Difference between revisions of "MITK-ModelFit"
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| − | ... | + | In many medical imaging applications, data fitting is an essential post-procession or analysis step. |
| + | Common examples are ADC calculations from a set of diffusion weighted MRI images and pharmacokinetic analysis of dynamic contrast enhanced (DCE) CT or MRI. | ||
| + | ModelFit provides an infrastructure within MITK for voxelwise fitting of 4D data sets. | ||
| + | |||
| + | The fitting routine, fitting infrastructure and result representation are separated from concrete models and fitting strategies and thus it can be utilized for any model fitting task on 4D data, regardless of imaging modality, fitting domain (temporal, spectral, etc.) or mathematical model. | ||
| + | This abstraction allows for implementation of own models for respective use-cases by the user, whilst not having to deal with the overhead of the fitting routine. | ||
| + | The embedding within MITK enables the user to perform fitting analysis within an eco-system of medical image processing in combination with all other relevant processing steps, such as image registration or segmentation. | ||
| + | |||
| + | The following ready-to-use applications in form of MITK Workbench plugins are offered: | ||
| + | * A general purpose fitting tool using formula parsing | ||
| + | * Pharmacokinetic analysis for DCE MRI using compartment models | ||
| + | * Pharmacokinetic analysis for dynamic PET using compartment models | ||
| + | * Semi-quantitative analysis for dynamic images using non-compartmental approaches | ||
| + | * Voxel-wise fit visualization for evaluation of fit quality | ||
| + | * Fitting of Z-spectra in chemical exchange saturation transfer (CEST) MRI | ||
| + | * T1/T2 Mapping from MRI acquisition with varying echo times | ||
| + | |||
| + | |||
| + | == Testdata == | ||
| + | === MRI DCE 2 compartment exchange model (2CXM) === | ||
| + | This data is used to validate our 2CXM model. The data was generated using JSim (National physiom projekt; http://www.physiome.org/jsim/). | ||
Revision as of 10:59, 16 July 2018
In many medical imaging applications, data fitting is an essential post-procession or analysis step. Common examples are ADC calculations from a set of diffusion weighted MRI images and pharmacokinetic analysis of dynamic contrast enhanced (DCE) CT or MRI. ModelFit provides an infrastructure within MITK for voxelwise fitting of 4D data sets.
The fitting routine, fitting infrastructure and result representation are separated from concrete models and fitting strategies and thus it can be utilized for any model fitting task on 4D data, regardless of imaging modality, fitting domain (temporal, spectral, etc.) or mathematical model. This abstraction allows for implementation of own models for respective use-cases by the user, whilst not having to deal with the overhead of the fitting routine. The embedding within MITK enables the user to perform fitting analysis within an eco-system of medical image processing in combination with all other relevant processing steps, such as image registration or segmentation.
The following ready-to-use applications in form of MITK Workbench plugins are offered:
- A general purpose fitting tool using formula parsing
- Pharmacokinetic analysis for DCE MRI using compartment models
- Pharmacokinetic analysis for dynamic PET using compartment models
- Semi-quantitative analysis for dynamic images using non-compartmental approaches
- Voxel-wise fit visualization for evaluation of fit quality
- Fitting of Z-spectra in chemical exchange saturation transfer (CEST) MRI
- T1/T2 Mapping from MRI acquisition with varying echo times
Testdata
MRI DCE 2 compartment exchange model (2CXM)
This data is used to validate our 2CXM model. The data was generated using JSim (National physiom projekt; http://www.physiome.org/jsim/).