Advanced Magnetic Resonance Imaging (MRI) Techniques: Technical Principles and Applications in Nanomedicine
Abstract
:Simple Summary
Abstract
1. Introduction
1.1. Nanoparticles
1.2. Magnetic Resonance Imaging
2. Functional Magnetic Resonance Imaging
2.1. Diffusion-Weighted Imaging MRI
2.2. Dynamic Contrast-Enhanced-MRI
2.3. BOLD-MRI
2.4. MR Spectroscopy
3. Radiomics
4. Nanoparticles and MRI Contrast Agents: Physical Principles and Clinical Setting
5. Nanomedicine, Treatment and Magnetic Resonance Imaging Assessment
6. Conclusions
Funding
Acknowledgments
Conflicts of Interest
References
- Li, Z.; Mu, Y.; Peng, C.; Lavin, M.F.; Shao, H.; Du, Z. Understanding the mechanisms of silica nanoparticles for nanomedicine. WIREs Nanomed. Nanobiotechnol. 2020, 13, e1658. [Google Scholar] [CrossRef] [PubMed]
- Kunz-Schughart, L.; Dubrovska, A.; Peitzsch, C.; Ewe, A.; Aigner, A.; Schellenburg, S.; Muders, M.H.; Hampel, S.; Cirillo, G.; Iemma, F.; et al. Nanoparticles for radiooncology: Mission, vision, challenges. Biomaterials 2017, 120, 155–184. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Sharma, A.; Kontodimas, K.; Bosmann, M. Nanomedicine: A Diagnostic and Therapeutic Approach to COVID-19. Front. Med. 2021, 8, 648005. [Google Scholar] [CrossRef] [PubMed]
- Fröhlich, E.; Wahl, R. Nanoparticles: Promising Auxiliary Agents for Diagnosis and Therapy of Thyroid Cancers. Cancers 2021, 13, 4063. [Google Scholar] [CrossRef]
- Bozzato, E.; Bastiancich, C.; Préat, V. Nanomedicine: A Useful Tool against Glioma Stem Cells. Cancers 2020, 13, 9. [Google Scholar] [CrossRef]
- Bellardita, L.; Colciago, R.R.; Frasca, S.; De Santis, M.C.; Gay, S.; Palorini, F.; La Rocca, E.; Valdagni, R.; Rancati, T.; Lozza, L. Breast cancer patient perspective on opportunities and challenges of a genetic test aimed to predict radio-induced side effects before treatment: Analysis of the Italian branch of the REQUITE project. Radiol. Med. 2021, 126, 1366–1373. [Google Scholar] [CrossRef]
- Bayda, S.; Adeel, M.; Tuccinardi, T.; Cordani, M.; Rizzolio, F. The History of Nanoscience and Nanotechnology: From Chemical–Physical Applications to Nanomedicine. Molecules 2020, 25, 112. [Google Scholar] [CrossRef] [Green Version]
- Tietze, R.; Zaloga, J.; Unterweger, H.; Lyer, S.; Friedrich, R.P.; Janko, C.; Pöttler, M.; Dürr, S.; Alexiou, C. Magnetic nanoparticle-based drug delivery for cancer therapy. Biochem. Biophys. Res. Commun. 2015, 468, 463–470. [Google Scholar] [CrossRef]
- Podgórna, K.; Szczepanowicz, K.; Piotrowski, M.; Gajdošová, M.; Štěpánek, F.; Warszynski, P. Gadolinium alginate nanogels for theranostic applications. Colloids Surf. B Biointerfaces 2017, 153, 183–189. [Google Scholar] [CrossRef]
- Novoselova, M.V.; German, S.V.; Abakumova, T.O.; Perevoschikov, S.V.; Sergeeva, O.V.; Nesterchuk, M.V.; Efimova, O.I.; Petrov, K.S.; Chernyshev, V.S.; Zatsepin, T.S.; et al. Multifunctional nanostructured drug delivery carriers for cancer therapy: Multimodal imaging and ultrasound-induced drug release. Colloids Surf. B Biointerfaces 2021, 200, 111576. [Google Scholar] [CrossRef]
- Amiri, M.; Salavati-Niasari, M.; Akbari, A. Magnetic nanocarriers: Evolution of spinel ferrites for medical applications. Adv. Colloid Interface Sci. 2019, 265, 29–44. [Google Scholar] [CrossRef]
- Nana, A.B.A.; Marimuthu, T.; Kondiah, P.P.D.; Choonara, Y.E.; Du Toit, L.C.; Pillay, V. Multifunctional Magnetic Nanowires: Design, Fabrication, and Future Prospects as Cancer Therapeutics. Cancers 2019, 11, 1956. [Google Scholar] [CrossRef] [Green Version]
- Coppola, A.; Platania, G.; Ticca, C.; De Mattia, C.; Bortolato, B.; Palazzi, M.F.; Vanzulli, A. Sensitivity of CE-MRI in detecting local recurrence after radical prostatectomy. Radiol. Med. 2020, 125, 683–690. [Google Scholar] [CrossRef]
- Nandwana, V.; De, M.; Chu, S.; Jaiswal, M.; Rotz, M.; Meade, T.J.; Dravid, V.P. Theranostic Magnetic Nanostructures (MNS) for Cancer. Cancer Treat. Res. 2015, 166, 51–83. [Google Scholar] [CrossRef] [Green Version]
- Hajba, L.; Guttman, A. The use of magnetic nanoparticles in cancer theranostics: Toward handheld diagnostic devices. Biotechnol. Adv. 2016, 34, 354–361. [Google Scholar] [CrossRef]
- Xu, Y.; Zheng, H.; Schumacher, D.; Liehn, E.A.; Slabu, I.; Rusu, M. Recent Advancements of Specific Functionalized Surfaces of Magnetic Nano- and Microparticles as a Theranostics Source in Biomedicine. ACS Biomater. Sci. Eng. 2021, 7, 1914–1932. [Google Scholar] [CrossRef]
- Bennett, K.M.; Jo, J.-I.; Cabral, H.; Bakalova, R.; Aoki, I. MR imaging techniques for nano-pathophysiology and theranostics. Adv. Drug Deliv. Rev. 2014, 74, 75–94. [Google Scholar] [CrossRef] [Green Version]
- Fusco, R.; Granata, V.; Sansone, M.; Rega, D.; Delrio, P.; Tatangelo, F.; Romano, C.; Avallone, A.; Pupo, D.; Giordano, M.; et al. Validation of the standardized index of shape tool to analyze DCE-MRI data in the assessment of neo-adjuvant therapy in locally advanced rectal cancer. Radiol. Med. 2021, 126, 1044–1054. [Google Scholar] [CrossRef]
- Sulheim, E.; Kim, J.; van Wamel, A.; Kim, E.; Snipstad, S.; Vidic, I.; Grimstad, I.H.; Widerøe, M.; Torp, S.H.; Lundgren, S.; et al. Multi-modal characterization of vasculature and nanoparticle accumulation in five tumor xenograft models. J. Control. Release 2018, 279, 292–305. [Google Scholar] [CrossRef]
- Bonferoni, M.C.; Rassu, G.; Gavini, E.; Sorrenti, M.; Catenacci, L.; Torre, M.L.; Perteghella, S.; Ansaloni, L.; Maestri, M.; Giunchedi, P. Electrochemotherapy of Deep-Seated Tumors: State of Art and Perspectives as Possible “EPR Effect Enhancer” to Improve Cancer Nanomedicine Efficacy. Cancers 2021, 13, 4437. [Google Scholar] [CrossRef]
- Gil, P.R. Nanotechnology Opens the Landscape of Personalized Medicine. Curr. Med. Chem. 2018, 25, 4552. [Google Scholar] [CrossRef]
- Zhu‡, L.; Zhou‡, Z.; Mao, H.; Yang, L. Magnetic nanoparticles for precision oncology: Theranostic magnetic iron oxide nanoparticles for image-guided and targeted cancer therapy. Nanomedicine 2017, 12, 73–87. [Google Scholar] [CrossRef] [Green Version]
- Pudakalakatti, S.; Enriquez, J.S.; McCowan, C.; Ramezani, S.; Davis, J.S.; Zacharias, N.M.; Bourgeois, D.; Constantinou, P.E.; Harrington, D.A.; Carson, D.; et al. Hyperpolarized MRI with silicon micro and nanoparticles: Principles and applications. WIREs Nanomed. Nanobiotechnol. 2021, 13, e1722. [Google Scholar] [CrossRef]
- Albano, D.; Stecco, A.; Micci, G.; Sconfienza, L.M.; Colagrande, S.; Reginelli, A.; Grassi, R.; Carriero, A.; Midiri, M.; Lagalla, R.; et al. Whole-body magnetic resonance imaging (WB-MRI) in oncology: An Italian survey. Radiol. Med. 2020, 126, 299–305. [Google Scholar] [CrossRef]
- Granata, V.; Grassi, R.; Fusco, R.; Setola, S.V.; Belli, A.; Ottaiano, A.; Nasti, G.; La Porta, M.; Danti, G.; Cappabianca, S.; et al. Intrahepatic cholangiocarcinoma and its differential diagnosis at MRI: How radiologist should assess MR features. Radiol. Med. 2021, 126, 1584–1600. [Google Scholar] [CrossRef]
- Rodrigues, H.F.; Capistrano, G.; Bakuzis, A.F. In vivo magnetic nanoparticle hyperthermia: A review on preclinical studies, low-field nano-heaters, noninvasive thermometry and computer simulations for treatment planning. Int. J. Hyperth. 2020, 37, 76–99. [Google Scholar] [CrossRef]
- Petralia, G.; Zugni, F.; Summers, P.E.; Colombo, A.; Pricolo, P.; Grazioli, L.; Colagrande, S.; Giovagnoni, A.; Padhani, A.R. Italian Working Group on Magnetic Resonance Whole-body magnetic resonance imaging (WB-MRI) for cancer screening: Recommendations for use. Radiol. Med. 2021, 1–17, 1434–1450. [Google Scholar] [CrossRef]
- Berardo, S.; Sukhovei, L.; Andorno, S.; Carriero, A.; Stecco, A. Quantitative bone marrow magnetic resonance imaging through apparent diffusion coefficient and fat fraction in multiple myeloma patients. Radiol. Med. 2020, 126, 445–452. [Google Scholar] [CrossRef]
- Furtado, A.R.R.; Moris, L.M.; Esmieu, S.; Cherubini, G.B.; Mantis, P. Low-field magnetic resonance imaging characteristics of multifocal vertebral lesions in dogs. Veter-Rec. 2021, 189, e78. [Google Scholar] [CrossRef]
- Sun, Y.; Zhu, Q.; Huang, M.; Shen, D.; Zhou, Y.; Feng, Q. Liver DCE-MRI registration based on sparse recovery of contrast agent curves. Med. Phys. 2021, 48, 6916–6929. [Google Scholar] [CrossRef]
- Chianca, V.; Albano, D.; Cuocolo, R.; Messina, C.; Gitto, S.; Brunetti, A.; Sconfienza, L.M. T2 mapping of the trapeziometacarpal joint and triangular fibrocartilage complex: A feasibility and reproducibility study at 1.5 T. Radiol. Med. 2019, 125, 306–312. [Google Scholar] [CrossRef] [PubMed]
- Sekine, T.; Murai, Y.; Orita, E.; Ando, T.; Takagi, R.; Amano, Y.; Matano, F.; Iwata, K.; Ogawa, M.; Obara, M.; et al. Cross-Comparison of 4-Dimensional Flow Magnetic Resonance Imaging and Intraoperative Middle Cerebral Artery Pressure Measurements Before and After Superficial Temporal Artery-Middle Cerebral Artery Bypass Surgery. Neurosurgery 2021, 89, 909–916. [Google Scholar] [CrossRef]
- Bilreiro, C.; Soler, J.C.; Ayuso, J.R.; Caseiro-Alves, F.; Ayuso, C. Diagnostic value of morphological enhancement patterns in the hepatobiliary phase of gadoxetic acid-enhanced MRI to distinguish focal nodular hyperplasia from hepatocellular adenoma. Radiol. Med. 2021, 126, 1379–1387. [Google Scholar] [CrossRef] [PubMed]
- Schwartz, J.A.; Shetty, A.M.; Price, R.E.; Stafford, R.J.; Wang, J.C.; Uthamanthil, R.K.; Pham, K.; McNichols, R.J.; Coleman, C.L.; Payne, J.D. Feasibility Study of Particle-Assisted Laser Ablation of Brain Tumors in Orthotopic Canine Model. Cancer Res. 2009, 69, 1659–1667. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zhang, Y.; Li, M.; Gao, X.; Chen, Y.; Liu, T. Nanotechnology in cancer diagnosis: Progress, challenges and opportunities. J. Hematol. Oncol. 2019, 12, 137. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Tombácz, E.; Turcu, R.; Socoliuc, V.; Vékás, L. Magnetic iron oxide nanoparticles: Recent trends in design and synthesis of magnetoresponsive nanosystems. Biochem. Biophys. Res. Commun. 2015, 468, 442–453. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Moskvin, M.; Babič, M.; Reis, S.; Cruz, M.M.; Ferreira, L.P.; Carvalho, M.D.; Lima, S.A.C.; Horák, D. Biological evaluation of surface-modified magnetic nanoparticles as a platform for colon cancer cell theranostics. Colloids Surf. B Biointerfaces 2018, 161, 35–41. [Google Scholar] [CrossRef]
- Chouhan, R.; Horvat, M.; Ahmed, J.; Alhokbany, N.; Alshehri, S.; Gandhi, S. Magnetic Nanoparticles—A Multifunctional Potential Agent for Diagnosis and Therapy. Cancers 2021, 13, 2213. [Google Scholar] [CrossRef]
- Shi, X.; Yang, W.; Ma, Q.; Lu, Y.; Xu, Y.; Bian, K.; Liu, F.; Shi, C.; Wang, H.; Shi, Y.; et al. Hemoglobin-mediated biomimetic synthesis of paramagnetic O2-evolving theranostic nanoprobes for MR imaging-guided enhanced photodynamic therapy of tumor. Theranostics 2020, 10, 11607–11621. [Google Scholar] [CrossRef]
- Quan, Q.; Xie, J.; Gao, H.; Yang, M.; Zhang, F.; Liu, G.; Lin, X.; Wang, A.; Eden, H.S.; Lee, S.; et al. HSA Coated Iron Oxide Nanoparticles as Drug Delivery Vehicles for Cancer Therapy. Mol. Pharm. 2011, 8, 1669–1676. [Google Scholar] [CrossRef] [Green Version]
- Yu, Z.; Gao, L.; Chen, K.; Zhang, W.; Zhang, Q.; Li, Q.; Hu, K. Nanoparticles: A New Approach to Upgrade Cancer Diagnosis and Treatment. Nanoscale Res. Lett. 2021, 16, 88. [Google Scholar] [CrossRef]
- Liao, J.; Jia, Y.; Chen, L.; Zhou, L.; Li, Q.; Qian, Z.; Niu, D.; Li, Y.; Li, P. Magnetic/Gold Core-Shell Hybrid Particles for Targeting and Imaging-Guided Photothermal Cancer Therapy. J. Biomed. Nanotechnol. 2019, 15, 2072–2089. [Google Scholar] [CrossRef]
- Hersi, A.-F.; Pistiolis, L.; Luberth, C.D.; Vikhe-Patil, E.; Nilsson, F.; Mohammed, I.; Bagge, R.O.; Wärnberg, F.; Eriksson, S.; Karakatsanis, A. Optimizing Dose and Timing in Magnetic Tracer Techniques for Sentinel Lymph Node Detection in Early Breast Cancers: The Prospective Multicenter SentiDose Trial. Cancers 2021, 13, 693. [Google Scholar] [CrossRef]
- Gatti, M.; Calandri, M.; Bergamasco, L.; Darvizeh, F.; Grazioli, L.; Inchingolo, R.; Ippolito, D.; Rousset, S.; Veltri, A.; Fonio, P.; et al. Characterization of the arterial enhancement pattern of focal liver lesions by multiple arterial phase magnetic resonance imaging: Comparison between hepatocellular carcinoma and focal nodular hyperplasia. Radiol. Med. 2020, 125, 348–355. [Google Scholar] [CrossRef]
- Palmisano, A.; Vignale, D.; Benedetti, G.; Del Maschio, A.; De Cobelli, F.; Esposito, A. Late iodine enhancement cardiac computed tomography for detection of myocardial scars: Impact of experience in the clinical practice. Radiol. Med. 2019, 125, 128–136. [Google Scholar] [CrossRef]
- Luo, D.; Wang, X.; Burda, C.; Basilion, J. Recent Development of Gold Nanoparticles as Contrast Agents for Cancer Diagnosis. Cancers 2021, 13, 1825. [Google Scholar] [CrossRef]
- Lazaro-Carrillo, A.; Calero, M.; Aires, A.; Cortajarena, A.L.; Simões, B.M.; Latorre, A.; Somoza, Á.; Clarke, R.B.; Miranda, R.; Villanueva, A. Tailored Functionalized Magnetic Nanoparticles to Target Breast Cancer Cells Including Cancer Stem-Like Cells. Cancers 2020, 12, 1397. [Google Scholar] [CrossRef]
- Canese, R.; Vurro, F.; Marzola, P. Iron Oxide Nanoparticles as Theranostic Agents in Cancer Immunotherapy. Nanomaterials 2021, 11, 1950. [Google Scholar] [CrossRef]
- Persano, S.; Das, P.; Pellegrino, T. Magnetic Nanostructures as Emerging Therapeutic Tools to Boost Anti-Tumour Immunity. Cancers 2021, 13, 2735. [Google Scholar] [CrossRef]
- Cai, X.; Zhu, Q.; Zeng, Y.; Zeng, Q.; Chen, X.; Zhan, Y. Manganese Oxide Nanoparticles As MRI Contrast Agents In Tumor Multimodal Imaging And Therapy. Int. J. Nanomed. 2019, 14, 8321–8344. [Google Scholar] [CrossRef] [Green Version]
- Al Faraj, A.; Shaik, A.P.; Shaik, A.S. Magnetic single-walled carbon nanotubes as efficient drug delivery nanocarriers in breast cancer murine model: Noninvasive monitoring using diffusion-weighted magnetic resonance imaging as sensitive imaging biomarker. Int. J. Nanomed. 2015, 10, 157–168. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Borresen, B.; Hansen, A.E.; Fliedner, F.P.; Henriksen, J.R.; Elema, D.R.; Brandt-Larsen, M.; Kristensen, L.K.; Kristensen, A.T.; Andresen, T.L.; Kjær, A. Noninvasive Molecular Imaging of the Enhanced Permeability and Retention Effect by (64)Cu-Liposomes: In Vivo Correlations with (68)Ga-RGD, Fluid Pressure, Diffusivity and (18)F-FDG. Int. J. Nanomed. 2020, 15, 8571–8581. [Google Scholar] [CrossRef] [PubMed]
- Kim, T.; Momin, E.; Choi, J.; Yuan, K.; Zaidi, H.; Kim, J.; Park, M.; Lee, N.; McMahon, M.T.; Quinones-Hinojosa, A.; et al. Mesoporous Silica-Coated Hollow Manganese Oxide Nanoparticles as Positive T1 Contrast Agents for Labeling and MRI Tracking of Adipose-Derived Mesenchymal Stem Cells. J. Am. Chem. Soc. 2011, 133, 2955–2961. [Google Scholar] [CrossRef] [PubMed]
- Crich, S.G.; Terreno, E.; Aime, S. Nano-sized and other improved reporters for magnetic resonance imaging of angiogenesis. Adv. Drug Deliv. Rev. 2017, 119, 61–72. [Google Scholar] [CrossRef] [PubMed]
- Brennan, G.; Bergamino, S.; Pescio, M.; Tofail, S.A.M.; Silien, C. The Effects of a Varied Gold Shell Thickness on Iron Oxide Nanoparticle Cores in Magnetic Manipulation, T1 and T2 MRI Contrasting, and Magnetic Hyperthermia. Nanomaterials 2020, 10, 2424. [Google Scholar] [CrossRef]
- Tomitaka, A.; Ota, S.; Nishimoto, K.; Arami, H.; Takemura, Y.; Nair, M. Dynamic magnetic characterization and magnetic particle imaging enhancement of magnetic-gold core–shell nanoparticles. Nanoscale 2019, 11, 6489–6496. [Google Scholar] [CrossRef]
- Petralia, G.; Summers, P.E.; Agostini, A.; Ambrosini, R.; Cianci, R.; Cristel, G.; Calistri, L.; Colagrande, S. Dynamic contrast-enhanced MRI in oncology: How we do it. Radiol. Med. 2020, 125, 1288–1300. [Google Scholar] [CrossRef]
- Bragg, A.; Candelaria, R.; Adrada, B.; Huang, M.; Rauch, G.; Santiago, L.; Scoggins, M.; Whitman, G. Imaging of Noncalcified Ductal Carcinoma In Situ. J. Clin. Imaging Sci. 2021, 11, 34. [Google Scholar] [CrossRef]
- Tamada, T.; Ueda, Y.; Ueno, Y.; Kojima, Y.; Kido, A.; Yamamoto, A. Diffusion-weighted imaging in prostate cancer. Magn. Reson. Mater. Phys. Biol. Med. 2021. [Google Scholar] [CrossRef]
- Ye, C.; Lin, Q.; Jin, Z.; Zheng, C.; Ma, S. Predictive effect of DCE-MRI and DWI in brain metastases from NSCLC. Open Med. 2021, 16, 1265–1275. [Google Scholar] [CrossRef]
- Assadsangabi, R.; Babaei, R.; Songco, C.; Ivanovic, V.; Bobinski, M.; Chen, Y.J.; Nabavizadeh, S.A. Multimodality oncologic evaluation of superficial neck and facial lymph nodes. Radiol. Med. 2021, 126, 1074–1084. [Google Scholar] [CrossRef]
- Chianca, V.; Albano, D.; Messina, C.; Vincenzo, G.; Rizzo, S.; Del Grande, F.; Sconfienza, L.M. An update in musculoskeletal tumors: From quantitative imaging to radiomics. Radiol. Med. 2021, 126, 1095–1105. [Google Scholar] [CrossRef]
- Cusumano, D.; Meijer, G.; Lenkowicz, J.; Chiloiro, G.; Boldrini, L.; Masciocchi, C.; Dinapoli, N.; Gatta, R.; Casà, C.; Damiani, A.; et al. A field strength independent MR radiomics model to predict pathological complete response in locally advanced rectal cancer. Radiol. Med. 2020, 126, 421–429. [Google Scholar] [CrossRef]
- Crimì, F.; Capelli, G.; Spolverato, G.; Bao, Q.R.; Florio, A.; Rossi, S.M.; Cecchin, D.; Albertoni, L.; Campi, C.; Pucciarelli, S.; et al. MRI T2-weighted sequences-based texture analysis (TA) as a predictor of response to neoadjuvant chemo-radiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC). Radiol. Med. 2020, 125, 1216–1224. [Google Scholar] [CrossRef]
- Sun, A.; Hayat, H.; Liu, S.; Tull, E.; Bishop, J.O.; Dwan, B.F.; Gudi, M.; Talebloo, N.; Dizon, J.R.; Li, W.; et al. 3D in vivo Magnetic Particle Imaging of Human Stem Cell-Derived Islet Organoid Transplantation Using a Machine Learning Algorithm. Front. Cell Dev. Biol. 2021, 9, 704483. [Google Scholar] [CrossRef]
- Qin, H.; Que, Q.; Lin, P.; Li, X.; Wang, X.-R.; He, Y.; Chen, J.-Q.; Yang, H. Magnetic resonance imaging (MRI) radiomics of papillary thyroid cancer (PTC): A comparison of predictive performance of multiple classifiers modeling to identify cervical lymph node metastases before surgery. Radiol. Med. 2021, 126, 1312–1327. [Google Scholar] [CrossRef]
- Santone, A.; Brunese, M.C.; Donnarumma, F.; Guerriero, P.; Mercaldo, F.; Reginelli, A.; Miele, V.; Giovagnoni, A.; Brunese, L. Radiomic features for prostate cancer grade detection through formal verification. Radiol. Med. 2021, 126, 688–697. [Google Scholar] [CrossRef]
- Turna, O.; Turna, I.F. Quantitative assessment of cervical spinal cord by diffusion tensor tractography in 3.0 T. Radiol. Med. 2020, 126, 83–88. [Google Scholar] [CrossRef]
- Haris, M.; Yadav, S.K.; Rizwan, A.; Singh, A.; Wang, E.; Hariharan, H.; Reddy, R.; Marincola, F.M. Molecular magnetic resonance imaging in cancer. J. Transl. Med. 2015, 13, 313. [Google Scholar] [CrossRef] [Green Version]
- Albano, D.; Cortese, M.C.; Duarte, A.; Messina, C.; Gitto, S.; Vicentin, I.; Coppola, A.; Galia, M.; Sconfienza, L.M. Predictive role of ankle MRI for tendon graft choice and surgical reconstruction. Radiol. Med. 2020, 125, 763–769. [Google Scholar] [CrossRef]
- Pelissier, M.; Ambarki, K.; Salleron, J.; Henrot, P. Maximum slope using ultrafast breast DCE-MRI at 1.5 Tesla: A potential tool for predicting breast lesion aggressiveness. Eur. Radiol. 2021, 31, 9556–9566. [Google Scholar] [CrossRef] [PubMed]
- Vasireddi, A.K.; Leo, M.E.; Squires, J.H. Magnetic resonance imaging of pediatric liver tumors. Pediatr. Radiol. 2021, 52, 177–188. [Google Scholar] [CrossRef] [PubMed]
- Galea, N.; Polizzi, G.; Gatti, M.; Cundari, G.; Figuera, M.; Faletti, R. Cardiovascular magnetic resonance (CMR) in restrictive cardiomyopathies. Radiol. Med. 2020, 125, 1072–1086. [Google Scholar] [CrossRef]
- Auer, T.A. Advanced MR techniques in glioblastoma imaging—upcoming challenges and how to face them. Eur. Radiol. 2021, 31, 6652–6654. [Google Scholar] [CrossRef]
- Zhang, Y.; Lin, Y.; Xing, Z.; Yao, S.; Cao, D.; Miao, W.-B. Non-invasive assessment of heterogeneity of gliomas using diffusion and perfusion MRI: Correlation with spatially co-registered PET. Acta Radiol. 2021. [Google Scholar] [CrossRef] [PubMed]
- Malagi, A.V.; Netaji, A.; Kumar, V.; Baidya Kayal, E.; Khare, K.; Das, C.J.; Calamante, F.; Mehndiratta, A. IVIM-DKI for differentiation between prostate cancer and benign prostatic hyperplasia: Comparison of 1.5 T vs. 3 T MRI. Magn. Reson. Mater. Phys. Biol. Med. 2021, 1–12. Available online: https://pubmed.ncbi.nlm.nih.gov/34052899/ (accessed on 14 February 2022). [CrossRef]
- Esposito, A.; Buscarino, V.; Raciti, D.; Casiraghi, E.; Manini, M.; Biondetti, P.; Forzenigo, L. Characterization of liver nodules in patients with chronic liver disease by MRI: Performance of the Liver Imaging Reporting and Data System (LI-RADS v.2018) scale and its comparison with the Likert scale. Radiol. Med. 2019, 125, 15–23. [Google Scholar] [CrossRef]
- Parlak, S.; Yazici, G.; Dolgun, A.; Ozgen, B. The evolution of bone marrow signal changes at the skull base in nasopharyngeal carcinoma patients treated with radiation therapy. Radiol. Med. 2021, 126, 818–826. [Google Scholar] [CrossRef]
- Scialpi, M.; Scialpi, P.; Martorana, E.; Torre, R.; Mancioli, F.A.; D’Andrea, A.; Di Blasi, A. Biparametric MRI with simplified PI-RADS (S-PI-RADS) for prostate cancer detection and management: What do radiologist need to know. Radiol. Med. 2021, 126, 1660–1661. [Google Scholar] [CrossRef]
- Radunsky, D.; Stern, N.; Nassar, J.; Tsarfaty, G.; Blumenfeld-Katzir, T.; Ben-Eliezer, N. Quantitative platform for accurate and reproducible assessment of transverse (T 2) relaxation time. NMR Biomed. 2021, 34, e4537. [Google Scholar] [CrossRef]
- Bontempi, P.; Scartoni, D.; Amelio, D.; Cianchetti, M.; Turkaj, A.; Amichetti, M.; Farace, P. Multicomponent T 2 relaxometry reveals early myelin white matter changes induced by proton radiation treatment. Magn. Reson. Med. 2021, 86, 3236–3245. [Google Scholar] [CrossRef]
- Raveendranath, V.; Nagarajan, K.; Umamageswari, A.; Srinidhi, S.; Kavitha, T. Three-dimensional magnetic resonance-based morphometry of pituitary stalk. Radiol. Med. 2018, 124, 206–210. [Google Scholar] [CrossRef]
- Takehara, Y. 4D Flow when and how? Radiol. Med. 2020, 125, 838–850. [Google Scholar] [CrossRef]
- Danti, G.; Flammia, F.; Matteuzzi, B.; Cozzi, D.; Berti, V.; Grazzini, G.; Pradella, S.; Recchia, L.; Brunese, L.; Miele, V. Gastrointestinal neuroendocrine neoplasms (GI-NENs): Hot topics in morphological, functional, and prognostic imaging. Radiol. Med. 2021, 126, 1497–1507. [Google Scholar] [CrossRef]
- Albano, D.; Benenati, M.; Bruno, A.; Bruno, F.; Calandri, M.; Caruso, D.; Cozzi, D.; De Robertis, R.; Gentili, F.; Grazzini, I.; et al. Imaging side effects and complications of chemotherapy and radiation therapy: A pictorial review from head to toe. Insights Imaging 2021, 12, 76. [Google Scholar] [CrossRef]
- Han, J.H.; Ahn, J.-H.; Kim, J.-S. Magnetic resonance elastography for evaluation of renal parenchyma in chronic kidney disease: A pilot study. Radiol. Med. 2020, 125, 1209–1215. [Google Scholar] [CrossRef]
- Fu, G.; Zhu, L.; Yang, K.; Zhuang, R.; Xie, J.; Zhang, F. Diffusion-Weighted Magnetic Resonance Imaging for Therapy Response Monitoring and Early Treatment Prediction of Photothermal Therapy. ACS Appl. Mater. Interfaces 2016, 8, 5137–5147. [Google Scholar] [CrossRef]
- Momeni, M.; Asadzadeh, M.; Mowla, K.; Hanafi, M.G.; Gharibvand, M.M.; Sahraeizadeh, A. Sensitivity and specificity assessment of DWI and ADC for the diagnosis of osteoporosis in postmenopausal patients. Radiol. Med. 2019, 125, 68–74. [Google Scholar] [CrossRef]
- Simón, M.; Jørgensen, J.T.; Norregaard, K.; Kjaer, A. 18F-FDG positron emission tomography and diffusion-weighted magnetic resonance imaging for response evaluation of nanoparticle-mediated photothermal therapy. Sci. Rep. 2020, 10, 7595. [Google Scholar] [CrossRef]
- Gunbey, H.P.; Has, A.C.; Aslan, K.; Saglam, D.; Avcı, U.; Sayıt, A.T.; Incesu, L. Microstructural white matter abnormalities in hypothyroidism evaluation with diffusion tensor imaging tract-based spatial statistical analysis. Radiol. Med. 2020, 126, 283–290. [Google Scholar] [CrossRef]
- Romano, A.; Covelli, E.; Confaloni, V.; Espagnet, M.C.R.; Butera, G.; Barbara, M.; Bozzao, A. Role of non-echo-planar diffusion-weighted images in the identification of recurrent cholesteatoma of the temporal bone. Radiol. Med. 2019, 125, 75–79. [Google Scholar] [CrossRef]
- Zhang, Y.; Zhu, Y.; Zhang, K.; Liu, Y.; Cui, J.; Tao, J.; Wang, Y.; Wang, S. Invasive ductal breast cancer: Preoperative predict Ki-67 index based on radiomics of ADC maps. Radiol. Med. 2019, 125, 109–116. [Google Scholar] [CrossRef]
- Afaq, A.; Andreou, A.; Koh, D.M. Diffusion-weighted magnetic resonance imaging for tumour response assessment: Why, when and how? Cancer Imaging 2010, 10, S179–S188. [Google Scholar] [CrossRef] [PubMed]
- Fornell-Perez, R.; Vivas-Escalona, V.; Aranda-Sanchez, J.; Gonzalez-Dominguez, M.C.; Rubio-Garcia, J.; Aleman-Flores, P.; Rodríguez, L.; Porcel-De-Peralta, G.; Loro, J. Primary and post-chemoradiotherapy MRI detection of extramural venous invasion in rectal cancer: The role of diffusion-weighted imaging. Radiol. Med. 2020, 125, 522–530. [Google Scholar] [CrossRef] [PubMed]
- Cutaia, G.; Tosto, G.; Cannella, R.; Bruno, A.; Leto, C.; Salvaggio, L.; Cutaia, S.; Lombardo, F.P.; Dispensa, N.; Giambelluca, D.; et al. Prevalence and clinical significance of incidental findings on multiparametric prostate MRI. Radiol. Med. 2019, 125, 204–213. [Google Scholar] [CrossRef] [PubMed]
- Sun, N.-N.; Ge, X.-L.; Liu, X.-S.; Xu, L.-L. Histogram analysis of DCE-MRI for chemoradiotherapy response evaluation in locally advanced esophageal squamous cell carcinoma. Radiol. Med. 2019, 125, 165–176. [Google Scholar] [CrossRef] [PubMed]
- Tang, L.; Wang, X.-J.; Baba, H.; Giganti, F. Gastric cancer and image-derived quantitative parameters: Part 2—a critical review of DCE-MRI and 18F-FDG PET/CT findings. Eur. Radiol. 2019, 30, 247–260. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Flaherty, K.T.; Rosen, M.A.; Heitjan, D.F.; Gallagher, M.L.; Schwartz, B.; Schnall, M.D.; O’Dwyer, P.J. Pilot study of DCE-MRI to predict progression-free survival with sorafenib therapy in renal cell carcinoma. Cancer Biol. Ther. 2008, 7, 496–501. [Google Scholar] [CrossRef] [Green Version]
- Gaudino, S.; Benenati, M.; Martucci, M.; Botto, A.; Infante, A.; Marrazzo, A.; Ramaglia, A.; Marziali, G.; Guadalupi, P.; Colosimo, C. Investigating dynamic susceptibility contrast-enhanced perfusion-weighted magnetic resonance imaging in posterior fossa tumors: Differences and similarities with supratentorial tumors. Radiol. Med. 2020, 125, 416–422. [Google Scholar] [CrossRef]
- Ippolito, D.; Drago, S.G.; Pecorelli, A.; Maino, C.; Querques, G.; Mariani, I.; Franzesi, C.T.; Sironi, S. Role of dynamic perfusion magnetic resonance imaging in patients with local advanced rectal cancer. World J. Gastroenterol. 2020, 26, 2657–2668. [Google Scholar] [CrossRef]
- Li, M.; Xu, X.; Xia, K.; Jiang, H.; Jiang, J.; Sun, J.; Lu, Z. Comparison of Diagnostic Performance between Perfusion-Related Intravoxel Incoherent Motion DWI and Dynamic Contrast-Enhanced MRI in Rectal Cancer. Comput. Math. Methods Med. 2021, 2021, 5095940. [Google Scholar] [CrossRef]
- Pietragalla, M.; Nardi, C.; Bonasera, L.; Mungai, F.; Taverna, C.; Novelli, L.; De Renzis, A.G.D.; Calistri, L.; Tomei, M.; Occhipinti, M.; et al. The role of diffusion-weighted and dynamic contrast enhancement perfusion-weighted imaging in the evaluation of salivary glands neoplasms. Radiol. Med. 2020, 125, 851–863. [Google Scholar] [CrossRef] [PubMed]
- Mungai, F.; Verrone, G.B.; Bonasera, L.; Bicci, E.; Pietragalla, M.; Nardi, C.; Berti, V.; Mazzoni, L.N.; Miele, V. Imaging biomarkers in the diagnosis of salivary gland tumors: The value of lesion/parenchyma ratio of perfusion-MR pharmacokinetic parameters. Radiol. Med. 2021, 126, 1345–1355. [Google Scholar] [CrossRef] [PubMed]
- Fusco, R.; Granata, V.; Maio, F.; Sansone, M.; Petrillo, A. Textural radiomic features and time-intensity curve data analysis by dynamic contrast-enhanced MRI for early prediction of breast cancer therapy response: Preliminary data. Eur. Radiol. Exp. 2020, 4, 8. [Google Scholar] [CrossRef] [PubMed]
- O’Connor, J.P.B.; Robinson, S.P.; Waterton, J.C. Imaging tumour hypoxia with oxygen-enhanced MRI and BOLD MRI. Br. J. Radiol. 2019, 92, 20180642. [Google Scholar] [CrossRef]
- Yang, H.; Yang, X.; Liu, H.; Long, H.; Hu, H.; Wang, Q.; Huang, R.; Shan, D.; Li, K.; Lai, W. Placebo modulation in orthodontic pain: A single-blind functional magnetic resonance study. Radiol. Med. 2021, 126, 1356–1365. [Google Scholar] [CrossRef]
- Li, C.; Liu, H.; Li, X.; Zhou, L.; Wang, R.; Zhang, Y. Application of BOLD-MRI in the classification of renal function in chronic kidney disease. Abdom. Radiol. 2018, 44, 604–611. [Google Scholar] [CrossRef] [Green Version]
- Glover, G.H. Overview of Functional Magnetic Resonance Imaging. Neurosurg. Clin. N. Am. 2011, 22, 133–139. [Google Scholar] [CrossRef] [Green Version]
- Shukla, G.; Alexander, G.S.; Bakas, S.; Nikam, R.; Talekar, K.; Palmer, J.D.; Shi, W. Advanced magnetic resonance imaging in glioblastoma: A review. Chin. Clin. Oncol. 2017, 6, 40. [Google Scholar] [CrossRef] [Green Version]
- Kauppinen, R.A.; Peet, A.C. Using magnetic resonance imaging and spectroscopy in cancer diagnostics and monitoring: Preclinical and clinical approaches. Cancer Biol. Ther. 2011, 12, 665–679. [Google Scholar] [CrossRef] [Green Version]
- Rothman, D.L.; de Graaf, R.A.; Hyder, F.; Mason, G.F.; Behar, K.L.; De Feyter, H.M. In Vivo (13) C and (1) H-[(13) C] MRS studies of neuroenergetics and neurotransmitter cycling, applications to neurological and psychiatric disease and brain cancer. NMR Biomed. 2019, 32, e4172. [Google Scholar] [CrossRef]
- Scapicchio, C.; Gabelloni, M.; Barucci, A.; Cioni, D.; Saba, L.; Neri, E. A deep look into radiomics. Radiol. Med. 2021, 126, 1296–1311. [Google Scholar] [CrossRef]
- Rossi, F.; Bignotti, B.; Bianchi, L.; Picasso, R.; Martinoli, C.; Tagliafico, A.S. Radiomics of peripheral nerves MRI in mild carpal and cubital tunnel syndrome. Radiol. Med. 2019, 125, 197–203. [Google Scholar] [CrossRef]
- Manikis, G.; Ioannidis, G.; Siakallis, L.; Nikiforaki, K.; Iv, M.; Vozlic, D.; Surlan-Popovic, K.; Wintermark, M.; Bisdas, S.; Marias, K. Multicenter DSC–MRI-Based Radiomics Predict IDH Mutation in Gliomas. Cancers 2021, 13, 3965. [Google Scholar] [CrossRef]
- Feng, Y.; Emerson, L.; Jeong, E.-K.; Parker, D.L.; Lu, Z.-R. Application of a biodegradable macromolecular contrast agent in dynamic contrast-enhanced MRI for assessing the efficacy of indocyanine green-enhanced photothermal cancer therapy. J. Magn. Reson. Imaging 2009, 30, 401–406. [Google Scholar] [CrossRef] [Green Version]
- Hu, H.-T.; Shan, Q.-Y.; Chen, S.-L.; Li, B.; Feng, S.-T.; Xu, E.-J.; Li, X.; Long, J.-Y.; Xie, X.-Y.; Lu, M.-D.; et al. CT-based radiomics for preoperative prediction of early recurrent hepatocellular carcinoma: Technical reproducibility of acquisition and scanners. Radiol. Med. 2020, 125, 697–705. [Google Scholar] [CrossRef]
- Nardone, V.; Reginelli, A.; Guida, C.; Belfiore, M.P.; Biondi, M.; Mormile, M.; Buonamici, F.B.; Di Giorgio, E.; Spadafora, M.; Tini, P.; et al. Delta-radiomics increases multicentre reproducibility: A phantom study. Med Oncol. 2020, 37, 38. [Google Scholar] [CrossRef]
- Zhang, L.; Kang, L.; Li, G.; Zhang, X.; Ren, J.; Shi, Z.; Li, J.; Yu, S. Computed tomography-based radiomics model for discriminating the risk stratification of gastrointestinal stromal tumors. Radiol. Med. 2020, 125, 465–473. [Google Scholar] [CrossRef]
- Daimiel Naranjo, I.; Gibbs, P.; Reiner, J.S.; Lo Gullo, R.; Sooknanan, C.; Thakur, S.B.; Jochelson, M.S.; Sevilimedu, V.; Morris, E.A.; Baltzer, P.A.; et al. Radiomics and Machine Learning with Multiparametric Breast MRI for Improved Diagnostic Accuracy in Breast Cancer Diagnosis. Diagnostics 2021, 11, 919. [Google Scholar] [CrossRef]
- Corradini, D.; Brizi, L.; Gaudiano, C.; Bianchi, L.; Marcelli, E.; Golfieri, R.; Schiavina, R.; Testa, C.; Remondini, D. Challenges in the Use of Artificial Intelligence for Prostate Cancer Diagnosis from Multiparametric Imaging Data. Cancers 2021, 13, 3944. [Google Scholar] [CrossRef]
- Yu, Y.; Heit, J.J.; Zaharchuk, G. Improving Ischemic Stroke Care with MRI and Deep Learning Artificial Intelligence. Top. Magn. Reson. Imaging 2021, 30, 187–195. [Google Scholar] [CrossRef]
- Neri, E.; Coppola, F.; Miele, V.; Bibbolino, C.; Grassi, R. Artificial intelligence: Who is responsible for the diagnosis? Radiol. Med. 2020, 125, 517–521. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Arita, Y.; Yoshida, S.; Kwee, T.C.; Akita, H.; Okuda, S.; Iwaita, Y.; Mukai, K.; Matsumoto, S.; Ueda, R.; Ishii, R.; et al. Diagnostic value of texture analysis of apparent diffusion coefficient maps for differentiating fat-poor angiomyolipoma from non-clear-cell renal cell carcinoma. Eur. J. Radiol. 2021, 143, 109895. [Google Scholar] [CrossRef] [PubMed]
- Fritz, B.; Fritz, J. Artificial intelligence for MRI diagnosis of joints: A scoping review of the current state-of-the-art of deep learning-based approaches. Skelet. Radiol. 2021, 51, 315–329. [Google Scholar] [CrossRef] [PubMed]
- Belfiore, M.P.; Urraro, F.; Grassi, R.; Giacobbe, G.; Patelli, G.; Cappabianca, S.; Reginelli, A. Artificial intelligence to codify lung CT in Covid-19 patients. Radiol. Med. 2020, 125, 500–504. [Google Scholar] [CrossRef]
- Shi, J.; Kantoff, P.W.; Wooster, R.; Farokhzad, O.C. Cancer nanomedicine: Progress, challenges and opportunities. Nat. Rev. Cancer 2017, 17, 20–37. [Google Scholar] [CrossRef]
- Lloyd-Parry, O.; Downing, C.; Aleisaei, E.; Jones, C.; Coward, K. Nanomedicine applications in women’s health: State of the art. Int. J. Nanomed. 2018, 13, 1963–1983. [Google Scholar] [CrossRef] [Green Version]
- Roca, A.G.; Gutiérrez, L.; Gavilán, H.; Fortes Brollo, M.E.; Veintemillas-Verdaguer, S.; del Puerto Morales, M. Design strategies for shape-controlled magnetic iron oxide nanoparticles. Adv. Drug Deliv. Rev. 2019, 138, 68–104. [Google Scholar] [CrossRef]
- Avasthi, A.; Caro, C.; Pozo-Torres, E.; Leal, M.P.; García-Martín, M.L. Magnetic Nanoparticles as MRI Contrast Agents. Top. Curr. Chem. 2020, 378, 40. [Google Scholar] [CrossRef]
- Sanz-Ortega, L.; Rojas, J.M.; Portilla, Y.; Pérez-Yagüe, S.; Barber, D.F. Magnetic Nanoparticles Attached to the NK Cell Surface for Tumor Targeting in Adoptive Transfer Therapies Does Not Affect Cellular Effector Functions. Front. Immunol. 2019, 10, 2073. [Google Scholar] [CrossRef] [Green Version]
- Albers, M.J.; Krieger, M.D.; Gonzalez-Gomez, I.; Gilles, F.H.; McComb, J.G.; Nelson, M.D., Jr.; Blüml, S. Proton-decoupled31P MRS in untreated pediatric brain tumors. Magn. Reson. Med. 2004, 53, 22–29. [Google Scholar] [CrossRef]
- Ding, C.; Wu, K.; Wang, W.; Guan, Z.; Wang, L.; Wang, X.; Wang, R.; Liu, L.; Fan, J. Synthesis of a cell penetrating peptide modified superparamagnetic iron oxide and MRI detection of bladder cancer. Oncotarget 2016, 8, 4718–4729. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Namestnikova, D.; Gubskiy, I.; Kholodenko, I.; Melnikov, P.; Sukhinich, K.; Gabashvili, A.; Vishnevskiy, D.; Soloveva, A.; Abakumov, M.; Vakhrushev, I.; et al. Methodological aspects of MRI of transplanted superparamagnetic iron oxide-labeled mesenchymal stem cells in live rat brain. PLoS ONE 2017, 12, e0186717. [Google Scholar] [CrossRef] [Green Version]
- Khalid, M.K.; Asad, M.; Henrich-Noack, P.; Sokolov, M.; Hintz, W.; Grigartzik, L.; Zhang, E.; Dityatev, A.; Van Wachem, B.; Sabel, B.A. Evaluation of Toxicity and Neural Uptake In Vitro and In Vivo of Superparamagnetic Iron Oxide Nanoparticles. Int. J. Mol. Sci. 2018, 19, 2613. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Gilad, A.A.; Walczak, P.; McMahon, M.; Bin Na, H.; Lee, J.; An, K.; Hyeon, T.; van Zijl, P.C.; Bulte, J.W. MR tracking of transplanted cells with “positive contrast” using manganese oxide nanoparticles. Magn. Reson. Med. 2008, 60, 1–7. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Lee, N.; Hyeon, T. Designed synthesis of uniformly sized iron oxide nanoparticles for efficient magnetic resonance imaging contrast agents. Chem. Soc. Rev. 2011, 41, 2575–2589. [Google Scholar] [CrossRef]
- Wang, X.; Xu, L.; Ren, Z.; Fan, M.; Zhang, J.; Qi, H.; Xu, M. A novel manganese chelated macromolecular MRI contrast agent based on O-carboxymethyl chitosan derivatives. Colloids Surf. B Biointerfaces 2019, 183, 110452. [Google Scholar] [CrossRef]
- Chis, A.A.; Dobrea, C.; Morgovan, C.; Arseniu, A.M.; Rus, L.L.; Butuca, A.; Juncan, A.M.; Totan, M.; Vonica-Tincu, A.L.; Cormos, G.; et al. Applications and Limitations of Dendrimers in Biomedicine. Molecules 2020, 25, 3982. [Google Scholar] [CrossRef]
- Tanifum, E.A.; Patel, C.; Liaw, M.E.; Pautler, R.G.; Annapragada, A.V. Hydrophilic fluorinated molecules for spectral 19F MRI. Sci. Rep. 2018, 8, 2889. [Google Scholar] [CrossRef] [Green Version]
- Zhu, W.; Xu, Y.; Jin, R.; Wu, C.; Ai, H. MRI Tracking of Dendritic Cells Loaded with Superparamagnetic Iron Oxide Nanoparticles. Cell Track. 2020, 2126, 107–116. [Google Scholar] [CrossRef]
- Gore, J.C.; Pham, W. Near-Infrared Dyes: Probe Development and Applications in Optical Molecular Imaging. Curr. Org. Synth. 2011, 8, 521–534. [Google Scholar] [CrossRef]
- Lian, X.; Wei, M.-Y.; Ma, Q. Nanomedicines for Near-Infrared Fluorescent Lifetime-Based Bioimaging. Front. Bioeng. Biotechnol. 2019, 7, 386. [Google Scholar] [CrossRef] [Green Version]
- Chen, T.; Mori, Y.; Inui-Yamamoto, C.; Komai, Y.; Tago, Y.; Yoshida, S.; Takabatake, Y.; Isaka, Y.; Ohno, K.; Yoshioka, Y. Polymer-brush-afforded SPIO Nanoparticles Show a Unique Biodistribution and MR Imaging Contrast in Mouse Organs. Magn. Reson. Med Sci. 2017, 16, 275–283. [Google Scholar] [CrossRef] [Green Version]
- Donahue, N.D.; Acar, H.; Wilhelm, S. Concepts of nanoparticle cellular uptake, intracellular trafficking, and kinetics in nanomedicine. Adv. Drug Deliv. Rev. 2019, 143, 68–96. [Google Scholar] [CrossRef]
- Schwartz-Duval, A.S.; Konopka, C.J.; Moitra, P.; Daza, E.A.; Srivastava, I.; Johnson, E.V.; Kampert, T.L.; Fayn, S.; Haran, A.; Dobrucki, L.W.; et al. Intratumoral generation of photothermal gold nanoparticles through a vectorized biomineralization of ionic gold. Nat. Commun. 2020, 11, 4530. [Google Scholar] [CrossRef]
- Yang, H.; Miao, Y.; Chen, L.; Li, Z.; Yang, R.; Xu, X.; Liu, Z.; Zhang, L.M.; Jiang, X. Redox-responsive nanoparticles from disulfide bond-linked poly-(N-epsilon-carbobenzyloxy-l-lysine)-grafted hyaluronan copolymers as theranostic nanoparticles for tumor-targeted MRI and chemotherapy. Int. J. Biol. Macromol. 2020, 148, 483–492. [Google Scholar] [CrossRef]
- Song, W.; Anselmo, A.C.; Huang, L. Nanotechnology intervention of the microbiome for cancer therapy. Nat. Nanotechnol. 2019, 14, 1093–1103. [Google Scholar] [CrossRef]
- Chaturvedi, V.K.; Singh, A.; Singh, V.K.; Singh, M.P. Cancer Nanotechnology: A New Revolution for Cancer Diagnosis and Therapy. Curr. Drug Metab. 2019, 20, 416–429. [Google Scholar] [CrossRef]
- Jiang, W.; Fang, H.; Liu, F.; Zhou, X.; Zhao, H.; He, X.; Guo, D. PEG-coated and Gd-loaded fluorescent silica nanoparticles for targeted prostate cancer magnetic resonance imaging and fluorescence imaging. Int. J. Nanomed. 2019, 14, 5611–5622. [Google Scholar] [CrossRef] [Green Version]
- Mason, E.E.; Mattingly, E.; Herb, K.; Sliwiak, M.; Franconi, S.; Cooley, C.Z.; Slanetz, P.J.; Wald, L.L. Concept for using magnetic particle imaging for intraoperative margin analysis in breast-conserving surgery. Sci. Rep. 2021, 11, 13456. [Google Scholar] [CrossRef]
- Ye, J.; Fu, G.; Yan, X.; Liu, J.; Wang, X.; Cheng, L.; Zhang, F.; Sun, P.Z.; Liu, G. Noninvasive magnetic resonance/photoacoustic imaging for photothermal therapy response monitoring. Nanoscale 2018, 10, 5864–5868. [Google Scholar] [CrossRef]
- Yingchoncharoen, P.; Kalinowski, D.S.; Richardson, D.R. Lipid-Based Drug Delivery Systems in Cancer Therapy: What Is Available and What Is Yet to Come. Pharmacol. Rev. 2016, 68, 701–787. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Su, H.; Wang, Y.; Liu, S.; Wang, Y.; Liu, Q.; Liu, G.; Chen, Q. Emerging transporter-targeted nanoparticulate drug delivery systems. Acta Pharm. Sin. B 2018, 9, 49–58. [Google Scholar] [CrossRef] [PubMed]
- Lee, G.Y.; Qian, W.P.; Wang, L.; Wang, Y.A.; Staley, C.A.; Satpathy, M.; Nie, S.; Mao, H.; Yang, L. Theranostic Nanoparticles with Controlled Release of Gemcitabine for Targeted Therapy and MRI of Pancreatic Cancer. ACS Nano 2013, 7, 2078–2089. [Google Scholar] [CrossRef]
- Lee, J.; Gordon, A.C.; Kim, H.; Park, W.; Cho, S.; Lee, B.; Larson, A.C.; Rozhkova, E.A.; Kim, D.-H. Targeted multimodal nano-reporters for pre-procedural MRI and intra-operative image-guidance. Biomaterials 2016, 109, 69–77. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Ng, T.S.; Wert, D.; Sohi, H.; Procissi, D.; Colcher, D.; Raubitschek, A.A.; Jacobs, R. Serial Diffusion MRI to Monitor and Model Treatment Response of the Targeted Nanotherapy CRLX101. Clin. Cancer Res. 2013, 19, 2518–2527. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Hussein, A.E.; Zagho, M.M.; Nasrallah, G.K.; Elzatahry, A.A. Recent advances in functional nanostructures as cancer photothermal therapy. Int. J. Nanomed. 2018, 13, 2897–2906. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zhang, F.; Cao, J.; Chen, X.; Yang, K.; Zhu, L.; Fu, G.; Huang, X.; Chen, X. Noninvasive Dynamic Imaging of Tumor Early Response to Nanoparticle-mediated Photothermal Therapy. Theranostics 2015, 5, 1444–1455. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Author | Year | Technique | Nanoparticle | Target |
---|---|---|---|---|
Jiang et al. [149] | 2019 | Magnetic resonance imaging (MRI)/fluorescence probes | Gadodiamide into fluorescent silica nanoparticles (NPs) | Prostate-specific membrane antigen (PSMA) receptor-positive PCa cells |
Mason et al. [150] | 2021 | Hand-held magnetic particle detector and a small-bore MPI scanner | Iron oxide | Breast cancer (intraoperative assessment of tumor margins) |
Ye at al. [151] | 2018 | Magnetic resonance temperature imaging (MRTI) and diffusion-weighted MRI (DWI) | Near-infrared (NIR) irradiated light-absorbing nanoparticles | Monitoring the vascular permeability and temperature status following PTT |
Lee et al. [154] | 2013 | MRI | Urokinase plasminogen activator receptor (uPAR)-targeted magnetic iron oxide nanoparticles (IONPs) + gemcitabine (Gem) | With MRI contrast enhancement by IONPs MRI detection in residual tumors following targeted delivery into uPAR-expressing tumor and stromal cells |
Lee et al. [155] | 2016 | Magnetic resonance imaging (MRI) and real-time upconversion luminescence imaging (UCL) | Transcatheter intra-arterial infusion of targeted multimodal Nd3+-doped upconversion nanoparticle (UCNP)+anti-CD44-monoclonal antibody | Discrimination of liver tumors from normal hepatic tissues in rats |
Quan et al. [40] | 2011 | MRI | Human serum albumin (HSA)-coated iron oxide nanoparticle (HINP) formula + doxorubicin | Tumor suppression effect on 4T1 murine breast cancer xenograft model |
Ng et al. [156] | 2013 | DWI | CRLX101 (cyclodextrin-based polymer particle containing the DNA topoisomerase I inhibitor camptothecin) | Temporal changes in ADC specified early CRLX101 treatment response |
Fu et al. [87] | 2016 | Diffusion-weighted magnetic resonance imaging (DW-MRI) | Photothermal therapy (PTT) | DW-MRI can be an accurate prognosis tool for PTT |
Zhang et al. [158] | 2015 | MRI | Nanoparticle-mediated photothermal therapy (PTT) using graphene oxide (GO) | Time- and temperature-dependent dynamic change of the MRI signal intensity in intratumor microenvironment |
Feng et al. [115] | 2009 | Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). | (Gd-DTPA)-cystamine copolymers (GDCC) | DCE-MRI with GDCC-40 is effective for assessing tumor early response to dye-enhanced photothermal therapy and detecting tumor relapse after treatment |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Bruno, F.; Granata, V.; Cobianchi Bellisari, F.; Sgalambro, F.; Tommasino, E.; Palumbo, P.; Arrigoni, F.; Cozzi, D.; Grassi, F.; Brunese, M.C.; et al. Advanced Magnetic Resonance Imaging (MRI) Techniques: Technical Principles and Applications in Nanomedicine. Cancers 2022, 14, 1626. https://doi.org/10.3390/cancers14071626
Bruno F, Granata V, Cobianchi Bellisari F, Sgalambro F, Tommasino E, Palumbo P, Arrigoni F, Cozzi D, Grassi F, Brunese MC, et al. Advanced Magnetic Resonance Imaging (MRI) Techniques: Technical Principles and Applications in Nanomedicine. Cancers. 2022; 14(7):1626. https://doi.org/10.3390/cancers14071626
Chicago/Turabian StyleBruno, Federico, Vincenza Granata, Flavia Cobianchi Bellisari, Ferruccio Sgalambro, Emanuele Tommasino, Pierpaolo Palumbo, Francesco Arrigoni, Diletta Cozzi, Francesca Grassi, Maria Chiara Brunese, and et al. 2022. "Advanced Magnetic Resonance Imaging (MRI) Techniques: Technical Principles and Applications in Nanomedicine" Cancers 14, no. 7: 1626. https://doi.org/10.3390/cancers14071626
APA StyleBruno, F., Granata, V., Cobianchi Bellisari, F., Sgalambro, F., Tommasino, E., Palumbo, P., Arrigoni, F., Cozzi, D., Grassi, F., Brunese, M. C., Pradella, S., di S. Stefano, M. L. M., Cutolo, C., Di Cesare, E., Splendiani, A., Giovagnoni, A., Miele, V., Grassi, R., Masciocchi, C., & Barile, A. (2022). Advanced Magnetic Resonance Imaging (MRI) Techniques: Technical Principles and Applications in Nanomedicine. Cancers, 14(7), 1626. https://doi.org/10.3390/cancers14071626