The objective of this study was to increase the spatial and temporal resolution of dynamic 3-dimensional (3D) magnetic resonance imaging (MRI) of lung volumes and diaphragm motion. To achieve this goal, we evaluate the utility of the proposed blind compressed sensing (BCS) algorithm to recover data from highly undersampled measurements.
We evaluated the performance of the BCS scheme to recover dynamic data sets from retrospectively and prospectively undersampled measurements. We also compared its performance against that of view-sharing, the nuclear norm minimization scheme, and the l 1 Fourier sparsity regularization scheme. Quantitative experiments were performed on a healthy subject using a fully sampled 2D data set with uniform radial sampling, which was retrospectively undersampled with 16 radial spokes per frame to correspond to an undersampling factor of 8. The images obtained from the 4 reconstruction schemes were compared with the fully sampled data using mean square error and normalized high-frequency error metrics. The schemes were also compared using prospective 3D data acquired on a Siemens 3 T TIM TRIO MRI scanner on 8 healthy subjects during free breathing. Two expert cardiothoracic radiologists (R1 and R2) qualitatively evaluated the reconstructed 3D data sets using a 5-point scale (0-4) on the basis of spatial resolution, temporal resolution, and presence of aliasing artifacts.
The BCS scheme gives better reconstructions (mean square error = 0.0232 and normalized high frequency = 0.133) than the other schemes in the 2D retrospective undersampling experiments, producing minimally distorted reconstructions up to an acceleration factor of 8 (16 radial spokes per frame). The prospective 3D experiments show that the BCS scheme provides visually improved reconstructions than the other schemes do. The BCS scheme provides improved qualitative scores over nuclear norm and l 1 Fourier sparsity regularization schemes in the temporal blurring and spatial blurring categories. The qualitative scores for aliasing artifacts in the images reconstructed by nuclear norm scheme and BCS scheme are comparable.
The comparisons of the tidal volume changes also show that the BCS scheme has less temporal blurring as compared with the nuclear norm minimization scheme and the l 1 Fourier sparsity regularization scheme. The minute ventilation estimated by BCS for tidal breathing in supine position (4 L/min) and the measured supine inspiratory capacity (1.5 L) is in good correlation with the literature. The improved performance of BCS can be explained by its ability to efficiently adapt to the data, thus providing a richer representation of the signal.
The feasibility of the BCS scheme was demonstrated for dynamic 3D free breathing MRI of lung volumes and diaphragm motion. A temporal resolution of ∼500 milliseconds, spatial resolution of 2.7 × 2.7 × 10 mm3, with whole lung coverage (16 slices) was achieved using the BCS scheme.
From the *Department of Electrical and Computer Engineering, The University of Iowa; †Department of Electrical Engineering, University of Southern California, Los Angeles; Departments of ‡Radiology and §Biomedical Engineering, The University of Iowa; and ∥Department of Radiology, University of Wisconsin School of Medicine and Public Health, Wisconsin.
Received for publication July 6, 2015; and accepted for publication, after revision, December 16, 2015.
Reprints: Sampada Bhave, MS, Department of Electrical and Computer Engineering, 4016 Seamans Center, University of Iowa, Iowa City, IA 52242. E-mail: firstname.lastname@example.org.
Conflicts of interest and sources of funding: Grant information: NSF CCF-1116067, ACS RSG-11-267-01-CCE, and ONR N00014-13-1-0202.
The authors report no conflicts of interest.