ESTRO 2023 - Abstract Book

S648

Monday 15 May 2023

ESTRO 2023

Twenty-six healthy volunteers had two MRI exams within a month. The RC of all qMRI values and structures is given in Table 1. Sixteen HN cancer patients have been included. Ten patients had xerostomia and 6 of these patients had also dysphagia. The ADC values between the cohorts were similar for all structures and their differences were smaller than the RC. T2 and FF values in the patients’ salivary glands and PCM are higher than RC compared to healthy volunteers and statistically significant (p<0,05). However, these differences are smaller between the patient cohorts, except for the T2 of the ipsilateral submandibular gland, which is higher than the RC between xerostomia and non-xerostomia cohort (Table 1).

Conclusion Baseline qMRI values (ADC,T2,FF) and RC for the salivary glands and PCM were determined. Differences of the T2 and FF values in these structures between healthy volunteers, toxicity and non-toxicity cohorts suggest that T2 and FF could be potentially used for spatial assessment of the radiation-induced damage. Further investigation including more structures and dosimetric analysis should be performed.

OC-0779 Reconstruction of lung tumour volumes from 4D-MRI: the use of a tumour-derived respiratory signal M. Warren 1 , A. Barrett 2 , N. Bhalla 3 , M. Brada 4 , D. Cobben 3,7 , J. McClelland 5 , L. Turtle 6 , J. Fenwick 4 1 The University of Liverpool, School of Health Sciences, Liverpool, United Kingdom; 2 Clatterbridge Cancer Centre, Medical Physics, Liverpool, United Kingdom; 3 Clatterbridge Cancer Centre, Clinical Oncology, Liverpool, United Kingdom; 4 The University of Liverpool, Molecular and Clinical Cancer Medicine, Liverpool, United Kingdom; 5 University College London, Medical Image Computing, London, United Kingdom; 6 Clatterbridge Cancer Centre, Radiotherapy, Liverpool, United Kingdom; 7 The University of Liverpool, Health Data Science, Liverpool, United Kingdom Purpose or Objective Visualisation of lung tumour volume on 4D-MRI requires slice data to be sorted and assigned to the correct breathing cycle phase. Sorting requires a respiratory signal, which is often extracted from normal tissue surrogates. As normal tissue signals