Noise Conscious Training of Non Local Neural Network Powered by Self Attentive Spectral Normalized Markovian Patch GAN for Low Dose CT Denoising

Calcium deblooming in coronary computed tomography angiography via semantic-oriented generative adversarial network

Calcium blooming artifact produced by calcified plaque in coronary computed tomography angiography (CCTA) is a significant contributor to false-positive results for radiologists. Most previous research focused on general noise reduction of CT images, while performance was limited when facing the blooming artifact. To address this problem, we designed an automated and robust semantics-oriented adversarial network that fully exploits the calcified plaques as semantic regions in the CCTA. The semantic features were extracted using a feature extraction module and implemented through a global-local fusion module, a generator with a semantic similarity module, and a matrix discriminator. The effectiveness of our network was validated both on a virtual and a clinical dataset. The clinical dataset consists of 372 CCTA and corresponding coronary angiogram (CAG) results, with the assistance of two cardiac radiologists (with 10 and 21 years of experience) for clinical evaluation. The proposed method effectively reduces artifacts for three major coronary arteries and significantly improves the specificity and positive predictive value for the diagnosis of coronary stenosis.

Asymmetric Convolution-based GAN Framework for Low-Dose CT Image Denoising

Noise reduction is essential to improve the diagnostic quality of low-dose CT (LDCT) images. In this regard, data-driven denoising methods based on generative adversarial networks (GAN) have shown promising results. However, custom designs with 2D convolution may not preserve the correlation of the local and global pixels, which results in the loss of high-frequency (edges/ boundaries of lesions) anatomical details. A recent state-of-the-art method demonstrates that using primitive GAN-based methods may introduce structural (shape) distortion. To address this issue, we develop a novel asymmetric convolution-based generator network (ACGNet), which is constructed by using one-dimensional (1D) asymmetric convolutions and a dynamic attention module (DAM). The 1D asymmetric convolutions (1 × 3 & 3 × 1) can intensify the representation power of square convolution kernels (3 × 3) in horizontal and vertical directions. Consequently, we integrated the highlighted low-level CT voxel details via purposed attention DAM with high-level CT-scan features. As a result, ACGNet efficiently preserves the local and global pixel relations in denoised LDCT images. Furthermore, we propose a novel neural structure preserving loss (NSPL) through which ACGNet learns the neighborhood structure of CT images, preventing structural (shape) distortion. In addition, the ACGNet can reconstruct the CT images with human-perceived quality via back-propagated gradients due to the feature-based NSPL loss. Finally, we include differential content loss in network optimization to restore high-frequency lesion boundaries. The proposed method outperforms many state-of-the-art methods on two publicly accessible datasets: the Mayo 2016 dataset (PSNR: 35.2015 dB, SSIM: 0.9560), and Low-dose CT image and projection dataset (PSNR: 35.2825 dB, SSIM: 0.9566).

CDAF-Net: A Contextual Contrast Detail Attention Feature Fusion Network for Low-Dose CT Denoising

Low-dose computed tomography (LDCT) is a specialized CT scan with a lower radiation dose than normal-dose CT. However, the reduced radiation dose can introduce noise and artifacts, affecting diagnostic accuracy. To enhance the LDCT image quality, we propose a Contextual Contrast Detail Attention Feature Fusion Network (CDAF-Net) for LDCT denoising. Firstly, the LDCT image, with dimensions 1 × H × W, is mapped to a feature map with dimensions C × H × W, and it is processed through the Contextual Contrast Detail Attention (CCDA) module and the Selective Kernel Feature Fusion (SKFF) module. The CCDA module combines a global contextual attention mechanism with detail-enhanced differential convolutions to better understand the overall semantics and structure of the LDCT image, capturing subtle changes and details. The SKFF module effectively merges shallow features extracted by the encoder with deep features from the decoder, integrating feature representations from different levels. This process is repeated across four different resolution feature maps, and the denoised LDCT image is output through a skip connection. We conduct experiments on the Mayo dataset, the LDCT-and-Projection-Data dataset, and the Piglet dataset. Specifically, the CDAF-Net achieves the optimal metrics with a PSNR of 33.7262 dB, an SSIM of 0.9254, and an RMSE of 5.3731 on the Mayo dataset. Improvements are also observed in head CT and ultra-low-dose chest CT images of the LDCT-and-Projection-Data dataset and the Piglet dataset. Experimental results show that the proposed CDAF-Net algorithm provides superior denoising performance compared with the state-of-the-art (SOTA) algorithms.

Real-World Low-Dose CT Image Denoising by Patch Similarity Purification

Reducing the radiation dose in CT scanning is important to alleviate the damage to the human health in clinical scenes. A promising way is to replace the normal-dose CT (NDCT) imaging by low-dose CT (LDCT) imaging with lower tube voltage and tube current. This often brings severe noise to the LDCT images, which adversely affects the diagnosis accuracy. Most of existing LDCT image denoising networks are trained either with synthetic LDCT images or real-world LDCT and NDCT image pairs with huge spatial misalignment. However, the synthetic noise is very different from the complex noise in real-world LDCT images, while the huge spatial misalignment brings inaccurate predictions of tissue structures in the denoised LDCT images. To well utilize real-world LDCT and NDCT image pairs for LDCT image denoising, in this paper, we introduce a new Patch Similarity Purification (PSP) strategy to construct high-quality training dataset for network training. Specifically, our PSP strategy first perform binarization for each pair of image patches cropped from the corresponding LDCT and NDCT image pairs. For each pair of binary masks, it then computes their similarity ratio by common mask calculation, and the patch pair can be selected as a training sample if their mask similarity ratio is higher than a threshold. By using our PSP strategy, each training set of our Rabbit and Patient datasets contain hundreds of thousands of real-world LDCT and NDCT image patch pairs with negligible misalignment. Extensive experiments demonstrate the usefulness of our PSP strategy on purifying the training data and the effectiveness of training LDCT image denoising networks on our datasets. The code and dataset are provided at https://github.com/TuTusong/PSP.

LungViT: Ensembling Cascade of Texture Sensitive Hierarchical Vision Transformers for Cross-Volume Chest CT Image-to-Image Translation

Chest computed tomography (CT) at inspiration is often complemented by an expiratory CT to identify peripheral airways disease. Additionally, co-registered inspiratory-expiratory volumes can be used to derive various markers of lung function. Expiratory CT scans, however, may not be acquired due to dose or scan time considerations or may be inadequate due to motion or insufficient exhale; leading to a missed opportunity to evaluate underlying small airways disease. Here, we propose LungViT- a generative adversarial learning approach using hierarchical vision transformers for translating inspiratory CT intensities to corresponding expiratory CT intensities. LungViT addresses several limitations of the traditional generative models including slicewise discontinuities, limited size of generated volumes, and their inability to model texture transfer at volumetric level. We propose a shifted-window hierarchical vision transformer architecture with squeeze-and-excitation decoder blocks for modeling dependencies between features. We also propose a multiview texture similarity distance metric for texture and style transfer in 3D. To incorporate global information into the training process and refine the output of our model, we use ensemble cascading. LungViT is able to generate large 3D volumes of size 320×320×320 . We train and validate our model using a diverse cohort of 1500 subjects with varying disease severity. To assess model generalizability beyond the development set biases, we evaluate our model on an out-of-distribution external validation set of 200 subjects. Clinical validation on internal and external testing sets shows that synthetic volumes could be reliably adopted for deriving clinical endpoints of chronic obstructive pulmonary disease.

DDT-Net: Dose-Agnostic Dual-Task Transfer Network for Simultaneous Low-Dose CT Denoising and Simulation

Deep learning (DL) algorithms have achieved unprecedented success in low-dose CT (LDCT) imaging and are expected to be a new generation of CT reconstruction technology. However, most DL-based denoising models often lack the ability to generalize to unseen dose data. Moreover, most simulation tools for LDCT typically operate on proprietary projection data, which is generally not accessible without an established collaboration with CT manufacturers. To alleviate these issues, in this work, we propose a dose-agnostic dual-task transfer network, termed DDT-Net, for simultaneous LDCT denoising and simulation. Concretely, the dual-task learning module is constructed to integrate the LDCT denoising and simulation tasks into a unified optimization framework by learning the joint distribution of LDCT and NDCT data. We approximate the joint distribution of continuous dose level data by training DDT-Net with discrete dose data, which can be generalized to denoising and simulation of unseen dose data. In particular, the mixed-dose training strategy adopted by DDT-Net can promote the denoising performance of lower-dose data. The paired dataset simulated by DDT-Net can be used for data augmentation to further restore the tissue texture of LDCT images. Experimental results on synthetic data and clinical data show that the proposed DDT-Net outperforms competing methods in terms of denoising and generalization performance at unseen dose data, and it also provides a simulation tool that can quickly simulate realistic LDCT images at arbitrary dose levels.

FSformer: A combined frequency separation network and transformer for LDCT denoising

Low-dose computed tomography (LDCT) has been widely concerned in the field of medical imaging because of its low radiation hazard to humans. However, under low-dose radiation scenarios, a large amount of noise/artifacts are present in the reconstructed image, which reduces the clarity of the image and is not conducive to diagnosis. To improve the LDCT image quality, we proposed a combined frequency separation network and Transformer (FSformer) for LDCT denoising. Firstly, FSformer decomposes the LDCT images into low-frequency images and multi-layer high-frequency images by frequency separation blocks. Then, the low-frequency components are fused with the high-frequency components of different layers to remove the noise in the high-frequency components with the help of the potential texture of low-frequency parts. Next, the estimated noise images can be obtained by using Transformer stage in the frequency aggregation denoising block. Finally, they are fed into the reconstruction prediction block to obtain improved quality images. In addition, a compound loss function with frequency loss and Charbonnier loss is used to guide the training of the network. The performance of FSformer has been validated and evaluated on AAPM Mayo dataset, real Piglet dataset and clinical dataset. Compared with previous representative models in different architectures, FSformer achieves the optimal metrics with PSNR of 33.7714 dB and SSIM of 0.9254 on Mayo dataset, the testing time is 1.825 s. The experimental results show that FSformer is a state-of-the-art (SOTA) model with noise/artifact suppression and texture/organization preservation. Moreover, the model has certain robustness and can effectively improve LDCT image quality.

LIT-Former: Linking In-Plane and Through-Plane Transformers for Simultaneous CT Image Denoising and Deblurring

This paper studies 3D low-dose computed tomography (CT) imaging. Although various deep learning methods were developed in this context, typically they focus on 2D images and perform denoising due to low-dose and deblurring for super-resolution separately. Up to date, little work was done for simultaneous in-plane denoising and through-plane deblurring, which is important to obtain high-quality 3D CT images with lower radiation and faster imaging speed. For this task, a straightforward method is to directly train an end-to-end 3D network. However, it demands much more training data and expensive computational costs. Here, we propose to link in-plane and through-plane transformers for simultaneous in-plane denoising and through-plane deblurring, termed as LIT-Former, which can efficiently synergize in-plane and through-plane sub-tasks for 3D CT imaging and enjoy the advantages of both convolution and transformer networks. LIT-Former has two novel designs: efficient multi-head self-attention modules (eMSM) and efficient convolutional feed-forward networks (eCFN). First, eMSM integrates in-plane 2D self-attention and through-plane 1D self-attention to efficiently capture global interactions of 3D self-attention, the core unit of transformer networks. Second, eCFN integrates 2D convolution and 1D convolution to extract local information of 3D convolution in the same fashion. As a result, the proposed LIT-Former synergizes these two sub-tasks, significantly reducing the computational complexity as compared to 3D counterparts and enabling rapid convergence. Extensive experimental results on simulated and clinical datasets demonstrate superior performance over state-of-the-art models. The source code is made available at https://github.com/hao1635/LIT-Former.

Is Attention all You Need in Medical Image Analysis? A Review

Medical imaging is a key component in clinical diagnosis, treatment planning and clinical trial design, accounting for almost 90% of all healthcare data. CNNs achieved performance gains in medical image analysis (MIA) over the last years. CNNs can efficiently model local pixel interactions and be trained on small-scale MI data. Despite their important advances, typical CNN have relatively limited capabilities in modelling "global" pixel interactions, which restricts their generalisation ability to understand out-of-distribution data with different "global" information. The recent progress of Artificial Intelligence gave rise to Transformers, which can learn global relationships from data. However, full Transformer models need to be trained on large-scale data and involve tremendous computational complexity. Attention and Transformer compartments ("Transf/Attention") which can well maintain properties for modelling global relationships, have been proposed as lighter alternatives of full Transformers. Recently, there is an increasing trend to co-pollinate complementary local-global properties from CNN and Transf/Attention architectures, which led to a new era of hybrid models. The past years have witnessed substantial growth in hybrid CNN-Transf/Attention models across diverse MIA problems. In this systematic review, we survey existing hybrid CNN-Transf/Attention models, review and unravel key architectural designs, analyse breakthroughs, and evaluate current and future opportunities as well as challenges. We also introduced an analysis framework on generalisation opportunities of scientific and clinical impact, based on which new data-driven domain generalisation and adaptation methods can be stimulated.

A Review of deep learning methods for denoising of medical low-dose CT images

To prevent patients from being exposed to excess of radiation in CT imaging, the most common solution is to decrease the radiation dose by reducing the X-ray, and thus the quality of the resulting low-dose CT images (LDCT) is degraded, as evidenced by more noise and streaking artifacts. Therefore, it is important to maintain high quality CT image while effectively reducing radiation dose. In recent years, with the rapid development of deep learning technology, deep learning-based LDCT denoising methods have become quite popular because of their data-driven and high-performance features to achieve excellent denoising results. However, to our knowledge, no relevant article has so far comprehensively introduced and reviewed advanced deep learning denoising methods such as Transformer structures in LDCT denoising tasks. Therefore, based on the literatures related to LDCT image denoising published from year 2016-2023, and in particular from 2020 to 2023, this study presents a systematic survey of current situation, and challenges and future research directions in LDCT image denoising field. Four types of denoising networks are classified according to the network structure: CNN-based, Encoder-Decoder-based, GAN-based, and Transformer-based denoising networks, and each type of denoising network is described and summarized from the perspectives of structural features and denoising performances. Representative deep-learning denoising methods for LDCT are experimentally compared and analyzed. The study results show that CNN-based denoising methods capture image details efficiently through multi-level convolution operation, demonstrating superior denoising effects and adaptivity. Encoder-decoder networks with MSE loss, achieve outstanding results in objective metrics. GANs based methods, employing innovative generators and discriminators, obtain denoised images that exhibit perceptually a closeness to NDCT. Transformer-based methods have potential for improving denoising performances due to their powerful capability in capturing global information. Challenges and opportunities for deep learning based LDCT denoising are analyzed, and future directions are also presented.

CT image denoising methods for image quality improvement and radiation dose reduction

With the ever-increasing use of computed tomography (CT), concerns about its radiation dose have become a significant public issue. To address the need for radiation dose reduction, CT denoising methods have been widely investigated and applied in low-dose CT images. Numerous noise reduction algorithms have emerged, such as iterative reconstruction and most recently, deep learning (DL)-based approaches. Given the rapid advancements in Artificial Intelligence techniques, we recognize the need for a comprehensive review that emphasizes the most recently developed methods. Hence, we have performed a thorough analysis of existing literature to provide such a review. Beyond directly comparing the performance, we focus on pivotal aspects, including model training, validation, testing, generalizability, vulnerability, and evaluation methods. This review is expected to raise awareness of the various facets involved in CT image denoising and the specific challenges in developing DL-based models.

A Systematic Literature Review of 3D Deep Learning Techniques in Computed Tomography Reconstruction

Computed tomography (CT) is used in a wide range of medical imaging diagnoses. However, the reconstruction of CT images from raw projection data is inherently complex and is subject to artifacts and noise, which compromises image quality and accuracy. In order to address these challenges, deep learning developments have the potential to improve the reconstruction of computed tomography images. In this regard, our research aim is to determine the techniques that are used for 3D deep learning in CT reconstruction and to identify the training and validation datasets that are accessible. This research was performed on five databases. After a careful assessment of each record based on the objective and scope of the study, we selected 60 research articles for this review. This systematic literature review revealed that convolutional neural networks (CNNs), 3D convolutional neural networks (3D CNNs), and deep learning reconstruction (DLR) were the most suitable deep learning algorithms for CT reconstruction. Additionally, two major datasets appropriate for training and developing deep learning systems were identified: 2016 NIH-AAPM-Mayo and MSCT. These datasets are important resources for the creation and assessment of CT reconstruction models. According to the results, 3D deep learning may increase the effectiveness of CT image reconstruction, boost image quality, and lower radiation exposure. By using these deep learning approaches, CT image reconstruction may be made more precise and effective, improving patient outcomes, diagnostic accuracy, and healthcare system productivity.

Sinogram domain angular upsampling of sparse-view micro-CT with dense residual hierarchical transformer and attention-weighted loss

Reduced angular sampling is a key strategy for increasing scanning efficiency of micron-scale computed tomography (micro-CT). Despite boosting throughput, this strategy introduces noise and extrapolation artifacts due to undersampling. In this work, we present a solution to this issue, by proposing a novel Dense Residual Hierarchical Transformer (DRHT) network to recover high-quality sinograms from 2×, 4× and 8× undersampled scans. DRHT is trained to utilize limited information available from sparsely angular sampled scans and once trained, it can be applied to recover higher-resolution sinograms from shorter scan sessions. Our proposed DRHT model aggregates the benefits of a hierarchical- multi-scale structure along with the combination of local and global feature extraction through dense residual convolutional blocks and non-overlapping window transformer blocks respectively. We also propose a novel noise-aware loss function named KL-L1 to improve sinogram restoration to full resolution. KL-L1, a weighted combination of pixel-level and distribution-level cost functions, leverages inconsistencies in noise distribution and uses learnable spatial weight maps to improve the training of the DRHT model. We present ablation studies and evaluations of our method against other state-of-the-art (SOTA) models over multiple datasets. Our proposed DRHT network achieves an average increase in peak signal to noise ratio (PSNR) of 17.73 dB and a structural similarity index (SSIM) of 0.161, for 8× upsampling, across the three diverse datasets, compared to their respective Bicubic interpolated versions. This novel approach can be utilized to decrease radiation exposure to patients and reduce imaging time for large-scale CT imaging projects.

Self-adaption and texture generation: A hybrid loss function for low-dose CT denoising

BACKGROUND

Deep learning has been successfully applied to low-dose CT (LDCT) denoising. But the training of the model is very dependent on an appropriate loss function. Existing denoising models often use per-pixel loss, including mean abs error (MAE) and mean square error (MSE). This ignores the difference in denoising difficulty between different regions of the CT images and leads to the loss of large texture information in the generated image.

PURPOSE

In this paper, we propose a new hybrid loss function that adapts to the noise in different regions of CT images to balance the denoising difficulty and preserve texture details, thus acquiring CT images with high-quality diagnostic value using LDCT images, providing strong support for condition diagnosis.

METHODS

We propose a hybrid loss function consisting of weighted patch loss (WPLoss) and high-frequency information loss (HFLoss). To enhance the model's denoising ability of the local areas which are difficult to denoise, we improve the MAE to obtain WPLoss. After the generated image and the target image are divided into several patches, the loss weight of each patch is adaptively and dynamically adjusted according to its loss ratio. In addition, considering that texture details are contained in the high-frequency information of the image, we use HFLoss to calculate the difference between CT images in the high-frequency information part.

RESULTS

Our hybrid loss function improves the denoising performance of several models in the experiment, and obtains a higher peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Moreover, through visual inspection of the generated results of the comparison experiment, the proposed hybrid function can effectively suppress noise and retain image details.

CONCLUSIONS

We propose a hybrid loss function for LDCT image denoising, which has good interpretation properties and can improve the denoising performance of existing models. And the validation results of multiple models using different datasets show that it has good generalization ability. By using this loss function, high-quality CT images with low radiation are achieved, which can avoid the hazards caused by radiation and ensure the disease diagnosis for patients.

Physics-informed sinogram completion for metal artifact reduction in CT imaging

Objective.Metal artifacts in the computed tomography (CT) imaging are unavoidably adverse to the clinical diagnosis and treatment outcomes. Most metal artifact reduction (MAR) methods easily result in the over-smoothing problem and loss of structure details near the metal implants, especially for these metal implants with irregular elongated shapes. To address this problem, we present the physics-informed sinogram completion (PISC) method for MAR in CT imaging, to reduce metal artifacts and recover more structural textures.Approach.Specifically, the original uncorrected sinogram is firstly completed by the normalized linear interpolation algorithm to reduce metal artifacts. Simultaneously, the uncorrected sinogram is also corrected based on the beam-hardening correction physical model, to recover the latent structure information in metal trajectory region by leveraging the attenuation characteristics of different materials. Both corrected sinograms are fused with the pixel-wise adaptive weights, which are manually designed according to the shape and material information of metal implants. To furtherly reduce artifacts and improve the CT image quality, a post-processing frequency split algorithm is adopted to yield the final corrected CT image after reconstructing the fused sinogram.Main results.We qualitatively and quantitatively evaluated the presented PISC method on two simulated datasets and three real datasets. All results demonstrate that the presented PISC method can effectively correct the metal implants with various shapes and materials, in terms of artifact suppression and structure preservation.Significance.We proposed a sinogram-domain MAR method to compensate for the over-smoothing problem existing in most MAR methods by taking advantage of the physical prior knowledge, which has the potential to improve the performance of the deep learning based MAR approaches.

U-Patch GAN: A Medical Image Fusion Method Based on GAN

Although medical imaging is frequently used to diagnose diseases, in complex diagnostic situations, specialists typically need to look at different modalities of image information. Creating a composite multimodal medical image can aid professionals in making quick and accurate diagnoses of diseases. The fused images of many medical image fusion algorithms, however, are frequently unable to precisely retain the functional and structural information of the source image. This work develops an end-to-end model based on GAN (U-Patch GAN) to implement the self-supervised fusion of multimodal brain images in order to enhance the fusion quality. The model uses the classical network U-net as the generator, and it uses the dual adversarial mechanism based on the Markovian discriminator (PatchGAN) to enhance the generator's attention to high-frequency information. To ensure that the network satisfies the Lipschitz continuity, we apply the spectral norm to each layer of the network. We also propose better adversarial loss and feature loss (feature matching loss and VGG-16 perceptual loss) based on the F-norm, which significantly enhance the quality of fused images. On public data sets, we performed a lot of tests. First, we studied how clinically useful the fused image was. The model's performance in single-slice images and continuous-slice images was then confirmed by comparison with other six most popular mainstream fusion approaches. Finally, we verify the effectiveness of the adversarial loss and feature loss.

DeflickerCycleGAN: Learning to Detect and Remove Flickers in a Single Image

Eliminating the flickers in digital images captured by rolling shutter cameras is a fundamental and important task in computer vision applications. The flickering effect in a single image stems from the mechanism of asynchronous exposure of rolling shutters employed by cameras equipped with CMOS sensors. In an artificial lighting environment, the light intensity captured at different time intervals varies due to the fluctuation of the power grid, ultimately resulting in the flickering artifact in the image. Up to date, there are few studies related to single image deflickering. Further, it is even more challenging to remove flickers without a priori information, e.g., camera parameters or paired images. To address these challenges, we propose an unsupervised framework termed DeflickerCycleGAN, which is trained on unpaired images for end-to-end single image deflickering. Besides the cycle-consistency loss to maintain the similarity of image contents, we meticulously design another two novel loss functions, i.e., gradient loss and flicker loss, to reduce the risk of edge blurring and color distortion. Moreover, we provide a strategy to determine whether an image contains flickers or not without extra training, which leverages an ensemble methodology based on the output of two previously trained markovian discriminators. Extensive experiments on both synthetic and real datasets show that our proposed DeflickerCycleGAN not only achieves excellent performance on flicker removal in a single image but also shows high accuracy and competitive generalization ability on flicker detection, compared to that of a well-trained classifier based on ResNet50.

A region-adaptive non-local denoising algorithm for low-dose computed tomography images

Low-dose computed tomography (LDCT) can effectively reduce radiation exposure in patients. However, with such dose reductions, large increases in speckled noise and streak artifacts occur, resulting in seriously degraded reconstructed images. The non-local means (NLM) method has shown potential for improving the quality of LDCT images. In the NLM method, similar blocks are obtained using fixed directions over a fixed range. However, the denoising performance of this method is limited. In this paper, a region-adaptive NLM method is proposed for LDCT image denoising. In the proposed method, pixels are classified into different regions according to the edge information of the image. Based on the classification results, the adaptive searching window, block size and filter smoothing parameter could be modified in different regions. Furthermore, the candidate pixels in the searching window could be filtered based on the classification results. In addition, the filter parameter could be adjusted adaptively based on intuitionistic fuzzy divergence (IFD). The experimental results showed that the proposed method performed better in LDCT image denoising than several of the related denoising methods in terms of numerical results and visual quality.

Structure-preserved meta-learning uniting network for improving low-dose CT quality

Objective.Deep neural network (DNN) based methods have shown promising performances for low-dose computed tomography (LDCT) imaging. However, most of the DNN-based methods are trained on simulated labeled datasets, and the low-dose simulation algorithms are usually designed based on simple statistical models which deviate from the real clinical scenarios, which could lead to issues of overfitting, instability and poor robustness. To address these issues, in this work, we present a structure-preserved meta-learning uniting network (shorten as 'SMU-Net') to suppress noise-induced artifacts and preserve structure details in the unlabeled LDCT imaging task in real scenarios.Approach.Specifically, the presented SMU-Net contains two networks, i.e., teacher network and student network. The teacher network is trained on simulated labeled dataset and then helps the student network train with the unlabeled LDCT images via the meta-learning strategy. The student network is trained on real LDCT dataset with the pseudo-labels generated by the teacher network. Moreover, the student network adopts the Co-teaching strategy to improve the robustness of the presented SMU-Net.Main results.We validate the proposed SMU-Net method on three public datasets and one real low-dose dataset. The visual image results indicate that the proposed SMU-Net has superior performance on reducing noise-induced artifacts and preserving structure details. And the quantitative results exhibit that the presented SMU-Net method generally obtains the highest signal-to-noise ratio (PSNR), the highest structural similarity index measurement (SSIM), and the lowest root-mean-square error (RMSE) values or the lowest natural image quality evaluator (NIQE) scores.Significance.We propose a meta learning strategy to obtain high-quality CT images in the LDCT imaging task, which is designed to take advantage of unlabeled CT images to promote the reconstruction performance in the LDCT environments.

Generation of synthetic ground glass nodules using generative adversarial networks (GANs)

BACKGROUND

Data shortage is a common challenge in developing computer-aided diagnosis systems. We developed a generative adversarial network (GAN) model to generate synthetic lung lesions mimicking ground glass nodules (GGNs).

METHODS

We used 216 computed tomography images with 340 GGNs from the Lung Image Database Consortium and Image Database Resource Initiative database. A GAN model retrieving information from the whole image and the GGN region was built. The generated samples were evaluated with visual Turing test performed by four experienced radiologists or pulmonologists. Radiomic features were compared between real and synthetic nodules. Performances were evaluated by area under the curve (AUC) at receiver operating characteristic analysis. In addition, we trained a classification model (ResNet) to investigate whether the synthetic GGNs can improve the performances algorithm and how performances changed as a function of labelled data used in training.

RESULTS

Of 51 synthetic GGNs, 19 (37%) were classified as real by clinicians. Of 93 radiomic features, 58 (62.4%) showed no significant difference between synthetic and real GGNs (p ≥ 0.052). The discrimination performances of physicians (AUC 0.68) and radiomics (AUC 0.66) were similar, with no-significantly different (p = 0.23), but clinicians achieved a better accuracy (AUC 0.74) than radiomics (AUC 0.62) (p < 0.001). The classification model trained on datasets with synthetic data performed better than models without the addition of synthetic data.

CONCLUSIONS

GAN has promising potential for generating GGNs. Through similar AUC, clinicians achieved better ability to diagnose whether the data is synthetic than radiomics.

Trainable joint bilateral filters for enhanced prediction stability in low-dose CT

Low-dose computed tomography (CT) denoising algorithms aim to enable reduced patient dose in routine CT acquisitions while maintaining high image quality. Recently, deep learning (DL)-based methods were introduced, outperforming conventional denoising algorithms on this task due to their high model capacity. However, for the transition of DL-based denoising to clinical practice, these data-driven approaches must generalize robustly beyond the seen training data. We, therefore, propose a hybrid denoising approach consisting of a set of trainable joint bilateral filters (JBFs) combined with a convolutional DL-based denoising network to predict the guidance image. Our proposed denoising pipeline combines the high model capacity enabled by DL-based feature extraction with the reliability of the conventional JBF. The pipeline's ability to generalize is demonstrated by training on abdomen CT scans without metal implants and testing on abdomen scans with metal implants as well as on head CT data. When embedding RED-CNN/QAE, two well-established DL-based denoisers in our pipeline, the denoising performance is improved by 10%/82% (RMSE) and 3%/81% (PSNR) in regions containing metal and by 6%/78% (RMSE) and 2%/4% (PSNR) on head CT data, compared to the respective vanilla model. Concluding, the proposed trainable JBFs limit the error bound of deep neural networks to facilitate the applicability of DL-based denoisers in low-dose CT pipelines.

Dilated Convolution ResNet with Boosting Attention Modules and Combined Loss Functions for LDCT Image Denoising

With the increasing concern regarding the radiation exposure of patients undergoing computed tomography (CT) scans, researchers have been using deep learning techniques to improve the quality of denoised low-dose CT (LDCT) images. In this paper, a cascaded dilated residual network (ResNet) with integrated attention modules, specifically spatial- and channel- attention modules, is proposed. This experiment demonstrated how these attention modules improved the denoised CT image by testing a simple ResNet with and without the modules. Further, an investigation regarding the effectiveness of per-pixel loss, perceptual loss via VGG16-Net, and structural dissimilarity loss functions is also covered through an ablation experiment. By knowing how these loss functions affect the output denoised images, a combination of the these loss function is then proposed which aims to prevent edge over-smoothing, enhance textural details and finally, preserve structural details on the denoised images. Finally, a bench testing was also done by comparing the visual and quantitative results of the proposed model with the state-of-the-art models such as block matching 3D (BM3D), patch-GAN and dilated convolution with edge detection layer (DRL-E-MP) for accuracy.

CCN-CL: A content-noise complementary network with contrastive learning for low-dose computed tomography denoising

In recent years, low-dose computed tomography (LDCT) has played an increasingly important role in the diagnosis CT to reduce the potential adverse effects of x-ray radiation on patients while maintaining the same diagnostic image quality. Current deep learning-based denoising methods applied to LDCT imaging only use normal dose CT (NDCT) images as positive examples to guide the denoising process. Recent studies on contrastive learning have proved that the original images as negative examples can also be helpful for network learning. Therefore, this paper proposes a novel content-noise complementary network with contrastive learning for an LDCT denoising task. First, to better train our proposed network, a contrastive learning loss, taking the NDCT image as a positive example and the original LDCT image as a negative example to guide the network learning is added. Furthermore, we also design a network structure that combines content-noise complementary learning strategy, attention mechanism, and deformable convolution for better network performance. In an evaluation study, we compare the performance of our designed network with some of the state-of-the-art methods in the 2016 NIH-AAPM-Mayo Clinic Low Dose CT Grand Challenge dataset. The quantitative and qualitative evaluation results demonstrate the feasibility and effectiveness of applying our proposed CCN-CL network model as a new deep learning-based LDCT denoising method.