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Deep Learning Based Detection and Correction of Cardiac MR Motion Artefacts During Reconstruction for High-Quality Segmentation
[article]
2020
arXiv
pre-print
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The method is based on our recently developed joint artefact detection and reconstruction method, which reconstructs high quality MR images from k-space using a joint loss function and essentially converts ...
Using a test set of 500 2D+time cine MR acquisitions from the UK Biobank data set, we achieve demonstrably good image quality and high segmentation accuracy in the presence of synthetic motion artefacts ...
Our novel idea of detecting and correcting artefacts from k-space for high segmentation accuracy has been shown to improve both reconstructed image quality and segmentation quality. ...
arXiv:1910.05370v4
fatcat:bq6pftuktvhufnycpepolrz2u4
Memory Efficient Model Based Deep Learning Reconstructions for High Spatial Resolution 3D Non-Cartesian Acquisitions
[article]
2022
arXiv
pre-print
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Significance: Block-wise learning allows for MBDL to be applied to high spatial resolution, 3D non-Cartesian datasets with improved image quality and significant reductions in reconstruction time relative ...
The goal of this work is to develop and apply a memory efficient method called block-wise learning that combines gradient checkpointing with patch-wise training to allow for fast and high-quality 3D non-Cartesian ...
Such algorithms have been successful in achieving high quality images primarily for 2D Cartesian reconstruction (Hammernik et al., 2018; Aggarwal, Mani and Jacob, 2019) . ...
arXiv:2204.13862v2
fatcat:widvl3uknjcvdfuc76iwaa2ai4
Reconstruction for Diverging-Wave Imaging Using Deep Convolutional Neural Networks
[article]
2020
arXiv
pre-print
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To deal with this limitation, we propose a convolutional neural networks (CNN) architecture for high-quality reconstruction of DW ultrasound images using a small number of transmissions. ...
A mapping between low-quality images and corresponding high-quality compounded reconstruction was learned by training the network using in vitro and in vivo samples. ...
., images). In this paper, we introduce a novel CNN architecture for high-quality reconstruction for DW imaging using a small number of DW transmissions. ...
arXiv:1911.03416v3
fatcat:nuu6czc7grgrfnzb57i3uwplku
Context-Sensitive Super-Resolution for Fast Fetal Magnetic Resonance Imaging
[chapter]
2017
Lecture Notes in Computer Science
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3D Magnetic Resonance Imaging (MRI) is often a trade-off between fast but low-resolution image acquisition and highly detailed but slow image acquisition. ...
the generation of high resolution images with sharp edges and fine scale detail. ...
image acquisition. ...
doi:10.1007/978-3-319-67564-0_12
fatcat:rxm4bt7nqfav5nnynpmme2utji
23 Na MRI in ischemic stroke: Acquisition time reduction using postprocessing with convolutional neural networks
2021
NMR in Biomedicine
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Long acquisition times are one of the most challenging aspects for its clinical establishment. K-space undersampling is an approach for acquisition time reduction, but generates noise and artifacts. ...
The reconstructions were performed with full dataset (FI) and with a simulated dataset an image that was acquired in 2.5 min (RI). ...
ACKNOWLEDGEMENTS The authors would like to thank Victor Saase for participating in the subjective image quality rating. Shradha Jain, Dr Michaela A.U. ...
doi:10.1002/nbm.4474
pmid:33480128
fatcat:a24y7jg6kbah3bqj2moifqgksq
A Deep Learning-based Integrated Framework for Quality-aware Undersampled Cine Cardiac MRI Reconstruction and Analysis
[article]
2022
arXiv
pre-print
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The framework enables active acquisition of radial k-space data, in which acquisition can be stopped as soon as acquired data are sufficient to produce high quality reconstructions and segmentations. ...
In this paper, we present a fully-automated, quality-controlled integrated framework for reconstruction, segmentation and downstream analysis of undersampled cine CMR data. ...
Traditionally, cine CMR acquisition, reconstruction and analysis have been considered as independent steps, despite the obvious inter-dependence between high-quality image reconstruction and high accuracy ...
arXiv:2205.01673v1
fatcat:erl7jduhyfelpddyzla7zyf4gq
High-resolution Petrous Bone Imaging Using Multi-slice Computerized Tomography
2001
Acta Oto-Laryngologica
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We de ned a data acquisition protocol for high-resolution (HR) temporal bone imaging using MSCT and assessed its impact on data acquisition and post-processing (PP). ...
The parameters image quality and diagnostic value of MSCT data were assessed for the cross-sectional source images as well as for 2-dimensional (2D) reformations and 3-dimensional (3D) reconstructions ...
This is especially important for petrous bone imaging as it is very dif cult to assess the complex internal architecture without using reformatted and:or reconstructed images (2, 6) . ...
doi:10.1080/00016480118263
pmid:11583399
fatcat:f2rwczm2zjhtngczvfue5mkd4q
Context-based image acquisition from memory in digital systems
2016
Journal of Real-Time Image Processing
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that efficiently trades image quality for reduced cost of the image acquisition process. ...
The sampled pixels are used to reconstruct an approximation to the ground truth, which is stored in a high-performance image buffer for further processing. ...
This trade-off between image quality and various costs of image acquisition is the key idea of the proposed CbIA framework that provides an alternative approach of designing hardware architectures for ...
doi:10.1007/s11554-016-0591-1
fatcat:rtu2eh5cdjaklm5uvaz54c5hpq
Compressed hyperspectral sensing
2015
Image Sensors and Imaging Systems 2015
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integration periods, without the need for successive frame acquisition. ...
A key shortcoming shared by all current methods lies in the high scanning repetition rates required for generating the complete 3D hyperspectral datacube. ...
In this work, we proposed a novel HSI architecture that can achieve high quality reconstruction of the hypercube Ipx, λq from a limited number of frames, without resorting to moving parts, by exploiting ...
doi:10.1117/12.2083282
fatcat:udl2aqlxnbavfju6sf4ukua7ne
From Compressed-Sensing to Artificial Intelligence-Based Cardiac MRI Reconstruction
2020
Frontiers in Cardiovascular Medicine
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However, CMR suffers from long acquisition times due to the need of obtaining images with high temporal and spatial resolution, different contrasts, and/or whole-heart coverage. ...
In this article, we provide an overview of the recent developments in the area of artificial intelligence for CMR image reconstruction. ...
for DL reconstruction, and high-quality MR images are obtained as an output in an end-to-end fashion. ...
doi:10.3389/fcvm.2020.00017
pmid:32158767
pmcid:PMC7051921
fatcat:xbge626jhzeulfjlssdlqnalze
Joint multi-contrast Variational Network reconstruction (jVN) with application to rapid 2D and 3D imaging
[article]
2019
arXiv
pre-print
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Complementary k-space sampling across imaging contrasts and Bunch-Phase/Wave-Encoding were used for data acquisition to improve the reconstruction at high accelerations. ...
Purpose: To improve the image quality of highly accelerated multi-channel MRI data by learning a joint variational network that reconstructs multiple clinical contrasts jointly. ...
We empirically observed that this improved the image quality for all evaluated reconstructions. ...
arXiv:1910.03273v1
fatcat:es3naqkmy5bcncjkxcp2v4z3wm
APIR-Net: Autocalibrated Parallel Imaging Reconstruction using a Neural Network
[article]
2019
arXiv
pre-print
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The experiments indicate that APIR-Net provides a promising alternative to the conventional parallel imaging methods, and results in improved image quality especially for low SNR acquisitions. ...
Deep learning has been successfully demonstrated in MRI reconstruction of accelerated acquisitions. ...
To conclude, APIR-Net provides a promising alternative to the conventional parallel imaging methods, and results in improved image quality especially for low SNR acquisitions. ...
arXiv:1909.09006v1
fatcat:luaglwjtszdrhj25zg4n22wg6q
A Multimodal Deep Network for the Reconstruction of T2W MR Images
[article]
2020
arXiv
pre-print
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In this paper, we present a deep learning method that is able to reconstruct subsampled MR images obtained by reducing the k-space data, while maintaining a high image quality that can be used to observe ...
Results prove the effectiveness of the proposed method in reconstructing subsampled MR images while saving execution time. ...
By using the proposed architecture, high image quality is achieved, thus enabling the recognition of brain injuries caused by the disease. ...
arXiv:1908.03009v2
fatcat:5a2cccj2r5ak3br2bt5brc4cya
Reconstruction in deep learning of highly under-sampled T2-weighted image with T1-weighted image
2018
Proceedings of the International Society for Magnetic Resonance in Medicine ... Scientific Meeting and Exhibition. International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition
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Discuss and Conclusion In this work, we propose a U-net architecture for reconstruction of high-quality T2WI with prior information of T1WI. ...
Our results demonstrate that the proposed method could achieve 8 or higher acceleration rate while keeping high image quality of the reconstructed T2WI. ...
pmid:30956568
pmcid:PMC6448782
fatcat:ec5aiagdebakpotbremt2fy3ua
Applications of Deep Learning to Neuro-Imaging Techniques
2019
Frontiers in Neurology
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/harmonizing images, improving image quality, lowering radiation and contrast dose, and shortening the duration of imaging studies. ...
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 ...
This approach produced high quality ASL images by denoising images without estimating its noise level. ...
doi:10.3389/fneur.2019.00869
pmid:31474928
pmcid:PMC6702308
fatcat:yki64mb57jhafduasd3hohfkgi
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