Med Phys
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Interests have been rapidly growing in the field of radiotherapy to replace CT with magnetic resonance imaging (MRI), due to superior soft tissue contrast offered by MRI and the desire to reduce unnecessary radiation dose. MR-only radiotherapy also simplifies clinical workflow and avoids uncertainties in aligning MR with CT. Methods, however, are needed to derive CT-equivalent representations, often known as synthetic CT (sCT), from patient MR images for dose calculation and DRR-based patient positioning. Synthetic CT estimation is also important for PET attenuation correction in hybrid PET-MR systems. We propose in this work a novel deep convolutional neural network (DCNN) method for sCT generation and evaluate its performance on a set of brain tumor patient images. ⋯ A DCNN model method was developed, and shown to be able to produce highly accurate sCT estimations from conventional, single-sequence MR images in near real time. Quantitative results also showed that the proposed method competed favorably with an atlas-based method, in terms of both accuracy and computation speed at test time. Further validation on dose computation accuracy and on a larger patient cohort is warranted. Extensions of the method are also possible to further improve accuracy or to handle multi-sequence MR images.
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To produce and maintain a database of National Institutes of Health (NIH) funding of the American Association of Physicists in Medicine (AAPM) members, to perform a top-level analysis of these data, and to make these data (hereafter referred to as the AAPM research database) available for the use of the AAPM and its members. ⋯ A database of NIH-funded research awarded to AAPM members has been developed and tested using a data mining approach, and a top-level analysis of funding trends has been performed. Current funding of AAPM members is lower than the historic mean. The database will be maintained by members of the Working group for the development of a research database (WGDRD) on an annual basis, and is available to the AAPM, its committees, working groups, and members for download through the AAPM electronic content website. A wide range of questions regarding financial and demographic funding trends can be addressed by these data. This report has been approved for publication by the AAPM Science Council.