Noam Nissan

Diffusion‐weighted breast MRI: Clinical applications and emerging techniques

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.

Diffusion-Tensor MR Imaging of the Breast: Hormonal Regulation

PURPOSE To investigate the parameters obtained with magnetic resonance (MR) diffusion-tensor imaging (DTI) of the breast throughout the menstrual cycle phases, during lactation, and after menopause, with and without hormone replacement therapy (HRT). MATERIALS AND METHODS All protocols were approved by the internal review board, and signed informed consent was obtained from all participants. Forty-five healthy volunteers underwent imaging by using T2-weighted and DTI MR sequences at 3 T. Premenopausal volunteers (n = 16) underwent imaging weekly, four times during one menstrual cycle. Postmenopausal volunteers (n = 19) and lactating volunteers (n = 10) underwent imaging once. The principal diffusion coefficients (λ1, λ2, and λ3), apparent diffusion coefficient (ADC), fractional anisotropy (FA), and maximal anisotropy (λ1-λ3) were calculated pixel by pixel for the fibroglandular tissue in the entire breast. RESULTS In all premenopausal volunteers, the DTI parameters exhibited high repeatability, remaining almost equal along the menstrual cycle, with a low mean within-subject coefficient of variance of λ1, λ2, λ3, and ADC (1%-2% for all) and FA (5%), as well as a high intraclass correlation of 0.92-0.98. The diffusion coefficients were significantly lower (a) in the group without HRT use as compared with the group with HRT use (P < .01) and premenopausal volunteers (P < .01) and (b) in the lactating volunteers as compared with the premenopausal volunteers (P < .005). No significant differences in DTI parameters were found between premenopausal volunteers free of oral contraceptives and those who used oral contraceptives (P = .28-0.82) and between premenopausal volunteers and postmenopausal volunteers who used HRT (P = .31-0.93). CONCLUSION DTI parameters are not sensitive to menstrual cycle changes, while menopause, long-term HRT, and presence of milk in lactating women affected the DTI parameters. Therefore, the timing for performing breast DTI is not restricted throughout the menstrual cycle, whereas the modulations in diffusion parameters due to HRT and lactation should be taken into account in DTI evaluation.

Diffusion Tensor Magnetic Resonance Imaging of the Pancreas

Purpose To develop a diffusion-tensor-imaging (DTI) protocol that is sensitive to the complex diffusion and perfusion properties of the healthy and malignant pancreas tissues. Materials and Methods Twenty-eight healthy volunteers and nine patients with pancreatic-ductal-adenocacinoma (PDAC), were scanned at 3T with T2-weighted and DTI sequences. Healthy volunteers were also scanned with multi-b diffusion-weighted-imaging (DWI), whereas a standard clinical protocol complemented the PDAC patients’ scans. Image processing at pixel resolution yielded parametric maps of three directional diffusion coefficients λ1, λ2, λ3, apparent diffusion coefficient (ADC), and fractional anisotropy (FA), as well as a λ1-vector map, and a main diffusion-direction map. Results DTI measurements of healthy pancreatic tissue at b-values 0,500 s/mm2yielded: λ1 = (2.65±0.35)×10−3, λ2 = (1.87±0.22)×10−3, λ3 = (1.20±0.18)×10−3, ADC = (1.91±0.22)×10−3 (all in mm2/s units) and FA = 0.38±0.06. Using b-values of 100,500 s/mm2 led to a significant reduction in λ1, λ2, λ3 and ADC (p<.0001) and a significant increase (p<0.0001) in FA. The reduction in the diffusion coefficients suggested a contribution of a fast intra-voxel-incoherent-motion (IVIM) component at b≤100 s/mm2, which was confirmed by the multi-b DWI results. In PDACs, λ1, λ2, λ3 and ADC in both 0,500 s/mm2 and 100,500 s/mm2 b-values sets, as well as the reduction in these diffusion coefficients between the two sets, were significantly lower in comparison to the distal normal pancreatic tissue, suggesting higher cellularity and diminution of the fast-IVIM component in the cancer tissue. Conclusion DTI using two reference b-values 0 and 100 s/mm2 enabled characterization of the water diffusion and anisotropy of the healthy pancreas, taking into account a contribution of IVIM. The reduction in the diffusion coefficients of PDAC, as compared to normal pancreatic tissue, and the smaller change in these coefficients in PDAC when the reference b-value was modified from 0 to 100 s/mm2, helped identifying the presence of malignancy.

Overcoming limitations in diffusion‐weighted MRI of breast by spatio‐temporal encoding

Evaluating the usefulness of diffusion‐weighted spatio‐temporal encoding (SPEN) methods to provide quantitative apparent diffusion coefficient (ADC)‐based characterizations of healthy and malignant human breast tissues, in comparison with results obtained using techniques based on spin‐echo echo planar imaging (SE‐EPI).

Can diffusion tensor anisotropy indices assist in breast cancer detection

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.

Advantages and drawbacks of breast DTI

In 1965, Stejskal and Tanner developed the pulsed gradient spin echo (PGSE) technique for measuring the diffusion coefficient in solution [1]. The discovery of MRI led to the development of diffusion imaging sequences particularly sequences based on echo planar imaging – EPI that facilitate fast clinical diffusion measurements. As a result of gradient strength limitations in most human scanners, it is necessary to use long finite-width gradient pulses in order to achieve high b values and hence, the short gradient pulse approximation breaks down. Nevertheless, it was shown by Zielinski and Sen [2] that diffusion experiments with long diffusion gradient pulses still measure a physical parameter reflecting self diffusion in systems that have open geometry and large amount of restriction as is the case in most tissues. Water diffusion in tissues presents a highly complex process as the system is composed of several different compartments with partial restriction processes within the compartments. Water diffusion coefficients of tissues are not merely affected by Brownian motion, but also by additional contributions of flow, restriction by cell membrane, extracellular tortuosity and exchange between tissue compartments. Water diffusion in tissues is often anisotropic due to restriction by membranes and walls of various micro-structures. Namely, anisotropic diffusion leads to variable diffusion coefficients for various directions and hence the diffusion coefficients are described by a diffusion tensor. The diffusion of water molecules in the mammary tissue presents a particular example of restricted movement in well defined microstructures composed of the ductal/ glandular trees. The, diffusion in parallel to the walls of the ducts is free; however, it is restricted in the directions perpendicular to the walls. The extent of restriction will depend on the experimental diffusion time versus the size of the ductal and glandular regions. Blockage of the ducts by cancer cells predominantly affects the free diffusion in parallel to the walls, reducing the diffusion coefficient in all directions, and consequently the extent of anisotropy. We have applied an experimental protocol to track this anisotropic motion using diffusion gradients in 30 to 60 directions and two b-values with a relatively high diffusion time [3]. In voxels with anisotropic water diffusion the distribution of the fraction of change in signal intensity in all directions of the field gradients assumed an anisotropic ellipsoid form, whereas in voxels with isotropic

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

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.

Monitoring In-Vivo the Mammary Gland Microstructure during Morphogenesis from Lactation to Post-Weaning Using Diffusion Tensor MRI

Lactation and the return to the pre-conception state during post-weaning are regulated by hormonal induced processes that modify the microstructure of the mammary gland, leading to changes in the features of the ductal / glandular tissue, the stroma and the fat tissue. These changes create a challenge in the radiological workup of breast disorder during lactation and early post-weaning. Here we present non-invasive MRI protocols designed to record in vivo high spatial resolution, T2-weighted images and diffusion tensor images of the entire mammary gland. Advanced imaging processing tools enabled tracking the changes in the anatomical and microstructural features of the mammary gland from the time of lactation to post-weaning. Specifically, by using diffusion tensor imaging (DTI) it was possible to quantitatively distinguish between the ductal / glandular tissue distention during lactation and the post-weaning involution. The application of the T2-weighted imaging and DTI is completely safe, non-invasive and uses intrinsic contrast based on differences in transverse relaxation rates and water diffusion rates in various directions, respectively. This study provides a basis for further in-vivo monitoring of changes during the mammary developmental stages, as well as identifying changes due to malignant transformation in patients with pregnancy associated breast cancer (PABC).

Removing silicone artifacts in diffusion-weighted breast MRI by means of shift-resolved spatiotemporally encoding.

Evaluate the usefulness of diffusion‐weighted spatiotemporally encoded (SPEN) methods to obtain apparent diffusion coefficient (ADC) maps of fibroglandular human breast tissue, in the presence of silicone implants.

Robust diffusion tensor imaging by spatiotemporal encoding: Principles and in vivo demonstrations.

Evaluate the usefulness of single‐shot and of interleaved spatiotemporally encoded (SPEN) methods to perform diffusion tensor imaging (DTI) under various preclinical and clinical settings.