Completion of Metal-Damaged Traces Based on Deep Learning in Sinogram Domain for Metal Artifacts Reduction in CT Images

In computed tomography (CT) images, the presence of metal artifacts leads to contaminated object structures. Theoretically, eliminating metal artifacts in the sinogram domain can correct projection deviation and provide reconstructed images that are more real. Contemporary methods that use deep networks for completing metal-damaged sinogram data are limited to discontinuity at the boundaries of traces, which, however, lead to secondary artifacts. This study modifies the traditional U-net and adds two sinogram feature losses of projection images—namely, continuity and consistency of projection data at each angle, improving the accuracy of the complemented sinogram data. Masking the metal traces also ensures the stability and reliability of the unaffected data during metal artifacts reduction. The projection and reconstruction results and various evaluation metrics reveal that the proposed method can accurately repair missing data and reduce metal artifacts in reconstructed CT images.

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Deep Learning Based Metal Artifacts Reduction in Post-operative Cochlear Implant CT Imaging

Lecture Notes in Computer Science - Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 ◽

10.1007/978-3-030-32226-7_14 ◽

2019 ◽

pp. 121-129 ◽

Cited By ~ 3

Author(s):

Zihao Wang ◽

Clair Vandersteen ◽

Thomas Demarcy ◽

Dan Gnansia ◽

Charles Raffaelli ◽

...

Keyword(s):

Deep Learning ◽

Cochlear Implant ◽

Ct Imaging ◽

Metal Artifacts ◽

Artifacts Reduction

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A deep Residual U-Net algorithm for automatic detection and quantification of ascites on the abdominopelvic computed tomography acquired in the emergency department (Preprint)

10.2196/preprints.34415 ◽

2021 ◽

Author(s):

Hoon Ko ◽

Jimi Huh ◽

Kyung Won Kim ◽

Heewon Chung ◽

Yousun Ko ◽

...

Keyword(s):

Computed Tomography ◽

Deep Learning ◽

Automatic Detection ◽

High Specificity ◽

Ct Images ◽

Free Fluid ◽

Detection Accuracy ◽

Abdominopelvic Ct ◽

Segmentation Accuracy ◽

Detection And Quantification

BACKGROUND Detection and quantification of intraabdominal free fluid (i.e., ascites) on computed tomography (CT) are essential processes to find emergent or urgent conditions in patients. In an emergent department, automatic detection and quantification of ascites will be beneficial. OBJECTIVE We aimed to develop an artificial intelligence (AI) algorithm for the automatic detection and quantification of ascites simultaneously using a single deep learning model (DLM). METHODS 2D deep learning models (DLMs) based on a deep residual U-Net, U-Net, bi-directional U-Net, and recurrent residual U-net were developed to segment areas of ascites on an abdominopelvic CT. Based on segmentation results, the DLMs detected ascites by classifying CT images into ascites images and non-ascites images. The AI algorithms were trained using 6,337 CT images from 160 subjects (80 with ascites and 80 without ascites) and tested using 1,635 CT images from 40 subjects (20 with ascites and 20 without ascites). The performance of AI algorithms was evaluated for diagnostic accuracy of ascites detection and for segmentation accuracy of ascites areas. Of these DLMs, we proposed an AI algorithm with the best performance. RESULTS The segmentation accuracy was the highest in the deep residual U-Net with a mean intersection over union (mIoU) value of 0.87, followed by U-Net, bi-directional U-Net, and recurrent residual U-net (mIoU values 0.80, 0.77, and 0.67, respectively). The detection accuracy was the highest in the deep residual U-net (0.96), followed by U-Net, bi-directional U-net, and recurrent residual U-net (0.90, 0.88, and 0.82, respectively). The deep residual U-net also achieved high sensitivity (0.96) and high specificity (0.96). CONCLUSIONS We propose the deep residual U-net-based AI algorithm for automatic detection and quantification of ascites on abdominopelvic CT scans, which provides excellent performance.

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Projection-to-image transform frame: a lightweight block reconstruction network (LBRN) for computed tomography

Physics in Medicine and Biology ◽

10.1088/1361-6560/ac4122 ◽

2021 ◽

Author(s):

Genwei Ma ◽

Xing Zhao ◽

Yining Zhu ◽

Huitao Zhang

Keyword(s):

Neural Network ◽

Computed Tomography ◽

Real Data ◽

Projection Data ◽

Ct Reconstruction ◽

Back Projection ◽

Image Block ◽

Metal Artifacts ◽

Reconstruction Process ◽

Memory Space

Abstract To solve the problem of learning based computed tomography (CT) reconstruction, several reconstruction networks were invented. However, applying neural network to tomographic reconstruction still remains challenging due to unacceptable memory space requirement. In this study, we presents a novel lightweight block reconstruction network (LBRN), which transforms the reconstruction operator into a deep neural network by unrolling the filter back-projection (FBP) method. Specifically, the proposed network contains two main modules, which, respectively, correspond to the filter and back-projection of FBP method. The first module of LBRN decouples the relationship of Radon transform between the reconstructed image and the projection data. Therefore, the following module, block back-projection module, can use the block reconstruction strategy. Due to each image block is only connected with part filtered projection data, the network structure is greatly simplified and the parameters of the whole network is dramatically reduced. Moreover, this approach is trained end-to-end, working directly from raw projection data and does not depend on any initial images. Five reconstruction experiments are conducted to evaluate the performance of the proposed LBRN: full angle, low-dose CT, region of interest (ROI), metal artifacts reduction and real data experiment. The results of the experiments show that the LBRN can be effectively introduced into the reconstruction process and has outstanding advantages in terms of different reconstruction problems.

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Deep learning based adaptive filtering for projection data noise reduction in x-ray computed tomography

15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine ◽

10.1117/12.2534838 ◽

2019 ◽

Author(s):

Tzu-Cheng Lee ◽

Jian Zhou ◽

Zhou Yu

Keyword(s):

Computed Tomography ◽

Deep Learning ◽

Noise Reduction ◽

Adaptive Filtering ◽

Projection Data ◽

Data Noise ◽

X Ray ◽

X Ray Computed

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Magnetic resonance image (MRI) synthesis from brain computed tomography (CT) images based on deep learning methods for magnetic resonance (MR)-guided radiotherapy

Quantitative Imaging in Medicine and Surgery ◽

10.21037/qims-19-885 ◽

2020 ◽

Vol 10 (6) ◽

pp. 1223-1236 ◽

Cited By ~ 3

Author(s):

Wen Li ◽

Yafen Li ◽

Wenjian Qin ◽

Xiaokun Liang ◽

Jianyang Xu ◽

...

Keyword(s):

Computed Tomography ◽

Deep Learning ◽

Magnetic Resonance ◽

Magnetic Resonance Image ◽

Ct Images ◽

Learning Methods ◽

Brain Computed Tomography

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Metal Artifacts Reduction in Computed Tomography: A Bilateral Reprojection Approach

2010 4th International Conference on Bioinformatics and Biomedical Engineering ◽

10.1109/icbbe.2010.5517743 ◽

2010 ◽

Cited By ~ 4

Author(s):

Lin Cheng ◽

Jinfeng Liu

Keyword(s):

Computed Tomography ◽

Metal Artifacts ◽

Artifacts Reduction

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An Early Prediction and Classification of Lung Nodule Diagnosis on CT Images Based on Hybrid Deep Learning Techniques

10.21203/rs.3.rs-1190013/v1 ◽

2022 ◽

Author(s):

Vijay Kumar Gugulothu ◽

Savadam Balaji

Keyword(s):

Computed Tomography ◽

Lung Cancer ◽

Deep Learning ◽

Early Stage ◽

Selection Process ◽

Lung Nodule ◽

Lung Cancer Screening ◽

Ct Images ◽

Learning Techniques ◽

Feature Extraction And Selection

Abstract Detection of malignant lung nodules at an early stage may allow for clinical interventions that increase the survival rate of lung cancer patients. The use of hybrid deep learning techniques to detect nodules will improve the sensitivity of lung cancer screening and the interpretation speed of lung scans.Accurate detection of lung nodes is an important step in computed tomography (CT) imaging to detect lung cancer. However, it is very difficult to identify strong nodes due to the diversity of lung nodes and the complexity of the surrounding environment.Here, we proposed alung nodule detection and classification with CT images based on hybrid deep learning (LNDC-HDL) techniques. First, we introduce achaotic bird swarm optimization (CBSO) algorithm for lung nodule segmentation using statistical information. Second, we illustrate anImproved Fish Bee (IFB) algorithm for feature extraction and selection process. Third, we develop hybrid classifier i.e. hybrid differential evolution based neural network (HDE-NN) for tumor prediction and classification.Experimental results have shown that the use of computed tomography, which demonstrates the efficiency and importance of the HDE-NN specific structure for detecting lung nodes on CT scans, increases sensitivity and reduces the number of false positives. The proposed method shows that the benefits of HDE-NN node detection can be reaped by combining clinical practice.

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Comparison of Supervised and Unsupervised Deep Learning Methods for Medical Image Synthesis between Computed Tomography and Magnetic Resonance Images

BioMed Research International ◽

10.1155/2020/5193707 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9

Author(s):

Yafen Li ◽

Wen Li ◽

Jing Xiong ◽

Jun Xia ◽

Yaoqin Xie

Keyword(s):

Computed Tomography ◽

Deep Learning ◽

Medical Image ◽

Image Synthesis ◽

Ct Images ◽

Learning Method ◽

Learning Methods ◽

Unsupervised Deep Learning ◽

Net Method ◽

Synthetic Images

Cross-modality medical image synthesis between magnetic resonance (MR) images and computed tomography (CT) images has attracted increasing attention in many medical imaging area. Many deep learning methods have been used to generate pseudo-MR/CT images from counterpart modality images. In this study, we used U-Net and Cycle-Consistent Adversarial Networks (CycleGAN), which were typical networks of supervised and unsupervised deep learning methods, respectively, to transform MR/CT images to their counterpart modality. Experimental results show that synthetic images predicted by the proposed U-Net method got lower mean absolute error (MAE), higher structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR) in both directions of CT/MR synthesis, especially in synthetic CT image generation. Though synthetic images by the U-Net method has less contrast information than those by the CycleGAN method, the pixel value profile tendency of the synthetic images by the U-Net method is closer to the ground truth images. This work demonstrated that supervised deep learning method outperforms unsupervised deep learning method in accuracy for medical tasks of MR/CT synthesis.

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