Abstract Book

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ESTRO 37

patient set-up and HLUT definition were considered during dose calculation to estimate confidence intervals for a set of dose volume metrics (figure 2). The intervals from orgCT were then used as pass or fail (i.e. at least one metric outside the interval) criterion for all dose volume metrics per simED.

conversion to synthetic Hounsfield Units (sHU). Atlas- based segments were used to separate bones, air cavities and soft tissues. The method included separate MR intensity to sHU conversion algorithms for each of the segments. The segmentation and conversion algorithms were scripted into a commercial medical image processing software. CT and MR image data of six pediatric patients (mean age of 2.4±0.4 years, images covered the torso from neck to thighs) was adopted for the construction of the atlas and the conversion algorithms. The method was tested for the same patients with a leave-one-out strategy. The sCT quality was evaluated by comparing sHUs to actual CT-based HUs. Dose calculation accuracy in the sCT images was quantified by three “virtual” target volumes in each patient. The target volumes were positioned into treatment sites lacking prior research; the lungs, the vertebra (thVI-X), and the liver. Five-field IMRT plans were constructed for the lung and liver cases and a three-field posterior plan for the thoracic vertebrae using 6 MV photons. Results The software transformed a standard T2-weighted MR image to a sCT image within ~5 minutes (3-5 minutes for atlas segmentation and 10-30 seconds for the MR intensity to sHU conversion). Figure 1 shows an example of a generated sCT, the original MR, and the reference CT image. The mean differences (CT-sCT) between sHUs and actual HUs were, 16±51, -20±29 and, -18±44 HUs for lung, soft tissues and, bone respectively. The dose-volume- histogram (DVH) parameter D98%, D50% and, D2% differences (CT-sCT) between target volumes in sCTs and real CTs in liver PTV were -0.4±0.4, -0.5±0.3 and, - 0.7±0.2 – in lung PTV 0.5±1.6, 0.1±1.4 and, -0.2±1.3 – in vertebra PTV 0.2±0.5, 0.1±0.2 and, 0.2±0.1, respectively. Figure 2 presents the DVH curves for sCT and CT images of the six cases for the lung, vertebrae, and liver plans, respectively.

Results SimED using a mean body ED as a single tissue class mostly failed the criterion, whereas water ED passed for four out of six patients. Two tissue classes representing fat and remaining soft tissue improved the result to five out of six patients. Best results were obtained for four tissue classes, adding air and bone, for which all patients passed the criterion. The results for dose volume metrics were in agreement with gamma pass rate analysis. Conclusion For MR-based treatment planning, synthetic CTs with two tissue classes, which could be segmented from an MR image, yield acceptable deviation from the standard CT- based plan, whereas one tissue class might not generally be sufficient. Four classes suggested fully acceptable dose distributions, while the segmentation of air and bone in the MR image is still a major challenge. EP-2086 Synthetic CT generation from standard T2- weighted MR image – lung, vertebrae, and liver targets L. Koivula 1 , F. Guerreiro 2 , B.W. Raaymakers 2 , J. Korhonen 3 1 Helsinki University Hospital, Department of Radiation Oncology, Helsinki, Finland 2 University Medical Center Utrecht, Department of Radiotherapy and Imaging Division, Utrecht, The Netherlands 3 Kymenlaakso Central Hospital, Medical Imaging and Radiation Therapy, Kotka, Finland Purpose or Objective MRI-only based radiotherapy treatment planning (RTP) and MRI-guided online adaptive RTP require the generation of a synthetic CT (sCT) from an MR image. Previously proposed sCT methods focus separately on particular body parts and treatment sites (mainly head & pelvis), which limits the sCT method application. This current work aims to develop a technique enabling the sCT generation for whole body standard T2-weighted MR scan. Material and Methods The proposed sCT method relies on an automatic-atlas- based segmentation followed by MR image intensity

Figure 1: Example of the MR, constructed sCT and, reference CT images.

Figure 2: DVH comparison of the three target sites. Conclusion The study shows feasibility of generating high quality sCT images from a standard T2-weighted MR image. The method can be applied either separately in different body parts or for larger body volumes. The work