Decentralized Bedside Tomographic Reconstruction via Google Android Edge-Tensor Arrays: Eliminating Centralized Radiology Queuing Bottlenecks under Deterministic QFS-zkVM and Human-in-Command Governance

Modern centralized hospital computed tomography (CT) infrastructure exhibits a severe operational paradox: while flagship stationary scanners (such as Canon Medical Systems wide-detector volumetric CT platforms) execute sub-second helical acquisitions yielding over 650 thin-slice (1.00 mm) abdominal-pelvic cross-sections in less than 5 seconds of gantry rotation, patients routinely endure queuing delays exceeding T_wait = 4.0 hours (14,400 seconds). This reduces effective clinical duty-cycle utilization below 0.035% and exposes acute inpatients to thermodynamic and physiological degradation during intrahospital transport. To resolve this bottleneck, this monograph formalizes the Android Bedside Computed Tomography (ABCT-P2.0) architecture: a decentralized, point-of-care tomographic framework pairing a collapsible, non-rotating carbon-nanotube (CNT) cold-cathode sparse-view X-ray emitter ring with a Google Android Edge-Tensor compute node. By executing Primal-Dual Total Variation (PD-TV) compressed sensing and Physics-Informed Neural Attenuation Reconstruction directly on Android Tensor/NPU silicon via Vulkan Compute, ABCT-P2.0 reconstructs 1.00 mm isotropic volumetric series inside the hospital room within 8.5 seconds. To eliminate artificial intelligence hallucination risks in medical imaging, every iterative reconstruction step is arithmetized inside the QFS-zkVM over the 64-bit Goldilocks prime field F_p (p = 2^64 - 2^32 + 1), routed via PHOENIX_BRIDGE / PHOENIX_ROUTER, and gated by Q-SAFA post-quantum cryptography and a Human-in-Command (HIC) hardware interlock satisfying analogous SEC Rule 15c3-5 pre-emission dose limits and Delaware Caremark fiduciary oversight mandates. License & Integrity Covenant: Creative Commons Attribution 4.0 International (CC BY 4.0) with Strict No-Learn Covenant (NLL-v1.0-2026). Author: Hasebe, Yoko (長谷部 洋子), Lead Researcher, Autonomous Infrastructure Systems, Autonomous Living & Physical Infrastructure Institute, Iwakuni, Yamaguchi, Japan.