Quantum-assisted tomographic image refinement with limited qubits for high-resolution imaging

We propose a quantum-assisted reconstruction framework for high-resolution tomographic imaging that reduces qubit/variable requirements by combining sinogram downsampling and region-wise iterative refinement. Previous quantum optimization–based CT reconstruction methods require solving QUBO (Quadratic Unconstrained Binary Optimization) problems over full-resolution image grids, which limits scalability under current hardware constraints. Our method addresses this by combining sinogram downscaling with region-wise iterative refinement, allowing reconstruction to begin from a reduced-resolution sinogram and image, and then to be progressively upscaled and optimized region by region. Each region is independently transformed into a compact QUBO problem and solved via a D-Wave hybrid quantum-classical solver while keeping the surrounding image fixed. The framework supports both full-view and sparse-view sinograms and is compatible with diverse acquisition geometries, including parallel-beam, fan-beam, cone-beam, synchrotron tomography, and electron tomography (ET). Adaptive region selection (including overlapping or boundary-covering subregions) enables targeted refinement of residual-error under a limited qubit/variable budget. Experimental validation on binary and integer-valued Shepp-Logan phantoms, together with an additional chest CT slice experiment, demonstrates accurate reconstructions under both dense-view and sparse-view angular sampling using reduced qubit/variable budgets. We observed that nearest-neighbor interpolation may cause edge artifacts that hinder convergence, which can be mitigated by smoother interpolation and Gaussian filtering. Sparse-view experiments indicate that the refinement pipeline can remain effective under angular undersampling in our discrete setting, suggesting potential relevance to low-dose imaging scenarios, while a dose–quality trade-off analysis is left for future work. These findings highlight the practicality of QUBO-based, quantum-assisted refinement for high-resolution tomographic reconstruction under current hardware constraints and provide a pathway for scaling as hybrid quantum optimization capabilities evolve.