ArticleProceedings of SPIE--the International Society for Optical Engineering2015
A MR-TRUS Registration Method for Ultrasound-Guided Prostate Interventions.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 11 citations in OpenAlex.
- Real-time multimodal image registration with partial intraoperative point-set data.Medical image analysis · 2021Article
- A planning study of focal dose escalations to multiparametric MRI-defined dominant intraprostatic lesions in prostate proton radiation therapy.The British journal of radiology · 2020Article
- Multiparametric MRI-guided dose boost to dominant intraprostatic lesions in CT-based High-dose-rate prostate brachytherapy.The British journal of radiology · 2019Article
- A Denoising Algorithm for CT Image Using Low-rank Sparse Coding.Proceedings of SPIE--the International Society for Optical Engineering · 2018Article
- A 3D Neurovascular Bundles Segmentation Method based on MR-TRUS Deformable Registration.Proceedings of SPIE--the International Society for Optical Engineering · 2015Article
- 3D Transrectal Ultrasound (TRUS) Prostate Segmentation Based on Optimal Feature Learning Framework.Proceedings of SPIE--the International Society for Optical EngineeringArticle
- Patch-Based Label Fusion for Automatic Multi-Atlas-Based Prostate Segmentation in MR Images.Proceedings of SPIE--the International Society for Optical EngineeringArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
In this paper, we report a MR-TRUS prostate registration method that uses a subject-specific prostate strain model to improve MR-targeted, US-guided prostate interventions (e.g., biopsy and radiotherapy). The proposed algorithm combines a subject-specific prostate biomechanical model with a B-spline transformation to register the prostate gland of the MRI to the TRUS images. The prostate biomechanical model was obtained through US elastography and a 3D strain map of the prostate was generated. The B-spline transformation was calculated by minimizing Euclidean distance between the normalized attribute vectors of landmarks on MR and TRUS prostate surfaces. This prostate tissue gradient map was used to constrain the B-spline-based transformation to predict and compensate for the internal prostate-gland deformation. This method was validated with a prostate-phantom experiment and a pilot study of 5 prostate-cancer patients. For the phantom study, the mean target registration error (TRE) was 1.3 mm. MR-TRUS registration was also successfully performed for 5 patients with a mean TRE less than 2 mm. The proposed registration method may provide an accurate and robust means of estimating internal prostate-gland deformation, and could be valuable for prostate-cancer diagnosis and treatment.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.