Derivation of the tumor position from external respiratory surrogates with periodical updating of the internal/external correlation.

In this work we develop techniques that can derive the tumor position from external respiratory surrogates (abdominal surface motion) through periodically updated internal/external correlation. A simple linear function is used to express the correlation between the tumor and surrogate motion. The function parameters are established during a patient setup session with the tumor and surrogate positions simultaneously measured at a 30 Hz rate. During treatment, the surrogate position, constantly acquired at 30 Hz, is used to derive the tumor position. Occasionally, a pair of radiographic images is acquired to enable the updating of the linear correlation function. Four update methods, two aggressive and two conservative, are investigated: (A1) shift line through the update point; (A2) re-fit line through the update point; (C1) re-fit line with extra weight to the update point; (C2) minimize the distances to the update point and previous line fit point. In the present study of eight lung cancer patients, tumor and external surrogate motion demonstrate a high degree of linear correlation which changes dynamically over time. It was found that occasionally updating the correlation function leads to more accurate predictions than using external surrogates alone. In the case of high imaging rates during treatment (greater than 2 Hz) the aggressive update methods (A1 and A2) are more accurate than the conservative ones (C1 and C2). The opposite is observed in the case of low imaging rates.

The Functional Magnetic Resonance Imaging Data Center (fMRIDC): the challenges and rewards of large-scale databasing of neuroimaging studies.

The Functional Magnetic Resonance Imaging Data Center (fMRIDC) (http://www.fmridc.org) was established in the Autumn of 1999 with the objective of creating a mechanism by which members of the neuroscientific community may more easily share functional neuroimaging data. Examples in other sciences offer proof of the usefulness and benefit that sharing data provides through encouraging growth and development in those fields. By building a publicly accessible repository of raw data from peer-reviewed studies, the Data Center hopes to create a similarly successful environment for the neurosciences. In this article, we discuss the continuum of data-sharing efforts and provide an overview of the scientific and practical difficulties inherent in managing various fMRI data-sharing approaches. Next, we detail the organization, design and foundation of the fMRIDC, ranging from its current capabilities to the issues involved in the submitting and requesting of data. We discuss how a publicly accessible database enables other fields to develop relevant tools that can aid in the growth of understanding of cognitive processes. Information retrieval and meta-analytic techniques can be used to search, sort and categorize study information with a view towards subjecting study data to secondary 'meta-' and 'mega-analyses'. In addition, we detail the technical and policy challenges that have had to be addressed in the formation of the Data Center. Among others, these include: human subject confidentiality issues; ensuring investigator's rights; heterogeneous data description and organization; development of search tools; and data transfer issues. We conclude with comments concerning the future of the fMRIDC effort, its role in promoting the sharing of neuroscientific data, and how this may alter the manner in which studies are published.