MITK Diffusion Imaging

Fritzsche, Klaus; Neher, Peter; Reicht, Ignaz; Bruggen, Thomas van; Goch, Caspar J.; Reisert, Marco; Nolden, Marco; Zelzer, Sascha; Meinzer, Hans-Peter; Stieltjes, Bram

doi:10.3414/me11-02-0031

Cited by 76 publications

(22 citation statements)

References 21 publications

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“…DTI was obtained after performing Insight Segmentation and Registration Toolkit (ITK, National Library of Medicine, http://www.itk.org)-based tensor reconstruction (57) on the preprocessed diffusion weighted images. The eddy-corrected diffusion weighted images were processed through MITK-Diffusion (58), which implements the Gibbs Tracking Algorithm (59), a global tractography method that reconstructs all brain fibers simultaneously while searching for a global optimum (56), and has outranked other tractography algorithms (60). The subset of tracts that passed through the chosen seed were extracted, and brain regions connected via the extracted tracts were identified based on individual brain atlases derived from running Freesurfer’s surface-based reconstruction pipeline (http://surfer.nmr.mgh.harvard.edu) on the T1-weighted anatomical image.…”

Section: Methodsmentioning

confidence: 99%

Omega-3 polyunsaturated fatty acid supplementation and white matter changes in major depression

Chhetry

Hezghia

Miller

et al. 2016

Journal of Psychiatric Research

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White matter abnormalities are implicated in major depressive disorder (MDD). As omega-3 polyunsaturated fatty acids (PUFAs) are low in MDD and affect myelination, we hypothesized that PUFA supplementation may alleviate depression through improving white matter integrity. Acutely depressed MDD patients (n=16) and healthy volunteers (HV, n=12) had 25-direction diffusion tensor imaging before and after 6 weeks of fish oil supplementation. Plasma phospholipid omega-3 PUFAs eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), and omega-6 PUFA arachidonic acid (AA) levels were determined before and after supplementation using high-throughput extraction and gas chromatography and expressed as a percentage of total phospholipids (PUFA%). Fractional anisotropy (FA) was computed using a least-squares-fit diffusion tensor with non-linear optimization. Regression analyses were performed with changes in PUFA levels or Hamilton Depression Rating Scale scores as predictors, voxel-wise difference maps of FA as outcome, covariates age and sex, with family-wise correction for multiple comparisons. Increases in plasma phospholipid DHA% (but not EPA% or AA%) after fish oil predicted increases in FA in MDD but not HV, in a cluster including genu and body of the corpus callosum, and anterior corona radiata and cingulum (cluster-level p<0.001, peak t-score=8.10, p=0.002). There was a trend for greater change in FA in MDD responders over nonresponders (t=−1.874, df=13.56, p=0.08). Decreased depression severity predicted increased FA in left corticospinal tract and superior longitudinal fasciculus (cluster-level p<0.001, peak t-score=5.04, p=0.0001). Increased FA correlated with increased DHA% and decreased depression severity after fish oil supplementation suggests therapeutic effects of omega-3 PUFAs may be related to improvements in white matter integrity.

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Section: Methodsmentioning

confidence: 99%

Omega-3 polyunsaturated fatty acid supplementation and white matter changes in major depression

Chhetry

Hezghia

Miller

et al. 2016

Journal of Psychiatric Research

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“…DW imaging-derived parameters were evaluated separately based on the IVIM model, [ 21 ] yielding the parameters perfusion fraction f and diffusion constant D , using open-source software developed in-house (MITK Diffusion, Version 2011) [ 22 ] . The parameter estimation was based on the assumption that the diffusion measurement is influenced mainly by 2 effects, a perfusion-related effect introduced by the molecules moving in the capillary network (pseudodiffusion coefficient, D *) and extravascular effects of passive diffusion ( D ).…”

Section: Methodsmentioning

confidence: 99%

The role of perfusion effects in monitoring of chemoradiotherapy of rectal carcinoma using diffusion-weighted imaging

et al. 2013

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Purpose: The aim of this study was to characterize and understand the therapy-induced changes in diffusion parameters in rectal carcinoma under chemoradiotherapy (CRT). The current literature shows conflicting results in this regard. We applied the intravoxel incoherent motion model, which allows for the differentiation between diffusion (D) and perfusion (f) effects, to further elucidate potential underlying causes for these divergent reports. Materials and methods: Eighteen patients with primary rectal carcinoma undergoing preoperative CRT were examined before, during, and after neoadjuvant CRT using diffusion-weighted imaging. Using the intravoxel incoherent motion approach, f and D were extracted and compared with postoperative tumor downstaging and volume. Results: Initial diffusion-derived parameters were within a narrow range (D1 = 0.94 ± 0.12 × 10−3 mm2/s). At follow-up, D rose significantly (D2 = 1.18 ± 0.13 × 10−3 mm2/s; P < 0.0001) and continued to increase significantly after CRT (D3 = 1.24 ± 0.14 × 10−3 mm2/s; P < 0.0001). The perfusion fraction f did not change significantly (f1 = 9.4 ± 2.0%, f2 = 9.4 ± 1.7%, f3 = 9.5 ± 2.7%). Mean volume (V) decreased significantly (V1 = 16,992 ± 13,083 mm3; V2 = 12,793 ± 8317 mm3, V3 = 9718 ± 6154 mm3). T-downstaging (10:18 patients) showed no significant correlation with diffusion-derived parameters. Conclusions: Conflicting results in the literature considering apparent diffusion coefficient (ADC) changes in rectal carcinoma under CRT for patients showing T-downstaging are unlikely to be due to perfusion effects. Our data support the view that under effective therapy, an increase in D/ADC can be observed.

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“…This popularity is also evident from the large number of software tools available for the analysis of diffusion-weighted images. Many of these tools are written in C/C++: 3D Slicer (Pieper et al, 2006), AFNI (Cox, 2012), MITK (Fritzsche et al, 2012), BrainVoyager QX (Goebel, 2012), DTI-Query/Quench (Sherbondy et al, 2005), FreeSurfer (Fischl, 2012), FSL-FDT (Smith et al, 2004), MedInria (Toussaint et al, 2007), MRtrix (Tournier et al, 2012), Diffusion Toolkit/Trackvis (Wang et al, 2007), FiberNavigator (Vaillancourt et al, 2011; Chamberland and Descoteaux, 2013). A few are written in other languages, such as R: TractoR (Clayden et al, 2011), Java: Camino (Cook et al, 2006) and Matlab: ExploreDTI (Leemans et al, 2009), AFQ (Yeatman et al, 2012) and others.…”

Section: Introductionmentioning

confidence: 99%

Dipy, a library for the analysis of diffusion MRI data

Garyfallidis

Brett

Amirbekian

et al. 2014

Front. Neuroinform.

1,061

882

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Diffusion Imaging in Python (Dipy) is a free and open source software project for the analysis of data from diffusion magnetic resonance imaging (dMRI) experiments. dMRI is an application of MRI that can be used to measure structural features of brain white matter. Many methods have been developed to use dMRI data to model the local configuration of white matter nerve fiber bundles and infer the trajectory of bundles connecting different parts of the brain. Dipy gathers implementations of many different methods in dMRI, including: diffusion signal pre-processing; reconstruction of diffusion distributions in individual voxels; fiber tractography and fiber track post-processing, analysis and visualization. Dipy aims to provide transparent implementations for all the different steps of dMRI analysis with a uniform programming interface. We have implemented classical signal reconstruction techniques, such as the diffusion tensor model and deterministic fiber tractography. In addition, cutting edge novel reconstruction techniques are implemented, such as constrained spherical deconvolution and diffusion spectrum imaging (DSI) with deconvolution, as well as methods for probabilistic tracking and original methods for tractography clustering. Many additional utility functions are provided to calculate various statistics, informative visualizations, as well as file-handling routines to assist in the development and use of novel techniques. In contrast to many other scientific software projects, Dipy is not being developed by a single research group. Rather, it is an open project that encourages contributions from any scientist/developer through GitHub and open discussions on the project mailing list. Consequently, Dipy today has an international team of contributors, spanning seven different academic institutions in five countries and three continents, which is still growing.

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MITK Diffusion Imaging

Abstract: The open source release of the modular MITK-DI tools will increase verifiability and comparability within the research community and will also be an important step towards bringing many of the current techniques towards clinical application.

Cited by 76 publications

References 21 publications

Omega-3 polyunsaturated fatty acid supplementation and white matter changes in major depression

Omega-3 polyunsaturated fatty acid supplementation and white matter changes in major depression

The role of perfusion effects in monitoring of chemoradiotherapy of rectal carcinoma using diffusion-weighted imaging

Dipy, a library for the analysis of diffusion MRI data

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