The initial process for creating a flexible three-dimensional computer-generated breast phantom based on empirical data is described. Dedicated breast computed-tomography data were processed to suppress noise and scatter artifacts in the reconstructed image set. An automated algorithm was developed to classify the breast into its primary components. A preliminary phantom defined using subdivision surfaces was generated from the segmented data. To demonstrate potential applications of the phantom, simulated mammographic image data were acquired of the phantom using a simplistic compression model and an analytic projection algorithm directly on the surface model. The simulated image was generated using a model for a polyenergetic cone-beam projection of the compressed phantom. The methods used to create the breast phantom generate resulting images that have a high level of tissue structure detail available and appear similar to actual mammograms. Fractal dimension measurements of simulated images of the phantom are comparatively similar to measurements from images of real human subjects. A realistic and geometrically defined breast phantom that can accurately simulate imaging data may have many applications in breast imaging research.

The authors report interim clinical results from an ongoing NIH-sponsored trial to evaluate digital chest tomosynthesis for improving detectability of small lung nodules. Twenty-one patients undergoing computed tomography ͑CT͒ to follow up lung nodules were consented and enrolled to receive an additional digital PA chest radiograph and digital tomosynthesis exam. Tomosynthesis was performed with a commercial CsI/a-Si flat-panel detector and a custom-built tube mover. Seventyone images were acquired in 11 s, reconstructed with the matrix inversion tomosynthesis algorithm at 5-mm plane spacing, and then averaged ͑seven planes͒ to reduce noise and low-contrast artifacts. Total exposure for tomosynthesis imaging was equivalent to that of 11 digital PA radiographs ͑comparable to a typical screen-film lateral radiograph or two digital lateral radiographs͒. CT scans ͑1.25-mm section thickness͒ were reviewed to confirm presence and location of nodules. Three chest radiologists independently reviewed tomosynthesis images and PA chest radiographs to confirm visualization of nodules identified by CT. Nodules were scored as: definitely visible, uncertain, or not visible. 175 nodules ͑diameter range 3.5-25.5 mm͒ were seen by CT and grouped according to size: Ͻ5, 5-10, and Ͼ10 mm. When considering as true positives only nodules that were scored definitely visible, sensitivities for all nodules by tomosynthesis and PA radiography were 70%͑Ϯ5%͒ and 22%͑Ϯ4%͒, respectively, ͑p Ͻ 0.0001͒. Digital tomosynthesis showed significantly improved sensitivity of detection of known small lung nodules in all three size groups, when compared to PA chest radiography.

Digital tomosynthesis is a technique that generates an arbitrary number of section images of a patient from a single pass of the x-ray tube. It is under investigation for application to a number of clinical detection tasks, and has recently been implemented in commercial devices for chest radiography. Tomosynthesis provides improved visibility of structures in the chest, such as pulmonary nodules, airways, and spine. This review article outlines the components of a typical tomosynthesis system, and presents examples of improved pulmonary nodule detection from a clinical trial in human subjects. Possible implementation strategies for use in chest imaging are discussed.

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