Hill D L, Batchelor P G, Holden M, Hawkes D J
Radiological Sciences, King's College London, Guy's Hospital, UK.
Phys Med Biol. 2001 Mar;46(3):R1-45. doi: 10.1088/0031-9155/46/3/201.
Radiological images are increasingly being used in healthcare and medical research. There is, consequently, widespread interest in accurately relating information in the different images for diagnosis, treatment and basic science. This article reviews registration techniques used to solve this problem, and describes the wide variety of applications to which these techniques are applied. Applications of image registration include combining images of the same subject from different modalities, aligning temporal sequences of images to compensate for motion of the subject between scans, image guidance during interventions and aligning images from multiple subjects in cohort studies. Current registration algorithms can, in many cases, automatically register images that are related by a rigid body transformation (i.e. where tissue deformation can be ignored). There has also been substantial progress in non-rigid registration algorithms that can compensate for tissue deformation, or align images from different subjects. Nevertheless many registration problems remain unsolved, and this is likely to continue to be an active field of research in the future.
放射影像在医疗保健和医学研究中的应用日益广泛。因此,人们普遍关注如何准确关联不同影像中的信息以用于诊断、治疗和基础科学研究。本文综述了用于解决这一问题的配准技术,并描述了这些技术的广泛应用。图像配准的应用包括将同一受试者不同模态的图像进行合并、对齐图像的时间序列以补偿扫描期间受试者的运动、介入过程中的图像引导以及在队列研究中对齐多个受试者的图像。在许多情况下,当前的配准算法能够自动配准通过刚体变换相关联的图像(即可以忽略组织变形的情况)。在能够补偿组织变形或对齐不同受试者图像的非刚性配准算法方面也取得了重大进展。然而,许多配准问题仍未得到解决,并且这在未来很可能仍是一个活跃的研究领域。