1
|
Afrough A, Mokhtari R, Feilberg KL. Simple MATLAB and Python scripts for multi-exponential analysis. MAGNETIC RESONANCE IN CHEMISTRY : MRC 2024. [PMID: 38813596 DOI: 10.1002/mrc.5453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 03/21/2024] [Accepted: 04/22/2024] [Indexed: 05/31/2024]
Abstract
Multi-exponential decay is prevalent in magnetic resonance spectroscopy, relaxation, and imaging. This paper describes simple MATLAB and Python functions and scripts for regularized multi-exponential analysis methods for 1D and 2D data and example test problems and experiments. Regularized least-squares solutions provide production-quality outputs with robust stopping rules in ~5 and ~20 lines of code for 1D and 2D inversions, respectively. The software provides an open-architecture simple solution for transforming exponential decay data to the distribution of their decay lifetimes. Examples from magnetic resonance relaxation of a complex fluid, a Danish North Sea crude oil, and fluid mixtures in porous materials-brine/crude oil mixture in North Sea reservoir chalk-are presented. Developed codes may be incorporated in other software or directly used by other researchers, in magnetic resonance relaxation, diffusion, and imaging or other physical phenomena that require multi-exponential analysis.
Collapse
Affiliation(s)
- Armin Afrough
- Danish Offshore Technology Centre, Technical University of Denmark, Kongens Lyngby, Denmark
- Interdisciplinary Nanoscience Center, Aarhus University, Aarhus, Denmark
| | - Rasoul Mokhtari
- Danish Offshore Technology Centre, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Karen L Feilberg
- Danish Offshore Technology Centre, Technical University of Denmark, Kongens Lyngby, Denmark
| |
Collapse
|
2
|
Palmin V, Mukhin A, Ivanova V, Perepukhov A, Nozik A. Automated component analysis in DOSY NMR using information criteria. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2023; 355:107541. [PMID: 37688831 DOI: 10.1016/j.jmr.2023.107541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 08/04/2023] [Accepted: 08/19/2023] [Indexed: 09/11/2023]
Abstract
This study introduces a model selection technique based on Bayesian information criteria for estimating the number of components in a mixture during Diffusion-Ordered Spectroscopy (DOSY) Nuclear Magnetic Resonance (NMR) data analysis. As the accuracy of this technique is dependent on the efficiency of parameter estimators, we further investigate the performance of the Weighted Least Squares (WLS) and Maximum a Posteriori (MAP) estimators. The WLS method, enhanced with meticulously tuned L2-regularization, effectively detects components when the difference in self-diffusion coefficients is more than two-fold, especially when the component with the smaller coefficient has a larger weight ratio. The MAP method, strengthened by a substantial database of prior information, exhibits outstanding precision, decreasing this threshold to 1.5 times. Both estimators provide weight ratio estimates with standard deviations of approximately around 1 percentage point, although the MAP method tends to overestimate the component with a larger self-diffusion coefficient. Deviations from the expected values can exceed 10 percentage points, often due to inaccuracies in component detection. The error estimates are determined using data resampling techniques derived from a large-scale 1000-point experiment and an additional five measurements from a single-component mixture. This approach allowed us to thoroughly examine data distribution characteristics, thereby laying a robust groundwork for future refinement efforts.
Collapse
Affiliation(s)
- Vladimir Palmin
- Moscow Institute of Physics and Technology (National Research University) - MIPT, 1 "A" Kerchenskaya st., Moscow, 117303, Russia.
| | - Andrey Mukhin
- Moscow Institute of Physics and Technology (National Research University) - MIPT, 1 "A" Kerchenskaya st., Moscow, 117303, Russia
| | - Valeriya Ivanova
- Moscow Institute of Physics and Technology (National Research University) - MIPT, 1 "A" Kerchenskaya st., Moscow, 117303, Russia
| | - Alexander Perepukhov
- Moscow Institute of Physics and Technology (National Research University) - MIPT, 1 "A" Kerchenskaya st., Moscow, 117303, Russia
| | - Alexander Nozik
- Moscow Institute of Physics and Technology (National Research University) - MIPT, 1 "A" Kerchenskaya st., Moscow, 117303, Russia
| |
Collapse
|
3
|
Williams J, Zheng Q, Sederman AJ, Mantle MD, Baart T, Guédon C, Gladden LF. In Situ Determination of Carbon Number Distributions of Mixtures of Linear Hydrocarbons Confined within Porous Media Using Pulsed Field Gradient NMR. Anal Chem 2020; 92:5125-5133. [PMID: 32142268 DOI: 10.1021/acs.analchem.9b05600] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Pulsed field gradient (PFG) NMR measurements, combined with a novel optimization method, are used to determine the composition of hydrocarbon mixtures of linear alkanes (C7-C16) in both the bulk liquid state and when imbibed within a porous medium of mean pore diameter 28.6 nm. The method predicts the average carbon number of a given mixture to an accuracy of ±1 carbon number and the mole fraction of a mixture component to within an average root-mean-square error of ±0.036 with just three calibration mixtures. Given that the method can be applied at any conditions of temperature and pressure at which the PFG NMR measurements are made, the method has the potential for application in characterizing hydrocarbon liquid mixtures inside porous media and at the operating conditions relevant to, for example, hydrocarbon recovery and heterogeneous catalysis.
Collapse
Affiliation(s)
- Jack Williams
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, United Kingdom
| | - Qingyuan Zheng
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, United Kingdom
| | - Andrew J Sederman
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, United Kingdom
| | - Mick D Mantle
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, United Kingdom
| | - Timothy Baart
- Shell Technology Centre Amsterdam, Grasweg 31, 1031 HW Amsterdam, The Netherlands
| | - Constant Guédon
- Shell Technology Centre Amsterdam, Grasweg 31, 1031 HW Amsterdam, The Netherlands
| | - Lynn F Gladden
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, United Kingdom
| |
Collapse
|
4
|
Resende MT, Linder C, Wiesman Z. 1
H LF‐NMR Energy Relaxation Time Characterization of the Chemical and Morphological Structure of PUFA‐Rich Linseed Oil During Oxidation With and Without Antioxidants. EUR J LIPID SCI TECH 2019. [DOI: 10.1002/ejlt.201800339] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Maysa T. Resende
- Plant Lipid Biotechnology Lab (PLBL)Department of Biotechnology EngineeringFaculty of Engineering SciencesBen Gurion University of the NegevBeer Sheva 84105Israel
| | - Charles Linder
- Plant Lipid Biotechnology Lab (PLBL)Department of Biotechnology EngineeringFaculty of Engineering SciencesBen Gurion University of the NegevBeer Sheva 84105Israel
| | - Zeev Wiesman
- Plant Lipid Biotechnology Lab (PLBL)Department of Biotechnology EngineeringFaculty of Engineering SciencesBen Gurion University of the NegevBeer Sheva 84105Israel
| |
Collapse
|
5
|
Resende MT, Campisi-Pinto S, Linder C, Wiesman Z. Multidimensional Proton Nuclear Magnetic Resonance Relaxation Morphological and Chemical Spectrum Graphics for Monitoring and Characterization of Polyunsaturated Fatty-Acid Oxidation. J AM OIL CHEM SOC 2019. [DOI: 10.1002/aocs.12182] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Maysa Teixeira Resende
- Plant Lipid Biotechnology Lab (PLBL), Department of Biotechnology Engineering, Faculty of Engineering Sciences; Ben Gurion University of the Negev; Ben Gurion Av. 1, Beer Sheva 84105 Israel
| | - Salvatore Campisi-Pinto
- Plant Lipid Biotechnology Lab (PLBL), Department of Biotechnology Engineering, Faculty of Engineering Sciences; Ben Gurion University of the Negev; Ben Gurion Av. 1, Beer Sheva 84105 Israel
| | - Charles Linder
- Plant Lipid Biotechnology Lab (PLBL), Department of Biotechnology Engineering, Faculty of Engineering Sciences; Ben Gurion University of the Negev; Ben Gurion Av. 1, Beer Sheva 84105 Israel
| | - Zeev Wiesman
- Plant Lipid Biotechnology Lab (PLBL), Department of Biotechnology Engineering, Faculty of Engineering Sciences; Ben Gurion University of the Negev; Ben Gurion Av. 1, Beer Sheva 84105 Israel
| |
Collapse
|
6
|
Reci A, de Kort DW, Sederman AJ, Gladden LF. Accelerating the estimation of 3D spatially resolved T 2 distributions. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2018; 296:93-102. [PMID: 30236617 DOI: 10.1016/j.jmr.2018.08.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2018] [Revised: 08/23/2018] [Accepted: 08/24/2018] [Indexed: 06/08/2023]
Abstract
Obtaining quantitative, 3D spatially-resolved T2 distributions (T2 maps) from magnetic resonance data is of importance in both medical and porous media applications. Due to the long acquisition time, there is considerable interest in accelerating the experiments by applying undersampling schemes during the acquisition and developing reconstruction techniques for obtaining the 3D T2 maps from the undersampled data. A multi-echo spin echo pulse sequence is used in this work to acquire the undersampled data according to two different sampling patterns: a conventional coherent sampling pattern where the same set of lines in k-space is sampled for all equally-spaced echoes in the echo train, and a proposed incoherent sampling pattern where an independent set of k-space lines is sampled for each echo. The conventional reconstruction technique of total variation regularization is compared to the more recent techniques of nuclear norm regularization and Nuclear Total Generalized Variation (NTGV) regularization. It is shown that best reconstructions are obtained when the data acquired using an incoherent sampling scheme are processed using NTGV regularization. Using an incoherent sampling pattern and NTGV regularization as the reconstruction technique, quantitative results are obtained at sampling percentages as low as 3.1% of k-space, corresponding to a 32-fold decrease in the acquisition time, compared to a fully sampled dataset.
Collapse
Affiliation(s)
- A Reci
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, United Kingdom
| | - D W de Kort
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, United Kingdom
| | - A J Sederman
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, United Kingdom.
| | - L F Gladden
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, United Kingdom
| |
Collapse
|