Health care, medical research and drug discovery rely increasingly on radiological images. Digital archives of such images are becoming commonplace, but currently lack interoperability. Grid technology could enable access to distributed radiological image archives. Image registration algorithms could then be applied to generate atlases authoritative reference datasets that describe anatomical structures or pathological changes from large cohorts. We have developed a prototype service based on registration programs that can be used remotely via the Globus Toolkit. To facilitate widespread testing of this emerging capability, we created a web-based workbench front-end that is geared towards atlas generation and makes the service accessible for clinicians and researchers. Background and Objectives Images of the human body, acquired through a variety of available modalities, play an increasingly important role in health care, medical research and drug testing. Many decision processes based on these images, such as making a diagnosis, rely on a radiologist making a visual assessment to identify abnormal tissue or to monitor change. There are currently only a few special medical areas, such as radiotherapy and reconstructive surgery planning, where advanced post-processing and interactive image analysis are routinely employed. Another area where quantitative results are usually required is drug development, but this is generally achieved either by subjective scoring based on visual assessments, or by manual or semi-automatic segmentation of the images for isolating and measuring target structures, such as anatomical features or lesions. Digital archives are quickly becoming the preferred mode of image storage for hospitals and research institutions, with many of them holding terabytes of data. Currently these image repositories tend to be isolated from one another, with poor interoperability between them. Enabling distributed access using Grid technology would make it possible to interact with these repositories and extract information that has previously been unobtainable. This has the potential to add significant value to the images for clinicians and researchers. To realize this added value, computationally expensive image processing algorithms are likely to be needed. An important example is image registration, which enables quantitative comparisons between images by determining the transformation required to match one to the other using varying numbers of degrees of freedom. The method can be used to quantify change over time in serial imaging studies, to fuse information from different modalities, or to fuse information from different subjects. When comparing subjects or groups of subjects, image registration can be used to generate atlases authoritative reference data sets that describe human anatomy and provide statistical information about sizes of structures or normal variations. Atlas generation is of particular potential benefit when applied to diffuse brain diseases, such as Alzheimer’s dementia. At present, imaging serves as an adjunct in the management of patients with dementia. A particularly useful method is serial magnetic resonance imaging (MRI) for monitoring disease progress. MRI can provide surrogate endpoint markers for assessing the efficacy of new dementia treatments in drug trials . Image registration of serial MRI’s is a proven research tool that identifies patterns of disease progression . So far, the cohorts that have been studied have been small. Processing was centralized and therefore time-intensive. It would be desirable to study large cohorts, requiring seamless access to distributed data sources and massive parallel processing facilities. The Grid infrastructure promises to provide both, thereby enabling interactive analysis. Making a first-time diagnosis of diffuse brain disease on MRI can be challenging, as pathological changes are often subtle and difficult to distinguish from normal age-related changes. When faced with such cases, radiologists may employ printed atlases generated from a normal individual to compare the patient’s images with. This approach, however, does not fully solve the problem, since the atlas will not usually be matched to the patient’s age and condition and there may be problems in comparing anatomical slices that are not well matched for spatial location. In addition, it introduces the new difficulty of distinguishing between normal anatomical and pathological variation among individuals. The ideal atlas reference would be one that is matched to the patient’s age, gender, background and medical history, that is geometrically aligned with the patient’s own cranium, and that also represents normal variability in structures of interest. This can be achieved with an interactive registration application that accesses a repository of MRI images, enables the selection of subjects that match the patient by selectable criteria, and provides quantitative comparisons of brain structure shapes and sizes a "dynamic brain atlas" . The goal of this work is to explore the possibilities and requirements for a Grid-based registration service that might help with decision support in health care and clinical research. We developed a basic service and created a prototype interface tool that enables non-technical users to submit registration processes as required for dynamic atlas generation.