Artifact and Detail Attention Generative Adversarial Networks for Low-Dose CT Denoising

PurposeThe challenge of cone-beam computed tomography (CBCT) is its low image quality, which limits its application for adaptive radiotherapy (ART). Despite recent substantial improvement in CBCT imaging using the deep learning method, the image quality still needs to be improved for effective ART application. Spurred by the advantages of transformers, which employs multi-head attention mechanisms to capture long-range contextual relations between image pixels, we proposed a novel transformer-based network (called TransCBCT) to generate synthetic CT (sCT) from CBCT. This study aimed to further improve the accuracy and efficiency of ART.Materials and methodsIn this study, 91 patients diagnosed with prostate cancer were enrolled. We constructed a transformer-based hierarchical encoder–decoder structure with skip connection, called TransCBCT. The network also employed several convolutional layers to capture local context. The proposed TransCBCT was trained and validated on 6,144 paired CBCT/deformed CT images from 76 patients and tested on 1,026 paired images from 15 patients. The performance of the proposed TransCBCT was compared with a widely recognized style transferring deep learning method, the cycle-consistent adversarial network (CycleGAN). We evaluated the image quality and clinical value (application in auto-segmentation and dose calculation) for ART need.ResultsTransCBCT had superior performance in generating sCT from CBCT. The mean absolute error of TransCBCT was 28.8 ± 16.7 HU, compared to 66.5 ± 13.2 for raw CBCT, and 34.3 ± 17.3 for CycleGAN. It can preserve the structure of raw CBCT and reduce artifacts. When applied in auto-segmentation, the Dice similarity coefficients of bladder and rectum between auto-segmentation and oncologist manual contours were 0.92 and 0.84 for TransCBCT, respectively, compared to 0.90 and 0.83 for CycleGAN. When applied in dose calculation, the gamma passing rate (1%/1 mm criterion) was 97.5% ± 1.1% for TransCBCT, compared to 96.9% ± 1.8% for CycleGAN.ConclusionsThe proposed TransCBCT can effectively generate sCT for CBCT. It has the potential to improve radiotherapy accuracy.

show abstract

“…Zhang et al. ( 29 ) employed the transformer blocks for low dose CT denoising and produced superior results. Liu et al.…”

Section: Introductionmentioning

confidence: 99%

A more effective CT synthesizer using transformers for cone-beam CT-guided adaptive radiotherapy

et al. 2022

View full text Add to dashboard Cite

show abstract

“…With the increased popularity of GANs in medical imaging [222], many researchers attempted to boost the performance of DL-based low-dose CT denoising methods [223][224][225][226]. Wolterink et al [225] was the first who adopted GANs for low-dose CT denoising.…”

Section: Medical Imagingmentioning

confidence: 99%

“…Yang et al [227] further augmented the loss functions in a Wasserstein GAN with perceptual loss [214] to replace noise artifacts with more plausible recovered details. Most recently, Zhang et al [224] further improved the low-dose denoising performance by adding edge-aware and noise-aware attention mechanisms in the generator. They also adopted a multi-scale discriminator to expand its receptive field and improve its judgemental capabilities.…”

Section: Medical Imagingmentioning

confidence: 99%

Image denoising in the deep learning era

2022

View full text Add to dashboard Cite

Over the last decade, the number of digital images captured per day witnessed a massive explosion. Nevertheless, the visual quality of photographs is often degraded by noise during image acquisition or transmission. With the re-emergence of deep neural networks, the performance of image denoising techniques has been substantially improved in recent years. The objective of this paper is to provide a comprehensive survey of recent advances in image denoising techniques based on deep neural networks. In doing so, we commence with a thorough description of the fundamental preliminaries of the image denoising problem followed by highlighting the benchmark datasets and the widely used metrics for objective assessments. Subsequently, we study the existing deep denoisers in the supervised and unsupervised categories and review the technical specifics of some representative methods within each category. Last but not least, we conclude the analysis by remarking on trends and challenges in the development of better state-of-the-art algorithms and future research.

show abstract

“…Since their inception in 2014, GANs have been successfully applied to many natural image tasks such as artistic style transfer 12,13 and image restoration, 14 as well as tasks within medical imaging such as imaging modality conversion (e.g. CT to PET or cone beam CT to helical CT), [15][16][17] denoising low dose CT, 18,19 and other medical image synthesis applications. [20][21][22] To address complications stemming from contrast media use and to reduce the need for contrast media without reducing contrast enhanced imaging orders, virtual contrast synthesis using GANs has become a very active area of research since the introduction of such GAN-based solutions with DyeFreeNet 23 in 2020.…”

Section: Introductionmentioning

confidence: 99%

Using a GAN for CT contrast enhancement to improve CNN kidney segmentation accuracy

Welland¹,

Melendez-Corres

Teng

et al. 2023

Medical Imaging 2023: Image Processing

View full text Add to dashboard Cite

Kidneys are most easily segmented by convolutional neural networks (CNN) on contrast enhanced CT (CECT) images, but their segmentation accuracy may be reduced when only non-contrast CT (NCCT) images are available. The purpose of this work was to investigate the improvement in segmentation accuracy when implementing a generative adversarial network (GAN) to create virtual contrast enhanced (vCECT) images from non-contrast inputs. A 2D cycleGAN model, incorporating an additional idempotent loss function to restrict the GAN from making unnecessary modifications to data already in the translated domain, was trained to generate virtual contrast enhanced images on 286 paired non-contrast and contrast enhanced inputs. A 3D CNN trained on contrast enhanced images was applied to segment the kidneys in a test set of 20 paired non-contrast and contrast enhanced images. The non-contrast images were converted to virtual contrast enhanced images, then kidneys in both image conditions were segmented by the CNN. Segmentation results were compared to analyst annotations on non-contrast images visually and by Dice Coefficient (DC). Segmentation on virtual contrast enhanced images were more complete with fewer extraneous detections compared to non-contrast images in 16/20 cases. Mean(±SD) DC was 0.88(±0.80), 0.90(±0.03), and 0.95(±0.05) for non-contrast, virtual contrast enhanced, and real contrast enhanced, respectively. Virtual contrast enhancement visually improved segmentation quality, poor performing cases had their performance improved resulting in an overall reduction in DC variation, and the minimum DC increased from 0.65 to 0.85. This work provides preliminary results demonstrating the potential effectiveness of using a GAN for virtual contrast enhancement to improve CNN-based kidney segmentation on non-contrast images.

show abstract

Artifact and Detail Attention Generative Adversarial Networks for Low-Dose CT Denoising

Cited by 43 publications

References 28 publications

A more effective CT synthesizer using transformers for cone-beam CT-guided adaptive radiotherapy

A more effective CT synthesizer using transformers for cone-beam CT-guided adaptive radiotherapy

Image denoising in the deep learning era

Using a GAN for CT contrast enhancement to improve CNN kidney segmentation accuracy

Contact Info

Product

Resources

About