Matthew Muckley

Research Engineer
Meta AI

Publications

You can also find my articles on my Google Scholar Profile.

Preprints

  1. I Huijben, M Douze, M Muckley, RV Sloun, J Verbeek. Residual Quantization with Implicit Neural Codebooks. arXiv preprint arXiv:2401.14732, 2024. [Preprint PDF]
  2. A Pokle, MJ Muckley, RTQ Chen, B Karrer. Training-free Linear Image Inversion via Flows. arXiv preprint arXiv:2310.04432, 2023. [Preprint PDF]
  3. RTQ Chen, M Le, M Muckley, M Nickel, K Ullrich. Latent discretization for continuous-time sequence compression. arXiv preprint arXiv:2212.13659 2022. [Preprint PDF]
  4. A Sriram*, MJ Muckley*, K Sinha, F Shamout, J Pineau, KJ Geras, L Azour, Y Aphinyanaphongs, N Yakubova, W Moore. COVID-19 Prognosis via Self-Supervised Representation Learning and Multi-Image Prediction. arXiv preprint arXiv:2101.04909, 2021. [Preprint PDF]
  5. J Zbontar*, F Knoll*, A Sriram*, T Murrell, Z Huang, MJ Muckley, A Defazio, R Stern, P Johnson, M Bruno, et al. fastMRI: An open dataset and benchmarks for accelerated MRI. arXiv preprint arXiv:1811.08839, 2018. [Preprint PDF]

Journal Articles

  1. A El-Nouby, MJ Muckley, K Ullrich, I Laptev, J Verbeek, H Jegou. Image Compression with Product Quantized Masked Image Modeling. Transactions on Machine Learning Research, 2023. [PDF]
  2. PM Johnson, DJ Lin, J Zbontar, CL Zitnick, A Sriram, M Muckley, JS Babb, M Kline, G Ciavarra, E Alaia, et al. Deep Learning Reconstruction Enables Prospectively Accelerated Clinical Knee MRI. Radiology, 2023. [PDF]
  3. A Radmanesh*, MJ Muckley*, T Murrell, E Lindsey, A Sriram, F Knoll, DK Sodickson, YW Lui. Exploring the Acceleration Limits of Deep Learning Variational Network–based Two-dimensional Brain MRI. Radiology: Artificial Intelligence, 4(6):e210313, 2022. [PDF]
  4. MJ Muckley*, B Riemenschneider*, A Radmanesh, S Kim, G Jeong, J Ko, Y Jun, H Shin, D Hwang, M Mostapha, et al. Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction. IEEE Transactions on Medical Imaging, 40(9):2306-2317, 2021. [PDF]
  5. MJ Muckley, B Ades-Aron, A Papaioannou, G Lemberskiy, E Solomon, YW Lui, DK Sodickson, E Fieremans, DS Novikov, F Knoll. Training a neural network for Gibbs and noise removal in diffusion MRI. Magnetic Resonance in Medicine, 85(1):413–428, 2021. [PDF]
  6. F Knoll*, T Murrell*, A Sriram*, N Yakubova, J Zbontar, M Rabbat, A Defazio, MJ Muckley, DK Sodickson, CL Zitnick, MP Recht. Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge. Magnetic Resonance in Medicine, 84(6):3054–3070, 2020. [PMC PDF]
  7. MP Recht, J Zbontar, DK Sodickson, F Knoll, N Yakubova, A Sriram, T Murrell, A Defazio, M Rabbat, L Rybak, et al. Using Deep Learning to Accelerate Knee MRI at 3 T: Results of an Interchangeability Study. American Journal of Roentgenology, 215(6):1421–1429, 2020. [PMC PDF]
  8. F Knoll*, J Zbontar*, A Sriram, MJ Muckley, M Bruno, A Defazio, M Parente, KJ Geras, Joe Katsnelson, H Chandarana, et al. fastMRI: A Publicly Available Raw k-Space and DICOM Dataset of Knee Images for Accelerated MR Image Reconstruction Using Machine Learning. Radiology: Artificial Intelligence, 2(1):e190007, 2020. [PDF]
  9. T Chitiboi, M Muckley, B Dane, C Huang, L Feng, H Chandarana. Pancreas deformation in the presence of tumors using feature tracking from free-breathing XD-GRASP MRI. Journal of Magnetic Resonance Imaging, 50(5):1633–1640, 2019. [PMC PDF]
  10. MJ Muckley, B Chen, T Vahle, T O’Donnell, F Knoll, AD Sodickson, DK Sodickson, R Otazo. Image reconstruction for interrupted-beam x-ray CT on diagnostic clinical scanners. Physics in Medicine and Biology, 64(15):155007, 2019. [PDF]
  11. B Chen, E Kobler, MJ Muckley, AD Sodickson, T O’Donnell, T Flohr, B Schmidt, DK Sodickson, R Otazo. SparseCT: System concept and design of multislit collimators. Medical Physics, 46(6):2589–2599, 2019. [PMC PDF]
  12. MA Cauble, MJ Muckley, M Fang, JA Fessler, K Welch, ED Rothman, BG Orr, LT Duong, MMB Holl. Estrogen depletion and drug treatment alter the microstructure of type I collagen in bone. Bone Reports, 5:243–251, 2016. [PDF]
  13. MJ Muckley, DC Noll, JA Fessler. Fast Parallel MR Image Reconstruction via B1-Based, Adaptive Restart, Iterative Soft Thresholding Algorithms (BARISTA). IEEE Transactions on Medical Imaging, 34(2):578–588, 2015. [PMC PDF]
  14. Z Zhong*, M Muckley*, S Agcaoglu, ME Grisham, H Zhao, M Orth, MS Lilburn, O Akkus, DM Karcher. The morphological, material-level, and ash properties of turkey femurs from 3 different genetic strains during production. Poultry Science, 91(11):2736–2746, 2012. [PDF]
  15. Z Xu, X Sun, J Liu, Q Song, M Muckley, O Akkus, YL Kim. Spectroscopic visualization of nanoscale deformation in bone: interaction of light with partially disordered nanostructure. Journal of Biomedical Optics, 15(6):060503, 2010. [PDF]

Conference Articles

  1. M Careil, MJ Muckley, J Verbeek, S Lathuilière. Towards image compression with perfect realism at ultra-low bitrates. In ICLR, 2024. [PDF]
  2. MJ Muckley, A El-Nouby, K Ullrich, H Jegou, J Verbeek. Improving Statistical Fidelity for Neural Image Compression with Implicit Local Likelihood Models. In ICML, pages 25426-25443, 2023. [PDF]
  3. T Bakker, MJ Muckley, A Romero-Soriano, M Drozdzal, L Pineda. On learning adaptive acquisition policies for undersampled multi-coil MRI reconstruction. In MIDL, pages 63-85, 2022. [PDF]
  4. Z Zhang, A Romero, MJ Muckley, P Vincent, L Yang, M Drozdzal. Reducing Uncertainty in Undersampled MRI Reconstruction With Active Acquisition. In CVPR, pages 2049–2058, 2019. [PDF]

Workshop Papers

Workshop and 4-page proceedings papers.

  1. W Xu, MJ Muckley, Y Dubois, K Ullrich. Revisiting Associative Compression: I Can’t Believe It’s Not Better. In ICML 2023 Workshop on Neural Compression, 2023. [PDF]
  2. PM Johnson, G Jeong, K Hammernik, J Schlemper, C Qin, J Duan, D Rueckert, J Lee, N Pezzotti, ED Weerdt, et al. Evaluation of the Robustness of Learned MR Image Reconstruction to Systematic Deviations Between Training and Test Data for the Models from the fastMRI Challenge. In MICCAI MLMIR Workshop, pages 25–34, 2021. [Preprint PDF]
  3. S Gong, M Muckley, N Wu, T Makino, GS Kim, L Heacock, L Moy, F Knoll, KJ Geras. Large-scale classification of breast MRI exams using deep convolutional networks. In Medical Imaging Meets NeurIPS Workshop, 2019. [PDF]
  4. PM Johnson, MJ Muckley, M Bruno, E Kobler, K Hammernik, T Pock, F Knoll. Joint multi-anatomy training of a variational network for reconstruction of accelerated magnetic resonance image acquisitions. In MICCAI MLMIR Workshop, pages 71–79, 2019. [Preprint PDF]
  5. MJ Muckley, B Chen, T O’Donnell, M Berner, T Allmendinger, K Stierstorfer, T Flohr, B Schmidt, A Sodickson, D Sodickson, R Otazo. Reconstruction of reduced-dose SparseCT data acquired with an interruped-beam prototype on a clinical scanner. In CT Meeting, pages 56–59, 2018. [PDF]
  6. B Chen, MJ Muckley, A Sodickson, T O’Donnell, M Berner, T Allmendinger, K Stierstorfer, T Flohr, B Schmidt, D Sodickson, R Otazo. First multislit collimator prototype for SparseCT: design, manufacturing and initial validation. In CT Meeting, pages 52–55, 2018. [PDF]
  7. E Kobler, MJ Muckley, B Chen, F Knoll, K Hammernik, T Pock, DK Sodickson, R Otazo. Variational network learning for low-dose CT. In CT Meeting, pages 430–434, 2018. [PDF]
  8. E Kobler, M Muckley, B Chen, F Knoll, K Hammernik, T Pock, D Sodickson, R Otazo. Variational deep learning for low-dose computed tomography. In ICASSP, pages 6687–6691, 2018.
  9. B Chen, M Muckley, A Sodickson, T O’Donnell, F Knoll, D Sodickson, R Otazo. Evaluation of SparseCT on patient data using realistic undersampling models. In SPIE Medical Imaging, volume 10573, page 1057342, 2018.
  10. M Muckley, B Chen, T Vahle, F Knoll, A Sodickson, DK Sodickson, R Otazo. Regularizer performance for SparseCT image reconstruction with practical subsampling. In Fully3D, pages 572–575, 2017. [PDF]
  11. B Chen, MJ Muckley, T O’Donnell, A Sodickson, T Flohr, K Stierstorfer, B Schmidt, F Knoll, A Primak, D Faul, et al. Realistic undersampling model for compressed sensing using a multi-slit collimator. In Fully3D, pages 314–317, 2017. [PDF]
  12. MJ Muckley, JA Fessler. Fast MR image reconstruction with orthogonal wavelet regularization via shift-variant shrinkage. In ICIP, pages 3651–3655, 2014. [PDF]

Conference Abstracts

  1. I Giannakopoulos, P Johnson, R Lattanzi, M Muckley. Improving variational network based 2D MRI reconstruction via feature-space data consistency. In ISMRM, 2023.
  2. F Knoll, MJ Muckley, YW Lui, DK Sodickson. Insights into the reliability of deep learning reconstructions with research challenges. In BASP Frontiers, page 62, 2023. [PDF]
  3. I Giannakopoulos, P Johnson, R Lattanzi, M Muckley. Improving variational network based 2D MRI reconstruction via feature-space data consistency. In ISMRM Data Sampling & Image Reconstruction Workshop, 2023.
  4. MJ Muckley, T Murrell, A Radmanesh, F Knoll, Z Huang, A Sriram, DK Sodickson, YW Lui. Properties of 2D MR image reconstructions with deep neural networks at high acceleration rates. In ISMRM, page 849, 2022.
  5. B Riemenschneider, MJ Muckley, A Radmanesh, S Kim, G Jeong, J Ko, Y Jun, H Shin, D Hwang, M Mostapha, et al. Results of the 2020 fastMRI brain reconstruction challenge. In ISMRM, page 63, 2021.
  6. MJ Muckley, T Murrell, S Booshan, H Chandarana, F Knoll, DK Sodickson. Unsupervised reconstruction of continuous dynamic radial acquisitions via CNN-NUFFT self-consistency. In ISMRM, page 3605, 2020.
  7. S Gong, M Muckley, N Wu, T Makino, GS Kim, L Heacock, L Moy, F Knoll, KJ Geras. Large-scale classification of breast MRI exams using deep convolutional networks. In ISMRM, page 569, 2020.
  8. T Murrell, MJ Muckley, F Knoll, H Chandarana, DK Sodickson. Self-supervised dynamic MR image reconstruction with a sequence-to-sequence NUFFT-CNN. In ISMRM Data Sampling & Image Reconstruction Workshop, 2020.
  9. MJ Muckley, R Stern, T Murrell, F Knoll. TorchKbNufft: A high-level, hardware-agnostic non-uniform fast Fourier transform. In ISMRM Data Sampling & Image Reconstruction Workshop, 2020. [PDF]
  10. MJ Muckley, A Papaioannou, B Ades-Aron, DK Sodickson, YW Lui, E Fieremans, DS Novikov, F Knoll. Learned Gibbs removal in partial Fourier acquisitions for diffusion MRI. In ISMRM, page 3402, 2019.
  11. F Knoll, M Muckley, J Zbontar, A Sriram, A Defazio, M Drozdzal, K Geras, M Bruno, M Parente, N Yakubova, et al. fastMRI: a publicly available raw k-space dataset for accelerated MRI reconstruction using machine learning. In ISMRM, page 657, 2019.
  12. Z Zhang, A Romero, MJ Muckley, P Vincent, and M Drozdzal. Active acquisition for MRI reconstruction. In ISMRM, page 1093, 2019.
  13. MJ Muckley, A Papaioannou, B Ades-Aron, DK Sodickson, YW Lui, E Fieremans, DS Novikov, F Knoll. Improving mean kurtosis measurements in diffusion MRI via learned Gibbs removal. In ISMRM Machine Learning Workshop, Part 2, 2018.
  14. MJ Muckley, JA Fessler, MVW Zibetti. Accelerating non-Cartesian, sparsity-promoting image reconstruction via line search FISTA. In ISMRM, page 2809, 2018. [PDF]
  15. MJ Muckley, L Feng, H Chandarana, DK Sodickson, R Otazo. Respiratory motion-field reconstruction using low-rank plus sparse (L+S) approach for dynamic MRI of the lungs. In ISMRM, page 3864, 2017.
  16. R Ramb, M Zenge, L Feng, MJ Muckley, C Forman, L Axel, DK Sodickson, R Otazo. Low-rank plus sparse tensor reconstruction for high-dimensional cardiac MRI. In ISMRM, page 1199, 2017.
  17. MJ Muckley, DC Noll, JA Fessler. Fast, iterative subsampled spiral reconstruction via circulant majorizers. In ISMRM, page 521, 2016.
  18. MJ Muckley, DC Noll, JA Fessler. Majorizer design for non-Cartesian MRI with sparsity-promoting regularization. In ISMRM Data Sampling & Image Reconstruction Workshop, 2016. [PDF]
  19. MJ Muckley, DC Noll, JA Fessler. Momentum optimization for iterative shrinkage algorithms in parallel MRI with sparsity-promoting regularization. In ISMRM, page 3413, 2015.
  20. MJ Muckley, Douglas C. Noll, and Jeffrey A. Fessler. Accelerating SENSE-type MR image reconstruction algorithms with incremental gradients. In ISMRM, page 4400, 2014. [PDF]
  21. MJ Muckley, SJ Peltier, DC Noll, JA Fessler. Model-based reconstruction for physiological noise correction in functional MRI. In ISMRM, page 2623, 2013. [PDF]
  22. MJ Muckley, SJ Peltier, JA Fessler, DC Noll. Group sparsity reconstruction for physiological noise correction in functional MRI. In ISMRM Data Sampling & Image Reconstruction Workshop, 2013. [PDF]
  23. M Muckley, S Agcaoglu, Z Zhong, H Zhao, M Grisham, D Karcher, M Orth, M Lilburn, O Akkus. The effect of selective breeding for body weight on geometric properties in turkey femurs. In ASME, pages 901–902, 2011.
  24. Z Zhong, S Agcaoglu, M Muckley, H Zhao, D Karcher, M Orth, M Lilburn, O Akkus. Changes in turkey femora mechanical properties resulting from selective breeding for body weight. In ASME, pages 897–898, 2011.