The increased utilisation of magnetic resonance imaging (MRI) in radiation therapy (RT) has led to the implementation of MRI simulators for RT treatment planning and influenced the development of MRI‐guided treatment systems. There is extensive literature on the advantages of MRI for tumour volume and organ‐at‐risk delineation compared to computed tomography. MRI provides both anatomical and functional information for RT treatment planning (RTP) as well as quantitative information to assess tumour response for adaptive treatment. Despite many advantages of MRI in RT, introducing an MRI simulator into a RT department is a challenge. Collaboration between radiographers and radiation therapists is paramount in making the best use of this technology. The cross‐disciplinary training of radiographers and radiation therapists alike is an area rarely discussed; however, it is becoming an important requirement due to detailed imaging needs for advanced RT treatment techniques and with the emergence of hybrid treatment systems. This article will discuss the initial experiences of a radiation oncology department in implementing a dedicated MRI simulator for RTP, with a focus on the training required for both radiographer and RT staff. It will also address the future of MRI in RT and the implementation of MRI‐guided treatment systems, such as MRI‐Linacs, and the role of both radiation therapists and radiographers in this technology.

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Toxicity to cardiac and coronary structures is an important late morbidity for patients undergoing left-sided breast radiotherapy. Many current studies have relied on estimates of cardiac doses assuming standardised anatomy, with a calculated increase in relative risk of 7.4% per Gy (mean heart dose). To provide individualised estimates for dose, delineation of various cardiac structures on patient images is required. Automatic multi-atlas based segmentation can provide a consistent, robust solution, however there are challenges to this method. We are aiming to develop and validate a cardiac atlas and segmentation framework, with a focus on the limitations and uncertainties in the process. We present a probabilistic approach to segmentation, which provides a simple method to incorporate inter-observer variation, as well as a useful tool for evaluating the accuracy and sources of error in segmentation. A dataset consisting of 20 planning computed tomography (CT) images of Australian breast cancer patients with delineations of 17 structures (including whole heart, four chambers, coronary arteries and valves) was manually contoured by three independent observers, following a protocol based on a published reference atlas, with verification by a cardiologist. To develop and validate the segmentation framework a leave-one-out cross-validation strategy was implemented. Performance of the automatic segmentations was evaluated relative to inter-observer variability in manually-derived contours; measures of volume and surface accuracy (Dice similarity coefficient (DSC) and mean absolute surface distance (MASD), respectively) were used to compare automatic segmentation to the consensus segmentation from manual contours. For the whole heart, the resulting segmentation achieved a DSC of , with a MASD of mm. Quantitative results, together with the analysis of probabilistic labelling, indicate the feasibility of accurate and consistent segmentation of larger structures, whereas this is not the case for many smaller structures, where a major limitation in segmentation accuracy is the inter-observer variability in manual contouring.

IGRT is widely used among Australian RT centres. On the basis of future plans of respondents, the installation of new imaging modalities is expected to increase for both planning and treatment.

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