Unpaired low-dose computed tomography image denoising using a progressive cyclical convolutional neural network.

BACKGROUND: Reducing the radiation dose from computed tomography (CT) can significantly reduce the radiation risk to patients. However, low-dose CT (LDCT) suffers from severe and complex noise interference that affects subsequent diagnosis and analysis. Recently, deep learning-based methods have shown superior performance in LDCT image-denoising tasks. However, most methods require many normal-dose and low-dose CT image pairs, which are difficult to obtain in clinical applications. Unsupervised methods, on the other hand, are more general. PURPOSE: Deep learning methods based on GAN networks have been widely used for unsupervised LDCT denoising, but the additional memory requirements of the model also hinder its further clinical application. To this end, we propose a simpler multi-stage denoising framework trained using unpaired data, the progressive cyclical convolutional neural network (PCCNN), which can remove the noise from CT images in latent space. METHODS: Our proposed PCCNN introduces a noise transfer model that transfers noise from LDCT to normal-dose CT (NDCT), denoised CT images generated from unpaired CT images, and noisy CT images. The denoising framework also contains a progressive module that effectively removes noise through multi-stage wavelet transforms without sacrificing high-frequency components such as edges and details. RESULTS: Compared with seven LDCT denoising algorithms, we perform a quantitative and qualitative evaluation of the experimental results and perform ablation experiments on each network module and loss function. On the AAPM dataset, compared with the contrasted unsupervised methods, our denoising framework has excellent denoising performance increasing the peak signal-to-noise ratio (PSNR) from 29.622 to 30.671, and the structural similarity index (SSIM) was increased from 0.8544 to 0.9199. The PCCNN denoising results were relatively optimal and statistically significant. In the qualitative result comparison, PCCNN without introducing additional blurring and artifacts, the resulting image has higher resolution and complete detail preservation, and the overall structural texture of the image is closer to NDCT. In visual assessments, PCCNN achieves a relatively balanced result in noise suppression, contrast retention, and lesion discrimination. CONCLUSIONS: Extensive experimental validation shows that our scheme achieves reconstruction results comparable to supervised learning methods and has performed well in image quality and medical diagnostic acceptability.

A cascade-based dual-domain data correction network for sparse view CT image reconstruction.

Computed tomography (CT) provides non-invasive anatomical structures of the human body and is also widely used for clinical diagnosis, but excessive ionizing radiation in X-rays can cause harm to the human body. Therefore, the researchers obtained sparse sinograms reconstructed sparse view CT images (SVCT) by reducing the amount of X-ray projection, thereby reducing the radiological effects caused by radiation. This paper proposes a cascade-based dual-domain data correction network (CDDCN), which can effectively combine the complementary information contained in the sinogram domain and the image domain to reconstruct high-quality CT images from sparse view sinograms. Specifically, several encoder-decoder subnets are cascaded in the sinogram domain to reconstruct artifact-free and noise-free CT images. In the encoder-decoder subnets, spatial-channel domain learning is designed to achieve efficient feature fusion through a group merging structure, providing continuous and elaborate pixel-level features and improving feature extraction efficiency. At the same time, to ensure that the original sinogram data collected can be retained, a sinogram data consistency layer is proposed to ensure the fidelity of the sinogram data. To further maintain the consistency between the reconstructed image and the reference image, a multi-level composite loss function is designed for regularization to compensate for excessive smoothing and distortion of the image caused by pixel loss and preserve image details and texture. Quantitative and qualitative analysis shows that CDDCN achieves competitive results in artifact removal, edge preservation, detail restoration, and visual improvement for sparsely sampled data under different views.

Multi-scale dense selective network based on border modeling for lung nodule segmentation.

PURPOSE: Accurate quantification of pulmonary nodules helps physicians to accurately diagnose and treat lung cancer. We try to improve the segmentation efficiency of irregular nodules while maintaining the segmentation accuracy of simple types of nodules. METHODS: In this paper, we obtain the unique edge part of pulmonary nodules and process it as a single branch stream, i.e., border stream, to explicitly model the nodule edge information. We propose a multi-scale dense selective network based on border modeling (BorDenNet). Its overall framework consists of a dual-branch encoder-decoder, which achieves parallel processing of classical image stream and border stream. We design a dense attention module to facilitate a strongly coupled status of feature images to focus on key regions of pulmonary nodules. Then, during the process of model decoding, the multi-scale selective attention module is proposed to establish long-range correlation relationships between different scale features, which further achieves finer feature discrimination and spatial recovery. We introduce border context enhancement module to mutually fuse and enhance the edge-related voxel features contained in the image stream and border stream and finally achieve the accurate segmentation of pulmonary nodules. RESULTS: We evaluate the BorDenNet rigorously on the lung public dataset LIDC-IDRI. For the segmentation of the target nodules, the average Dice score is 92.78[Formula: see text], the average sensitivity is 91.37[Formula: see text], and the average Hausdorff distance is 3.06 mm. We further test on a private dataset from Shanxi Provincial People's Hospital, which verifies the excellent generalization of BorDenNet. Our BorDenNet relatively improves the segmentation efficiency for multi-type nodules such as adherent pulmonary nodules and ground-glass pulmonary nodules. CONCLUSION: Accurate segmentation of irregular pulmonary nodules can obtain important clinical parameters, which can be used as a guide for clinicians and improve clinical efficiency.

Low-dose computed tomography image reconstruction via a multistage convolutional neural network with autoencoder perceptual loss network.

Background: Computed tomography (CT) is widely used in medical diagnoses due to its ability to non-invasively detect the internal structures of the human body. However, CT scans with normal radiation doses can cause irreversible damage to patients. The radiation exposure is reduced with low-dose CT (LDCT), although considerable speckle noise and streak artifacts in CT images and even structural deformation may result, significantly undermining its diagnostic capability. Methods: This paper proposes a multistage network framework which gradually divides the entire process into 2-staged sub-networks to complete the task of image reconstruction. Specifically, a dilated residual convolutional neural network (DRCNN) was used to denoise the LDCT image. Then, the learned context information was combined with the channel attention subnet, which retains local information, to preserve the structural details and features of the image and textural information. To obtain recognizable characteristic details, we introduced a novel self-calibration module (SCM) between the 2 stages to reweight the local features, which realizes the complementation of information at different stages while refining feature information. In addition, we also designed an autoencoder neural network, using a self-supervised learning scheme to train a perceptual loss neural network specifically for CT images. Results: We evaluated the diagnostic quality of the results and performed ablation experiments on the loss function and network structure modules to verify each module's effectiveness in the network. Our proposed network architecture obtained high peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and visual information fidelity (VIF) values in terms of quantitative evaluation. In the analysis of qualitative results, our network structure maintained a better balance between eliminating image noise and preserving image details. Experimental results showed that our proposed network structure obtained better metrics and visual evaluation. Conclusions: This study proposed a new LDCT image reconstruction method by combining autoencoder perceptual loss networks with multistage convolutional neural networks (MSCNN). Experimental results showed that the newly proposed method has performance than other methods.