ArticleJournal of medical imaging (Bellingham, Wash.)2016
Subject-specific four-dimensional liver motion modeling based on registration of dynamic MRI.
Article in Journal of medical imaging (Bellingham, Wash.), 2016. 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
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Who cites it
7 citing papers in PubMed, 12 citations in OpenAlex.
- Motion modeling from 4D MR images of liver simulating phantom.Journal of applied clinical medical physics · 2022Article
- Simulated accuracy assessment of small footprint body-mounted probe alignment device for MRI-guided cryotherapy of abdominal lesions.Medical physics · 2020Article
- Automatic self-gated 4D-MRI construction from free-breathing 2D acquisitions applied on liver images.International journal of computer assisted radiology and surgery · 2019Article
- Image registration assessment in radiotherapy image guidance based on control chart monitoring.Journal of medical imaging (Bellingham, Wash.) · 2018Article
- Respiratory motion correction for free-breathing 3D abdominal MRI using CNN-based image registration: a feasibility study.The British journal of radiology · 2018Article
- Automatic error correction using adaptive weighting for vessel-based deformable image registration.Biomedical engineering letters · 2017Article
- Deformable image registration for tissues with large displacements.Journal of medical imaging (Bellingham, Wash.) · 2017Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Magnetic resonance-guided high intensity focused ultrasound treatment of the liver is a promising noninvasive technique for ablation of liver lesions. For the technique to be used in clinical practice, however, the issue of liver motion needs to be addressed. A subject-specific four-dimensional liver motion model is presented that is created based on registration of dynamically acquired magnetic resonance data. This model can be used for predicting the tumor motion trajectory for treatment planning and to indicate the tumor position for treatment guidance. The performance of the model was evaluated on a dynamic scan series that was not used to build the model. The method achieved an average Dice coefficient of 0.93 between the predicted and actual liver profiles and an average vessel misalignment of 3.0 mm. The model performed robustly, with a small variation in the results per subject. The results demonstrate the potential of the model to be used for MRI-guided treatment of liver lesions. Furthermore, the model can possibly be applied in other image-guided therapies, for instance radiotherapy of the liver.
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Registered trials
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