Multi-planar 2.5D U-Net for image quality enhancement of dental cone-beam CT.

Cone-beam computed tomography (CBCT) can provide 3D images of a targeted area with the advantage of lower dosage than multidetector computed tomography (MDCT; also simply referred to as CT). However, in CBCT, due to the cone-shaped geometry of the X-ray source and the absence of post-patient collimation, the presence of more scattering rays deteriorates the image quality compared with MDCT. CBCT is commonly used in dental clinics, and image artifacts negatively affect the radiology workflow and diagnosis. Studies have attempted to eliminate image artifacts and improve image quality; however, a vast majority of that work sacrificed structural details of the image. The current study presents a novel approach to reduce image artifacts while preserving details and sharpness in the original CBCT image for precise diagnostic purposes. We used MDCT images as reference high-quality images. Pairs of CBCT and MDCT scans were collected retrospectively at a university hospital, followed by co-registration between the CBCT and MDCT images. A contextual loss-optimized multi-planar 2.5D U-Net was proposed. Images corrected using this model were evaluated quantitatively and qualitatively by dental clinicians. The quantitative metrics showed superior quality in output images compared to the original CBCT. In the qualitative evaluation, the generated images presented significantly higher scores for artifacts, noise, resolution, and overall image quality. This proposed novel approach for noise and artifact reduction with sharpness preservation in CBCT suggests the potential of this method for diagnostic imaging.

Unified treatment of exact and approximate scalar electromagnetic wave scattering.

Under conditions of strong scattering, a dilemma often arises regarding the best numerical method to use. Main competitors are the Born series, the beam propagation method, and direct solution of the Lippmann-Schwinger equation. However, analytical relationships between the three methods have not yet, to our knowledge, been explicitly stated. Here, we bridge this gap in the literature. In addition to overall insight about aspects of optical scattering that are best numerically captured by each method, our approach allows us to derive approximate error bounds to be expected under various scattering conditions.

Recurrent neural network reveals transparent objects through scattering media.

Scattering generally worsens the condition of inverse problems, with the severity depending on the statistics of the refractive index gradient and contrast. Removing scattering artifacts from images has attracted much work in the literature, including recently the use of static neural networks. S. Li et al. [Optica5(7), 803 (2018)10.1364/OPTICA.5.000803] trained a convolutional neural network to reveal amplitude objects hidden by a specific diffuser; whereas Y. Li et al. [Optica5(10), 1181 (2018)10.1364/OPTICA.5.001181] were able to deal with arbitrary diffusers, as long as certain statistical criteria were met. Here, we propose a novel dynamical machine learning approach for the case of imaging phase objects through arbitrary diffusers. The motivation is to strengthen the correlation among the patterns during the training and to reveal phase objects through scattering media. We utilize the on-axis rotation of a diffuser to impart dynamics and utilize multiple speckle measurements from different angles to form a sequence of images for training. Recurrent neural networks (RNN) embedded with the dynamics filter out useful information and discard the redundancies, thus quantitative phase information in presence of strong scattering. In other words, the RNN effectively averages out the effect of the dynamic random scattering media and learns more about the static pattern. The dynamical approach reveals transparent images behind the scattering media out of speckle correlation among adjacent measurements in a sequence. This method is also applicable to other imaging applications that involve any other spatiotemporal dynamics.

Reliable Multivalued Conductance States in TaO x Memristors through Oxygen Plasma-Assisted Electrode Deposition with in Situ-Biased Conductance State Transmission Electron Microscopy Analysis.

Transition metal oxide-based memristors have widely been proposed for applications toward artificial synapses. In general, memristors have two or more electrically switchable stable resistance states that device researchers see as an analogue to the ion channels found in biological synapses. The mechanism behind resistive switching in metal oxides has been divided into electrochemical metallization models and valence change models. The stability of the resistance states in the memristor vary widely depending on: oxide material, electrode material, deposition conditions, film thickness, and programming conditions. So far, it has been extremely challenging to obtain reliable memristors with more than two stable multivalued states along with endurances greater than ∼1000 cycles for each of those states. Using an oxygen plasma-assisted sputter deposition method of noble metal electrodes, we found that the metal-oxide interface could be deposited with substantially lower interface roughness observable at the nanometer scale. This markedly improved device reliability and function, allowing for a demonstration of memristors with four completely distinct levels from ∼6 × 10-6 to ∼4 × 10-8 S that were tested up to 104 cycles per level. Furthermore through a unique in situ transmission electron microscopy study, we were able to verify a redox reaction-type model to be dominant in our samples, leading to the higher degree of electrical state controllability. For solid-state synapse applications, the improvements to electrical properties will lead to simple device structures, with an overall power and area reduction of at least 1000 times when compared to SRAM.