Dynamic Target Tracking Method Based on Medical Imaging

Qin, Guofeng; Qin, Jiahao; Xia, Qiufang; Zou, Jianghuang; Lin, Pengpeng; Ren, Chengkun; Wang, Ruihan

doi:10.3389/fphys.2022.894282

Cited by 1 publication

(2 citation statements)

References 25 publications

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“…X-ray target tracking refers to the process of continuously monitoring and accurately tracking the position and motion of the target in an X-ray image or sequence of X-ray images ( 11 ). X-ray target tracking typically involves the following steps: image acquisition, pre-processing, target localization, motion estimation, tracking, and updating.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning-based target tracking with X-ray images for radiotherapy: a narrative review

Liu,

Geng,

Huang

et al. 2024

Quant Imaging Med Surg

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Background and Objective As one of the main treatment modalities, radiotherapy (RT) (also known as radiation therapy) plays an increasingly important role in the treatment of cancer. RT could benefit greatly from the accurate localization of the gross tumor volume and circumambient organs at risk (OARs). Modern linear accelerators (LINACs) are typically equipped with either gantry-mounted or room-mounted X-ray imaging systems, which provide possibilities for marker-less tracking with two-dimensional (2D) kV X-ray images. However, due to organ overlapping and poor soft tissue contrast, it is challenging to track the target directly and precisely with 2D kV X-ray images. With the flourishing development of deep learning in the field of image processing, it is possible to achieve real-time marker-less tracking of targets with 2D kV X-ray images in RT using advanced deep-learning frameworks. This article sought to review the current development of deep learning-based target tracking with 2D kV X-ray images and discuss the existing limitations and potential solutions. Finally, it also discusses some common challenges and potential future developments. Methods Manual searches of the Web of Science, and PubMed, and Google Scholar were carried out to retrieve English-language articles. The keywords used in the searches included “radiotherapy, radiation therapy, motion tracking, target tracking, motion estimation, motion monitoring, X-ray images, digitally reconstructed radiographs, deep learning, convolutional neural network, and deep neural network”. Only articles that met the predetermined eligibility criteria were included in the review. Ultimately, 23 articles published between March 2019 and December 2023 were included in the review. Key Content and Findings In this article, we narratively reviewed deep learning-based target tracking with 2D kV X-ray images in RT. The existing limitations, common challenges, possible solutions, and future directions of deep learning-based target tracking were also discussed. The use of deep learning-based methods has been shown to be feasible in marker-less target tracking and real-time motion management. However, it is still quite challenging to directly locate tumor and OARs in real-time with 2D kV X-ray images, and more technical and clinical efforts are needed. Conclusions Deep learning-based target tracking with 2D kV X-ray images is a promising method in motion management during RT. It has the potential to track the target in real time, recognize motion, reduce the extended margin, and better spare the normal tissue. However, it still has many issues that demand prompt attention, and further development before it can be put into clinical practice.

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Section: Introductionmentioning

confidence: 99%

“…X-ray target tracking is commonly used in various applications, including medical imaging, industrial inspection, security systems, etc. ( 11 , 15 , 16 ).…”

Section: Introductionmentioning

confidence: 99%