Erez Eyal

Parametric diffusion tensor imaging of the breast

Objectives:To investigate the ability of parametric diffusion tensor imaging (DTI), applied at 3 Tesla, to dissect breast tissue architecture and evaluate breast lesions. Materials and Methods:All protocols were approved and a signed informed consent was obtained from all subjects. The study included 21 healthy women, 26 women with 33 malignant lesions, and 14 women with 20 benign lesions. Images were recorded at 3 Tesla with a protocol optimized for breast DTI at a spatial resolution of 1.9 × 1.9 × (2–2.5) mm3. Image processing algorithms and software, applied at pixel resolution, yielded vector maps of prime diffusion direction and parametric maps of the 3 orthogonal diffusion coefficients and of the fractional anisotropy and maximal anisotropy. Results:The DTI-derived vector maps and parametric maps revealed the architecture of the entire mammary fibroglandular tissue and allowed a reliable detection of malignant lesions. Cancer lesions exhibited significantly lower values of the orthogonal diffusion coefficients, &lgr;1, &lgr;2, &lgr;3, and of the maximal anisotropy index &lgr;1-&lgr;3 as compared with normal breast tissue (P < 0.0001) and to benign breast lesions (P < 0.0009 and 0.004, respectively). Maps of &lgr;1 exhibited the highest contrast-to-noise ratio enabling delineation of the cancer lesions. These maps also provided high sensitivity/specificity of 95.6%/97.7% for differentiating cancers from benign lesions, which were similar to the sensitivity/specificity of dynamic contrast-enhanced magnetic resonance imaging of 94.8%/92.9%. Maps of &lgr;1-&lgr;3 provided a secondary independent diagnostic parameter with high sensitivity of 92.3%, but low specificity of 69.5% for differentiating cancers from benign lesions. Conclusion:Mapping the diffusion tensor parameters at high spatial resolution provides a potential novel means for dissecting breast architecture. Parametric maps of &lgr;1 and &lgr;1-&lgr;3 facilitate the detection and diagnosis of breast cancer.

Principal component analysis of breast DCE-MRI adjusted with a model-based method

To investigate a fast, objective, and standardized method for analyzing breast dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) applying principal component analysis (PCA) adjusted with a model‐based method.

Non-Invasive Imaging of Barriers to Drug Delivery in Tumors

Solid tumors often develop high interstitial fluid pressure (IFP) as a result of increased water leakage and impaired lymphatic drainage, as well as changes in the extracellular matrix composition and elasticity. This high fluid pressure forms a barrier to drug delivery and hence, resistance to therapy. We have developed techniques based on contrast enhanced magnetic resonance imaging for mapping in tumors the vascular and transport parameters determining the delivery efficiency of blood borne substances. Sequential images are recorded during continuous infusion of a Gd-based contrast agent and analyzed according to a new physiological model, yielding maps of microvascular transfer constants, as well as outward convective interstitial transfer constants and steady state interstitial contrast agent concentrations both reflecting IFP distribution. We further demonstrated in non small cell human lung cancer xenografts the capability of our techniques to monitor in vivo collagenase induced increase in contrast agent delivery as a result of decreased IFP. These techniques can be applied to test drugs that affect angiogenesis and modulate interstitial fluid pressure and has the potential to be extended to cancer patients for assessing resistance to drug delivery.

Principal component analysis of dynamic contrast enhanced mri in human prostate cancer

Objectives:To develop and evaluate a fast, objective and standardized method for image processing of dynamic contrast enhanced MRI of the prostate based on principal component analysis (PCA). Materials and Methods:The study was approved by the institutional internal review board; signed informed consent was obtained. MRI of the prostate at 3 Tesla was performed in 21 patients with biopsy proven cancers before radical prostatectomy. Seven 3-dimensional gradient echo datesets, 2 pre and 5 post-gadopentetate dimeglumine injection (0.1 mmol/kg), were acquired within 10.5 minutes at high spatial resolution. PCA of dynamic intensity-scaled (IS) and enhancement-scaled (ES) datasets and analysis by the 3-time points (3TP) method were applied using the latter method for adjusting the PCA eigenvectors. Results:PCA of 7 IS datasets and 6 ES datasets yielded their corresponding eigenvectors and eigenvalues. The first IS-eigenvector captured the major part of the signal variance because of a signal change between the precontrast and the first postcontrast arising from the inhomogeneous surface coil reception profile. The next 2 IS-eigenvectors and the 2 dominant ES-eigenvectors captured signal changes because of tissue contrast-enhancement, whereas the remaining eigenvectors captured noise changes. These eigenvectors were adjusted by rotation to reach congruence with the wash-in and wash-out kinetic parameters defined according to the 3TP method. The IS and ES-eigenvectors and rotation angles were highly reproducible across patients enabling the calculation of a general rotated eigenvector base that served to rapidly and objectively calculate diagnostically relevant projection coefficient maps for new cases. We found for the a priori selected prostate cancer patients that the projection coefficients of the IS-2nd eigenvector provided a higher accuracy for detecting biopsy proven cancers (94% sensitivity, 67% specificity, 80% ppv, and 89% npv) than the projection coefficients of the ES-2nd rotated and non rotated eigenvectors. Conclusions:PCA adjusted to correlate with physiological parameters selects a dominant eigenvector, free of the inhomogeneous radio-frequency field reception-profile and noise-components. Projection coefficient maps of this eigenvector provide a fast, objective, and standardized means for visualizing prostate cancer.

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.

Combination of model-free and model-based analysis of dynamic contrast enhanced MRI for breast cancer diagnosis

Dynamic contrast enhancement (DCE) is the leading technique in magnetic resonance imaging for cancer detection and diagnosis. However, there are large variations in the reported sensitivity and specificity of this method that result from the wide range of contrast-enhanced MRI sequences and protocols, image processing methods, and interpretation criteria. Analysis methods can be divided to physiological based models that take into account the vascular and tissue specific features that influence tracer perfusion, and to model free algorithms that decompose enhancement patterns in order to segment and classify different tissue types. Inhere we present a general hybrid method for analyzing dynamic contrast enhanced images integrating a mathematical, model-free technique with a model derived approach that characterizes tissue microvasculature function. We demonstrate the application of the method for breast cancer diagnosis. A brief description of this approach was recently presented for the diagnosis of prostate cancer. The model free method employed principal component analysis and yielded eigen-vectors of which two were relevant for characterizing breast malignancy. The physiological relevance of the two eigen-vectors was revealed by a quantitative correlation with the model based three time point technique. Projection maps of the eigen-vector that specifically related to the wash-out rate of the contrast agent depicted with high accuracy breast cancer. Overall, this hybrid method is fast, standardized, and yields parametric images characterizing tissue microvascular function. It can improve breast cancer detection and be potentially extended as a computer-aided tool for the detection and diagnosis of other cancers.

Automatic three-dimensional segmentation of MR images applied to the rat uterus

We introduce an automatic 3D multiscale automatic segmentation algorithm for delineating specific organs in Magnetic Resonance images (MRI). The algorithm can process several modalities simultaneously, and handle both isotropic and anisotropic data in only linear time complexity. It produces a hierarchical decomposition of MRI scans. During this segmentation process a rich set of features describing the segments in terms of intensity, shape and location are calculated, reflecting the formation of the hierarchical decomposition. We show that this method can delineate the entire uterus of the rat abdomen in 3D MR images utilizing a combination of scanning protocols that jointly achieve high contrast between the uterus and other abdominal organs and between inner structures of the rat uterus. Both single and multi-channel automatic segmentation demonstrate high correlation to a manual segmentation. While the focus here is on the rat uterus, the general approach can be applied to recognition in 2D, 3D and multi-channel medical images.