ArticleInsights into imaging2023
Development and validation of a four-dimensional registration technique for DCE breast MRI.
Article in Insights into imaging, 2023. 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.
- Fractal analysis of dynamic stress myocardial CT perfusion decouples diagnostic accuracy for obstructive coronary artery disease from remote flow.Japanese journal of radiology · 2026Article
- A groupwise multiresolution network for DCE-MRI image registration.Scientific reports · 2025Article
- Longitudinal registration of TMagnetic resonance imaging · 2024Article
- Image quality assessment using deep learning in high b-value diffusion-weighted breast MRI.Scientific reports · 2023Article
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Authors and funding
3 authors.
Funding
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Abstract
backgroundPatient motion can degrade image quality of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) due to subtraction artifacts. By objectively and subjectively assessing the impact of principal component analysis (PCA)-based registration on pretreatment DCE-MRIs of breast cancer patients, we aim to validate four-dimensional registration for DCE breast MRI.
resultsAfter applying a four-dimensional, PCA-based registration algorithm to 154 pretreatment DCE-MRIs of histopathologically well-described breast cancer patients, we quantitatively determined image quality in unregistered and registered images. For subjective assessment, we ranked motion severity in a clinical reading setting according to four motion categories (0: no motion, 1: mild motion, 2: moderate motion, 3: severe motion with nondiagnostic image quality). The median of images with either moderate or severe motion (median category 2, IQR 0) was reassigned to motion category 1 (IQR 0) after registration. Motion category and motion reduction by registration were correlated (Spearman's rho: 0.83, p < 0.001). For objective assessment, we performed perfusion model fitting using the extended Tofts model and calculated its volume transfer coefficient K
conclusionsFour-dimensional, PCA-based image registration improves image quality of breast DCE-MRI by correcting for motion artifacts in subtraction images and reduces uncertainty in quantitative perfusion modeling. The improvement is most pronounced when moderate-to-severe motion artifacts are present.
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