ESTRO 2023 - Abstract Book

S277

Sunday 14 May 2023

ESTRO 2023

parameters typically multiple images are acquired through which a model is fitted. Movement between these images could affect the accuracy and repeatability of the quantitative MRI parameters. For example, breathing motion causes blurring in the b-value images of DWI when multiple individual b-value images are averaged. Cardiac motion causes signal drop-out mostly seen in the upper left lobe of the liver. In DCE-MRI motion results in more noise in or even corruption of the signal intensity time curves, which makes quantification with tracer kinetic modelling more difficult. Therefore, quantitative MRI in moving targets, such as liver and lung, is even more challenging than in the pelvic region. Here we focus on the effect of breathing motion on the quantification of DWI. There are two main strategies to deal with breathing motion: during acquisition or afterwards with offline analysis strategies. In the acquisition of DWI data breath-hold and respiratory-triggered approaches are typically used to deal with the breathing motion. Offline motion correction has been used for DWI in the liver, where individual b-value images were registered before averaging and calculating the ADC. The advantage of this approach is that the data can be acquired in free breathing, which saves time and is easier for the patient and technicians compared to breath-hold or respiratory- triggered approaches. Motion compensation results in an improvement in image quality and signal-to-noise of the DWI images. Test-retest measurements are used to investigate the effect of motion compensation strategies on the repeatability of the ADC, which is important to distinguish treatment-related changes for response assessment. SP-0366 Dealing with legacy treatment data L. Wilson 1 , A. Bryce-Atkinson 2 , F. Pirlepesov 1 , F. Xie 1 , A. Faught 1 , M. Aznar 2 , M. van Herk 2 , E. Vasquez Osorio 2 1 St. Jude Children's Research Hospital, Radiation Oncology, Memphis, USA; 2 University of Manchester, Division of Cancer Sciences, Faculty of Biology, Medicine and Health, Manchester, United Kingdom Abstract Text Legacy treatment data are a necessary evil for many radiotherapy researchers. Legacy data are images, structure contours, and dose distributions associated with treatment plans that were created, and are likely still stored in historical, retired treatment planning systems. Legacy data are commonly required by those looking to study the link between radiotherapy exposures and late effects with long latency periods of years to decades. Other researchers, however, may also find themselves considering the prospect of resurrecting legacy data to bolster cohort numbers when studying rare diseases and/or effects, or to improve data variety for advanced analytical techniques like data mining and deep learning. Despite their value, working with legacy data can be intimidating and onerous. We at St. Jude Children’s Research Hospital have found it necessary to leverage treatment data created as many as 25 years ago for all of the above-mentioned reasons. Our journey to recover and employ legacy data involved inconsistent software versions, outdated data formats, questionable data veracity, database crashes, and many other hurdles. This presentation will draw on our experiences and focus on the challenges associated with retrieving and using legacy treatment data, as well as strategies to overcome them. The objective will be to provide attendees with: realistic expectations for working with legacy treatment data, appreciation for the value of legacy treatment data, and strategies for overcoming, or avoiding entirely, some potential obstacles. Abstract Text In oncology, follow-up imaging is designed to assess treatment response, detect secondary cancers, or record treatment- induced toxicities. Regular follow-up imaging offers a method to visualise a patient’s natural progression through time, where they may grow or undergo large anatomical changes. Such imaging provides information about the course of disease that you cannot obtain from images acquired before or during treatment. The value of using follow-up images has been demonstrated in a range of research applications including early response assessment, automated cancer detection, and quantitative toxicity measurement. Often, this type of research requires prospective data collection of high-quality, standardised imaging at a specified frequency. It is possible to develop new imaging sequences for optimal follow-up, though this should not come at the cost of patient comfort. Increasingly, researchers are interested in the use of ‘real-world’ retrospective data. With retrospective research, the original purpose of imaging is often not the same purpose the image is required for in research. This means images are often sub-optimal for the task at hand - a frustrating reality for researchers hoping to perform a retrospective analysis of follow-up images. There are additional challenges in using ‘real-world’ follow-up imaging, as adherence rates can be low, and imaging is often performed at local centres where different protocols will be implemented. Inclusion criteria for typical research methods (e.g., single modality) are too strict in this context. Situations exist where researchers are required to ‘make the most’ of the data they already have. For example, in late-effects research 5+ years of follow-up data is required before investigation. Advances in image processing can help us ‘deal with follow-up images’ in research. Super-resolution can increase the quality of low-resolution data eliminating the need for costly and time-consuming sequences in clinical practice. Image synthesis can create new datasets that replicate imaging protocols that are otherwise impractical to acquire. Alternatively, information can be combined from multiple images to improve the quality of existing datasets. Joint Symposium: ESTRO-AAPM: Big data, big headache SP-0367 Dealing with follow-up images A. Davey 1 1 The University of Manchester, Division of Cancer Sciences, Manchester, United Kingdom

SP-0368 Dealing with public datasets B. Anderson 1 1 UC San Diego Health, Radiation Medicine & Applied Sciences, La Jolla, USA

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