All Classes Interface Summary Class Summary Enum Summary Exception Summary
| Class |
Description |
| ArrayConverter |
The Class ArrayConverter.
|
| ArrayConverter.SeparatedData |
The Class SeparatedData.
|
| Custom_Format_Importer |
The Custom_Format_Importer class is in charge of handling importation MRI data
from NIFTI, multiple NIFTI or BRUKER formats.
|
| FijiRelax_Gui |
The PlugInFrame holding the main window the FijiRelax GUI, when called from the Fiji interface.
|
| HyperMap |
HyperMap class is the data packager for the variety of possible data processed by FijiRelax.
|
| JAMAMatrix |
The Class JAMAMatrix.
|
| LMA |
A class which implements the Levenberg-Marquardt Algorithm
(LMA) fit for non-linear, multidimensional parameter space
for any multidimensional fit function.
|
| LMAFunction |
Implement this for your fit function.
|
| LMAMatrix |
The matrix to be used in LMA.
|
| LMAMatrix.InvertException |
The Class InvertException.
|
| LMAMultiDimFunction |
Implement this multidimensional function y = (x[], a[]) for your fit purposes.
|
| LMDualCurveFitterNoBias |
Implements the MRLMA capabilities for fitting exponential curves to match observation points
Copyright (C) 2022 io.github.rocsg
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
|
| LMDualCurveFitterNoBias.T1Mono |
The Class T1Mono.
|
| LMDualCurveFitterNoBias.T1MonoBias |
The Class T1MonoBias.
|
| LMDualCurveFitterNoBias.T1MonoRice |
The Class T1MonoRice.
|
| LMDualCurveFitterNoBias.T1T2DefaultMonoRice |
The Class T1T2DefaultMonoRice.
|
| LMDualCurveFitterNoBias.T1T2DefaultMultiRice |
The Class T1T2DefaultMultiRice.
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| LMDualCurveFitterNoBias.T1T2Mono |
The Class T1T2Mono.
|
| LMDualCurveFitterNoBias.T1T2MonoBias |
The Class T1T2MonoBias.
|
| LMDualCurveFitterNoBias.T1T2MonoRice |
The Class T1T2MonoRice.
|
| LMDualCurveFitterNoBias.T1T2Multi |
The Class T1T2Multi.
|
| LMDualCurveFitterNoBias.T1T2MultiBias |
The Class T1T2MultiBias.
|
| LMDualCurveFitterNoBias.T1T2MultiRice |
The Class T1T2MultiRice.
|
| LMDualCurveFitterNoBias.T2Mono |
The Class T2Mono.
|
| LMDualCurveFitterNoBias.T2MonoBias |
The Class T2MonoBias.
|
| LMDualCurveFitterNoBias.T2MonoRice |
The Class T2MonoRice.
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| LMDualCurveFitterNoBias.T2Multi |
The Class T2Multi.
|
| LMDualCurveFitterNoBias.T2MultiBias |
The Class T2MultiBias.
|
| LMDualCurveFitterNoBias.T2MultiRice |
The Class T2MultiRice.
|
| MRDataType |
This enum define different common MRI imaging sequence.
|
| MRI_HyperCurvesExplorer |
MRI_HyperCurvesExplorer is the PlugInFrame describing the curve explorer of FijiRelax, an user-friendly tool for exploration of T1 T2 relaxation curves coming from T1 and T2 sequence
Copyright (C) 2022 io.github.rocsg
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
|
| MRLMA |
A class which implements the Levenberg-Marquardt Algorithm
(LMA) fit for non-linear, multidimensional parameter space
for any multidimensional fit function.
|
| MRUtils |
This class is a utility class to hold many helpers used for simulating MRI echoes, computing rice noise estimation, reading information into TIFF metadata.
|
| NoiseManagement |
This enum define cases of noise management.
|
| RiceEstimator |
This class provides utilities to estimate MRI relaxation parameters in presence of a Rice noise
Rice noise affects the signal in a way that make it complicated to invert : its moments depend on the value of the unaltered signal
For more information, refer to Fernandez et al. 2023 FijiRelax: Fast and noise-corrected estimation of MRI relaxation maps in 3D + t (in prep.)
|
| SimplexDualCurveFitterNoBias |
A simplex-based solution to curve fitting of exponential functions over MRI observations points
This one is preferred as default solution, as it produce way less outliers than Levenberg implementation, is faster, and converges toward the same values in most cases
Copyright (C) 2022 io.github.rocsg
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
|