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

Abstract

BackgroundMany medical imaging techniques utilize fitting approaches for quantitative parameter estimation and analysis. Common examples are pharmacokinetic modeling in dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI)/computed tomography (CT), apparent diffusion coefficient calculations and intravoxel incoherent motion 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.ResultsWe present a framework for medical image fitting tasks that is included in the Medical Imaging Interaction Toolkit 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.ConclusionsProviding 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.

DeepImageTranslator V2: analysis of multimodal medical images using semantic segmentation maps generated through deep learning

“…In recent years, numerous open-source software tools have been reported in the field of medical image processing [13][14][15][16][17]. One growing area of development is the popularization of deep learning methods through the creation of user-friendly tools with a graphical interface.…”

De-Identification of Facial Features in Magnetic Resonance Images: Software Development Using Deep Learning Technology

“…3D facial reconstruction of high-resolution MRI can be generated by a freeware MRI viewer [ 19 ]. Moreover, the faces of patients in MR images from publicly available data can be revealed ( Figure 2 ).…”

Madym: A C++ toolkit for quantitative DCE-MRI analysis

“…These include standalone tools such as ROCK-ETSHIP (Barnes et al, 2015), DCE@urLAB (Ortuño et al, 2013), and DCEMRI.jl [Smith et al (2015), and packages for R: DATforDCEMRI (Ferl, 2011), dcemriS4 (Whitcher & Schmid, 2011) or python: pydcemri [welchpydcemri:2017. In terms of providing a fully open-source, cross platform solution that runs complete end-to-end pipelines, MITK-ModelFit (Debus et al, 2019) (also implemented in C++) has probably the closest functionality to Madym. They differ in that MITK-ModelFit builds into the wider MITK and ITK toolkits, with the advantage of providing a richer set of additional pre/post processing options not available in Madym (eg image registration and segmentation), at the expense of requiring a much larger install.…”