Fully automatic deformable registration of pretreatment MRI/CT for image-guided prostate radiotherapy planning

التفاصيل البيبلوغرافية
العنوان: Fully automatic deformable registration of pretreatment MRI/CT for image-guided prostate radiotherapy planning
المؤلفون: Catherine Cheze Le Rest, Nicolas Boussion, Antoine Valeri, Dimitris Visvikis, J.P. Tasu, Guillaume Dardenne, Iyas Hamdan, Julien Bert
المصدر: Medical physics. 44(12)
سنة النشر: 2016
مصطلحات موضوعية: Male, Computer science, Multimodal Imaging, 030218 nuclear medicine & medical imaging, Image (mathematics), 03 medical and health sciences, Automation, 0302 clinical medicine, Prostate, medicine, Image Processing, Computer-Assisted, Prostate radiotherapy, Humans, Computer vision, Radiation treatment planning, Multimodal imaging, business.industry, Radiotherapy Planning, Computer-Assisted, Soft tissue, Prostatic Neoplasms, General Medicine, Magnetic Resonance Imaging, Visualization, Hausdorff distance, medicine.anatomical_structure, 030220 oncology & carcinogenesis, Artificial intelligence, business, Tomography, X-Ray Computed, Radiotherapy, Image-Guided
الوصف: Purpose In prostate radiotherapy, dose distribution may be calculated on CT images, while the MRI can be used to enhance soft tissue visualization. Therefore, a registration between MR and CT images could improve the overall treatment planning process, by improving visualization with a demonstrated interobserver delineation variability when segmenting the prostate, which in turn can lead to a more precise planning. This registration must compensate for prostate deformations caused by changes in size and form between the acquisitions of both modalities. Methods We present a fully automatic MRI/CT nonrigid registration method for prostate radiotherapy treatment planning. The proposed registration methodology is a two-step registration process involving both a rigid and a nonrigid registration step. The registration is constrained to volumes of interest in order to improve robustness and computational efficiency. The method is based on the maximization of the mutual information in combination with a deformation field parameterized by cubic B-Splines. Results The proposed method was validated on eight clinical patient datasets. Quantitative evaluation, using Hausdorff distance between prostate volumes in both images, indicated that the overall registration errors is 1.6 ± 0.2 mm, with a maximum error of less than 2.3 mm, for all patient datasets considered in this study. Conclusions The proposed approach provides a promising solution for an effective and accurate prostate radiotherapy treatment planning since it satisfies the desired clinical accuracy.
تدمد: 2473-4209
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a3333849c05c77b8e3bf98c22e8caa83
https://pubmed.ncbi.nlm.nih.gov/29044630
Rights: CLOSED
رقم الانضمام: edsair.doi.dedup.....a3333849c05c77b8e3bf98c22e8caa83
قاعدة البيانات: OpenAIRE