This paper describes the implementation of a resampling ﬁlter for Diffusion Tensor Images (DTI) in the Insight ToolKit (ITK). ITK already contains a ﬁlter for resampling scalar and vector images as well as several transformation and interpolation classes. However, due to the directional nature of DT images, using the existing classes would result in losing the structural information of the image.
 We developed a new resampling ﬁlter, speciﬁc to DTI, that preserves the structure by applying a rotation directly on the tensors while performing the transformation of the image. New transformation and interpolator classes have also been implemented to handle tensors correctly. The new transformation classes are based on algorithms described by D.C. Alexander et al. Finally, three ﬁlters have been written to correct symmetric semi-deﬁnite matrices that would no longer be positive after the resampling process and project them into the tensors’ space. In addition, a software based on the new classes has been developed and is provided with this article.

An ITK Implementation of a Diffusion Tensor Images Resampling Filter

This document describes the implementation of image samplers using the Insight Toolkit ITK url{www.itk.org}. Image samplers take a set of `picks' from an image and store them in an array. A sample consists of the location of the pick (a point) and the corresponding image intensity (a value). Image samplers are useful for image registration, where samples are drawn from the fixed image in order to compute the similarity measure. Together with an image sampler base class, we introduce the following image samplers: 1) a full sampler that draws all voxel coordinates from the input image, 2) a grid sampler that draws samples from a user-specified regular voxel grid, 3 and 4) two random samplers that uniformly draw a user-specified number of samples from the input image.

This paper is accompanied with the source code, input data, parameters and output data that the authors used for validating the algorithm described in this paper.

An image sampling framework for the ITK

The Johns Hopkins University Surgical Assistant Workstation (SAW) modular software framework provides support for robotic devices, imaging sensors and a visualization pipeline for rapid prototyping, primarily for surgical research systems. We extend the SAW architecture for "steady hand" micromanipulation in biomanipulation laboratory tasks. The proposed system integrates direct "steady-hand" manipulation, teleoperation, and automated manipulation in a single two-handed system. System architecture, implementation and initial performance measurement experiments are detailed.

A modular architecture for biomanipulation

Teleoperation is currently the dominant form of instrument control for surgical robotics. For example, the mechanical masters in the da Vinci surgical system provides a stable, precise interface for teleoperation, allowing execution of delicate tasks in complex surgical procedures. However, conventional teleoperation also adds the complexity of false feedback to the user from the interaction of the master manipulators with each other and other elements of the surgical console. Conventional teleoperation also has other inherent performance and accuracy limitations due to the mechanical devices integrated in such a system. As an alternative, we outline a hands-free system that could be improved without significant hardware redesign, and provide a sterile master workspace. Our ReachIN prototype is implemented by extending the Johns Hopkins University Surgical Assistant Workstation (SAW) framework to include additional vision interfaces and control methods. We present the design of this prototype and results from initial validation experiments.

ReachIN: A Modular Vision Based Interface for Teleoperation

This document describes a project that can compare many images simultaneously in a synchronized manner. Such an application can be used in any ITK/VTK image processing pipelines; the programmer can use it to quickly visualize and compare intermediate results of used filters within the pipeline.The goals of this project are two folds: (i) to be integrated in gdb pretty debuggers (for visual debugging of ITK pipelines), and, (ii) to provide a visual comparison widget for image-to-image plugins (ITK or VTK based filters).
This project is part of the Gofigure2 development effort, an open-source, cross-platform application for visualizing, processing and analyzing of multidimensional microscopy data. 

A simple Qt based comparison program for ITK and VTK images.

Research and development of image guided surgery techniques requires software that provides a high degree of flexibility, with easy access to reliable functionality for common tasks. The goal of this paper is to highlight this claim in the context of image guided cardiac surgery applications. Cardiac surgery traditionally involves very invasive surgical procedures. Minimally invasive surgical techniques can greatly reduce the invasiveness of these procedures, thus reducing patient trauma. Implementing new image guidance systems for cardiac surgery is a complex process, requiring the integration of a wide variety of physical and software systems, involving both research and clinical personnel. Modular software design is essential to accommodate this complexity. Furthermore, software architecture must be highly flexible in order to facilitate both the adaptability necessary in research, and the tested reliability needed for clinical applications. Over the past decade, our laboratory has created a comprehensive system for research and development, testing and implementation of new surgical guidance techniques in the operating room. This paper provides an overview of our approach to the development and implementation of image guided therapy software for cardiovascular surgeries, ranging from the simple (needle guidance for central line access) to highly complex and experimental (intracardiac beating heart mitral valve repair/replacement). 

Flexible architecture and modularity requirements for image guided cardiovascular surgeries

This paper describes implementation of local structure tensor and anisotropic enhancement diffusion filters using the Insight Toolkit. The anisotropic diffusion filters are implemented using ITK's finite difference solver framework. The filters are used to implement the 3D edge-enhancing diffusion ( EED), coherence-enhancing diffusion (CED) and hybrid diffusion with continuous switch(HDCS) noise filtering algorithms described in Mendrik et al[1].

The most up-to-date version of the code presented in this paper is distributed with the TubeTK project:
http://public.kitware.com/Wiki/TubeTK


Implementation of local structure tensor and enhancement anisotropic diffusion filters in ITK

This documents describes the filter itk::HistogramMatchingQuadEdgeMeshFilter. It takes an input (source) mesh and a reference mesh, normalizes the scalar values between two meshes by histogram matching, and generates an output mesh which has a similar histogram as the input mesh. 
This paper is accompanied with the source code, input data, parameters and output data that we used for validating the algorithm described in this paper. This adheres to the fundamental principle that scientific publications must facilitate reproducibility of the reported results.

Histogram Matching Mesh Filter

This documents is about the filter itk::RescaleScalarsQuadEdgeMeshFilter. It
does the point-wise linear transformation to the scalar values of the input
mesh. This paper is accompanied with the source code, input data, parameters and output data that we used for validating the algorithm described in this paper. This adheres to the fundamental principle that scientific publications must facilitate reproducibility of the reported results.

Rescale Scalars Mesh Filter

This article describes the filter itk::AssignScalarValuesQuadEdgeMeshFilter. It takes two meshes, one as an input and the other as a source to assign scalar values of the nodes from the source mesh to the input mesh. Both of the two meshes should have the same number of nodes. This paper is accompanied with the source code, input data, parameters and output data that we used for validating the algorithm described in this paper. This adheres to the fundamental principle that scientific publications must facilitate reproducibility of the reported results.