2 research outputs found
ROCKETSHIP: a flexible and modular software tool for the planning, processing and analysis of dynamic MRI studies
Background:
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a promising technique to characterize pathology and evaluate treatment response. However, analysis of DCE-MRI data is complex and benefits from concurrent analysis of multiple kinetic models and parameters. Few software tools are currently available that specifically focuses on DCE-MRI analysis with multiple kinetic models. Here, we developed ROCKETSHIP, an open-source, flexible and modular software for DCE-MRI analysis. ROCKETSHIP incorporates analyses with multiple kinetic models, including data-driven nested model analysis.
Results:
ROCKETSHIP was implemented using the MATLAB programming language. Robustness of the software to provide reliable fits using multiple kinetic models is demonstrated using simulated data. Simulations also demonstrate the utility of the data-driven nested model analysis. Applicability of ROCKETSHIP for both preclinical and clinical studies is shown using DCE-MRI studies of the human brain and a murine tumor model.
Conclusion:
A DCE-MRI software suite was implemented and tested using simulations. Its applicability to both preclinical and clinical datasets is shown. ROCKETSHIP was designed to be easily accessible for the beginner, but flexible enough for changes or additions to be made by the advanced user as well. The availability of a flexible analysis tool will aid future studies using DCE-MRI
Image Based Biomarkers from Magnetic Resonance Modalities: Blending Multiple Modalities, Dimensions and Scales.
The successful analysis and processing of medical
imaging data is a multidisciplinary work that requires the
application and combination of knowledge from diverse fields,
such as medical engineering, medicine, computer science and
pattern classification. Imaging biomarkers are biologic features
detectable by imaging modalities and their use offer the prospect
of more efficient clinical studies and improvement in both
diagnosis and therapy assessment. The use of Dynamic Contrast
Enhanced Magnetic Resonance Imaging (DCE-MRI) and its
application to the diagnosis and therapy has been extensively
validated, nevertheless the issue of an appropriate or optimal
processing of data that helps to extract relevant biomarkers
to highlight the difference between heterogeneous tissue still
remains. Together with DCE-MRI, the data extracted from
Diffusion MRI (DWI-MR and DTI-MR) represents a promising
and complementary tool. This project initially proposes the
exploration of diverse techniques and methodologies for the
characterization of tissue, following an analysis and classification
of voxel-level time-intensity curves from DCE-MRI data mainly
through the exploration of dissimilarity based representations
and models. We will explore metrics and representations to
correlate the multidimensional data acquired through diverse
imaging modalities, a work which starts with the appropriate
elastic registration methodology between DCE-MRI and DWI-
MR on the breast and its corresponding validation.
It has been shown that the combination of multi-modal MRI
images improve the discrimination of diseased tissue. However the fusion
of dissimilar imaging data for classification and segmentation purposes is
not a trivial task, there is an inherent difference in information domains,
dimensionality and scales. This work also proposes a multi-view consensus
clustering methodology for the integration of multi-modal MR images
into a unified segmentation of tumoral lesions for heterogeneity assessment. Using a variety of metrics and distance functions this multi-view
imaging approach calculates multiple vectorial dissimilarity-spaces for
each one of the MRI modalities and makes use of the concepts behind
cluster ensembles to combine a set of base unsupervised segmentations
into an unified partition of the voxel-based data. The methodology is
specially designed for combining DCE-MRI and DTI-MR, for which a
manifold learning step is implemented in order to account for the geometric constrains of the high dimensional diffusion information.The successful analysis and processing of medical
imaging data is a multidisciplinary work that requires the
application and combination of knowledge from diverse fields,
such as medical engineering, medicine, computer science and
pattern classification. Imaging biomarkers are biologic features
detectable by imaging modalities and their use offer the prospect
of more efficient clinical studies and improvement in both
diagnosis and therapy assessment. The use of Dynamic Contrast
Enhanced Magnetic Resonance Imaging (DCE-MRI) and its
application to the diagnosis and therapy has been extensively
validated, nevertheless the issue of an appropriate or optimal
processing of data that helps to extract relevant biomarkers
to highlight the difference between heterogeneous tissue still
remains. Together with DCE-MRI, the data extracted from
Diffusion MRI (DWI-MR and DTI-MR) represents a promising
and complementary tool. This project initially proposes the
exploration of diverse techniques and methodologies for the
characterization of tissue, following an analysis and classification
of voxel-level time-intensity curves from DCE-MRI data mainly
through the exploration of dissimilarity based representations
and models. We will explore metrics and representations to
correlate the multidimensional data acquired through diverse
imaging modalities, a work which starts with the appropriate
elastic registration methodology between DCE-MRI and DWI-
MR on the breast and its corresponding validation.
It has been shown that the combination of multi-modal MRI
images improve the discrimination of diseased tissue. However the fusion
of dissimilar imaging data for classification and segmentation purposes is
not a trivial task, there is an inherent difference in information domains,
dimensionality and scales. This work also proposes a multi-view consensus
clustering methodology for the integration of multi-modal MR images
into a unified segmentation of tumoral lesions for heterogeneity assessment. Using a variety of metrics and distance functions this multi-view
imaging approach calculates multiple vectorial dissimilarity-spaces for
each one of the MRI modalities and makes use of the concepts behind
cluster ensembles to combine a set of base unsupervised segmentations
into an unified partition of the voxel-based data. The methodology is
specially designed for combining DCE-MRI and DTI-MR, for which a
manifold learning step is implemented in order to account for the geometric constrains of the high dimensional diffusion information