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Compressed sensing MRI seeks to accelerate MRI acquisition processes by sampling fewer k-space measurements and then reconstructing the missing data algorithmically.
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Thomas Cover · 1999
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“Image quality assessment: From error visibility to structural similarity”
Zhou Wang, Alan. Bovik, Hamid. Sheikh and Eero. Simoncelli · 2004
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“Estimation of non-normalized statistical models by score matching.”
Aapo Hyvärinen and Peter Dayan · 2005
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“Parallel MR imaging”
Anagha Deshmane, Vikas Gulani, Mark Griswold and Nicole Seiberlich · 2012
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“Auto-encoding variational bayes”
Diederik Kingma and Max Welling · 2013
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“ESPIRiT—an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA”
Martin Uecker, Peng Lai, Mark Murphy, Patrick Virtue, Michael Elad, John Pauly, Shreyas Vasanawala and Michael Lustig · 2014
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“UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age”
Cathie Sudlow, John Gallacher, Naomi Allen, Valerie Beral, Paul Burton, John Danesh, Paul Downey, Paul Elliott, Jane Green and Martin Landray · 2015
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“fastMRI: An open dataset and benchmarks for accelerated MRI”
Jure Zbontar, Florian Knoll, Anuroop Sriram, Tullie Murrell, Zhengnan Huang, Matthew Muckley, Aaron Defazio, Ruben Stern, Patricia Johnson and Mary Bruno · 2018
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“The unreasonable effectiveness of deep features as a perceptual metric”
Richard Zhang, Phillip Isola, Alexei Efros, Eli Shechtman and Oliver Wang · 2018
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“SigPy: a python package for high performance iterative reconstruction”
Frank Ong and Michael Lustig · 2019
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“Denoising diffusion probabilistic models”
Jonathan Ho, Ajay Jain and Pieter Abbeel · 2020
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“SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluation”
Arjun Desai, Andrew Schmidt, Elka Rubin, Christopher Sandino, Marianne Black, Valentina Mazzoli, Kathryn Stevens, Robert Boutin, Christopher Re and Garry Gold · 2021
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“Classifier-Free Diffusion Guidance”
Jonathan Ho and Tim Salimans · 2021
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“Robust compressed sensing mri with deep generative priors”
Ajil Jalal, Marius Arvinte, Giannis Daras, Eric Price, Alexandros Dimakis and Jonathan Tamir · 2021
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“Stochastic Solutions for Linear Inverse Problems using the Prior Implicit in a Denoiser”
Zahra Kadkhodaie and Eero Simoncelli · 2021
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“Learning transferable visual models from natural language supervision”
Alec Radford, Jong Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin and Jack Clark · 2021
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“Denoising Diffusion Implicit Models”
Jiaming Song, Chenlin Meng and Stefano Ermon · 2021
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“Score-Based Generative Modeling through Stochastic Differential Equations”
Yang Song, Jascha Sohl-Dickstein, Diederik. Kingma, Abhishek Kumar, Stefano Ermon and Ben Poole · 2021
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“fastMRI+: Clinical Pathology Annotations for Knee and Brain Fully Sampled Multi-Coil MRI Data”
Ruiyang Zhao, Burhaneddin Yaman, Yuxin Zhang, Russell Stewart, Austin Dixon, Florian Knoll, Zhengnan Huang, Yvonne Lui, Michael Hansen and Matthew Lungren · 2021
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“Score-based diffusion models for accelerated MRI”
Hyungjin Chung and Jong Ye · 2022
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“Denoising Diffusion Restoration Models”
Bahjat Kawar, Michael Elad, Stefano Ermon and Jiaming Song · 2022
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“Brain imaging generation with latent diffusion models”
Walter Pinaya, Petru-Daniel Tudosiu, Jessica Dafflon, Pedro Da, Virginia Fernandez, Parashkev Nachev, Sebastien Ourselin and M Cardoso · 2022
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“DeepFloyd-IF: High-Quality Text-to-Image Synthesis” Accessed: 2024-09-12, https://github.com/deepfloyd/IF , 2023
DeepFloyd Team · 2023
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“A vision–language foundation model for the generation of realistic chest x-ray images”
Christian Bluethgen, Pierre Chambon, Jean-Benoit Delbrouck, Rogier van Sluijs, Małgorzata Połacin, Juan Zambrano, Tanishq Abraham, Shivanshu Purohit, Curtis Langlotz and Akshay Chaudhari · 2024
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“Classifier-Free Guidance is a Predictor-Corrector”
Arwen Bradley and Preetum Nakkiran · 2024
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“Video generation models as world simulators”, 2024
Tim Brooks, Bill Peebles, Connor Holmes, Will DePue, Yufei Guo, Li Jing, David Schnurr, Joe Taylor, Troy Luhman, Eric Luhman, Clarence Ng, Ricky Wang and Aditya Ramesh · 2024
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“MediSyn: Text-Guided Diffusion Models for Broad Medical 2D and 3D Image Synthesis”
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“DreamFusion: Text-to-3D using 2D Diffusion”
Ben Poole, Ajay Jain, Jonathan. Barron and Ben Mildenhall · 2022
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“High-resolution image synthesis with latent diffusion models”
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser and Björn Ommer · 2022
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“Photorealistic text-to-image diffusion models with deep language understanding”
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Kamyar Ghasemipour, Raphael Gontijo, Burcu Karagol and Tim Salimans · 2022
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“Solving Inverse Problems in Medical Imaging with Score-Based Generative Models”
Yang Song, Liyue Shen, Lei Xing and Stefano Ermon · 2022
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“Diffusion Posterior Sampling for General Noisy Inverse Problems”
Hyungjin Chung, Jeongsol Kim, Michael Mccann, Marc Klasky and Jong Ye · 2023
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“Luciddreamer: Domain-free generation of 3d gaussian splatting scenes”
Jaeyoung Chung, Suyoung Lee, Hyeongjin Nam, Jaerin Lee and Kyoung Lee · 2023
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“GenerateCT: Text-Conditional Generation of 3D Chest CT Volumes”
Ibrahim Hamamci, Sezgin Er, Anjany Sekuboyina, Enis Simsar, Alperen Tezcan, Ayse Simsek, Sevval Esirgun, Furkan Almas, Irem Dogan and Muhammed Dasdelen · 2023
Cited alongside, same era.
Joseph Cho, Cyril Zakka, Rohan Shad, Ross Wightman, Akshay Chaudhari and William Hiesinger · 2024
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“Cfg++: Manifold-constrained classifier free guidance for diffusion models”
Hyungjin Chung, Jeongsol Kim, Geon Park, Hyelin Nam and Jong Ye · 2024
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“Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problems”
Hyungjin Chung, Suhyeon Lee and Jong Ye · 2024
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“Prompt-tuning Latent Diffusion Models for Inverse Problems”
Hyungjin Chung, Jong Ye, Peyman Milanfar and Mauricio Delbracio · 2024
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“A survey on diffusion models for inverse problems”
Giannis Daras, Hyungjin Chung, Chieh-Hsin Lai, Yuki Mitsufuji, Jong Ye, Peyman Milanfar, Alexandros Dimakis and Mauricio Delbracio · 2024
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“Novel View Synthesis with Pixel-Space Diffusion Models”
Noam Elata, Bahjat Kawar, Yaron Ostrovsky-Berman, Miriam Farber and Ron Sokolovsky · 2024
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“Controllable text-to-image synthesis for multi-modality MR images”
Kyuri Kim, Yoonho Na, Sung-Joon Ye, Jimin Lee, Sung Ahn, Ji Park and Hwiyoung Kim · 2024
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“The multimodal brain tumor image segmentation benchmark (BRATS)”
Bjoern Menze, Andras Jakab, Stefan Bauer, Jayashree Kalpathy-Cramer, Keyvan Farahani, Justin Kirby, Yuliya Burren, Nicole Porz, Johannes Slotboom and Roland Wiest · 2024
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“Solving linear inverse problems provably via posterior sampling with latent diffusion models”
Litu Rout, Negin Raoof, Giannis Daras, Constantine Caramanis, Alex Dimakis and Sanjay Shakkottai · 2024
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“Diffusion Models as Data Mining Tools”
Ioannis Siglidis, Aleksander Holynski, Alexei Efros, Mathieu Aubry and Shiry Ginosar · 2024
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“Towards General Text-guided Image Synthesis for Customized Multimodal Brain MRI Generation”
Yulin Wang, Honglin Xiong, Kaicong Sun, Shuwei Bai, Ling Dai, Zhongxiang Ding, Jiameng Liu, Qian Wang, Qian Liu and Dinggang Shen · 2024
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“Dreamsampler: Unifying diffusion sampling and score distillation for image manipulation”
Jeongsol Kim, Geon Park and Jong Ye · 2025
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