Treatment planning plays an important role in the process of radiotherapy (RT). The quality of the treatment plan directly and significantly affects patient treatment outcomes. In the past decades, technological advances in computer and software have promoted the development of RT treatment planning systems with sophisticated dose calculation and optimization algorithms. Treatment planners now have greater flexibility in designing highly complex RT treatment plans in order to mitigate the damage to healthy tissues better while maximizing radiation dose to tumor targets. Nevertheless, treatment planning is still largely a time-inefficient and labor-intensive process in current clinical practice. Artificial intelligence, including machine learning (ML) and deep learning (DL), has been recently used to automate RT treatment planning and has gained enormous attention in the RT community due to its great promises in improving treatment planning quality and efficiency. In this article, we reviewed the historical advancement, strengths, and weaknesses of various DL-based automated RT treatment planning techniques. We have also discussed the challenges, issues, and potential research directions of DL-based automated RT treatment planning techniques.
In this Letter, the loss and gain characteristics of an unconventional In x Ga 1−x As∕GaAs asymmetrical step well structure consisting of variable indium contents of In x Ga 1−x As materials are measured and analyzed for the first time, to the best of our knowledge. This special well structure is formed based on the indium-rich effect from the material growth process. The loss and gain are obtained by optical pumping and photoluminescence (PL) spectrum measurement at dual facets of an edge-emitting device. Unlike conventional quasi-rectangle wells, the asymmetrical step well may lead to a hybrid strain configuration containing both compressive and tensile strains and, thus, special loss and gain characteristics. The results will be very helpful in the development of multiple wavelength InGaAs-based semiconductor lasers.
In this paper, an absolute-phase unwrapping and speckle suppression approach to reconstruct a three-dimensional (3-D) image of an object with laser digital holography is described. This method offers three advantages to enhance the performance of the phase reconstruction technique. First, both speckle suppression and phase unwrapping are processed in the complex amplitude domain rather than in the single phase or amplitude domain. With this approach, the phase details of the object are better preserved upon phase reconstruction. Second, the proposed algorithm requires no threshold determination and thus achieves self-adaptive speckle suppression and robust phase unwrapping, in contrast to other methods. Finally, an improved dual-domain image denoising method is applied to further remove speckle-remnant-induced phase distortion. Ideal 3-D phase reconstruction results are obtained both theoretically and experimentally for the first time.
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