A method of film dosimetry for high energy photon beams is proposed which reduces the required film calibration exposures to a set of films obtained for a small radiation field size and shallow depth (6 cm x 6 cm at 5 cm depth). It involves modification of a compression type polystyrene film phantom to include thin lead foils parallel to the vertical film plane at approximately 1 cm from both sides of the film emulsion. The foils act as high atomic number filters which remove low energy Compton scatter photons that otherwise would cause the film sensitivity to change with field size and depth. The proposed method is best described as "lateral scatter filtering." To validate the proposed method, central axis depth doses and isodose curves for a 4 MV photon beam were determined from films exposed within the modified phantom and the results compared with ionization chamber measurements. When no lateral filtering was used, for field sizes of 6 cm x 6 cm and 25 cm x 25 cm, this comparison demonstrated up to a 65% difference between film and ionization chamber central axis depth dose measurements. When using the lateral scatter filtering technique, less than a 4% difference was observed for these field sizes.

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A significant metric in federal mammography quality standards is the phantom image quality assessment. The present work seeks to demonstrate that automated image analyses for American College of Radiology (ACR) mammographic accreditation phantom (MAP) images may be performed by a computer with objectivity, once a human acceptance level has been established. Twelve MAP images were generated with different x-ray techniques and digitized. Nineteen medical physicists in diagnostic roles (five of which were specially trained in mammography) viewed the original film images under similar conditions and provided individual scores for each test object (fibrils, microcalcifications, and nodules). Fourier domain template matching, used for low-level processing, combined with derivative filters, for intermediate-level processing, provided translation and rotation-independent localization of the test objects in the MAP images. The visibility classification decision was modeled by a Bayesian classifer using threshold contrast. The 50% visibility contrast threshold established by the trained observers' responses were: fibrils 1.010, microcalcifications 1.156, and nodules 1.016. Using these values as an estimate of human observer performance and given the automated localization of test objects, six images were graded with the computer algorithm. In all but one instance, the algorithm scored the images the same as the diagnostic physicists. In the case where it did not, the margin of disagreement was 10% due to the fact that the human scoring did not allow for half-visible fibrils (agreement occurred for the other test objects). The implication from this is that an operator-independent, machine-based scoring of MAP images is feasible and could be used as a tool to help eliminate the effect of observer variability within the current system, given proper, consistent digitization is performed.

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