Hormonal effect on time-dependent diffusion of the breast fibroglandular tissue

Sungheon Gene Kim^{1,2}, Eric Sigmund^{1,2}, Melanie Moccaldi^{1,2}, Thorsten Feiweier^{3}, and Linda Moy^{1,2}

Seven asymptomatic,
premenopausal women (32±7 years) were scanned twice; once in Week2 (follicular
phase) of the menstrual cycle and again in Week4 (luteal phase). All scans were
performed using a whole-body MAGNETOM Trio 3T scanner (Siemens Healthcare, Erlangen, Germany) with
a 7-element breast coil array. We measured diffusion using a prototype
stimulated-echo acquisition mode (STEAM)-DTI sequence with an echo-planar
imaging (EPI) readout and SPAIR fat suppression (TR/TE = 11500/45 ms, matrix =
192x132x10, resolution = 2.1x2.1x5 mm^{3}, 6 directions, 3 averages,
parallel imaging factor = 2) with two different b-values (0 and 500 s/mm^{2}).
The acquisition was repeated using 4 different diffusion times (Δ=69, 173, 466
and 903 ms) by varying the mixing time.

Diffusion-weighted
images were corrected for eddy-current-induced distortion using a non-affine
registration. The contribution of the imaging gradients was included in the
b-matrix for tensor estimation. Parametric maps of mean diffusivity (MD),
fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD)
were derived from the diffusion eigenvalues (λ_{1}, λ_{2} and λ_{3})
with AD = λ_{1} and RD = (λ_{2}+λ_{3})/2. Regions of
interest (ROI) were manually drawn to select the FGT in the bilateral breast of
all imaging slices. For the voxels in the ROI, selection criteria of AD >
1.0 μm^{2}/ms and AD < 2.7 μm^{2}/ms were used to exclude any
voxels with a substantial partial-volume effect, with unsuppressed fat or
noise, respectively. The selected voxels were then used to quantify the
diffusion characteristics of the FGT using a bootstrapping analysis: the eigenvalues
were calculated from the average data of randomly selected 30% of the selected voxels
for each diffusion time; this process was repeated 500 times to calculate the
mean and standard deviation of the FGT diffusion metrics. With the RD values of
diffusion times 69 and 173 ms, surface-to-volume ratio (SVR) was calculated
using the Mitra short time limit model^{3}: $$D(\Delta)=D_0[1-SVR\frac{4\sqrt{D_0\Delta}}{6\sqrt{\pi}}]$$

1. Cho G, Leite APK, Baete S, et al. Diffusion Tensor Imaging (DTI) at Multiple Diffusion Times in Mammary Fibroglandular Tissue and Cancerous Lesions. Proceedings of the 21st Annual Meeting of ISMRM 2013; p.3373

2. Teruel J, Cho G, Ostenson J, et al. Stimulated echo diffusion tensor imaging with varying diffusion times as a probe of breast tissue. Proceedings of the 23rd Annual Meeting of ISMRM 2015;p.0883.

3. Mitra PP, Sen PN, Schwartz LM. Short-time behavior of the diffusion coefficient as a geometrical probe of porous media. Physical review B, Condensed matter. 1993;47(14):8565-74.

4. Taneri F, Kurukahvecioglu O, Akyurek N, et al. Microanatomy of milk ducts in the nipple. European surgical research Europaische chirurgische Forschung Recherches chirurgicales europeennes. 2006;38(6):545-9.

5. Rusby JE, Brachtel EF, Michaelson JS, et al. Breast duct anatomy in the human nipple: three-dimensional patterns and clinical implications. Breast cancer research and treatment. 2007;106(2):171-9

Figure 1: Representative images of raw images with b=0 (a,b) and MD (c, d) measured at
diffusion time Δ=69 ms (a,c) and 173 ms (b, d).

Figure 2: Average
eigenvalues (black; O for λ_{1}, flipped-Δ for λ_{2}, and Δ for λ_{3})
from the ROIs of all subjects at the follicular phase (a) and the
luteal phase (b). Red markers and lines represent RD ((λ_{2}+λ_{3})/2). Error bars are for
standard deviations.

Figure 3: Comparison of average AD and RD between Week2 and Week4 at two diffusion times,
69 and 173 ms. The error bars represents standard deviations.

Figure 4: Comparison of average SVR
between Week2 and Week4. The error bars represents standard deviations.

Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)

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