A Fidelity-Preserving Reconstruction Framework for Low-Dose CT, Patient State Space Modeling, and Quantum-Enabled Clinical Intelligence

This technical white paper presents SQK QMEDIC-QMLIR and Quantum Longitudinal Diagnostic Intelligence (QLDI), a reconstruction-first medical imaging framework for low-dose CT (LDCT), fidelity-preserving image reconstruction, and future patient state-space modeling. QMEDIC-QMLIR is designed to improve foundational CT image quality by operating earlier in the reconstruction workflow, using sinogram/projection-domain information when available, rather than relying only on downstream DICOM image post-processing. The framework aims to support artifact reduction, low-contrast detectability, subtle-structure preservation, uncertainty-aware validation, and higher-quality inputs for radiologists and downstream medical AI systems. The white paper describes QMLIR, QPINN, QPR, and QLDI; outlines the platform architecture, Quantum-HPC strategy, clinical validation direction, FDA 510(k) readiness planning, commercialization roadmap, and validation metric framework; and positions QMEDIC as a research-use and development-stage reconstruction-enabling platform for clinically practical LDCT. This document is provided for research collaboration, technical review, and commercialization discussion. QMEDIC is not presented as a cleared or authorized medical device, and any future clinical use would require appropriate validation, regulatory review, and clearance or authorization.