Towards 3D Dynamic MRI of the Lung using Blind Compressed Sensing
Sajan Goud Lingala 1 , Yasir Baqqal 1 , John Newell 1 , Dingxin Wang 2 , Jessica C Sieren 1 , Daniel Thedens 1 , and Mathews Jacob 1
University of Iowa, Iowa city, Iowa, United
Medical Solutions, Minneapolis, MN, United States
3-D dynamic MRI of the lung is a promising tool to
assess lung function and mechanics. Compared to
multi-slice 2D-DMRI, 3-D acquisitions enables the
accurate estimation of lung volumes and its variations.
However, its full potential is not clinically realized
due to restricted spatio-temporal resolutions and volume
coverage. In this work, we propose to employ a blind
compressed sensing (BCS) scheme to overcome existing
trade-offs with 3D-DMRI. The BCS scheme exploits the
sparsity of the dynamic dataset in a dictionary of
temporal bases that are estimated from the measurements.
Since the bases are learnt from the data at hand, they
are more representative of the temporal variations
within the data, and are expected to provide sparser
representations than compressed sensing (CS) schemes
that utilize predetermined bases. In addition, it does
not require any assumptions on the breathing conditions.
Additionally, we propose to combine BCS with parallel
imaging and golden angle (GA) radial sampling; the
combination offers superior incoherence properties. With
the BCS scheme, we show feasibilities of imaging the
lung during normal breathing at spatial resolutions and
time resolutions of upto (2.37mm2, 0.72 sec) slice
coverage (16 slices, 4 mm thickness)
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