ProFit‐1D—A 1D fitting software and open‐source validation data sets

Borbáth, T; Murali‐Manohar, Saipavitra; Dorst, Johanna; Wright, Andrew Martin; Henning, A

doi:10.1002/mrm.28941

Cited by 16 publications

(14 citation statements)

References 45 publications

(98 reference statements)

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“…Unlike jMRUI, FSL‐MRS, 18 and Tarquin, 70 it does not handle spectroscopic imaging data processing or analysis. Also, unlike ProFit 16,27 and FiTAID, 17 it does not handle multi‐dimensional data in spectral dimensions. And similar to all other packages, Vespa is affected by changes to manufacturer DICOM/proprietary file format changes and the need for backward compatibility.…”

Section: Discussionmentioning

confidence: 99%

“…The path toward the current LCM data analysis consensus has inspired the creation of a variety of software applications, toolboxes, and libraries, both commercial and open source. These include packages for RF pulse design and characterization, 4–11 spectral simulation and basis function creation, 4,9–15 and various data processing and spectral analysis applications 9,10,16–30 . Some packages perform a single function, whereas others were designed, or have evolved, to play multiple roles.…”

Section: Introductionmentioning

confidence: 99%

“…These include packages for RF pulse design and characterization, [4][5][6][7][8][9][10][11] spectral simulation and basis function creation, 4,[9][10][11][12][13][14][15] and various data processing and spectral analysis applications. 9,10,[16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] Some packages perform a single function, whereas others were designed, or have evolved, to play multiple roles. For example, the initial release of many spectral analysis packages has often focused on one data type or organ system, such as spectrally edited brain spectra and medium-to-long TE single-voxel spectroscopy (SVS) data, in order to address a particular need or work with protocols prevalent at that time.…”

Section: Introductionmentioning

confidence: 99%

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Vespa: Integrated applications for RF pulse design, spectral simulation and MRS data analysis

Soher,

Semanchuk,

Todd

et al. 2023

Magnetic Resonance in Med

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Purpose The Vespa package (Versatile Simulation, Pulses, and Analysis) is described and demonstrated. It provides workflows for developing and optimizing linear combination modeling (LCM) fitting for 1H MRS data using intuitive graphical user interface interfaces for RF pulse design, spectral simulation, and MRS data analysis. Command line interfaces for embedding workflows in MR manufacturer platforms and utilities for synthetic dataset creation are included. Complete provenance is maintained for all steps in workflows. Theory and Methods Vespa is written in Python for compatibility across operating systems. It embeds the PyGAMMA spectral simulation library for spectral simulation. Multiprocessing methods accelerate processing and visualization. Applications use the Vespa database for results storage and cross‐application access. Three projects demonstrate pulse, sequence, simulation, and data analysis workflows: (1) short TE semi‐LASER single‐voxel spectroscopy (SVS) LCM fitting, (2) optimizing MEGA‐PRESS (MEscher–GArwood Point RESolved Spectroscopy) flip angle and LCM fitting, and (3) creating a synthetic short TE dataset. Results The LCM workflows for in vivo basis set creation and spectral analysis showed reasonable results for both the short TE semi‐LASER and MEGA‐PRESS. Examples of pulses, simulations, and data fitting are shown in Vespa application interfaces for various steps to demonstrate the interactive workflow. Conclusion Vespa provides an efficient and extensible platform for characterizing RF pulses, pulse design, spectral simulation optimization, and automated LCM fitting via an interactive platform. Modular design and command line interface make it easy to embed in other platforms. As open source, it is free to the MRS community for use and extension. Vespa source code and documentation are available through GitHub.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Vespa: Integrated applications for RF pulse design, spectral simulation and MRS data analysis

Soher,

Semanchuk,

Todd

et al. 2023

Magnetic Resonance in Med

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“…Up to 12 distinct MM resonances, estimated from HRMAS signals 69,70 and also obtained from Cudalbu et al, 30 were imposed for the tested signals at the following frequency positions: 0.9 ppm (MM0.9), 1.22 ppm (MM1. [29][30][31][35][36][37][38] The corresponding MM basis set signals are automatically simulated with Lorentzian or Gaussian line shapes (default or user assigned) using the estimated linewidth a MM of the methyl MM0.9 (0.9 ppm) peak. The latter was estimated for the assessment of N trunc .…”

Section: Basis Set Simulationmentioning

confidence: 99%

“…Most of the time domain and frequency domain fitting algorithms (semiparametric techniques) used for quantification of short and long echo time signals were reviewed in Graveron‐Demilly 27 . Linear Combination of Models (LCModel), 35 QUantitation based on quantum ESTimation (QUEST), 29 Automated Quantitation of Short Echo Time MRS Signals (AQSES), 36 and TARQUIN, 37 among others (e.g., 38 ), are extensively used. These techniques are based on the knowledge of a metabolite signal or spectrum basis set either measured or simulated, and on nonlinear least‐squares (NLLS) fitting.…”

Section: Introductionmentioning

confidence: 99%

Quantification of short echo time MRS signals with improved version of QUantitation based on quantum ESTimation algorithm

Starčuková

Su²,

Graveron‐Demilly

2023

NMR in Biomedicine

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Magnetic resonance spectroscopy offers information about metabolite changes in the organism, which can be used in diagnosis. While short echo time proton spectra exhibit more distinguishable metabolites compared with proton spectra acquired with long echo times, their quantification (and providing estimates of metabolite concentrations) is more challenging. They are hampered by a background signal, which originates mainly from macromolecules (MM) and mobile lipids. An improved version of the quantification algorithm QUantitation based on quantum ESTimation (QUEST), with MM prior knowledge (QUEST‐MM), dedicated to proton signals and invoking appropriate prior knowledge on MM, is proposed and tested. From a single acquisition, it enables better metabolite quantification, automatic estimation of the background, and additional automatic quantification of MM components, thus improving its applicability in the clinic. The proposed algorithm may facilitate studies that involve patients with pathological MM in the brain. QUEST‐MM and three QUEST‐based strategies for quantifying short echo time signals are compared in terms of bias‐variance trade‐off and Cramér–Rao lower bound estimates. The performances of the methods are evaluated through extensive Monte Carlo studies. In particular, the histograms of the metabolite and MM amplitude distributions demonstrate the performances of the estimators. They showed that QUEST‐MM works better than QUEST (Subtract approach) and is a good alternative to QUEST when measured MM signal is unavailable or unsuitable. Quantification with QUEST‐MM is shown for 1H in vivo rat brain signals obtained with the SPECIAL pulse sequence at 9.4 T, and human brain signals obtained, respectively, with STEAM at 4 T and PRESS at 3 T. QUEST‐MM is implemented in jMRUI and will be available for public use from version 7.1.

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Diffusion‐weighted MR spectroscopy: Consensus, recommendations, and resources from acquisition to modeling

Ligneul,

Najac,

Döring

et al. 2023

Magnetic Resonance in Med

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Brain cell structure and function reflect neurodevelopment, plasticity, and aging; and changes can help flag pathological processes such as neurodegeneration and neuroinflammation. Accurate and quantitative methods to noninvasively disentangle cellular structural features are needed and are a substantial focus of brain research. Diffusion‐weighted MRS (dMRS) gives access to diffusion properties of endogenous intracellular brain metabolites that are preferentially located inside specific brain cell populations. Despite its great potential, dMRS remains a challenging technique on all levels: from the data acquisition to the analysis, quantification, modeling, and interpretation of results. These challenges were the motivation behind the organization of the Lorentz Center workshop on “Best Practices & Tools for Diffusion MR Spectroscopy” held in Leiden, the Netherlands, in September 2021. During the workshop, the dMRS community established a set of recommendations to execute robust dMRS studies. This paper provides a description of the steps needed for acquiring, processing, fitting, and modeling dMRS data, and provides links to useful resources.

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ProFit‐1D—A 1D fitting software and open‐source validation data sets

Abstract: This is an open access article under the terms of the Creat ive Commo ns Attri bution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 16 publications

References 45 publications

Vespa: Integrated applications for RF pulse design, spectral simulation and MRS data analysis

Vespa: Integrated applications for RF pulse design, spectral simulation and MRS data analysis

Quantification of short echo time MRS signals with improved version of QUantitation based on quantum ESTimation algorithm

Diffusion‐weighted MR spectroscopy: Consensus, recommendations, and resources from acquisition to modeling

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