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Items: 1 to 20 of 106

1.

Comparison of Dixon Sequences for Estimation of Percent Breast Fibroglandular Tissue.

Ledger AE, Scurr ED, Hughes J, Macdonald A, Wallace T, Thomas K, Wilson R, Leach MO, Schmidt MA.

PLoS One. 2016 Mar 24;11(3):e0152152. doi: 10.1371/journal.pone.0152152. eCollection 2016.

2.

Influence of fat-water separation and spatial resolution on automated volumetric MRI measurements of fibroglandular breast tissue.

Wengert GJ, Pinker-Domenig K, Helbich TH, Vogl WD, Clauser P, Bickel H, Marino MA, Magometschnigg HF, Baltzer PA.

NMR Biomed. 2016 Jun;29(6):702-8. doi: 10.1002/nbm.3516. Epub 2016 Apr 7.

PMID:
27061174
3.

Accuracy of fully automated, quantitative, volumetric measurement of the amount of fibroglandular breast tissue using MRI: correlation with anthropomorphic breast phantoms.

Wengert GJ, Pinker K, Helbich TH, Vogl WD, Spijker SM, Bickel H, Polanec SH, Baltzer PA.

NMR Biomed. 2017 Jun;30(6). doi: 10.1002/nbm.3705. Epub 2017 Mar 10.

PMID:
28295818
4.

Comparison of 3-point Dixon imaging and fuzzy C-means clustering methods for breast density measurement.

Clendenen TV, Zeleniuch-Jacquotte A, Moy L, Pike MC, Rusinek H, Kim S.

J Magn Reson Imaging. 2013 Aug;38(2):474-81. doi: 10.1002/jmri.24002. Epub 2013 Jan 4.

PMID:
23292922
5.
6.

Using deep learning to segment breast and fibroglandular tissue in MRI volumes.

Dalmış MU, Litjens G, Holland K, Setio A, Mann R, Karssemeijer N, Gubern-Mérida A.

Med Phys. 2017 Feb;44(2):533-546. doi: 10.1002/mp.12079.

PMID:
28035663
7.

Inter- and intra-observer agreement of BI-RADS-based subjective visual estimation of amount of fibroglandular breast tissue with magnetic resonance imaging: comparison to automated quantitative assessment.

Wengert GJ, Helbich TH, Woitek R, Kapetas P, Clauser P, Baltzer PA, Vogl WD, Weber M, Meyer-Baese A, Pinker K.

Eur Radiol. 2016 Nov;26(11):3917-3922. Epub 2016 Apr 23.

8.

Clinical applicability and relevance of fibroglandular tissue segmentation on routine T1 weighted breast MRI.

Pujara AC, Mikheev A, Rusinek H, Rallapalli H, Walczyk J, Gao Y, Chhor C, Pysarenko K, Babb JS, Melsaether AN.

Clin Imaging. 2017 Mar - Apr;42:119-125. doi: 10.1016/j.clinimag.2016.12.002. Epub 2016 Dec 6.

PMID:
27951458
9.

Quantitative Volumetric K-Means Cluster Segmentation of Fibroglandular Tissue and Skin in Breast MRI.

Niukkanen A, Arponen O, Nykänen A, Masarwah A, Sutela A, Liimatainen T, Vanninen R, Sudah M.

J Digit Imaging. 2018 Aug;31(4):425-434. doi: 10.1007/s10278-017-0031-1.

PMID:
29047034
10.

T1-weighted fat-suppressed imaging of the pelvis with a dual-echo Dixon technique: initial clinical experience.

Beddy P, Rangarajan RD, Kataoka M, Moyle P, Graves MJ, Sala E.

Radiology. 2011 Feb;258(2):583-9. doi: 10.1148/radiol.10100912. Epub 2010 Nov 15.

PMID:
21079201
11.

Automated fibroglandular tissue segmentation and volumetric density estimation in breast MRI using an atlas-aided fuzzy C-means method.

Wu S, Weinstein SP, Conant EF, Kontos D.

Med Phys. 2013 Dec;40(12):122302. doi: 10.1118/1.4829496.

12.

T1-weighted 3D dynamic contrast-enhanced MRI of the breast using a dual-echo Dixon technique at 3 T.

Dogan BE, Ma J, Hwang K, Liu P, Yang WT.

J Magn Reson Imaging. 2011 Oct;34(4):842-51. doi: 10.1002/jmri.22705. Epub 2011 Jul 18.

PMID:
21769987
13.

Fat-suppressed, three-dimensional T1-weighted imaging using high-acceleration parallel acquisition and a dual-echo Dixon technique for gadoxetic acid-enhanced liver MRI at 3 T.

Yoon JH, Lee JM, Yu MH, Kim EJ, Han JK, Choi BI.

Acta Radiol. 2015 Dec;56(12):1454-62. doi: 10.1177/0284185114561038. Epub 2014 Dec 5.

PMID:
25480475
14.

Comparison between gadolinium-enhanced 2D T1-weighted gradient-echo and spin-echo sequences in the detection of active multiple sclerosis lesions on 3.0T MRI.

Aymerich FX, Auger C, Alcaide-Leon P, Pareto D, Huerga E, Corral JF, Mitjana R, Sastre-Garriga J, Montalban X, Rovira A.

Eur Radiol. 2017 Apr;27(4):1361-1368. doi: 10.1007/s00330-016-4503-3. Epub 2016 Jul 25.

PMID:
27456965
15.

Mammographic density, MRI background parenchymal enhancement and breast cancer risk.

Pike MC, Pearce CL.

Ann Oncol. 2013 Nov;24 Suppl 8:viii37-viii41. doi: 10.1093/annonc/mdt310.

16.

The impact of bilateral salpingo-oophorectomy on breast MRI background parenchymal enhancement and fibroglandular tissue.

Price ER, Brooks JD, Watson EJ, Brennan SB, Comen EA, Morris EA.

Eur Radiol. 2014 Jan;24(1):162-8. doi: 10.1007/s00330-013-2993-9. Epub 2013 Aug 28.

PMID:
23982290
17.

Quantitative 3D breast magnetic resonance imaging fibroglandular tissue analysis and correlation with qualitative assessments: a feasibility study.

Ha R, Mema E, Guo X, Mango V, Desperito E, Ha J, Wynn R, Zhao B.

Quant Imaging Med Surg. 2016 Apr;6(2):144-50. doi: 10.21037/qims.2016.03.03.

18.

Feasibility of a three-step magnetic resonance imaging approach for the assessment of hepatic steatosis in an asymptomatic study population.

Hetterich H, Bayerl C, Peters A, Heier M, Linkohr B, Meisinger C, Auweter S, Kannengießer SA, Kramer H, Ertl-Wagner B, Bamberg F.

Eur Radiol. 2016 Jun;26(6):1895-904. doi: 10.1007/s00330-015-3966-y. Epub 2015 Sep 4.

PMID:
26340812
19.

The relationship of breast density in mammography and magnetic resonance imaging in high-risk women and women with breast cancer.

Albert M, Schnabel F, Chun J, Schwartz S, Lee J, Klautau Leite AP, Moy L.

Clin Imaging. 2015 Nov-Dec;39(6):987-92. doi: 10.1016/j.clinimag.2015.08.001. Epub 2015 Aug 6.

20.

Fat suppression techniques for breast MRI: Dixon versus spectral fat saturation for 3D T1-weighted at 3 T.

Kalovidouri A, Firmenich N, Delattre BMA, Picarra M, Becker CD, Montet X, Botsikas D.

Radiol Med. 2017 Oct;122(10):731-742. doi: 10.1007/s11547-017-0782-2. Epub 2017 Jun 22.

PMID:
28643295

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