Table 1.

Review of literature reporting breast tissue composition analyses using breast magnetic resonance imaging (MRI).

Studies	MRI Protocol	Method for Gland Measurement from MR Images	Validation with Mammographic Density	General Comments
(Poon et al., 1992)	T1 by Look-Locker technique; T2 by multiple echo sequences; and relative water content by a pair of fat- and water- suppressed images	Relative water content of whole breast; T1 and T2 relaxation time for middle slice of breast only; and fourth moment of T2 pixel histogram	Yes, with 4 categories of Wolfe's classification (n=23 women)	Mean relative water to fat content, mean T1 relaxation time, and fourth moment of T2 relaxation time can distinguish DY and N1 patterns of Wolfe, but not mean T2 relaxation time; not used in epidemiologic studies.
(Graham et al., 1996)	MR spectrum by hybrid Dixon method, conventional frequency encoding to obtain 1D image of fat and water; T2 decay from breast volume of interest only by a CPMG sequence of hard pulse	Relative water and fat content estimated from MR spectrum peak area, and first moment of continuous distribution of T2 decay curve by a software	Yes, r>0.60 with semi-automatic interactive thresholding segmentation in n=42 mammograms	Water to fat content associated with sociodemographic risk factors for breast cancer, mean T2 decay associated only with family hitory of breast cancer and BMI.
(Lee et al., 1997)	T1 weighted spoiled gradient echo fast low-angle shot sequence	Manual segmentation of each slice; semi- automatic, assuming a two compartment model by solving two equations (mean MR intensity of the breast × total breast volume = fat volume × fat MR intensity + gland volume × gland MR intensity; and total breast volume=fat volume + gland volume)	Yes, r=0.63 with visual scoring in steps of 5% from 5%–95% in n=40 women	%-Glandular tissue associated with age change
(Klifa et al., 2004) (Klifa et al., 2010)	3D fat suppressed spoiled gradient echo pulse sequences, non-contrast imaging	Semi-automatic identification of breast ROI (Bezier splines and Lapalacian of Gaussian Filter); quantification of gland tissue by unsupervised fuzzy c-means clustering, manual delineation, and/or segmentation of signal intensity histogram by interactive thresholding algorithm.	Yes, r>0.75 with visual 4 categorical scoring (n=30), semi-automatic thresholding segmentation (n=10) and manual delineation of dense area and automatic pixel counting (n=35) of film- screen mammograms	Good reproducibility on replicate images; not validated for studying breast cancer risk factors
(Wei et al., 2004)	Coronal 3D SPGR (spoiled gradient recalled echo) pre- contrast T1-weighted	Semi-automatic isolation of breast ROI, interactive thresholding segmentation of gland from fat in MR images slice-by-slice	Yes, r=0.91 using an in-house software Mammogram Density ESTmator based on interactive thresholding segmentation and with visual scoring by radiologist	Not used for studying risk factors of breast cancer
(Boston et al., 2005)	3D spoiled gradient echo inversion recovery sequence for T1 map construction	Segmentation of T1 histogram into gland and fat using a logistic function that described the probability of a voxel containing glandular tissue to be a function of T1 of the voxel, mean T1 times of fat and gland peaks, respectively, and maximum slope of the logistic curve.	No, concpetual approach developed with phantom and tested in human cases	Empirical logistic model allowed for accurate segmentation of fat and parenchyma in breast phantoms
(Khazen et al., 2008) (Thompson et al., 2009)	Pre-contrast T1 weighted MR	Interactive thresholding segmentation, corrected for non-uniformity using proton density map (MRIBview software)	Yes, r>0.75, visual scoring using 21 point-scale and segmentation by an interactive thresholding algorithm using Cumulus software (n=138 in 2008 study and n=513 in 2009 MARIB study with matched MRI and film-screen mammogram)	Mammograms overestimate breast density, protocol time consuming (n=138), applied to MARIBS study (n=513) that validated the association of breast density with several known risk factors for breast cancer (in 2009 study)
(Eng-Wong et al., 2008)	T1-weighted spoiled gradient- echo with fat suppression per protocol by Yao et al., 2005	User interface software to automatically segment breast region of interest from the rest of body organs, fuzzy c-means based on pixel distance to edge and pixel MR signal intensity to classify tissues into three types, gland, fat and skin (Yao et al., 2005)	Yes, r>0.7 by a semi-automatic interactive thresholding segmentation of pixel intensity histogram of film-screen mammograms (n=20 women)	Raloxifene treatment for 1–2 yrs did not affect mammographic density (n=20), but decreased glandular tissue volume measured by MRI in 27 women (not a randomized trial)
(Ertas et al., 2009)	Proton density weighted and pre- and post-contrast T1 weighted images acquired using 3D spoiled gradient echo pulase sequences, a modification of Khazan et al 2008	Segmentation by Interactive thresholding based on signal intensity uniformity corrected pre- contrast T1 weighted image using a software MRIBView; automated fuzzy c-means clustering based on dual phase T1 estimate histograms, i.e., mean T1 estimate of pre-contrast histogram and the post-initial enhancement changes (n=20)	No	Compostions of breast tissue correlated well between results from interactive thresholding histogram segmentation method and two points fuzzy c-means clustering algorithm on pre-contrast signal intensity histogram and post-initial enhancement changes; not validated for breast cancer risk factors in epidemiologic studies
(Nie et al., 2008) (Nie et al., 2010a) (Nie et al., 2010b)	Non-fat saturated T1-weighted, fast 3D SPGR pulse sequence	Semi-automatic isolation of breast ROI (n=11) and skin removal (n=50), fuzzy c-mean classification to exclude air/lung, B-spline curve- fitting to exclude chest wall muscle; adaptive FCM to isolate dense tissue	No	In 2010 study (n=321), age and race were found to be strong predictors of gland tissue content.