Name
Title | ||
|---|---|---|
Robustness of Quantitative Compressive Sensing MRI: The Effect of Random Undersampling Patterns on Derived Parameters for DCE- and DSC-MRI. |
Abstract | ||
|---|---|---|
Compressive sensing (CS) in Cartesian magnetic resonance imaging (MRI) involves random partial Fourier acquisitions. The random nature of these acquisitions can lead to variance in reconstruction errors. In quantitative MRI, variance in the reconstructed images translates to an uncertainty in the derived quantitative maps. We show that for a spatially regularized 2 ×-accelerated human breast CS DCE-MRI acquisition with a 1922 matrix size, the coefficients of variation (CoVs) in voxel-level parameters due to the random acquisition are 1.1%, 0.96%, and 1.5% for the tissue parameters Ktrans, ve, and vp, with an average error in the mean of -2.5%, -2.0%, and -3.7%, respectively. Only 5% of the acquisition schemes had a systematic underestimation larger than than 4.2%, 3.7%, and 6.1%, respectively. For a 2× -accelerated rat brain CS DSC-MRI study with a 642 matrix size, the CoVs due to the random acquisition were 19%, 9.5%, and 15% for the cerebral blood flow and blood volume and mean transit time, respectively, and the average errors in the tumor mean were 9.2%, 0.49%, and -7.0%, respectively. Across 11000 different CS reconstructions, we saw no outliers in the distribution of parameters, suggesting that, despite the random undersampling schemes, CS accelerated quantitative MRI may have a predictable level of performance. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/TMI.2011.2172216 | IEEE Trans. Med. Imaging |
Keywords | Field | DocType |
cerebral blood flow,random variable,image reconstruction,acceleration,nonuniform sampling,haemodynamics,histograms,blood volume,compressed sensing,coefficient of variation,magnetic resonance imaging,magnetic resonance image,diagnostic imaging,random variables,biomedical imaging | Iterative reconstruction,Computer vision,Random variable,Medical imaging,Outlier,Undersampling,Cerebral blood flow,Artificial intelligence,Nuclear magnetic resonance,Sample size determination,Mathematics,Magnetic resonance imaging | Journal |
Volume | Issue | ISSN |
31 | 2 | 0278-0062 |
Citations | PageRank | References |
6 | 0.72 | 4 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| David S. Smith | 1 | 50 | 5.85 |
| Xia Li | 2 | 6 | 1.39 |
| James V. Gambrell | 3 | 6 | 0.72 |
| Lori R. Arlinghaus | 4 | 11 | 3.35 |
| C. Chad Quarles | 5 | 6 | 0.72 |
| Thomas E. Yankeelov | 6 | 20 | 7.14 |
| E. Brian Welch | 7 | 45 | 16.66 |