A vectorized Levenberg-Marquardt model fitting algorithm for efficient post-processing of cardiac T 1 mapping MRI

• Published in: Computers in biology and medicine Vol. 96; pp. 106 - 115
• Main Authors: Liu, Shufang, Bustin, Aurelien, Ferry, Pauline, Codreanu, Andrei, Burschka, Darius, Menini, Anne, Odille, Freddy
• Format: Journal Article
• Language: English
• Published: United States Elsevier Limited 01.05.2018
• Subjects: Algorithms; Cardiac Imaging Techniques - methods; Contrast agents; Curve fitting; Data acquisition; Data recovery; Heart; Heart - diagnostic imaging; Heart diseases; Humans; Image acquisition; Image Interpretation, Computer-Assisted - methods; Internal Medicine; Investigations; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Mapping; Mathematical models; NMR; Noise; Noise reduction; Nuclear magnetic resonance; Other; Parameter estimation; Parameters; Post-production processing; Registration; Regularization; Robustness; Sequences; T 1 mapping; MR parameter mapping; Relaxometry; Curve fitting; Cardiac MR; T mapping
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2018.03.009

AbstractPurposeT 1 mapping is an emerging MRI research tool to assess diseased myocardial tissue. Recent research has been focusing on the image acquisition protocol and motion correction, yet little attention has been paid to the curve fitting algorithm. MethodsAfter nonrigid registration of the image series, a vectorized Levenberg-Marquardt (LM) technique is proposed to improve the robustness of the curve fitting algorithm by allowing spatial regularization of the parametric maps. In addition, a region-based initialization is proposed to improve the initial guess of the T 1 value. The algorithm was validated with cardiac T 1 mapping data from 16 volunteers acquired with saturation-recovery (SR) and inversion-recovery (IR) techniques at 3T, both pre- and post-injection of a contrast agent. Signal models of T 1 relaxation with 2 and 3 parameters were tested. ResultsThe vectorized LM fitting showed good agreement with its pixel-wise version but allowed reduced calculation time (60 s against 696 s on average in Matlab with 256 × 256 × 8(11) images). Increasing the spatial regularization parameter led to noise reduction and improved precision of T 1 values in SR sequences. The region-based initialization was particularly useful in IR data to reduce the variability of the blood T 1. ConclusionsWe have proposed a vectorized curve fitting algorithm allowing spatial regularization, which could improve the robustness of the curve fitting, especially for myocardial T 1 mapping with SR sequences.