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

Ping N Wang; Julia V Velikina; Roberta M Strigel; Leah C Henze Bancroft; Alexey A Samsonov; Ty A Cashen; Kang Wang; Frederick Kelcz; Kevin M Johnson; Frank R Korosec; Ali Ersoz; James H Holmes

doi:10.1002/mrm.28628

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

Journal article

Peer reviewed

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

Ping N Wang, Julia V Velikina, Roberta M Strigel, Leah C Henze Bancroft, Alexey A Samsonov, Ty A Cashen, Kang Wang, Frederick Kelcz, Kevin M Johnson, Frank R Korosec, …

Magnetic resonance in medicine, Vol.85(6), pp.3071-3084

06/2021

DOI: 10.1002/mrm.28628

PMCID: PMC11542549

PMID: 33306217

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Abstract

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. 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. 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. 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.

Magnetic Resonance Imaging

Artifacts

Breast - diagnostic imaging

Contrast Media

Image Interpretation, Computer-Assisted

Details

Title: Subtitle: Comparison of data-driven and general temporal constraints on compressed sensing for breast DCE MRI
Creators: 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 - 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 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 - University of Iowa, Radiology
Resource Type: Journal article
Publication Details: Magnetic resonance in medicine, Vol.85(6), pp.3071-3084
DOI: 10.1002/mrm.28628
PMID: 33306217
PMCID: PMC11542549
NLM abbreviation: Magn Reson Med
ISSN: 0740-3194
eISSN: 1522-2594
Grant note: R01 EB027087 / NIBIB NIH HHS R21 EB018483 / NIBIB NIH HHS P30 CA014520 / NCI NIH HHS
Language: English
Date published: 06/2021
Academic Unit: Radiology
Record Identifier: 9984119796802771

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