Search Results - "Basic Science - Reconstruction algorithms and artificial intelligence"

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

“…Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic resonance imaging (MRI), a critical tool in diagnostic radiology. This…”
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Deep learning for automatic segmentation of thigh and leg muscles by 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

“…Objective In this study we address the automatic segmentation of selected muscles of the thigh and leg through a supervised deep learning approach. Material…”
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Compressed SVD-based L + S model to reconstruct undersampled dynamic MRI data using parallel architecture by Shafique, Muhammad, Qazi, Sohaib Ayaz, Omer, Hammad

“…Background Magnetic Resonance Imaging (MRI) is a highly demanded medical imaging system due to high resolution, large volumetric coverage, and ability to…”
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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 by 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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Image distortion correction for MRI in low field permanent magnet systems with strong B0 inhomogeneity and gradient field nonlinearities by 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…”
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s2MRI-ADNet: an interpretable deep learning framework integrating Euclidean-graph representations of Alzheimer’s disease solely from structural MRI by 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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MRI acquisition and reconstruction cookbook: recipes for reproducibility, served with real-world flavour by 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. As methods become more sophisticated, compute-heavy, and…”
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MRI recovery with self-calibrated denoisers without fully-sampled data by Shafique, Muhammad, Liu, Sizhuo, Schniter, Philip, Ahmad, Rizwan

“…Objective Acquiring fully sampled training data is challenging for many MRI applications. We present a self-supervised image reconstruction method, termed…”
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Exploring the potential performance of 0.2 T low-field unshielded MRI scanner using deep learning techniques by Li, Lei, He, Qingyuan, Wei, Shufeng, Wang, Huixian, Wang, Zheng, Yang, Wenhui

“…Objective Using deep learning-based techniques to overcome physical limitations and explore the potential performance of 0.2 T low-field unshielded MRI in…”
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A densely interconnected network for deep learning accelerated MRI by 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. Materials and methods A…”
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Large-scale 3D non-Cartesian coronary MRI reconstruction using distributed memory-efficient physics-guided deep learning with limited training data by 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…”
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Deep learning initialized compressed sensing (Deli-CS) in volumetric spatio-temporal subspace reconstruction by 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…”
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Cross2SynNet: cross-device–cross-modal synthesis of routine brain MRI sequences from CT with brain lesion by Jiang, Minbo, Wang, Shuai, Song, Zhiwei, Song, Limei, Wang, Yi, Zhu, Chuanzhen, Zheng, Qiang

“…Objectives CT and MR are often needed to determine the location and extent of brain lesions collectively to improve diagnosis. However, patients with acute…”
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Deep-learning-based image reconstruction with limited data: generating synthetic raw data using deep learning by 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. However, raw…”
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Stop moving: MR motion correction as an opportunity for artificial intelligence by Zhou, Zijian, Hu, Peng, Qi, Haikun

“…Subject motion is a long-standing problem of magnetic resonance imaging (MRI), which can seriously deteriorate the image quality. Various prospective and…”
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Self-supervised learning for MRI reconstruction: a review and new perspective by Li, Xinzhen, Huang, Jinhong, Sun, Guanglong, Yang, Zihan

“…Objective To review the latest developments in self-supervised deep learning (DL) techniques for magnetic resonance imaging (MRI) reconstruction, emphasizing…”
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Accelerating multi-coil MR image reconstruction using weak supervision by 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…”
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An unsupervised method for MRI recovery: deep image prior with structured sparsity by 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. Materials and methods The…”
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Quantitative image quality metrics enable resource-efficient quality control of clinically applied AI-based reconstructions in MRI by 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. Recent…”
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Learning to deep learning: statistics and a paradigm test in selecting a UNet architecture to enhance MRI by Sharma, Rishabh, Tsiamyrtzis, Panagiotis, Webb, Andrew G., Leiss, Ernst L., Tsekos, Nikolaos V.

“…Objective This study aims to assess the statistical significance of training parameters in 240 dense UNets (DUNets) used for enhancing low Signal-to-Noise…”
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