Fetching the paper…
Reading the bibliography…
Magnetic resonance imaging (MRI) is a common and life-saving medical imaging technique.
On the rician distribution of noisy mri data
Anders H Andersen · 1996
Earlier work this paper cites.
Sense: sensitivity encoding for fast mri
Klaas P Pruessmann, Markus Weiger, Markus B Scheidegger, and Peter Boesiger · 1999
Earlier work this paper cites.
Looking into the functional architecture of the brain with diffusion MRI
Denis Le Bihan · 2003
Earlier work this paper cites.
Estimation of non-normalized statistical models by score matching
Aapo Hyvärinen · 2005
Earlier work this paper cites.
Artifacts and pitfalls in diffusion MRI
Denis Le Bihan, Cyril Poupon, Alexis Amadon, and Franck Lethimonnier · 2006
Earlier work this paper cites.
No-reference image quality metrics for structural MRI
Jeffrey P. Woodard and Monica P. Carley-Spencer · 2006
Earlier work this paper cites.
Comprehensive quantification of signal-to-noise ratio and g-factor for image-based and k-space-based parallel imaging reconstructions
Philip M Robson, Aaron K Grant, Ananth J Madhuranthakam, Riccardo Lattanzi, Daniel K Sodickson, and Charles A McKenzie · 2008
Earlier work this paper cites.
Tweedie’s formula and selection bias
Bradley Efron · 2011
Earlier work this paper cites.
The parkinson progression marker initiative (ppmi)
Kenneth Marek, Danna Jennings, Shirley Lasch, Andrew Siderowf, Caroline Tanner, Tanya Simuni, Chris Coffey, Karl Kieburtz, Emily Flagg, Sohini Chowdhury, Werner Poewe, Brit Mollenhauer, Paracelsus-Elena Klinik, Todd Sherer, Mark Frasier, Claire Meunier, Alice Rudolph, Cindy Casaceli, John Seibyl, Susan Mendick, Norbert Schuff, Ying Zhang, Arthur Toga, Karen Crawford, Alison Ansbach, Pasquale De Blasio, Michele Piovella, John Trojanowski, Les Shaw, Andrew Singleton, Keith Hawkins, Jamie Eberling, Deborah Brooks, David Russell, Laura Leary, Stewart Factor, Barbara Sommerfeld, Penelope Hogarth, Emily Pighetti, Karen Williams, David Standaert, Stephanie Guthrie, Robert Hauser, Holly Delgado, Joseph Jankovic, Christine Hunter, Matthew Stern, Baochan Tran, Jim Leverenz, Marne Baca, Sam Frank, Cathi-Ann Thomas, Irene Richard, Cheryl Deeley, Linda Rees, Fabienne Sprenger, Elisabeth Lang, Holly Shill, Sanja Obradov, Hubert Fernandez, Adrienna Winters, Daniela Berg, Katharina Gauss, Douglas Galasko, Deborah Fontaine, Zoltan Mari, Melissa Gerstenhaber, David Brooks, Sophie Malloy, Paolo Barone, Katia Longo, Tom Comery, Bernard Ravina, Igor Grachev, Kim Gallagher, Michelle Collins, Katherine L. Widnell, Suzanne Ostrowizki, Paulo Fontoura, Tony Ho, Johan Luthman, Marcel van der Brug, Alastair D. Reith, and Peggy Taylor · 2011
Earlier work this paper cites.
Blind/referenceless image spatial quality evaluator
Anish Mittal, Anush K. Moorthy, and Alan C. Bovik · 2011
Earlier work this paper cites.
Dipy, a library for the analysis of diffusion MRI data
Eleftherios Garyfallidis, Matthew Brett, Bagrat Amirbekian, Ariel Rokem, Stéfan van der Walt, Maxime Descoteaux, and Ian Nimmo-Smith · 2014
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
Earlier work this paper cites.
Stanford HARDI surfaces
Ariel Rokem · 2016
Earlier work this paper cites.
Simultaneous NODDI and GFA parameter map generation from subsampled q-space imaging using deep learning
Eric K. Gibbons, Kyler K. Hodgson, Akshay S. Chaudhari, Lorie G. Richards, Jennifer J. Majersik, Ganesh Adluru, and Edward V.R. DiBella · 2018
Earlier work this paper cites.
Noise2noise: Learning image restoration without clean data
Jaakko Lehtinen, Jacob Munkberg, Jon Hasselgren, Samuli Laine, Tero Karras, Miika Aittala, and Timo Aila · 2018
Earlier work this paper cites.
Unsupervised learning with stein’s unbiased risk estimator
Christopher A Metzler, Ali Mousavi, Reinhard Heckel, and Richard G Baraniuk · 2018
Earlier work this paper cites.
Deep image prior
Dmitry Ulyanov, Andrea Vedaldi, and Victor S. Lempitsky · 2018
Earlier work this paper cites.
Noise2self: Blind denoising by self-supervision
Joshua Batson and Loïc Royer · 2019
Cited alongside, same era.
Utility of deep learning super-resolution in the context of osteoarthritis MRI biomarkers
Akshay S. Chaudhari, Kathryn J. Stevens, Jeff P. Wood, Amit K. Chakraborty, Eric K. Gibbons, Zhongnan Fang, Arjun D. Desai, Jin Hyung Lee, Garry E. Gold, and Brian A. Hargreaves · 2019
Cited alongside, same era.
Noise2void - learning denoising from single noisy images
Alexander Krull, Tim-Oliver Buchholz, and Florian Jug · 2019
Cited alongside, same era.
High-quality self-supervised deep image denoising
Samuli Laine, Tero Karras, Jaakko Lehtinen, and Timo Aila · 2019
Cited alongside, same era.
Comparison of objective image quality metrics to expert radiologists’ scoring of diagnostic quality of MR images
Allister Mason, James Rioux, Sharon E. Clarke, Andreu Costa, Matthias Schmidt, Valerie Keough, Thien Huynh, and Steven Beyea · 2019
Cited alongside, same era.
Neighbor2neighbor: Self-supervised denoising from single noisy images
Tao Huang, Songjiang Li, Xu Jia, Huchuan Lu, and Jianzhuang Liu · 2021
Later among the works it cites.
Noise2score: Tweedie’s approach to self-supervised image denoising without clean images
Kwanyoung Kim and Jong Chul Ye · 2021
Later among the works it cites.
Diffwave: A versatile diffusion model for audio synthesis
Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, and Bryan Catanzaro · 2021
Later among the works it cites.
Diffusion probabilistic models for 3d point cloud generation
Shitong Luo and Wei Hu · 2021
Later among the works it cites.
Recorrupted-to-recorrupted: Unsupervised deep learning for image denoising
Tongyao Pang, Huan Zheng, Yuhui Quan, and Hui Ji · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Cited alongside, same era.
Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
Cited alongside, same era.
Prospective deployment of deep learning in mri: A framework for important considerations, challenges, and recommendations for best practices
Akshay S. Chaudhari, Christopher M. Sandino, Elizabeth K. Cole, David B. Larson, Garry E. Gold, Shreyas S. Vasanawala, Matthew P. Lungren, Brian A. Hargreaves, and Curtis P. Langlotz · 2020
Cited alongside, same era.
Patch2self: Denoising diffusion MRI with self-supervised learning
Shreyas Fadnavis, Joshua Batson, and Eleftherios Garyfallidis · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Solving linear inverse problems using the prior implicit in a denoiser
Zahra Kadkhodaie and Eero P Simoncelli · 2020
Cited alongside, same era.
Accelerating prostate diffusion-weighted MRI using a guided denoising convolutional neural network: Retrospective feasibility study
Elena A. Kaye, Emily A. Aherne, Cihan Duzgol, Ida Häggström, Erich Kobler, Yousef Mazaheri, Maggie M. Fung, Zhigang Zhang, Ricardo Otazo, Hebert A. Vargas, and Oguz Akin · 2020
Cited alongside, same era.
Fully unsupervised diversity denoising with convolutional variational autoencoders
Mangal Prakash, Alexander Krull, and Florian Jug · 2021
Later among the works it cites.
Image super-resolution via iterative refinement
Chitwan Saharia, Jonathan Ho, William Chan, Tim Salimans, David J. Fleet, and Mohammad Norouzi · 2021
Later among the works it cites.
Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2021
Later among the works it cites.
Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2021
Later among the works it cites.
3d shape generation and completion through point-voxel diffusion
Linqi Zhou, Yilun Du, and Jiajun Wu · 2021
Later among the works it cites.
MR image denoising and super-resolution using regularized reverse diffusion
Hyungjin Chung, Eun Sun Lee, and Jong Chul Ye · 2022
Later among the works it cites.
Arjun D Desai, Beliz Gunel, Batu M Ozturkler, Harris Beg, Shreyas Vasanawala, Brian A Hargreaves, Christopher Ré, John M Pauly, and Akshay S Chaudhari · 2022
Later among the works it cites.
BDDM: Bilateral denoising diffusion models for fast and high-quality speech synthesis
Max W. Y. Lam, Jun Wang, Dan Su, and Dong Yu · 2022
Later among the works it cites.
Progressive distillation for fast sampling of diffusion models
Tim Salimans and Jonathan Ho · 2022
Later among the works it cites.
Solving inverse problems in medical imaging with score-based generative models
Yang Song, Liyue Shen, Lei Xing, and Stefano Ermon · 2022
Later among the works it cites.
Learning fast samplers for diffusion models by differentiating through sample quality
Daniel Watson, William Chan, Jonathan Ho, and Mohammad Norouzi · 2022
Later among the works it cites.
Geodiff: A geometric diffusion model for molecular conformation generation
Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, and Jian Tang · 2022
Later among the works it cites.