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Purpose: The purpose of this experiment was to: 1) Determine if a commercially available Al2O3 detector system used for monitoring personnel exposure could be adapted for use as a radiation therapy dosimetry system; and 2) Evaluate the system's performance as an in‐vivo dosimeter and its ability to measure absolute surface dose, isocenter dose, and normal tissues dose in a phantom as part of patient‐specific IMRT quality assurance. Method and Materials: The dosimeters were evaluated for: 1) Signal decay; 2) Field size dependence; 3) Energy dependence; and 4) Angular dependence using the Landauer, InLight MicroStar system. In‐Vivo dosimetry measurements were taken for 22 patients treated on a Varian 21EX. The Landauer system was also tested for its ability to measure absolute dose from helical tomotherapy treatments. Results: The variation between dosimeters was evaluated and found to be ±1.6%. The dosimeters appeared to over‐respond in the first 10 minutes, however, after 10 minutes the chips were within 1 percent of the steady‐state reading. Unlike other detectors, the Al2O3 dosimeters showed no field size, energy, or angular dependence. The agreement between the dosimeters and the calculated doses for the in‐vivo dosimetry patients was 2.2±6.1 cGy or 3.7±2.5%. The dosimeters were also tested for their ability to measure absolute dose inside an IMRT phantom. The agreement between the dosimeters and the calculated doses was 0.1±5.3 cGy or 0.7±6.7%. Conclusion: Al2O3 dosimeters can be a convenient, inexpensive alternative to TLDs, MOSFETS, and Diodes. The agreement between calculated and measured doses for in‐vivo dosimetry and IMRT QA is comparable to TLDs, MOSFETS, and Diodes. The dosimeters can be quickly read and analyzed after 10 minutes (to allow time for signal decay). The dosimeters do not appear to have an energy, field size, or angular dependence. In addition, the detectors can be erased and re‐used.

The most common form of cancer among women in both developed and developing countries is breast cancer. The early detection and diagnosis of this disease is significant because it may reduce the number of deaths caused by breast cancer and improve the quality of life of those effected. Computer-aided detection (CADe) and computer-aided diagnosis (CADx) methods have shown promise in recent years for aiding in the human expert reading analysis and improving the accuracy and reproducibility of pathology results. One significant application of CADe and CADx is for breast cancer screening using mammograms. In image processing and machine learning research, relevant results have been produced by sparse analysis methods to represent and recognize imaging patterns. However, application of sparse analysis techniques to the biomedical field is challenging, as the objects of interest may be obscured because of contrast limitations or background tissues, and their appearance may change because of anatomical variability. We introduce methods for label-specific and label-consistent dictionary learning to improve the separation of benign breast masses from malignant breast masses in mammograms. We integrated these approaches into our Spatially Localized Ensemble Sparse Analysis (SLESA) methodology. We performed 10- and 30-fold cross validation (CV) experiments on multiple mammography datasets to measure the classification performance of our methodology and compared it to deep learning models and conventional sparse representation. Results from these experiments show the potential of this methodology for separation of malignant from benign masses as a part of a breast cancer screening workflow.

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