Low-dose computed tomography (LDCT) is desired due to prevalence and ionizing radiation of CT, but suffers elevated noise. To improve LDCT image quality, an image-domain denoising method based on cycle-consistent generative adversarial network (CycleGAN) is developed and compared with two other variants, IdentityGAN and GAN-CIRCLE. Different from supervised deep learning methods, these unpaired methods can effectively learn image translation from the low-dose domain to the full-dose (FD) domain without the need of aligning FDCT and LDCT images. The results on real and synthetic patient CT data show that these methods can achieve peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) comparable to, if not better than, the other state-of-the-art denoising methods. Among CycleGAN, IdentityGAN, and GAN-CIRCLE, the later achieves the best denoising performance with the shortest computation time. Subsequently, GAN-CIRCLE is used to demonstrate that the increasing number of training patches and of training patients can improve denoising performance. Finally, two nonoverlapping experiments, i.e., no counterparts of FDCT and LDCT images in the training data, further demonstrate the effectiveness of unpaired learning methods. This work paves the way for applying unpaired deep learning methods to enhance LDCT images without requiring aligned FD and low-dose images from the same patient. Index Terms-Cycle consistency, deep-learning-based denoising, generative adversarial network (GAN), low-dose computed tomography (LDCT), unpaired learning. I. INTRODUCTION C OMPUTED tomography (CT) is a common technique to produce cross-sectional images of the human body to find abnormalities through X-ray scanning around the patient in modern medicine. However, ionizing radiation in X-ray CT can potentially introduce adverse effects on patients [1], [2]. To alleviate this problem, low-dose CT (LDCT) is pursued

With the goal of developing a total-body small-animal PET system with a high spatial resolution of ∼0.5 mm and a high sensitivity >10% for mouse/rat studies, we simulated four scanners using the graphical processing unit-based Monte Carlo simulation package (gPET) and compared their performance in terms of spatial resolution and sensitivity. We also investigated the effect of depth-of-interaction (DOI) resolution on the spatial resolution. All the scanners are built upon 128 DOI encoding dual-ended readout detectors with lutetium yttrium oxyorthosilicate (LYSO) arrays arranged in 8 detector rings. The solid angle coverages of the four scanners are all ∼0.85 steradians. Each LYSO element has a cross-section of 0.44 × 0.44 mm2 and the pitch size of the LYSO arrays are all 0.5 mm. The four scanners can be divided into two groups: (1) H2RS110-C10 and H2RS110-C20 with 40 × 40 LYSO arrays, a ring diameter of 110 mm and axial length of 167 mm, and (2) H2RS160-C10 and H2RS160-C20 with 60 × 60 LYSO arrays, a diameter of 160 mm and axial length of 254 mm. C10 and C20 denote the crystal thickness of 10 and 20 mm, respectively. The simulation results show that all scanners have a spatial resolution better than 0.5 mm at the center of the field-of-view (FOV). The radial resolution strongly depends on the DOI resolution and radial offset, but not the axial resolution and tangential resolution. Comparing the C10 and C20 designs, the former provides better resolution, especially at positions away from the center of the FOV, whereas the latter has 2× higher sensitivity (∼10% versus ∼20%). This simulation study provides evidence that the 110 mm systems are a good choice for total-body mouse studies at a lower cost, whereas the 160 mm systems are suited for both total-body mouse and rat studies.

We experimentally demonstrate C-band 2 × 56 Gb/s pulse-amplitude modulation (PAM)-4 signal transmission over 100 km standard single-mode fiber (SSMF) using 18 GHz direct-modulated lasers (DMLs) and direct detection, without inline optical amplifier. A delay interferometer (DI) at the transmitter side is used to extend the transmission reach from 40 to 100 km. A digital Volterra filter at the receiver side is used to mitigate the nonlinear distortions. We obtain an average bit error ratio (BER) of 1.5 × 10(-3) for 2 × 56 Gb/s PAM-4 signal after 100 km SSMF transmission at the optimal input power, which is below the 7% forward error correction (FEC) threshold (3.8 × 10(-3)).

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