Artificial Intelligence-based Motion Tracking in Cancer Radiotherapy: A Review

Radiotherapy aims to deliver a prescribed dose to the tumor while sparing neighboring organs at risk (OARs). Increasingly complex treatment techniques such as volumetric modulated arc therapy (VMAT), stereotactic radiosurgery (SRS), stereotactic body radiotherapy (SBRT), and proton therapy have been developed to deliver doses more precisely to the target. While such technologies have improved dose delivery, the implementation of intra-fraction motion management to verify tumor position at the time of treatment has become increasingly relevant. Recently, artificial intelligence (AI) has demonstrated great potential for real-time tracking of tumors during treatment. However, AI-based motion management faces several challenges including bias in training data, poor transparency, difficult data collection, complex workflows and quality assurance, and limited sample sizes. This review serves to present the AI algorithms used for chest, abdomen, and pelvic tumor motion management/tracking for radiotherapy and provide a literature summary on the topic. We will also discuss the limitations of these algorithms and propose potential improvements.Comment: 36 pages, 5 Figures, 4 Table

Artificial Intelligence in Radiation Therapy

Artificial intelligence (AI) has great potential to transform the clinical workflow of radiotherapy. Since the introduction of deep neural networks, many AI-based methods have been proposed to address challenges in different aspects of radiotherapy. Commercial vendors have started to release AI-based tools that can be readily integrated to the established clinical workflow. To show the recent progress in AI-aided radiotherapy, we have reviewed AI-based studies in five major aspects of radiotherapy including image reconstruction, image registration, image segmentation, image synthesis, and automatic treatment planning. In each section, we summarized and categorized the recently published methods, followed by a discussion of the challenges, concerns, and future development. Given the rapid development of AI-aided radiotherapy, the efficiency and effectiveness of radiotherapy in the future could be substantially improved through intelligent automation of various aspects of radiotherapy

Management of Motion and Anatomical Variations in Charged Particle Therapy:Past, Present, and Into the Future

The major aim of radiation therapy is to provide curative or palliative treatment to cancerous malignancies while minimizing damage to healthy tissues. Charged particle radiotherapy utilizing carbon ions or protons is uniquely suited for this task due to its ability to achieve highly conformal dose distributions around the tumor volume. For these treatment modalities, uncertainties in the localization of patient anatomy due to inter- and intra-fractional motion present a heightened risk of undesired dose delivery. A diverse range of mitigation strategies have been developed and clinically implemented in various disease sites to monitor and correct for patient motion, but much work remains. This review provides an overview of current clinical practices for inter and intra-fractional motion management in charged particle therapy, including motion control, current imaging and motion tracking modalities, as well as treatment planning and delivery techniques. We also cover progress to date on emerging technologies including particle-based radiography imaging, novel treatment delivery methods such as tumor tracking and FLASH, and artificial intelligence and discuss their potential impact towards improving or increasing the challenge of motion mitigation in charged particle therapy

Deep learning for tomographic reconstruction with limited data

Tomography is a powerful technique to non-destructively determine the interior structure of an object.Usually, a series of projection images (e.g.\ X-ray images) is acquired from a range of different positions.from these projection images, a reconstruction of the object's interior is computed. Many advanced applications require fast acquisition, effectively limiting the number of projection images and imposing a level of noise on these images. These limitations result in artifacts (deficiencies) in the reconstructed images. Recently, deep neural networks have emerged as a powerful technique to remove these limited-data artifacts from reconstructed images, often outperformingconventional state-of-the-art techniques. To perform this task, the networks are typically trained on a dataset of paired low-quality and high-quality images of similar objects. This is a major obstacle to their use in many practical applications. In this thesis, we explore techniques to employ deep learning in advanced experiments where measuring additional objects is not possible.Financial support was provided by the Netherlands Organisation for Scientific Research (NWO), programme 639.073.506Number theory, Algebra and Geometr

High resolution laboratory x-ray tomography for biomedical research : From design to application

Laboratory x-ray micro- and nano-tomography are emerging techniques in biomedical research. Through the use of phase-contrast, sufficient contrast can be achieved in soft tissue to support medical studies. With ongoing developments of x-ray sources and detectors, biomedical studies can increasingly be performed at the laboratory and do not necessary require synchrotron radiation. Particularly nano-focus x-ray sources offer new possibilities for the study of soft tissue. However, with increasing resolution, the complexity and stability requirements on laboratory systems advance as well. This thesis describes the design and implementation of two systems: a micro- CT and a nano-CT, which are used for biomedical imaging.To increase the resolution of the micro-CT, super-resolution imaging is adopted and evaluated for x-ray ima- ging, grating-based imaging and computed tomography utilising electromagnetic stepping of the x-ray source to acquire shifted low-resolution images to estimate a high-resolution image. The experiments have shown that super-resolution can significantly improve the resolution in 2D and 3D imaging, but also that upscaling during the reconstruction can be a viable approach in tomography, which does not require additional images.Element-specific information can be obtained by using photon counting detectors with energy-discriminating thresholds. By performing a material decomposition, a dataset can be split into multiple different materials. Tissue contains a variety of elements with absorption edges in the range of 4 – 11 keV, which can be identified by placing energy thresholds just below and above these edges, as we have demonstrated using human atherosclerotic plaques.An evaluation of radiopaque dyes as alternative contrast agent to identify vessels in lung tissue was performed using phase contrast micro-tomography. We showed that the dye solutions have a sufficiently low density to not cause any artefacts while still being able to separate them from the tissue and distinguish them from each other.Finally, the design and implementation of the nano-CT system is discussed. The system performance is assessed in 2D and 3D, achieving sub-micron resolution and satisfactory tissue contrast through phase contrast. Applica- tion examples are presented using lung tissue, a mouse heart, and freeze dried leaves