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Fu M, Zhang N, Huang Z, Zhou C, Zhang X, Yuan J, He Q, Yang Y, Zheng H, Liang D, Wu FX, Fan W, Hu Z. OIF-Net: An Optical Flow Registration-Based PET/MR Cross-Modal Interactive Fusion Network for Low-Count Brain PET Image Denoising. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:1554-1567. [PMID: 38096101 DOI: 10.1109/tmi.2023.3342809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
The short frames of low-count positron emission tomography (PET) images generally cause high levels of statistical noise. Thus, improving the quality of low-count images by using image postprocessing algorithms to achieve better clinical diagnoses has attracted widespread attention in the medical imaging community. Most existing deep learning-based low-count PET image enhancement methods have achieved satisfying results, however, few of them focus on denoising low-count PET images with the magnetic resonance (MR) image modality as guidance. The prior context features contained in MR images can provide abundant and complementary information for single low-count PET image denoising, especially in ultralow-count (2.5%) cases. To this end, we propose a novel two-stream dual PET/MR cross-modal interactive fusion network with an optical flow pre-alignment module, namely, OIF-Net. Specifically, the learnable optical flow registration module enables the spatial manipulation of MR imaging inputs within the network without any extra training supervision. Registered MR images fundamentally solve the problem of feature misalignment in the multimodal fusion stage, which greatly benefits the subsequent denoising process. In addition, we design a spatial-channel feature enhancement module (SC-FEM) that considers the interactive impacts of multiple modalities and provides additional information flexibility in both the spatial and channel dimensions. Furthermore, instead of simply concatenating two extracted features from these two modalities as an intermediate fusion method, the proposed cross-modal feature fusion module (CM-FFM) adopts cross-attention at multiple feature levels and greatly improves the two modalities' feature fusion procedure. Extensive experimental assessments conducted on real clinical datasets, as well as an independent clinical testing dataset, demonstrate that the proposed OIF-Net outperforms the state-of-the-art methods.
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Dashtbani Moghari M, Sanaat A, Young N, Moore K, Zaidi H, Evans A, Fulton RR, Kyme AZ. Reduction of scan duration and radiation dose in cerebral CT perfusion imaging of acute stroke using a recurrent neural network. Phys Med Biol 2023; 68:165005. [PMID: 37327792 DOI: 10.1088/1361-6560/acdf3a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Accepted: 06/16/2023] [Indexed: 06/18/2023]
Abstract
Objective. Cerebral CT perfusion (CTP) imaging is most commonly used to diagnose acute ischaemic stroke and support treatment decisions. Shortening CTP scan duration is desirable to reduce the accumulated radiation dose and the risk of patient head movement. In this study, we present a novel application of a stochastic adversarial video prediction approach to reduce CTP imaging acquisition time.Approach. A variational autoencoder and generative adversarial network (VAE-GAN) were implemented in a recurrent framework in three scenarios: to predict the last 8 (24 s), 13 (31.5 s) and 18 (39 s) image frames of the CTP acquisition from the first 25 (36 s), 20 (28.5 s) and 15 (21 s) acquired frames, respectively. The model was trained using 65 stroke cases and tested on 10 unseen cases. Predicted frames were assessed against ground-truth in terms of image quality and haemodynamic maps, bolus shape characteristics and volumetric analysis of lesions.Main results. In all three prediction scenarios, the mean percentage error between the area, full-width-at-half-maximum and maximum enhancement of the predicted and ground-truth bolus curve was less than 4 ± 4%. The best peak signal-to-noise ratio and structural similarity of predicted haemodynamic maps was obtained for cerebral blood volume followed (in order) by cerebral blood flow, mean transit time and time to peak. For the 3 prediction scenarios, average volumetric error of the lesion was overestimated by 7%-15%, 11%-28% and 7%-22% for the infarct, penumbra and hypo-perfused regions, respectively, and the corresponding spatial agreement for these regions was 67%-76%, 76%-86% and 83%-92%.Significance. This study suggests that a recurrent VAE-GAN could potentially be used to predict a portion of CTP frames from truncated acquisitions, preserving the majority of clinical content in the images, and potentially reducing the scan duration and radiation dose simultaneously by 65% and 54.5%, respectively.
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Affiliation(s)
- Mahdieh Dashtbani Moghari
- School of Biomedical Engineering, Faculty of Engineering and Information Technologies, The University of Sydney, Sydney, Australia
| | - Amirhossein Sanaat
- Geneva University Hospitals, Division of Nuclear Medicine & Molecular Imaging, CH-1205 Geneva, Switzerland
| | - Noel Young
- Department of Radiology, Westmead Hospital, Sydney, Australia
- Medical imaging group, School of Medicine, Western Sydney University, Sydney, Australia
| | - Krystal Moore
- Department of Radiology, Westmead Hospital, Sydney, Australia
| | - Habib Zaidi
- Geneva University Hospitals, Division of Nuclear Medicine & Molecular Imaging, CH-1205 Geneva, Switzerland
| | - Andrew Evans
- Department of Aged Care & Stroke, Westmead Hospital, Sydney, Australia
- School of Health Sciences, University of Sydney, Sydney, Australia
| | - Roger R Fulton
- School of Health Sciences, University of Sydney, Sydney, Australia
- Department of Medical Physics, Westmead Hospital, Sydney, Australia
- The Brain & Mind Centre, The University of Sydney, Sydney, Australia
| | - Andre Z Kyme
- School of Biomedical Engineering, Faculty of Engineering and Information Technologies, The University of Sydney, Sydney, Australia
- The Brain & Mind Centre, The University of Sydney, Sydney, Australia
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Zhu L, Han Y, Xi X, Fu H, Tan S, Liu M, Yang S, Liu C, Li L, Yan B. STEDNet: Swin transformer-based encoder-decoder network for noise reduction in low-dose CT. Med Phys 2023. [PMID: 36708286 DOI: 10.1002/mp.16249] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 11/04/2022] [Accepted: 01/17/2023] [Indexed: 01/29/2023] Open
Abstract
BACKGROUND Low-dose computed tomography (LDCT) can reduce the dose of X-ray radiation, making it increasingly significant for routine clinical diagnosis and treatment planning. However, the noise introduced by low-dose X-ray exposure degrades the quality of CT images, affecting the accuracy of clinical diagnosis. Purpose The noises, artifacts, and high-frequency components are similarly distributed in LDCT images. Transformer can capture global context information in an attentional manner to create distant dependencies on targets and extract more powerful features. In this paper, we reduce the impact of image errors on the ability to retain detailed information and improve the noise suppression performance by fully mining the distribution characteristics of image information. METHODS This paper proposed an LDCT noise and artifact suppressing network based on Swin Transformer. The network includes a noise extraction sub-network and a noise removal sub-network. The noise extraction and removal capability are improved using a coarse extraction network of high-frequency features based on full convolution. The noise removal sub-network improves the network's ability to extract relevant image features by using a Swin Transformer with a shift window as an encoder-decoder and skip connections for global feature fusion. Also, the perceptual field is extended by extracting multi-scale features of the images to recover the spatial resolution of the feature maps. The network uses a loss constraint with a combination of L1 and MS-SSIM to improve and ensure the stability and denoising effect of the network. RESULTS The denoising ability and clinical applicability of the methods were tested using clinical datasets. Compared with DnCNN, RED-CNN, CBDNet and TSCN, the STEDNet method shows a better denoising effect on RMSE and PSNR. The STEDNet method effectively removes image noise and preserves the image structure to the maximum extent, making the reconstructed image closest to the NDCT image. The subjective and objective analysis of several sets of experiments shows that the method in this paper can effectively maintain the structure, edges, and textures of the denoised images while having good noise suppression performance. In the real data evaluation, the RMSE of this method is reduced by 18.82%, 15.15%, 2.25%, and 1.10% on average compared with DnCNN, RED-CNN, CBDNet, and TSCNN, respectively. The average improvement of PSNR is 9.53%, 7.33%, 2.65%, and 3.69%, respectively. CONCLUSIONS This paper proposed a LDCT image denoising algorithm based on end-to-end training. The method in this paper can effectively improve the diagnostic performance of CT images by constraining the details of the images and restoring the LDCT image structure. The problem of increased noise and artifacts in CT images can be solved while maintaining the integrity of CT image tissue structure and pathological information. Compared with other algorithms, this method has better denoising effects both quantitatively and qualitatively.
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Affiliation(s)
- Linlin Zhu
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
| | - Yu Han
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
| | - Xiaoqi Xi
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
| | - Huijuan Fu
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
| | - Siyu Tan
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
| | - Mengnan Liu
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
| | - Shuangzhan Yang
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
| | - Chang Liu
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China.,School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou, China
| | - Lei Li
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
| | - Bin Yan
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
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牛 善, 刘 宏, 刘 沛, 张 梦, 李 硕, 梁 礼, 李 楠, 刘 国. [Nonlocal low-rank and sparse matrix decomposition for low-dose cerebral perfusion CT image restoration]. NAN FANG YI KE DA XUE XUE BAO = JOURNAL OF SOUTHERN MEDICAL UNIVERSITY 2022; 42:1309-1316. [PMID: 36210703 PMCID: PMC9550540 DOI: 10.12122/j.issn.1673-4254.2022.09.06] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Indexed: 01/24/2023]
Abstract
OBJECTIVE To present a nonlocal low-rank and sparse matrix decomposition (NLSMD) method for low-dose cerebral perfusion CT image restoration. METHODS Low-dose cerebral perfusion CT images were first partitioned into a matrix, and the low- rank and sparse matrix decomposition model was constructed to obtain high-quality low-dose cerebral perfusion CT images. The cerebral hemodynamic parameters were calculated from the restored high-quality CT images. RESULTS In the phantom study, the average structured similarity (SSIM) value of the sequential images obtained by filtered back-projection (FBP) algorithm was 0.9438, which was increased to 0.9765 using the proposed algorithm; the SSIM values of cerebral blood flow (CBF) and cerebral blood volume (CBV) map obtained by FBP algorithm were 0.7005 and 0.6856, respectively, which were increased using the proposed algorithm to 0.7871 and 0.7972, respectively. CONCLUSION The proposed method can effectively suppress noises in low-dose cerebral perfusion CT images to obtain accurate cerebral hemodynamic parameters.
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Affiliation(s)
- 善洲 牛
- 赣南师范大学数学与计算机科学学院,江西 赣州 341000School of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, China
- 赣南师范大学赣州市计算成像重点实验室,江西 赣州 341000Ganzhou Key Laboratory of Computational Imaging, Gannan Normal University, Ganzhou 341000, China
| | - 宏 刘
- 赣南师范大学数学与计算机科学学院,江西 赣州 341000School of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, China
- 赣南师范大学赣州市计算成像重点实验室,江西 赣州 341000Ganzhou Key Laboratory of Computational Imaging, Gannan Normal University, Ganzhou 341000, China
| | - 沛沄 刘
- 赣南师范大学数学与计算机科学学院,江西 赣州 341000School of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, China
- 赣南师范大学赣州市计算成像重点实验室,江西 赣州 341000Ganzhou Key Laboratory of Computational Imaging, Gannan Normal University, Ganzhou 341000, China
| | - 梦真 张
- 赣南师范大学数学与计算机科学学院,江西 赣州 341000School of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, China
- 赣南师范大学赣州市计算成像重点实验室,江西 赣州 341000Ganzhou Key Laboratory of Computational Imaging, Gannan Normal University, Ganzhou 341000, China
| | - 硕 李
- 赣南师范大学数学与计算机科学学院,江西 赣州 341000School of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, China
- 赣南师范大学赣州市计算成像重点实验室,江西 赣州 341000Ganzhou Key Laboratory of Computational Imaging, Gannan Normal University, Ganzhou 341000, China
| | - 礼境 梁
- 赣南师范大学数学与计算机科学学院,江西 赣州 341000School of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, China
- 赣南师范大学赣州市计算成像重点实验室,江西 赣州 341000Ganzhou Key Laboratory of Computational Imaging, Gannan Normal University, Ganzhou 341000, China
| | - 楠 李
- 赣南师范大学经济管理学院,江西 赣州 341000School of Economics and Management, Gannan Normal University, Ganzhou 341000, China
| | - 国良 刘
- 赣南医学院医学信息工程学院,江西 赣州 341000School of Medical Information Engineering, Gannan Medical University, Ganzhou 341000, China
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Zeng D, Zeng C, Zeng Z, Li S, Deng Z, Chen S, Bian Z, Ma J. Basis and current state of computed tomography perfusion imaging: a review. Phys Med Biol 2022; 67. [PMID: 35926503 DOI: 10.1088/1361-6560/ac8717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 08/04/2022] [Indexed: 12/30/2022]
Abstract
Computed tomography perfusion (CTP) is a functional imaging that allows for providing capillary-level hemodynamics information of the desired tissue in clinics. In this paper, we aim to offer insight into CTP imaging which covers the basics and current state of CTP imaging, then summarize the technical applications in the CTP imaging as well as the future technological potential. At first, we focus on the fundamentals of CTP imaging including systematically summarized CTP image acquisition and hemodynamic parameter map estimation techniques. A short assessment is presented to outline the clinical applications with CTP imaging, and then a review of radiation dose effect of the CTP imaging on the different applications is presented. We present a categorized methodology review on known and potential solvable challenges of radiation dose reduction in CTP imaging. To evaluate the quality of CTP images, we list various standardized performance metrics. Moreover, we present a review on the determination of infarct and penumbra. Finally, we reveal the popularity and future trend of CTP imaging.
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Affiliation(s)
- Dong Zeng
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Cuidie Zeng
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Zhixiong Zeng
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Sui Li
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Zhen Deng
- Department of Neurology, Nanfang Hospital, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Sijin Chen
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Zhaoying Bian
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Jianhua Ma
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
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Zeng D, Wang L, Geng M, Li S, Deng Y, Xie Q, Li D, Zhang H, Li Y, Xu Z, Meng D, Ma J. Noise-Generating-Mechanism-Driven Unsupervised Learning for Low-Dose CT Sinogram Recovery. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2022. [DOI: 10.1109/trpms.2021.3083361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Sudarshan VP, Reddy PK, Gubbi J, Purushothaman B. Forward Model and Deep Learning Based Iterative Deconvolution for Robust Dynamic CT Perfusion. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:3543-3546. [PMID: 34892004 DOI: 10.1109/embc46164.2021.9630969] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Perfusion maps obtained from low-dose computed tomography (CT) data suffer from poor signal to noise ratio. To enhance the quality of the perfusion maps, several works rely on denoising the low-dose CT (LD-CT) images followed by conventional regularized deconvolution. Recent works employ deep neural networks (DNN) for learning a direct mapping between the noisy and the clean perfusion maps ignoring the convolution-based forward model. DNN-based methods are not robust to practical variations in the data that are seen in real-world applications such as stroke. In this work, we propose an iterative framework that combines the perfusion forward model with a DNN-based regularizer to obtain perfusion maps directly from the LD-CT dynamic data. To improve the robustness of the DNN, we leverage the anatomical information from the contrast-enhanced LD-CT images to learn the mapping between low-dose and standard-dose perfusion maps. Through empirical experiments, we show that our model is robust both qualitatively and quantitatively to practical perturbations in the data.
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牛 善, 刘 宏, 刘 沛, 张 梦, 邱 洋, 黎 钰, 谢 国, 刘 国, 卢 绍. [Low-dose cerebral perfusion CT image restoration using prior image constrained diffusion tensor]. NAN FANG YI KE DA XUE XUE BAO = JOURNAL OF SOUTHERN MEDICAL UNIVERSITY 2021; 41:1226-1233. [PMID: 34549715 PMCID: PMC8527232 DOI: 10.12122/j.issn.1673-4254.2021.08.15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Indexed: 01/24/2023]
Abstract
OBJECTIVE We propose an efficient method to reduce the noise in low-dose cerebral perfusion CT images using prior image constrained diffusion tensor to reduce the radiation dose in brain CT examination. METHODS By utilizing the redundant information in cerebral perfusion CT images, we embedded the complementary structure information in prior images into lowdose cerebral perfusion CT image restoration process to suppress the image noise and artifacts.We first calculated the diffusion tensor for the low-dose cerebral perfusion CT image and prior image separately and then constructed a prior image constrained diffusion tensor (PICDT) to incorporate the structure information from the prior image into low-dose image restoration process. RESULTS In experiments with the Shepp-Logan phantom, the SSIM value of CBF map obtained by the proposed algorithm was increased by 63% as compared with that of the FBP algorithm.In analysis of the clinical dataset, the SSIM value of CBF map obtained by the proposed algorithm was increased by 45% as compared with that of FBP algorithm. CONCLUSION The proposed method can effectively reduce noises and artifacts of low-dose cerebral perfusion CT images while maintaining the structural details to obtain accurate cerebral hemodynamic maps.
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Affiliation(s)
- 善洲 牛
- 赣南师范大学数学与计算机科学学院, 江西 赣州 341000School of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, China
| | - 宏 刘
- 赣南师范大学数学与计算机科学学院, 江西 赣州 341000School of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, China
| | - 沛沄 刘
- 赣南师范大学数学与计算机科学学院, 江西 赣州 341000School of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, China
| | - 梦真 张
- 赣南师范大学数学与计算机科学学院, 江西 赣州 341000School of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, China
| | - 洋 邱
- 赣南师范大学数学与计算机科学学院, 江西 赣州 341000School of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, China
| | - 钰 黎
- 赣南师范大学数学与计算机科学学院, 江西 赣州 341000School of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, China
| | - 国强 谢
- 赣南师范大学数学与计算机科学学院, 江西 赣州 341000School of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, China
| | - 国良 刘
- 赣南医学院医学信息工程学院, 江西 赣州 341000School of Medical Information Engineering of Gannan Medical University, Ganzhou 341000, China
| | - 绍辉 卢
- 赣南医学院第一附属医院, 江西 赣州 341000First Affiliated Hospital of Gannan Medical University, Ganzhou 341000, China
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Jolivet F, Lesaint J, Fournier C, Garcin M, Brambilla A. An Efficient One-Step Method for Spectral CT Based on an Approximate Linear Model. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2021. [DOI: 10.1109/trpms.2020.3015598] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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10
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Li S, Zeng D, Bian Z, Li D, Zhu M, Huang J, Ma J. Learning non-local perfusion textures for high-quality computed tomography perfusion imaging. Phys Med Biol 2021; 66. [PMID: 33910178 DOI: 10.1088/1361-6560/abfc90] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 04/28/2021] [Indexed: 11/11/2022]
Abstract
Background. Computed tomography perfusion (CTP) imaging plays a critical role in the acute stroke syndrome assessment due to its widespread availability, speed of image acquisition, and relatively low cost. However, due to its repeated scanning protocol, CTP imaging involves a substantial radiation dose, which might increase potential cancer risks.Methods. In this work, we present a novel deep learning model called non-local perfusion texture learning network (NPTN) for high-quality CTP imaging at low-dose cases. Specifically, considering abundant similarities in the CTP images, i.e. latent self-similarities within the non-local region in the CTP images, we firstly search the most similar pixels from the adjacent frames within a fixed search window to obtain the non-local similarities and to construct non-local textures vector. Then, both the low-dose frame and these non-local textures from adjacent frames are fed into a convolution neural network to predict high-quality CTP images, which can help better characterize the structure details and contrast variants in the targeted CTP image rather than simply utilizing the targeted frame itself. The residual learning strategy and batch normalization are utilized to boost the performance of the convolution neural network. In the experiment, the CTP images of 31 patients with suspected stroke disease are collected to demonstrate the performance of the presented NPTN method.Results. The results show the presented NPTN method obtains superior performance compared with the competing methods. From numerical value, at all dose levels, the presented NPTN method has achieved around 3.0 dB improvement of average PSNR, an increase of around 1.4% of average SSIM, and a decrease of around 4.8% of average RMSE in the low-dose CTP reconstruction task, and also has achieved an increase of around 3.4% of average SSIM and a decrease of around 61.1% of average RMSE in the cerebral blood flow (CBF) estimation task.Conclusions. The presented NPTN method can obtain high-quality CTP images and estimate high-accuracy CBF map by characterizing more structure details and contrast variants in the CTP image and outperform the competing methods at low-dose cases.
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Affiliation(s)
- Sui Li
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China
| | - Dong Zeng
- College of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, People's Republic of China.,Guangdong Artificial Intelligence and Digital Economy Laboratory (Guangzhou), Guangzhou 510335, People's Republic of China
| | - Zhaoying Bian
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China
| | - Danyang Li
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China
| | - Manman Zhu
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China
| | - Jing Huang
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China
| | - Jianhua Ma
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China.,Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, People's Republic of China.,Guangdong Artificial Intelligence and Digital Economy Laboratory (Guangzhou), Guangzhou 510335, People's Republic of China
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Slart RHJA, Williams MC, Juarez-Orozco LE, Rischpler C, Dweck MR, Glaudemans AWJM, Gimelli A, Georgoulias P, Gheysens O, Gaemperli O, Habib G, Hustinx R, Cosyns B, Verberne HJ, Hyafil F, Erba PA, Lubberink M, Slomka P, Išgum I, Visvikis D, Kolossváry M, Saraste A. Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT. Eur J Nucl Med Mol Imaging 2021; 48:1399-1413. [PMID: 33864509 PMCID: PMC8113178 DOI: 10.1007/s00259-021-05341-z] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 03/25/2021] [Indexed: 12/18/2022]
Abstract
In daily clinical practice, clinicians integrate available data to ascertain the diagnostic and prognostic probability of a disease or clinical outcome for their patients. For patients with suspected or known cardiovascular disease, several anatomical and functional imaging techniques are commonly performed to aid this endeavor, including coronary computed tomography angiography (CCTA) and nuclear cardiology imaging. Continuous improvement in positron emission tomography (PET), single-photon emission computed tomography (SPECT), and CT hardware and software has resulted in improved diagnostic performance and wide implementation of these imaging techniques in daily clinical practice. However, the human ability to interpret, quantify, and integrate these data sets is limited. The identification of novel markers and application of machine learning (ML) algorithms, including deep learning (DL) to cardiovascular imaging techniques will further improve diagnosis and prognostication for patients with cardiovascular diseases. The goal of this position paper of the European Association of Nuclear Medicine (EANM) and the European Association of Cardiovascular Imaging (EACVI) is to provide an overview of the general concepts behind modern machine learning-based artificial intelligence, highlights currently prefered methods, practices, and computational models, and proposes new strategies to support the clinical application of ML in the field of cardiovascular imaging using nuclear cardiology (hybrid) and CT techniques.
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Affiliation(s)
- Riemer H J A Slart
- Medical Imaging Centre, Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO 9700 RB, Groningen, The Netherlands.
- Faculty of Science and Technology Biomedical, Photonic Imaging, University of Twente, Enschede, The Netherlands.
| | - Michelle C Williams
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging facility QMRI, Edinburgh, UK
| | - Luis Eduardo Juarez-Orozco
- Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Christoph Rischpler
- Department of Nuclear Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Marc R Dweck
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging facility QMRI, Edinburgh, UK
| | - Andor W J M Glaudemans
- Medical Imaging Centre, Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO 9700 RB, Groningen, The Netherlands
| | | | - Panagiotis Georgoulias
- Department of Nuclear Medicine, Faculty of Medicine, University of Thessaly, University Hospital of Larissa, Larissa, Greece
| | - Olivier Gheysens
- Department of Nuclear Medicine, Cliniques Universitaires Saint-Luc and Institute of Clinical and Experimental Research (IREC), Université catholique de Louvain (UCLouvain), Brussels, Belgium
| | | | - Gilbert Habib
- APHM, Cardiology Department, La Timone Hospital, Marseille, France
- IRD, APHM, MEPHI, IHU-Méditerranée Infection, Aix Marseille Université, Marseille, France
| | - Roland Hustinx
- Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, ULiège, Liège, Belgium
| | - Bernard Cosyns
- Department of Cardiology, Centrum voor Hart en Vaatziekten, Universitair Ziekenhuis Brussel, 101 Laarbeeklaan, 1090, Brussels, Belgium
| | - Hein J Verberne
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location AMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Fabien Hyafil
- Department of Nuclear Medicine, DMU IMAGINA, Georges-Pompidou European Hospital, Assistance Publique - Hôpitaux de Paris, F-75015, Paris, France
- University of Paris, PARCC, INSERM, F-75006, Paris, France
| | - Paola A Erba
- Medical Imaging Centre, Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO 9700 RB, Groningen, The Netherlands
- Department of Nuclear Medicine (P.A.E.), University of Pisa, Pisa, Italy
- Department of Translational Research and New Technology in Medicine (P.A.E.), University of Pisa, Pisa, Italy
| | - Mark Lubberink
- Department of Surgical Sciences/Radiology, Uppsala University, Uppsala, Sweden
- Medical Physics, Uppsala University Hospital, Uppsala, Sweden
| | - Piotr Slomka
- Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Ivana Išgum
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location AMC, University of Amsterdam, Amsterdam, The Netherlands
- Department of Biomedical Engineering and Physics, Amsterdam UMC - location AMC, University of Amsterdam, 1105, Amsterdam, AZ, Netherlands
| | | | - Márton Kolossváry
- MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, 68 Városmajor Street, Budapest, Hungary
| | - Antti Saraste
- Turku PET Centre, Turku University Hospital, University of Turku, Turku, Finland
- Heart Center, Turku University Hospital, Turku, Finland
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Wu D, Ren H, Li Q. Self-Supervised Dynamic CT Perfusion Image Denoising With Deep Neural Networks. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2021. [DOI: 10.1109/trpms.2020.2996566] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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13
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Rastogi A, Yalavarthy PK. SpiNet: A deep neural network for Schatten p-norm regularized medical image reconstruction. Med Phys 2021; 48:2214-2229. [PMID: 33525049 DOI: 10.1002/mp.14744] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 12/30/2020] [Accepted: 01/19/2021] [Indexed: 11/07/2022] Open
Abstract
PURPOSE To propose a generic deep learning based medical image reconstruction model (named as SpiNet) that can enforce any Schatten p-norm regularization with 0 < p ≤ 2, where the p can be learnt (or fixed) based on the problem at hand. METHODS Model-based deep learning architecture for solving inverse problems consists of two parts, a deep learning based denoiser and an iterative data consistency solver. The former has either L2 norm or L1 norm enforced on it, which are convex and can be easily minimized. This work proposes a method to enforce any p norm on the noise prior where 0 < p ≤ 2. This is achieved by using Majorization-Minimization algorithm, which upper bounds the cost function with a convex function, thus can be easily minimized. The proposed SpiNet has the capability to work for a fixed p or it can learn p based on the data. The network was tested for solving the inverse problem of reconstructing magnetic resonance (MR) images from undersampled k space data and the results were compared with a popular model-based deep learning architecture MoDL which enforces L2 norm along with other compressive sensing-based algorithms. This comparison between MoDL and proposed SpiNet was performed for undersampling rates (R) of 2×, 4×, 6×, 8×, 12×, 16×, and 20×. Multiple figures of merit such as PSNR, SSIM, and NRMSE were utilized in this comparison. A two-tailed t test was performed for all undersampling rates and for all metrices for proving the superior performance of proposed SpiNet compared to MoDL. For training and testing, the same dataset that was utilized in MoDL implementation was deployed. RESULTS The results indicate that for all undersampling rates, the proposed SpiNet shows higher PSNR and SSIM and lower NRMSE than MoDL. However, for low undersampling rates of 2× and 4×, there is no significant difference in performance of proposed SpiNet and MoDL in terms of PSNR and NRMSE. This can be expected as the learnt p value is close to 2 (norm enforced by MoDL). For higher undersampling rates ≥6×, SpiNet significantly outperforms MoDL in all metrices with improvement as high as 4 dB in PSNR and 0.5 points in SSIM. CONCLUSION As deep learning based medical image reconstruction methods are gaining popularity, the proposed SpiNet provides a generic framework to incorporate Schatten p-norm regularization with 0 <p ≤ 2 with an added advantage of providing superior performance compared to its counterparts. The proposed SpiNet also has useful addition of Schatten p-norm value in regularization term being automatically chosen based on the available training data.
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Affiliation(s)
- Aditya Rastogi
- Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, 560012, India
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Chang S, Chen X, Duan J, Mou X. A CNN-Based Hybrid Ring Artifact Reduction Algorithm for CT Images. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2021. [DOI: 10.1109/trpms.2020.2983391] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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15
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Hasan AM, Mohebbian MR, Wahid KA, Babyn P. Hybrid-Collaborative Noise2Noise Denoiser for Low-Dose CT Images. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2021. [DOI: 10.1109/trpms.2020.3002178] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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16
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Xie S, Yang T. Artifact Removal in Sparse-Angle CT Based on Feature Fusion Residual Network. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2021. [DOI: 10.1109/trpms.2020.3000789] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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17
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Gong Y, Shan H, Teng Y, Tu N, Li M, Liang G, Wang G, Wang S. Parameter-Transferred Wasserstein Generative Adversarial Network (PT-WGAN) for Low-Dose PET Image Denoising. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2021; 5:213-223. [PMID: 35402757 PMCID: PMC8993163 DOI: 10.1109/trpms.2020.3025071] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2023]
Abstract
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 details, and ii) a task-specific initialization based on transfer learning is developed to train PT-WGAN using trainable parameters transferred from a pretrained model, which significantly improves the training efficiency of PT-WGAN. The experimental results on clinical data show that the proposed network can suppress image noise more effectively while preserving better image fidelity than recently published state-of-the-art methods. We make our code available at https://github.com/90n9-yu/PT-WGAN.
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Affiliation(s)
- Yu Gong
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China, and Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Hongming Shan
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai 200433, China, and the Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 201210, China
| | - Yueyang Teng
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China, and the Key Laboratory of Intelligent Computing in Medical Images, Ministry of Education, Shenyang 110169, China
| | - Ning Tu
- PET-CT/MRI Center and Molecular Imaging Center, Wuhan University Renmin Hospital, Wuhan, 430060, China
| | - Ming Li
- Neusoft Medical Systems Co., Ltd, Shenyang 110167, China
| | - Guodong Liang
- Neusoft Medical Systems Co., Ltd, Shenyang 110167, China
| | - Ge Wang
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180 USA
| | - Shanshan Wang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
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Torres-Velázquez M, Chen WJ, Li X, McMillan AB. Application and Construction of Deep Learning Networks in Medical Imaging. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2021; 5:137-159. [PMID: 34017931 PMCID: PMC8132932 DOI: 10.1109/trpms.2020.3030611] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Deep learning (DL) approaches are part of the machine learning (ML) subfield concerned with the development of computational models to train artificial intelligence systems. DL models are characterized by automatically extracting high-level features from the input data to learn the relationship between matching datasets. Thus, its implementation offers an advantage over common ML methods that often require the practitioner to have some domain knowledge of the input data to select the best latent representation. As a result of this advantage, DL has been successfully applied within the medical imaging field to address problems, such as disease classification and tumor segmentation for which it is difficult or impossible to determine which image features are relevant. Therefore, taking into consideration the positive impact of DL on the medical imaging field, this article reviews the key concepts associated with its evolution and implementation. The sections of this review summarize the milestones related to the development of the DL field, followed by a description of the elements of deep neural network and an overview of its application within the medical imaging field. Subsequently, the key steps necessary to implement a supervised DL application are defined, and associated limitations are discussed.
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Affiliation(s)
- Maribel Torres-Velázquez
- Department of Biomedical Engineering, College of Engineering, University of Wisconsin-Madison, Madison, WI 53705 USA
| | - Wei-Jie Chen
- Department of Electrical and Computer Engineering, College of Engineering, University of Wisconsin-Madison, Madison, WI 53705 USA
| | - Xue Li
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI 53705 USA
| | - Alan B McMillan
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI 53705 USA, and also with the Department of Medical Physics, University of Wisconsin-Madison, Madison, WI 53705 USA
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19
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Dashtbani Moghari M, Zhou L, Yu B, Young N, Moore K, Evans A, Fulton RR, Kyme AZ. Efficient radiation dose reduction in whole-brain CT perfusion imaging using a 3D GAN: Performance and clinical feasibility. Phys Med Biol 2021; 66. [PMID: 33621965 DOI: 10.1088/1361-6560/abe917] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 02/23/2021] [Indexed: 02/08/2023]
Abstract
Dose reduction in cerebral CT perfusion (CTP) imaging is desirable but is accompanied by an increase in noise that can compromise the image quality and the accuracy of image-based haemodynamic modelling used for clinical decision support in acute ischaemic stroke. The few reported methods aimed at denoising low-dose CTP images lack practicality by considering only small sections of the brain or being computationally expensive. Moreover, the prediction of infarct and penumbra size and location - the chief means of decision support for treatment options - from denoised data has not been explored using these approaches. In this work, we present the first application of a 3D generative adversarial network (3D GAN) for predicting normal-dose CTP data from low-dose CTP data. Feasibility of the approach was tested using real data from 30 acute ischaemic stroke patients in conjunction with low dose simulation. The 3D GAN model was applied to 64^3 voxel patches extracted from two different configurations of the CTP data- frame-based and stacked. The method led to whole-brain denoised data being generated for haemodynamic modelling within 90 seconds. Accuracy of the method was evaluated using standard image quality metrics and the extent to which the clinical content and lesion characteristics of the denoised CTP data were preserved. Results showed an average improvement of 5.15-5.32 dB PSNR and 0.025-0.033 SSIM for CTP images and 2.66-3.95 dB PSNR and 0.036-0.067 SSIM for functional maps at 50% and 25% of normal dose using GAN model in conjunction with a stacked data regime for image synthesis. Consequently, the average lesion volumetric error reduced significantly (p-value < 0.05) by 18-29% and dice coefficient improved significantly by 15-22%. We conclude that GAN-based denoising is a promising practical approach for reducing radiation dose in CTP studies and improving lesion characterisation.
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Affiliation(s)
- Mahdieh Dashtbani Moghari
- Biomedical Engineering, Faculty of Engineering and Computer Science, Darlington Campus, The University of Sydney, NSW, 2006, AUSTRALIA
| | - Luping Zhou
- The University of Sydney, Sydney, 2006, AUSTRALIA
| | - Biting Yu
- University of Wollongong, Wollongong, New South Wales, AUSTRALIA
| | - Noel Young
- Radiology, Westmead Hospital, Sydney, New South Wales, AUSTRALIA
| | - Krystal Moore
- Westmead Hospital, Sydney, New South Wales, AUSTRALIA
| | - Andrew Evans
- Aged Care & Stroke, Westmead Hospital, Sydney, New South Wales, AUSTRALIA
| | - Roger R Fulton
- Faculty of Health Sciences, University of Sydney, 94 Mallett Street, Camperdown, Sydney, New South Wales, 2050, AUSTRALIA
| | - Andre Z Kyme
- Brain & Mind Research Institute, University of Sydney, Sydney, NSW 2006, Sydney, New South Wales, AUSTRALIA
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20
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Kandarpa VSS, Bousse A, Benoit D, Visvikis D. DUG-RECON: A Framework for Direct Image Reconstruction Using Convolutional Generative Networks. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2021. [DOI: 10.1109/trpms.2020.3033172] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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21
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DPIR-Net: Direct PET Image Reconstruction Based on the Wasserstein Generative Adversarial Network. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2021. [DOI: 10.1109/trpms.2020.2995717] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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22
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Whiteley W, Panin V, Zhou C, Cabello J, Bharkhada D, Gregor J. FastPET: Near Real-Time Reconstruction of PET Histo-Image Data Using a Neural Network. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2021. [DOI: 10.1109/trpms.2020.3028364] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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23
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Kande NA, Dakhane R, Dukkipati A, Yalavarthy PK. SiameseGAN: A Generative Model for Denoising of Spectral Domain Optical Coherence Tomography Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:180-192. [PMID: 32924938 DOI: 10.1109/tmi.2020.3024097] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Optical coherence tomography (OCT) is a standard diagnostic imaging method for assessment of ophthalmic diseases. The speckle noise present in the high-speed OCT images hampers its clinical utility, especially in Spectral-Domain Optical Coherence Tomography (SDOCT). In this work, a new deep generative model, called as SiameseGAN, for denoising Low signal-to-noise ratio (LSNR) B-scans of SDOCT has been developed. SiameseGAN is a Generative Adversarial Network (GAN) equipped with a siamese twin network. The siamese network module of the proposed SiameseGAN model helps the generator to generate denoised images that are closer to groundtruth images in the feature space, while the discriminator helps in making sure they are realistic images. This approach, unlike baseline dictionary learning technique (MSBTD), does not require an apriori high-quality image from the target imaging subject for denoising and takes less time for denoising. Moreover, various deep learning models that have been shown to be effective in performing denoising task in the SDOCT imaging were also deployed in this work. A qualitative and quantitative comparison on the performance of proposed method with these state-of-the-art denoising algorithms has been performed. The experimental results show that the speckle noise can be effectively mitigated using the proposed SiameseGAN along with faster denoising unlike existing approaches.
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Matsuura M, Zhou J, Akino N, Yu Z. Feature-Aware Deep-Learning Reconstruction for Context-Sensitive X-ray Computed Tomography. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2021. [DOI: 10.1109/trpms.2020.3040882] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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25
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Xu M, Hu D, Luo F, Liu F, Wang S, Wu W. Limited-Angle X-Ray CT Reconstruction Using Image Gradient ℓ₀-Norm With Dictionary Learning. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2021. [DOI: 10.1109/trpms.2020.2991887] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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26
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Zhang F, Zhang M, Qin B, Zhang Y, Xu Z, Liang D, Liu Q. REDAEP: Robust and Enhanced Denoising Autoencoding Prior for Sparse-View CT Reconstruction. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2021. [DOI: 10.1109/trpms.2020.2989634] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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27
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Wang F, Henninen TR, Keller D, Erni R. Noise2Atom: unsupervised denoising for scanning transmission electron microscopy images. Appl Microsc 2020; 50:23. [PMID: 33580362 PMCID: PMC7818366 DOI: 10.1186/s42649-020-00041-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 09/17/2020] [Indexed: 11/10/2022] Open
Abstract
We propose an effective deep learning model to denoise scanning transmission electron microscopy (STEM) image series, named Noise2Atom, to map images from a source domain [Formula: see text] to a target domain [Formula: see text], where [Formula: see text] is for our noisy experimental dataset, and [Formula: see text] is for the desired clear atomic images. Noise2Atom uses two external networks to apply additional constraints from the domain knowledge. This model requires no signal prior, no noise model estimation, and no paired training images. The only assumption is that the inputs are acquired with identical experimental configurations. To evaluate the restoration performance of our model, as it is impossible to obtain ground truth for our experimental dataset, we propose consecutive structural similarity (CSS) for image quality assessment, based on the fact that the structures remain much the same as the previous frame(s) within small scan intervals. We demonstrate the superiority of our model by providing evaluation in terms of CSS and visual quality on different experimental datasets.
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Affiliation(s)
- Feng Wang
- Electron Microscopy Center, Empa, Swiss Federal Laboratories for Materials Science and Technology, Überlandstr. 129, Dübendorf, CH-8600, Switzerland.
| | - Trond R Henninen
- Electron Microscopy Center, Empa, Swiss Federal Laboratories for Materials Science and Technology, Überlandstr. 129, Dübendorf, CH-8600, Switzerland
| | - Debora Keller
- Electron Microscopy Center, Empa, Swiss Federal Laboratories for Materials Science and Technology, Überlandstr. 129, Dübendorf, CH-8600, Switzerland
| | - Rolf Erni
- Electron Microscopy Center, Empa, Swiss Federal Laboratories for Materials Science and Technology, Überlandstr. 129, Dübendorf, CH-8600, Switzerland
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Feng J, Deng J, Li Z, Sun Z, Dou H, Jia K. End-to-end Res-Unet based reconstruction algorithm for photoacoustic imaging. BIOMEDICAL OPTICS EXPRESS 2020; 11:5321-5340. [PMID: 33014617 PMCID: PMC7510873 DOI: 10.1364/boe.396598] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 07/30/2020] [Accepted: 08/20/2020] [Indexed: 05/03/2023]
Abstract
Recently, deep neural networks have attracted great attention in photoacoustic imaging (PAI). In PAI, reconstructing the initial pressure distribution from acquired photoacoustic (PA) signals is a typically inverse problem. In this paper, an end-to-end Unet with residual blocks (Res-Unet) is designed and trained to solve the inverse problem in PAI. The performance of the proposed algorithm is explored and analyzed by comparing a recent model-resolution-based regularization algorithm (MRR) with numerical and physical phantom experiments. The improvement obtained in the reconstructed images was more than 95% in pearson correlation and 39% in peak signal-to-noise ratio in comparison to the MRR. The Res-Unet also achieved superior performance over the state-of-the-art Unet++ architecture by more than 18% in PSNR in simulation experiments.
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Affiliation(s)
- Jinchao Feng
- Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
- Beijing Laboratory of Advanced Information Networks, Beijing 100124, China
| | - Jianguang Deng
- Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
- Beijing Laboratory of Advanced Information Networks, Beijing 100124, China
| | - Zhe Li
- Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
- Beijing Laboratory of Advanced Information Networks, Beijing 100124, China
| | - Zhonghua Sun
- Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
- Beijing Laboratory of Advanced Information Networks, Beijing 100124, China
| | - Huijing Dou
- Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Kebin Jia
- Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
- Beijing Laboratory of Advanced Information Networks, Beijing 100124, China
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Tian C, Fei L, Zheng W, Xu Y, Zuo W, Lin CW. Deep learning on image denoising: An overview. Neural Netw 2020; 131:251-275. [PMID: 32829002 DOI: 10.1016/j.neunet.2020.07.025] [Citation(s) in RCA: 175] [Impact Index Per Article: 43.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 06/17/2020] [Accepted: 07/21/2020] [Indexed: 01/19/2023]
Abstract
Deep learning techniques have received much attention in the area of image denoising. However, there are substantial differences in the various types of deep learning methods dealing with image denoising. Specifically, discriminative learning based on deep learning can ably address the issue of Gaussian noise. Optimization models based on deep learning are effective in estimating the real noise. However, there has thus far been little related research to summarize the different deep learning techniques for image denoising. In this paper, we offer a comparative study of deep techniques in image denoising. We first classify the deep convolutional neural networks (CNNs) for additive white noisy images; the deep CNNs for real noisy images; the deep CNNs for blind denoising and the deep CNNs for hybrid noisy images, which represents the combination of noisy, blurred and low-resolution images. Then, we analyze the motivations and principles of the different types of deep learning methods. Next, we compare the state-of-the-art methods on public denoising datasets in terms of quantitative and qualitative analyses. Finally, we point out some potential challenges and directions of future research.
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Affiliation(s)
- Chunwei Tian
- Bio-Computing Research Center, Harbin Institute of Technology, Shenzhen, Shenzhen, 518055, Guangdong, China; Shenzhen Key Laboratory of Visual Object Detection and Recognition, Shenzhen, 518055, Guangdong, China
| | - Lunke Fei
- School of Computers, Guangdong University of Technology, Guangzhou, 510006, Guangdong, China
| | - Wenxian Zheng
- Tsinghua Shenzhen International Graduate School, Shenzhen, 518055, Guangdong, China
| | - Yong Xu
- Bio-Computing Research Center, Harbin Institute of Technology, Shenzhen, Shenzhen, 518055, Guangdong, China; Shenzhen Key Laboratory of Visual Object Detection and Recognition, Shenzhen, 518055, Guangdong, China; Peng Cheng Laboratory, Shenzhen, 518055, Guangdong, China.
| | - Wangmeng Zuo
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, Heilongjiang, China; Peng Cheng Laboratory, Shenzhen, 518055, Guangdong, China
| | - Chia-Wen Lin
- Department of Electrical Engineering and the Institute of Communications Engineering, National Tsing Hua University, Hsinchu, Taiwan
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30
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Li Z, Zhou S, Huang J, Yu L, Jin M. Investigation of Low-Dose CT Image Denoising Using Unpaired Deep Learning Methods. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2020; 5:224-234. [PMID: 33748562 DOI: 10.1109/trpms.2020.3007583] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Low-dose computed tomography (LDCT) is desired due to prevalence and ionizing radiation of CT, but suffers elevated noise. To improve LDCT image quality, an image-domain denoising method based on cycle-consistent generative adversarial network ("CycleGAN") is developed and compared with two other variants, IdentityGAN and GAN-CIRCLE. Different from supervised deep learning methods, these unpaired methods can effectively learn image translation from the low-dose domain to the full-dose (FD) domain without the need of aligning FDCT and LDCT images. The results on real and synthetic patient CT data show that these methods can achieve peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) comparable to, if not better than, the other state-of-the-art denoising methods. Among CycleGAN, IdentityGAN, and GAN-CIRCLE, the later achieves the best denoising performance with the shortest computation time. Subsequently, GAN-CIRCLE is used to demonstrate that the increasing number of training patches and of training patients can improve denoising performance. Finally, two non-overlapping experiments, i.e. no counterparts of FDCT and LDCT images in the training data, further demonstrate the effectiveness of unpaired learning methods. This work paves the way for applying unpaired deep learning methods to enhance LDCT images without requiring aligned full-dose and low-dose images from the same patient.
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Affiliation(s)
- Zeheng Li
- Computer Science and Engineering Department, University of Texas at Arlington, Arlington, TX 76019 USA
| | - Shiwei Zhou
- Physics Department, University of Texas at Arlington, Arlington, TX 76019 USA
| | - Junzhou Huang
- Computer Science and Engineering Department, University of Texas at Arlington, Arlington, TX 76019 USA
| | - Lifeng Yu
- Department of Radiology at Mayo Clinic, Rochester, MN 55905 USA
| | - Mingwu Jin
- Physics Department, University of Texas at Arlington, Arlington, TX 76019 USA
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31
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Gao Y, Liang Z, Zhang H, Yang J, Ferretti J, Bilfinger T, Yaddanapudi K, Schweitzer M, Bhattacharji P, Moore W. A Task-dependent Investigation on Dose and Texture in CT Image Reconstruction. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2020; 4:441-449. [PMID: 33907724 PMCID: PMC8075295 DOI: 10.1109/trpms.2019.2957459] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Localizing and characterizing clinically-significant lung nodules, a potential precursor to lung cancer, at the lowest achievable radiation dose is demanded to minimize the stochastic radiation effects from x-ray computed tomography (CT). A minimal dose level is heavily dependent on the image reconstruction algorithms and clinical task, in which the tissue texture always plays an important role. This study aims to investigate the dependence through a task-based evaluation at multiple dose levels and variable textures in reconstructions with prospective patient studies. 133 patients with a suspicious pulmonary nodule scheduled for biopsy were recruited and the data was acquired at120kVp with three different dose levels of 100, 40 and 20mAs. Three reconstruction algorithms were implemented: analytical filtered back-projection (FBP) with optimal noise filtering; statistical Markov random field (MRF) model with optimal Huber weighting (MRF-H) for piecewise smooth reconstruction; and tissue-specific texture model (MRF-T) for texture preserved statistical reconstruction. Experienced thoracic radiologists reviewed and scored all images at random, blind to the CT dose and reconstruction algorithms. The radiologists identified the nodules in each image including the 133 biopsy target nodules and 66 other non-target nodules. For target nodule characterization, only MRF-T at 40mAs showed no statistically significant difference from FBP at 100mAs. For localizing both the target nodules and the non-target nodules, some as small as 3mm, MRF-T at 40 and 20mAs levels showed no statistically significant difference from FBP at 100mAs, respectively. MRF-H and FBP at 40 and 20mAs levels performed statistically differently from FBP at 100mAs. This investigation concluded that (1) the textures in the MRF-T reconstructions improves both the tasks of localizing and characterizing nodules at low dose CT and (2) the task of characterizing nodules is more challenging than the task of localizing nodules and needs more dose or enhanced textures from reconstruction.
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Affiliation(s)
- Yongfeng Gao
- Department of Radiology, Stony Brook University, Stony Brook, NY
11794, USA
| | - Zhengrong Liang
- Departments of Radiology, Biomedical Engineering, Computer Science,
and Electrical Engineering, Stony Brook University, Stony Brook, NY 11794,
USA
| | - Hao Zhang
- Departments of Radiology and Biomedical Engineering, Stony Brook
University, Stony Brook, NY 11794, USA and now with the Department of
Radiation Oncology, Stanford University, Stanford, CA 94035, USA
| | - Jie Yang
- Department of Family, Population and Preventive Medicine, Stony
Brook University, Stony Brook, NY 11794, USA
| | - John Ferretti
- Department of Radiology, Stony Brook University, Stony Brook, NY
11794, USA
| | - Thomas Bilfinger
- Department of Surgery, Stony Brook University, Stony Brook, NY
11794, USA)
| | | | - Mark Schweitzer
- Department of Radiology, Stony Brook University, Stony Brook, NY
11794, USA
| | - Priya Bhattacharji
- Department of Radiology, Stony Brook University, Stony Brook, NY
11794, USA, and now with the Department of Radiology, New York University,
New York, NY 10016, USA
| | - William Moore
- Department of Radiology, Stony Brook University, Stony Brook, NY
11794, USA, and now with the Department of Radiology, New York University,
New York, NY 10016, USA
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