Prog Med Phys.  2020 Sep;31(3):81-98. 10.14316/pmp.2020.31.3.81.

Nuclear Medicine Physics: Review of Advanced Technology

• Oh JS

• KMID: 2507481
• DOI: http://doi.org/10.14316/pmp.2020.31.3.81

Abstract

This review aims to provide a brief, comprehensive overview of advanced technologies of nuclear medicine physics, with a focus on recent developments from both hardware and software perspectives. Developments in image acquisition/reconstruction, especially the time-of-flight and point spread function, have potential advantages in the image signal-to-noise ratio and spatial resolution. Modern detector materials and devices (including lutetium oxyorthosilicate, cadmium zinc tellurium, and silicon photomultiplier) as well as modern nuclear medicine imaging systems (including positron emission tomography [PET]/computerized tomography [CT], whole-body PET, PET/magnetic resonance [MR], and digital PET) enable not only high-quality digital image acquisition, but also subsequent image processing, including image reconstruction and postreconstruction methods. Moreover, theranostics in nuclear medicine extend the usefulness of nuclear medicine physics far more than quantitative image-based diagnosis, playing a key role in personalized/precision medicine by raising the importance of internal radiation dosimetry in nuclear medicine. Now that deep-learning-based image processing can be incorporated in nuclear medicine image acquisition/processing, the aforementioned fields of nuclear medicine physics face the new era of Industry 4.0. Ongoing technological developments in nuclear medicine physics are leading to enhanced image quality and decreased radiation exposure as well as quantitative and personalized healthcare.

Keyword

Nuclear medicine physics; Advanced techniques; Theranostics; Precision medicine; Deep learning; Personalized healthcare

Reference

References

1. Cherry SR, Phelps ME, Sorenson JA. 2012. Physics in nuclear medicine. 4th ed. Saunders/Elsevier;Philadelphia: DOI: 10.1016/B978-1-4160-5198-5.00001-0.

2. Anderson CJ, Ling X, Schlyer DJ, Cutler CS. 2019. A short history of nuclear medicine. Radiopharmaceutical chemistry. Springer;Cham: p. 11–16. DOI: 10.1007/978-3-319-98947-1_2.

3. Wagner HN Jr. 1998; A brief history of positron emission tomography (PET). Semin Nucl Med. 28:213–220. DOI: 10.1016/S0001-2998(98)80027-5.

4. L'Annunziata MF. 2007. Radioactivity: introduction and history. Elsevier;Amsterdam:

5. Hertz S, Roberts A. 1946; Radioactive iodine in the study of thyroid physiology; the use of radioactive iodine therapy in hyperthyroidism. J Am Med Assoc. 131:81–86. DOI: 10.1001/jama.1946.02870190005002. PMID: 21025609.

6. McCready VR. 2000; Milestones in nuclear medicine. Eur J Nucl Med. 27(1 Suppl):S49–S79. DOI: 10.1007/s002590050518. PMID: 10654157.

7. Ansell G, Rotblat J. 1948; Radioactive iodine as a diagnostic aid for intrathoracic goitre. Br J Radiol. 21:552–558. DOI: 10.1259/0007-1285-21-251-552. PMID: 18893269.

8. Cassen B, Curtis L, Reed CW. 1950. A sensitive directional gamma-ray detector. United States Atomic Energy Commission;Oak Ridge, USA: p. 78–81. UCLA-49.

9. Mallard J, Trott NG. 1979; Some aspects of the history of nuclear medicine in the United Kingdom. Semin Nucl Med. 9:203–217. DOI: 10.1016/S0001-2998(79)80034-3. PMID: 116364.

10. Mallard JR. 1987; Hevesy memorial medal lecture 1985. Some call it laziness: I call it deep thought (with apologies to Garfield). Nucl Med Commun. 8:691–710. DOI: 10.1097/00006231-198709000-00001. PMID: 3684109.

11. Kuhl DE, Edwards RQ. 1968; Reorganizing data from transverse section scans of the brain using digital processing. Radiology. 91:975–983. DOI: 10.1148/91.5.975. PMID: 5681332.

12. Nellemann P, Hines H, Braymer W, Muehllehner G, Geagan M. 1995; Performance characteristics of a dual head SPECT scanner with PET capability. IEEE Trans Nucl Sci. 3:1751–1755. DOI: 10.1109/NSSMIC.1995.501924.

13. Jones T. 1996; The imaging science of positron emission tomography. Eur J Nucl Med. 23:807–813. DOI: 10.1007/BF00843711. PMID: 8662121.

14. Moses WW. 2011; Fundamental limits of spatial resolution in PET. Nucl Instrum Methods Phys Res A. 648 Supplement 1:S236–S240. DOI: 10.1016/j.nima.2010.11.092. PMID: 21804677. PMCID: PMC3144741.

15. Townsend DW. 2008; Combined positron emission tomography-computed tomography: the historical perspective. Semin Ultrasound CT MR. 29:232–235. DOI: 10.1053/j.sult.2008.05.006. PMID: 18795489. PMCID: PMC2777694.

16. Surti S, Kuhn A, Werner ME, Perkins AE, Kolthammer J, Karp JS. 2007; Performance of Philips Gemini TF PET/CT scanner with special consideration for its time-of-flight imaging capabilities. J Nucl Med. 48:471–480. PMID: 17332626.

17. Karp JS, Surti S, Daube-Witherspoon ME, Muehllehner G. 2008; Benefit of time-of-flight in PET: experimental and clinical results. J Nucl Med. 49:462–470. DOI: 10.2967/jnumed.107.044834. PMID: 18287269. PMCID: PMC2639717.

18. Jakoby BW, Bercier Y, Conti M, Casey ME, Bendriem B, Townsend DW. 2011; Physical and clinical performance of the mCT time-of-flight PET/CT scanner. Phys Med Biol. 56:2375–2389. DOI: 10.1088/0031-9155/56/8/004. PMID: 21427485.

19. Bettinardi V, Presotto L, Rapisarda E, Picchio M, Gianolli L, Gilardi MC. 2011; Physical performance of the new hybrid PET∕CT Discovery-690. Med Phys. 38:5394–5411. DOI: 10.1118/1.3635220. PMID: 21992359.

20. Lecoq P. 2017; Pushing the limits in time-of-flight PET imaging. IEEE Trans Radiat Plasma Med Sci. 1:473–485. DOI: 10.1109/TRPMS.2017.2756674.

21. Berg E, Cherry SR. 2018; Innovations in instrumentation for positron emission tomography. Semin Nucl Med. 48:311–331. DOI: 10.1053/j.semnuclmed.2018.02.006. PMID: 29852942. PMCID: PMC5986096.

22. van der Vos CS, Koopman D, Rijnsdorp S, Arends AJ, Boellaard R, van Dalen JA, et al. 2017; Quantification, improvement, and harmonization of small lesion detection with state-of-the-art PET. Eur J Nucl Med Mol Imaging. 44(Suppl 1):4–16. DOI: 10.1007/s00259-017-3727-z. PMID: 28687866. PMCID: PMC5541089.

23. Son HJ, Oh JS, Roh JH, Seo SW, Oh M, Lee SJ, et al. 2019; Differences in gray and white matter 18F-THK5351 uptake between behavioral-variant frontotemporal dementia and other dementias. Eur J Nucl Med Mol Imaging. 46:357–366. DOI: 10.1007/s00259-018-4125-x. PMID: 30109402.

24. Son HJ, Oh JS, Oh M, Kim SJ, Lee JH, Roh JH, et al. 2020; The clinical feasibility of deep learning-based classification of amyloid PET images in visually equivocal cases. Eur J Nucl Med Mol Imaging. 47:332–341. DOI: 10.1007/s00259-019-04595-y. PMID: 31811343.

25. Wahl RL, Jacene H, Kasamon Y, Lodge MA. 2009; From RECIST to PERCIST: evolving considerations for PET response criteria in solid tumors. J Nucl Med. 50 Suppl 1(Suppl 1):122S–150S. DOI: 10.2967/jnumed.108.057307. PMID: 19403881. PMCID: PMC2755245.

26. Strother SC, Casey ME, Hoffman EJ. 1990; Measuring PET scanner sensitivity: relating countrates to image signal-to-noise ratios using noise equivalents counts. IEEE Trans Nucl Sci. 37:783–788. DOI: 10.1109/23.106715.

27. Campagnolo RE, Garderet P, Vacher J. 1979. Tomographie par emetteurs positrons avec mesure de temps de vol. Colloque national sur le traitement du signal. Rennes Cedex;Nice:

28. Vandenberghe S, Mikhaylova E, D'Hoe E, Mollet P, Karp JS. 2016; Recent developments in time-of-flight PET. EJNMMI Phys. 3:3. DOI: 10.1186/s40658-016-0138-3. PMID: 26879863. PMCID: PMC4754240.

29. Ter-Pogossian MM, Mullani NA, Ficke DC, Markham J, Snyder DL. 1981; Photon time-of-flight-assisted positron emission tomography. J Comput Assist Tomogr. 5:227–239. DOI: 10.1097/00004728-198104000-00014. PMID: 6971303.

30. Laval M, Gariod R, Allemand R, Cormorèche E, Moszynski M. The "LETI" positron tomograph architecture and time-of-flight improvements. Paper presented at: Workshop on Time of Flight Tomography. 1982 May 17-19; St Louis, USA.

31. Yamamoto M, Ficke DC, Ter-Pogossian MM. 1982; Experimental assessment of the gain achieved by the utilization of time-of-flight information in a positron emission tomograph (Super PETT I). IEEE Trans Med Imaging. 1:187–192. DOI: 10.1109/TMI.1982.4307571. PMID: 18238274.

32. Budinger TF. 1983; Time-of-flight positron emission tomography: status relative to conventional PET. J Nucl Med. 24:73–78. PMID: 6336778.

33. Wong WH. 1988; PET camera performance design evaluation for BGO and BaF2 scintillators (non-time-of-flight). J Nucl Med. 29:338–347. PMID: 3258027.

34. Mallon A, Grangeat P. 1992; Three-dimensional PET reconstruction with time-of-flight measurement. Phys Med Biol. 37:717–729. DOI: 10.1088/0031-9155/37/3/016. PMID: 1565699.

35. Patterson D. 50 years of computer architecture: from the mainframe CPU to the domain-specific tpu and the open RISC-V instruction set. Paper presented at: 2018 IEEE International Solid - State Circuits Conference - (ISSCC). 2018 Feb 11-15; San Francisco, USA. DOI: 10.1109/ISSCC.2018.8310168.

36. Cherry SR, Jones T, Karp JS, Qi J, Moses WW, Badawi RD. 2018; Total-body PET: maximizing sensitivity to create new opportunities for clinical research and patient care. J Nucl Med. 59:3–12. DOI: 10.2967/jnumed.116.184028. PMID: 28935835. PMCID: PMC5750522.

37. Badawi RD, Shi H, Hu P, Chen S, Xu T, Price PM, et al. 2019; First human imaging studies with the EXPLORER total-body PET scanner. J Nucl Med. 60:299–303. DOI: 10.2967/jnumed.119.226498. PMID: 30733314. PMCID: PMC6424228.

38. Panin VY, Kehren F, Michel C, Casey M. 2006; Fully 3-D PET reconstruction with system matrix derived from point source measurements. IEEE Trans Med Imaging. 25:907–921. DOI: 10.1109/TMI.2006.876171. PMID: 16827491.

39. Takamochi K, Yoshida J, Murakami K, Niho S, Ishii G, Nishimura M, et al. 2005; Pitfalls in lymph node staging with positron emission tomography in non-small cell lung cancer patients. Lung Cancer. 47:235–242. DOI: 10.1016/j.lungcan.2004.08.004. PMID: 15639722.

40. Bellevre D, Blanc Fournier C, Switsers O, Dugué AE, Levy C, Allouache D, et al. 2014; Staging the axilla in breast cancer patients with 18F-FDG PET: how small are the metastases that we can detect with new generation clinical PET systems? Eur J Nucl Med Mol Imaging. 41:1103–1112. DOI: 10.1007/s00259-014-2689-7. PMID: 24562642.

41. Kuhnert G, Boellaard R, Sterzer S, Kahraman D, Scheffler M, Wolf J, et al. 2016; Impact of PET/CT image reconstruction methods and liver uptake normalization strategies on quantitative image analysis. Eur J Nucl Med Mol Imaging. 43:249–258. DOI: 10.1007/s00259-015-3165-8. PMID: 26280981.

42. Erlandsson K, Buvat I, Pretorius PH, Thomas BA, Hutton BF. 2012; A review of partial volume correction techniques for emission tomography and their applications in neurology, cardiology and oncology. Phys Med Biol. 57:R119–R159. DOI: 10.1088/0031-9155/57/21/R119. PMID: 23073343.

43. Rousset O, Rahmim A, Alavi A, Zaidi H. 2007; Partial volume correction strategies in PET. PET Clin. 2:235–249. DOI: 10.1016/j.cpet.2007.10.005. PMID: 27157875.

44. Hoffman EJ, Huang SC, Phelps ME. 1979; Quantitation in positron emission computed tomography: 1. Effect of object size. J Comput Assist Tomogr. 3:299–308. DOI: 10.1097/00004728-197906000-00001. PMID: 438372.

45. Rousset O, Ma Y, Kamber M, Evans AC. 1993; 3D simulations of radiotracer uptake in deep nuclei of human brain. Comput Med Imaging Graph. 17:373–379. DOI: 10.1016/0895-6111(93)90031-H. PMID: 8306312.

46. Rousset OG, Ma Y, Evans AC. 1998; Correction for partial volume effects in PET: principle and validation. J Nucl Med. 39:904–911. PMID: 9591599.

47. Schafer RW, Mersereau RM, Richards MA. 1981; Constrained iterative restoration algorithms. Proc IEEE. 69:432–450. DOI: 10.1109/PROC.1981.11987.

48. Carasso A. 1999; Linear and nonlinear image deblurring: a documented study. SIAM J Numer Anal. 36:1659–1689. DOI: 10.1137/S0036142997320413.

49. Rudin L, Osher S, Fatemi E. 1992; Nonlinear total variation based noise removal algorithms. Phys D. 60:259–268. DOI: 10.1016/0167-2789(92)90242-F.

50. Teo BK, Seo Y, Bacharach SL, Carrasquillo JA, Libutti SK, Shukla H, et al. 2007; Partial-volume correction in PET: validation of an iterative postreconstruction method with phantom and patient data. J Nucl Med. 48:802–810. DOI: 10.2967/jnumed.106.035576. PMID: 17475970.

51. Green PJ. 1990; Bayesian reconstructions from emission tomography data using a modified EM algorithm. IEEE Trans Med Imaging. 9:84–93. DOI: 10.1109/42.52985. PMID: 18222753.

52. Thomas BA, Erlandsson K, Modat M, Thurfjell L, Vandenberghe R, Ourselin S, et al. 2011; The importance of appropriate partial volume correction for PET quantification in Alzheimer's disease. Eur J Nucl Med Mol Imaging. 38:1104–1119. DOI: 10.1007/s00259-011-1745-9. PMID: 21336694.

53. Cysouw MCF, Kramer GM, Schoonmade LJ, Boellaard R, de Vet HCW, Hoekstra OS. 2017; Impact of partial-volume correction in oncological PET studies: a systematic review and meta-analysis. Eur J Nucl Med Mol Imaging. 44:2105–2116. DOI: 10.1007/s00259-017-3775-4. PMID: 28776088. PMCID: PMC5656693.

54. Erlandsson K, Hutton BF. 2010; Partial volume correction in SPECT using anatomical information and iterative FBP. Tsinghua Sci Technol. 15:50–55. DOI: 10.1016/S1007-0214(10)70008-0.

55. Boening G, Pretorius PH, King MA. 2006; Study of relative quantification of Tc-99m with partial volume effect and spillover correction for SPECT oncology imaging. IEEE Trans Nucl Sci. 53:1205–1212. DOI: 10.1109/TNS.2006.871406.

56. Lambrou T, Groves AM, Erlandsson K, Screaton N, Endozo R, Win T, et al. 2011; The importance of correction for tissue fraction effects in lung PET: preliminary findings. Eur J Nucl Med Mol Imaging. 38:2238–2246. DOI: 10.1007/s00259-011-1906-x. PMID: 21874321.

57. Boellaard R. 2009; Standards for PET image acquisition and quantitative data analysis. J Nucl Med. 50 Suppl 1:11S–20S. DOI: 10.2967/jnumed.108.057182. PMID: 19380405.

58. Boellaard R. 2011; Methodological aspects of multicenter studies with quantitative PET. Methods Mol Biol. 727:335–349. DOI: 10.1007/978-1-61779-062-1_18. PMID: 21331942.

59. Boellaard R. 2012; Mutatis mutandis: harmonize the standard! J Nucl Med. 53:1–3. DOI: 10.2967/jnumed.111.094763. PMID: 22159159.

60. Aide N, Lasnon C, Veit-Haibach P, Sera T, Sattler B, Boellaard R. 2017; EANM/EARL harmonization strategies in PET quantification: from daily practice to multicentre oncological studies. Eur J Nucl Med Mol Imaging. 44(Suppl 1):17–31. DOI: 10.1007/s00259-017-3740-2. PMID: 28623376. PMCID: PMC5541084.

61. Ferretti A, Chondrogiannis S, Rampin L, Bellan E, Marzola MC, Grassetto G, et al. 2018; How to harmonize SUVs obtained by hybrid PET/CT scanners with and without point spread function correction. Phys Med Biol. 63:235010. DOI: 10.1088/1361-6560/aaee27. PMID: 30474620.

62. Joshi A, Koeppe RA, Fessler JA. 2009; Reducing between scanner differences in multi-center PET studies. Neuroimage. 46:154–159. DOI: 10.1016/j.neuroimage.2009.01.057. PMID: 19457369. PMCID: PMC4308413.

63. Oh JS, Kang BC, Roh JL, Kim JS, Cho KJ, Lee SW, et al. 2015; Intratumor textural heterogeneity on pretreatment (18)F-FDG pET images predicts response and survival after chemoradiotherapy for hypopharyngeal cancer. Ann Surg Oncol. 22:2746–2754. DOI: 10.1245/s10434-014-4284-3. PMID: 25487968.

64. Kim JW, Oh JS, Roh JL, Kim JS, Choi SH, Nam SY, et al. 2015; Prognostic significance of standardized uptake value and metabolic tumour volume on 18F-FDG PET/CT in oropharyngeal squamous cell carcinoma. Eur J Nucl Med Mol Imaging. 42:1353–1361. DOI: 10.1007/s00259-015-3051-4. PMID: 26067088.

65. Ha SC, Oh JS, Roh JL, Moon H, Kim JS, Cho KJ, et al. 2017; Pretreatment tumor SUVmax predicts disease-specific and overall survival in patients with head and neck soft tissue sarcoma. Eur J Nucl Med Mol Imaging. 44:33–40. DOI: 10.1007/s00259-016-3456-8. PMID: 27448574.

66. Lim WS, Oh JS, Roh JL, Kim JS, Kim SJ, Choi SH, et al. 2018; Prediction of distant metastasis and survival in adenoid cystic carcinoma using quantitative 18F-FDG PET/CT measurements. Oral Oncol. 77:98–104. DOI: 10.1016/j.oraloncology.2017.12.013. PMID: 29362133.

67. Cherry SR, Shao Y, Silverman RW, Meadors K, Siegel S, Chatziioannou A, et al. 1997; MicroPET: a high resolution PET scanner for imaging small animals. IEEE Trans Nucl Sci. 44:1161–1166. DOI: 10.1109/23.596981.

68. Wienhard K, Schmand M, Casey ME, Baker K, Bao J, Eriksson L, et al. 2002; The ECAT HRRT: performance and first clinical application of the new high resolution research tomograph. IEEE Trans Nucl Sci. 49:104–110. DOI: 10.1109/TNS.2002.998689.

69. Schmand M, Eriksson L, Casey ME, Andreaco MS, Melcher C, Wienhard K, et al. 1998; Performance results of a new DOI detector block for a high resolution PET-LSO research tomograph HRRT. IEEE Trans Nucl Sci. 45:3000–3006. DOI: 10.1109/23.737656.

70. Spinks TJ, Bloomfield PM. A comparison of count rate performance for 15O-water blood flow studies in the CTI HR+ and accel tomographs in 3D mode. Paper presented at: 2002 IEEE Nuclear Science Symposium Conference Record. 2002 Nov 10-16; Norfolk, USA.

71. Muehllrhner G, Karp JS, Surti S. 2002; Design considerations for PET scanners. Q J Nucl Med. 46:16–23. PMID: 12072842.

72. Lewellen TK. 1998; Time-of-flight PET. Semin Nucl Med. 28:268–275. DOI: 10.1016/S0001-2998(98)80031-7. PMID: 32542512. PMCID: PMC7295929.

73. Moses WW, Derenzo SE. 1999; Prospects for time-of-flight PET using LSO scintillator. IEEE Trans Nucl Sci. 46:474–478. DOI: 10.1109/23.775565.

74. Han S, Kim YH, Ahn JM, Kang SJ, Oh JS, Shin E, et al. 2018; Feasibility of dynamic stress 201Tl/rest 99mTc-tetrofosmin single photon emission computed tomography for quantification of myocardial perfusion reserve in patients with stable coronary artery disease. Eur J Nucl Med Mol Imaging. 45:2173–2180. DOI: 10.1007/s00259-018-4057-5. PMID: 29858614.

75. Boellaard R, Quick HH. 2015; Current image acquisition options in PET/MR. Semin Nucl Med. 45:192–200. DOI: 10.1053/j.semnuclmed.2014.12.001. PMID: 25841274.

76. Cabello J, Ziegler SI. 2018; Advances in PET/MR instrumentation and image reconstruction. Br J Radiol. 91:20160363. DOI: 10.1259/bjr.20160363. PMID: 27376170. PMCID: PMC5966194.

77. Hofmann M, Pichler B, Schölkopf B, Beyer T. 2009; Towards quantitative PET/MRI: a review of MR-based attenuation correction techniques. Eur J Nucl Med Mol Imaging. 36 Suppl 1:S93–S104. DOI: 10.1007/s00259-008-1007-7. PMID: 19104810.

78. Mehranian A, Zaidi H. 2015; Emission-based estimation of lung attenuation coefficients for attenuation correction in time-of-flight PET/MR. Phys Med Biol. 60:4813–4833. DOI: 10.1088/0031-9155/60/12/4813. PMID: 26047036.

79. Benoit D, Ladefoged CN, Rezaei A, Keller SH, Andersen FL, Højgaard L, et al. 2016; Optimized MLAA for quantitative non-TOF PET/MR of the brain. Phys Med Biol. 61:8854–8874. DOI: 10.1088/1361-6560/61/24/8854. PMID: 27910823.

80. Cheng JC, Salomon A, Yaqub M, Boellaard R. 2016; Investigation of practical initial attenuation image estimates in TOF-MLAA reconstruction for PET/MR. Med Phys. 43:4163. DOI: 10.1118/1.4953634. PMID: 27370136.

81. Samarin A, Burger C, Wollenweber SD, Crook DW, Burger IA, Schmid DT, et al. 2012; PET/MR imaging of bone lesions--implications for PET quantification from imperfect attenuation correction. Eur J Nucl Med Mol Imaging. 39:1154–1160. DOI: 10.1007/s00259-012-2113-0. PMID: 22526955.

82. Hofmann M, Bezrukov I, Mantlik F, Aschoff P, Steinke F, Beyer T, et al. 2011; MRI-based attenuation correction for whole-body PET/MRI: quantitative evaluation of segmentation- and atlas-based methods. J Nucl Med. 52:1392–1399. DOI: 10.2967/jnumed.110.078949. PMID: 21828115.

83. Frach T, Prescher G, Degenhardt C, de Gruyter R, Schmitz A, Ballizany R. The digital silicon photomultiplier- principle of operation and intrinsic detector performance. Paper presented at: 2009 IEEE Nuclear Science Symposium Conference Record (NSS/MIC). 2009 Oct 24-Nov 1; Orlando, USA. DOI: 10.1109/NSSMIC.2009.5402143.

84. Degenhardt C, Prescher G, Frach T, Thon A, de Gruyter R, Schmitz A, et al. The digital silicon photomultiplier- a novel sensor for the detection of scintillation light. Paper presented at: 2009 IEEE Nuclear Science Symposium Conference Record (NSS/MIC). 2009 Oct 24-Nov 1; Orlando, USA. DOI: 10.1109/NSSMIC.2009.5402190.

85. Degenhardt C, Rodrigues P, Trindade A, Zwaans B, Mülhens O, Dorscheid R, et al. Performance evaluation of a prototype Positron Emission Tomography scanner using Digital Photon Counters (DPC). Paper presented at: 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC). 2012 Oct 27-Nov 3; Anaheim, USA. DOI: 10.1109/NSSMIC.2012.6551643.

86. Zhang J, Binzel K, Bardos P, Nagar V, Knopp M, Zhang B, et al. 2015; FDG dose reduction potential of a next generation digital detector PET/CT system: initial clinical demonstration in wholebody imaging. J Nucl Med. 56(Suppl 3):1823.

87. Narayanan M, Andreyev A, Bai C, Miller M, Hu Z. 2016; TOF-benefits on the philips digital PET/CT scanner: evaluation of faster convergence and reduced scan times. J Nucl Med. 57(Suppl 2):201.

88. Ahn BC. 2016; Personalized medicine based on theranostic radioiodine molecular imaging for differentiated thyroid cancer. Biomed Res Int. 2016:1680464. DOI: 10.1155/2016/1680464. PMID: 27239470. PMCID: PMC4864569.

89. Yordanova A, Eppard E, Kürpig S, Bundschuh RA, Schönberger S, Gonzalez-Carmona M, et al. 2017; Theranostics in nuclear medicine practice. Onco Targets Ther. 10:4821–4828. DOI: 10.2147/OTT.S140671. PMID: 29042793.

90. Choudhury P, Gupta M. 2017; Personalized & precision medicine in cancer: a theranostic approach. Curr Radiopharm. 10:166–170. DOI: 10.2174/1874471010666170728094008. PMID: 28758574.

91. Koh JM, Kim ES, Ryu JS, Hong SJ, Kim WB, Shong YK. 2003; Effects of therapeutic doses of 131I in thyroid papillary carcinoma patients with elevated thyroglobulin level and negative 131I whole-body scan: comparative study. Clin Endocrinol (Oxf). 58:421–427. DOI: 10.1046/j.1365-2265.2003.01733.x. PMID: 12641624.

92. Jun S, Lee JJ, Park SH, Kim TY, Kim WB, Shong YK, et al. 2015; Prediction of treatment response to 131I therapy by diffuse hepatic uptake intensity on post-therapy whole-body scan in patients with distant metastases of differentiated thyroid cancer. Ann Nucl Med. 29:603–612. DOI: 10.1007/s12149-015-0983-5. PMID: 25980591.

93. Lee N, Oh I, Chae SY, Jin S, Oh SJ, Lee SJ, et al. 2019; Radiation dosimetry of [18F]GP1 for imaging activated glycoprotein IIb/IIIa receptors with positron emission tomography in patients with acute thromboembolism. Nucl Med Biol. 72-73:45–48. DOI: 10.1016/j.nucmedbio.2019.07.003. PMID: 31330411.

94. Willowson KP, Eslick E, Ryu H, Poon A, Bernard EJ, Bailey DL. 2018; Feasibility and accuracy of single time point imaging for renal dosimetry following 177Lu-DOTATATE ('Lutate') therapy. EJNMMI Phys. 5:33. DOI: 10.1186/s40658-018-0232-9. PMID: 30569328.

95. Visvikis D, Cheze Le Rest C, Jaouen V, Hatt M. 2019; Artificial intelligence, machine (deep) learning and radio(geno)mics: definitions and nuclear medicine imaging applications. Eur J Nucl Med Mol Imaging. 46:2630–2637. DOI: 10.1007/s00259-019-04373-w. PMID: 31280350.

96. Uribe CF, Mathotaarachchi S, Gaudet V, Smith KC, Rosa-Neto P, Bénard F, et al. 2019; Machine learning in nuclear medicine: part 1-introduction. J Nucl Med. 60:451–458. DOI: 10.2967/jnumed.118.223495. PMID: 30733322.

97. Goodfellow I, Bengio Y, Courville A. 2016. Deep learning. MIT Press;Cambridge:

98. Choi H. 2018; Deep learning in nuclear medicine and molecular imaging: current perspectives and future directions. Nucl Med Mol Imaging. 52:109–118. DOI: 10.1007/s13139-017-0504-7. PMID: 29662559. PMCID: PMC5897260.

99. Weber GM, Mandl KD, Kohane IS. 2014; Finding the missing link for big biomedical data. JAMA. 311:2479–2480. DOI: 10.1001/jama.2014.4228. PMID: 24854141.

100. Chicklore S, Goh V, Siddique M, Roy A, Marsden PK, Cook GJ. 2013; Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis. Eur J Nucl Med Mol Imaging. 40:133–140. DOI: 10.1007/s00259-012-2247-0. PMID: 23064544.

101. Cook GJ, Yip C, Siddique M, Goh V, Chicklore S, Roy A, et al. 2013; Are pretreatment 18F-FDG PET tumor textural features in non-small cell lung cancer associated with response and survival after chemoradiotherapy? J Nucl Med. 54:19–26. DOI: 10.2967/jnumed.112.107375. PMID: 23204495.

102. Ganeshan B, Goh V, Mandeville HC, Ng QS, Hoskin PJ, Miles KA. 2013; Non-small cell lung cancer: histopathologic correlates for texture parameters at CT. Radiology. 266:326–336. DOI: 10.1148/radiol.12112428. PMID: 23169792.

103. Ha S, Choi H, Cheon GJ, Kang KW, Chung JK, Kim EE, et al. 2014; Autoclustering of non-small cell lung carcinoma subtypes on (18)F-FDG PET using texture analysis: a preliminary result. Nucl Med Mol Imaging. 48:278–286. DOI: 10.1007/s13139-014-0283-3. PMID: 26396632. PMCID: PMC4571663.

104. Lee HS, Oh JS, Park YS, Jang SJ, Choi IS, Ryu JS. 2016; Differentiating the grades of thymic epithelial tumor malignancy using textural features of intratumoral heterogeneity via (18)F-FDG PET/CT. Ann Nucl Med. 30:309–319. DOI: 10.1007/s12149-016-1062-2. PMID: 26868139.

105. Aerts H. 2016; Radiomics: there is more than meets the eye in medical imaging. Proc SPIE. 9785:2016SPIE.9785E..0OA. DOI: 10.1117/12.2214251.

106. Hatt M, Tixier F, Visvikis D, Cheze Le Rest C. 2017; Radiomics in PET/CT: more than meets the eye? J Nucl Med. 58:365–366. DOI: 10.2967/jnumed.116.184655. PMID: 27811126.

107. Choi H, Jin KH. Alzheimer's disease neuroimaging initiative. 2018; Predicting cognitive decline with deep learning of brain metabolism and amyloid imaging. Behav Brain Res. 344:103–109. DOI: 10.1016/j.bbr.2018.02.017. PMID: 29454006.

108. Choi H, Kim YK, Yoon EJ, Lee JY, Lee DS. Alzheimer's Disease Neuroimaging Initiative. 2020; Cognitive signature of brain FDG PET based on deep learning: domain transfer from Alzheimer's disease to Parkinson's disease. Eur J Nucl Med Mol Imaging. 47:403–412. DOI: 10.1007/s00259-019-04538-7. PMID: 31768599.

109. Choi H, Ha S, Im HJ, Paek SH, Lee DS. 2017; Refining diagnosis of Parkinson's disease with deep learning-based interpretation of dopamine transporter imaging. Neuroimage Clin. 16:586–594. DOI: 10.1016/j.nicl.2017.09.010. PMID: 28971009.

110. Ryoo HG, Choi H, Lee DS. 2020; Deep learning-based interpretation of basal/acetazolamide brain perfusion SPECT leveraging unstructured reading reports. Eur J Nucl Med Mol Imaging. 47:2186–2196. DOI: 10.1007/s00259-019-04670-4. PMID: 31912255.

111. Huang B, Chen Z, Wu PM, Ye Y, Feng ST, Wong CO, et al. 2018; Fully automated delineation of gross tumor volume for head and neck cancer on PET-CT using deep learning: a dual-center study. Contrast Media Mol Imaging. 2018:8923028. DOI: 10.1155/2018/8923028. PMID: 30473644. PMCID: PMC6220410.

112. Chen L, Shen C, Zhou Z, Maquilan G, Albuquerque K, Folkert MR, et al. 2019; Automatic PET cervical tumor segmentation by combining deep learning and anatomic prior. Phys Med Biol. 64:085019. DOI: 10.1088/1361-6560/ab0b64. PMID: 30818303. PMCID: PMC7098064.

113. Lindgren Belal S, Sadik M, Kaboteh R, Enqvist O, Ulén J, Poulsen MH, et al. 2019; Deep learning for segmentation of 49 selected bones in CT scans: first step in automated PET/CT-based 3D quantification of skeletal metastases. Eur J Radiol. 113:89–95. DOI: 10.1016/j.ejrad.2019.01.028. PMID: 30927965.

114. Park J, Bae S, Seo S, Park S, Bang JI, Han JH, et al. 2019; Measurement of glomerular filtration rate using quantitative SPECT/CT and deep-learning-based kidney segmentation. Sci Rep. 9:4223. DOI: 10.1038/s41598-019-40710-7. PMID: 30862873. PMCID: PMC6414660.

115. Wang Y, Yu B, Wang L, Zu C, Lalush DS, Lin W, et al. 2018; 3D conditional generative adversarial networks for high-quality PET image estimation at low dose. Neuroimage. 174:550–562. DOI: 10.1016/j.neuroimage.2018.03.045. PMID: 29571715. PMCID: PMC6410574.

116. Ouyang J, Chen KT, Gong E, Pauly J, Zaharchuk G. 2019; Ultra-low-dose PET reconstruction using generative adversarial network with feature matching and task-specific perceptual loss. Med Phys. 46:3555–3564. DOI: 10.1002/mp.13626. PMID: 31131901. PMCID: PMC6692211.

117. Song TA, Chowdhury SR, Yang F, Dutta J. 2020; PET image super-resolution using generative adversarial networks. Neural Netw. 125:83–91. DOI: 10.1016/j.neunet.2020.01.029. PMID: 32078963. PMCID: PMC7136141.

118. Choi H, Lee DS. Alzheimer's Disease Neuroimaging Initiative. 2018; Generation of structural MR images from amyloid PET: application to MR-less quantification. J Nucl Med. 59:1111–1117. DOI: 10.2967/jnumed.117.199414. PMID: 29217736. PMCID: PMC6910644.

119. Hwang D, Kim KY, Kang SK, Seo S, Paeng JC, Lee DS, et al. 2018; Improving the accuracy of simultaneously reconstructed activity and attenuation maps using deep learning. J Nucl Med. 59:1624–1629. DOI: 10.2967/jnumed.117.202317. PMID: 29449446.

120. Hwang D, Kang SK, Kim KY, Seo S, Paeng JC, Lee DS, et al. 2019; Generation of PET attenuation map for whole-body time-of-flight 18F-FDG PET/MRI using a deep neural network trained with simultaneously reconstructed activity and attenuation maps. J Nucl Med. 60:1183–1189. DOI: 10.2967/jnumed.118.219493. PMID: 30683763.

121. Gong K, Yang J, Kim K, El Fakhri G, Seo Y, Li Q. 2018; Attenuation correction for brain PET imaging using deep neural network based on Dixon and ZTE MR images. Phys Med Biol. 63:125011. DOI: 10.1088/1361-6560/aac763. PMID: 29790857. PMCID: PMC6031313.