Abstract
Grid technologies and infrastructures can contribute to harnessing the full power of computer-aided image analysis into clinical research and practice. Given the volume of data, the sensitivity of medical information, and the joint complexity of medical datasets and computations expected in clinical practice, the challenge is to fill the gap between the grid middleware and the requirements of clinical applications. This chapter reports on the goals, achievements and lessons learned from the AGIR (Grid Analysis of Radiological Data) project. AGIR addresses this challenge through a combined approach. On one hand, leveraging the grid middleware through core grid medical services (data management, responsiveness, compression, and workflows) targets the requirements of medical data processing applications. On the other hand, grid-enabling a panel of applications ranging from algorithmic research to clinical use cases both exploits and drives the development of the services.
TopIntroduction
Harnessing the full power of computer-aided image analysis into clinical research and practice remains an open issue. Given the amount of data produced by X-ray Computed Tomography (CT), Magnetic Resonance Imaging (MRI), or PET-scan, and the difficulty to interpret medical images, algorithms for medical image analysis, processing, and diagnostic assistance have been developed these last 15 years or so. Some of these algorithms have reached a high level of usability and proved to have a real impact in the clinical domain. However, their widespread adoption by clinicians is not realized yet. Two stringent examples, amongst many others, are radiotherapy, which could greatly benefit of exploiting advances in segmentation and registration algorithms, and intra-operative situations as well as intervention planning, which could exploit modern high-performance computing systems for augmented reality (Kikinis, 1998). In the 90’s, G.A Moore has coined the term “crossing the chasm” (Moore, 1991) for the issue of selling high-tech products to mainstream customers. Crossing the long-lasting chasm between, on one hand the advances in computer science and engineering in the field of medical images analysis, and on the other hand clinical research and practice, is a challenge of the same nature.
This chapter reports on the goals, achievements and lessons learned from the AGIR (Grid Analysis of Radiological Data) project. AGIR is a multi-disciplinary collaboration funded by the French ministry of research under the ACI/ANR scheme, for the 2004-2007 period. AGIR gathers collaborators from eight laboratories, including computer scientists, middleware experts and physicians. The central tenet of AGIR is that grids can help crossing the chasm, despite their recent apparition, because they introduce a change in paradigm in the access to high-end resources. Grids provide seamless scaling of the development, validation, and exploitation cycles of analysis methods: the same infrastructure allows multiple user communities to access and manipulate medical data, and to explore individual images or create augmented reality situations through compute-intensive visualization methods; the same infrastructure provides the computing power needed to validate algorithms on large datasets and to process complete databases for the most demanding applications such as epidemiology.
Filling the gap between the clinical applications and the grid middleware raises many specific issues, ranging from computer science basic research to legal concerns, as will be exemplified in section 2. Addressing all these issues is an active research and technology area, and a new scientific community is emerging (Berry, 2003). In this framework, the specific objectives of AGIR are to define and validate:
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New grid services that address some of the requirements of complex medical image processing and data manipulation applications; these services are described individually in section 3.
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New medical image processing algorithms making advantage of the underlying grid infrastructure for compute and data intensive needs; sections 4 to 6 report on these developments.
The method is to confront the expertise of medical and computer science teams, specialized in clinical applications, medical images analysis algorithms, grids and distributed systems, around a few paradigmatic medical applications, in order to get a cross-section of the middleware, algorithmic and medical issues. A specific application (section 7) was a “bottom-up” approach targeting the immediate development of a telemedicine platform, whose requirements and results were to be confronted with the more long-term activities in AGIR.
TopGrid-Enabling Medical Image Analysis
Challenges and Issues
Grid computing has been considered to tackle some of the requirements of the medical image analysis community (Montagnat, 2004; Aloiso, 2005). The scenarios investigated in AGIR (see section 4-6) led to the identification of common area-specific grid services:
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Medical databases management in order to handle large amounts of sensitive data. Federation of distributed data sources is needed e.g. for large scale testing involved in algorithms validation.
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Optimized and reliable data transfers even on low-end networks, e.g. to support humanitarian medicine.
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Efficient execution of analysis pipelines on a distributed grid infrastructure in order to match the computing requirements of the most demanding applications such as cardiac sequences analysis.
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Responsiveness needed by human guided procedures such as assisted segmentation and surgery planning.
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Production quality support to ensure sustainable grid usability.
Key Terms in this Chapter
Workflow: A description of a process involving the ordered execution of different tasks. A workflow graph describes the tasks involved and their dependencies. The dependencies might be temporal or due to data exchanges needed between tasks (data flow).
Telemedicine: Clinical practice where medical information is transferred via telephone, the Internet or other networks for the purpose of consulting, and sometimes remote medical procedures or examinations.
Quality of Service: The ability to provide different priority to different applications, users, or data flows, or to guarantee a certain level of performance to a data flow.
Image Registration: Evaluating the transformation compensating for the difference in location, pose (rigid registration) and possibly scale or shape (non-rigid registration) between the images. Different images can thus be spatially aligned in one common coordinate system before further analysis. For instance,. rigid registration of images of the head is used to correct for the different positioning of the subject in the image acquisition device at two distinct times points.
Data Compression: Encoding information using fewer bits than the unencoded representation of data would. Lossy compression methods do not allow to fully retrieve the original data, while lossless compression methods do.
Performance Evaluation: Assessment of the technical characteristics of algorithms used in medical image analysis (e.g. registration accuracy). In some cases, a well established reference or gold standard exists and can be used for evaluation. In other cases, there is no direct way of comparing a result to the known reference. Statistical procedures can be used to define an approximate reference, or Bronze Standard.
Responsiveness: In the context of human-computer-interaction, responsiveness of an interactive system describes how quickly it responds to user input.
DICOM (Digital Imaging and Communications in Medicine): DICOM is standards to ensure the interoperability in the processing of medical image. In particular, DICOM defines standard for image and data formats, image transmission, query or store images, print and display.
Medical Imaging: The techniques and processes used to create digital images of the human body (or parts of it) for clinical purposes (diagnosis and therapy) or medical science (including the study of normal anatomy and function).