Literature DB >> 26794935

MR Fingerprinting for Rapid Quantitative Abdominal Imaging.

Yong Chen1, Yun Jiang1, Shivani Pahwa1, Dan Ma1, Lan Lu1, Michael D Twieg1, Katherine L Wright1, Nicole Seiberlich1, Mark A Griswold1, Vikas Gulani1.   

Abstract

PURPOSE: To develop a magnetic resonance (MR) "fingerprinting" technique for quantitative abdominal imaging.
MATERIALS AND METHODS: This HIPAA-compliant study had institutional review board approval, and informed consent was obtained from all subjects. To achieve accurate quantification in the presence of marked B0 and B1 field inhomogeneities, the MR fingerprinting framework was extended by using a two-dimensional fast imaging with steady-state free precession, or FISP, acquisition and a Bloch-Siegert B1 mapping method. The accuracy of the proposed technique was validated by using agarose phantoms. Quantitative measurements were performed in eight asymptomatic subjects and in six patients with 20 focal liver lesions. A two-tailed Student t test was used to compare the T1 and T2 results in metastatic adenocarcinoma with those in surrounding liver parenchyma and healthy subjects.
RESULTS: Phantom experiments showed good agreement with standard methods in T1 and T2 after B1 correction. In vivo studies demonstrated that quantitative T1, T2, and B1 maps can be acquired within a breath hold of approximately 19 seconds. T1 and T2 measurements were compatible with those in the literature. Representative values included the following: liver, 745 msec ± 65 (standard deviation) and 31 msec ± 6; renal medulla, 1702 msec ± 205 and 60 msec ± 21; renal cortex, 1314 msec ± 77 and 47 msec ± 10; spleen, 1232 msec ± 92 and 60 msec ± 19; skeletal muscle, 1100 msec ± 59 and 44 msec ± 9; and fat, 253 msec ± 42 and 77 msec ± 16, respectively. T1 and T2 in metastatic adenocarcinoma were 1673 msec ± 331 and 43 msec ± 13, respectively, significantly different from surrounding liver parenchyma relaxation times of 840 msec ± 113 and 28 msec ± 3 (P < .0001 and P < .01) and those in hepatic parenchyma in healthy volunteers (745 msec ± 65 and 31 msec ± 6, P < .0001 and P = .021, respectively).
CONCLUSION: A rapid technique for quantitative abdominal imaging was developed that allows simultaneous quantification of multiple tissue properties within one 19-second breath hold, with measurements comparable to those in published literature.

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Year:  2016        PMID: 26794935      PMCID: PMC4819902          DOI: 10.1148/radiol.2016152037

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  26 in total

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2.  MR fingerprinting using fast imaging with steady state precession (FISP) with spiral readout.

Authors:  Yun Jiang; Dan Ma; Nicole Seiberlich; Vikas Gulani; Mark A Griswold
Journal:  Magn Reson Med       Date:  2014-12-09       Impact factor: 4.668

3.  Accelerating magnetic resonance fingerprinting (MRF) using t-blipped simultaneous multislice (SMS) acquisition.

Authors:  Huihui Ye; Dan Ma; Yun Jiang; Stephen F Cauley; Yiping Du; Lawrence L Wald; Mark A Griswold; Kawin Setsompop
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4.  Quantitative evaluation of liver cirrhosis using T1 relaxation time with 3 tesla MRI before and after oxygen inhalation.

Authors:  Kyung Ah Kim; Mi-Suk Park; In-Seong Kim; Berthold Kiefer; Woo-Suk Chung; Myeong-Jin Kim; Ki Whang Kim
Journal:  J Magn Reson Imaging       Date:  2012-03-05       Impact factor: 4.813

5.  Transmit B1+ field inhomogeneity and T1 estimation errors in breast DCE-MRI at 3 tesla.

Authors:  Kyunghyun Sung; Bruce L Daniel; Brian A Hargreaves
Journal:  J Magn Reson Imaging       Date:  2013-01-04       Impact factor: 4.813

6.  Rapid volumetric T1 mapping of the abdomen using three-dimensional through-time spiral GRAPPA.

Authors:  Yong Chen; Gregory R Lee; Gunhild Aandal; Chaitra Badve; Katherine L Wright; Mark A Griswold; Nicole Seiberlich; Vikas Gulani
Journal:  Magn Reson Med       Date:  2015-05-18       Impact factor: 4.668

7.  MR vascular fingerprinting: A new approach to compute cerebral blood volume, mean vessel radius, and oxygenation maps in the human brain.

Authors:  T Christen; N A Pannetier; W W Ni; D Qiu; M E Moseley; N Schuff; G Zaharchuk
Journal:  Neuroimage       Date:  2013-12-07       Impact factor: 6.556

8.  Magnetic Resonance Fingerprinting - a promising new approach to obtain standardized imaging biomarkers from MRI.

Authors: 
Journal:  Insights Imaging       Date:  2015-03-24

9.  Magnetic resonance fingerprinting.

Authors:  Dan Ma; Vikas Gulani; Nicole Seiberlich; Kecheng Liu; Jeffrey L Sunshine; Jeffrey L Duerk; Mark A Griswold
Journal:  Nature       Date:  2013-03-14       Impact factor: 49.962

10.  Tumour T1 changes in vivo are highly predictive of response to chemotherapy and reflect the number of viable tumour cells--a preclinical MR study in mice.

Authors:  Claudia Weidensteiner; Peter R Allegrini; Melanie Sticker-Jantscheff; Vincent Romanet; Stephane Ferretti; Paul M J McSheehy
Journal:  BMC Cancer       Date:  2014-02-14       Impact factor: 4.430

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  65 in total

1.  Radiomic biomarkers informative of cancerous transformation in neurofibromatosis-1 plexiform tumors.

Authors:  J Uthoff; F A De Stefano; K Panzer; B W Darbro; T S Sato; R Khanna; D E Quelle; D K Meyerholz; J Weimer; J C Sieren
Journal:  J Neuroradiol       Date:  2018-06-27       Impact factor: 3.447

Review 2.  Magnetic resonance fingerprinting: an overview.

Authors:  Charit Tippareddy; Walter Zhao; Jeffrey L Sunshine; Mark Griswold; Dan Ma; Chaitra Badve
Journal:  Eur J Nucl Med Mol Imaging       Date:  2021-05-26       Impact factor: 9.236

3.  Three-dimensional MR Fingerprinting for Quantitative Breast Imaging.

Authors:  Yong Chen; Ananya Panda; Shivani Pahwa; Jesse I Hamilton; Sara Dastmalchian; Debra F McGivney; Dan Ma; Joshua Batesole; Nicole Seiberlich; Mark A Griswold; Donna Plecha; Vikas Gulani
Journal:  Radiology       Date:  2018-10-30       Impact factor: 11.105

4.  Magnetic resonance fingerprinting with quadratic RF phase for measurement of T2 * simultaneously with δf , T1 , and T2.

Authors:  Charlie Yi Wang; Simone Coppo; Bhairav Bipin Mehta; Nicole Seiberlich; Xin Yu; Mark Alan Griswold
Journal:  Magn Reson Med       Date:  2018-10-30       Impact factor: 4.668

5.  Deep Learning for Fast and Spatially Constrained Tissue Quantification From Highly Accelerated Data in Magnetic Resonance Fingerprinting.

Authors:  Zhenghan Fang; Yong Chen; Mingxia Liu; Lei Xiang; Qian Zhang; Qian Wang; Weili Lin; Dinggang Shen
Journal:  IEEE Trans Med Imaging       Date:  2019-02-13       Impact factor: 10.048

6.  Slice profile and B1 corrections in 2D magnetic resonance fingerprinting.

Authors:  Dan Ma; Simone Coppo; Yong Chen; Debra F McGivney; Yun Jiang; Shivani Pahwa; Vikas Gulani; Mark A Griswold
Journal:  Magn Reson Med       Date:  2017-01-11       Impact factor: 4.668

7.  Magnetic resonance fingerprinting of the pancreas at 1.5 T and 3.0 T.

Authors:  Eva M Serrao; Dimitri A Kessler; Bruno Carmo; Lucian Beer; Kevin M Brindle; Guido Buonincontri; Ferdia A Gallagher; Fiona J Gilbert; Edmund Godfrey; Martin J Graves; Mary A McLean; Evis Sala; Rolf F Schulte; Joshua D Kaggie
Journal:  Sci Rep       Date:  2020-10-16       Impact factor: 4.379

8.  31 P magnetic resonance fingerprinting for rapid quantification of creatine kinase reaction rate in vivo.

Authors:  Charlie Y Wang; Yuchi Liu; Shuying Huang; Mark A Griswold; Nicole Seiberlich; Xin Yu
Journal:  NMR Biomed       Date:  2017-09-15       Impact factor: 4.044

9.  Repeatability of magnetic resonance fingerprinting T1 and T2 estimates assessed using the ISMRM/NIST MRI system phantom.

Authors:  Yun Jiang; Dan Ma; Kathryn E Keenan; Karl F Stupic; Vikas Gulani; Mark A Griswold
Journal:  Magn Reson Med       Date:  2016-10-27       Impact factor: 4.668

10.  Multi-frequency interpolation in spiral magnetic resonance fingerprinting for correction of off-resonance blurring.

Authors:  Jason Ostenson; Ryan K Robison; Nicholas R Zwart; E Brian Welch
Journal:  Magn Reson Imaging       Date:  2017-07-08       Impact factor: 2.546

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