![]() The Corpus Callosum (CC) is the largest white matter structure in the human brain. It implements standard techniques, such as diffusion tensor fitting, mapping fractional anisotropy and mean diffusivity, deterministic and probabilistic tractography TORTOISE, which are contained in two main modules: DIFF_PREP, tool for image resampling, motion, eddy current distortion and susceptibility induced EPI distortion corrections, and for re-orientation of data to a common space DIFF_CALC, tool for tensor fitting, error analysis, color map visualization, and ROI analysis and finally, FSL, a command-line based comprehen-sive tool of image analysis and statistical tools for fMRI, MRI, and DTI data. Three tools that are often used in the initial pipeline steps are: Camino, a free, open-source, object-oriented tool for analysis and reconstruction of Diffusion MRI data, tractography, and connectivity mapping. 1 The first group of available tools implements DTI preprocessing, such as artifacts correction and tensor estimation, alongside processing techniques such as connectivity calculations and fiber tracking. Information about such tools summarized in this section was obtained in the Neuroimaging Tools and Resources Collaboratory (NITRC), an award-winning free webbased resource that offers comprehensive information on an everexpanding scope of neuroinformatics software and data. Our results quantify the maturation of white matter fiber tracts in neonates. Geometrical and diffusion based features of the tracts are then utilized to compare premature babies to term babies. We tested our method using neonatal data and successfully extracted some of the limbic, association and commissural fibers, all of which are typically difficult to obtain by direct tractography. Here, we introduce a post-processing method that overcomes the difficulties described above, allowing the determination of reliable tracts in newborns. As a result, the water molecules' movements are not as constrained as in older brains, making it even harder to define structure using diffusion profiles. ![]() Furthermore, axons are not yet fully myelinated in these subjects. These image acquisition protocols are implemented with the aim of reducing motion artifacts that may be produced by the movement of the neonate's head during the scanning session. In imaging studies of neonates, particularly in the clinical setting, diffusion tensor imaging-based tractography is typically unreliable due to the use of fast acquisition protocols that yield low resolution and signal-to-noise ratio (SNR). CaPTk’s long-term goal is to provide widely-used technology to make use of advanced quantitative imaging analytics in cancer prediction, diagnosis and prognosis, leading toward a better understanding of the biological mechanisms of cancer development. For the latter it facilitates the use of complex algorithms for clinically-relevant studies through a user-friendly interface, eliminating the prerequisite of a substantial computational background. For the former it provides i) an efficient image viewer offering the ability of integrating new algorithms, and ii) a library of readily-available clinically-relevant algorithms, allowing batch-processing of multiple subjects. The target audience of CaPTk consists of both computational scientists and clinical experts. This functionality describes specialized, as well as general-purpose, image analysis algorithms developed during active multi-disciplinary collaborative research studies to address real clinical requirements. CaPTk builds upon established open-source software toolkits, such as the Insight Toolkit (ITK) and OpenCV, to bring together advanced computational functionality. The primary aim of this platform is to enable swift and efficient translation of cutting-edge academic research into clinically useful tools relating to clinical quantification, analysis, predictive modeling, decision-making, and reporting workflow. The purpose of this manuscript is to provide an overview of the technical specifications and architecture of the Cancer imaging Phenomics Toolkit (CaPTk a cross-platform, open-source, easy-to-use, and extensible software platform for analyzing 2D and 3D images, currently focusing on radiographic scans of brain, breast, and lung cancer.
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