Reconstruction of dynamic contrast enhanced magnetic resonance imaging of the breast with temporal constraints.
A number of methods using temporal and spatial constraints have been proposed for reconstruction of undersampled dynamic magnetic resonance imaging (MRI) data. The complex data can be constrained or regularized in a number of different ways, for example, the time derivative of the magnitude and phase image voxels can be constrained separately or jointly. Intuitively, the performance of different regularizations will depend on both the data and the chosen temporal constraints. Here, a complex temporal total variation (TV) constraint was compared to the use of separate real and imaginary constraints, and to a magnitude constraint alone. Projection onto Convex Sets (POCS) with a gradient descent method was used to implement the diverse temporal constraints in reconstructions of DCE MRI data. For breast DCE data, serial POCS with separate real and imaginary TV constraints was found to give relatively poor results while serial/parallel POCS with a complex temporal TV constraint and serial POCS with a magnitude-only temporal TV constraint performed well with an acceleration factor as large as R=6. In the tumor area, the best method was found to be parallel POCS with complex temporal TV constraint. This method resulted in estimates for the pharmacokinetic parameters that were linearly correlated to those estimated from the fully-sampled data, with K(trans,R=6)=0.97 K(trans,R=1)+0.00 with correlation coefficient r=0.98, k(ep,R=6)=0.95 k(ep,R=1)+0.00 (r=0.85). These results suggest that it is possible to acquire highly undersampled breast DCE-MRI data with improved spatial and/or temporal resolution with minimal loss of image quality.
Chen
Liyong
L
Department of Bioengineering, University of Utah, Salt Lake City, UT 84108, USA.
Schabel
Matthias C
MC
DiBella
Edward V R
EV
D000465:Algorithms; D001940:Breast / Q000473:pathology*; D001943:Breast Neoplasms / Q000175:diagnosis*; D003287:Contrast Media; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
D003287:Contrast Media
Magn Reson Med. 2008 Feb;59(2):365-73
18228595
IEEE Trans Med Imaging. 1991;10(4):548-53
18222860
Magn Reson Med. 2004 Mar;51(3):559-67
15004798
Magn Reson Med. 2009 Jan;61(1):103-16
19097216
Med Phys. 2008 Feb;35(2):660-3
18383687
J Magn Reson Imaging. 1993 Nov-Dec;3(6):925-8
8280985
Magn Reson Med. 2008 Mar;59(3):463-8
18306398
Magn Reson Med. 2006 Jan;55(1):30-40
16342275
IEEE Trans Med Imaging. 2008 Apr;27(4):538-47
18390350
Conf Proc IEEE Eng Med Biol Soc. 2005;2005:1434-7
17282469
Magn Reson Med. 1999 Nov;42(5):813-28
10542340
Magn Reson Med. 2004 Dec;52(6):1397-406
15562485
Magn Reson Med. 2003 Nov;50(5):1031-42
14587014
J Magn Reson Imaging. 1999 Sep;10(3):223-32
10508281
J Magn Reson Imaging. 1997 Jul-Aug;7(4):716-23
9243393
Magn Reson Med. 1993 Jul;30(1):51-9
8371675
Magn Reson Med. 1995 Apr;33(4):506-14
7776881
Magn Reson Med. 2007 Jun;57(6):1027-36
17534924
Magn Reson Med. 2006 Nov;56(5):993-1000
17036301
Radiology. 1994 Dec;193(3):777-81
7972823
J Magn Reson Imaging. 1993 Jul-Aug;3(4):671-5
8347963
Magn Reson Imaging Clin N Am. 2009 May;17(2):351-62
19406363
Magn Reson Med. 2004 Apr;51(4):858-62
15065262