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Performance Comparison of Compressed Sensing Algorithms for Accelerating T 1ρ Mapping of Human Brain
Author(s) -
Me Rajiv G.,
Zibetti Marcelo V.W.,
Jain Rajan,
Ge Yulin,
Regatte Ravinder R.
Publication year - 2021
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.27421
Subject(s) - algorithm , compressed sensing , mathematics , image quality , iterative reconstruction , computer science , linear regression , pearson product moment correlation coefficient , artificial intelligence , pattern recognition (psychology) , statistics , image (mathematics)
Background 3D‐T 1ρ mapping is useful to quantify various neurologic disorders, but data are currently time‐consuming to acquire. Purpose To compare the performance of five compressed sensing (CS) algorithms—spatiotemporal finite differences (STFD), exponential dictionary (EXP), 3D‐wavelet transform (WAV), low‐rank (LOW) and low‐rank plus sparse model with spatial finite differences (L + S SFD)—for 3D‐T 1ρ mapping of the human brain with acceleration factors (AFs) of 2, 5, and 10. Study Type Retrospective. Subjects Eight healthy volunteers underwent T 1ρ imaging of the whole brain. Field Strength/Sequence The sequence was fully sampled 3D Cartesian ultrafast gradient echo sequence with a customized T 1ρ preparation module on a clinical 3T scanner. Assessment The fully sampled data was undersampled by factors of 2, 5, and 10 and reconstructed with the five CS algorithms. Image reconstruction quality was evaluated and compared to the SENSE reconstruction of the fully sampled data (reference) and T 1ρ estimation errors were assessed as a function of AF. Statistical Tests Normalized root mean squared errors (nRMSE) and median normalized absolute deviation (MNAD) errors were calculated to compare image reconstruction errors and T 1ρ estimation errors, respectively. Linear regression plots, Bland–Altman plots, and Pearson correlation coefficients (CC) are shown. Results For image reconstruction quality, at AF = 2, EXP transforms had the lowest mRMSE (1.56%). At higher AF values, STFD performed better, with the smallest errors (3.16% at AF = 5, 4.32% at AF = 10). For whole‐brain quantitative T 1ρ mapping, at AF = 2, EXP performed best (MNAD error = 1.62%). At higher AF values (AF = 5, 10), the STFD technique had the least errors (2.96% at AF = 5, 4.24% at AF = 10) and the smallest variance from the reference T 1ρ estimates. Data Conclusion This study demonstrates the use of different CS algorithms that may be useful in reducing the scan time required to perform volumetric T 1ρ mapping of the brain. Level of Evidence 2. Technical Efficacy Stage 1.