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A Synergized Pulsing-Imaging Network (SPIN) [article]

Qing Lyu, Tao Xu, Hongming Shan, Ge Wang
2018 arXiv   pre-print
For the first time, in this paper we view data acquisition and the image reconstruction as the two key parts of an integrated MRI process, and optimize both the pulse sequence and the reconstruction scheme  ...  Currently, the deep neural network is the mainstream for machine learning, and being actively developed for biomedical imaging applications with an increasing emphasis on tomographic reconstruction for  ...  The neural network was trained using samples from natural images on ImageNet, and fine-tuned with only tens of MR images of interest (T1-or T2-weighted images).  ... 
arXiv:1805.12006v1 fatcat:7qpb32sjhfhhjh7gyxeypdu6h4

Perspective: magnetic nanoparticles in theranostic applications [article]

Annelies Coene, Jonathan Leliaert
2022 arXiv   pre-print
Secondly ,we introduce the concept of smart theranostics based on feedback mechanisms between the particle applications and their supporting imaging procedure to enhance the performance of both and allow  ...  The latter research track also includes hybrid models in which physics-based and data-driven models are combined to overcome challenges of applications with limited data, as well as to uncover unknown  ...  The surrogate model is enhanced by optimizing the mappings between the input (P) and output (S) space of the coarse and fine model, see Fig. 6 .  ... 
arXiv:2201.06058v1 fatcat:tvvccudt55dxxfwnm3mkcquyzm

Potential of compressed sensing in quantitative MR imaging of cancer

David S. Smith, Xia Li, Richard G. Abramson, C. Chad Quarles, Thomas E. Yankeelov, E. Brian Welch
2013 Cancer Imaging  
With custom CS acquisition and reconstruction strategies, one can quickly obtain a small subset of the full data and then iteratively reconstruct images that are consistent with the acquired data and sparse  ...  We finally illustrate applications of the technique by describing examples of CS in dynamic contrast-enhanced MRI and dynamic susceptibility contrast MRI.  ...  We thank the National Institutes of Health for funding through NCI U01 CA142565, NCI 1R01CA129961, NCI R25CA092043, and NCI P30 CA68485.  ... 
doi:10.1102/1470-7330.2013.0041 pmid:24434808 pmcid:PMC3893904 fatcat:gawyr7c575htzfhot67ywrhwxa

A Hybrid Geometric Morphometric Deep Learning Approach for Cut and Trampling Mark Classification

Courtenay, Huguet, González-Aguilera, Yravedra
2019 Applied Sciences  
An example of this equifinality can be found in the differentiation between anthropic cut marks and other traces on bone produced by natural agents, such as that of sedimentary abrasion and trampling.  ...  Here, we combine Geometric Morphometric data, 3D digital microscopy, and Deep Learning Neural Networks to provide a means of empirically classifying taphonomic traces on bone.  ...  experimental programs to truly fine tune these results and examine their limitations.  ... 
doi:10.3390/app10010150 fatcat:mo2kao7p3vc5zfmiivtwsolhka

HYDRA: Hybrid Deep Magnetic Resonance Fingerprinting [article]

Pingfan Song, Yonina C. Eldar, Gal Mazor, Miguel Rodrigues
2019 arXiv   pre-print
, reconstruction accuracy, and storage requirements.  ...  The designed network is trained on synthesized MRF data simulated with the Bloch equations and fast imaging with steady state precession (FISP) sequences, and then it takes a temporal MRF signal as input  ...  fast and accurate MRF reconstruction. • A series of numerical experiments are conducted to evaluate the proposed approach on both synthetic data and phantom data.  ... 
arXiv:1902.02882v1 fatcat:dcburf44lzbplm3lv7oo7j43oy

Adversary-aware Multimodal Neural Networks for Cancer Susceptibility Prediction from Multi-omics Data

Md. Rezaul Karim, Tanhim Islam, Christoph Lange, Dietrich Rebholz-Schuhmann, Stefan Decker
2022 IEEE Access  
In this paper, we propose an adversaryaware multimodal convolutional autoencoder (MCAE) model for cancer susceptibility prediction from multi-omics data consisting of copy number variations (CNVs), miRNA  ...  A critical and challenging usage scenario for AI is aiding the treatment of cancerous conditions. Providing accurate diagnosis for cancer is a challenging problem in precision oncology.  ...  Each fit is iterated for 50 epochs during the fine-tuning, by setting margin m = 1 : 2 for the OOD detection.  ... 
doi:10.1109/access.2022.3175816 fatcat:fdpdj6i7xzherkz5tklhftazbm

An overview of deep learning in medical imaging focusing on MRI

Alexander Selvikvåg Lundervold, Arvid Lundervold
2018 Zeitschrift für Medizinische Physik  
As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI.  ...  Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry.  ...  Our work was financially supported by the Bergen Research Foundation through the project "Computational medical imaging and machine learning -methods, infrastructure and applications".  ... 
doi:10.1016/j.zemedi.2018.11.002 fatcat:kkimovnwcrhmth7mg6h6cpomjm

Applications of artificial intelligence and deep learning in molecular imaging and radiotherapy

Hossein Arabi, Habib Zaidi
2020 European Journal of Hybrid Imaging  
To this end, the applications of artificial intelligence in five generic fields of molecular imaging and radiation therapy, including PET instrumentation design, PET image reconstruction quantification  ...  and segmentation, image denoising (low-dose imaging), radiation dosimetry and computer-aided diagnosis, and outcome prediction are discussed.  ...  Arabi and Zaidi European Journal of Hybrid Imaging (2020) image reconstruction would be completely replaced by a fast and seemingly black-box deep learning network (Haggstrom et al. 2019) .  ... 
doi:10.1186/s41824-020-00086-8 pmid:34191161 fatcat:6qp2s2jwebbglk5gi3pfvozpie

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,  ...  and even prediction of therapy responses.  ...  In addition, to expert parameter tuning of scanners always required to optimize reconstruction performance, especially in the presence of sensor non-idealities and noise (20) .  ... 
doi:10.3389/fneur.2019.00869 pmid:31474928 pmcid:PMC6702308 fatcat:yki64mb57jhafduasd3hohfkgi

Integration of Deep Learning and Active Shape Models for More Accurate Prostate Segmentation in 3D MR Images

Massimo Salvi, Bruno De Santi, Bianca Pop, Martino Bosco, Valentina Giannini, Daniele Regge, Filippo Molinari, Kristen M. Meiburger
2022 Journal of Imaging  
In this work, we propose a fully automated hybrid approach for prostate gland segmentation in MR images using an initial segmentation of prostate volumes using a custom-made 3D deep network (VNet-T2),  ...  While the deep network focuses on three-dimensional spatial coherence of the shape, the ASM relies on local image information and this joint effort allows for improved segmentation of the organ contours  ...  The quantitative differences between this hybrid approach and the 3D network may not be large, but the improved accuracy could prove to be critical for specific clinical applications, allowing the conservation  ... 
doi:10.3390/jimaging8050133 fatcat:w7ikpzz2izh6xeshwm6zovznp4

Off-resonance artifact correction for magnetic resonance imaging: a review [article]

Melissa W. Haskell, Jon-Fredrik Nielsen, Douglas C. Noll
2022 arXiv   pre-print
In this review, we describe sources of off-resonance in MRI, how off-resonance affects images, and strategies to prevent and correct for off-resonance.  ...  In magnetic resonance imaging (MRI), inhomogeneity in the main magnetic field used for imaging, referred to as off-resonance, can lead to image artifacts ranging from mild to severe depending on the application  ...  Jeffrey Fessler for helpful discussions on model based image reconstructions. Images from Figure 2  ... 
arXiv:2205.01028v1 fatcat:b4re36pzmnfjre4tbbgylk7eh4

A review of automated image understanding within 3D baggage computed tomography security screening

Andre Mouton, Toby P. Breckon
2015 Journal of X-Ray Science and Technology  
We discuss the recent and most pertinent advancements and identify topics for future research within the challenging automated image understanding for baggage security screening CT.  ...  The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes  ...  The exploitation of such a priori knowledge allows for the development of algorithms designed or fine-tuned for particular tasks or anatomical structures [53] .  ... 
doi:10.3233/xst-150508 pmid:26409422 fatcat:piczzsf3ojhjxo4h5ve3e7jpgi

Stability of AI-Enabled Diagnosis of Parkinson's Disease: A Study Targeting Substantia Nigra in Quantitative Susceptibility Mapping Imaging

Bin Xiao, Naying He, Qian Wang, Feng Shi, Zenghui Cheng, Ewart Mark Haacke, Fuhua Yan, Dinggang Shen
2021 Frontiers in Neuroscience  
Purpose: Parkinson's disease (PD) diagnosis algorithms based on quantitative susceptibility mapping (QSM) and image algorithms rely on substantia nigra (SN) labeling.  ...  : The logistic regression model and CNN model were first compared for classification between PD patients and healthy controls (HC), given different sets of SN labeling.  ...  FIGURE 1 | 1 FIGURE 1 | The framework of the quantitative susceptibility mapping-based Parkinson's disease diagnosis model.  ... 
doi:10.3389/fnins.2021.760975 pmid:34887722 pmcid:PMC8650720 fatcat:t3sl5ynunnggrliqauqhbrq75y

Deep learning in photoacoustic imaging: a review

Handi Deng, Hui Qiao, Qionghai Dai, Cheng Ma
2021 Journal of Biomedical Optics  
When applied in PAI, DL can effectively process images, improve reconstruction quality, fuse information, and assist quantitative analysis. DL has become a powerful tool in PAI.  ...  For PA imaging (PAI), non-ideal signal detection deteriorates image quality, and quantitative PAI (QPAI) remains challenging due to the unknown light fluence spectra in deep tissue.  ...  The authors would like to thank Youwei Bao and Xiangxiu Zhang for assistance with figure copyright application.  ... 
doi:10.1117/1.jbo.26.4.040901 pmid:33837678 pmcid:PMC8033250 fatcat:uwutps2wfbfztfo6bvbv6g5f6y

Survey and Evaluation of RGB-D SLAM

Shishun Zhang, Longyu Zheng, Wenbing Tao
2021 IEEE Access  
Newcombe, Thomas Schops, Thomas Whelan and Angela Dai for opening the source codes of their methods for us, and the developers of PCL for making many methods evaluated in this paper publicly available.  ...  London for providing their datasets.  ...  The optimization is performed using an iteratively reweighted Gauss-Newton algorithm in a coarse-to-fine scheme [19] .  ... 
doi:10.1109/access.2021.3053188 fatcat:g4uhbbm7l5hzlopdhesbovepiu
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