59 research outputs found
Cycle-Consistent Generative Adversarial Network: Effect on Radiation Dose Reduction and Image Quality Improvement in Ultralow-Dose CT for Evaluation of Pulmonary Tuberculosis
OBJECTIVE: To investigate the image quality of ultralow-dose CT (ULDCT) of the chest reconstructed using a cycle-consistent generative adversarial network (CycleGAN)-based deep learning method in the evaluation of pulmonary tuberculosis. MATERIALS AND METHODS: Between June 2019 and November 2019, 103 patients (mean age, 40.8 ± 13.6 years; 61 men and 42 women) with pulmonary tuberculosis were prospectively enrolled to undergo standard-dose CT (120 kVp with automated exposure control), followed immediately by ULDCT (80 kVp and 10 mAs). The images of the two successive scans were used to train the CycleGAN framework for image-to-image translation. The denoising efficacy of the CycleGAN algorithm was compared with that of hybrid and model-based iterative reconstruction. Repeated-measures analysis of variance and Wilcoxon signed-rank test were performed to compare the objective measurements and the subjective image quality scores, respectively. RESULTS: With the optimized CycleGAN denoising model, using the ULDCT images as input, the peak signal-to-noise ratio and structural similarity index improved by 2.0 dB and 0.21, respectively. The CycleGAN-generated denoised ULDCT images typically provided satisfactory image quality for optimal visibility of anatomic structures and pathological findings, with a lower level of image noise (mean ± standard deviation [SD], 19.5 ± 3.0 Hounsfield unit [HU]) than that of the hybrid (66.3 ± 10.5 HU, p 0.908). The CycleGAN-generated images showed the highest contrast-to-noise ratios for the pulmonary lesions, followed by the model-based and hybrid iterative reconstruction. The mean effective radiation dose of ULDCT was 0.12 mSv with a mean 93.9% reduction compared to standard-dose CT. CONCLUSION: The optimized CycleGAN technique may allow the synthesis of diagnostically acceptable images from ULDCT of the chest for the evaluation of pulmonary tuberculosis
Multi-frame-based Cross-domain Image Denoising for Low-dose Computed Tomography
Computed tomography (CT) has been used worldwide for decades as one of the
most important non-invasive tests in assisting diagnosis. However, the ionizing
nature of X-ray exposure raises concerns about potential health risks such as
cancer. The desire for lower radiation dose has driven researchers to improve
the reconstruction quality, especially by removing noise and artifacts.
Although previous studies on low-dose computed tomography (LDCT) denoising have
demonstrated the effectiveness of learning-based methods, most of them were
developed on the simulated data collected using Radon transform. However, the
real-world scenario significantly differs from the simulation domain, and the
joint optimization of denoising with modern CT image reconstruction pipeline is
still missing. In this paper, for the commercially available third-generation
multi-slice spiral CT scanners, we propose a two-stage method that better
exploits the complete reconstruction pipeline for LDCT denoising across
different domains. Our method makes good use of the high redundancy of both the
multi-slice projections and the volumetric reconstructions while avoiding the
collapse of information in conventional cascaded frameworks. The dedicated
design also provides a clearer interpretation of the workflow. Through
extensive evaluations, we demonstrate its superior performance against
state-of-the-art methods
Deep Generative Adversarial Networks: Applications in Musculoskeletal Imaging
In recent years, deep learning techniques have been applied in musculoskeletal radiology to increase the diagnostic potential of acquired images. Generative adversarial networks (GANs), which are deep neural networks that can generate or transform images, have the potential to aid in faster imaging by generating images with a high level of realism across multiple contrast and modalities from existing imaging protocols. This review introduces the key architectures of GANs as well as their technical background and challenges. Key research trends are highlighted, including: (a) reconstruction of high-resolution MRI; (b) image synthesis with different modalities and contrasts; (c) image enhancement that efficiently preserves high-frequency information suitable for human interpretation; (d) pixel-level segmentation with annotation sharing between domains; and (e) applications to different musculoskeletal anatomies. In addition, an overview is provided of the key issues wherein clinical applicability is challenging to capture with conventional performance metrics and expert evaluation. When clinically validated, GANs have the potential to improve musculoskeletal imaging.ope
Diffusion Probabilistic Priors for Zero-Shot Low-Dose CT Image Denoising
Denoising low-dose computed tomography (CT) images is a critical task in
medical image computing. Supervised deep learning-based approaches have made
significant advancements in this area in recent years. However, these methods
typically require pairs of low-dose and normal-dose CT images for training,
which are challenging to obtain in clinical settings. Existing unsupervised
deep learning-based methods often require training with a large number of
low-dose CT images or rely on specially designed data acquisition processes to
obtain training data. To address these limitations, we propose a novel
unsupervised method that only utilizes normal-dose CT images during training,
enabling zero-shot denoising of low-dose CT images. Our method leverages the
diffusion model, a powerful generative model. We begin by training a cascaded
unconditional diffusion model capable of generating high-quality normal-dose CT
images from low-resolution to high-resolution. The cascaded architecture makes
the training of high-resolution diffusion models more feasible. Subsequently,
we introduce low-dose CT images into the reverse process of the diffusion model
as likelihood, combined with the priors provided by the diffusion model and
iteratively solve multiple maximum a posteriori (MAP) problems to achieve
denoising. Additionally, we propose methods to adaptively adjust the
coefficients that balance the likelihood and prior in MAP estimations, allowing
for adaptation to different noise levels in low-dose CT images. We test our
method on low-dose CT datasets of different regions with varying dose levels.
The results demonstrate that our method outperforms the state-of-the-art
unsupervised method and surpasses several supervised deep learning-based
methods. Codes are available in https://github.com/DeepXuan/Dn-Dp
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