Diffusion-Tensor MR Imaging of the Breast: Hormonal Regulation

Nissan, Noam; Furman‐Haran, Edna; Shapiro-Feinberg, Myra; Grobgeld, Dov; Degani, Hadassa

doi:10.1148/radiol.14132084

Cited by 55 publications

(37 citation statements)

References 43 publications

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“…Another example demonstrating the sensitivity of the tensor to the size of the ducts is shown in the example of a lactating volunteer in Figure 14. Since milk is a colloid of fat globules within a water-based fluid that contains dissolved carbohydrates and protein aggregates with minerals, the viscosity of milk is higher than that of the normal water fluid in the ducts, and thus, the water diffusion coefficients of lactating breasts are lower than those of premenopausal women 24 . Furthermore, the ducts near the nipple are large and the restriction in the directions orthogonal to the ducts is lower than in the non-lactating breast, and consequently the anisotropy indices are lower as well.…”

Section: Representative Resultsmentioning

confidence: 99%

“…More recently, diffusion weighted MRI and the resulting maps of apparent diffusion coefficient (ADC) have been evaluated as a complement method to DCE-MRI and it was shown that ADC values can help distinguish between cancers, benign lesions and normal breast tissue 9,10 . In addition, studies of breast diffusion tensor imaging (DTI) were initiated in healthy volunteers and patients with breast lesions at field strength of 1.5 T [11][12][13][14][15] and of 3 T [16][17][18][19][20][21][22][23][24] . Most of these studies reported ADC and fractional anisotropy (FA) values 11,12,14,15,[20][21][22][23] and found these two parameters to be reproducible with ADC values more reproducible than FA 13,20 .…”

Section: Introductionmentioning

confidence: 99%

“…The results of these studies indicated that malignant lesions exhibit low ADC values as compared to normal tissue and benign lesions, however, conflicting results were reported on the values and diagnostic capability of FA 11,12,14,[20][21][22][23] . In a set of 3 T-DTI studies the values of the three tensor eigenvalues and eigenvectors in the breast tissue frame were reported as well, and the results were presented in vector maps of the main eigenvector and parametric maps of the eigenvalues, ADC, FA and a maximal anisotropy index [16][17][18][19]24 . In these studies the main diffusion eigenvalue and the maximal anisotropy were shown to serve as the most sensitive independent parameters for the detection and diagnosis of cancer lesions…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Nissan

Furman‐Haran

Feinberg-Shapiro

et al. 2014

JoVE

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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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Section: Representative Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Nissan

Furman‐Haran

Feinberg-Shapiro

et al. 2014

JoVE

Self Cite

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show abstract

“…This requires the standardization of DTI parameters and assessing the normative range as well as improved understanding of factors that influence these values. Few publications have specifically focused on characterizing normal breast tissue [10, 11] and to our knowledge no group has evaluated the effects of fibroglandular tissue composition (FGT) and background parenchymal enhancement (BPE) on DTI measurements. Our goal was to measure DTI parameters at 3T in normal breast tissue and assess the influences of FGT composition and BPE.…”

Section: Introductionmentioning

confidence: 99%

Diffusion tensor imaging in the normal breast: influences of fibroglandular tissue composition and background parenchymal enhancement

Plaza¹,

Morris²,

Thakur³

2016

Clinical Imaging

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Objective To evaluate effects of fibroglandular tissue (FGT) composition and background parenchymal enhancement (BPE) on diffusion tensor imaging (DTI) parameters in normal breast tissue. Methods DTI analysis was performed on 35 breasts with regions of interest drawn to include only normal tissue. Breasts were dichotomized by FGT composition and by BPE; DTI parameters were compared. Results The λ1 principal diffusion coefficient was lower in breasts with moderate/marked BPE versus those with minimal/mild BPE (p=0.039). All other parameters were unaffected. Conclusion λ1 is sensitive to differences in BPE within normal breast tissue which should be taken into account in DTI evaluation.

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“…These provide a complete description of the geometric and diffusion properties of the tensor [42]. Recent studies [43,44] applying DTI-derived vector and parametric maps indicate that the breast architecture can thereby be evaluated with high spatial resolution. Comparisons between twenty-six women with 33 cancerous breast lesions and fourteen women with 20 benign breast lesions revealed lower (p < 0.0009) orthogonal diffusion coefficients λ 1 , λ 2 , λ 3 in the malignant lesions, with a specificity of 98 %.…”

Section: Diffusion-weighted Mri For Breast Cancer Diagnosticsmentioning

confidence: 99%

Padé optimization of noise-corrupted magnetic resonance spectroscopic time signals from fibroadenoma of the breast

Belkić

2014

J Math Chem

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We apply the fast Padé transform (FPT) to time signals as encoded via magnetic resonance spectroscopy (MRS) in breast fibroadenoma. Realistic levels of noise are considered. The conventional fast Fourier transform (FFT) is also used for comparison with the FPT. For N = 2048 signal points, the FFT generated uninformative total shape spectra with only a few distorted peaks, whereas the FPT yielded converged envelope spectra at partial signal length N P = 1700. To match the FPT based at time signals sampled at N = 2048, the FFT requires N = 65536 signal points, i.e. a 32-fold lengthening of each transient. Via the parametric FPT, at N P = 1700 all the resonances were resolved and metabolite concentrations precisely computed, including those that were almost completely overlapping (phosphocholine and phosphoethanolamine whose chemical shifts are separated by 0.001 parts per million). The multi-faceted signal-noise separation (SNS) procedure was applied through identification of pole-zero cancellations, zero or near zero amplitudes plus the stability test against different levels of noise. Via SNS, all the spurious resonances were confidently identified, thus leaving only genuine metabolites in the output list. Practical implications are underscored: the high resolution of the FPT will shorten the examination time of the patient. Using the FPT, the cancer biomarker phosphocholine, plus other informative metabolites can be identified and their concentrations exactly determined.

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Diffusion-Tensor MR Imaging of the Breast: Hormonal Regulation

Cited by 55 publications

References 43 publications

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Diffusion tensor imaging in the normal breast: influences of fibroglandular tissue composition and background parenchymal enhancement

Padé optimization of noise-corrupted magnetic resonance spectroscopic time signals from fibroadenoma of the breast

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