ClinicalTrials.gov
public full text
Public Domain
Use of Compressed Sensing in Breast MRI
Breast MRI is increasingly important in breast screening imaging. It is currently based on dynamic sequences after contrast injection whose temporal resolution must be less than 90 seconds with dynamic acquisitions at different times. These curves profiles are designed to differentiate benign from malignant lesions. Recently, Mann et al, (2014) showed that increasing the temporal resolution of dynamic acquisitions; lesion enhancement curves over time were more accurate than the curves usually performed. It seems necessary to work on sequences having better temporal resolution without compromising however spatial resolution.
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- SliceBridge: context-consistent repair of corrupted slice intervals in T1-weighted MRI#822250
- Beyond MSE: Rician Likelihood Denoising for Self-Supervised Cardiac $T2$ and $T1ρ$ MRI#1115733
- MIAR: Medical Image Super-Resolution With Autoregressive Modeling#1062006
- Vasculature-informed spatial smoothing of white matter functional magnetic resonance imaging.#1361
- Harnessing Magnitude-Only and Complex Measurements for Improved Dynamic MRI Reconstruction with Learned Priors#463207
Record · ID 877466
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive