Start from loaded data
A representative spectrum or dataset is loaded in the Clinical Viewer. This measured signal provides the context for defining a simple model and judging whether the fit is plausible.
Expert tutorial
Make a simple TDFDFit model, apply it, and create spectroscopic maps
This page preserves the old tutorial video for creating a first prior-knowledge TDFDFit model and applying it to data. In version 3.0 alpha this remains expert training material: clinical users should normally start with the automatic TDFDFit model creator, while spectroscopists can use the expert modelling frame directly.
Legacy expert walkthrough video
The video demonstrates the older workflow for defining a simple TDFDFit model, applying that model, and creating fitted spectroscopic maps. It is preserved here because it teaches the modelling logic, but the exact GUI actions should be reviewed against the current version 3.0.1 Alpha modelling frame before using it as final training material.
Legacy tutorial video: first TDFDFit prior-knowledge model, application to spectra, and creation of spectroscopic maps.
Where this fits in version 3
Version 3.0.1 Alpha introduces two complementary paths. The clinical path uses the automatic TDFDFit model creator to create a valid model for the loaded dataset and then applies that model through the parallel CPU fitting workflow.
The expert path uses the TDFDFit modelling frame to construct, inspect, optimize, and constrain model components manually. This video belongs mainly to the expert path, because it assumes that the user understands the spectroscopy model and the consequences of prior knowledge choices.
Workflow described by the tutorial
Create a simple model
The expert modelling frame is used to define the model components and their prior-knowledge behaviour. In v3, this can be compared with the automatic model creator route.
Apply the model
The model is applied to spectra with TDFDFit. Version 3.0 alpha adds the parallel pthreads-TDFDFit CPU fitting route for faster fitting of selected spectra or MRSI datasets.
Create maps
Fitted parameters can be mapped spatially to create spectroscopic images. These maps should be interpreted together with fitting quality, preprocessing choices, and anatomical context.
Recommended v3 follow-up
After reviewing the legacy video, use the current written pages to learn the v3 workflow. The automatic model creator is the safer starting point for clinical users. The expert modelling frame page documents manual model construction, prior-knowledge editing, and model inspection in more detail.