We have modified the current VTK volume rendering on GPU to allow simultaneous rendering of two volumes, each of them with its own color and opacity transfer functions.
These changes have led to the creation of two new C++ classes and several GLSL shaders. We explain the modifications made to the original classes and shaders and we discuss possible additional improvements. A C++ demo code shows how to use the new classes.


GPU Volume Ray Casting of two Volumes within VTK

In this document, we report our activities on the subcontract with Kitware where we carefully study the upcoming ITK v4 pipeline design to help the ITK v4 development team to add support of modern dataflow architectures to ITK.

HyperFlow and ITK v4 Integration: Exploring the use of a modern parallel dataflow architecture in ITK

Open interfaces/frameworks increase the pace of research and discovery by promoting collaborations within and between clinicians, biomedical engineers, and software developers. These systems usually employ modular architectures to model the complexity involved in surgical assistance systems. Hence, standards are critical to allow for cross-framework interoperability of systems, and modules. SACAI workshop aims to establish a forum for discussing systems and architectures for computer assisted discussing intervention (CAI) applications. Continuing the success of the series, the workshop will focus on open interfaces, interoperability, and standards. In addition, this year, we will discuss clinical applications and future directions of CAI systems and frameworks. The workshop will continue to include the robust discussion between industry and academia. 


Fifth International Workshop on Systems and Architectures for Computer Assisted Interventions

Skull-stripping (or brain extraction) is an important pre-processing step in neuroimage analysis. This document describes a skull-stripping filter implemented using the Insight Toolkit ITK, which we named itk::StripTsImageFilter. It is a composite filter based on existing ITK classes. The filter has been implemented with usability, robustness, speed and versatility in mind, rather than accuracy. This makes it useful for many pre-processing tasks in neuroimage analysis. This paper is accompanied by the source code, input data and a testing environment.

A Skull-Stripping Filter for ITK

This document describes the workflow for unwarping structural MR images, in particular T1-weighted SPGR and MP-RAGE scans, using reference scans of the Magphan(R) EMR051 Quantitative Imaging Phantom (a.k.a. ADNI Phantom) and the tools of the Computational Morphometry Toolkit (CMTK).

Structural MRI Unwarping Using CMTK

Registration of multiple MR sequences remains a challenging problem. The Insight Toolkit (ITK) implements the Mattes’ mutual information metric for multimodality registration. Here, example source code, data, and executable files to implement the Mattes’ mutual information metric in ITK are provided. Multiple MR sequences of the knee are used as example images. This serves as a companion manuscript for a permanent archive of the source code, executable file and example data and results. 

Mattes’ Mutual Information Metric for Multimodality Registration of DESS and T2 Mapped Knee Articular MR Sequences

Acquiring data sets for stereo or multi-view reconstruction with known ground truth is costly and time consuming. Freely available data sets mostly focus on specific scene conditions and might not reflect the intended application scenario. We propose a GPU accelerated ray tracing framework that allows to generate realistic images with adjustable camera properties and projection geometry. Due to the nature of ray tracing, ground truth data can easily be obtained. The system also supports projectors as used e.g. for structured light systems. This facilitates to study interaction effects between different pattern designs, camera and projector properties, as well as the utilized reconstruction algorithm.

On the Generation of Ground Truth Data for Depth Reconstruction

medInria is a free medical imaging software developed at Inria, which aims at 
providing clinicians with state-of-the-art algorithms dedicated to medical image
processing and visualization. Efforts have been made to simplify the user 
interface, while keeping high-level algorithms. In this particular article,
we will concentrate on its use in preoperative preparation for cardiac
interventions, and how we handle the difficulties arising from the lack of standard
format for data types such as meshes or fibers, the absence of a common
programming interface for data processing algorithms, notably registration, and
the issues of visualisation where display conventions would be beneficial.

Cardiac Interventional Guidance using Multimodal Data Processing and Visualisation: medInria as an Interoperability Platform

We present preliminary results of our work to develop an augmented display for improved visualization
of the prostate and surrounding critical anatomy for robot-assisted prostate surgery. Prostate
cancer is the second leading cause of cancer-related deaths in men in the United States. Early stage
prostate cancer is potentially cured by surgery, which can be performed in a traditional, open fashion
or laparoscopically. Recently, robot-assisted laparoscopic radical prostatectomy (RALP) using the da
Vinci surgical robot system has gained wide acceptance. Robotic systems improve surgeon dexterity
by incorporating additional degrees of freedom at the end of the tools and offer increased precision
and stability of movements. However, since the procedure is performed through small incisions, this
technique reduces free sight and tactile feedback compared to open surgery. Surgeons also lose the ability
to palpate the prostate to locate tumors and other critical structures such as neurovascular bundles
(NVB). Surgeons must rely on visual cues from the video monitor and mentally correlate them with
the underlying anatomy, often using information from medical images obtained prior to the procedure.
Further complications arise from local deformations in the prostate tissue that occurs throughout the
prostatectomy procedure due to the interaction between the surgical instruments and the prostate tissue.
Subsequently, the anatomical model generated pre-operatively will need to be updated during the procedure
to reflect this deformation. To address the clinical need for more accurate and reliable guidance,
we are developing a navigation system that provides surgeons with an augmented reality (AR) view that
fuses a pre-operative MRI model of the prostate, tumor and surrounding tissues with the da Vinci system
laparoscopic video, while compensating for non-rigid prostate tissue deformation using intra-operative
transrectal ultrasound (TRUS) imaging. The work focuses on three components: (i) Developing and
evaluating algorithm for registration of MRI and ultrasound images (2) Building and testing an augmented
reality, with 3D Slicer, Surgical Assistant Workstation (SAW) and the da Vinci surgical robotic
system. (3) Evaluate the system using a multi-modality prostate phantom.

Robot assisted prostate surgery using augmented reality with deformable models

This paper describes the software integration of a treatment planning system (TPS), based on the open-source 3D Slicer package, with the Small Animal Radiation Research Platform (SARRP).The TPS is designed to enable researchers to replicate their clinical techniques, allow for image fusion with other imaging modalities, and provide dose computation and graphical visualization of treatment plans consisting of multiple x-ray beams and conformable arcs. The dose computation is implemented on a GPU to achieve high performance; the dose volume for a typical treatment plan can be computed in less than a minute.
