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Adaptive histogram equalization (AHE) and intensity windowing have been compared using psychophysical observer studies. Experienced radiologists were shown clinical CT (computerized tomographic) images of the chest. Into some of the images, appropriate artificial lesions were introduced; the physicians were then shown the images processed with both AHE and intensity windowing. They were asked to assess the probability that a given image contained the artificial lesion, and their accuracy was measured. The results of these experiments show that for this particular diagnostic task, there was no significant difference in the ability of the two methods to depict luminance contrast; thus, further evaluation of AHE using controlled clinical trials is indicated.

Data sharing is increasingly recognized as critical to cross-disciplinary research and to assuring scientific validity. Despite National Institutes of Health and National Science Foundation policies encouraging data sharing by grantees, little data sharing of clinical data has in fact occurred. A principal reason often given is the potential of inadvertent violation of the Health Insurance Portability and Accountability Act privacy regulations. While regulations specify the components of private health information that should be protected, there are no commonly accepted methods to de-identify clinical data objects such as images. This leads institutions to take conservative risk-averse positions on data sharing. In imaging trials, where images are coded according to the Digital Imaging and Communications in Medicine (DICOM) standard, the complexity of the data objects and the flexibility of the DICOM standard have made it especially difficult to meet privacy protection objectives. The recent release of DICOM Supplement 142 on image de-identification has removed much of this impediment. This article describes the development of an open-source software suite that implements DICOM Supplement 142 as part of the National Biomedical Imaging Archive (NBIA). It also describes the lessons learned by the authors as NBIA has acquired more than 20 image collections encompassing over 30 million images.

Computed tomography (CT) has become the first modality to provide the patient's self-brain map for stereotactic neurosurgery. This paper describes our development of a nearly artifact-free stereotactic frame designed for CT imaging. The surgical procedure is performed within the CT scanner itself. The scanner's computer, via a new software program, spatially integrates the new stereotactic frame with the CT images of the patient and with the scanner gantry to provide rapid coordinate determinations, calculate potential probe trajectories, obtain target accuracy within 1 mm, and observe for any procedural complications. Our initial clinical experience with this system is described.

In the era of health information exchanges, there are trade-offs to consider when sharing a patient's medical record among all providers that a patient might choose.

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