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

Abstract

BackgroundReducing 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.PurposeDeep 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.MethodsOur 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.ResultsCompared 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.ConclusionsExtensive 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.

Opportunities for artificial intelligence in radiation protection

“…In einer anderen Studie wurde ein progressives zyklisches neuronales Faltungsnetzwerk („progressive cyclic convolutional neural network“, PCCNN) für den Einsatz in Niedrigdosis-CT-Bildern vorgestellt [ 5 ]. Das Modell wurde entwickelt, um Rauschen aus CT-Bildern zu entfernen und gleichzeitig den Speicherbedarf zu minimieren, wodurch es sich besser für den klinischen Einsatz eignet.…”

Node2Node: Self-Supervised Cardiac Diffusion Tensor Image Denoising Method

“…For instance, Zhang et al proposed a denoising convolutional neural network (DnCNN) [17] which can achieve a better performance than traditional block matching and 3D collaborative filtering (BM3D) denoising methods [18] with an extremely simple architecture. Currently, CNN-based denoising methods have been widely used in X-ray [19], PET [20,21], CT [22,23], and MRI images [24]. Regarding DW images, Wang et al first proposed a joint denoising CNN (JD-CNN) model which can effectively reduce noise by taking multiple b-value DW images as the multichannel input of the network [25].…”