Comparison of Algorithms to Compute Relaxation Time Maps in Magnetic Resonance Imaging

Authors:
Open Access
Publisher website
Google Scholar
Abstract
Magnetic resonance imaging (MRI) is a valuable diagnostic tool that provides detailed information about the structure and function of tissues in the human body. In particular, measuring relaxation times, such as T1 and T2, can provide important insights into the composition and properties of different tissues. Accurate relaxation time mapping is therefore critical for clinical diagnosis and treatment planning, as it can help to identify and characterize pathological conditions, monitor disease progression, and guide interventions. However, the computation of relaxation time maps in MRI is a complex and challenging task that requires sophisticated mathematical algorithms. Thus, there is a need for robust and accurate algorithms that can reliably extract the desired information from MRI data. This article compares the performance of the Reduced Dimension Nonlinear Least Squares (RD-NLS) algorithm versus several widely used algorithms to compute relaxation times in MRI, such as Levenberg-Marquardt and Nelder-Mead. RD-NLS simplifies the search space for the optimum fit by leveraging the partial linear relationship between signal intensity and model parameters. The comparison was performed on several datasets and signal models, resulting in T1 and T2 maps. The algorithms were evaluated based on their fit error, with the RD-NLS algorithm showing a lower error than other fit-ting algorithms. The improvement was particularly notable in T1 maps, with less of a difference in T2 maps. Additionally, the average T1 values computed with different algorithms differed by up to 14 ms, indicating the importance of algorithm selection. These results suggest that the RD-NLS algorithm outperforms other commonly used algorithms for computing relaxation times in MRI.
Funding Information
  • Consejería de Sanidad, Comunidad de Madrid (S2017/BMD-3875, PID2019-10656RJ-I00, PID2021-123238OB-I00, PDC2021-121696-I00, 823854, HR20-00075)
  • Ministerio de Economía y Competitividad (S2017/BMD-3875, PID2019-10656RJ-I00, PID2021-123238OB-I00, PDC2021-121696-I00, 823854, HR20-00075)
  • European Union’s Horizon 2020 Research and Innovation Program (S2017/BMD-3875, PID2019-10656RJ-I00, PID2021-123238OB-I00, PDC2021-121696-I00, 823854, HR20-00075)
  • BBVA Foundation (Ayudas a Equipos de investigación científica Biomedicina 2018) (S2017/BMD-3875, PID2019-10656RJ-I00, PID2021-123238OB-I00, PDC2021-121696-I00, 823854, HR20-00075)
  • LaCaixa Foundation (S2017/BMD-3875, PID2019-10656RJ-I00, PID2021-123238OB-I00, PDC2021-121696-I00, 823854, HR20-00075)
  • Fundación Contra la Hipertensión Pulmonar (S2017/BMD-3875, PID2019-10656RJ-I00, PID2021-123238OB-I00, PDC2021-121696-I00, 823854, HR20-00075)

This publication has 28 references indexed in Scilit: