ArticleMagnetic resonance in medicine2015
Deformable registration for quantifying longitudinal tumor changes during neoadjuvant chemotherapy.
Article in Magnetic resonance in medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 35 citations in OpenAlex.
- Prediction of Treatment Response to Neoadjuvant Chemotherapy for Breast Cancer via Early Changes in Tumor Heterogeneity Captured by DCE-MRI Registration.Scientific reports · 2019Trial
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- Inter-fraction deformable image registration using unsupervised deep learning for CBCT-guided abdominal radiotherapy.Physics in medicine and biology · 2023Article
- Supine magnetic resonance image registration for breast surgery: insights on material mechanics.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- Article
- Incorporating radiomics into clinical trials: expert consensus endorsed by the European Society of Radiology on considerations for data-driven compared to biologically driven quantitative biomarkers.European radiology · 2021Article
- Learning Multiparametric Biomarkers for Assessing MR-Guided Focused Ultrasound Treatment of Malignant Tumors.IEEE transactions on bio-medical engineering · 2021Article
- Voxelwise and Regional Brain Apparent Diffusion Coefficient Changes on MRI from Birth to 6 Years of Age.Radiology · 2021Article
- Radiomics of Tumor Heterogeneity in Longitudinal Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Predicting Response to Neoadjuvant Chemotherapy in Breast Cancer.Frontiers in molecular biosciences · 2021Article
- 4D-CT deformable image registration using multiscale unsupervised deep learning.Physics in medicine and biology · 2020Article
- Quantifying local tumor morphological changes with Jacobian map for prediction of pathologic tumor response to chemo-radiotherapy in locally advanced esophageal cancer.Physics in medicine and biology · 2018Article
- Genetic mapping of molar size relations identifies inhibitory locus for third molars in mice.Heredity · 2018Article
- Image Registration for Microwave Tomography of the Breast Using Priors From Nonsimultaneous Previous Magnetic Resonance Images.IEEE journal of electromagnetics, RF and microwaves in medicine and biology · 2018Article
- Quantitative image analysis for evaluation of tumor response in clinical oncology.Chronic diseases and translational medicine · 2018Article
- Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome.Journal of medical imaging (Bellingham, Wash.) · 2018Article
- Efficient Segmentation of a Breast in B-Mode Ultrasound Tomography Using Three-Dimensional GrabCut (GC3D).Sensors (Basel, Switzerland) · 2017Article
- Using clinically acquired MRI to construct age-specific ADC atlases: Quantifying spatiotemporal ADC changes from birth to 6-year old.Human brain mapping · 2017Article
- Brain extraction in pediatric ADC maps, toward characterizing neuro-development in multi-platform and multi-institution clinical images.NeuroImage · 2015Article
- Performance of single and multi-atlas based automated landmarking methods compared to expert annotations in volumetric microCT datasets of mouse mandibles.Frontiers in zoology · 2015Article
- Comparative evaluation of registration algorithms in different brain databases with varying difficulty: results and insights.IEEE transactions on medical imaging · 2014Article
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Authors and funding
11 authors at 1 institution in 1 country.
Funding
Abstract
purposeTo evaluate DRAMMS, an attribute-based deformable registration algorithm, compared to other intensity-based algorithms, for longitudinal breast MRI registration, and to show its applicability in quantifying tumor changes over the course of neoadjuvant chemotherapy.
methodsBreast magnetic resonance images from 14 women undergoing neoadjuvant chemotherapy were analyzed. The accuracy of DRAMMS versus five intensity-based deformable registration methods was evaluated based on 2,380 landmarks independently annotated by two experts, for the entire image volume, different image subregions, and patient subgroups. The registration method with the smallest landmark error was used to quantify tumor changes, by calculating the Jacobian determinant maps of the registration deformation.
resultsDRAMMS had the smallest landmark errors (6.05 ± 4.86 mm), followed by the intensity-based methods CC-FFD (8.07 ± 3.86 mm), NMI-FFD (8.21 ± 3.81 mm), SSD-FFD (9.46 ± 4.55 mm), Demons (10.76 ± 6.01 mm), and Diffeomorphic Demons (10.82 ± 6.11 mm). Results show that registration accuracy also depends on tumor versus normal tissue regions and different patient subgroups.
conclusionsThe DRAMMS deformable registration method, driven by attribute-matching and mutual-saliency, can register longitudinal breast magnetic resonance images with a higher accuracy than several intensity-matching methods included in this article. As such, it could be valuable for more accurately quantifying heterogeneous tumor changes as a marker of response to treatment.
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