Mapping the diffusion tensor parameters at high spatial resolution provides a potential novel means for dissecting breast architecture. Parametric maps of λ1 and λ1-λ3 facilitate the detection and diagnosis of breast cancer.

A wide range of dynamic-contrast-enhanced (DCE) sequences and protocols, image processing methods, and interpretation criteria have been developed and evaluated over the last 20 years. In particular, attempts have been made to better understand the origin of the contrast observed in breast lesions using physiological models that take into account the vascular and tissue-specific features that influence tracer perfusion. In addition, model-free algorithms to decompose enhancement patterns in order to segment and classify different breast tissue types have been developed. This review includes a description of the mechanism of contrast enhancement by gadolinium-based contrast agents, followed by the current status of the physiological models used to analyze breast DCE-MRI and related critical issues. We further describe more recent unsupervised and supervised methods that use a range of different common algorithms. The model-based and model-free methods strive to achieve scientific accuracy and high clinical performance--both important goals yet to be reached.

Novel estrogen-conjugated pyridine-containing Gd(III) and Eu(III) contrast agents (EPTA-Gd/Eu) were designed and effectively synthesized. Convenient to administration and MRI experiments, both EPTA-Gd and EPTA-Eu are soluble in water. The EPTA-Gd selectively binds with a micromolar affinity to the estrogen receptor and induces proliferation of human breast cancer cells. The EPTA-Gd is not lethal and does not cause any adverse effects when administrated intravenously. It enhances T1 and T2 nuclear relaxation rates of water and serves as a selective contrast agent for localizing the estrogen receptor by MRI.

Purpose: To investigate a fast, objective, and standardized method for analyzing breast dynamic contrastenhanced magnetic resonance imaging (DCE-MRI) applying principal component analysis (PCA) adjusted with a model-based method.Materials and Methods: 3D gradient-echo DCE breast images of 31 malignant and 38 benign lesions, recorded on a 1.5T scanner, were retrospectively analyzed by PCA and by the model-based three-timepoints (3TP) method.Results: Intensity-scaled (IS) and enhancement-scaled (ES) datasets were reduced by PCA yielding a first IS-eigenvector that captured the signal variation between fat and fibroglandular tissue; two IS-eigenvectors and the two first ES-eigenvectors captured contrast-enhanced changes, whereas the remaining eigenvectors captured predominantly noise changes. Rotation of the two contrast-related eigenvectors led to a high congruence between the projection coefficients and the 3TP parameters. The ES-eigenvectors and the rotation angle were highly reproducible across malignant lesions, enabling calculation of a general rotated eigenvector base. Receiver operating characteristic (ROC) curve analysis of the projection coefficients of the two eigenvectors indicated high sensitivity of the first rotated eigenvector to detect lesions (area under the curve [AUC] > 0.97) and of the second rotated eigenvector to differentiate malignancy from benignancy (AUC ¼ 0.87). Conclusion:PCA adjusted with a model-based method provided a fast and objective computer-aided diagnostic tool for breast DCE-MRI.

Breast cancer is the most common cause of cancer among women worldwide. Early detection of breast cancer has a critical role in improving the quality of life and survival of breast cancer patients. In this paper a new approach for the detection of breast cancer is described, based on tracking the mammary architectural elements using diffusion tensor imaging (DTI).The paper focuses on the scanning protocols and image processing algorithms and software that were designed to fit the diffusion properties of the mammary fibroglandular tissue and its changes during malignant transformation. The final output yields pixel by pixel vector maps that track the architecture of the entire mammary ductal glandular trees and parametric maps of the diffusion tensor coefficients and anisotropy indices.The efficiency of the method to detect breast cancer was tested by scanning women volunteers including 68 patients with breast cancer confirmed by histopathology findings. Regions with cancer cells exhibited a marked reduction in the diffusion coefficients and in the maximal anisotropy index as compared to the normal breast tissue, providing an intrinsic contrast for delineating the boundaries of malignant growth. Overall, the sensitivity of the DTI parameters to detect breast cancer was found to be high, particularly in dense breasts, and comparable to the current standard breast MRI method that requires injection of a contrast agent. Thus, this method offers a completely non-invasive, safe and sensitive tool for breast cancer detection.