Výsledky vyhledávání - Basic Science - Reconstruction algorithms and artificial intelligence

Deep learning for accelerated and robust MRI reconstruction Autor Heckel, Reinhard, Jacob, Mathews, Chaudhari, Akshay, Perlman, Or, Shimron, Efrat

“…), a critical tool in diagnostic radiology. This review paper provides a comprehensive overview of recent advances in DL for MRI reconstruction, and focuses on various DL approaches and architectures designed to improve image quality…”
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A low-rank deep image prior reconstruction for free-breathing ungated spiral functional CMR at 0.55 T and 1.5 T Autor Hamilton, Jesse I., Truesdell, William, Galizia, Mauricio, Burris, Nicholas, Agarwal, Prachi, Seiberlich, Nicole

“…Objective This study combines a deep image prior with low-rank subspace modeling to enable real-time (free-breathing and ungated) functional cardiac imaging on…”
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Stop moving: MR motion correction as an opportunity for artificial intelligence Autor Zhou, Zijian, Hu, Peng, Qi, Haikun

“… Furthermore, besides motion-corrected MRI reconstruction, how estimated motion is applied in other downstream tasks is briefly introduced, aiming to strengthen the interaction between different research areas…”
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MRI acquisition and reconstruction cookbook: recipes for reproducibility, served with real-world flavour Autor Tamir, Jonathan I., Blumenthal, Moritz, Wang, Jiachen, Oved, Tal, Shimron, Efrat, Zaiss, Moritz

“…MRI acquisition and reconstruction research has transformed into a computation-driven field…”
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Image distortion correction for MRI in low field permanent magnet systems with strong B0 inhomogeneity and gradient field nonlinearities Autor Koolstra, Kirsten, O’Reilly, Thomas, Börnert, Peter, Webb, Andrew

“…Objective To correct for image distortions produced by standard Fourier reconstruction techniques on low field permanent magnet MRI systems with strong B 0 inhomogeneity and gradient field nonlinearities…”
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Deep learning for automatic segmentation of thigh and leg muscles Autor Agosti, Abramo, Shaqiri, Enea, Paoletti, Matteo, Solazzo, Francesca, Bergsland, Niels, Colelli, Giulia, Savini, Giovanni, Muzic, Shaun I., Santini, Francesco, Deligianni, Xeni, Diamanti, Luca, Monforte, Mauro, Tasca, Giorgio, Ricci, Enzo, Bastianello, Stefano, Pichiecchio, Anna

“… Several automatic methods, based mainly on machine learning and deep learning algorithms, have recently been proposed to discriminate between skeletal muscle, bone, subcutaneous and intermuscular adipose tissue…”
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Large-scale 3D non-Cartesian coronary MRI reconstruction using distributed memory-efficient physics-guided deep learning with limited training data Autor Zhang, Chi, Piccini, Davide, Demirel, Omer Burak, Bonanno, Gabriele, Roy, Christopher W., Yaman, Burhaneddin, Moeller, Steen, Shenoy, Chetan, Stuber, Matthias, Akçakaya, Mehmet

“…Object To enable high-quality physics-guided deep learning (PG-DL) reconstruction of large-scale 3D non-Cartesian coronary MRI by overcoming challenges of hardware limitations and limited training data availability…”
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Deep learning initialized compressed sensing (Deli-CS) in volumetric spatio-temporal subspace reconstruction Autor Schauman, S. Sophie, Iyer, Siddharth S., Sandino, Christopher M., Yurt, Mahmut, Cao, Xiaozhi, Liao, Congyu, Ruengchaijatuporn, Natthanan, Chatnuntawech, Itthi, Tong, Elizabeth, Setsompop, Kawin

“…Object Spatio-temporal MRI methods offer rapid whole-brain multi-parametric mapping, yet they are often hindered by prolonged reconstruction times or prohibitively burdensome hardware requirements…”
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Deep-learning-based image reconstruction with limited data: generating synthetic raw data using deep learning Autor Zijlstra, Frank, While, Peter Thomas

“…Object Deep learning has shown great promise for fast reconstruction of accelerated MRI acquisitions by learning from large amounts of raw data…”
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Quantitative image quality metrics enable resource-efficient quality control of clinically applied AI-based reconstructions in MRI Autor White, Owen A., Shur, Joshua, Castagnoli, Francesca, Charles-Edwards, Geoff, Whitcher, Brandon, Collins, David J., Cashmore, Matthew T. D., Hall, Matt G., Thomas, Spencer A., Thompson, Andrew, Harrison, Ciara A., Hopkinson, Georgina, Koh, Dow-Mu, Winfield, Jessica M.

“…Objective AI-based MRI reconstruction techniques improve efficiency by reducing acquisition times whilst maintaining or improving image quality…”
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Compressed SVD-based L + S model to reconstruct undersampled dynamic MRI data using parallel architecture Autor Shafique, Muhammad, Qazi, Sohaib Ayaz, Omer, Hammad

“… Advanced image reconstruction algorithms have been used in literature to overcome these undersampling artifacts…”
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Accelerating multi-coil MR image reconstruction using weak supervision Autor Atalık, Arda, Chopra, Sumit, Sodickson, Daniel K.

“…Deep-learning-based MR image reconstruction in settings where large fully sampled dataset collection is infeasible requires methods that effectively use both under-sampled and fully sampled datasets…”
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Self-supervised learning for MRI reconstruction: a review and new perspective Autor Li, Xinzhen, Huang, Jinhong, Sun, Guanglong, Yang, Zihan

“… (MRI) reconstruction, emphasizing their potential to overcome the limitations of supervised methods dependent on fully sampled k-space data…”
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Improved reconstruction for highly accelerated propeller diffusion 1.5 T clinical MRI Autor Yarach, Uten, Chatnuntawech, Itthi, Setsompop, Kawin, Suwannasak, Atita, Angkurawaranon, Salita, Madla, Chakri, Hanprasertpong, Charuk, Sangpin, Prapatsorn

“…) constrained reconstruction to enhance the SNR. Furthermore, we enhanced both the speed and SNR by employing Convolutional Neural Networks (CNNs…”
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Deep learning for efficient reconstruction of highly accelerated 3D FLAIR MRI in neurological deficits Autor Liebrand, Luka C., Karkalousos, Dimitrios, Poirion, Émilie, Emmer, Bart J., Roosendaal, Stefan D., Marquering, Henk A., Majoie, Charles B. L. M., Savatovsky, Julien, Caan, Matthan W. A.

“…) with respect to image quality and reconstruction times when 12-fold accelerated scans of patients with neurological deficits are reconstructed…”
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MRI recovery with self-calibrated denoisers without fully-sampled data Autor Shafique, Muhammad, Liu, Sizhuo, Schniter, Philip, Ahmad, Rizwan

“… We present a self-supervised image reconstruction method, termed ReSiDe, capable of recovering images solely from undersampled data…”
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An unsupervised method for MRI recovery: deep image prior with structured sparsity Autor Sultan, Muhammad Ahmad, Chen, Chong, Liu, Yingmin, Gil, Katarzyna, Zareba, Karolina, Ahmad, Rizwan

“…Objective To propose and validate an unsupervised MRI reconstruction method that does not require fully sampled k-space data…”
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A densely interconnected network for deep learning accelerated MRI Autor Ottesen, Jon André, Caan, Matthan W. A., Groote, Inge Rasmus, Bjørnerud, Atle

“…Objective To improve accelerated MRI reconstruction through a densely connected cascading deep learning reconstruction framework…”
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s2MRI-ADNet: an interpretable deep learning framework integrating Euclidean-graph representations of Alzheimer’s disease solely from structural MRI Autor Song, Zhiwei, Li, Honglun, Zhang, Yiyu, Zhu, Chuanzhen, Jiang, Minbo, Song, Limei, Wang, Yi, Ouyang, Minhui, Hu, Fang, Zheng, Qiang

“…Objective To establish a multi-dimensional representation solely on structural MRI (sMRI) for early diagnosis of AD. Methods A total of 3377 participants’ sMRI…”
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Exploring the potential performance of 0.2 T low-field unshielded MRI scanner using deep learning techniques Autor Li, Lei, He, Qingyuan, Wei, Shufeng, Wang, Huixian, Wang, Zheng, Yang, Wenhui

“… of 0.2 T low-field unshielded MRI in terms of imaging quality and speed. Methods First, fast and high-quality unshielded imaging is achieved using active electromagnetic shielding and basic super-resolution…”
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