Volume 32, Issue 4, October 2022, Pages 365-376

Cancer accounts for nearly 10 million deaths in 2020 and is a leading cause of death worldwide. Radiation therapy is an effective modality to cure cancer. The ultimate goal of successful radiation therapy is to accurately deliver a safe and therapeutic dose of radiation to cancerous target cells while limiting radiation to the healthy tissue in the beam pathway and the surrounding normal tissue. With advancing radiation treatment technology, better imaging techniques are imperative for safe, high-dose delivery. Artificial intelligence developments on image-guided radiation therapy technologies, which range from Kilovoltage and Megavoltage modalities to two-dimensional and three-dimensional techniques, are discussed in depth.

Section snippetsIntroduction to X-Ray Guidance in Radiation Therapy

Artificial intelligence (AI), which is also referred to as machine intelligence, usually denotes intelligence demonstrated by machines.1 Generally speaking, AI describes machines that mimic cognitive functions (such as learning) of the human mind. AI, as a concept, was proposed back in the 1950s. However, recent advances in computing capability allow the development of high-level features learning models to represent complex relationships within observational data. Figure 1 shows representative

Review of X-Ray Modalities in Radiation Therapy

The primary goal and the ideal scenario of radiation therapy are to deliver a useful dose of radiation to the target cancerous cells while sparing the surrounding healthy tissues and tissues along the radiation beam path2. Although it is impossible to achieve complete sparing of the surrounding healthy tissues, the objective of safe delivery of radiation therapy (RT) is to keep the entrance, exit, and scatter doses as low as possible by employing various treatment techniques. As RT technologies

Cone-Beam CT Reconstruction

X-ray cone-beam CT (CBCT) with 2D detectors is capable of obtaining high-resolution projection images with a relatively simple scanner geometry. It is often used for interventional radiology, dental CT, besides modern radiotherapy. For the cone-beam geometry, Feldkamp Davis and Kress (FDK) algorithm6 has been extensively used as the standard reconstruction method.7, 8, 9 Unfortunately, the FDK algorithm for CBCT usually suffers from cone-beam artifacts as the cone-angle increases xxx. Some

Remaining Challenges and Future Work

Similar to most AI applications, massive amounts of imaging data are required to successfully train the AI model to ensure that the applications are ready to be accurately deployed in routine care. Various co-factors can heavily influence the performance of clinical AI systems (eg, patient demographics, disease stages or subtypes, genotypes, and more). Given the large number of combinations of the co-factors, data from one institution are often insufficient to train a robust AI model.

Conclusion

RT imaging techniques have made strides to deliver more conformal therapeutic radiation to target tissues while limiting radiation to healthy tissues. X-ray is still the most available and important imaging modality for patient and tumor localization in image-guided radiotherapy. As discussed, the formation of the 2D X-ray and 3D CT images, image processing, and registration can be markedly improved for tumor and normal organ localization using artificial intelligence techniques. This is

Conflicts of Interest

All authors declare that they have no conflicts of interest.

Acknowledgments

The authors would like to thank many researchers who were involved in the related work at our institute.

References (59)KL Moore et al.Experience-based quality control of clinical intensity-modulated radiotherapy planning

Int J Radiat Oncol Biol Phys

(2011)

K Kuriyama et al.A new irradiation unit constructed of self-moving gantry-CT and linac

Int J Radiat Oncol Biol Phys

(2003)

S Siddique et al.Artificial intelligence in radiotherapy

Rep Practical Oncol Radiotherapy

(2020)

L Xing et al.Artificial Intelligence in Medicine

(2020)

S Gianfaldoni et al.An overview on radiotherapy: from its history to its current applications in dermatology

Open Access Maced J Med Sci

(2017)

T MackieHistory of tomotherapy

Phys Med Biol

(2006)

R Timmerman et al.Image Guided and Adaptive Radiation Therapy

(2009)

X Tang et al.A three-dimensional weighted cone beam filtered backprojection (CB-FBP) algorithm for image reconstruction in volumetric CT under a circular source trajectory

Phys Med Biol

(2005)

Y Wang et al.Modified FDK algorithm for cone-beam reconstruction with efficient weighting scheme

IEEE

(2006)

X Tang et al.A three-dimensional-weighted cone beam filtered backprojection (CB-FBP) algorithm for image reconstruction in volumetric CT-helical scanning

Phys Med Biol

(2006)

HK TuyAn inversion formula for cone-beam reconstruction

SIAM J Appl Math

(1983)

BD SmithImage reconstruction from cone-beam projections: necessary and sufficient conditions and reconstruction methods

IEEE Trans Med Imaging

(1985)

IJ Kalet et al.Knowledge-based computer systems for radiotherapy planning

Am J Clin Oncol

(1990)

M Mardani et al.Deep-Learning based prediction of achievable dose for personalizing inverse treatment planning

Int J Radiat Oncol Biol Phys

(2016)

X Chen et al.A feasibility study on an automated method to generate patient-specific dose distributions for radiotherapy using deep learning

Med Phys

(2019)

V Kearney et al.DoseNet: a volumetric dose prediction algorithm using 3D fully-convolutional neural networks

Phys Med Biol

(2018)

B Ibragimov et al.Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks

Med Phys

(2017)

M Ma et al.Incorporating dosimetric features into the prediction of 3D VMAT dose distributions using deep convolutional neural network

Phys Med Biol

(2019)

W Zhao et al.A model-based scatter artifacts correction for cone beam CT

Med Phys

(2016)

N Mail et al.The influence of bowtie filtration on cone-beam CT image quality

Med Phys

(2009)

J Boone et al.An analytical model of the scattered radiation distribution in diagnostic radiology

Med Phys

(1988)

RS Thing et al.Optimizing cone beam CT scatter estimation in egs_cbct for a clinical and virtual chest phantom

Med Phys

(2014)

L Zhu et al.Scatter correction method for X-ray CT using primary modulation: theory and preliminary results

IEEE

(2006)

J Wang et al.Scatter correction for cone-beam computed tomography using moving blocker strips: a preliminary study

Med Phys

(2010)

J Maier et al.Deep Scatter Estimation (DSE): accurate real-time scatter estimationfor X-Ray CT using a deep convolutional neural network

J Nondestr Eval

(2017)

DC Hansen et al.ScatterNet: a convolutional neural network for cone-beam CT intensity correction

Med Phys

(2018)

Y Jiang et al.Scatter correction of cone-beam CT using a deep residual convolution neural network (DRCNN)

Phys Med Biol

(2019)

T Niu et al.Shading correction for on-board cone-beam CT in radiation therapy using planning MDCT images

Med Phys

(2010)

EY Sidky et al.Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization

Phys Med Biol

(2008)

View full text

© 2022 Elsevier Inc. All rights reserved.