Diffusion-weighted MRI (DWI) holds potential to improve the detection and biological characterization of breast cancer. DWI is increasingly being incorporated into breast MRI protocols to address some of the shortcomings of routine clinical breast MRI. Potential benefits include improved differentiation of benign and malignant breast lesions, assessment and prediction of therapeutic efficacy, and noncontrast detection of breast cancer. The breast presents a unique imaging environment with significant physiologic and inter-subject variations, as well as specific challenges to achieving reliable high quality diffusion-weighted MR images. Technical innovations are helping to overcome many of the image quality issues that have limited widespread use of DWI for breast imaging. Advanced modeling approaches to further characterize tissue perfusion, complexity, and glandular organization may expand knowledge and yield improved diagnostic tools.

Level of Evidence: 5

J. Magn. Reson. Imaging 2016

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Diffusion-weighted breast MRI: Clinical applications and emerging techniques

Diffusion-weighted imaging (DWI) is increasingly being incorporated into routine breast MRI protocols in many institutions worldwide, and there are abundant breast DWI indications ranging from lesion detection and distinguishing malignant from benign tumors to assessing prognostic biomarkers of breast cancer and predicting treatment response. DWI has the potential to serve as a noncontrast MR screening method. Beyond apparent diffusion coefficient (ADC) mapping, which is a commonly used quantitative DWI measure, advanced DWI models such as intravoxel incoherent motion (IVIM), non-Gaussian diffusion MRI, and diffusion tensor imaging (DTI) are extensively exploited in this field, allowing the characterization of tissue perfusion and architecture and improving diagnostic accuracy without the use of contrast agents. This review will give a summary of the clinical literature along with future directions. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;52:70-90.

Diffusion MRI of the breast: Current status and future directions

PURPOSE To evaluate whether the various anisotropy indices derived from breast diffusion tensor imaging (DTI) can characterize the healthy breast structure and differentiate cancer from normal breast tissue. MATERIALS AND METHODS Six healthy volunteers and retrospectively selected 24 breast cancer patients were imaged at 3T. DTI included two b-values 0 and 700 sec/mm2 with 20-64 gradient directions and TE of 120 or 90 msec. The normalized anisotropy indices: fractional anisotropy (FA), relative anisotropy (RA), and 1-volume ratio (1-VR), as well as the absolute maximal anisotropy index (λ1 -λ3 ) were compared. RESULTS The spatial distribution of the various anisotropy indices in healthy volunteers exhibited a high congruence (Pearson correlation coefficients range: 0.79-1.0). All indices showed a statistically significant reduction (P < 0.001) following shortening of the diffusion time. Significantly lower λ1 -λ3 values were found in cancers as compared to normal breast tissue (P < 6.0 × 10-7 ), while the values of the normalized indices in cancers were not significantly different from those in normal breast tissue (P < 0.65 for FA, P < 0.6 for RA, and P < 0.2 for 1-VR). The contrast-to-noise ratio of λ1 -λ3 was significantly higher (P < 0.001) than those of the normalized anisotropy indices, and the area under the curve in a receiver operating characteristic analysis exhibited the highest value for λ1 -λ3 (0.89 ± 0.04 vs. 0.51-0.54 for the other anisotropy indices). CONCLUSION Water diffusion anisotropy in the healthy breast can be similarly mapped by the normalized indices and by λ1 -λ3 . However, the normalized anisotropy indices fail to differentiate cancer from normal breast tissue, whereas λ1 -λ3 can assist in differentiating cancer from normal breast tissue. J. Magn. Reson. Imaging 2016;44:1624-1632.

Can diffusion tensor anisotropy indices assist in breast cancer detection

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

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Tracking the mammary architectural features and detecting breast cancer with magnetic resonance diffusion tensor imaging.

PURPOSE To evaluate the diagnostic performances of the diffusion tensor imaging (DTI) parameters in the diagnosis of breast cancer and to investigate the variations in DTI parameters according to the breast cancer biomarkers. MATERIALS AND METHODS At 3.0 Tesla (T), DTI was performed in 85 patients with 92 enhancing breast lesions. λ1 , λ2 , λ3 , mean diffusivity (MD), radial diffusivity (RD), fractional anisotropy (FA), relative anisotropy (RA), and geodesic anisotropy (GA) were studied and compared with diffusion-weighted imaging-derived apparent diffusion coefficient. Lesions were analyzed according to BIRADS lexicon. Logistic regression models were constructed to determine the contribution of DTI to the specificity and the accuracy of DCE-MRI. Breast cancer biomarkers; estrogen receptor (ER), HER-2 status, and Ki-67 were correlated with DTI in malignant cases. RESULTS Malignant lesions exhibited significantly lower MD, RD, λ1 , λ2 , λ3 and higher FA, RA, GA values (P <  0.001). Logistic regression models showed that MD, RD, λ1 , λ2 , λ3 , FA, and RA increase the specificity of the DCE-MRI (from 83.0% to 89.4-93.6%; P <  0.05). Higher RD, λ2 , λ3 and lower FA, RA, and GA values were observed in ER-negative breast cancer (P <  0.05). Ki-67 showed significant, negative correlation with FA, RA, GA, λ1 -λ3 and λ1 -λ2 (r = -0.336 to -0.435; P <  0.05). CONCLUSION Besides its ability to differentiate malignant breast lesions, DTI improves the specificity of conventional 3.0T breast MRI and shows correlation with biomarkers ER and Ki-67. LEVEL OF EVIDENCE 1 J. Magn. Reson. Imaging 2017;45:660-672.

Yan Hu

Papers

Optimization of the parameters for diffusion tensor magnetic resonance imaging data acquisition for breast fiber tractography at 1.5 T.