Multimod Data Manager: a tool for data fusion.

Nowadays biomedical engineers regularly have to combine data from multiple medical imaging modalities, biomedical measurements and computer simulations and this can demand the knowledge of many specialised software tools. Acquiring this knowledge to the depth necessary to perform the various tasks can require considerable time and thus divert the researcher from addressing the actual biomedical problems. The aim of the present study is to describe a new application called the Multimod Data Manager, distributed as a freeware, which provides the end user with a fully integrated environment for the fusion and manipulation of all biomedical data. The Multimod Data Manager is generated using a software application framework, called the Multimod Application Framework, which is specifically designed to support the rapid development of computer aided medicine applications. To understand the general logic of the Data Manager, we first introduce the framework from which it is derived. We then illustrate its use by an example--the development of a complete subject-specific musculo-skeletal model of the lower limb from the Visible Human medical imaging data to be used for predicting the stresses in the skeleton during gait. While the Data Manager is clearly still only at the prototype stage, we believe that it is already capable of being used to solve a large number of problems common to many biomedical engineering activities.

The multimod application framework: a rapid application development tool for computer aided medicine.

This paper describes a new application framework (OpenMAF) for rapid development of multimodal applications in computer-aided medicine. MAF applications are multimodal in data, in representation, and in interaction. The framework supports almost any type of biomedical data, including DICOM datasets, motion-capture recordings, or data from computer simulations (e.g. finite element modeling). The interactive visualization approach (multimodal display) helps the user interpret complex datasets, providing multiple representations of the same data. In addition, the framework allows multimodal interaction by supporting the simultaneous use of different input-output devices like 3D trackers, stereoscopic displays, haptics hardware and speech recognition/synthesis systems. The Framework has been designed to run smoothly even on limited power computers, but it can take advantage of all hardware capabilities. The Framework is based on a collection of portable libraries and it can be compiled on any platform that supports OpenGL, including Windows, MacOS X and any flavor of Unix/linux.

Aneurysm identification by analysis of the blood-vessel skeleton.

At least 1% of the general population have an aneurysm (or possibly more) in their cerebral blood vessels. If an aneurysm ruptures, it kills the patient in up to 60% of cases. In order to choose the optimal treatment, clinicians have to monitor the development of the aneurysm in time. Nowadays, aneurysms are typically identified manually, which means that the monitoring is often imprecise since the identification is observer dependent. As a result, the number of misdiagnosed cases may be large. This paper proposes a fast semi-automatic method for the identification of aneurysms which is based on the analysis of the skeleton of blood vessels. Provided that the skeleton is accurate, the results achieved by our method have been deemed acceptable by expert clinicians.

Octree rasterization: accelerating high-quality out-of-core GPU volume rendering.

We present a novel approach for GPU-based high-quality volume rendering of large out-of-core volume data. By focusing on the locations and costs of ray traversal, we are able to significantly reduce the rendering time over traditional algorithms. We store a volume in an octree (of bricks); in addition, every brick is further split into regular macrocells. Our solutions move the branch-intensive accelerating structure traversal out of the GPU raycasting loop and introduce an efficient empty-space culling method by rasterizing the proxy geometry of a view-dependent cut of the octree nodes. This rasterization pass can capture all of the bricks that the ray penetrates in a per-pixel list. Since the per-pixel list is captured in a front-to-back order, our raycasting pass needs only to cast rays inside the tighter ray segments. As a result, we achieve two levels of empty space skipping: the brick level and the macrocell level. During evaluation and testing, this technique achieved 2 to 4 times faster rendering speed than a current state-of-the-art algorithm across a variety of data sets.

Nonedge-specific adaptive scheme for highly robust blind motion deblurring of natural imagess.

Blind motion deblurring estimates a sharp image from a motion blurred image without the knowledge of the blur kernel. Although significant progress has been made on tackling this problem, existing methods, when applied to highly diverse natural images, are still far from stable. This paper focuses on the robustness of blind motion deblurring methods toward image diversity-a critical problem that has been previously neglected for years. We classify the existing methods into two schemes and analyze their robustness using an image set consisting of 1.2 million natural images. The first scheme is edge-specific, as it relies on the detection and prediction of large-scale step edges. This scheme is sensitive to the diversity of the image edges in natural images. The second scheme is nonedge-specific and explores various image statistics, such as the prior distributions. This scheme is sensitive to statistical variation over different images. Based on the analysis, we address the robustness by proposing a novel nonedge-specific adaptive scheme (NEAS), which features a new prior that is adaptive to the variety of textures in natural images. By comparing the performance of NEAS against the existing methods on a very large image set, we demonstrate its advance beyond the state-of-the-art.

SOA-based digital library services and composition in biomedical applications.

Carefully collected, high-quality data are crucial in biomedical visualization, and it is important that the user community has ready access to both this data and the high-performance computing resources needed by the complex, computational algorithms that will process it. Biological researchers generally require data, tools and algorithms from multiple providers to achieve their goals. This paper illustrates our response to the problems that result from this. The Living Human Digital Library (LHDL) project presented in this paper has taken advantage of Web Services to build a biomedical digital library infrastructure that allows clinicians and researchers not only to preserve, trace and share data resources, but also to collaborate at the data-processing level.

Multimodal fusion of biomedical data at different temporal and dimensional scales.

The introduction of integrative approaches to biomedical research (integrative biology, physiome, Virtual Physiological Human, etc.) poses original problems to computer aided medicine: the need to operate with large amounts of data that are strongly heterogeneous in structure, format and even in the knowledge domain that generated them; the need to integrate all of these data into a coherent whole; the further complication imposed by the fact that more and more frequently these data are captured at very different dimensional and/or temporal scales. The present study describes a first attempt at providing an interactive visualisation environment for homogeneous biomedical data defined over radically different spatial or temporal scales. In particular, we describe new strategies for the management of the dimensional information of highly heterogeneous data types; the management of temporal multiscaling; for 3D unstructured spatial multiscale visualisation and the related interaction paradigms and user interface. Preliminary results with a prototype implementation based on the OpenMAF application framework (http://www.openmaf.org) indicate that it is possible to develop effective environments for interactive visualisation of multiscale biomedical data.

The Virtual Physiological Human - a European initiative for in silico human modelling -.

The Virtual Physiological Human (VPH) is an initiative, strongly supported by the European Commission (EC), that seeks to develop an integrated model of human physiology at multiple scales from the whole body through the organ, tissue, cell and molecular levels to the genomic level. VPH had its beginnings in 2005 with informal discussions amongst like-minded scientists which led to the STEP project, a Coordination Action funded by the EC that began in early 2006. The STEP project greatly accelerated the progress of the VPH and proved to be a catalyst for wide-ranging discussions within Europe and for outreach activities designed to develop a broad international approach to the huge scientific and technological challenges involved in this area. This paper provides an overview of the VPH and the developments it has engendered in the rapidly expanding worldwide activities associated with the physiome. It then uses one particular project, the Living Human Project, to illustrate the type of advances that are taking place to further the aims of the VPH and similar initiatives worldwide.