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Quantum Compressed Sensing Tomographic Reconstruction Algorithm
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DOI：10.1109/TQE.2026.3681530.png)
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Abstract
En 中文
Computed tomography (CT) is a nondestructive technique for observing internal images and has proven highly valuable in medical diagnostics. Recent advances in quantum computing have begun to influence tomographic reconstruction techniques. The quantum tomographic reconstruction algorithm is less affected by artifacts or noise than classical algorithms by using the square function of the difference between pixels obtained by projecting CT images in quantum superposition states and pixels obtained from experimental data. In particular, by using quantum linear systems, a fast quadratic unconstrained binary optimization (QUBO) model formulation for quantum tomographic reconstruction is possible. In this article, we formulate the QUBO model for quantum compressed sensing tomographic reconstruction, which is a linear combination of the QUBO model for quantum tomographic reconstruction and the QUBO model for total variation in quantum superposition-state CT images. In our experiments, we used sinograms obtained by using the Radon transform of Shepp-Logan images and body CT images. We evaluate the performance of the new algorithm by reconstructing CT images using a hybrid solver with the QUBO model computed from each sinogram. The new algorithm was able to obtain a solution within five projection images for 30 & times; 30 image samples and within six projection images for 60 & times; 60 image samples, reconstructing error-free CT images. We anticipate that quantum compressed sensing tomographic reconstruction algorithms could significantly reduce the total radiation dose when quantum computing performance advances.
Keywords:
Circuits
Filtering
Filters
Circuits and systems
Quantum circuit
Media Access Control
Pixel
Wireless Access in Vehicular Environments
Communications technology
TV
Quantum compressed sensing
quantum compressed sensing tomographic reconstruction (QCSTR) algorithm
quantum optimization
quantum tomographic reconstruction (QTR)
quantum tomography
Journal
I
IF:
4.6
Papers:
52
Citations:
0
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Cited Papers
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