Page 94 - ITPS-7-3

INNOSC Theranostics and
Pharmacological Sciences Medical imaging technology

doi: 10.1007/s11042-023-17326-1 doi: 10.1162/15353500200404127

2. Rix A, Lederle W, Theek B, et al. Advanced ultrasound 12. Kwee RM, Kwee TC. Whole-body MRI for preventive health
technologies for diagnosis and therapy. J Nucl Med. screening: A systematic review of the literature. J Magn
2018;59(5):740-746. Reson Imaging. 2019;50(5):1489-1503.
doi: 10.2967/jnumed.117.200030 doi: 10.1002/jmri.26736
3. Tubiana M. Wilhelm Conrad Röntgen and the discovery of 13. Buxton RB. The physics of functional magnetic resonance
X-rays. Bull Acad Natl Med. 1996;180(1):97-108. imaging (fMRI). Rep Prog Phys. 2013;76(9):096601.
4. Withers PJ, Bouman C, Carmignato S, et al. X-ray computed doi: 10.1088/0034-4885/76/9/096601
tomography. Nat Rev Methods Prim. 2021;1(1):18. 14. 1Hansen SB, Bender D. Advancement in production of
doi: 10.1038/s43586-021-00015-4 radiotracers. Semin Nuclear Med. 2022;52(3):266-275.
5. Brenner DJ, Hall EJ. Computed Tomography--an doi: 10.1053/j.semnuclmed.2021.10.003
increasing source of radiation exposure. New Engl J Med. 15. Sarıgül M, Ozyildirim BM, Avci M. Differential convolutional
2007;357(22):2277-2284. neural network. Neural Netw. 2019;116:279-287.
doi: 10.1056/NEJMra072149 doi: 10.1016/j.neunet.2019.04.025
6. Pullicino P, du Boulay GH, Kendall BE. Xenon enhancement 16. Lee JG, Jun S, Cho YW, et al. Deep learning in
for computed tomography of the spinal cord. Neuroradiology. medical imaging: General overview. Korean J Radiol.
1979;18(2):63-66. 2017;18(4):570-584.
doi: 10.1007/bf00344823 doi: 10.3348/kjr.2017.18.4.570
7. Willemink MJ, Noël PB. The evolution of image 17. Yasin M, Sarıgül M, Avci M. Logarithmic learning
reconstruction for CT-from filtered back projection to differential convolutional neural network. Neural Netw.
artificial intelligence. Eur Radiol. 2019;29(5):2185-2195. 2024;172:106114.
doi: 10.1007/s00330-018-5810-7 doi: 10.1016/j.neunet.2024.106114
8. Xian JF, Chen M, Jin ZY. Magnetic resonance imaging 18. Savadjiev P, Chong J, Dohan A, et al. Demystification of
in clinical medicine: Current status and potential AI-driven medical image interpretation: Past, present and
future developments in China. Chin Med J (Engl). future. Eur Radiol. 2019;29(3):1616-1624.
2015;128(5):569-570. doi: 10.1007/s00330-018-5674-x
doi: 10.4103/0366-6999.151637 19. Tatsugami F, Nakaura T, Yanagawa M, et al. Recent
advances in artificial intelligence for cardiac CT: Enhancing
9. Moran CM, Thomson AJW. Preclinical ultrasound diagnosis and prognosis prediction. Diagn Interv Imaging.
imaging-a review of techniques and imaging applications. 2023;104(11):521-528.
Front Phys. 2020;8:124.
doi: 10.1016/j.diii.2023.06.011
doi: 10.3389/fphy.2020.00124
20. Zhu B, Liu JZ, Cauley SF, Rosen BR, Rosen MS. Image
10. Wang RF, Liu M. Study on neuroreceptor imaging with reconstruction by domain-transform manifold learning.
radionuclide tracing in vivo. Beijing Da Xue Xue Bao Yi Xue Nature. 2018;555(7697):487-492.
Ban = J Peking Univ Health Sci. 2007;39(5):550-554.
doi: 10.1038/nature25988
11. Gremlich HU, Martínez V, Kneuer R, et al. Noninvasive
assessment of gastric emptying by near-infrared fluorescence 21. Zaharchuk G, Davidzon G. Artificial intelligence for
reflectance imaging in mice: Pharmacological validation optimization and interpretation of PET/CT and PET/MR
with tegaserod, cisapride, and clonidine. Mol Imaging. images. Semin Nucl Med. 2021;51(2):134-142.
2004;3(4):303-311. doi: 10.1053/j.semnuclmed.2020.10.001

Volume 7 Issue 3 (2024) 10 doi: 10.36922/itps.3360