Advancing Therapeutics through MRI Contrast Agents: Current Status and Future Prospects
Main Article Content
Keywords
Advancing, Therapeutics, MRI, Contrast, Agents
Abstract
In recent years, magnetic resonance imaging has emerged as a highly promising method for the detection of severe illnesses. Its remarkable spatiotemporal resolution and user-friendliness have made it an essential clinical diagnostic instrument. However, there are situations in which poor contrast in MRI poses difficulties that need the use of contrast agents. Researchers have worked hard to improve the accuracy of viewing sick body regions by combining MRI with other imaging modalities to maximize its synergistic potential and altering the contrast agents.
References
Akbas, E., Unal, F., & Yuzbasioglu, D. (2023). Cellular toxicities of gadolinium‐based contrast agents used in magnetic resonance imaging. Journal of Applied Toxicology, 43(7), 958-972.
Amin, J., Sharif, M., Haldorai, A., Yasmin, M., & Nayak, R. S. (2022). Brain tumor detection and classification using machine learning: a comprehensive survey. Complex & intelligent systems, 8(4), 3161-3183.
Antwi‐Baah, R., Wang, Y., Chen, X., & Yu, K. (2022). Metal‐Based Nanoparticle Magnetic Resonance Imaging Contrast Agents: Classifications, Issues, and Countermeasures toward their Clinical Translation. Advanced Materials Interfaces, 9(9), 2101710.
Caspani, S., Magalhães, R., Araújo, J. P., & Sousa, C. T. (2020). Magnetic nanomaterials as contrast agents for MRI. Materials, 13(11), 2586.
Comelli, A., Dahiya, N., Stefano, A., Vernuccio, F., Portoghese, M., Cutaia, G., ... & Yezzi, A. (2021). Deep learning-based methods for prostate segmentation in magnetic resonance imaging. Applied Sciences, 11(2), 782.
Costelloe, C. M., Amini, B., & Madewell, J. E. (2020, April). Risks and benefits of gadolinium-based contrast-enhanced MRI. In Seminars in Ultrasound, CT and MRI (Vol. 41, No. 2, pp. 170-182). WB Saunders.
Daksh, S., Kaul, A., Deep, S., & Datta, A. (2022). Current advancement in the development of manganese complexes as magnetic resonance imaging probes. Journal of Inorganic Biochemistry, 112018.
David, D. S., & Arun, L. (2020). Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Artech J. Eff. Res. Eng. Technol, 1, 57-63.
Dekker, H. M., Stroomberg, G. J., Van der Molen, A. J., & Prokop, M. (2024). Review of strategies to reduce the contamination of the water environment by gadolinium-based contrast agents. Insights into Imaging, 15(1), 1-14.
Del Bene, M., Carone, G., Porto, E., Barbotti, A., Messina, G., Tringali, G., ... & Casali, C. (2022). Neurophysiology-Guided Laser Interstitial Thermal Therapy: A Synergistic Approach For Motor Function Preservation. Technical Note. World Neurosurgery, 168, 165-172.
Dong, S., Wang, P., & Abbas, K. (2021). A survey on deep learning and its applications. Computer Science Review, 40, 100379.
Foster, D., & Larsen, J. (2023). Polymeric Metal Contrast Agents for T1-Weighted Magnetic Resonance Imaging of the Brain. ACS Biomaterials Science & Engineering, 9(3), 1224-1242.
Gawi Ermi, A. S. (2021). Copper Sulfide Manganese Nanoparticles for Multimodality Imaging and Therapy.
Geraldes, C. F., Castro, M. M. C., & Peters, J. A. (2021). Mn (III) porphyrins as potential MRI contrast agents for diagnosis and MRI-guided therapy. Coordination Chemistry Reviews, 445, 214069.
Gupta, A., Caravan, P., Price, W. S., Platas-Iglesias, C., & Gale, E. M. (2020). Applications for transition-metal chemistry in contrast-enhanced magnetic resonance imaging. Inorganic chemistry, 59(10), 6648-6678.
Iki, N., Nakane, R., Masuya-Suzuki, A., Ozawa, Y., Maruoka, T., Iiyama, M., ... & Aoki, I. (2023). MRI Contrasting Agent Based on Mn-MOF-74 Nanoparticles with Coordinatively Unsaturated Sites. Molecular Imaging and Biology, 1-9.
Jeon, M., Halbert, M. V., Stephen, Z. R., & Zhang, M. (2021). Iron oxide nanoparticles as T1 contrast agents for magnetic resonance imaging: fundamentals, challenges, applications, and prospectives. Advanced Materials, 33(23), 1906539.
Kalman, F. K., Nagy, V., Váradi, B., Garda, Z., Molnar, E., Trencsenyi, G., ... & Tircsó, G. (2020). Mn (II)-based MRI contrast agent candidate for vascular imaging. Journal of Medicinal Chemistry, 63(11), 6057-6065.
Khalilnejad, M., Mortezazadeh, T., & Shayan, R. G. (2021). Application of Manganese Oxide (MnO) nanoparticles in multimodal molecular imaging and cancer therapy: a review. Nanomed J, 8, 166-78.
Lersy, F., Boulouis, G., Clément, O., Desal, H., Anxionnat, R., Berge, J., ... & Cotton, F. (2020). Consensus Guidelines of the French Society of Neuroradiology (SFNR) on the use of Gadolinium-Based Contrast agents (GBCAs) and related MRI protocols in Neuroradiology. Journal of Neuroradiology, 47(6), 441-449.
Li, H., Lee, C. H., Chia, D., Lin, Z., Huang, W., & Tan, C. H. (2022). Machine learning in prostate MRI for prostate cancer: current status and future opportunities. Diagnostics, 12(2), 289.
Li, L., Gu, P., Hao, M., Xiang, X., Feng, Y., Zhu, X., ... & Song, E. (2021). Bacteria‐Targeted MRI Probe‐Based Imaging Bacterial Infection and Monitoring Antimicrobial Therapy In Vivo. Small, 17(44), 2103627.
Li, Z., Wang, C., Chen, J., Lian, X., Xiong, C., Tian, R., ... & Tian, J. (2021). uPAR targeted phototheranostic metal-organic framework nanoprobes for MR/NIR-II imaging-guided therapy and surgical resection of glioblastoma. Materials & Design, 198, 109386.
Magadza, T., & Viriri, S. (2021). Deep learning for brain tumor segmentation: a survey of state-of-the-art. Journal of Imaging, 7(2), 19.
Masoudi, B., Daneshvar, S., & Razavi, S. N. (2021). A multi-modal fusion of features method based on deep belief networks to diagnosis schizophrenia disease. International Journal of Wavelets, Multiresolution and Information Processing, 19(03), 2050088.
Meng, Z., Huang, H., Huang, D., Zhang, F., & Mi, P. (2021). Functional metal–organic framework-based nanocarriers for accurate magnetic resonance imaging and effective eradication of breast tumor and lung metastasis. Journal of Colloid and Interface Science, 581, 31-43.
Molaei, M. J. (2023). Turmeric-derived gadolinium-doped carbon quantum dots for multifunctional fluorescence imaging and MRI contrast agent. Journal of Luminescence, 257, 119692.
Ouyang, B., Yang, Q., Wang, X., He, H., Ma, L., Yang, Q., ... & Cai, C. (2022). Single‐shot T2 mapping via multi‐echo‐train multiple overlapping‐echo detachment planar imaging and multitask deep learning. Medical Physics, 49(11), 7095-7107.
Rahmati, S., & David, A. E. (2024). A review of design criteria for cancer-targeted, nanoparticle-based MRI contrast agents. Applied Materials Today, 37, 102087.
Reig, B., Heacock, L., Geras, K. J., & Moy, L. (2020). Machine learning in breast MRI. Journal of Magnetic Resonance Imaging, 52(4), 998-1018.
Setia, A., Mehata, A. K., Priya, V., Pradhan, A., Prasanna, P., Mohan, S., & Muthu, M. S. (2024). Nanoparticles for Thrombus Diagnosis and Therapy: Emerging Trends in Thrombus-theranostics. Nanotheranostics, 8(2), 127.
Tu, Y., Lin, S., Qiao, J., Zhuang, Y., & Zhang, P. (2022). Alzheimer’s disease diagnosis via multimodal feature fusion. Computers in Biology and Medicine, 148, 105901.
Wald, L. L., McDaniel, P. C., Witzel, T., Stockmann, J. P., & Cooley, C. Z. (2020). Low‐cost and portable MRI. Journal of Magnetic Resonance Imaging, 52(3), 686-696.
Wang, J., Jia, Y., Wang, Q., Liang, Z., Han, G., Wang, Z., ... & Ling, D. (2021). An ultrahigh‐field‐tailored T1–T2 dual‐mode MRI contrast agent for high‐performance vascular imaging. Advanced Materials, 33(2), 2004917.
Wang, P., Yang, J., Zhou, B., Hu, Y., Xing, L., Xu, F., ... & Shi, X. Supporting Information Antifouling Manganese Oxide Nanoparticles: Synthesis, Characterization, and Applications for Enhanced MR Imaging of Tumors.
Xiu, W., Gan, S., Wen, Q., Qiu, Q., Dai, S., Dong, H., ... & Wang, L. (2020). Biofilm microenvironment-responsive nanotheranostics for dual-mode imaging and hypoxia-relief-enhanced photodynamic therapy of bacterial infections. Research.
Xu, X., Duan, J., Liu, Y., Kuang, Y., Duan, J., Liao, T., ... & Li, C. (2021). Multi-stimuli responsive hollow MnO2-based drug delivery system for magnetic resonance imaging and combined chemo-chemodynamic cancer therapy. Acta Biomaterialia, 126, 445-462.
Xue, Y., Xiao, B., Xia, Z., Dai, L., Xia, Q., Zhong, L., ... & Zhu, J. (2023). A New OATP‐Mediated Hepatobiliary‐Specific Mn (II)‐Based MRI Contrast Agent for Hepatocellular Carcinoma in Mice: A Comparison With Gd‐EOB‐DTPA. Journal of Magnetic Resonance Imaging.
Yamanakkanavar, N., Choi, J. Y., & Lee, B. (2020). MRI segmentation and classification of human brain using deep learning for diagnosis of Alzheimer’s disease: a survey. Sensors, 20(11), 3243.
Yang, J., Feng, J., Yang, S., Xu, Y., & Shen, Z. (2023). Exceedingly Small Magnetic Iron Oxide Nanoparticles for T1‐Weighted Magnetic Resonance Imaging and Imaging‐Guided Therapy of Tumors. Small, 19(49), 2302856.
Zhao, J., Li, D., Kassam, Z., Howey, J., Chong, J., Chen, B., & Li, S. (2020). Tripartite-GAN: Synthesizing liver contrast-enhanced MRI to improve tumor detection. Medical image analysis, 63, 101667.
Zheng, R., Guo, J., Cai, X., Bin, L., Lu, C., Singh, A., ... & Liu, J. (2022). Manganese complexes and manganese-based metal-organic frameworks as contrast agents in MRI and chemotherapeutics agents: Applications and prospects. Colloids and Surfaces B: Biointerfaces, 213, 112432.
Zhou, Y., Song, Z., Han, X., Li, H., & Tang, X. (2021). Prediction of Alzheimer’s disease progression based on magnetic resonance imaging. ACS Chemical Neuroscience, 12(22), 4209-4223.
Zhu, H., Li, B., Chan, C. Y., Ling, B. L. Q., Tor, J., Oh, X. Y., ... & Loh, X. J. (2023). Advances in single-component inorganic nanostructures for photoacoustic imaging guided photothermal therapy. Advanced Drug Delivery Reviews, 192, 114644
Amin, J., Sharif, M., Haldorai, A., Yasmin, M., & Nayak, R. S. (2022). Brain tumor detection and classification using machine learning: a comprehensive survey. Complex & intelligent systems, 8(4), 3161-3183.
Antwi‐Baah, R., Wang, Y., Chen, X., & Yu, K. (2022). Metal‐Based Nanoparticle Magnetic Resonance Imaging Contrast Agents: Classifications, Issues, and Countermeasures toward their Clinical Translation. Advanced Materials Interfaces, 9(9), 2101710.
Caspani, S., Magalhães, R., Araújo, J. P., & Sousa, C. T. (2020). Magnetic nanomaterials as contrast agents for MRI. Materials, 13(11), 2586.
Comelli, A., Dahiya, N., Stefano, A., Vernuccio, F., Portoghese, M., Cutaia, G., ... & Yezzi, A. (2021). Deep learning-based methods for prostate segmentation in magnetic resonance imaging. Applied Sciences, 11(2), 782.
Costelloe, C. M., Amini, B., & Madewell, J. E. (2020, April). Risks and benefits of gadolinium-based contrast-enhanced MRI. In Seminars in Ultrasound, CT and MRI (Vol. 41, No. 2, pp. 170-182). WB Saunders.
Daksh, S., Kaul, A., Deep, S., & Datta, A. (2022). Current advancement in the development of manganese complexes as magnetic resonance imaging probes. Journal of Inorganic Biochemistry, 112018.
David, D. S., & Arun, L. (2020). Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Artech J. Eff. Res. Eng. Technol, 1, 57-63.
Dekker, H. M., Stroomberg, G. J., Van der Molen, A. J., & Prokop, M. (2024). Review of strategies to reduce the contamination of the water environment by gadolinium-based contrast agents. Insights into Imaging, 15(1), 1-14.
Del Bene, M., Carone, G., Porto, E., Barbotti, A., Messina, G., Tringali, G., ... & Casali, C. (2022). Neurophysiology-Guided Laser Interstitial Thermal Therapy: A Synergistic Approach For Motor Function Preservation. Technical Note. World Neurosurgery, 168, 165-172.
Dong, S., Wang, P., & Abbas, K. (2021). A survey on deep learning and its applications. Computer Science Review, 40, 100379.
Foster, D., & Larsen, J. (2023). Polymeric Metal Contrast Agents for T1-Weighted Magnetic Resonance Imaging of the Brain. ACS Biomaterials Science & Engineering, 9(3), 1224-1242.
Gawi Ermi, A. S. (2021). Copper Sulfide Manganese Nanoparticles for Multimodality Imaging and Therapy.
Geraldes, C. F., Castro, M. M. C., & Peters, J. A. (2021). Mn (III) porphyrins as potential MRI contrast agents for diagnosis and MRI-guided therapy. Coordination Chemistry Reviews, 445, 214069.
Gupta, A., Caravan, P., Price, W. S., Platas-Iglesias, C., & Gale, E. M. (2020). Applications for transition-metal chemistry in contrast-enhanced magnetic resonance imaging. Inorganic chemistry, 59(10), 6648-6678.
Iki, N., Nakane, R., Masuya-Suzuki, A., Ozawa, Y., Maruoka, T., Iiyama, M., ... & Aoki, I. (2023). MRI Contrasting Agent Based on Mn-MOF-74 Nanoparticles with Coordinatively Unsaturated Sites. Molecular Imaging and Biology, 1-9.
Jeon, M., Halbert, M. V., Stephen, Z. R., & Zhang, M. (2021). Iron oxide nanoparticles as T1 contrast agents for magnetic resonance imaging: fundamentals, challenges, applications, and prospectives. Advanced Materials, 33(23), 1906539.
Kalman, F. K., Nagy, V., Váradi, B., Garda, Z., Molnar, E., Trencsenyi, G., ... & Tircsó, G. (2020). Mn (II)-based MRI contrast agent candidate for vascular imaging. Journal of Medicinal Chemistry, 63(11), 6057-6065.
Khalilnejad, M., Mortezazadeh, T., & Shayan, R. G. (2021). Application of Manganese Oxide (MnO) nanoparticles in multimodal molecular imaging and cancer therapy: a review. Nanomed J, 8, 166-78.
Lersy, F., Boulouis, G., Clément, O., Desal, H., Anxionnat, R., Berge, J., ... & Cotton, F. (2020). Consensus Guidelines of the French Society of Neuroradiology (SFNR) on the use of Gadolinium-Based Contrast agents (GBCAs) and related MRI protocols in Neuroradiology. Journal of Neuroradiology, 47(6), 441-449.
Li, H., Lee, C. H., Chia, D., Lin, Z., Huang, W., & Tan, C. H. (2022). Machine learning in prostate MRI for prostate cancer: current status and future opportunities. Diagnostics, 12(2), 289.
Li, L., Gu, P., Hao, M., Xiang, X., Feng, Y., Zhu, X., ... & Song, E. (2021). Bacteria‐Targeted MRI Probe‐Based Imaging Bacterial Infection and Monitoring Antimicrobial Therapy In Vivo. Small, 17(44), 2103627.
Li, Z., Wang, C., Chen, J., Lian, X., Xiong, C., Tian, R., ... & Tian, J. (2021). uPAR targeted phototheranostic metal-organic framework nanoprobes for MR/NIR-II imaging-guided therapy and surgical resection of glioblastoma. Materials & Design, 198, 109386.
Magadza, T., & Viriri, S. (2021). Deep learning for brain tumor segmentation: a survey of state-of-the-art. Journal of Imaging, 7(2), 19.
Masoudi, B., Daneshvar, S., & Razavi, S. N. (2021). A multi-modal fusion of features method based on deep belief networks to diagnosis schizophrenia disease. International Journal of Wavelets, Multiresolution and Information Processing, 19(03), 2050088.
Meng, Z., Huang, H., Huang, D., Zhang, F., & Mi, P. (2021). Functional metal–organic framework-based nanocarriers for accurate magnetic resonance imaging and effective eradication of breast tumor and lung metastasis. Journal of Colloid and Interface Science, 581, 31-43.
Molaei, M. J. (2023). Turmeric-derived gadolinium-doped carbon quantum dots for multifunctional fluorescence imaging and MRI contrast agent. Journal of Luminescence, 257, 119692.
Ouyang, B., Yang, Q., Wang, X., He, H., Ma, L., Yang, Q., ... & Cai, C. (2022). Single‐shot T2 mapping via multi‐echo‐train multiple overlapping‐echo detachment planar imaging and multitask deep learning. Medical Physics, 49(11), 7095-7107.
Rahmati, S., & David, A. E. (2024). A review of design criteria for cancer-targeted, nanoparticle-based MRI contrast agents. Applied Materials Today, 37, 102087.
Reig, B., Heacock, L., Geras, K. J., & Moy, L. (2020). Machine learning in breast MRI. Journal of Magnetic Resonance Imaging, 52(4), 998-1018.
Setia, A., Mehata, A. K., Priya, V., Pradhan, A., Prasanna, P., Mohan, S., & Muthu, M. S. (2024). Nanoparticles for Thrombus Diagnosis and Therapy: Emerging Trends in Thrombus-theranostics. Nanotheranostics, 8(2), 127.
Tu, Y., Lin, S., Qiao, J., Zhuang, Y., & Zhang, P. (2022). Alzheimer’s disease diagnosis via multimodal feature fusion. Computers in Biology and Medicine, 148, 105901.
Wald, L. L., McDaniel, P. C., Witzel, T., Stockmann, J. P., & Cooley, C. Z. (2020). Low‐cost and portable MRI. Journal of Magnetic Resonance Imaging, 52(3), 686-696.
Wang, J., Jia, Y., Wang, Q., Liang, Z., Han, G., Wang, Z., ... & Ling, D. (2021). An ultrahigh‐field‐tailored T1–T2 dual‐mode MRI contrast agent for high‐performance vascular imaging. Advanced Materials, 33(2), 2004917.
Wang, P., Yang, J., Zhou, B., Hu, Y., Xing, L., Xu, F., ... & Shi, X. Supporting Information Antifouling Manganese Oxide Nanoparticles: Synthesis, Characterization, and Applications for Enhanced MR Imaging of Tumors.
Xiu, W., Gan, S., Wen, Q., Qiu, Q., Dai, S., Dong, H., ... & Wang, L. (2020). Biofilm microenvironment-responsive nanotheranostics for dual-mode imaging and hypoxia-relief-enhanced photodynamic therapy of bacterial infections. Research.
Xu, X., Duan, J., Liu, Y., Kuang, Y., Duan, J., Liao, T., ... & Li, C. (2021). Multi-stimuli responsive hollow MnO2-based drug delivery system for magnetic resonance imaging and combined chemo-chemodynamic cancer therapy. Acta Biomaterialia, 126, 445-462.
Xue, Y., Xiao, B., Xia, Z., Dai, L., Xia, Q., Zhong, L., ... & Zhu, J. (2023). A New OATP‐Mediated Hepatobiliary‐Specific Mn (II)‐Based MRI Contrast Agent for Hepatocellular Carcinoma in Mice: A Comparison With Gd‐EOB‐DTPA. Journal of Magnetic Resonance Imaging.
Yamanakkanavar, N., Choi, J. Y., & Lee, B. (2020). MRI segmentation and classification of human brain using deep learning for diagnosis of Alzheimer’s disease: a survey. Sensors, 20(11), 3243.
Yang, J., Feng, J., Yang, S., Xu, Y., & Shen, Z. (2023). Exceedingly Small Magnetic Iron Oxide Nanoparticles for T1‐Weighted Magnetic Resonance Imaging and Imaging‐Guided Therapy of Tumors. Small, 19(49), 2302856.
Zhao, J., Li, D., Kassam, Z., Howey, J., Chong, J., Chen, B., & Li, S. (2020). Tripartite-GAN: Synthesizing liver contrast-enhanced MRI to improve tumor detection. Medical image analysis, 63, 101667.
Zheng, R., Guo, J., Cai, X., Bin, L., Lu, C., Singh, A., ... & Liu, J. (2022). Manganese complexes and manganese-based metal-organic frameworks as contrast agents in MRI and chemotherapeutics agents: Applications and prospects. Colloids and Surfaces B: Biointerfaces, 213, 112432.
Zhou, Y., Song, Z., Han, X., Li, H., & Tang, X. (2021). Prediction of Alzheimer’s disease progression based on magnetic resonance imaging. ACS Chemical Neuroscience, 12(22), 4209-4223.
Zhu, H., Li, B., Chan, C. Y., Ling, B. L. Q., Tor, J., Oh, X. Y., ... & Loh, X. J. (2023). Advances in single-component inorganic nanostructures for photoacoustic imaging guided photothermal therapy. Advanced Drug Delivery Reviews, 192, 114644
Article Sidebar
Published
Feb 22, 2023
Article Details
How to Cite
Abdulaziz Ghazi Alotaibi, Ahmed Zuwayyid Almoutiri, Sarah Qaed Alharbi, Alanoud Mosfer Alomar, Ahmed Nisha Alotaibi , Sami Ahmed Alhamri, Hamad Abdelaziz Alhelayel , Fahad Awad Al-harbi , Mohammed Hassan Daghriri , Abdulaziz Abdulrahman AlKhalawi. (2023). Advancing Therapeutics through MRI Contrast Agents: Current Status and Future Prospects. Journal of Population Therapeutics and Clinical Pharmacology, 30(2), 432–447. https://doi.org/10.53555/jptcp.v30i2.4841
Section
Articles
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
All articles published in JPTCP are licensed under Copyright Creative Commons Attribution-NonCommercial 4.0 International License.