The Fourth Dimension: Development and Clinical Implementation of 4D Imaging for Real-Time Visualization of Dynamic Physiological Processes
Abstract
Background: Traditional medical imaging modalities have historically provided static, two- or three-dimensional anatomical snapshots, limiting comprehension of inherently dynamic physiological processes. The evolution toward four-dimensional (4D) imaging—incorporating the temporal dimension—represents a paradigm shift in diagnostic medicine, enabling visualization of dynamic phenomena in real-time. Aim: This narrative review comprehensively examines the development, technical foundations, and expanding clinical implementation of 4D imaging techniques across major modalities, focusing on their role in capturing and quantifying physiological dynamics. Methods: A systematic literature search of PubMed, IEEE Xplore, and Scopus databases (2015-2025) was conducted, with emphasis on technological innovations, validation studies, and clinical outcome research. Results: Significant advancements in 4D ultrasound, CT, MRI, and nuclear medicine have enabled unprecedented visualization of cardiac motion, respiratory dynamics, blood flow patterns, and metabolic processes. Key applications include fetal cardiac assessment, functional neuroimaging, radiotherapy planning, and intraoperative guidance. Despite transformative potential, challenges persist in data management, standardization, and clinical integration. Conclusion: 4D imaging has matured from research curiosity to clinical reality, providing novel insights into physiological function and pathology. Successful implementation requires continued technological refinement, establishment of standardized protocols, and validation through prospective clinical trials to fully realize its potential in personalized medicine.
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References
Addetia, K., Mazzanti, A., Maragna, R., Monti, L., Yamat, M., Kukavica, D., ... & Lang, R. M. (2023). Value of 3D echocardiography in the diagnosis of arrhythmogenic right ventricular cardiomyopathy. European Heart Journal-Cardiovascular Imaging, 24(5), 664-677. https://doi.org/10.1093/ehjci/jeac172
Aljaafari, L., Bird, D., Buckley, D. L., Al-Qaisieh, B., & Speight, R. (2024). A systematic review of 4D magnetic resonance imaging techniques for abdominal radiotherapy treatment planning. Physics and Imaging in Radiation Oncology, 31, 100604. https://doi.org/10.1016/j.phro.2024.100604
Barteit, S., Lanfermann, L., Bärnighausen, T., Neuhann, F., & Beiersmann, C. (2021). Augmented, mixed, and virtual reality-based head-mounted devices for medical education: systematic review. JMIR serious games, 9(3), e29080. https://doi.org/10.2196/29080
Baschnagel, A. M., Flakus, M. J., Wallat, E. M., Wuschner, A. E., Chappell, R. J., Bayliss, R. A., ... & Bayouth, J. E. (2024). A Phase 2 Randomized Clinical Trial Evaluating 4-Dimensional Computed Tomography Ventilation-Based Functional Lung Avoidance Radiation Therapy for Non-Small Cell Lung Cancer. International Journal of Radiation Oncology, Biology, Physics, 119(5), 1393-1402.
Fave, X., Zhang, L., Yang, J., Mackin, D., Balter, P., Gomez, D., ... & Court, L. (2017). Delta-radiomics features for the prediction of patient outcomes in non–small cell lung cancer. Scientific reports, 7(1), 588. https://doi.org/10.1038/s41598-017-00665-z
Forghani, R. (2020). Precision digital oncology: emerging role of radiomics-based biomarkers and artificial intelligence for advanced imaging and characterization of brain tumors. Radiology: Imaging Cancer, 2(4), e190047. https://doi.org/10.1148/rycan.2020190047
Fornacon-Wood, I., Faivre-Finn, C., O’Connor, J. P., & Price, G. J. (2020). Radiomics as a personalized medicine tool in lung cancer: Separating the hope from the hype. Lung Cancer, 146, 197-208. https://doi.org/10.1016/j.lungcan.2020.05.028
Fu, Y., Lei, Y., Wang, T., Higgins, K., Bradley, J. D., Curran, W. J., ... & Yang, X. (2020). LungRegNet: an unsupervised deformable image registration method for 4D‐CT lung. Medical physics, 47(4), 1763-1774. https://doi.org/10.1002/mp.14065
Grimbergen, G., Pötgens, G. G., Eijkelenkamp, H., Raaymakers, B. W., Intven, M. P., & Meijer, G. J. (2023). Feasibility of delivered dose reconstruction for MR-guided SBRT of pancreatic tumors with fast, real-time 3D cine MRI. Radiotherapy and Oncology, 182, 109506. https://doi.org/10.1016/j.radonc.2023.109506
Hyun, C. M., Kim, H. P., Lee, S. M., Lee, S., & Seo, J. K. (2018). Deep learning for undersampled MRI reconstruction. Physics in Medicine & Biology, 63(13), 135007. DOI: 10.1088/1361-6560/aac71a
Jiang, P., Liu, L., Qiao, H., Xu, X., Zheng, Y., Lin, L., ... & Xue, Y. (2025). Anatomical location-related hemodynamic variations are associated with atherosclerosis in the middle cerebral artery: a preliminary cross-sectional 4D flow and 3D vessel wall MRI study. Quantitative Imaging in Medicine and Surgery, 15(4), 3585. https://doi.org/10.21037/qims-24-1733
Keall, P. J., Mageras, G. S., Balter, J. M., Emery, R. S., Forster, K. M., Jiang, S. B., ... & Yorke, E. (2006). The management of respiratory motion in radiation oncology report of AAPM Task Group 76 a. Medical physics, 33(10), 3874-3900. https://doi.org/10.1118/1.2349696
Kerna, N. A., Carsrud, N. V., Ngwu, D. C., Holets, H. M., Flores, J. V., Pruitt, K. D., & Kadivi, K. (2024). Transformative Precision: Investigative Summary of PET/CT-Guided Radiation Therapy Simulation in Comprehensive Cancer Management. European Journal of Theoretical and Applied Sciences, 2(1), 623-633. DOI: 10.59324/ejtas.2024.2(1).54
Khalifa, M., Hamza, H. M., & Hosny, K. M. (2025). De-speckling of medical ultrasound image using metric-optimized knowledge distillation. Scientific Reports, 15(1), 23703. https://doi.org/10.1038/s41598-025-07115-1
Lin, Y., Xu, Y., Lin, J., Fu, L., Sun, H., Huang, Z., ... & Xie, S. (2022). Improving CT-guided transthoracic biopsy diagnostic yield of lung masses using intraprocedural CT and prior PET/CT fusion imaging. BMC Pulmonary Medicine, 22(1), 311. https://doi.org/10.1186/s12890-022-02108-6
Markl, M., & Hope, M. D. (2022). 4D flow imaging—state of the art. Annals of Cardiothoracic Surgery, 11(4), 468. https://doi.org/10.21037/acs-2021-bav-15
McCollough, C. H., Rajendran, K., & Leng, S. (2023). Standardization and quantitative imaging with photon-counting detector CT. Investigative radiology, 58(7), 451-458. DOI: 10.1097/RLI.0000000000000948
Miller, T., Bello, U. M., Tsang, C. S., Winser, S. J., Ying, M. T., & Pang, M. Y. (2024). Using ultrasound elastography to assess non-invasive, non-pharmacological interventions for musculoskeletal stiffness: a systematic review and meta-analysis. Disability and Rehabilitation, 46(16), 3549-3563. https://doi.org/10.1080/09638288.2023.2252744
O’Connor, E. E., Sullivan, E. V., Chang, L., Hammoud, D. A., Wilson, T. W., Ragin, A. B., ... & Ances, B. M. (2023). Imaging of brain structural and functional effects in people with human immunodeficiency virus. The Journal of Infectious Diseases, 227(Supplement_1), S16-S29. https://doi.org/10.1093/infdis/jiac387
Petersen, S. E., Aung, N., Sanghvi, M. M., Zemrak, F., Fung, K., Paiva, J. M., ... & Neubauer, S. (2016). Reference ranges for cardiac structure and function using cardiovascular magnetic resonance (CMR) in Caucasians from the UK Biobank population cohort. Journal of cardiovascular magnetic resonance, 19(1), 18. https://doi.org/10.1186/s12968-017-0327-9
Rivera‐Rivera, L. A., Vikner, T., Eisenmenger, L., Johnson, S. C., & Johnson, K. M. (2024). Four‐dimensional flow MRI for quantitative assessment of cerebrospinal fluid dynamics: Status and opportunities. NMR in Biomedicine, 37(7), e5082. https://doi.org/10.1002/nbm.5082
Robinson, J. D., Rose, M. J., Joh, M., Jarvis, K., Schnell, S., Barker, A. J., ... & Markl, M. (2019). 4-D flow magnetic-resonance-imaging-derived energetic biomarkers are abnormal in children with repaired tetralogy of Fallot and associated with disease severity. Pediatric radiology, 49(3), 308-317. https://doi.org/10.1007/s00247-018-4312-8
Sindoni, A., Minutoli, F., Pontoriero, A., Iatì, G., Baldari, S., & Pergolizzi, S. (2016). Usefulness of four dimensional (4D) PET/CT imaging in the evaluation of thoracic lesions and in radiotherapy planning: Review of the literature. Lung Cancer, 96, 78-86. https://doi.org/10.1016/j.lungcan.2016.03.019
Sugeng, L., Mor-Avi, V., Weinert, L., Niel, J., Ebner, C., Steringer-Mascherbauer, R., ... & Nesser, H. J. (2010). Multimodality comparison of quantitative volumetric analysis of the right ventricle. JACC: Cardiovascular Imaging, 3(1), 10-18. https://doi.org/10.1016/j.jcmg.2009.09.017
Tayebi Arasteh, S., Romanowicz, J., Pace, D. F., Golland, P., Powell, A. J., Maier, A. K., ... & Moghari, M. H. (2023). Automated segmentation of 3D cine cardiovascular magnetic resonance imaging. Frontiers in Cardiovascular Medicine, 10, 1167500. https://doi.org/10.3389/fcvm.2023.1167500
Tamaki, N., Kotani, T., Nishimura, M., & Kaji, T. (2022). Dynamic whole-body FDG-PET imaging for oncology studies. Clinical and Translational Imaging, 10(3), 249-258. https://doi.org/10.1007/s40336-022-00479-8
van der Bie, J., van Straten, M., Booij, R., Bos, D., Dijkshoorn, M. L., Hirsch, A., ... & Budde, R. P. (2023). Photon-counting CT: review of initial clinical results. European journal of radiology, 163, 110829. https://doi.org/10.1016/j.ejrad.2023.110829
Voigt, J. U., & Cvijic, M. (2019). 2-and 3-dimensional myocardial strain in cardiac health and disease. JACC: Cardiovascular Imaging, 12(9), 1849-1863. https://doi.org/10.1016/j.jcmg.2019.01.044
Willemink, M. J., De Jong, P. A., Leiner, T., De Heer, L. M., Nievelstein, R. A., Budde, R. P., & Schilham, A. M. (2013). Iterative reconstruction techniques for computed tomography Part 1: technical principles. European radiology, 23(6), 1623-1631. https://doi.org/10.1007/s00330-012-2765-y
Xin, M., Li, L., Wang, C., Shao, H., Liu, J., & Zhang, C. (2023). Pilot study on 11C-CFT dynamic imaging using total-body PET/CT: biodistribution and radiation dosimetry in Parkinson's disease. Frontiers in Neurology, 14, 1153779. https://doi.org/10.3389/fneur.2023.1153779
Yang, G., Yu, S., Dong, H., Slabaugh, G., Dragotti, P. L., Ye, X., ... & Firmin, D. (2017). DAGAN: deep de-aliasing generative adversarial networks for fast compressed sensing MRI reconstruction. IEEE transactions on medical imaging, 37(6), 1310-1321. https://doi.org/10.1109/TMI.2017.2785879
Zhang, Y., Knopf, A. C., Weber, D. C., & Lomax, A. J. (2015). Improving 4D plan quality for PBS-based liver tumour treatments by combining online image guided beam gating with rescanning. Physics in Medicine & Biology, 60(20), 8141. DOI: 10.1088/0031-9155/60/20/8141
Zhang, J., Xiao, S., Zhu, Y., Zhang, Z., Cao, H., Xie, M., & Zhang, L. (2024). Advances in the application of artificial intelligence in fetal echocardiography. Journal of the American Society of Echocardiography, 37(5), 550-561. https://doi.org/10.1016/j.echo.2023.12.013
Zhou, X. Z., Lu, K., Zhai, D. C., Cui, M. M., Liu, Y., Wang, T. T., ... & Cai, W. (2023). The image quality and diagnostic performance of CT perfusion-derived CT angiography versus that of conventional CT angiography. Quantitative Imaging in Medicine and Surgery, 13(10), 7294. https://doi.org/10.21037/qims-22-988
Authors
Copyright (c) 2025 Wael Ayed Alahmadi, Ali Marwi Al Mokhalfi, Mohammad Abdulghani Hajomar, Mohammed Obaid Alharbi, Awadh Rashdan A Alharbi, Abdallah Rajeh Al Gohani, Nejaa Nasser Alharbi, Naif Salah Aljohani, Turki Awadhallah T Aljabri, Tawfek Awdalh Alofi
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