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Imaging"],"abstract":"<jats:p>Periodic artifacts such as ringing (Gibbs), herringbone (spike\/corduroy), and zipper patterns degrade the quality of brain MRI. We present a reproducible framework that (i) synthetically generates periodic artifacts with controllable severity directly in k-space, (ii) normalizes pattern orientation through a Radon-guided alignment step, and (iii) corrects them in the wavelet domain using a 2D DWT (AA\/AD\/DA\/DD) with a band-weighted loss. The evaluation was conducted using DLBS T1-weighted 3T MRI volumes with synthetically generated periodic artifacts. It combined global image-quality metrics (SSIM, PSNR) with per-band metrics to quantify how correction concentrates on high-frequency components, and included ablation studies, mixed-artifact stress tests, and structural preservation analyses. Compared with several baseline architectures, the proposed approach shows improvements in structural fidelity and a reduction in periodic patterns (SSIM: 0.985\u00b10.022; PSNR: 43.337\u00b15.364; reduction in concentrated error in high-frequency bands), while preserving unaffected structures. These findings indicate that, within a controlled synthetic benchmark, aligning the pattern orientation prior to learning and optimizing correction in the wavelet domain enables suppression of synthetically generated periodic artifacts while limiting over-smoothing.<\/jats:p>","DOI":"10.3390\/jimaging12040153","type":"journal-article","created":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T07:57:55Z","timestamp":1775116675000},"page":"153","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Radon-Guided Wavelet-Domain Attention U-Net for Periodic Artifact Suppression in Brain MRI"],"prefix":"10.3390","volume":"12","author":[{"given":"Jesus David","family":"Rios-Perez","sequence":"first","affiliation":[{"name":"Departamento de Ciencias de la Computaci\u00f3n y de la Decisi\u00f3n, Facultad de Minas, Universidad Nacional de Colombia, Medell\u00edn 050035, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-6235-3967","authenticated-orcid":false,"given":"German","family":"Sanchez-Torres","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda, Grupo de Investigaci\u00f3n y Desarrollo en Sistemas y Computaci\u00f3n, Universidad del Magdalena, Santa Marta 470004, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0378-028X","authenticated-orcid":false,"given":"John W.","family":"Branch-Bedoya","sequence":"additional","affiliation":[{"name":"Departamento de Ciencias de la Computaci\u00f3n y de la Decisi\u00f3n, Facultad de Minas, Universidad Nacional de Colombia, Medell\u00edn 050035, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8488-8753","authenticated-orcid":false,"given":"Camilo Andres","family":"Laiton-Bonadiez","sequence":"additional","affiliation":[{"name":"Departamento de Ciencias de la Computaci\u00f3n y de la Decisi\u00f3n, Facultad de Minas, Universidad Nacional de Colombia, Medell\u00edn 050035, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2095","DOI":"10.1002\/mrm.28832","article-title":"Suppression of Artifact-Generating Echoes in Cine Dense Using Deep Learning","volume":"86","author":"Abdi","year":"2021","journal-title":"Magn. Reson. Med."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.mri.2023.10.002","article-title":"Convolutional Network Denoising for Acceleration of Multi-Shot Diffusion Mri","volume":"105","author":"Alus","year":"2024","journal-title":"Magn. Reson. Imaging"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1007\/s00062-021-01121-2","article-title":"Deep Learning Image Processing Enables 40% Faster Spinal MR Scans Which Match or Exceed Quality of Standard of Care","volume":"32","author":"Bash","year":"2021","journal-title":"Clin. Neuroradiol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Boudissa, S., Kanli, G., Perlo, D., Jaquet, T., and Keunen, O. (2024). Addressing Artefacts in Anatomical Mr Images: A k-Space-Based Approach. Proceedings of the 2024 IEEE International Symposium on Biomedical Imaging (ISBI), Athens, Greece, 27\u201330 May 2024, IEEE.","DOI":"10.1109\/ISBI56570.2024.10635199"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1413","DOI":"10.1002\/jmri.27255","article-title":"Combined Denoising and Suppression of Transient Artifacts in Arterial Spin Labeling Mri Using Deep Learning","volume":"52","author":"Hales","year":"2020","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chen, Y.-J., Chang, Y.-J., Wen, S.-C., Shi, Y., Xu, X., Ho, T.-Y., Jia, Q., Huang, M., and Zhuang, J. (2020). Zero-Shot Medical Image Artifact Reduction. Proceedings of the 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Iowa City, IA, USA, 3\u20137 April 2020, IEEE.","DOI":"10.1109\/ISBI45749.2020.9098566"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Chen, Y.-J., Chang, Y.-J., Wen, S.-C., Xu, X., Huang, M., Yuan, H., Zhuang, J., Shi, Y., and Ho, T.-Y. (2021). \u201cone-Shot\u201d Reduction of Additive Artifacts in Medical Images. Proceedings of the 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Houston, TX, USA, 2\u20139 December 2021, IEEE.","DOI":"10.1109\/BIBM52615.2021.9669372"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Cui, L., Jiang, B., Zhao, X., and Chai, S. (2024). Cardiac Mri Image Enhancement Based on Gan Network. Proceedings of the 2024 43rd Chinese Control Conference (CCC), Kunming, China, 28\u201331 July 2024, IEEE.","DOI":"10.23919\/CCC63176.2024.10661588"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"6531","DOI":"10.21037\/qims-24-455","article-title":"Deep Learning-Based Reconstruction for 3-Dimensional Heavily T2-Weighted Fat-Saturated Magnetic Resonance (Mr) Myelography in Epidural Fluid Detection: Image Quality and Diagnostic Performance","volume":"14","author":"Kim","year":"2024","journal-title":"Quant. Imaging Med. Surg."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Patel, V., Wang, A., Monk, A.P., and Schneider, M.T.-Y. (2024). Enhancing Knee Mr Image Clarity through Image Domain Super-Resolution Reconstruction. Bioengineering, 11.","DOI":"10.3390\/bioengineering11020186"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ryu, K.H., Baek, H.J., Gho, S.-M., Ryu, K., Kim, D.-H., Park, S.E., Ha, J.Y., Cho, S.B., and Lee, J.S. (2020). Validation of Deep Learning-Based Artifact Correction on Synthetic Flair Images in a Different Scanning Environment. J. Clin. Med., 9.","DOI":"10.3390\/jcm9020364"},{"key":"ref_12","unstructured":"Singh, R., and Kaur, L. (2021). Magnetic Resonance Image Denoising Using Patchwise Convolutional Neural Networks. Proceedings of the 2021 8th International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India, 17\u201319 March 2021, IEEE."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"012029","DOI":"10.1088\/1742-6596\/2089\/1\/012029","article-title":"Noise-Residue Learning Convolutional Network Model for Magnetic Resonance Image Enhancement","volume":"2089","author":"Singh","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3378","DOI":"10.1007\/s00261-021-02964-6","article-title":"Novel Deep Learning-Based Noise Reduction Technique for Prostate Magnetic Resonance Imaging","volume":"46","author":"Wang","year":"2021","journal-title":"Abdom. Radiol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3846","DOI":"10.1007\/s00330-020-07461-w","article-title":"Improvement of Late Gadolinium Enhancement Image Quality Using a Deep Learning-Based Reconstruction Algorithm and Its Influence on Myocardial Scar Quantification","volume":"31","author":"Hassing","year":"2021","journal-title":"Eur. Radiol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1002\/mrm.29036","article-title":"Scan-Specific Artifact Reduction in k-Space (Spark) Neural Networks Synergize with Physics-Based Reconstruction to Accelerate Mri","volume":"87","author":"Arefeen","year":"2022","journal-title":"Magn. Reson. Med."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.mri.2023.10.001","article-title":"Aliasnet: Alias Artefact Suppression Network for Accelerated Phase-Encode Mri","volume":"105","author":"Chandra","year":"2024","journal-title":"Magn. Reson. Imaging"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"095005","DOI":"10.1088\/1361-6560\/abf278","article-title":"Ground-Truth-Free Deep Learning for Artefacts Reduction in 2d Radial Cardiac Cine Mri Using a Synthetically Generated Dataset","volume":"66","author":"Chen","year":"2021","journal-title":"Phys. Med. Biol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1109\/TMI.2019.2930318","article-title":"Spatio-Temporal Deep Learning-Based Undersampling Artefact Reduction for 2d Radial Cine Mri with Limited Training Data","volume":"39","author":"Kofler","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1007\/978-3-031-34048-2_42","article-title":"Neural Implicit K-Space for Binning-Free Non-Cartesian Cardiac Mr Imaging","volume":"13939 LNCS","author":"Huang","year":"2023","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"e23131","DOI":"10.1002\/ima.23131","article-title":"Reconstruction of Cardiac Cine Mri Using Motion-Guided Deformable Alignment and Multi-Resolution Fusion","volume":"34","author":"Han","year":"2024","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2483","DOI":"10.1002\/mrm.30021","article-title":"Just-Net: Jointly Unrolled Cross-Domain Optimization Based Spatio-Temporal Reconstruction Network for Accelerated 3d Myelin Water Imaging","volume":"91","author":"Lee","year":"2024","journal-title":"Magn. Reson. Med."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1002\/mrm.28834","article-title":"Real-Time Deep Artifact Suppression Using Recurrent u-Nets for Low-Latency Cardiac Mri","volume":"86","author":"Jaubert","year":"2021","journal-title":"Magn. Reson. Med."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2179","DOI":"10.1002\/mrm.29374","article-title":"Fresco: Flow Reconstruction and Segmentation for Low-Latency Cardiac Output Monitoring Using Deep Artifact Suppression and Segmentation","volume":"88","author":"Jaubert","year":"2022","journal-title":"Magn. Reson. Med."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1002\/mrm.29453","article-title":"Signal Intensity Informed Multi-Coil Encoding Operator for Physics-Guided Deep Learning Reconstruction of Highly Accelerated Myocardial Perfusion Cmr","volume":"89","author":"Demirel","year":"2023","journal-title":"Magn. Reson. Med."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2014","DOI":"10.1002\/ima.22567","article-title":"An Edge Guided Cascaded U-Net Approach for Accelerated Magnetic Resonance Imaging Reconstruction","volume":"31","author":"Dhengre","year":"2021","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1195","DOI":"10.1002\/mrm.28485","article-title":"Multi-Domain Convolutional Neural Network (Md-Cnn) for Radial Reconstruction of Dynamic Cardiac Mri","volume":"85","author":"Fahmy","year":"2021","journal-title":"Magn. Reson. Med."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Gao, Z., and Zhou, S.K. (2024). Rethinking Dual-Domain Undersampled Mri Reconstruction: Domain-Specific Design from the Perspective of the Receptive Field. Proceedings of the 2024 IEEE International Symposium on Biomedical Imaging (ISBI), Athens, Greece, 27\u201330 May 2024, IEEE.","DOI":"10.1109\/ISBI56570.2024.10635264"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Fatania, K., Pirkl, C.M., Menzel, M.I., Hall, P., and Golbabaee, M. (2022). A Plug-and-Play Approach to Multiparametric Quantitative Mri: Image Reconstruction Using Pre-Trained Deep Denoisers. Proceedings\u2014International Symposium on Biomedical Imaging, Kolkata, India, 28\u201331 March 2022, IEEE.","DOI":"10.1109\/ISBI52829.2022.9761603"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1088","DOI":"10.1109\/JSTSP.2020.2998402","article-title":"Rare: Image Reconstruction Using Deep Priors Learned without Groundtruth","volume":"14","author":"Liu","year":"2020","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1859","DOI":"10.1109\/TMI.2023.3240862","article-title":"Hierarchical Perception Adversarial Learning Framework for Compressed Sensing Mri","volume":"42","author":"Gao","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.mri.2022.10.010","article-title":"Undersampling Artifact Reduction for Free-Breathing 3d Stack-of-Radial Mri Based on a Deep Adversarial Learning Network","volume":"95","author":"Gao","year":"2023","journal-title":"Magn. Reson. Imaging"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1109\/TMI.2023.3314747","article-title":"Reconformer: Accelerated Mri Reconstruction Using Recurrent Transformer","volume":"43","author":"Guo","year":"2024","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1109\/MSP.2019.2950432","article-title":"Structured Low-Rank Algorithms: Theory, Magnetic Resonance Applications, and Links to Machine Learning","volume":"37","author":"Jacob","year":"2020","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.mri.2021.08.005","article-title":"Deep Artifact Suppression for Spiral Real-Time Phase Contrast Cardiac Magnetic Resonance Imaging in Congenital Heart Disease","volume":"83","author":"Jaubert","year":"2021","journal-title":"Magn. Reson. Imaging"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1148\/radiol.220634","article-title":"Emerging Technology in Musculoskeletal Mri and Ct","volume":"306","author":"Kijowski","year":"2023","journal-title":"Radiology"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1002\/jmri.29108","article-title":"Recent Developments in Speeding up Prostate Mri","volume":"60","author":"Mir","year":"2024","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"106218","DOI":"10.1016\/j.bspc.2024.106218","article-title":"Dlgan: Undersampled Mri Reconstruction Using Deep Learning Based Generative Adversarial Network","volume":"93","author":"Noor","year":"2024","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"105947","DOI":"10.1016\/j.compbiomed.2022.105947","article-title":"Hiwdnet: A Hybrid Image-Wavelet Domain Network for Fast Magnetic Resonance Image Reconstruction","volume":"151","author":"Tong","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"614","DOI":"10.1007\/978-3-030-87199-4_58","article-title":"Uncertainty-Guided Progressive Gans for Medical Image Translation","volume":"12903 LNCS","author":"Upadhyay","year":"2021","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_41","unstructured":"Abinesh, R., Yogeshkumar, V.G., Sarabesh, T.J., and Nandhini, S. (2024). Deformable Convolution Network for Reconstruction of Misaligned Mri Images and Classification of Brain Tumor in the Aligned Images. Proceedings of the 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Kirtipur, Nepal, 3\u20135 October 2024, IEEE."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1007\/978-3-031-72114-4_21","article-title":"Deformation-Aware Segmentation Network Robust to Motion Artifacts for Brain Tissue Segmentation Using Disentanglement Learning","volume":"15009 LNCS","author":"Jung","year":"2024","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"5923","DOI":"10.1007\/s00330-020-07006-1","article-title":"Reduction of Respiratory Motion Artifacts in Gadoxetate-Enhanced Mr with a Deep Learning-Based Filter Using Convolutional Neural Network","volume":"30","author":"Kromrey","year":"2020","journal-title":"Eur. Radiol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"510","DOI":"10.6009\/jjrt.2024-1408","article-title":"Reduction of motion artifacts in liver mri using deep learning with high-pass filtering","volume":"80","author":"Mio","year":"2024","journal-title":"Nihon Hoshasen Gijutsu Gakkai Zasshi"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Lim, A., Lo, J., Wagner, M.W., Ertl-Wagner, B., and Sussman, D. (2023). Motion Artifact Correction in Fetal Mri Based on a Generative Adversarial Network Method. Biomed. Signal Process. Control, 81.","DOI":"10.1016\/j.bspc.2022.104484"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Liu, Y., Diao, J., Zhou, Z., Qi, H., and Hu, P. (2024). Cardiac Cine Mri Motion Correction Using Diffusion Models. Proceedings of the 2024 IEEE International Symposium on Biomedical Imaging (ISBI), Athens, Greece, 27\u201330 May 2024, IEEE.","DOI":"10.1109\/ISBI56570.2024.10635444"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/TCI.2023.3347917","article-title":"Annealed Score-Based Diffusion Model for Mr Motion Artifact Reduction","volume":"10","author":"Oh","year":"2024","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2170","DOI":"10.1109\/TMI.2021.3073381","article-title":"Cine Cardiac Mri Motion Artifact Reduction Using a Recurrent Neural Network","volume":"40","author":"Lyu","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1097\/RMR.0000000000000249","article-title":"Applying Artificial Intelligence to Mitigate Effects of Patient Motion or Other Complicating Factors on Image Quality","volume":"29","author":"Nguyen","year":"2020","journal-title":"Top. Magn. Reson. Imaging"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"3125","DOI":"10.1109\/TMI.2021.3089708","article-title":"Unpaired Mr Motion Artifact Deep Learning Using Outlier-Rejecting Bootstrap Aggregation","volume":"40","author":"Oh","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"4001","DOI":"10.1109\/TMI.2020.3008930","article-title":"Deep Learning-Based Detection and Correction of Cardiac Mr Motion Artefacts during Reconstruction for High-Quality Segmentation","volume":"39","author":"Oksuz","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Rawat, U., Batra, V., Sharma, R.K., Kulandhaivel, M., Mukuntharaj, C., and Dongre, D. (2023). High Quality Segmentation Using Deep Learning Centered Detection and Correction of Cardiac Mr Motion Artefacts throughout Reconstruction. Proceedings of the 2023 6th International Conference on Contemporary Computing and Informatics (IC3I), Gautam Buddha Nagar, India, 14\u201316 September 2023, IEEE.","DOI":"10.1109\/IC3I59117.2023.10397682"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Tripathi, V.R., Tibdewal, M.N., and Mishra, R. (2024). A Survey on Motion Artifact Correction in Magnetic Resonance Imaging for Improved Diagnostics. SN Comput. Sci., 5.","DOI":"10.1007\/s42979-023-02596-1"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2598","DOI":"10.1002\/mp.16844","article-title":"Mri Motion Artifact Reduction Using a Conditional Diffusion Probabilistic Model (Mar-Cdpm)","volume":"51","author":"Safari","year":"2024","journal-title":"Med. Phys."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"115057","DOI":"10.1088\/1361-6560\/ad4845","article-title":"Unsupervised Mri Motion Artifact Disentanglement: Introducing Maudgan","volume":"69","author":"Safari","year":"2024","journal-title":"Phys. Med. Biol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1080\/13682199.2023.2196494","article-title":"Hybrid Deep Autoencoder Network Based Adaptive Cross Guided Bilateral Filter for Motion Artifacts Correction and Denoising from Mri","volume":"72","author":"Samuel","year":"2023","journal-title":"Imaging Sci. J."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1002\/mrm.27917","article-title":"Unsupervised Learning of a Deep Neural Network for Metal Artifact Correction Using Dual-Polarity Readout Gradients","volume":"83","author":"Kwon","year":"2020","journal-title":"Magn. Reson. Med."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"195002","DOI":"10.1088\/1361-6560\/abb02c","article-title":"Truncation Compensation and Metallic Dental Implant Artefact Reduction in Pet\/Mri Attenuation Correction Using Deep Learning-Based Object Completion","volume":"65","author":"Arabi","year":"2020","journal-title":"Phys. Med. Biol."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1737","DOI":"10.1007\/s00256-024-04624-4","article-title":"Managing Hardware-Related Metal Artifacts in Mri: Current and Evolving Techniques","volume":"53","author":"Feuerriegel","year":"2024","journal-title":"Skelet. Radiol."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Ranzini, M.B.M., Groothuis, I., Klaser, K., Cardoso, M.J., Henckel, J., Ourselin, S., Hart, A., and Modat, M. (2020). Combining Multimodal Information for Metal Artefact Reduction: An Unsupervised Deep Learning Framework. Proceedings of the 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), owa City, IA, USA, 3\u20137 April 2020, IEEE.","DOI":"10.1109\/ISBI45749.2020.9098633"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"012021","DOI":"10.1088\/1742-6596\/1714\/1\/012021","article-title":"Hybrid Learning Model for Metal Artifact Reduction","volume":"1714","author":"Bedi","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.mri.2022.05.016","article-title":"Epi Susceptibility Correction Introduces Significant Differences Far from Local Areas of High Distortion","volume":"92","author":"Begnoche","year":"2022","journal-title":"Magn. Reson. Imaging"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1002\/mrm.29653","article-title":"Unsupervised Cycle-Consistent Network Using Restricted Subspace Field Map for Removing Susceptibility Artifacts in Epi","volume":"90","author":"Bao","year":"2023","journal-title":"Magn. Reson. Med."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.mri.2020.04.004","article-title":"An Unsupervised Deep Learning Technique for Susceptibility Artifact Correction in Reversed Phase-Encoding Epi Images","volume":"71","author":"Duong","year":"2020","journal-title":"Magn. Reson. Imaging"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Duong, S.T.M., Phung, S.L., Bouzerdoum, A., Ang, S.P., and Schira, M.M. (2021). Correcting Susceptibility Artifacts of Mri Sensors in Brain Scanning: A 3d Anatomy-Guided Deep Learning Approach. Sensors, 21.","DOI":"10.3390\/s21072314"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"110247","DOI":"10.1016\/j.mri.2024.110247","article-title":"Deep Learning Corrects Artifacts in Raser Mri Profiles","volume":"115","author":"Becker","year":"2025","journal-title":"Magn. Reson. Imaging"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1355","DOI":"10.1002\/mrm.29285","article-title":"Cancellation of Streak Artifacts in Radial Abdominal Imaging Using Interference Null Space Projection","volume":"88","author":"Fu","year":"2022","journal-title":"Magn. Reson. Med."},{"key":"ref_68","first-page":"258","article-title":"Feature-Guided Deep Learning Reduces Signal Loss and Increases Lesion Cnr in Diffusion-Weighted Imaging of the Liver","volume":"34","author":"Saake","year":"2024","journal-title":"Z. Med. Ethik"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"957","DOI":"10.1007\/s00256-023-04501-6","article-title":"Cerebrospinal Fluid Flow Artifact Reduction with Deep Learning to Optimize the Evaluation of Spinal Canal Stenosis on Spine Mri","volume":"53","author":"Kim","year":"2024","journal-title":"Skelet. Radiol."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"846","DOI":"10.1038\/s41597-025-04847-7","article-title":"The Dallas Lifespan Brain Study","volume":"12","author":"Park","year":"2024","journal-title":"Sci. Data"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1038","DOI":"10.1002\/jmri.23642","article-title":"Physics of MRI: A Primer","volume":"35","author":"Plewes","year":"2012","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1186\/1532-429X-15-41","article-title":"Cardiovascular Magnetic Resonance Artefacts","volume":"15","author":"Ferreira","year":"2013","journal-title":"J. Cardiovasc. Magn. Reson."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1007\/s10334-020-00863-3","article-title":"Characterization of Hardware-Related Spatial Distortions for IR-PETRA Pulse Sequence Using a Brain Specific Phantom","volume":"34","author":"Ahmadian","year":"2021","journal-title":"Magn. Reson. Mater. Phys."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1007\/s00256-020-03597-4","article-title":"Metal Artifacts of Hip Arthroplasty Implants at 1.5-T and 3.0-T: A Closer Look into the B1 Effects","volume":"50","author":"Khodarahmi","year":"2021","journal-title":"Skelet. Radiol."},{"key":"ref_75","first-page":"1270","article-title":"Pediatric Magnetic Resonance Imaging: Faster Is Better","volume":"53","author":"Bedoya","year":"2023","journal-title":"Pediatr. Radiol."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1221","DOI":"10.1007\/s13534-024-00425-9","article-title":"A Review of Deep Learning-Based Reconstruction Methods for Accelerated MRI Using Spatiotemporal and Multi-Contrast Redundancies","volume":"14","author":"Kim","year":"2024","journal-title":"Biomed. Eng. Lett."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"518","DOI":"10.1007\/s12194-024-00784-z","article-title":"Improving Image Quality Using the Pause Function Combination to PROPELLER Sequence in Brain MRI: A Phantom Study","volume":"17","author":"Saotome","year":"2024","journal-title":"Radiol. Phys. Technol."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"e190007","DOI":"10.1148\/ryai.2020190007","article-title":"fastMRI: A Publicly Available Raw k-Space and DICOM Dataset of Knee Images for Accelerated MR Image Reconstruction Using Machine Learning","volume":"2","author":"Knoll","year":"2020","journal-title":"Radiol. Artif. Intell."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"1242","DOI":"10.1007\/s00330-006-0470-4","article-title":"Artifacts in Body MR Imaging: Their Appearance and How to Eliminate Them","volume":"17","author":"Stadler","year":"2007","journal-title":"Eur. Radiol."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Pietsch, M., Christiaens, D., Hajnal, J.V., and Tournier, J.-D. (2021). dStripe: Slice Artefact Correction in Diffusion MRI via Constrained Neural Network. Med. Image Anal., 74.","DOI":"10.1016\/j.media.2021.102255"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Ossowski, J., Wang, X., Li, S., Devinsky, O., Martin, S.P., and Pardoe, H.R. (2021). Localized Motion Artifact Reduction on Brain MRI Using Deep Learning with Effective Data Augmentation Techniques. Proceedings of the 2021 International Joint Conference on Neural Networks (IJCNN), Shenzhen, China, 18\u201322 July 2021, IEEE.","DOI":"10.1109\/IJCNN52387.2021.9534191"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Shetty, N.R., Patnaik, L.M., Prasad, N.H., and Nalini, N. (2018). Frequency Domain Technique to Remove Herringbone Artifact from Magnetic Resonance Images of Brain and Morphological Segmentation for Detection of Tumor. Proceedings of the Emerging Research in Computing, Information, Communication and Applications, Springer.","DOI":"10.1007\/978-981-10-4741-1"},{"key":"ref_83","first-page":"88","article-title":"A Review on Brain Magnetic Resonance Imaging Artifacts: Description, Causes and their Elimination","volume":"4","author":"Meshram","year":"2015","journal-title":"Int. J. Adv. Inf. Sci. Technol. (IJAIST)"},{"key":"ref_84","unstructured":"Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., and Kainz, B. (2018). Attention U-Net: Learning Where to Look for the Pancreas. arXiv."},{"key":"ref_85","unstructured":"Miravete Zararaza, C., Gaspar Lorenz, F.J., and Rodrigo Cardiel, C. (2024). La Transformaci\u00f3n Wavelet y Sus Aplicaciones en el Procesamiento de Im\u00e1genes, Universidad de Zaragoza."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI, Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"106151","DOI":"10.1016\/j.cmpb.2021.106151","article-title":"Projection-Based Cascaded U-Net Model for MR Image Reconstruction","volume":"207","author":"Aghabiglou","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1007\/s11760-021-02030-0","article-title":"Multiscale U-Net-Based Accelerated Magnetic Resonance Imaging Reconstruction","volume":"16","author":"Dhengre","year":"2022","journal-title":"SIViP"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Zabihi, S., Rahimian, E., Asif, A., and Mohammadi, A. (2021, January 19\u201322). SepUnet: Depthwise Separable Convolution Integrated U-Net For MRI Reconstruction. Proceedings of the 2021 IEEE International Conference on Image Processing (ICIP), Anchorage, AK, USA. Available online: https:\/\/ieeexplore.ieee.org\/abstract\/document\/9506285.","DOI":"10.1109\/ICIP42928.2021.9506285"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"105909","DOI":"10.1016\/j.cmpb.2020.105909","article-title":"Brain MRI Artefact Detection and Correction Using Convolutional Neural Networks","volume":"199","author":"Oksuz","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"119411","DOI":"10.1016\/j.neuroimage.2022.119411","article-title":"Stacked U-Nets with Self-Assisted Priors towards Robust Correction of Rigid Motion Artifact in Brain MRI","volume":"259","author":"Lee","year":"2022","journal-title":"NeuroImage"},{"key":"ref_92","first-page":"195","article-title":"Deep Learning with U-Net for Motion Artifact Reduction in Brain MRI","volume":"8","year":"2025","journal-title":"Vasc. Endovasc. Rev."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1007\/s10334-023-01127-6","article-title":"Learning to Deep Learning: Statistics and a Paradigm Test in Selecting a UNet Architecture to Enhance MRI","volume":"37","author":"Sharma","year":"2024","journal-title":"Magn. Reson. Mater. Phys."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"13587","DOI":"10.1007\/s00521-021-05983-y","article-title":"U-Net Based Analysis of MRI for Alzheimer\u2019s Disease Diagnosis","volume":"33","author":"Fan","year":"2021","journal-title":"Neural Comput. Applic"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative Adversarial Networks","volume":"63","author":"Goodfellow","year":"2014","journal-title":"Commun. ACM"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1109\/JBHI.2020.2986926","article-title":"Multi-Scale Self-Guided Attention for Medical Image Segmentation","volume":"25","author":"Sinha","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Souibgui, M.A., Biswas, S., Jemni, S.K., Kessentini, Y., Forn\u00e9s, A., Llad\u00f3s, J., and Pal, U. (2022). DocEnTr: An End-to-End Document Image Enhancement Transformer. Proceedings of the 2022 26th International Conference on Pattern Recognition (ICPR), Montreal, QC, Canada, 21\u201325 August 2022, IEEE.","DOI":"10.1109\/ICPR56361.2022.9956101"},{"key":"ref_98","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2021). An Image Is Worth 16x16 Words: Transformers for Image Recognition at Scale. arXiv."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/4\/153\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T08:59:23Z","timestamp":1775552363000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/4\/153"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,2]]},"references-count":98,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["jimaging12040153"],"URL":"https:\/\/doi.org\/10.3390\/jimaging12040153","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,2]]}}}