No abstract

We report an investigation of GeSn-based p-i-n photodiodes with an active GeSn layer that is almost fully strained. The results show that (a) the response of the Ge/GeSn/Ge heterojunction photodiodes is stronger than that of the reference Ge-based photodiodes at photon energies above the 0.8 eV direct bandgap of bulk Ge (<1.55 μm), and (b) the optical response extends to lower energy regions (1.55–1.80 μm wavelengths) as characterized by the strained GeSn bandgap. A cusp-like spectral characteristic is observed for samples with high Sn contents, which is attributed to the significant strain-induced energy splitting of heavy and light hole bands. This work represents a step forward in developing GeSn-based infrared photodetectors.

No abstract

No abstract

Purpose Metal artifact is a quite common problem in diagnostic dental computed tomography (CT) images. Due to the high attenuation of heavy materials such as metal, severe global artifacts can occur in reconstructions. Typical metal artifact reduction (MAR) techniques segment out the metal regions and estimate the corrupted projection data by various interpolation methods. However, interpolations are not accurate and introduce new artifacts or even deform the teeth in the reconstructed image. This work presents a new strategy to take advantage of the power of deep learning for metal artifact reduction. Method The analysis first uses coarse reconstructions from simulated locally interpolated data affected by metal fillings as a starting point. A deep learning network is then trained using the simulated data and applied to practical data. Thus, an easily implemented three‐step MAR method is formed: Firstly, use the acquired projection data to create a preliminary image reconstruction with linearly interpolated data for the metal‐related projections. Secondly, a deep learning network is used to remove the artifacts from the linear interpolation and recover the nonmetal region information. Thirdly, the method adds the ROI reconstruction of the metal regions. The structures behind the shading artifacts in the direct filtered back‐projection (FBP) reconstruction can be partially recovered by interpolation‐based MAR (I‐MAR) with the network further correcting for interpolation errors. The key to this method is that the linear interpolation reconstruction errors can be easily simulated to train a network and the effectiveness of the network can be easily generalized to I‐MAR results in real situations. Results We trained a network with a simulation dataset and validated the network against a separate simulation dataset. Then, the network was tested using simulation data that did not overlap with the training/validation datasets and real patient datasets. Both tests gave encouraging results with accurate tooth structure recovery and few artifacts. The relative root mean square error and structure similarity index method indexes were significantly improved in the tests. The method was also evaluated by two experienced dentists who gave positive evaluations. Conclusions This work presents a strategy to build a transferable learning from simulations to practical systems for metal artifact reduction using a supervised deep learning method. The system transforms the MAR analyses to an interpolation‐artifact reduction problem to recover structural details from the coarse interpolation reconstruction. In this way, training data from simulations with ground truth labels can easily model the similar features in real data with I‐MAR as the bridge. The network can seamlessly optimize both simulations and real data. The whole method is easily implemented with little computational cost. Test results demonstrated that this is an effective MAR method applicable to practical dental CT systems.