The Future of Medical Imaging

Author(s): Luigi Landini* .

Journal Name: Current Pharmaceutical Design

Volume 24 , Issue 46 , 2018

[1]
Lawonn K, Smit K, Buhler K, et al. A Survey on Multimodal Medical Data Visualization. Comput Graph Forum 2018; 37(1): 413-38.
[2]
Xue Li, Zhang Xue-Ning, Li Xiao-Dong , et al. Multimodality imaging in nanomedicine and nanotheranostics. Cancer Biol Med 2016; 13: 339-48.
[http://dx.doi.org/10.20892/j.issn.2095-3941.2016.0055]
[3]
Wu M, Shu J. Multimodal Molecular Imaging: Current Status and Future Directions. Contrast Media Mol Imag 2018 Article ID 1382183.
[http://dx.doi.org/https://doi.org/10.1155/2018/1382183.]
[4]
Santarelli MF, Vanello N, Scipioni M, et al. New Imaging Frontiers in Cardiology: Fast and Quantitative Maps from Raw Data. Curr Pharm Des 2017; 23(22): 3268-84.
[http://dx.doi.org/10.2174/1381612823666170328143348]
[5]
Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiology 2016; 278(2): 563-77.
[6]
Lafata K, Cai J, Wang C, et al. Spatial-temporal variability of radiomic features and its effect on the classification of lung cancer histology. Phys Medand Biol 2018.
[http://dx.doi.org/10.1088/1361-6560/aae56a]
[7]
Nioche C, Orlhac F, Boughdad S, et al. LIFEx: A Freeware for Radiomic Feature Calculation in Multimodality Imaging to Accelerate Advances in the Characterization of Tumor Heterogeneity. Cancer Res 2018; 4786-4789
[http://dx.doi.org/10.1158/0008-5472.CAN-18-0125]
[8]
Beckmann N, Kaltashov IA, Windhorst AD. Editorial: Invivo Imaging in Pharmacological Research. Front Pharmacol 2017; 7: 1-2.
[9]
Britto S. Sandanaraj, Rainer Kneuer, Nicolau Beckmann. Optical and magnetic resonance imaging as complementary modalities in drug discovery. Future Med Chem 2010; 2(3): 317-37.
[10]
Vandenberghe S, Marsden PK. PET-MRI: a review of challenges and solutions in the development of integrated multimodality imaging. Phys in Med& Biol 2015; 60(4): R115-54.
[11]
Hatt M, Tixier F, Visvikis D, et al. Radiomics in PET/CT: More Than Meets the Eye? J Nucl Med 2017; 58: 365-6.
[http://dx.doi.org/10.2967/jnumed.116.184655]
[12]
Lambin P, Leijenaar RT, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 2017; 14(12): 749-62.
[13]
Traverso A, Wee L, Dekker A, et al. Repeatability and Reproducibility of Radiomic Features: A Systematic Review. Int J Radiat Oncol Biol Phys 2018; 1-16.
[http://dx.doi.org/10.1016/j.ijrobp.2018.05.053]
[14]
Budan F, Szigeti K, Weszl M, et al. Novel radiomics evaluation of bone formation utilizing multimodal (SPECT/X-ray CT) in vivo imaging. PLoS ONE 2018; 13(9): 1-12. e0204423.
[http://dx.doi.org/https://doi.org/10.1371/journal.]
[15]
Radiomics and radiogenomics for precision Radiotherapy. Jof Radiat Res (Tokyo) 2018; 59(S1): i25-31.
[http://dx.doi.org/10.1093/jrr/rrx102]
[16]
Reuzé S, Schernberg A, Orlhac F, et al. Radiomics in Nuclear Medicine Applied to Radiation Therapy: Methods, Pitfalls, and Challenges. Int J Radiat Oncol Biol Phys 2018.
[http://dx.doi.org/10.1016/j.ijrobp.2018.05.022]
[17]
Palombelli E, Marini S, Sacchi L, Bellazzi R. Patient similarity for precision medicine: A systematic review. J Biomed Inform 2018; 83: 87-96.
[18]
Arimura H, Soufi M. Kamezawa et al. Radiomics with artificial intelligence for precision medicine in radiation therapy. ournal of Radiation Research, , Volume 60, Issue 1, 1 January 2019, Pages 150–157.
[http://dx.doi.org/10.1093/jrr/rry077]
[19]
Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol 2017; 2(4): 230-43.
[http://dx.doi.org/10.1136/svn-2017-000101]
[20]
Yong Xue, Shihui Chen, Jing Qin, et al. Application of Deep Learning in Automated Analysis of Molecular Images in Cancer: A Survey. Contrast Media Mol Imaging 2017; 9512370
[http://dx.doi.org/10.1155/2017/9512370]
[21]
Chen H, Engkvist O, Wang Y, et al. The rise of deep learning in drug discovery. Drug Discov Today 2018; 23(6): 1241-50.
[22]
Mehta N, Pandit A. Concurrence of big data analytics and healthcare: A systematic review. Int J Med Inform 2018; 114: 57-65.
[http://dx.doi.org/10.1016/j.ijmedinf.2018.03.013]
[23]
Zhou XM, Scott XJ, Chaudhury XB, et al. Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches. AJNR Am J Neuroradiol 2018; 39(2): 208-16.
[http://dx.doi.org/10.3174/ajnr.A5391]
[24]
Wright CL, Binzel K, Zhang J, et al. Advanced Functional Tumor Imaging and Precision Nuclear Medicine Enabled by Digital PET Technologies. Contrast Media Mol Imaging 2017 Article ID; 5260305.
[http://dx.doi.org/10.1155/2017/5260305]
[25]
Friedrich MG. The Future of Cardiovascular Magnetic Resonance Imaging. Eur Heart J 2017; 38(22): 1698-701.
[26]
Maria Isabel Vargas, Pascal Martelli, Lijing Xin Clinical Neuroimaging Using 7 T MRI: Challenges and Prospects. J Neuroimaging 2018; 28: 5-13.
[http://dx.doi.org/10.1111/jon.12481]
[27]
David B. Douglas, Clifford A.Wilke, J. David Augmented Reality: Advances in Diagnostic Imaging. Multimodal Technologies and Interact 2017; 1(29)
[http://dx.doi.org/10.3390/mti1040029]
[28]
Capellini K, Vignali E, Costa E, et al. Computational fluid dynamic study for aTAA hemodynamics: an integrated image-based and RBF mesh morphing approach. J Biomech Eng 2018.
[http://dx.doi.org/10.1115/1.4040940]


Rights & PermissionsPrintExport Cite as

Article Details

VOLUME: 24
ISSUE: 46
Year: 2018
Page: [5487 - 5488]
Pages: 2
DOI: 10.2174/138161282446190426115124

Article Metrics

PDF: 20
HTML: 3
EPUB: 1
PRC: 1