Bangerter NK, Morrell G, Grech-Sollars M. Magnetic resonance imaging. In: bioengineering innovative solutions for cancer. Cambridge: Academic Press; 2019. p. 163–94.
Khanduri S. Magnetic resonance imaging physics. In: textbook of radiology for CT and MRI technicians with MCQs. 2018;109–109. https://doi.org/10.5005/jp/books/14192.
Carré A, Battistella E, Niyoteka S, Sun R, Deutsch E, Robert C. AutoComBat: a generic method for harmonizing MRI-based radiomic features. Sci Reports. 2022;12(1):12762. https://doi.org/10.1038/s41598-022-16609-1.
Fratini M, Abdollahzadeh A, DiNuzzo M, Salo RA, Maugeri L, Cedola A, et al. Multiscale imaging approach for studying the central nervous system: methodology and perspective. Front Neurosci. 2020;14:72. https://doi.org/10.3389/fnins.2020.00072.
Lakshmi MJ, Nagaraja RS. Brain tumor magnetic resonance image classification: a deep learning approach. Soft Comput. 2022;26(13):6245–53. https://doi.org/10.3390/cancers15164172.
Han KM, Ham BJ. How inflammation affects the brain in depression: a review of functional and structural MRI studies. J Clin Neurol. 2021;17(4):503. https://doi.org/10.3988/jcn.2021.17.4.503.
Noor MB, Zenia NZ, Kaiser MS, Mamun SA, Mahmud M. Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia. Brain Inform. 2020;7:1–21.
IXI Brain Development Dataset. https://brain-development.org/ixi-dataset/.
Tixier F, Jaouen V, Hognon C, Gallinato O, Colin T, Visvikis D. Evaluation of conventional and deep learning based image harmonization methods in radiomics studies. Phys Med Biol. 2021;66(24):245009. https://doi.org/10.1088/1361-6560/ac39e5.
Lawrence E, Vegvari C, Ower A, Hadjichrysanthou C, De Wolf F, Anderson RM. A systematic review of longitudinal studies which measure Alzheimer’s disease biomarkers. J Alzheimer’s Dis. 2017. https://doi.org/10.3233/JAD-170261.
Pomponio R, Erus G, Habes M, Doshi J, Srinivasan D, Mamourian E, et al. Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan. Neuroimage. 2020;208:116450. https://doi.org/10.1016/j.neuroimage.2019.116450.
Wang J, Lan C, Liu C, Ouyang Y, Qin T, Lu W, Chen Y, Zeng W, Philip SY. Generalizing to unseen domains: a survey on domain generalization. IEEE Trans Knowl Data Eng. 2022;35(8):8052–72.
Guan H, Liu M. Domain adaptation for medical image analysis: a survey. IEEE Trans Biomed Eng. 2021;69(3):1173–85.
Chen C, Dou Q, Chen H, Qin J, Heng PA. Unsupervised bidirectional cross-modality adaptation via deeply synergistic image and feature alignment for medical image segmentation. IEEE Trans Med Imaging. 2020;39(7):2494–505.
Zhou K, Liu Z, Qiao Y, Xiang T, Loy CC. Domain generalization: a survey. IEEE Trans Pattern Anal Mach Intell. 2022;45(4):4396–415.
Zhang J, Zuo L, Dewey BE, Remedios SW, Hays SP, Pham DL, Prince JL, Carass A. Harmonization-enriched domain adaptation with light fine-tuning for multiple sclerosis lesion segmentation. InMed Imaging 2024 Clin Biomed Imaging. 2024;12930:635–41.
Tanaka SC, Yamashita A, Yahata N, Itahashi T, Lisi G, Yamada T, et al. A multi-site, multi-disorder resting-state magnetic resonance image database. Sci Data. 2021;8(1):227. https://doi.org/10.6084/m9.figshare.14716329.
Stamoulou E, Spanakis C, Manikis GC, Karanasiou G, Grigoriadis G, Foukakis T, et al. Harmonization strategies in multicenter MRI-based radiomics. J Imaging. 2022;8(11):303. https://doi.org/10.3390/jimaging8110303.
Orlhac F, Eertink JJ, Cottereau AS, Zijlstra JM, Thieblemont C, Meignan M, et al. A guide to ComBat harmonization of imaging biomarkers in multicenter studies. J Nuclear Med. 2022;63(2):172–9.
Hu F, Chen AA, Horng H, Bashyam V, Davatzikos C, Alexander-Bloch A, et al. Image harmonization: a review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization. Neuroimage. 2023;20:120125.
Roca V, Kuchcinski G, Pruvo JP, Manouvriez D, Leclerc X, Lopes R. A three-dimensional deep learning model for inter-site harmonization of structural MR images of the brain: extensive validation with a multicenter dataset. Heliyon. 2023. https://doi.org/10.1016/j.heliyon.2023.e22647.
Ayaz A, Al KY, Amirrajab S, Lorenz C, Weese J, Pluim J, et al. Brain MR image simulation for deep learning based medical image analysis networks. Comput Methods Programs Biomed. 2024;248:108115.
Klemenz AC, Albrecht L, Manzke M, Dalmer A, Böttcher B, Surov A, et al. Improved image quality in CT pulmonary angiography using deep learning-based image reconstruction. Sci Reports. 2024;14(1):2494.
Deng L, Lan Q, Zhi Q, Huang S, Wang J, Yang X. Deep learning-based 3D brain multimodal medical image registration. Med Biol Eng Comput. 2024;62(2):505–19.
Wen G, Shim V, Holdsworth SJ, Fernandez J, Qiao M, Kasabov N, et al. Machine learning for brain MRI data harmonisation: a systematic review. Bioengineering. 2023;10(4):397. https://doi.org/10.3390/bioengineering10040397.
Mali SA, Ibrahim A, Woodruff HC, Andrearczyk V, Müller H, Primakov S, et al. Making radiomics more reproducible across scanner and imaging protocol variations: a review of harmonization methods. J Personal Med. 2021;11(9):842. https://doi.org/10.3390/jpm11090842.
Bayer JMM, Thompson PM, Ching CRK, Liu M, Chen A, Panzenhagen AC, et al. Site effects how-to and when: an overview of retrospective techniques to accommodate site effects in multi-site neuroimaging analyses. Front Neurol. 2022;13:923988.
Zuo L, Liu Y, Prince JL, Carass A. An overview of disentangled representation learning for MR image harmonization. Deep Learn Med Image Anal. 2024;1:135–52.
Yang J, Liu J, Xu N, Huang J. TVT: transferable vision transformer for unsupervised domain adaptation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2023; 520–530.
Ko K, Yeom T, Lee M. SuperstarGAN: generative adversarial networks for image-to-image translation in large-scale domains. Neural Netw. 2023;162:330–9. https://doi.org/10.1016/j.neunet.2023.02.042.
Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12(3):e1001779. https://doi.org/10.1371/journal.pmed.1001779.
Di Martino A, Yan CG, Li Q, Denio E, Castellanos FX, Alaerts K, et al. The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol Psychiatry. 2014;19(6):659–67. https://doi.org/10.1038/mp.2013.78.
Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Green RC, et al. The Alzheimer’s disease neuroimaging initiative 3: continued innovation for clinical trial improvement. Alzheimer’s Dementia. 2017;13(5):561–71. https://doi.org/10.1016/j.jalz.2016.10.006.
Casey BJ, Cannonier T, Conley MI, Cohen AO, Barch DM, Heitzeg MM, et al. The Adolescent brain cognitive development (ABCD) study: imaging acquisition across 21 sites. Dev Cogn Neurosci. 2018;32:43–54. https://doi.org/10.1016/j.dcn.2018.03.001.
Ellis KA, Bush AI, Darby D, De Fazio D, Foster J, Hudson P, et al. The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging: methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of Alzheimer’s disease. Int Psychogeriatrics. 2009;21(4):672–87. https://doi.org/10.1017/S1041610209009405.
Marcus DS, Wang TH, Parker J, Csernansky JG, Morris JC, Buckner RL. Open Access Series of Imaging Studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. J Cognit Neurosci. 2007;19(9):1498–507. https://doi.org/10.1162/jocn.2007.19.9.1498.
Marcus DS, Fotenos AF, Csernansky JG, Morris JC, Buckner RL. Open access series of imaging studies: longitudinal MRI data in nondemented and demented older adults. J Cognit Neurosci. 2010. https://doi.org/10.1162/jocn.2009.21407.
LaMontagne PJ, Keefe S, Lauren W, Xiong C, Grant EA, Moulder KL, et al. OASIS-3: longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and Alzheimer disease. MedRxiv. 2019. https://doi.org/10.1101/2019.12.13.19014902.
Ogbole GI, Adeyomoye AO, Badu-Peprah A, Mensah Y, Nzeh DA. Survey of magnetic resonance imaging availability in West Africa. Pan Afr Med J. 2018. https://doi.org/10.11604/pamj.2018.30.240.14000.
Rutt BK, Lee DH. The impact of field strength on image quality in MRI. J Magnet Reson Imaging. 1996;6(1):57–62. https://doi.org/10.1002/jmri.1880060111.
Ahmed SY, Hassan FF. Optimizing imaging resolution in brain MRI: understanding the impact of technical factors. J Med Life. 2023;16(6):920. https://doi.org/10.25122/jml-2022-0212.
Thrower SL, Al Feghali KA, Luo D, Paddick I, Hou P, Briere T, Li J, McAleer MF, McGovern SL, Woodhouse KD, Yeboa DN. The effect of slice thickness on contours of brain metastases for stereotactic radiosurgery. Adv Radiat Oncol. 2021;6(4):100708. https://doi.org/10.1016/j.adro.2021.100708.
Ma YJ, Jang H, Chang EY, Hiniker A, Head BP, Lee RR, Corey-Bloom J, Bydder GM, Du J. Ultrashort echo time (UTE) magnetic resonance imaging of myelin: technical developments and challenges. Quant Imaging Med Surg. 2020;10(6):1186.
Nazarpoor M. The effect of inversion times on the minimum signal intensity of the contrast agent concentration using inversion recovery t1-weighted fast imaging sequence. Med J Islamic Republic Iran. 2014;28:128.
Morrell GR, Schabel MC. An analysis of the accuracy of magnetic resonance flip angle measurement methods. Phys Med Biol. 2010;55(20):6157.
Sijbers J, Scheunders P, Bonnet N, Van Dyck D, Raman E. Quantification and improvement of the signal-to-noise ratio in a magnetic resonance image acquisition procedure. Magnet Reson Imaging. 1996;14(10):1157–63.
Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process. 2004;13(4):600–12. https://doi.org/10.1109/TIP.2003.819861.
Ravano V, Démonet JF, Damian D, Meuli R, Piredda GF, Huelnhagen T, Maréchal B, Thiran JP, Kober T, Richiardi J. Neuroimaging harmonization using cGANs: image similarity metrics poorly predict cross-protocol volumetric consistency. Int Workshop Mach Learn Clin Neuroimag. 2022;18:83–92.
Parida A, Jiang Z, Anwar SM, Foreman N, Stence N, Fisher MJ, Packer RJ, Avery RA, Linguraru MG. Harmonization across imaging locations (HAIL): one-shot learning for brain MRI. arXiv. 2023. https://doi.org/10.48550/arXiv.2308.11047.
Treder MS, Codrai R, Tsvetanov KA. Quality assessment of anatomical MRI images from generative adversarial networks: human assessment and image quality metrics. J Neurosci Methods. 2022;374:109579.
Tronchin L, Sicilia R, Cordelli E, Ramella S, Soda P. Evaluating GANs in medical imaging. In: Engelhardt S, Oksuz I, Zhu D, Yuan Y, Mukhopadhyay A, Heller N, Huang SX, Nguyen H, Sznitman R, Xue Y, editors. Deep generative models, and data augmentation, labelling, and imperfections. MICCAI. Cham: Springer International Publishing; 2021. p. 112–21.
Bińkowski M, Sutherland DJ, Arbel M, Gretton A. Demystifying mmd gans. arXiv. 2018. https://doi.org/10.48550/arXiv.1801.01401.
Zhang R, Isola P, Efros AA, Shechtman E, Wang O. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2018; 586–595.
Cackowski S, Barbier EL, Dojat M, Christen T. Imunity: a generalizable VAE-GAN solution for multicenter MR image harmonization. Med Image Anal. 2023;88:102799. https://doi.org/10.1016/j.media.2023.102799.
Sinha S, Thomopoulos SI, Lam P, Muir A, Thompson PM. Alzheimer’s disease classification accuracy is improved by MRI harmonization based on attention-guided generative adversarial networks. Int Sympos Med Inform Proc Anal. 2021;12088:180–9. https://doi.org/10.1117/12.2606155.
Shao M, Zuo L, Carass A, Zhuo J, Gullapalli RP, Prince JL. Evaluating the impact of MR image harmonization on thalamus deep network segmentation. Med Imaging 2022 Image Proc. 2022;12032:115–21. https://doi.org/10.1117/12.2613159.
Taha AA, Hanbury A. Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med Imaging. 2015;15:1–28.
Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells WM, Frangi AF, editors. Medical Image computing and computer-assisted intervention–MICCAI. Springer International Publishing: Cham; 2015. p. 234–41.
Dewey BE, Zhao C, Carass A, Oh J, Calabresi PA, van Zijl PCM, et al. Deep harmonization of inconsistent MR data for consistent volume segmentation. In: Gooya A, Goksel O, Oguz I, Burgos N, editors., et al., Simulation and synthesis in medical imaging: third international workshop, SASHIMI 2018, held in conjunction with MICCAI. Springer International Publishing: Cham; 2018. p. 20–30.
Shiri I, Ghafarian P, Geramifar P, Leung KHY, Ghelichoghli M, Oveisi M, et al. Direct attenuation correction of brain PET images using only emission data via a deep convolutional encoder-decoder (Deep-DAC). Eur Radiol. 2019;29:6867–79.
Zhang S, Niu Y. LcmUNet: a lightweight network combining CNN and MLP for real-time medical image segmentation. Bioengineering. 2023;10(6):712. https://doi.org/10.3390/bioengineering10060712.
Dewey BE, Zhao C, Reinhold JC, Carass A, Fitzgerald KC, Sotirchos ES, et al. DeepHarmony: a deep learning approach to contrast harmonization across scanner changes. Magn Reson Imaging. 2019;64:160–70. https://doi.org/10.1016/j.mri.2019.05.041.
Bottani S, Thibeau-Sutre E, Maire A, Stroër S, Dormont D, Colliot O, et al. Homogenization of brain MRI from a clinical data warehouse using contrast-enhanced to non-contrast-enhanced image translation with U-Net derived models. Med Imaging. 2022;12032:576–82. https://doi.org/10.1117/12.2608565.
Osman AF, Tamam NM. Deep learning-based convolutional neural network for intramodality brain MRI synthesis. J Appl Clin Med Phys. 2022;23(4):e13530. https://doi.org/10.1002/acm2.13530.
Zhu JY, Park T, Isola P, Efros AA. Unpaired image-to-image translation using cycle-consistent adversarial networks. Proc IEEE Int Con Comput Vision. 2017. https://doi.org/10.1109/ICCV.2017.244.
Komandur D, Gupta U, Chattopadhyay T, Dhinagar NJ, Sophia I, California S, et al. Unsupervised harmonization of brain MRI using 3D CycleGANs and its effect on brain age prediction. Int Sympos Med Inform Proc Anal (SIPAIM). 2023. https://doi.org/10.1109/SIPAIM56729.2023.10373501.
Modanwal G, Vellal A, Buda M, Mazurowski MA. MRI image harmonization using cycle-consistent generative adversarial network. Med Imaging. 2020;11314:259–64. https://doi.org/10.1117/12.2551301.
Bashyam VM, Doshi J, Erus G, Srinivasan D, Abdulkadir A, Habes M, et al. Medical image harmonization using deep learning based canonical mapping: toward robust and generalizable learning in imaging. arXiv. 2020. https://doi.org/10.48550/arXiv.2010.05355.
Yan W, Fu Z, Sui J, Calhoun VD. ‘Harmless’ adversarial network harmonization approach for removing site effects and improving reproducibility in neuroimaging studies. Ann Int Conf IEEE Eng Med Biol Soc (EMBC). 2022. https://doi.org/10.1109/EMBC48229.2022.9871061.
Zhao F, Wu Z, Wang L, Lin W, Xia S, Shen D, Li G. UNC/UMN baby connectome project consortium. Harmonization of infant cortical thickness using surface-to-surface cycle-consistent adversarial networks. In: Shen D, Liu T, Peters TM, Staib LH, Essert C, Zhou S, Yap P-T, Khan A, editors. International conference on medical image computing and computer-assisted intervention. Cham: Springer International Publishing; 2019. p. 475–83.
Huang X, Liu MY, Belongie S, Kautz J. Multimodal unsupervised image-to-image translation. In: Proceedings of the European conference on computer vision (ECCV). 2018. p. 172–89.
Liu M, Maiti P, Thomopoulos S, Zhu A, Chai Y, Kim H, et al. Style transfer using generative adversarial networks for multi-site MRI harmonization. Med Image Comput Comput Assist Intervent MICCAI. 2021. https://doi.org/10.1002/hbm.26422.
Liu M, Zhu AH, Maiti P, Thomopoulos SI, Gadewar S, Chai Y, et al. Style transfer generative adversarial networks to harmonize multisite MRI to a single reference image to avoid overcorrection. Hum Brain Mapp. 2023;44(14):4875–92. https://doi.org/10.1002/hbm.26422.
Saxena S, Teli MN. Comparison and analysis of image-to-image generative adversarial networks: a survey. arXiv. 2021. https://doi.org/10.48550/arXiv.2112.12625.
Bashyam VM, Doshi J, Erus G, Srinivasan D, Abdulkadir A, Singh A, et al. Deep generative medical image harmonization for improving cross-site generalization in deep learning predictors. J Magnet Reson Imaging. 2022;55(3):908–16. https://doi.org/10.1002/jmri.27908.
Roca V, Kuchcinski G, Pruvo JP, Manouvriez D, Lopes R. IGUANe: a 3D generalizable CycleGAN for multicenter harmonization of brain MR images. arXiv. 2024. https://doi.org/10.48550/arXiv.2402.03227.
Bai Z, Wang P, Xiao T, He T, Han Z, Zhang Z, Shou MZ. Hallucination of multimodal large language models: a survey. arXiv. 2024. https://doi.org/10.48550/arXiv.2404.18930.
Sarkar P, Ebrahimi S, Etemad A, Beirami A, Arık SÖ, Pfister T. Mitigating object hallucination via data augmented contrastive tuning. arXiv. 2024. https://doi.org/10.48550/arXiv.2405.18654.
Liang J, Yang X, Huang Y, Li H, He S, Hu X, Chen Z, Xue W, Cheng J, Ni D. Sketch guided and progressive growing GAN for realistic and editable ultrasound image synthesis. Med Image Anal. 2022;79:102461.
Miyato T, Kataoka T, Koyama M, Yoshida Y. Spectral normalization for generative adversarial networks. ArXiv. 2018. https://doi.org/10.48550/arXiv.1802.05957.
Kingma DP, Welling M. Auto-encoding variational bayes. ArXiv. 2013. https://doi.org/10.48550/arXiv.1312.6114.
Baur C, Denner S, Wiestler B, Navab N, Albarqouni S. Autoencoders for unsupervised anomaly segmentation in brain MR images: a comparative study. Med Image Anal. 2021;69:101952. https://doi.org/10.1016/j.media.2020.101952.
Torbati ME, Tudorascu DL, Minhas DS, Maillard P, Decarli CS, Jae Hwang S. Multi-scanner harmonization of paired neuroimaging data via structure preserving embedding learning. Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021; 3284–3293.
Jadon S. An overview of deep learning architectures in few-shot learning domain. arXiv. 2020. https://doi.org/10.48550/arXiv.2008.06365.
Pachetti E, Colantonio S. A systematic review of few-shot learning in medical imaging. ArXiv. 2023. https://doi.org/10.48550/arXiv.2309.11433.
Kotia J, Kotwal A, Bharti R, Mangrulkar R. Few shot learning for medical imaging. Mach Learn Algorithms Indust Appl. 2021.
Fatania K, Clark A, Frood R, Scarsbrook A, Al-Qaisieh B, Currie S, et al. Harmonisation of scanner-dependent contrast variations in magnetic resonance imaging for radiation oncology, using style-blind auto-encoders. Phys Imaging Radiat Oncol. 2022;22:115–22. https://doi.org/10.1016/j.phro.2022.05.005.
Jeong H, Byun H, Kang DU, Lee J. BlindHarmony.” Blind” Harmonization for MR Images via Flow model. Proceedings of the IEEE/CVF International Conference on Computer Vision. 2023; 21129–21139.
Beizaee F, Desrosiers C, Lodygensky GA, Dolz J. Harmonizing flows: unsupervised MR harmonization based on normalizing flows. International Conference on Information Processing in Medical Imaging. 2023; 347–359.
Vigneshwaran V, Wilms M, Camacho MI, Souza R, Forkert N. Improved multi-site Parkinson’s disease classification using neuroimaging data with counterfactual inference. In: Medical Imaging with Deep Learning. PMLR; 2024. p. 1304–17.
He K, Gan C, Li Z, Rekik I, Yin Z, Ji W, et al. Transformers in medical image analysis. Intell Med. 2023;3(1):59–78. https://doi.org/10.1016/j.imed.2022.07.002.
Torbunov D, Huang Y, Yu H, Huang J, Yoo S, Lin M, et al. Uvcgan: UNET vision transformer cycle-consistent GAN for unpaired image-to-image translation. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2023; 702–712.
Yao X, Lou A, Li H, Hu D, Lu D, Liu H, Wang J, Stoebner Z, Johnson H, Long JD, Paulsen JS. Novel application of the attention mechanism on medical image harmonization. Med Imaging. 2023;12464:184–94. https://doi.org/10.1117/12.2654392.
Hu X, Zhou X, Huang Q, Shi Z, Sun L, Li Q. Qs-attn: Query-selected attention for contrastive learning in i2i translation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022; 18291–18300.
Han D, Yu R, Li S, Wang J, Yang Y, Zhao Z, Wei Y, Cong S. MR Image harmonization with transformer. In 2023 IEEE International Conference on Mechatronics and Automation (ICMA). 2023; 2448–2453.
Dinsdale NK, Jenkinson M, Namburete AIL. Deep learning-based unlearning of dataset bias for MRI harmonisation and confound removal. Neuroimage. 2021. https://doi.org/10.1016/j.neuroimage.2020.117689.
Guan H, Liu Y, Yang E, Yap PT, Shen D, Liu M. Multi-site MRI harmonization via attention-guided deep domain adaptation for brain disorder identification. Med Image Anal. 2021;71:102076. https://doi.org/10.1016/j.media.2021.102076.
Wolleb J, Sandkühler R, Bieder F, Barakovic M, Hadjikhani N, Papadopoulou A, et al. Learn to ignore: domain adaptation for multi-site MRI analysis. International Conference on Medical Image Computing and Computer-Assisted Intervention. 2022; 725–735.
Dewey BE, Zuo L, Carass A, He Y, Liu Y, Mowry EM, et al. A disentangled latent space for cross-site MRI harmonization. International Conference on Medical Image Computing and Computer-Assisted Intervention. 2020; 720–729.
Zuo L, Dewey BE, Carass A, Liu Y, He Y, Calabresi PA, et al. Information-based disentangled representation learning for unsupervised MR harmonization. International Conference on Information Processing in Medical Imaging. 2021; 346–359.
Zuo L, Liu Y, Xue Y, Han S, Bilgel M, Resnick SM, et al. Disentangling a single MR modality. MICCAI Workshop on Data Augmentation, Labelling, and Imperfections. Cham: Springer Nature Switzerland; 2022. p. 54–63.
Liu S, Yap P-T. Learning multi-site harmonization of magnetic resonance images without traveling human phantoms. Commun Eng. 2024;3(1):6.
Li H, Gopal S, Sekuboyina A, Zhang J, Niu C, Pirkl C, et al. Unpaired MR image homogenisation by disentangled representations and its uncertainty. Uncertainty for safe utilization of machine learning in medical imaging, and perinatal imaging, placental and preterm image analysis: 3rd international workshop. Cham: Springer International Publishing; 2021. p. 44–53.
Zhao F, Wu Z, Zhu D, Liu T, Gilmore J, Lin W, Wang L, Li G. Disentangling Site Effects with Cycle-Consistent Adversarial Autoencoder for Multi-site Cortical Data Harmonization. International Conference on Medical Image Computing and Computer-Assisted Intervention. 2023; 369–379
Wu M, Zhang L, Yap PT, Lin W, Zhu H, Liu M. Structural MRI harmonization via disentangled latent energy-based style translation. In: Cao X, Xuanang X, Rekik I, Cui Z, Ouyang X, editors. International Workshop on machine learning in medical imaging. Cham: Springer Nature Switzerland; 2023. p. 1–11.
Zuo L, Liu Y, Xue Y, Dewey BE, Bilgel M, Mowry EM, et al. HACA3: a unified approach for multi-site MR image harmonization. Comput Med Imaging Graphics. 2023;109:102285. https://doi.org/10.1016/j.compmedimag.2023.102285.
Wang X, Chen H, Tang SA, Wu Z, Zhu W. Disentangled representation learning. ArXiv. 2022. https://doi.org/10.48550/arXiv.2211.11695.
Jog A, Carass A, Roy S, Pham DL, Prince JL. Random forest regression for magnetic resonance image synthesis. Med Image Anal. 2017;35:475–88. https://doi.org/10.1016/j.media.2016.08.009.
Chang X, Cai X, Dan Y, Song Y, Lu Q, Yang G, et al. Self-supervised learning for multi-center magnetic resonance imaging harmonization without traveling phantoms. Phys Med Biol. 2022;67(14):145004. https://doi.org/10.1088/1361-6560/ac7b66.
Yurt M, Dalmaz O, Dar S, Ozbey M, Tınaz B, Oguz K, Çukur T. Semi-supervised learning of MRI synthesis without fully-sampled ground truths. IEEE Trans Med Imaging. 2022;41(12):3895–906.
Grigorescu I, Vanes L, Uus A, Batalle D, Cordero-Grande L, Nosarti C, et al. Harmonized segmentation of neonatal brain MRI. Front Neurosci. 2021;15:662005. https://doi.org/10.3389/fnins.2021.662005.
Tor-Diez C, Porras AR, Packer RJ, Avery RA, Linguraru MG. Unsupervised MRI homogenization: application to pediatric anterior visual pathway segmentation. Machine learning in medical imaging: MICCAI. 2020. 180–188.
An L, Chen J, Chen P, Zhang C, He T, Chen C, Zhou JH, Yeo BT. Of Aging LS, Alzheimer’s disease neuroimaging initiative. Goal-specific brain MRI harmonization. Neuroimage. 2022;263:119570. https://doi.org/10.1016/j.neuroimage.2022.119570.
Jin C-B, Kim H, Liu M, Jung W, Joo S, Park E, et al. Deep CT to MR synthesis using paired and unpaired data. Sensors. 2019;19(10):2361. https://doi.org/10.3390/s19102361.
Wang T, Lei Y, Fu Y, Wynne JF, Curran WJ, Liu T, et al. A review on medical imaging synthesis using deep learning and its clinical applications. J Appl Clin Med Phys. 2021;22(1):11–36. https://doi.org/10.1002/acm2.13121.
Pan Y, Liu M, Lian C, Zhou T, Xia Y, Shen D. Synthesizing missing PET from MRI with cycle-consistent generative adversarial networks for Alzheimer’s disease diagnosis. Med Image Comput Comput Assist Intervent MICCAI. 2018. https://doi.org/10.1007/978-3-030-00931-1_52.
Zhang J, Cui Z, Jiang C, Zhang J, Gao F, Shen D. Mapping in cycles: dual-domain PET-CT synthesis framework with cycle-consistent constraints. International Conference on Medical Image Computing and Computer-Assisted Intervention. 2022; 758–767.
Sharma R, Tsiamyrtzis P, Webb AG, Leiss EL, Tsekos NV. Learning to deep learning: statistics and a paradigm test in selecting a UNet architecture to enhance MRI. Magn Reson Mater Phys Biol Med. 2023;21:1–22.
Sharma R, Tsiamyrtzis P, Webb AG, Seimenis I, Loukas C, Leiss E, Tsekos NV. A deep learning approach to upscaling “low-quality” MR Images: an in silico comparison study based on the UNet framework. Appl Sci. 2022;12(22):11758.
Atanda OG, Ismaila W, Afolabi AO, Awodoye OA, Falohun AS, Oguntoye JP. Statistical Analysis of a deep learning based trimodal biometric system using paired sampling T-Test. International Conference on Science, Engineering and Business for Sustainable Development Goals (SEB-SDG). IEEE. 2023;1:1–10.
Akter S, Shamrat FJ, Chakraborty S, Karim A, Azam S. COVID-19 detection using deep learning algorithm on chest X-ray images. Biology. 2021;10(11):1174.
Azad B, Azad R, Eskandari S, Bozorgpour A, Kazerouni A, Rekik I, Merhof D. Foundational models in medical imaging: a comprehensive survey and future vision. ArXiv. 2023. https://doi.org/10.48550/arXiv.2310.18689.
Add Comment