ESTRO 37 Abstract book

S1163

ESTRO 37

Purpose or Objective To investigate the potential impact of 68Ga-DOTATOC PET/CT in addition to MRI and CT for retrospectively assessing the GTV delineation of meningiomas of the skull base in patients treated with particle therapy. Material and Methods Sixteen patients (median age 51.4 y) with skull base meningiomas underwent 68Ga-DOTATOC PET/CT for diagnosis followed by constract-enhanced (CE) MRI and CT for treatment planning with particle therapy. GTV for treatment was defined on the CE-MRI fused to the simulation CT (CT sim ) integrating information from the PET/CT. Three methods to delineate the PET volume were evaluated: manual (PET man ), semiautomatic (42% threshold of SUV max , PET SUV42% ) and an automatic adaptive thresholding method incorporating PET reconstruction parameters (PET auto ) (iTA, TA, Udine). PET man volumes were delineated by the same nuclear medicine physician, while PET SUV42% and PET auto volumes were determined by medical physicists. The PET/CT, CE-RMI and CT sim were co-registered with a deformable image registration algorithm using the MIM Software (v. 6.1) and tumor volumes and positions were determined. The overlapping region of MRI and PET man resulted in GTV common . Results The analyzed lesions had a median SUV max of 12.1 ± 6.8 (range: 3.2-32.6). Overall, the GTV-MRI was larger than GTV-PET man in 7 patients (43.8%), smaller in 6 (37.5%) and almost the same in 3 (18.8%). The median value of the different volumes were: GTV-MRI = 14.3 ± 37.2 cc, GTV- PET man = 14.4 ± 47.0 cc, GTV common = 12.2 ± 33.6 cc, GTV- PET SUV42% = 8.3 ± 18.3 cc, GTV-PET auto = 11.3 ± 26.2 cc. As expected, volumes determined by the fixed threshold technique were always smaller than GTV-PET man (D = - 51.0 ± 32.7%) and GTV-PET auto (D = - 21.5 ± 12.7%) since they do not take into account variations in tumor heterogeneity. The largest differences were observed for lesion showing a high SD of SUV. The median difference between GTV-PET man and GTV-PET auto was 39.6 ± 29.5 cc. Conclusion 68Ga-DOTATOC-PET/CT seems to improve the target volume delineation in skull base meningiomas, often leading to a reduction of GTV compared with results from MRI. The automatic adaptive thresholding delineation method may provide robust and reliable tool to help physician in segmenting PET images, reducing inter- and intra-observer variability. EP-2111 Improving the spatial accuracy of PET-guided dose painting through constrained PET image deblurring D. Di Perri 1,2 , S. Guérit 3 , N. Christian 4 , A. Robert 5 , J. Lee 1 , X. Geets 1,2 1 Université catholique de Louvain, IREC/MIRO, Brussels, Belgium 2 Cliniques universitaires Saint-Luc, Radiation oncology, Brussels, Belgium 3 Université catholique de Louvain, ICTM/ELEN, Louvain- la-Neuve, Belgium 4 Hôpital de Jolimont-Lobbes, Radiation oncology, La Louvière, Belgium 5 Université catholique de Louvain, IREC/EPID, Brussels, Belgium Purpose or Objective PET-guided dose painting (DP) aims at improving the efficacy of radiation therapy by focusing the treatment on radioresistant tumour regions identified through PET imaging. Importantly, this strategy depends on the spatial accuracy of the images which are used to guide the treatment. However, PET images are blurred as a result of the low spatial resolution of PET imaging systems, which may lead to inaccurate biological mapping of the tumour. In this context, we evaluated the performance of a novel PET image deblurring algorithm.

Material and Methods We retrieved the database from Christian et al. [1], containing FDG autoradiographs of 14 mice tumours and the corresponding registered PET images. Next, we applied a novel iterative deblurring algorithm to these PET images. This algorithm involves a positivity constraint and local dynamic constraints on the PET signal admissible range, to avoid artifacts and noise amplification. Finally, we analysed whether image deblurring allowed to increase the similarity between PET images and the corresponding autoradiographs, using two indexes: the Spearman’s correlation coefficient (r S ) and the structural similarity index (SSIM). The SSIM is a novel index which tries to mimic the human visual perception [2]. Results PET image deblurring increased the visual impression of similarity between autoradiographs and PET images (as illustrated in the attached figure). Quantitatively, deblurring increased the mean r S between autoradiography and PET images from 0.787 to 0.795 (p = 0.44), and the mean SSIM from 0.702 to 0.756 (p = 0.035).

Conclusion The considered deblurring algorithm improved the spatial accuracy of PET images. This constitutes a step towards better biological mapping of the tumour in the context of PET-guided DP. 1. Christian N, Lee JA, Bol A, et al. The limitation of PET imaging for biological adaptive-IMRT assessed in animal models. Radiother Oncol. 2009;91:101-6. 2. Sampat MP, Wang Z, Gupta S, et al. Complex wavelet structural similarity: a new image similarity index. IEEE Trans Image Process. 2009;18:2385-401.