A deep learning method for eliminating head motion artifacts in computed tomography

2021 ◽  
Author(s):  
Bin Su ◽  
Yuting Wen ◽  
Yanyan Liu ◽  
Shu Liao ◽  
Jianwei Fu ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Motion Artifacts ◽  
Head Motion ◽  
Learning Method

A Deep Learning Method to Estimate Conventional Dose Computed Tomography Scans from Reduced Dose Acquisitions: Effect on Emphysema Quantification

2019 ◽  
Author(s):  
L.A. Zavala Mondragón ◽  
S.M. Humphries ◽  
L. Gallardo Estrella ◽  
E.M. van Rikxoort ◽  
D.A. Lynch ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Learning Method ◽  
Reduced Dose ◽  
Conventional Dose ◽  
Computed Tomography Scans

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

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.

scholarly journals Deep Learning Method for Mandibular Canal Segmentation in Dental Cone Beam Computed Tomography Volumes

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Joel Jaskari ◽  
Jaakko Sahlsten ◽  
Jorma Järnstedt ◽  
Helena Mehtonen ◽  
Kalle Karhu ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Cone Beam Computed Tomography ◽  
Mandibular Canal ◽  
Cone Beam ◽  
Learning Method

3D Densenet Deep Learning Based Preoperative Computed Tomography for Detecting Myasthenia Gravis in Patients with Thymoma

2019 ◽  
Author(s):  
Ying Zhu ◽  
Zhenguo Liu ◽  
Yujie Yuan ◽  
Lei Yang ◽  
Kefeng Wang ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Myasthenia Gravis
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