Abstract
T1-weighted (T1w) MRI has low frequency intensity artifacts due to magnetic field inhomogeneities. Removal of these biases in T1w MRI images is a critical preprocessing step to ensure spatially consistent image interpretation. N4ITK bias field correction, the current state-of-the-art, is implemented in such a way that makes it difficult to port between different pipelines and workflows, thus making it hard to reimplement and reproduce results across local, cloud, and edge platforms. Moreover, N4ITK is opaque to optimization before and after its application, meaning that methodological development must work around the inhomogeneity correction step. Given the importance of bias fields correction in structural preprocessing and flexible implementation, we pursue a deep learning approximation / reinterpretation of the N4ITK bias fields correction to create a method which is portable, flexible, and fully differentiable. In this paper, we trained a deep learning network “DeepN4” on eight independent cohorts from 72 different scanners and age ranges with N4ITK-corrected T1w MRI and bias field for supervision in log space. We found that we can closely approximate N4ITK bias fields correction with naïve networks. We evaluate the peak signal to noise ratio (PSNR) in test dataset against the N4ITK corrected images. The median PSNR of corrected images between N4ITK and DeepN4 was 47.96 dB. In addition, we assess the DeepN4 model on eight additional external datasets and show the generalizability of the approach. This study establishes that incompatible N4ITK preprocessing steps can be closely approximated by naïve deep neural networks, facilitating more flexibility. All code and models are released at https://github.com/MASILab/DeepN4.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Availability of Data and Material
The data used for the analysis of this article are confidential due to privacy or other restrictions.
Code Availability
The code used for this article is available from https://github.com/MASILab/DeepN4.
References
Agrawal, A., Amos, B., Barratt, S., Boyd, S., Diamond, S., & Kolter, J. Z. (2019). Differentiable convex optimization layers. Advances in neural information processing systems (Vol. 32).
Ashburner, J., & Friston, K. J. (2005). Unified segmentation. NeuroImage, 26(3), 839–851.
Avants, B. B., Tustison, N., & Song, G. (2009). Advanced normalization tools (ANTS). Insight J, 2(365), 1–35.
Axel, L., Costantini, J., & Listerud, J. (1987). Intensity correction in surface-coil MR imaging. AJR American Journal of Roentgenology, 148(2), 418–420.
Bakas, S., Akbari, H., Sotiras, A., et al. (2017). Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Scientific Data, 4(1), 1–13.
Beekly, D. L., Ramos, E. M., Lee, W. W., et al. (2007). The National Alzheimer’s Coordinating Center (NACC) database: The uniform data set. Alzheimer Disease & Associated Disorders, 21(3), 249–258.
Beekly, D. L., Ramos, E. M., van Belle, G., et al. (2004). The national Alzheimer’s coordinating center (NACC) database: An Alzheimer disease database. Alzheimer Disease & Associated Disorders, 18(4), 270–277.
Besser, L. M., Kukull, W. A., Teylan, M. A., et al. (2018). The revised National Alzheimer’s Coordinating Center’s Neuropathology Form—available data and new analyses. Journal of Neuropathology & Experimental Neurology, 77(8), 717–726.
Brinkmann, B. H., Manduca, A., & Robb, R. A. (1998). Optimized homomorphic unsharp masking for MR grayscale inhomogeneity correction. IEEE Transactions on Medical Imaging, 17(2), 161–171.
Chen, C., Qin, C., Qiu, H., Ouyang, C., Wang, S., Chen, L., & Rueckert, D. (2020). Realistic adversarial data augmentation for MR image segmentation. Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part I 23 (pp. 667–677). Springer International Publishing.
Chuang, K.-H., Wu, P.-H., Li, Z., Fan, K.-H., & Weng, J.-C. (2022). Deep learning network for integrated coil inhomogeneity correction and brain extraction of mixed MRI data. Scientific Reports, 12(1), 8578.
Damadian, R. (1971). Tumor detection by nuclear magnetic resonance. Science, 171(3976), 1151–1153.
Esteban, O., Markiewicz, C. J., Blair, R. W., et al. (2019). fMRIPrep: A robust preprocessing pipeline for functional MRI. Nature Methods, 16(1), 111–116.
Fischl, B. (2012). FreeSurfer. Neuroimage, 62(2), 774–781.
Froeling, M., Tax, C. M., Vos, S. B., Luijten, P. R., & Leemans, A. (2017). “MASSIVE” brain dataset: Multiple acquisitions for standardization of structural imaging validation and evaluation. Magnetic Resonance in Medicine, 77(5), 1797–1809.
Gaillochet, M., Tezcan, K. C., & Konukoglu, E. (2020). Joint reconstruction and bias field correction for undersampled MR imaging. International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 44–52). Cham: Springer International Publishing.
Gispert, J. D., Reig, S., Pascau, J., Vaquero, J. J., García-Barreno, P., & Desco, M. (2004). Method for bias field correction of brain T1-weighted magnetic resonance images minimizing segmentation error. Human Brain Mapping, 22(2), 133–144.
Goldfryd, T., Gordon, S., & Raviv, T. R. (2021). Deep semi-supervised bias field correction of Mr images (pp. 1836–1840). IEEE.
Gorgolewski, K., Burns, C. D., Madison, C., et al. (2011). Nipype: A flexible, lightweight and extensible neuroimaging data processing framework in python. Frontiers in Neuroinformatics, 5, 13.
Harms, M. P., Somerville, L. H., Ances, B. M., et al. (2018). Extending the Human Connectome Project across ages: Imaging protocols for the Lifespan Development and Aging projects. NeuroImage, 183, 972–984.
Huo, Y., Xu, Z., Xiong, Y., et al. (2019). 3D whole brain segmentation using spatially localized atlas network tiles. NeuroImage, 194, 105–119.
IXI Dataset - Information eXtraction from images. (2020). Biomedical Image Analysis Group, Imperial College London. https://brain-development.org/ixi-dataset/
Jack, C. R., Jr., Bernstein, M. A., Fox, N. C., et al. (2008). The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. Journal of Magnetic Resonance Imaging: An Official Journal of the International Society for Magnetic Resonance in Medicine, 27(4), 685–691.
Jefferson, A. L., Gifford, K. A., Acosta, L. M. Y., et al. (2016). The Vanderbilt Memory & Aging Project: Study design and baseline cohort overview. Journal of Alzheimer’s Disease, 52(2), 539–559.
Johnson, K. A. (2016). Basic proton MR imaging: tissue signal characteristics. Harvard Medical School. Archived from the original on, 03-05.
Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint. arXiv:14126980
LaMontagne, P. J., Benzinger, T. L., Morris, J. C., et al. (2019). OASIS-3: longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and Alzheimer disease. MedRxiv, 2019.12. 13.19014902.
Landman, B. A., Huang, A. J., Gifford, A., et al. (2011). Multi-parametric neuroimaging reproducibility: A 3-T resource study. NeuroImage, 54(4), 2854–2866.
Mascalchi, M., Marzi, C., Giannelli, M., et al. (2018). Histogram analysis of DTI-derived indices reveals pontocerebellar degeneration and its progression in SCA2. PLoS ONE, 13(7), e0200258.
Mihara, H., Iriguchi, N., & Ueno, S. (1998). A method of RF inhomogeneity correction in MR imaging. Magnetic Resonance Materials in Physics, Biology and Medicine, 7(2), 115–120.
Miller, N., Liu, Y., Krivochenitser, R., & Rokers, B. (2019). Linking neural and clinical measures of glaucoma with diffusion magnetic resonance imaging (dMRI). PLoS ONE, 14(5), e0217011.
Narayana, P., Brey, W., Kulkarni, M., & Sievenpiper, C. (1988). Compensation for surface coil sensitivity variation in magnetic resonance imaging. Magnetic Resonance Imaging, 6(3), 271–274.
Payares-Garcia, D., Mateu, J., & Schick, W. (2023). NeuroNorm: An R package to standardize multiple structural MRI. Neurocomputing, 550, 126493.
Pineda, L., Fan, T., Monge, M., et al. (2022). Theseus: A library for differentiable nonlinear optimization. Advances in Neural Information Processing Systems, 35, 3801–3818.
Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18 (pp. 234–241). Springer International Publishing.
Sacktor, N., Soldan, A., Grega, M., Farrington, L., Cai, Q., Wang, M. C., & Albert, M. (2017). The BIOCARD index: a summary measure to predict onset of mild cognitive impairment (P1. 095). AAN Enterprises.
Schiffmann, R., & van der Knaap, M. S. (2009). Invited article: An MRI-based approach to the diagnosis of white matter disorders. Neurology, 72(8), 750–759.
Schilling, K. G., Blaber, J., Hansen, C., et al. (2020). Distortion correction of diffusion weighted MRI without reverse phase-encoding scans or field-maps. PLoS ONE, 15(7), e0236418.
Shock, N. W. (1984). Normal human aging: The Baltimore longitudinal study of aging (No. 84). US Department of Health and Human Services, Public Health Service, National Institutes of Health, National Institute on Aging, Gerontology Research Center.
Simkó, A., Löfstedt, T., Garpebring, A., Nyholm, T., & Jonsson, J. (2022). MRI bias field correction with an implicitly trained CNN. International Conference on Medical Imaging with Deep Learning (pp. 1125–1138). PMLR.
Simmons, A., Tofts, P. S., Barker, G. J., & Arridge, S. R. (1994). Sources of intensity nonuniformity in spin echo images at 1.5 T. Magnetic Resonance in Medicine, 32(1), 121–128.
Sled, J. G., Zijdenbos, A. P., & Evans, A. C. (1998). A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Transactions on Medical Imaging, 17(1), 87–97.
Song, S., Zheng, Y., & He, Y. (2017). A review of methods for bias correction in medical images. Biomedical Engineering Review, 1(1).
Sridhara, S. N., Akrami, H., Krishnamurthy, V., & Joshi, A. A. (2021). Bias field correction in 3D-MRIs using convolutional autoencoders. Medical Imaging 2021: Image Processing (Vol. 11596, pp. 671–676). SPIE.
Tournier, J. D., Calamante, F., & Connelly, A. (2012). MRtrix: Diffusion tractography in crossing fiber regions. International Journal of Imaging Systems and Technology, 22(1), 53–66.
Tustison, N. J., Avants, B. B., Cook, P. A., et al. (2010). N4ITK: Improved N3 bias correction. IEEE Transactions on Medical Imaging, 29(6), 1310–1320.
Tustison, N. J., Cook, P. A., Holbrook, A. J., et al. (2021). The ANTsX ecosystem for quantitative biological and medical imaging. Scientific Reports, 11(1), 9068.
Van Essen, D. C., Smith, S. M., Barch, D. M., et al. (2013). The WU-Minn human connectome project: An overview. NeuroImage, 80, 62–79.
Vovk, U., Pernus, F., & Likar, B. (2007). A review of methods for correction of intensity inhomogeneity in MRI. IEEE Transactions on Medical Imaging, 26(3), 405–421.
Wan, F., Smedby, Ö., & Wang, C. (2019). Simultaneous MR knee image segmentation and bias field correction using deep learning and partial convolution. Medical Imaging 2019: Image Processing (Vol. 10949, pp. 61–67). SPIE.
Weintraub, S., Besser, L., Dodge, H. H., et al. (2018). Version 3 of the Alzheimer Disease Centers’ neuropsychological test battery in the Uniform Data Set (UDS). Alzheimer Disease and Associated Disorders, 32(1), 10.
Weintraub, S., Salmon, D., Mercaldo, N., et al. (2009). The Alzheimer’s disease centers’ uniform data set (UDS): The neuropsychological test battery. Alzheimer Disease and Associated Disorders, 23(2), 91.
Xu, Y., Wang, Y., Hu, S., & Du, Y. (2022). Deep convolutional neural networks for bias field correction of brain magnetic resonance images. The Journal of Supercomputing, 78(16), 17943–17968.
Yaniv, Z., Lowekamp, B. C., Johnson, H. J., & Beare, R. (2018). SimpleITK image-analysis notebooks: A collaborative environment for education and reproducible research. Journal of Digital Imaging, 31(3), 290–303.
Acknowledgements
This work was supported by the National Institutes of Health under award numbers R01EB017230, 1K01EB032898, K01-AG073584 and T32GM007347, and in part by the National Center for Research Resources, Grant UL1 RR024975-01, UL1-TR000445 and UL1-TR002243 (Vanderbilt Clinical Translational Science Award), S10-OD023680 (Vanderbilt’s High-Performance Computer Cluster for Biomedical Research) and U24-AG074855. The Vanderbilt Institute for Clinical and Translational Research (VICTR) is funded by the National Center for Advancing Translational Sciences (NCATS) Clinical Translational Science Award (CTSA) Program, Award Number 5UL1TR002243-03.
We acknowledge the data provided by several initiatives: ADNI: Data collection and sharing for ADNI were supported by National Institutes of Health Grant U01-AG024904 and Department of Defense (award number W81XWH-12-2-0012). ADNI is also funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.
BLSA: The BLSA is supported by the Intramural Research Program, National Institute on Aging, NIH.
NACC: The NACC database is funded by NIA/NIH Grant U24 AG072122. NACC data are contributed by the NIA-funded ADRCs: P30 AG062429 (PI James Brewer, MD, PhD), P30 AG066468 (PI Oscar Lopez, MD), P30 AG062421 (PI Bradley Hyman, MD, PhD), P30 AG066509 (PI Thomas Grabowski, MD), P30 AG066514 (PI Mary Sano, PhD), P30 AG066530 (PI Helena Chui, MD), P30 AG066507 (PI Marilyn Albert, PhD), P30 AG066444 (PI John Morris, MD), P30 AG066518 (PI Jeffrey Kaye, MD), P30 AG066512 (PI Thomas Wisniewski, MD), P30 AG066462 (PI Scott Small, MD), P30 AG072979 (PI David Wolk, MD), P30 AG072972 (PI Charles DeCarli, MD), P30 AG072976 (PI Andrew Saykin, PsyD), P30 AG072975 (PI David Bennett, MD), P30 AG072978 (PI Ann McKee, MD), P30 AG072977 (PI Robert Vassar, PhD), P30 AG066519 (PI Frank LaFerla, PhD), P30 AG062677 (PI Ronald Petersen, MD, PhD), P30 AG079280 (PI Eric Reiman, MD), P30 AG062422 (PI Gil Rabinovici, MD), P30 AG066511 (PI Allan Levey, MD, PhD), P30 AG072946 (PI Linda Van Eldik, PhD), P30 AG062715 (PI Sanjay Asthana, MD, FRCP), P30 AG072973 (PI Russell Swerdlow, MD), P30 AG066506 (PI Todd Golde, MD, PhD), P30 AG066508 (PI Stephen Strittmatter, MD, PhD), P30 AG066515 (PI Victor Henderson, MD, MS), P30 AG072947 (PI Suzanne Craft, PhD), P30 AG072931 (PI Henry Paulson, MD, PhD), P30 AG066546 (PI Sudha Seshadri, MD), P20 AG068024 (PI Erik Roberson, MD, PhD), P20 AG068053 (PI Justin Miller, PhD), P20 AG068077 (PI Gary Rosenberg, MD), P20 AG068082 (PI Angela Jefferson, PhD), P30 AG072958 (PI Heather Whitson, MD), P30 AG072959 (PI James Leverenz, MD).
VMAP: Study data were obtained from the Vanderbilt Memory and Aging Project (VMAP). VMAP data were collected by Vanderbilt Memory and Alzheimer’s Center Investigators at Vanderbilt University Medical Center. This work was supported by NIA grants R01-AG034962 (PI: Jefferson), R01-AG056534 (PI: Jefferson), R01-AG062826 (PI: Gifford), and Alzheimer’s Association IIRG-08-88733 (PI: Jefferson).
BIOCARD: The BIOCARD study is supported by a grant from the National Institute on Aging (NIA): U19-AG03365. The BIOCARD Study consists of 7 Cores and 2 projects with the following members: (1) The Administrative Core (Marilyn Albert, Corinne Pettigrew, Barbara Rodzon); (2) the Clinical Core (Marilyn Albert, Anja Soldan, Rebecca Gottesman, Corinne Pettigrew, Leonie Farrington, Maura Grega, Gay Rudow, Rostislav Brichko, Scott Rudow, Jules Giles, Ned Sacktor); (3) the Imaging Core (Michael Miller, Susumu Mori, Anthony Kolasny, Hanzhang Lu, Kenichi Oishi, Tilak Ratnanather, Peter vanZijl, Laurent Younes); (4) the Biospecimen Core (Abhay Moghekar, Jacqueline Darrow, Alexandria Lewis, Richard O’Brien); (5) the Informatics Core (Roberta Scherer, Ann Ervin, David Shade, Jennifer Jones, Hamadou Coulibaly, Kathy Moser, Courtney Potter); the (6) Biostatistics Core (Mei-Cheng Wang, Yuxin Zhu, Jiangxia Wang); (7) the Neuropathology Core (Juan Troncoso, David Nauen, Olga Pletnikova, Karen Fisher); (8) Project 1 (Paul Worley, Jeremy Walston, Mei-Fang Xiao), and (9) Project 2 (Mei-Cheng Wang, Yifei Sun, Yanxun Xu.
OASIS-3: Data were provided in part by OASIS for the OASIS-3 cohort: Longitudinal Multimodal Neuroimaging: Principal Investigators: T. Benzinger, D. Marcus, J. Morris; NIH P30 AG066444, P50 AG00561, P30 NS09857781, P01 AG026276, P01 AG003991, R01 AG043434, UL1 TR000448, R01 EB009352. AV-45 doses were provided by Avid Radiopharmaceuticals, a wholly owned subsidiary of Eli Lilly. HCP: Data were provided [in part] by the Human Connectome Project, WU- Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
The Alzheimer’s Disease Neuroimaging Initiative (ADNI) – Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.
The BIOCARD Study Team – Data used in preparation of this article were derived from BIOCARD study data, supported by grant U19 –AG033655 from the National Institute on Aging. The BIOCARD study team did not participate in the analysis or writing of this report, however, they contributed to the design and implementation of the study. A listing of BIOCARD investigators may be accessed at: https://www.biocard-se.org/public/Core%20Groups.html.
Author information
Authors and Affiliations
Consortia
Contributions
Conceptualization: Praitayini Kanakaraj, Eleftherios Garyfallidis, Bennett A. Landman and Daniel Moyer. Data curation: Praitayini Kanakaraj, Nancy R. Newlin, Michael E. Kim, Chenyu Gao, Kimberly R. Pechman, Derek Archer, Timothy Hohman, Angela Jefferson, Lori L. Beason-Held, Susan M. Resnick, The Alzheimer’s Disease Neuroimaging Initiative (ADNI), The BIOCARD Study Team. Formal analysis: Praitayini Kanakaraj, Tianyuan Yao, and Daniel Moyer. Funding acquisition: Bennett A. Landman. Methodology: Praitayini Kanakaraj, Leon Y. Cai, Ho Hin Lee, and Daniel Moyer. Resources: Bennett A. Landman. Software: Praitayini Kanakaraj, Leon Y. Cai and Tianyuan Yao. Supervision: Daniel Moyer. Validation: Praitayini Kanakaraj, Eleftherios Garyfallidis, Kurt G. Schilling, and Adam Anderson. Writing – original draft: Praitayini Kanakaraj. Writing – review & editing: Nancy R. Newlin, Michael E. Kim, Chenyu Gao, Kimberly R. Pechman, Derek Archer, Timothy Hohman, Angela Jefferson, Lori L. Beason-Held, Susan M. Resnick, Adam Anderson, Kurt G. Schilling, Bennett A. Landman, and Daniel Moyer.
Corresponding author
Ethics declarations
Declaration of Generative AI and AI-Assisted Technologies in the Writing Process
During the preparation of this work the authors used ChatGPT 3.5, an AI language model developed by OpenAI, in order to assist in rephrasing the text in this paper for clarification. After using this tool, the authors reviewed and edited the content as needed and takes full responsibility for the content of the publication.
Ethics Approval
Not applicable.
Consent to Participate
Not applicable.
Consent to Publication
Not applicable.
Competing Interest
The authors declare no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Kanakaraj, P., Yao, T., Cai, L.Y. et al. DeepN4: Learning N4ITK Bias Field Correction for T1-weighted Images. Neuroinform 22, 193–205 (2024). https://doi.org/10.1007/s12021-024-09655-9
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12021-024-09655-9