A Noise-level-aware Framework for PET Image Denoising
Center of Advanced Medical Computing and Analysis, MGH/HMS（麻省总医院/哈佛医学院高级医学计算与分析中心）；Johns Hopkins University（约翰斯·霍普金斯大学）；GE Healthcare（通用电气医疗）；University of Bern（伯尔尼大学）
Ye Li, Jianan Cui, Junyu Chen, Guodong Zeng, Scott Wollenweber, Floris Jansen, Se-In Jang, Kyungsang Kim, Kuang Gong, Quanzheng Li
In PET, the amount of relative (signal-dependent) noise present in different body regions can be significantly different and is inherently related to the number of counts present in that region. The number of counts in a region depends, in principle and among other factors, on the total administered activity, scanner sensitivity, image acquisition duration, radiopharmaceutical tracer uptake in the region, and patient local body morphometry surrounding the region. In theory, less amount of denoising operations is needed to denoise a high-count (low relative noise) image than images a low-count (high relative noise) image, and vice versa. The current deep-learning-based methods for PET image denoising are predominantly trained on image appearance only and have no special treatment for images of different noise levels. Our hypothesis is that by explicitly providing the local relative noise level of the input image to a deep convolutional neural network (DCNN), the DCNN can outperform itself trained on image appearance only. To this end, we propose a noise-level-aware framework denoising framework that allows embedding of local noise level into a DCNN. The proposed is trained and tested on 30 and 15 patient PET images acquired on a GE Discovery MI PET/CT system. Our experiments showed that the increases in both PSNR and SSIM from our backbone network with relative noise level embedding (NLE) versus the same network without NLE were statistically significant with p<0.001, and the proposed method significantly outperformed a strong baseline method by a large margin.
Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School（哈佛医学院麻省总医院戈登医学成像中心）；Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital, Harvard Medical School（哈佛医学院麻省总医院高级医学计算与分析中心）；University of Texas MD Anderson Cancer Center（德克萨斯大学MD安德森癌症中心）
Due to various physical degradation factors and limited counts received, PET image quality needs further improvements. The denoising diffusion probabilistic models (DDPM) are distribution learning-based models, which try to transform a normal distribution into a specific data distribution based on iterative refinements. In this work, we proposed and evaluated different DDPM-based methods for PET image denoising. Under the DDPM framework, one way to perform PET image denoising is to provide the PET image and/or the prior image as the network input. Another way is to supply the prior image as th…
This paper evaluates the performance of supervised and unsupervised deep learning models for denoising positron emission tomography (PET) images in the presence of reduced acquisition times. Our experiments consider 212 studies (56908 images), and evaluate the models using 2D (RMSE, SSIM) and 3D (SUVpeak and SUVmax error for the regions of interest) metrics. It was shown that, in contrast to previous studies, supervised models (ResNet, Unet, SwinIR) outperform unsupervised models (pix2pix GAN and CycleGAN with ResNet backbone and various auxiliary losses) in the reconstruction of 2D PET images…
Hamamatsu Photonics K. K.（滨松光子学株式会社）；Hamamatsu University School of Medicine（滨松医科大学）
Although supervised convolutional neural networks (CNNs) often outperform conventional alternatives for denoising positron emission tomography (PET) images, they require many low- and high-quality reference PET image pairs. Herein, we propose an unsupervised 3D PET image denoising method based on an anatomical information-guided attention mechanism. The proposed magnetic resonance-guided deep decoder (MR-GDD) utilizes the spatial details and semantic features of MR-guidance image more effectively by introducing encoder-decoder and deep decoder subnetworks. Moreover, the specific shapes and pat…
Cycle-Constrained Adversarial Denoising Convolutional Network for PET Image Denoising: Multi-Dimensional Validation on Large Datasets with Reader Study and Real Low-Dose Data
Low-resolution and signal-dependent noise distribution in positron emission tomography (PET) images makes denoising process an inevitable step prior to qualitative and quantitative image analysis tasks. Conventional PET denoising methods either over-smooth small-sized structures due to resolution limitation or make incorrect assumptions about the noise characteristics. Therefore, clinically important quantitative information may be corrupted. To address these challenges, we introduced a novel approach to remove signal-dependent noise in the PET images where the noise distribution was considere…
UniPET: a universal network for high-quality PET image denoising across varied dose reduction factors
School of Biological Science and Medical Engineering（生物科学与医学工程学院）；State Key Laboratory of Software Development Environment（软件开发环境国家重点实验室）；Key Laboratory of Biomechanics, Mechanobiology of Ministry of Education（教育部生物力学与机械生物学重点实验室）；Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University（北京生物医学工程先进创新中心，北航）；Department of Biomedical Engineering（生物医学工程系）；Department of Gynecology Oncology（妇科肿瘤科）；National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital（国家癌症中心/国家癌症临床研究中心/癌症医院）；Chinese Academy of Medical Sciences（中国医学科学院）；Peking Union Medical College（北京协和医学院）；Xiaomi Corporation（小米公司）
Exploiting network optimization stability for enhanced PET image denoising using deep image prior
Central Research Laboratory, Hamamatsu Photonics K. K.（滨松光子学株式会社中央研究所）；Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology (QST)（国立量子科学技术研究开发机构量子医学科学研究所）；J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida（佛罗里达大学J. Crayton Pruitt家族生物医学工程系）
A Kernel Space-based Multidimensional Sparse Model for Dynamic PET Image Denoising
Zhejiang Polytechnic University of Mechanical and Electrical Engineering（浙江机电职业技术学院）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥国家综合科学中心人工智能研究院）；School of Electrical and Optical Engineering（南京理工大学电气与光学工程学院）；Zhejiang Lab（浙江实验室）；School of Intelligent Science and Technology（智能科学与技术学院）；Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences（杭州高等研究院，中国科学院大学）；Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau（澳门大学生物医学成像实验室）；College of Optical Science and Engineering（光学工程学院）；College of Biomedical Engineering and Instrument Science（生物医学工程与仪器科学学院）
U-TTT: Towards Generalizable PET Image Denoising via Test-Time Training
School of Biological Science and Medical Engineering, Beihang University（北京航空航天大学生物科学与医学工程学院）；Department of Biomedical Engineering, Tsinghua University（清华大学生物医学工程系）；School of Aerospace Engineering, Tsinghua University（清华大学航天航空学院）；ByteDance Inc.（字节跳动有限公司）
Spach Transformer: Spatial and Channel-wise Transformer Based on Local and Global Self-attentions for PET Image Denoising
Gordon Center for Medical Imaging（戈登医学影像中心）；Center for Advanced Medical Computing and Analysis（高级医学计算与分析中心）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；University of Florida（佛罗里达大学）；University of Texas MD Anderson Cancer Center（德克萨斯大学MD安德森癌症中心）；Johns Hopkins University（约翰斯·霍普金斯大学）
针对 PET 图像噪声及三维全局自注意力计算成本高的问题，提出融合局部与全局 MSA 的空间通道编解码 Transformer，并在多示踪剂数据集上验证其去噪性能优于现有深度学习架构。
Sichuan University（四川大学）；MinFound Medical Systems Co., Ltd（明峰医疗系统股份有限公司）
Positron emission tomography (PET) reconstruction has become an ill-posed inverse problem due to low-count projection data, and a robust algorithm is urgently required to improve imaging quality. Recently, the deep image prior (DIP) has drawn much attention and has been successfully applied in several image restoration tasks, such as denoising and inpainting, since it does not need any labels (reference image). However, overfitting is a vital defect of this framework. Hence, many methods have been proposed to mitigate this problem, and DeepRED is a typical representation that combines DIP and …
We propose a fully-convolutional neural-network architecture for image denoising which is simple yet powerful. Its structure allows to exploit the gradual nature of the denoising process, in which shallow layers handle local noise statistics, while deeper layers recover edges and enhance textures. Our method advances the state-of-the-art when trained for different noise levels and distributions (both Gaussian and Poisson). In addition, we show that making the denoiser class-aware by exploiting semantic class information boosts performance, enhances textures and reduces artifacts.
MPI-CBG/PKS (CSBD)（马克斯·普朗克分子细胞生物学与遗传学研究所/普朗克计算生物学中心（德累斯顿系统生物学中心））；Brno University of Technology（布尔诺理工大学）
Today, Convolutional Neural Networks (CNNs) are the leading method for image denoising. They are traditionally trained on pairs of images, which are often hard to obtain for practical applications. This motivates self-supervised training methods such as Noise2Void~(N2V) that operate on single noisy images. Self-supervised methods are, unfortunately, not competitive with models trained on image pairs. Here, we present 'Probabilistic Noise2Void' (PN2V), a method to train CNNs to predict per-pixel intensity distributions. Combining these with a suitable description of the noise, we obtain a compl…
The increasing demand for high image quality in mobile devices brings forth the need for better computational enhancement techniques, and image denoising in particular. At the same time, the images captured by these devices can be categorized into a small set of semantic classes. However simple, this observation has not been exploited in image denoising until now. In this paper, we demonstrate how the reconstruction quality improves when a denoiser is aware of the type of content in the image. To this end, we first propose a new fully convolutional deep neural network architecture which is sim…
This paper presents a novel context-aware image denoising algorithm that combines an adaptive image smoothing technique and color reduction techniques to remove perturbation from adversarial images. Adaptive image smoothing is achieved using auto-threshold canny edge detection to produce an accurate edge map used to produce a blurred image that preserves more edge features. The proposed algorithm then uses color reduction techniques to reconstruct the image using only a few representative colors. Through this technique, the algorithm can reduce the effects of adversarial perturbations on image…
Texas A&M University（得克萨斯农工大学）；Nanyang Technological University（南洋理工大学）；The University of Sydney（悉尼大学）
Several recent works discussed application-driven image restoration neural networks, which are capable of not only removing noise in images but also preserving their semantic-aware details, making them suitable for various high-level computer vision tasks as the pre-processing step. However, such approaches require extra annotations for their high-level vision tasks, in order to train the joint pipeline using hybrid losses. The availability of those annotations is yet often limited to a few image sets, potentially restricting the general applicability of these methods to denoising more unseen …
Nanyang Technological University（南洋理工大学）；Alibaba Group（阿里巴巴集团）；Tianjin University（天津大学）；State Administration of Cultural Heritage（国家文物局）
Image denoising can remove natural noise that widely exists in images captured by multimedia devices due to low-quality imaging sensors, unstable image transmission processes, or low light conditions. Recent works also find that image denoising benefits the high-level vision tasks, e.g., image classification. In this work, we try to challenge this common sense and explore a totally new problem, i.e., whether the image denoising can be given the capability of fooling the state-of-the-art deep neural networks (DNNs) while enhancing the image quality. To this end, we initiate the very first attem…
Volumetric Conditional Score-based Residual Diffusion Model for PET/MR Denoising
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Zhejiang University（浙江大学）；Tsinghua University（清华大学）；Neuroscience Research Institute（神经科学研究所）；Gachon University of Medicine and Science（嘉泉医科大学）
BiEvLight: Bi-level Learning of Task-Aware Event Refinement for Low-Light Image Enhancement
College of Computer and Data Science, Fuzhou University（福州大学计算机与数据科学学院）；College of Computer Science and Technology, Shandong Technology and Business University（山东科技职业大学计算机科学与技术学院）
WIA-LD2ND: Wavelet-based Image Alignment for Self-supervised Low-Dose CT Denoising
School of Computer Science, Wuhan University（武汉大学计算机学院）；School of Computer Science, Central China Normal University（华中师范大学计算机学院）；University of Louisville（路易斯维尔大学）
FedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET Denoising
Yale University（耶鲁大学）；University of Southern California（南加州大学）；University of California Irvine（加州大学欧文分校）；University of Science and Technology of China（中国科学技术大学）；Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院）；Inselspital, Bern University Hospital（伯尔尼大学医院 Inselspital）；University of Bern（伯尔尼大学）；Technical University of Munich（慕尼黑工业大学）
MDAA-Diff: CT-Guided Multi-Dose Adaptive Attention Diffusion Model for PET Denoising
School of Biomedical Engineering, Southern Medical University（南方医科大学生物医学工程学院）；School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）；Nanfang PET Center, Nanfang Hospital Southern Medical University（南方医科大学南芳医院PET中心）
End-to-end Triple-domain PET Enhancement: A Hybrid Denoising-and-reconstruction Framework for Reconstructing Standard-dose PET Images from Low-dose PET Sinograms
ShanghaiTech University（上海科技大学）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）；Shanghai Clinical Research and Trial Center（上海临床研究与试验中心）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
提出端到端三域 TriPLET 框架，融合投影域去噪、小波域重建和图像域对抗评估，从低剂量 PET 正弦图恢复标准剂量图像，并取得最优相似度与信噪比。
Gating is commonly used in PET imaging to reduce respiratory motion blurring and facilitate more sophisticated motion correction methods. In the applications of low dose PET, however, reducing injection dose causes increased noise and reduces signal-to-noise ratio (SNR), subsequently corrupting the motion estimation/correction steps, causing inferior image quality. To tackle these issues, we first propose a Siamese adversarial network (SAN) that can efficiently recover high dose gated image volume from low dose gated image volume. To ensure the appearance consistency between the recovered gate…
Northeastern University（东北大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Rensselaer Polytechnic Institute（伦斯勒理工学院）
Due to the widespread use of positron emission tomography (PET) in clinical practice, the potential risk of PET-associated radiation dose to patients needs to be minimized. However, with the reduction in the radiation dose, the resultant images may suffer from noise and artifacts that compromise diagnostic performance. In this paper, we propose a parameter-transferred Wasserstein generative adversarial network (PT-WGAN) for low-dose PET image denoising. The contributions of this paper are twofold: i) a PT-WGAN framework is designed to denoise low-dose PET images without compromising structural…
Self is the Best Learner: CT-free Ultra-Low-Dose PET Organ Segmentation via Collaborating Denoising and Segmentation Learning
School of Biomedical Engineering, Southern Medical University（生物医学工程学院，南方医科大学）；School of Biomedical Engineering, Shanghai Jiao Tong University（生物医学工程学院，上海交通大学）；Nanfang PET Center, Nanfang Hospital Southern Medical University（南芳PET中心，南方医科大学南芳医院）；Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University（广东省医学图像处理重点实验室，南方医科大学）；Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University（广东省医学影像与诊断技术工程实验室，南方医科大学）；Pazhou Lab（琶洲实验室）
Positron Emission Tomography (PET) is an essential technique in many clinical applications that allows for quantitative imaging at the molecular level. This study aims to develop a denoising method using novel dilated convolutional neural network to recover full-count images from low-count images. We adopted similar hierarchal structure from the conventional uNet and incorporated dilated kernels in each convolution to allow the network to observe larger, and perhaps, more robust, features within the image. Our dNet were trained alongside a uNet for comparison. Our 2.5D model used a training se…
Gordon Center for Medical Imaging（戈登医学影像中心）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Martinos Center for Biomedical Imaging（马蒂诺斯生物医学影像中心）；University of California, Davis（加州大学戴维斯分校）
Direct reconstruction methods have been developed to estimate parametric images directly from the measured PET sinograms by combining the PET imaging model and tracer kinetics in an integrated framework. Due to limited counts received, signal-to-noise-ratio (SNR) and resolution of parametric images produced by direct reconstruction frameworks are still limited. Recently supervised deep learning methods have been successfully applied to medical imaging denoising/reconstruction when large number of high-quality training labels are available. For static PET imaging, high-quality training labels c…
Quantitative accuracy and thus diagnostic precision in Emission Tomography is impaired by the inherent random characteristics of the data acquisition leading to statistical image noise. Edge preserving spatial variation regularized iterative image reconstruction approaches require case-specific control parameter adaptation for optimized contrast-vs-noise tradeoff. For MLEM reconstruction, we propose and evaluate iRDF which automatically adapts RDP edge preservation parameters according to local image noise and PET data characteristics. In order to distinguish between clustered noise spots and …
Fed-NDIF: A Noise-Embedded Federated Diffusion Model For Low-Count Whole-Body PET Denoising
Yale University（耶鲁大学）；Northwestern University（西北大学）；Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院）；Inselspital, Bern University Hospital, University of Bern（伯尔尼大学医院 Inselspital，伯尔尼大学）；Technical University of Munich（慕尼黑工业大学）；Yale School of Medicine（耶鲁医学院）
Indian Institute of Technology (IIT) Bombay（印度理工学院孟买分校）；IITB-Monash Research Academy（印度理工学院孟买分校-莫纳什研究学院）；Monash University（莫纳什大学）
Radiation exposure in positron emission tomography (PET) imaging limits its usage in the studies of radiation-sensitive populations, e.g., pregnant women, children, and adults that require longitudinal imaging. Reducing the PET radiotracer dose or acquisition time reduces photon counts, which can deteriorate image quality. Recent deep-neural-network (DNN) based methods for image-to-image translation enable the mapping of low-quality PET images (acquired using substantially reduced dose), coupled with the associated magnetic resonance imaging (MRI) images, to high-quality PET images. However, s…
Positron Emission Tomography (PET) is an important tool for studying Alzheimer's disease (AD). PET scans can be used as diagnostics tools, and to provide molecular characterization of patients with cognitive disorders. However, multiple tracers are needed to measure glucose metabolism (18F-FDG), synaptic vesicle protein (11C-UCB-J), and $β$-amyloid (11C-PiB). Administering multiple tracers to patient will lead to high radiation dose and cost. In addition, access to PET scans using new or less-available tracers with sophisticated production methods and short half-life isotopes may be very limit…
Two-Phase Multi-Dose-Level PET Image Reconstruction with Dose Level Awareness
School of Computer Science, Sichuan University（四川大学计算机学院）；School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University（上海科技大学生物医学工程学院 & 先进医学材料与器件国家重点实验室）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）
High noise and low spatial resolution are two key confounding factors that limit the qualitative and quantitative accuracy of PET images. AI models for image denoising and deblurring are becoming increasingly popular for post-reconstruction enhancement of PET images. We present here a detailed review of recent efforts for AI-based PET image enhancement with a focus on network architectures, data types, loss functions, and evaluation metrics. We also highlight emerging areas in this field that are quickly gaining popularity, identify barriers to large-scale adoption of AI models for PET image e…
