ArticleJournal of medical imaging (Bellingham, Wash.)2019
Deformable registration of PET/CT and ultrasound for disease-targeted focal prostate brachytherapy.
Article in Journal of medical imaging (Bellingham, Wash.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- The use of deep learning in interventional radiotherapy (brachytherapy): A review with a focus on open source and open data.Zeitschrift fur medizinische Physik · 2024Review
- Clinical Efficacy and Openness to New Challenges of Low Dose Rate Brachytherapy for Prostate Cancer.Current oncology (Toronto, Ont.) · 2023Review
- Anatomy-guided multimodal registration by learning segmentation without ground truth: Application to intraprocedural CBCT/MR liver segmentation and registration.Medical image analysis · 2021Article
- Automatic multi-organ segmentation in computed tomography images using hierarchical convolutional neural network.Journal of medical imaging (Bellingham, Wash.) · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
We propose a deformable registration algorithm for prostate-specific membrane antigen (PSMA) PET/CT and transrectal ultrasound (TRUS) fusion. Accurate registration of PSMA PET to intraoperative TRUS will allow physicians to customize dose planning based on the regions involved. The inputs to the registration algorithm are the PET/CT and TRUS volumes as well as the prostate segmentations. PET/CT and TRUS volumes are first rigidly registered by maximizing the overlap between the segmented prostate binary masks. Three-dimensional anatomical landmarks are then automatically extracted from the boundary as well as within the prostate. Then, a deformable registration is performed using a regularized thin plate spline where the landmark localization error is optimized between the extracted landmarks that are in correspondence. The proposed algorithm was evaluated on 25 prostate cancer patients treated with low-dose-rate brachytherapy. We registered the postimplant CT to TRUS using the proposed algorithm and computed target registration errors (TREs) by comparing implanted seed locations. Our approach outperforms state-of-the-art methods, with significantly lower (
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