ClinicalTrials.gov
public full text
Public Domain
DTi Diffusion Findings in Breast MRI Screening and Diagnostic
Introduction The most common breast cancer originates within the breast milk ducts, and is called ductal carcinoma. After the stage where the cancer is found only within the ducts, it invades the breast tissue beyond the ducts, and becomes invasive cancer (Infiltrating Duct Carcinoma). Breast cancer located within the ducts is called intra ductal carcinoma in situ- DCIS. DCIS is the developing cancer and usually does not metastasize. Another type of breast cancer originates within the breast lobules and is call lobular carcinoma. Recently at the Weizmann Institute, Prof. Hadassa Degani and her group developed and imaging technique using breast MRI. This technique allows to map the three-dimensional structure of the milk ducts in the breast. The method is based on measuring the random movement (Brownian motion) of water molecules (diffusion) in different directions in the breast tissue, collecting diffusion images by using magnetic field gradients in different directions, allowing imaging of Diffusion tensor Imaging -DTI. Movement of water molecules within a unified solution is usually random and uniform in all directions, but the internal structures in biological systems (such as cells, pipelines, fiber) restrict the movement of water in certain directions. The DTI method exploits this feature by measuring the diffusion in various directions, thus enabling calculating images of biological structures \[1\]. This method was proved to be effective in mapping the brain's white matter fibers and is used routinely in clinical tests used to monitor neurological injuries \[2-4\]. This method was used for milk ducts mapping within breast tissue at the Weizmann Institute. The system that was developed at the Weizmann Institute, showed that the movement of water molecules along the milk ducts is faster than their movement across the ducts and therefore it was possible using this method to map the breast ductal system. Also this method showed that it is possible to detect and
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
- What Factors Affect Breast Cancer Neoadjuvant Chemotherapy Efficacy?#916263
- Feasibility Study of Vessel Analysis for Noninvasive Diagnosis of Malignancy in Breast Tumors#882692
- Role of MRI Breast in Detection of Ductal Carcinoma in Situ#1162870
- Evaluation of radiological features for breast tumour classification in clinical screening with machine learning methods#492891
- Breast Cancer Detection: An Evaluation of Machine Learning, Ensemble Learning, and Deep Learning Algorithms.#489653
Record · ID 1044822
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