4.5 Article

Comparison of data-driven and general temporal constraints on compressed sensing for breast DCE MRI

期刊

MAGNETIC RESONANCE IN MEDICINE
卷 85, 期 6, 页码 3071-3084

出版社

WILEY
DOI: 10.1002/mrm.28628

关键词

breast; compressed sensing; DCE MRI; dynamic imaging

资金

  1. National Institutes of Health [R21EB018483, R01EB027087, P30CA014520]
  2. GE Healthcare
  3. Research and Development Grant from the Departments of Radiology and Medical Physics, University of Wisconsin

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The MOCCO reconstruction was able to capture rapid lesion kinetics and provide high image quality across a range of optimal regularization values. In comparison, the CS-TV method demonstrated either high spatial resolution with reduced temporal accuracy at large regularization values or recovery of rapid lesion kinetics with reduced image quality at low regularization values. Simulations showed that MOCCO with radial acquisition is a robust imaging technique for improving temporal fidelity while maintaining high spatial resolution and image quality in bilateral breast DCE MRI.
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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