Jun Kyungtaek, Lee Hyunju
Quantum Research Center, QTomo Inc., Cheongju, Chungcheongbuk-do, 28535, South Korea.
Chungbuk Quantum Research Center, Chungbuk National University, Cheongju, Chungcheongbuk-Do, 28644, South Korea.
Sci Rep. 2025 Jul 1;15(1):20649. doi: 10.1038/s41598-025-08453-w.
Computed tomography (CT) is an important imaging technique used in medical analysis of the internal structure of the human body. Previously, image segmentation methods were required after acquiring reconstructed CT images to obtain segmented CT images which made it susceptible to errors from both reconstruction and segmentation algorithms. However, this paper introduces a new approach using an advanced quantum optimization algorithm called quadratic unconstrained binary optimization (QUBO) for CT image segmentation. This algorithm allows CT image reconstruction and segmentation to be performed simultaneously. This algorithm segments CT images by minimizing the difference between a sinogram in a superposition state with qubits, obtained using the mathematical projection including the Radon transform, and the experimentally acquired sinogram from X-ray images for various angles. Furthermore, we leveraged X-ray mass attenuation coefficients to reduce the number of logical qubits required for our quantum optimization algorithm, and we employed D-Wave's hybrid solver to solve the optimization problem. We compared the segmentation results of our algorithm with those of classical algorithms using X-ray images of actual tooth samples to validate the results of our algorithm. The comparison revealed that, after undergoing appropriate image post-processing, our algorithm's segmentation results matched those of classical algorithms that perform segmentation after reconstruction, except for some pixels at the boundary. We expect that the new quantum optimization CT algorithm will bring about great advancements in medical imaging.
计算机断层扫描（CT）是一种重要的成像技术，用于对人体内部结构进行医学分析。以前，在获取重建的CT图像后需要进行图像分割方法，以获得分割后的CT图像，这使得其容易受到重建和分割算法两方面的误差影响。然而，本文介绍了一种新方法，使用一种称为二次无约束二进制优化（QUBO）的先进量子优化算法进行CT图像分割。该算法允许同时进行CT图像重建和分割。该算法通过最小化使用包括拉东变换的数学投影获得的处于叠加态且带有量子比特的正弦图与从不同角度的X射线图像实验获取的正弦图之间的差异来分割CT图像。此外，我们利用X射线质量衰减系数来减少我们的量子优化算法所需的逻辑量子比特数量，并使用D-Wave的混合求解器来解决优化问题。我们使用实际牙齿样本的X射线图像将我们算法的分割结果与经典算法的分割结果进行比较，以验证我们算法的结果。比较结果表明，经过适当的图像后处理后，除了边界处的一些像素外，我们算法的分割结果与重建后进行分割的经典算法的结果相匹配。我们期望新的量子优化CT算法将在医学成像方面带来巨大进步。
