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Wang PN, Velikina JV, Strigel RM, Henze Bancroft LC, Samsonov AA, Cashen TA, Wang K, Kelcz F, Johnson KM, Korosec FR, Ersoz A, Holmes JH. Comparison of data-driven and general temporal constraints on compressed sensing for breast DCE MRI. Magn Reson Med 2021; 85:3071-3084. [PMID: 33306217 DOI: 10.1002/mrm.28628] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 11/10/2020] [Accepted: 11/11/2020] [Indexed: 12/20/2022]
Abstract
PURPOSE Current breast DCE-MRI strategies provide high sensitivity for cancer detection but are known to be insufficient in fully capturing rapidly changing contrast kinetics at high spatial resolution across both breasts. Advanced acquisition and reconstruction strategies aim to improve spatial and temporal resolution and increase specificity for disease characterization. In this work, we evaluate the spatial and temporal fidelity of a modified data-driven low-rank-based model (known as MOCCO, model consistency condition) compressed-sensing (CS) reconstruction compared to CS with temporal total variation with radial acquisition for high spatial-temporal breast DCE MRI. METHODS Reconstruction performance was characterized using numerical simulations of a golden-angle stack-of-stars breast DCE-MRI acquisition at 5-second temporal resolution. Specifically, MOCCO was compared with CS total variation and conventional SENSE reconstructions. The temporal model for MOCCO was prelearned over the source data, whereas CS total variation was performed using a first-order temporal gradient sparsity transform. RESULTS The MOCCO reconstruction was able to capture rapid lesion kinetics while providing high image quality across a range of optimal regularization values. It also recovered kinetics in small lesions (1.5 mm) in line-profile analysis and error images, whereas g-factor maps showed relatively low and constant values with no significant artifacts. The CS-TV method demonstrated either recovery of high spatial resolution with reduced temporal accuracy using large regularization values, or recovery of rapid lesion kinetics with reduced image quality using low regularization values. CONCLUSION Simulations demonstrated that MOCCO with radial acquisition provides a robust imaging technique for improving temporal fidelity, while maintaining high spatial resolution and image quality in the setting of bilateral breast DCE MRI.
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Affiliation(s)
- Ping N Wang
- Department of Medical Physics, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Julia V Velikina
- Department of Medical Physics, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Roberta M Strigel
- Department of Medical Physics, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
- Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
- Carbone Cancer Center, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Leah C Henze Bancroft
- Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Alexey A Samsonov
- Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Ty A Cashen
- Global MR Applications & Workflow, GE Healthcare, Madison, Wisconsin, USA
| | - Kang Wang
- Global MR Applications & Workflow, GE Healthcare, Madison, Wisconsin, USA
| | - Frederick Kelcz
- Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Kevin M Johnson
- Department of Medical Physics, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
- Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Frank R Korosec
- Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Ali Ersoz
- MR Engineering, GE Healthcare, Waukesha, Wisconsin, USA
| | - James H Holmes
- Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
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