Next Article in Journal
Imaging Neurodegenerative Metabolism in Amyotrophic Lateral Sclerosis with Hyperpolarized [1-13C]pyruvate MRI
Previous Article in Journal
Magnetic Susceptibility Source Separation Solely from Gradient Echo Data: Histological Validation
Article

The Influence of Data-Driven Compressed Sensing Reconstruction on Quantitative Pharmacokinetic Analysis in Breast DCE MRI

1
Department of Medical Physics, University of Wisconsin-Madison, 1111 Highland Avenue, Madison, WI 53705, USA
2
Department of Radiology, University of Wisconsin-Madison, 600 Highland Avenue, Madison, WI 53792, USA
3
Carbone Cancer Center, University of Wisconsin-Madison, 600 Highland Avenue, Madison, WI 53792, USA
4
Department of Radiology, University of Iowa, 169 Newton Road, Iowa City, IA 52333, USA
5
Holden Comprehensive Cancer Center, University of Iowa, 169 Newton Road, Iowa City, IA 52333, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Emilio Quaia
Tomography 2022, 8(3), 1552-1569; https://doi.org/10.3390/tomography8030128
Received: 14 May 2022 / Revised: 7 June 2022 / Accepted: 9 June 2022 / Published: 14 June 2022
(This article belongs to the Section Cancer Imaging)
Radial acquisition with MOCCO reconstruction has been previously proposed for high spatial and temporal resolution breast DCE imaging. In this work, we characterize MOCCO across a wide range of temporal contrast enhancement in a digital reference object (DRO). Time-resolved radial data was simulated using a DRO with lesions in different PK parameters. The under sampled data were reconstructed at 5 s temporal resolution using the data-driven low-rank temporal model for MOCCO, compressed sensing with temporal total variation (CS-TV) and more conventional low-rank reconstruction (PCB). Our results demonstrated that MOCCO was able to recover curves with Ktrans values ranging from 0.01 to 0.8 min−1 and fixed Ve = 0.3, where the fitted results are within a 10% bias error range. MOCCO reconstruction showed less impact on the selection of different temporal models than conventional low-rank reconstruction and the greater error was observed with PCB. CS-TV showed overall underestimation in both Ktrans and Ve. For the Monte-Carlo simulations, MOCCO was found to provide the most accurate reconstruction results for curves with intermediate lesion kinetics in the presence of noise. Initial in vivo experiences are reported in one patient volunteer. Overall, MOCCO was able to provide reconstructed time-series data that resulted in a more accurate measurement of PK parameters than PCB and CS-TV. View Full-Text
Keywords: breast DCE-MRI; compressed sensing; quantitative imaging breast DCE-MRI; compressed sensing; quantitative imaging
Show Figures

Figure 1

MDPI and ACS Style

Wang, P.N.; Velikina, J.V.; Bancroft, L.C.H.; Samsonov, A.A.; Kelcz, F.; Strigel, R.M.; Holmes, J.H. The Influence of Data-Driven Compressed Sensing Reconstruction on Quantitative Pharmacokinetic Analysis in Breast DCE MRI. Tomography 2022, 8, 1552-1569. https://doi.org/10.3390/tomography8030128

AMA Style

Wang PN, Velikina JV, Bancroft LCH, Samsonov AA, Kelcz F, Strigel RM, Holmes JH. The Influence of Data-Driven Compressed Sensing Reconstruction on Quantitative Pharmacokinetic Analysis in Breast DCE MRI. Tomography. 2022; 8(3):1552-1569. https://doi.org/10.3390/tomography8030128

Chicago/Turabian Style

Wang, Ping N., Julia V. Velikina, Leah C.H. Bancroft, Alexey A. Samsonov, Frederick Kelcz, Roberta M. Strigel, and James H. Holmes. 2022. "The Influence of Data-Driven Compressed Sensing Reconstruction on Quantitative Pharmacokinetic Analysis in Breast DCE MRI" Tomography 8, no. 3: 1552-1569. https://doi.org/10.3390/tomography8030128

Find Other Styles

Article Access Map by Country/Region

1
Back to TopTop