to appear novice- or expert-like (differing in suggestion quantity and response accuracy). We logged behavioral measures (number of passes, suggestion acceptance rate), collected subjective ratings (trust, perceived efficiency), and conducted semi-structured interviews. Participants experienced an initial novelty effect and a subsequent dip in trust that recovered over time. Their efficiency improved as they shifted from a two-pass to a one-pass video inspection approach. Evaluators ultimately rated the experienced CA as significantly more efficient, trustworthy, and comprehensive, despite not perceiving expertise differences early on. We conclude with design implications for adapting AI expertise to enable calibrated human-AI collaboration.

cs.HC

TeamPortal: Exploring Virtual Reality Collaboration Through Shared and Manipulating Parallel Views

Virtual Reality (VR) offers a unique collaborative experience, with parallel views playing a pivotal role in Collaborative Virtual Environments by supporting the transfer and delivery of items. Sharing and manipulating partners' views provides users with a broader perspective that helps them identify the targets and partner actions. We proposed TeamPortal accordingly and conducted two user studies with 72 participants (36 pairs) to investigate the potential benefits of interactive, shared perspectives in VR collaboration. Our first study compared ShaView and TeamPortal against a baseline in a collaborative task that encompassed a series of searching and manipulation tasks. The results show that TeamPortal significantly reduced movement and increased collaborative efficiency and social presence in complex tasks. Following the results, the second study evaluated three variants: TeamPortal+, SnapTeamPortal+, and DropTeamPortal+. The results show that both SnapTeamPortal+ and DropTeamPortal+ improved task efficiency and willingness to further adopt these technologies, though SnapTeamPortal+ reduced co-presence. Based on the findings, we proposed three design implications to inform the development of future VR collaboration systems.

cs.HC

Towards Massive Interaction with Generalist Robotics: A Systematic Review of XR-enabled Remote Human-Robot Interaction Systems

The rising interest of generalist robots seek to create robots with versatility to handle multiple tasks in a variety of environments, and human will interact with such robots through immersive interfaces. In the context of human-robot interaction (HRI), this survey provides an exhaustive review of the applications of extended reality (XR) technologies in the field of remote HRI. We developed a systematic search strategy based on the PRISMA methodology. From the initial 2,561 articles selected, 100 research papers that met our inclusion criteria were included. We categorized and summarized the domain in detail, delving into XR technologies, including augmented reality (AR), virtual reality (VR), and mixed reality (MR), and their applications in facilitating intuitive and effective remote control and interaction with robotic systems. The survey highlights existing articles on the application of XR technologies, user experience enhancement, and various interaction designs for XR in remote HRI, providing insights into current trends and future directions. We also identified potential gaps and opportunities for future research to improve remote HRI systems through XR technology to guide and inform future XR and robotics research.

cs.HC