Constrained CycleGAN for Effective Generation of Ultrasound Sector Images of Improved Spatial Resolution
arXiv:2309.00995 · doi:10.1088/1361-6560/acd236
Abstract
Objective. A phased or a curvilinear array produces ultrasound (US) images with a sector field of view (FOV), which inherently exhibits spatially-varying image resolution with inferior quality in the far zone and towards the two sides azimuthally. Sector US images with improved spatial resolutions are favorable for accurate quantitative analysis of large and dynamic organs, such as the heart. Therefore, this study aims to translate US images with spatially-varying resolution to ones with less spatially-varying resolution. CycleGAN has been a prominent choice for unpaired medical image translation; however, it neither guarantees structural consistency nor preserves backscattering patterns between input and generated images for unpaired US images. Approach. To circumvent this limitation, we propose a constrained CycleGAN (CCycleGAN), which directly performs US image generation with unpaired images acquired by different ultrasound array probes. In addition to conventional adversarial and cycle-consistency losses of CycleGAN, CCycleGAN introduces an identical loss and a correlation coefficient loss based on intrinsic US backscattered signal properties to constrain structural consistency and backscattering patterns, respectively. Instead of post-processed B-mode images, CCycleGAN uses envelope data directly obtained from beamformed radio-frequency signals without any other non-linear postprocessing. Main Results. In vitro phantom results demonstrate that CCycleGAN successfully generates images with improved spatial resolution as well as higher peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) compared with benchmarks. Significance. CCycleGAN-generated US images of the in vivo human beating heart further facilitate higher quality heart wall motion estimation than benchmarks-generated ones, particularly in deep regions.
References in corpus (12)
- Adam: A Method for Stochastic Optimization
- Denoising Diffusion Probabilistic Models
- Deep Residual Learning for Image Recognition
- Instance Normalization: The Missing Ingredient for Fast Stylization
- Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
- Diffusion Models Beat GANs on Image Synthesis
- Perceptual Adversarial Networks for Image-to-Image Transformation
- Cycle Consistent Adversarial Denoising Network for Multiphase Coronary CT Angiography
- Freehand Ultrasound Image Simulation with Spatially-Conditioned Generative Adversarial Networks
- Translating and Segmenting Multimodal Medical Volumes with Cycle- and Shape-Consistency Generative Adversarial Network
- Synthesizing CT from Ultrashort Echo-Time MR Images via Convolutional Neural Networks
- Non-Uniform Diffusion Models