地图集(解剖学)
磁共振成像
分割
放射治疗计划
计算机科学
放射治疗
人工智能
计算机断层摄影术
计算机视觉
图像配准
核医学
放射科
医学
图像(数学)
解剖
作者
Ninon Burgos,Filipa Guerreiro,Jamie R. McClelland,Benoît Presles,Marc Modat,Simeon Nill,David P. Dearnaley,Nandita M. deSouza,Uwe Oelfke,Antje Knopf,Sébastien Ourselin,M. Jorge Cardoso
标识
DOI:10.1088/1361-6560/aa66bf
摘要
To tackle the problem of magnetic resonance imaging (MRI)-only radiotherapy treatment planning (RTP), we propose a multi-atlas information propagation scheme that jointly segments organs and generates pseudo x-ray computed tomography (CT) data from structural MR images (T1-weighted and T2-weighted). As the performance of the method strongly depends on the quality of the atlas database composed of multiple sets of aligned MR, CT and segmented images, we also propose a robust way of registering atlas MR and CT images, which combines structure-guided registration, and CT and MR image synthesis. We first evaluated the proposed framework in terms of segmentation and CT synthesis accuracy on 15 subjects with prostate cancer. The segmentations obtained with the proposed method were compared using the Dice score coefficient (DSC) to the manual segmentations. Mean DSCs of 0.73, 0.90, 0.77 and 0.90 were obtained for the prostate, bladder, rectum and femur heads, respectively. The mean absolute error (MAE) and the mean error (ME) were computed between the reference CTs (non-rigidly aligned to the MRs) and the pseudo CTs generated with the proposed method. The MAE was on average HU and the ME HU. We then performed a dosimetric evaluation by re-calculating plans on the pseudo CTs and comparing them to the plans optimised on the reference CTs. We compared the cumulative dose volume histograms (DVH) obtained for the pseudo CTs to the DVH obtained for the reference CTs in the planning target volume (PTV) located in the prostate, and in the organs at risk at different DVH points. We obtained average differences of in the PTV for , and between and 0.05% in the PTV, bladder, rectum and femur heads for D mean and . Overall, we demonstrate that the proposed framework is able to automatically generate accurate pseudo CT images and segmentations in the pelvic region, potentially bypassing the need for CT scan for accurate RTP.
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