AI 中文总结
探讨医学图像采集与重建，回顾各成像模态物理及采集过程，推导通用框架，讨论多种重建方法及计算特性，研究高效计算考量，展示成像物理、数学建模与高效计算整合可实现准确且可扩展的医学图像重建。
AI 中文摘要
医学成像系统如CT、MRI、PET和SPECT并不直接采集图像，而是测量编码解剖或生理信息的物理信号，通过解决反问题进行图像重建。随着数据集增大，图像重建成为计算瓶颈。先进重建方法虽提高图像质量，但计算成本大增。本文给出统一计算视角，回顾成像物理和数据采集过程，推导通用数学框架，讨论多种重建方法及其计算特性，还探讨高效计算考量，展示三者整合能实现准确且可扩展的医学图像重建。
英文摘要
Medical imaging systems such as CT, MRI, PET, and SPECT do not directly acquire images. Instead, they measure physical signals that encode anatomical or physiological information, and image reconstruction recovers the underlying image by solving an inverse problem. Although these imaging modalities are governed by different imaging physics, they share a common computational framework that naturally connects medical physics, linear algebra, probability, numerical optimization, and efficient computing. As medical imaging systems acquire increasingly large and higher-dimensional datasets, image reconstruction has become one of the primary computational bottlenecks in modern medical imaging. Advanced reconstruction methods, including analytical reconstruction, iterative optimization, and statistical model-based reconstruction, substantially improve image quality while reducing radiation dose or scan time, but at significantly increased computational cost. Efficient computing has therefore become essential for achieving clinically practical reconstruction times. This chapter presents a unified computational perspective on medical image acquisition and reconstruction across CT, MRI, PET, and SPECT. It first reviews the imaging physics and data acquisition process for each modality and derives a generalized mathematical framework for image reconstruction. Building on this framework, the chapter discusses analytical, iterative, and statistical reconstruction methods together with their computational characteristics. Finally, it examines efficient computing considerations, including optimization algorithms, physics-aware forward operators, memory-efficient implementations, and parallel computing strategies. Together, these topics demonstrate how the integration of imaging physics, mathematical modeling, and efficient computing enables accurate and scalable medical image reconstruction.
Commentsbook chapter for textbook "Medical Image Vision Handbook"
