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Applications of Deep Learning to Neuro-Imaging Techniques

Guangming Zhu, Bin Jiang, Liz Tong, Yuan Xie, Greg Zaharchuk, Max Wintermark
2019 Frontiers in Neurology  
Many clinical applications based on deep learning and pertaining to radiology have been proposed and studied in radiology for classification, risk assessment, segmentation tasks, diagnosis, prognosis,  ...  There are many other innovative applications of AI in various technical aspects of medical imaging, particularly applied to the acquisition of images, ranging from removing image artifacts, normalizing  ...  (68) proposed a non-reference approach to automatically detect the presence of motion artifacts on MRI images.  ... 
doi:10.3389/fneur.2019.00869 pmid:31474928 pmcid:PMC6702308 fatcat:yki64mb57jhafduasd3hohfkgi

Automatic Artifact Detection Algorithm in Fetal MRI

Adam Lim, Justin Lo, Matthias W. Wagner, Birgit Ertl-Wagner, Dafna Sussman
2022 Frontiers in Artificial Intelligence  
It makes use of a CNN ensemble approach where the first CNN aims to identify and classify any artifacts in the image, and the second CNN uses regression to determine the severity of the detected artifacts  ...  We propose a novel algorithm, RISE-Net, that can consistently, automatically, and objectively detect artifacts in 3D fetal MRI.  ...  DISCUSSION We introduced and evaluated a novel CNN algorithm that can automatically detect and grade the severity of artifacts in wholebody fetal MRI scans.  ... 
doi:10.3389/frai.2022.861791 pmid:35783351 pmcid:PMC9244144 fatcat:y5ywqqhxaraitgiblt2qvmayf4

Front Matter: Volume 10574

Proceedings of SPIE, Elsa D. Angelini, Bennett A. Landman
2018 Medical Imaging 2018: Image Processing  
using a Base 36 numbering system employing both numerals and letters.  ...  Publication of record for individual papers is online in the SPIE Digital Library. SPIEDigitalLibrary.org Paper Numbering: Proceedings of SPIE follow an e-First publication model.  ...  . of SPIE Vol. 10574 1057401-7 Automatic detection of the inner ears in head CT images using deep convolutional neural networks [10574-78] 10574 28 Multiorgan structures detection using deep convolutional  ... 
doi:10.1117/12.2315755 fatcat:jdfbaent6vhu5dwlrqrqt66vce

A Tetrahedron-Based Heat Flux Signature for Cortical Thickness Morphometry Analysis [chapter]

Yonghui Fan, Gang Wang, Natasha Lepore, Yalin Wang
2018 Lecture Notes in Computer Science  
Decisions Aggregated CNN 392 Respiratory Motion Modelling using cGANs 398 A deep learning-based method for automated performance evaluation in Transesoesophageal Echocardiography 405 Dilatation of Lateral  ...  474 Computing CNN Loss and Gradients for Pose Estimation with Riemannian Geometry 477 Multi-Modal Synthesis of ASL-MRI Features with KPLS Regression on Heterogeneous Data 480 Deep supervision with additional  ... 
doi:10.1007/978-3-030-00931-1_48 pmid:30338317 pmcid:PMC6191198 fatcat:dqhvpm5xzrdqhglrfftig3qejq

Motion Correction For 3D Volumetric Reconstruction from 2D Multi-Slice Stacks in Fetal MRI

Bin Chen
2020 Scientific Journal of Research & Reviews  
While each individual slice generally immunes to motion artifact, the whole volume, consisting of multiple 2D motion-free slices, still needs several minutes to acquire. The motion during  ...  MRI by nature is a relatively slow technique. The detailed volumetric data usually take several minutes to collect, making it susceptible to motion artifacts.  ...  As one of the biggest challenges in fetal MRI, motion correction has been studied from patient motion prevention, MRI pulse sequence development and post processing.  ... 
doi:10.33552/sjrr.2020.02.000549 fatcat:smk4ryfnrjglnpd7s23ntxc4pq

Fast Fully Automatic Segmentation of the Human Placenta from Motion Corrupted MRI [chapter]

Amir Alansary, Konstantinos Kamnitsas, Alice Davidson, Rostislav Khlebnikov, Martin Rajchl, Christina Malamateniou, Mary Rutherford, Joseph V. Hajnal, Ben Glocker, Daniel Rueckert, Bernhard Kainz
2016 Lecture Notes in Computer Science  
Moreover, image acquisition is corrupted by motion artifacts from both fetal and maternal movements.  ...  a cohort mixed with cases of intrauterine fetal growth restriction using varying scan parameters.  ...  We thank NVIDIA for the donation of a Tesla K40 GPU. Medical Interaction Toolkit (http://mitk.org/) was used for some of the figures.  ... 
doi:10.1007/978-3-319-46723-8_68 fatcat:jjolbmavvvd4pjqfjzonax7lwe

Automated Multiclass Artifact Detection in Diffusion MRI Volumes via 3D Residual Squeeze-and-Excitation Convolutional Neural Networks

Nabil Ettehadi, Pratik Kashyap, Xuzhe Zhang, Yun Wang, David Semanek, Karan Desai, Jia Guo, Jonathan Posner, Andrew F. Laine
2022 Frontiers in Human Neuroscience  
"good" quality) of the dMRI volumes or focus on detecting a single type of artifact (e.g., motion, Eddy currents, etc.).  ...  Diffusion MRI (dMRI) is widely used to investigate neuronal and structural development of brain. dMRI data is often contaminated with various types of artifacts.  ...  motion, out of FOV, low SNR, and MRI miscellaneous artifacts).  ... 
doi:10.3389/fnhum.2022.877326 pmid:35431841 pmcid:PMC9005752 fatcat:6bevfuiwnjblfjiidk5uuynrt4

Development and application of artificial intelligence in cardiac imaging

Beibei Jiang, Ning Guo, Yinghui Ge, Lu Zhang, Matthijs Oudkerk, Xueqian Xie
2020 British Journal of Radiology  
As a result, 24 and 14 studies using CT and MRI, respectively, were included and summarized.  ...  In this review, we describe the technical aspects of artificial intelligence (AI) in cardiac imaging, starting with radiomics, basic algorithms of deep learning and application tasks of algorithms, until  ...  Another example is the use of CNN to improve image quality or to correct motion artifact, which can be the main barrier for a clear cardiac image and is beyond human capability.  ... 
doi:10.1259/bjr.20190812 pmid:32017605 pmcid:PMC7465846 fatcat:kab3xoqh5vhyvcedntigw3aplm

Application of Artificial Intelligence to Cardiovascular Computed Tomography

Dong Hyun Yang
2021 Korean Journal of Radiology  
This review summarizes the latest research on the application of deep learning to cardiovascular CT.  ...  The areas covered range from image quality improvement to automatic analysis of CT images, including methods such as calcium scoring, image segmentation, and coronary artery evaluation.  ...  [25] applied a CNN technique to motion artifact detection and quantification in coronary arteries on CT, which is potentially useful for reducing motion artifacts.  ... 
doi:10.3348/kjr.2020.1314 pmid:34402240 pmcid:PMC8484158 fatcat:33n4ufdbs5ho7cyev4ng7qrdca

Reduction of respiratory motion artifacts in gadoxetate-enhanced MR with a deep learning–based filter using convolutional neural network

M.-L. Kromrey, D. Tamada, H. Johno, S. Funayama, N. Nagata, S. Ichikawa, J.-P. Kühn, H. Onishi, U. Motosugi
2020 European Radiology  
This method can be of high clinical value in subjects with failing breath-hold in the scan. • This study presents a newly developed deep learning-based filter for artifact reduction using convolutional  ...  To reveal the utility of motion artifact reduction with convolutional neural network (MARC) in gadoxetate disodium-enhanced multi-arterial phase MRI of the liver.  ...  Taking these findings into account, in our study, we developed a deep learning filter based on CNN, which works in combination with multi-arterial phase acquisition using DISCO to achieve motion artifact  ... 
doi:10.1007/s00330-020-07006-1 pmid:32556463 fatcat:agqouj4tezgnhek2s7hvmtv3b4

Artificial intelligence in pediatric and adult congenital cardiac MRI: an unmet clinical need

Arghavan Arafati, Peng Hu, J. Paul Finn, Carsten Rickers, Andrew L. Cheng, Hamid Jafarkhani, Arash Kheradvar
2019 Cardiovascular Diagnosis and Therapy  
We include an overview of AI definitions, different architectures, and details on well-known methods. This paper reviews the major deep learning concepts used for analyses of patients with CHD.  ...  The algorithms based on artificial intelligence (AI), and in particular, deep learning, have rapidly become a methodology of choice for analyzing CMR.  ...  Acknowledgments The authors would also like to acknowledge Saeed Karimi Bidhendi for providing the images in 9th illustration. from his work-in-progress study on automatic segmentation of tetralogy of  ... 
doi:10.21037/cdt.2019.06.09 pmid:31737539 pmcid:PMC6837938 fatcat:gcbodzcxszcvjgs2nelzvz3ika

Deep Learning in MR Image Processing

Doohee Lee, Jingu Lee, Jingyu Ko, Jaeyeon Yoon, Kanghyun Ryu, Yoonho Nam
2019 Investigative Magnetic Resonance Imaging  
In this article, we introduce the basic concepts of deep learning and review recent studies on various MR image processing applications. 82 Deep Learning in MRI | Doohee Lee, et al. directions of deep  ...  Deep Learning: a Brief Overview Deep learning is a branch of machine learning based on the use of multiple layers to learn data representations, and can be applied to both supervised and unsupervised learning  ...  Acknowledgments This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A1B03030772).  ... 
doi:10.13104/imri.2019.23.2.81 fatcat:txjrlwhklbh47nxbwiq55xkhva

Diagnostic Image Quality Assessment and Classification in Medical Imaging: Opportunities and Challenges [article]

Jeffrey Ma, Ukash Nakarmi, Cedric Yue Sik Kin, Christopher Sandino, Joseph Y. Cheng, Ali B. Syed, Peter Wei, John M. Pauly, Shreyas Vasanawala
2019 arXiv   pre-print
Magnetic Resonance Imaging (MRI) suffers from several artifacts, the most common of which are motion artifacts. These artifacts often yield images that are of non-diagnostic quality.  ...  This motivates the need to develop an automated framework capable of accessing medical image quality and detecting diagnostic and non-diagnostic images.  ...  Current solutions to automatically detect and correct such artifacts are suboptimal. Despite the extensive training of MR technologists who operate scanners, these artifacts commonly go unrecognized.  ... 
arXiv:1912.02907v1 fatcat:hv3igzbf6fendfpxrguayvhdum

Front Matter: Volume 12032

Ivana Išgum, Olivier Colliot
2022 Medical Imaging 2022: Image Processing  
of SPIE at the time of publication.  ...  using a Base 36 numbering system employing both numerals and letters.  ...  diffusion-weighted data using tagged magnetic resonance imaging [ models for organ contouring in head and neck radiotherapy [12032-13] 0G Automatic classification of MRI contrasts using a deep siamese  ... 
doi:10.1117/12.2638192 fatcat:ikfgnjefaba2tpiamxoftyi6sa

Detection of Hepatocellular Carcinoma in Contrast-Enhanced Magnetic Resonance Imaging Using Deep Learning Classifier: A Multi-Center Retrospective Study

Junmo Kim, Ji Hye Min, Seon Kyoung Kim, Soo-Yong Shin, Min Woo Lee
2020 Scientific Reports  
We also confirmed whether the lesion detected by our deep learning model is a true lesion using a class activation map.  ...  Hepatocellular carcinoma (HCC) is one of the most common malignant tumors and a leading cause of cancer-related death worldwide.  ...  transient severe motion artifacts in the arterial phase 26, 27 .  ... 
doi:10.1038/s41598-020-65875-4 pmid:32527998 pmcid:PMC7289813 fatcat:xxyudk7e2vhadmeykyey7i3dyu
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