ArticleMedical image analysis2015
Patient-specific biomechanical model as whole-body CT image registration tool.
Article in Medical image analysis, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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
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Who cites it
9 citing papers in PubMed, 23 citations in OpenAlex.
- Biomechanics-driven dose stress metrics for radiation-induced acute xerostomia prediction among head and neck radiation therapy.Physical and engineering sciences in medicine · 2025Article
- Voxel-wise body composition analysis using image registration of a three-slice CT imaging protocol: methodology and proof-of-concept studies.Biomedical engineering online · 2024Article
- An image registration method for voxel-wise analysis of whole-body oncological PET-CT.Scientific reports · 2022Article
- Computer simulation of tumour resection-induced brain deformation by a meshless approach.International journal for numerical methods in biomedical engineering · 2022Article
- Suite of meshless algorithms for accurate computation of soft tissue deformation for surgical simulation.Medical image analysis · 2019Article
- Accuracy of semi-automated versus manual localisation of liver tumours in CT-guided ablation procedures.European radiology · 2018Article
- Automated identification and reduction of artifacts in cine four-dimensional computed tomography (4DCT) images using respiratory motion model.International journal of computer assisted radiology and surgery · 2017Article
- Biomechanical model for computing deformations for whole-body image registration: A meshless approach.International journal for numerical methods in biomedical engineering · 2016Article
- The Parametric Model of the Human Mandible Coronoid Process Created by Method of Anatomical Features.Computational and mathematical methods in medicine · 2015Article
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 3 institutions in 4 countries.
Funding
Abstract
Whole-body computed tomography (CT) image registration is important for cancer diagnosis, therapy planning and treatment. Such registration requires accounting for large differences between source and target images caused by deformations of soft organs/tissues and articulated motion of skeletal structures. The registration algorithms relying solely on image processing methods exhibit deficiencies in accounting for such deformations and motion. We propose to predict the deformations and movements of body organs/tissues and skeletal structures for whole-body CT image registration using patient-specific non-linear biomechanical modelling. Unlike the conventional biomechanical modelling, our approach for building the biomechanical models does not require time-consuming segmentation of CT scans to divide the whole body into non-overlapping constituents with different material properties. Instead, a Fuzzy C-Means (FCM) algorithm is used for tissue classification to assign the constitutive properties automatically at integration points of the computation grid. We use only very simple segmentation of the spine when determining vertebrae displacements to define loading for biomechanical models. We demonstrate the feasibility and accuracy of our approach on CT images of seven patients suffering from cancer and aortic disease. The results confirm that accurate whole-body CT image registration can be achieved using a patient-specific non-linear biomechanical model constructed without time-consuming segmentation of the whole-body images.
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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.