AI 中文总结
研究针对压缩感知 MRI 性能依赖采样分布的问题，提出基于 QUBO 公式的自适应框架，通过回顾性实验验证其在多指标上优于静态策略，简化池实验表明 D-Wave 求解器有可比质量，为自适应 MRI 采样提供实用框架。
AI 中文摘要
压缩感知通过从欠采样的 k 空间重建图像来加速磁共振成像（MRI），但其性能很大程度上取决于采样分布。我们提出了一种自适应框架，使用固定基数二次无约束二进制优化（QUBO）公式顺序选择笛卡尔相位编码线。目标结合了对中心 k 空间的偏好、先前采集测量的信号能量信息以及鼓励空间分散采样的成对项。该公式与经典退火和量子退火硬件兼容。回顾性实验使用模拟的八线圈 3D MRI 数据；用并行回火解决 QUBO 问题，并用 SENSE 和总变差正则化重建图像。在 20%和 10%采样率下，与评估的静态笛卡尔策略相比，该方法在 PSNR、SSIM、NMSE 和 HFEN 方面有所改善。在简化池实验中，D-Wave 量子-经典混合求解器实现了与可变密度泊松盘采样相当的重建质量。虽然这些结果未确立量子计算优势，但直接 QUBO 表示为自适应 MRI 采样提供了实用框架，未来可能受益于量子退火硬件的进步。前瞻性扫描仪验证和系统的量子-经典基准测试仍然必要。
英文摘要
Compressed sensing accelerates MRI by reconstructing images from undersampled k-space, but performance depends strongly on sampling distribution. We propose an adaptive framework that selects Cartesian phase-encode lines sequentially using a fixed-cardinality quadratic unconstrained binary optimization (QUBO) formulation. The objective combines a preference for central k-space, signal-energy information from previously acquired measurements, and pairwise terms that encourage spatially dispersed sampling. The formulation is compatible with classical annealing and quantum-annealing hardware. Retrospective experiments used simulated eight-coil 3D MRI data; QUBO problems were solved with parallel tempering, and images were reconstructed with SENSE and total-variation regularization. At 20% and 10% sampling, the proposed method improved PSNR, SSIM, NMSE, and HFEN compared with the evaluated static Cartesian strategies, including variable-density Poisson-disc sampling, although gains varied with resolution, acceleration, and noise level. In a reduced-pool experiment, a D-Wave quantum-classical hybrid solver achieved reconstruction quality comparable to variable-density Poisson-disc sampling, demonstrating feasibility on current quantum optimization infrastructure. While these results do not establish quantum computational advantage, the direct QUBO representation provides a practical framework for adaptive MRI sampling and may benefit from future advances in quantum-annealing hardware. Prospective scanner validation and systematic quantum-classical benchmarking remain necessary.
