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Chapter 10 explores the most recent and fascinating tools from the artificial intelligence (AI) world and their use in ultrasound-guided radiotherapy.

Chapter 1 is an introduction to the topics covered in the book, describing image guided and adaptive radiotherapy, and the rationale for its use. It also contains a history of ultrasound in radiotherapy.

The first challenge encountered when introducing ultrasound into radiotherapy is described in chapter 3: localising the probe in the simulation suite and in the treatment room to enable comparison of the positions of anatomical structures at the two stages. The solutions available are introduced together with a detailed description and analysis of the sources of error and uncertainty. After the probe has been localised, the next steps are to evaluate if the image acquired is of sufficient quality and segment all the relevant structures of interest.

This is a practical guide to the implementation of 3D/4D ultrasound imaging in radiography. Among its features are the coverage of the technology utilised for ultrasound-guided radiotherapy, clinical need and the advantages of using ultrasound. It is a useful tool for users that incorporates implementation, potential errors, uncertainties and training. This is a comprehensive review of the state-of-the-art technologies, which also looks at the future direction of this exciting field. Researchers, students, hospital physicists and radiographers will all find this book of use as it guides them through current clinical situation and examines the full potential of ultrasound in radiotherapy.

In chapter 5 the role of ultrasound in the first stage of the radiotherapy workflow, planning, is introduced: it can be used to identify the anatomical structures important for radiotherapy planning, in particular the treatment target. Practical challenges are highlighted, for example, how to acquire the simulation CT scan with the ultrasound probe in place avoiding CT imaging artefacts that would compromise the planning process.

The loss of skeletal muscle mass is recognized as a complication of several chronic diseases and is associated with increased mortality and a decreased quality of life. Relevant and reliable animal models in which muscle wasting can be monitored noninvasively over time are instrumental to investigate and develop new therapies. In this work, we developed a fully automatic deep learning algorithm for segmentation of micro cone beam computed tomography images of the lower limb muscle complex in mice and subsequent muscle mass calculation. A deep learning algorithm was trained on manually segmented data from 32 mice. Muscle wet mass measurements were obtained from 47 mice and served as a data set for model validation and reverse model validation. The automatic algorithm performance was similar to 150 times faster than manual segmentation. Reverse validation of the algorithm showed high quantitative metrics (i.e., a Dice similarity coefficient of 0.93. a Hausdorff distance of 0.4 mm, and a center of mass displacement of 0.1 mm). substantiating the robustness and accuracy of the model. A high correlation (R-2 = 0.92) was obtained between the computed tomography-derived muscle mass measurements and the muscle wet masses. Longitudinal follow-up revealed time-dependent changes in muscle mass that separated control from lung tumor-bearing mice, which was confirmed as cachexia. In conclusion, this deep learning model for automated assessment of the lower limb muscle complex provides highly accurate noninvasive longitudinal evaluation of skeletal muscle mass. Furthermore, it facilitates the workflow and increases the amount of data derived from mouse studies while reducing the animal numbers.NEW & NOTEWORTHY This deep learning application enables highly accurate noninvasive longitudinal evaluation of skeletal muscle mass changes in mice with minimal requirement for operator involvement in the data analysis. It provides a unique opportunity to increase and analyze

This is a practical guide to the implementation of 3D/4D ultrasound imaging in radiography. Among its features are the coverage of the technology utilised for ultrasound-guided radiotherapy, clinical need and the advantages of using ultrasound. It is a useful tool for users that incorporates implementation, potential errors, uncertainties and training. This is a comprehensive review of the state-of-the-art technologies, which also looks at the future direction of this exciting field. Researchers, students, hospital physicists and radiographers will all find this book of use as it guides them through current clinical situation and examines the full potential of ultrasound in radiotherapy. Key Features Technology used for ultrasound guided RT Clinical need and advantages of using ultrasound Practical guide to implementation, including errors, uncertainties and training Comprehensive review of state-of-the-art Critical evaluation of field and future directions

Ultrasound guidance is not in widespread use in prostate cancer radiotherapy workflows. This can be partially attributed to the need for image interpretation by a trained operator during ultrasound image acquisition. In this work, a one-class regressor, based on DenseNet and Gaussian processes, was implemented to automatically assess the quality of transperineal ultrasound images of the male pelvic region. The implemented deep learning approach was tested on 300 transperineal ultrasound images and it achieved a scoring accuracy of 94%, a specificity of 95% and a sensitivity of 92% with respect to the majority vote of 3 experts, which was comparable with the results of these experts. This is the first step toward a fully automatic workflow, which could potentially remove the need for ultrasound image interpretation and make real-time volumetric organ tracking in the radiotherapy environment using ultrasound more appealing.

Objective: Preclinical biological research is mandatory for developing new drugs to investigate the toxicity and efficacy of the drug. In this paper, the focus is on radiobiological research as an example of advanced preclinical biological research. In radiobiology, recent technological advances have produced novel research platforms which can precisely irradiate targets in animals and use advanced onboard image-guidance, mimicking the clinical radiotherapy environment. These platforms greatly facilitate complex research combining several agents simultaneously (in our example, radiation and non-radiation agents). Since these modern platform can produce a large amount of wide-ranging data, one of the main impediments in preclinical research platforms is a proper data management system for preclinical studies.Methods: A preclinical data management system, inspired by current radiotherapy clinical data management systems was designed. The system was designed with InterSystems technology, i.e. a programmable Enterprise Service Bus solution. New DICOM animal imaging standards are used such as DICOM suppl. 187 for storing small animal acquisition context and the DICOM second generation course model.Results: A small animal big data warehouse environment for research is designed to work with modern image-guided precision research platforms. Its modular design includes (1) a study workflow manager, (2) a data manager, and (3) a storage manager. The system provides interfaces to, e.g. preclinical treatment planning systems and data analysis plug-ins, and guides the user efficiently through the many steps involved in preclinical research. The system manages various data source locations, and arranges access to the data centrally.Conclusion: A novel preclinical data management system can be designed to improve preclinical workflow, facilitate data exchange between researchers, and support translation to clinical trials.Advances in knowledge: A pre

Objective: Lung cancer is the deadliest cancer worldwide. To increase treatment potential for lung cancer, pre-clinical models that allow testing and follow up of clinically relevant treatment modalities are essential. Therefore, we developed a single-nodule-based orthotopic non-small cell lung cancer tumor model which can be monitored using multimodal non-invasive imaging to select the optimal image-guided radiation treatment plan.Methods: An orthotopic non-small cell lung cancer model in NMRI-nude mice was established to investigate the complementary information acquired from 80 kVp microcone-beam CT (micro-CBCT) and bioluminescence imaging (BLI) using different angles and filter settings. Different micro-CBCT-based radiation-delivery plans were evaluated based on their dose-volume histogram metrics of tumor and organs at risk to select the optimal treatment plan.Results: H1299 cell suspensions injected directly into the lung render exponentially growing single tumor nodules whose CBCT-based volume quantification strongly correlated with BLI-integrated intensity. Parallel-opposed single angle beam plans through a single lung are preferred for smaller tumors, whereas for larger tumors, plans that spread the radiation dose across healthy tissues are favored.Conclusions: Closely mimicking a clinical setting for lung cancer with highly advanced preclinical radiation treatment planning is possible in mice developing orthotopic lung tumors.Advances in knowledge: BLI and CBCT imaging of orthotopic lung tumors provide complementary information in a temporal manner. The optimal radiotherapy plan is tumor volume-dependent.