MITK-ModelFit: A generic open-source framework for model fits and their exploration in medical imaging -- design, implementation and application on the example of DCE-MRI

TL;DR

MITK-ModelFit is an open-source, flexible framework integrated into MITK that enables easy development, extension, and application of model fitting in various medical imaging modalities, demonstrated with DCE-MRI analysis.

Contribution

The paper introduces a generic, modular framework for medical image fitting tasks that is easily adaptable, integrated into MITK, and supports diverse models and workflows.

Findings

Validated on digital phantoms with competitive accuracy.

Provides feature-rich tools for pharmacokinetic analysis.

Supports both interactive and automated processing.

Abstract

Many medical imaging techniques utilize fitting approaches for quantitative parameter estimation and analysis. Common examples are pharmacokinetic modeling in DCE MRI/CT, ADC calculations and IVIM modeling in diffusion-weighted MRI and Z-spectra analysis in chemical exchange saturation transfer MRI. Most available software tools are limited to a special purpose and do not allow for own developments and extensions. Furthermore, they are mostly designed as stand-alone solutions using external frameworks and thus cannot be easily incorporated natively in the analysis workflow. We present a framework for medical image fitting tasks that is included in MITK, following a rigorous open-source, well-integrated and operating system independent policy. Software engineering-wise, the local models, the fitting infrastructure and the results representation are abstracted and thus can be easily adapted to any model fitting task on image data, independent of image modality or model. Several ready-to-use libraries for model fitting and use-cases, including fit evaluation and visualization, were implemented. Their embedding into MITK allows for easy data loading, pre- and post-processing and thus a natural inclusion of model fitting into an overarching workflow. As an example, we present a comprehensive set of plug-ins for the analysis of DCE MRI data, which we validated on existing and novel digital phantoms, yielding competitive deviations between fit and ground truth. Providing a very flexible environment, our software mainly addresses developers of medical imaging software that includes model fitting algorithms and tools. Additionally, the framework is of high interest to users in the domain of perfusion MRI, as it offers feature-rich, freely available, validated tools to perform pharmacokinetic analysis on DCE MRI data, with both interactive and automatized batch processing workflows.