Peter F. Neher

The challenge of mapping the human connectome based on diffusion tractography

Tractography based on non-invasive diffusion imaging is central to the study of human brain connectivity. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain data set with ground truth tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups. Here, we report the encouraging finding that most state-of-the-art algorithms produce tractograms containing 90% of the ground truth bundles (to at least some extent). However, the same tractograms contain many more invalid than valid bundles, and half of these invalid bundles occur systematically across research groups. Taken together, our results demonstrate and confirm fundamental ambiguities inherent in tract reconstruction based on orientation information alone, which need to be considered when interpreting tractography and connectivity results. Our approach provides a novel framework for estimating reliability of tractography and encourages innovation to address its current limitations.Though tractography is widely used, it has not been systematically validated. Here, authors report results from 20 groups showing that many tractography algorithms produce both valid and invalid bundles.

The DTI Challenge: Toward Standardized Evaluation of Diffusion Tensor Imaging Tractography for Neurosurgery

Diffusion tensor imaging (DTI) tractography reconstruction of white matter pathways can help guide brain tumor resection. However, DTI tracts are complex mathematical objects and the validity of tractography‐derived information in clinical settings has yet to be fully established. To address this issue, we initiated the DTI Challenge, an international working group of clinicians and scientists whose goal was to provide standardized evaluation of tractography methods for neurosurgery. The purpose of this empirical study was to evaluate different tractography techniques in the first DTI Challenge workshop.

MITK diffusion imaging

BACKGROUND Diffusion-MRI provides a unique window on brain anatomy and insights into aspects of tissue structure in living humans that could not be studied previously. There is a major effort in this rapidly evolving field of research to develop the algorithmic tools necessary to cope with the complexity of the datasets. OBJECTIVES This work illustrates our strategy that encompasses the development of a modularized and open software tool for data processing, visualization and interactive exploration in diffusion imaging research and aims at reinforcing sustainable evaluation and progress in the field. METHODS In this paper, the usability and capabilities of a new application and toolkit component of the Medical Imaging and Interaction Toolkit (MITK, www.mitk.org), MITK-DI, are demonstrated using in-vivo datasets. RESULTS MITK-DI provides a comprehensive software framework for high-performance data processing, analysis and interactive data exploration, which is designed in a modular, extensible fashion (using CTK) and in adherence to widely accepted coding standards (e.g. ITK, VTK). MITK-DI is available both as an open source software development toolkit and as a ready-to-use installable application. CONCLUSIONS 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.

Tractography-based connectomes are dominated by false-positive connections

Fiber tractography based on non-invasive diffusion imaging is at the heart of connectivity studies of the human brain. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain dataset with ground truth white matter tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups. While most state-of-the-art algorithms reconstructed 90% of ground truth bundles to at least some extent, on average they produced four times more invalid than valid bundles. About half of the invalid bundles occurred systematically in the majority of submissions. Our results demonstrate fundamental ambiguities inherent to tract reconstruction methods based on diffusion orientation information, with critical consequences for the approach of diffusion tractography in particular and human connectivity studies in general.

Fiberfox: Facilitating the creation of realistic white matter software phantoms

Phantom‐based validation of diffusion‐weighted image processing techniques is an important key to innovation in the field and is widely used. Openly available and user friendly tools for the flexible generation of tailor‐made datasets for the specific tasks at hand can greatly facilitate the work of researchers around the world.

Strengths and weaknesses of state of the art fiber tractography pipelines – A comprehensive in-vivo and phantom evaluation study using Tractometer

Many different tractography approaches and corresponding isolated evaluation attempts have been presented over the last years, but a comparative and quantitative evaluation of tractography algorithms still remains a challenge, particularly in-vivo. The recently presented evaluation framework Tractometer is the first attempt to approach this challenge in a quantitative, comparative, persistent and open-access way. Tractometer is currently based on the evaluation of several global connectivity and tract-overlap metrics on hardware phantom data. The work presented in this paper focuses on extending Tractometer with a metric that enables the assessment of the local consistency of tractograms with the underlying image data that is not only applicable to phantom dataset but allows the quantitative and purely data-driven evaluation of in-vivo tractography. We furthermore present an extensive reference-based evaluation study of 25,000 tractograms obtained on phantom and in-vivo datasets using the presented local metric as well as all the methods already established in Tractometer. The experiments showed that the presented local metric successfully reflects the behavior of in-vivo tractography under different conditions and that it is consistent with the results of previous studies. Additionally our experiments enabled a multitude of conclusions with implications for fiber tractography in general, including recommendations regarding optimal choice of a local modeling technique, tractography algorithm, and parameterization, confirming and complementing the results of earlier studies.

MITK global tractography

Fiber tracking algorithms yield valuable information for neurosurgery as well as automated diagnostic approaches. However, they have not yet arrived in the daily clinical practice. In this paper we present an open source integration of the global tractography algorithm proposed by Reisert et.al.1 into the open source Medical Imaging Interaction Toolkit (MITK) developed and maintained by the Division of Medical and Biological Informatics at the German Cancer Research Center (DKFZ). The integration of this algorithm into a standardized and open development environment like MITK enriches accessibility of tractography algorithms for the science community and is an important step towards bringing neuronal tractography closer to a clinical application. The MITK diffusion imaging application, downloadable from www.mitk.org, combines all the steps necessary for a successful tractography: preprocessing, reconstruction of the images, the actual tracking, live monitoring of intermediate results, postprocessing and visualization of the final tracking results. This paper presents typical tracking results and demonstrates the steps for pre- and post-processing of the images.

A Machine Learning Based Approach to Fiber Tractography Using Classifier Voting

Current tractography pipelines incorporate several modelling assumptions about the nature of the diffusion-weighted signal. We present an approach that tracks fiber pathways based on a random forest classification and voting process, guiding each step of the streamline progression by directly processing raw signal intensities. We evaluated our approach quantitatively and qualitatively using phantom and in vivo data. The presented machine learning based approach to fiber tractography is the first of its kind and our experiments showed auspicious performance compared to 12 established state of the art tractography pipelines. Due to its distinctly increased sensitivity and specificity regarding tract connectivity and morphology, the presented approach is a valuable addition to the repertoire of currently available tractography methods and promises to be beneficial for all applications that build upon tractography results.

Fiber tractography using machine learning

We present a fiber tractography approach based on a random forest classification and voting process, guiding each step of the streamline progression by directly processing raw diffusion-weighted signal intensities. For comparison to the state-of-the-art, i.e. tractography pipelines that rely on mathematical modeling, we performed a quantitative and qualitative evaluation with multiple phantom and in vivo experiments, including a comparison to the 96 submissions of the ISMRM tractography challenge 2015. The results demonstrate the vast potential of machine learning for fiber tractography.

Model-Based Super-Resolution of Diffusion MRI

This work introduces a model-based super-resolution reconstruction (SRR) technique for achieving high-resolution diffusion-weighted MRI. Diffusion-weighted imaging (DWI) is a key technique for investigating white matter non-invasively. However, due to hardware and imaging time constraints, the technique offers limited spatial resolution. A SRR technique was recently proposed to address this limitation. This approach is attractive because it can produce high-resolution DWI data without the need for onerously long scan time. However, the technique treats individual DWI data from different diffusion-sensitizing gradients as independent, which in fact are coupled through the common underlying tissue. The proposed technique addresses this issue by explicitly accounting for this intrinsic coupling between DWI scans from different gradients. The key technical advance is in introducing a forward model that predicts the DWI data from all the diffusion gradients by the underpinning tissue microstructure. As a proof-of-concept, we show that the proposed SRR approach provides more accurate reconstruction results than the current SRR technique with synthetic white matter phantoms.