Skip to main content
Hauptrubriken
CME Facharzt-Training Webinare Zeitschriften Bücher e.Medpedia Podcast
Service
Jobbörse Info & Hilfe
Fachgebiete
Anästhesiologie Allgemeinmedizin Arbeitsmedizin Augenheilkunde Chirurgie Dermatologie Gynäkologie und Geburtshilfe HNO Innere Medizin Kardiologie Neurologie Onkologie und Hämatologie Orthopädie und Unfallchirurgie Pädiatrie Pathologie Psychiatrie Radiologie Rechtsmedizin Urologie Zahnmedizin
Themen
Klimawandel und Gesundheit Neues aus dem Markt COVID-19 Praxis und Beruf Seltene Erkrankungen GOÄ & EBM Panorama
Sammlungen
Kasuistiken Algorithmen & Infografiken Blickdiagnosen Arthropedia FlapFinder (für Dermatochirurgen) Videos Kongressberichterstattung Medizin für Apothekerinnen und Apotheker Für Ärztinnen und Ärzte in Weiterbildung Für Medizinstudierende
Erweiterte Suche
Anmelden
nach oben
Nuclear Medicine and Molecular Imaging

06.02.2024 | Review

A Review of Machine Learning Approaches for Brain Positron Emission Tomography Data Analysis

verfasst von: Ke Xu, Hakmook Kang

Erschienen in: Nuclear Medicine and Molecular Imaging

Einloggen, um Zugang zu erhalten

Abstract

Positron emission tomography (PET) imaging has moved forward the development of medical diagnostics and research across various domains, including cardiology, neurology, infection detection, and oncology. The integration of machine learning (ML) algorithms into PET data analysis has further enhanced their capabilities of including disease diagnosis and classification, image segmentation, and quantitative analysis. ML algorithms empower researchers and clinicians to extract valuable insights from complex big PET datasets, which enabling automated pattern recognition, predictive health outcome modeling, and more efficient data analysis. This review explains the basic knowledge of PET imaging, statistical methods for PET image analysis, and challenges of PET data analysis. We also discussed the improvement of analysis capabilities by combining PET data with machine learning algorithms and the application of this combination in various aspects of PET image research. This review also highlights current trends and future directions in PET imaging, emphasizing the driving and critical role of machine learning and big PET image data analytics in improving diagnostic accuracy and personalized medical approaches. Integration between PET imaging will shape the future of medical diagnosis and research.
Literatur
1.
Zainab TA-S, Al-Sharify TA, Al-Sharify NT, Naser HY. A critical review on medical imaging techniques (CT and PET scans) in the medical field. IOP Conf Ser: Mater Sci Eng. 2020;870:012043.
2.
Vaquero JJ, Kinahan P. Positron emission tomography: current challenges and opportunities for technological advances in clinical and preclinical imaging systems. Annu Rev Biomed Eng. 2015;17:385–414.PubMedPubMedCentralCrossRef
3.
Townsend DW, Carney JP, Yap JT, Hall NC. PET/CT today and tomorrow. J Nucl Med. 2004;45(Suppl 1):4S-14S.PubMed
4.
Kitson SL, Cuccurullo V, Ciarmiello A, Salvo D, Mansi L. Clinical applications of positron emission tomography (PET) imaging in medicine: oncology, brain diseases and cardiology. Curr Radiopharm. 2009;2:224–53.CrossRef
5.
Sepehrizadeh T, Jong I, DeVeer M, Malhotra A. PET/MRI in paediatric disease. Eur J Radiol. 2021;144: 109987.PubMedCrossRef
6.
7.
8.
Kazakauskaite E, Zaliaduonyte-Peksiene D, Rumbinaite E, Kersulis J, Kulakiene I, Jurkevicius R. Positron emission tomography in the diagnosis and management of coronary artery disease. Medicina (Kaunas). 2018;54:47.PubMedCrossRef
9.
Santos BS, Ferreira MJ. Positron emission tomography in ischemic heart disease. Rev Port Cardiol (Engl Ed). 2019;38:599–608.PubMedCrossRef
10.
11.
12.
Fahim Ul H, Cook GJ. PET/CT in oncology. Clin Med (Lond). 2012;12:368–72.
13.
Saif MW, Tzannou I, Makrilia N, Syrigos K. Role and cost effectiveness of PET/CT in management of patients with cancer. Yale J Biol Med. 2010;83:53–65.PubMedPubMedCentral
14.
Duffy IR, Boyle AJ, Vasdev N. Improving PET imaging acquisition and analysis with machine learning: a narrative review with focus on Alzheimer’s disease and oncology. Mol Imaging. 2019;18:1536012119869070.PubMedPubMedCentralCrossRef
15.
Choi H, Ha S, Kang H, Lee H, Lee DS, Alzheimer’s Disease Neuroimaging I. Deep learning only by normal brain PET identify unheralded brain anomalies. EBioMedicine. 2019;43:447–53.PubMedPubMedCentralCrossRef
16.
Frood R, Clark M, Burton C, Tsoumpas C, Frangi AF, Gleeson F, et al. Utility of pre-treatment FDG PET/CT-derived machine learning models for outcome prediction in classical Hodgkin lymphoma. Eur Radiol. 2022;32:7237–47.PubMedPubMedCentralCrossRef
17.
Fujima N, Andreu-Arasa VC, Meibom SK, Mercier GA, Salama AR, Truong MT, Sakai O. Deep learning analysis using FDG-PET to predict treatment outcome in patients with oral cavity squamous cell carcinoma. Eur Radiol. 2020;30:6322–30.PubMedCrossRef
18.
19.
Wang S, Cao G, Wang Y, Liao S, Wang Q, Shi J, et al. Review and prospect: artificial intelligence in advanced medical imaging. Front Radiol. 2021;1: 781868.PubMedPubMedCentralCrossRef
20.
Ng JM, Azuma K, Kelley C, Pencek R, Radikova Z, Laymon C, et al. PET imaging reveals distinctive roles for different regional adipose tissue depots in systemic glucose metabolism in nonobese humans. Am J Physiol Endocrinol Metab. 2012;303:E1134–41.PubMedPubMedCentralCrossRef
21.
Ombao H, Lindquist M, Thompson W, Aston J. Handbook of neuroimaging data analysis. Boca Raton: CRC Press; 2017.
22.
Bailey DL, Maisey MN, Townsend DW, Valk PE. Positron emission tomography: Basic Sciences. London: Springer; 2005.
23.
Ziegler SI. Positron emission tomography: principles, technology, and recent developments. Nucl Phys A. 2005;752:679–87.CrossRefADS
24.
25.
26.
Boellaard R. Standards for PET image acquisition and quantitative data analysis. J Nucl Med. 2009;50(Suppl 1):11S-20S.PubMedCrossRef
27.
Kinahan PE, Fletcher JW. Positron emission tomography-computed tomography standardized uptake values in clinical practice and assessing response to therapy. Semin Ultrasound CT MR. 2010;31:496–505.PubMedPubMedCentralCrossRef
28.
29.
Subramaniam RM. Precision medicine and PET/computed tomography: challenges and implementation. PET Clin. 2017;12:1–5.PubMedCrossRef
30.
Lodge MA, Chaudhry MA, Wahl RL. Noise considerations for PET quantification using maximum and peak standardized uptake value. J Nucl Med. 2012;53:1041–7.PubMedCrossRef
31.
Dai D, Boroomand S. A review of artificial intelligence to enhance the security of big data systems: state-of-art, methodologies, applications, and challenges. Arch Comput Methods Eng. 2022;29:1291–309.CrossRef
32.
Du-Harpur X, Watt FM, Luscombe NM, Lynch MD. What is AI? Applications of artificial intelligence to dermatology. Br J Dermatol. 2020;183:423–30.PubMedPubMedCentralCrossRef
33.
El Naqa I, Murphy MJ. What Is Machine Learning? In: El Naqa I, Li R, Murphy MJ, editors. Machine learning in radiation oncology: theory and applications. Cham: Springer International Publishing; 2015. p. 3–11.CrossRef
34.
Mamdani M, Slutsky AS. Artificial intelligence in intensive care medicine. Intensive Care Med. 2021;47:147–9.PubMedCrossRef
35.
Zhou Z-H. Learnware: on the future of machine learning. Front Comp Sci. 2016;10:589–90.CrossRef
36.
Singh A, Thakur N, Sharma A, editors. A review of supervised machine learning algorithms. 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom); 2016 16–18 March 2016.
37.
Cunningham P, Cord M, Delany SJ. Supervised Learning. In: Cord M, Cunningham P, editors. Machine learning techniques for multimedia: case studies on organization and retrieval. Berlin, Heidelberg: Springer Berlin Heidelberg; 2008. p. 21–49.
38.
Ghahramani Z. Unsupervised learning. In: Bousquet O, von Luxburg U, Rätsch G, editors. Advanced lectures on machine learning: ML summer schools 2003, Canberra, Australia, February 2 - 14, 2003, Tübingen, Germany, August 4 - 16, 2003, Revised Lectures. Berlin, Heidelberg: Springer Berlin Heidelberg; 2004. p. 72–112.
39.
Hady MFA, Schwenker F. Semi-supervised learning. In: Bianchini M, Maggini M, Jain LC, editors. Handbook on neural information processing. Berlin, Heidelberg: Springer, Berlin Heidelberg; 2013. p. 215–39.CrossRef
40.
Loh WY. Classification and regression trees. WIREs Data Mining Knowledge Discovery. 2011;1:14–23.
41.
El-Dahshan E-SA, Mohsen HM, Revett K, Salem A-BM. Computer-aided diagnosis of human brain tumor through MRI: a survey and a new algorithm. Expert Syst Appl. 2014;41:5526–45.
42.
43.
Ezugwu AE, Ikotun AM, Oyelade OO, Abualigah L, Agushaka JO, Eke CI, Akinyelu AA. A comprehensive survey of clustering algorithms: state-of-the-art machine learning applications, taxonomy, challenges, and future research prospects. Eng Appl Artif Intell. 2022;110: 104743.CrossRef
44.
Zhang T, Yang B, editors. Big data dimension reduction using PCA. 2016 IEEE International Conference on Smart Cloud (SmartCloud); 2016 18–20 Nov. 2016.
45.
Wiens J, Shenoy ES. Machine learning for healthcare: on the verge of a major shift in healthcare epidemiology. Clin Infect Dis. 2018;66:149–53.PubMedCrossRef
46.
47.
Seo S, Kim SJ, Lee DS, Lee JS. Recent advances in parametric neuroreceptor mapping with dynamic PET: basic concepts and graphical analyses. Neurosci Bull. 2014;30:733–54.PubMedPubMedCentralCrossRef
48.
Muzi M, O’Sullivan F, Mankoff DA, Doot RK, Pierce LA, Kurland BF, et al. Quantitative assessment of dynamic PET imaging data in cancer imaging. Magn Reson Imaging. 2012;30:1203–15.PubMedPubMedCentralCrossRef
49.
Guo Q, Owen DR, Rabiner EA, Turkheimer FE, Gunn RN. A graphical method to compare the in vivo binding potential of PET radioligands in the absence of a reference region: application to [(1)(1)C]PBR28 and [(1)(8)F]PBR111 for TSPO imaging. J Cereb Blood Flow Metab. 2014;34:1162–8.PubMedPubMedCentralCrossRef
50.
Eary JF, Mankoff DA. Tumor metabolic rates in sarcoma using FDG PET. J Nucl Med. 1998;39:250–4.PubMed
51.
Mumcuoglu EU, Leahy RM, Cherry SR. Bayesian reconstruction of PET images: methodology and performance analysis. Phys Med Biol. 1996;41:1777–807.PubMedCrossRef
52.
Hatt M, Cheze le Rest C, Turzo A, Roux C, Visvikis D. A fuzzy locally adaptive Bayesian segmentation approach for volume determination in PET. IEEE Trans Med Imaging. 2009;28:881–93.PubMedPubMedCentralCrossRef
53.
Zhou J, Coatrieux JL, Bousse A, Shu H, Luo L. A Bayesian MAP-EM algorithm for PET image reconstruction using wavelet transform. IEEE Trans Nucl Sci. 2007;54:1660–9.CrossRefADS
54.
Ouyang X, Wong WH, Johnson VE, Hu X, Chen CT. Incorporation of correlated structural images in PET image reconstruction. IEEE Trans Med Imaging. 1994;13:627–40.PubMedCrossRef
55.
Buvat I. A non-parametric bootstrap approach for analysing the statistical properties of SPECT and PET images. Phys Med Biol. 2002;47:1761–75.PubMedCrossRef
56.
Karakatsanis NA, Zhou Y, Lodge MA, Casey ME, Wahl RL, Rahmim A, editors. Quantitative whole-body parametric PET imaging incorporating a generalized Patlak model. 2013 IEEE Nuclear Science Symposium and Medical Imaging Conference (2013 NSS/MIC); 2013 27 Oct.-2 Nov. 2013.
57.
Zhang X, Xiong Z, Wu Y, Cai W, Tseng JR, Gambhir SS, Chen X. Quantitative PET imaging of tumor integrin alphavbeta3 expression with 18F-FRGD2. J Nucl Med. 2006;47:113–21.PubMed
58.
Garcia Vicente AM, Soriano Castrejon A, Cruz Mora MA, Gonzalez Ageitos A, Munoz Sanchez Mdel M, Leon Martin A et al. Semi-quantitative lymph node assessment of (18)F-FDG PET/CT in locally advanced breast cancer: correlation with biological prognostic factors. Eur J Nucl Med Mol Imaging. 2013;40:72–9.
59.
Liu G, Yu H, Shi D, Hu P, Hu Y, Tan H, et al. Short-time total-body dynamic PET imaging performance in quantifying the kinetic metrics of (18)F-FDG in healthy volunteers. Eur J Nucl Med Mol Imaging. 2022;49:2493–503.PubMedCrossRef
60.
Foldvary N, Lee N, Hanson MW, Coleman RE, Hulette CM, Friedman AH, et al. Correlation of hippocampal neuronal density and FDG-PET in mesial temporal lobe epilepsy. Epilepsia. 1999;40:26–9.PubMedCrossRef
61.
Kumar R, Chauhan A, Zhuang H, Chandra P, Schnall M, Alavi A. Clinicopathologic factors associated with false negative FDG-PET in primary breast cancer. Breast Cancer Res Treat. 2006;98:267–74.PubMedCrossRef
62.
Khalaf M, Abdel-Nabi H, Baker J, Shao Y, Lamonica D, Gona J. Relation between nodule size and 18F-FDG-PET SUV for malignant and benign pulmonary nodules. J Hematol Oncol. 2008;1:13.PubMedPubMedCentralCrossRef
63.
Shokouhi S, Claassen D, Kang H, Ding Z, Rogers B, Mishra A, et al. Longitudinal progression of cognitive decline correlates with changes in the spatial pattern of brain 18F-FDG PET. J Nucl Med. 2013;54:1564–9.PubMedCrossRef
64.
Pyka T, Gempt J, Hiob D, Ringel F, Schlegel J, Bette S, et al. Textural analysis of pre-therapeutic [18F]-FET-PET and its correlation with tumor grade and patient survival in high-grade gliomas. Eur J Nucl Med Mol Imaging. 2016;43:133–41.PubMedCrossRef
65.
66.
Chen DL, Cheriyan J, Chilvers ER, Choudhury G, Coello C, Connell M, et al. Quantification of lung PET images: challenges and opportunities. J Nucl Med. 2017;58:201–7.PubMedPubMedCentralCrossRef
67.
Defrise M, Kinahan PE, Michel CJ. Image reconstruction algorithms in PET. In: Bailey DL, Townsend DW, Valk PE, Maisey MN, editors. Positron emission tomography: basic sciences. London: Springer, London; 2005. p. 63–91.CrossRef
68.
Townsend DW. Physical principles and technology of clinical PET imaging. Ann Acad Med Singap. 2004;33:133–45.PubMedCrossRef
69.
Klyuzhin IS, Cheng JC, Bevington C, Sossi V. Use of a tracer-specific deep artificial neural net to denoise dynamic PET images. IEEE Trans Med Imaging. 2020;39:366–76.PubMedCrossRef
70.
Rousset OG, Ma Y, Evans AC. Correction for partial volume effects in PET: principle and validation. J Nucl Med. 1998;39:904–11.PubMed
71.
Thomas BA, Erlandsson K, Modat M, Thurfjell L, Vandenberghe R, Ourselin S, Hutton BF. The importance of appropriate partial volume correction for PET quantification in Alzheimer’s disease. Eur J Nucl Med Mol Imaging. 2011;38:1104–19.PubMedCrossRef
72.
Armanious K, Hepp T, Kustner T, Dittmann H, Nikolaou K, La Fougere C, et al. Independent attenuation correction of whole body [(18)F]FDG-PET using a deep learning approach with Generative Adversarial Networks. EJNMMI Res. 2020;10:53.PubMedPubMedCentralCrossRef
73.
Levin CS, Hoffman EJ. Calculation of positron range and its effect on the fundamental limit of positron emission tomography system spatial resolution. Phys Med Biol. 1999;44:781–99.PubMedCrossRef
74.
75.
Kennedy JA, Israel O, Frenkel A, Bar-Shalom R, Azhari H. Super-resolution in PET imaging. IEEE Trans Med Imaging. 2006;25:137–47.PubMedCrossRef
76.
Strauss LG, Clorius JH, Schlag P, Lehner B, Kimmig B, Engenhart R, et al. Recurrence of colorectal tumors: PET evaluation. Radiology. 1989;170:329–32.PubMedCrossRef
77.
Cherry SR, Jones T, Karp JS, Qi J, Moses WW, Badawi RD. Total-body PET: maximizing sensitivity to create new opportunities for clinical research and patient care. J Nucl Med. 2018;59:3–12.PubMedPubMedCentralCrossRef
78.
Song AK, Hett K, Eisma JJ, McKnight CD, Elenberger J, Stark AJ, et al. Parasagittal dural space hypertrophy and amyloid-beta deposition in Alzheimer’s disease. Brain Commun. 2023;5:fcad128.PubMedPubMedCentralCrossRef
79.
Campbell DL, Kang H, Shokouhi S. Application of Haralick texture features in brain [(18)F]-florbetapir positron emission tomography without reference region normalization. Clin Interv Aging. 2017;12:2077–86.PubMedPubMedCentralCrossRef
80.
Ennis GE, Betthauser TJ, Koscik RL, Chin NA, Christian BT, Asthana S, et al. The relationship of insulin resistance and diabetes to tau PET SUVR in middle-aged to older adults. Alzheimers Res Ther. 2023;15:55.PubMedPubMedCentralCrossRef
81.
Hwang D, Kang SK, Kim KY, Choi H, Lee JS. Comparison of deep learning-based emission-only attenuation correction methods for positron emission tomography. Eur J Nucl Med Mol Imaging. 2022;49(6):1833–42.PubMedCrossRef
82.
Choi H, Lee DS, Alzheimer’s Disease Neuroimaging I. Generation of structural MR images from amyloid PET: application to MR-less quantification. J Nucl Med. 2018;59(7):1111–7.PubMedPubMedCentralCrossRef
83.
Lee J, Ha S, Kim REY, Lee M, Kim D, Lim HK. Development of amyloid PET analysis pipeline using deep learning-based brain MRI segmentation-a comparative validation study. Diagnostics (Basel). 2022;12(3):623.PubMedPubMedCentralCrossRef
84.
Kim JY, Suh HY, Ryoo HG, Oh D, Choi H, Paeng JC, et al. Amyloid PET quantification via end-to-end training of a deep learning. Nucl Med Mol Imaging. 2019;53(5):340–8.PubMedPubMedCentralCrossRef
85.
Duchesnay E, Cachia A, Boddaert N, Chabane N, Mangin JF, Martinot JL, et al. Feature selection and classification of imbalanced datasets: application to PET images of children with autistic spectrum disorders. Neuroimage. 2011;57:1003–14.PubMedCrossRef
86.
Kocher M, Ruge MI, Galldiks N, Lohmann P. Applications of radiomics and machine learning for radiotherapy of malignant brain tumors. Strahlenther Onkol. 2020;196:856–67.PubMedPubMedCentralCrossRef
87.
Ding Y, Sohn JH, Kawczynski MG, Trivedi H, Harnish R, Jenkins NW, et al. A Deep Learning model to predict a diagnosis of Alzheimer disease by using. Radiology. 2019;290:456–64.PubMedCrossRef
88.
Romeo V, Clauser P, Rasul S, Kapetas P, Gibbs P, Baltzer PAT, et al. AI-enhanced simultaneous multiparametric 18F-FDG PET/MRI for accurate breast cancer diagnosis. Eur J Nucl Med Mol Imaging. 2022;49:596–608.PubMedCrossRef
89.
Miller RJH, Huang C, Liang JX, Slomka PJ. Artificial intelligence for disease diagnosis and risk prediction in nuclear cardiology. J Nucl Cardiol. 2022;29:1754–62.PubMedCrossRef
90.
Cheebsumon P, Yaqub M, van Velden FH, Hoekstra OS, Lammertsma AA, Boellaard R. Impact of [(1)(8)F]FDG PET imaging parameters on automatic tumour delineation: need for improved tumour delineation methodology. Eur J Nucl Med Mol Imaging. 2011;38:2136–44.PubMedPubMedCentralCrossRef
91.
Li L, Zhao X, Lu W, Tan S. Deep Learning for variational multimodality tumor segmentation in PET/CT. Neurocomputing (Amst). 2020;392:277–95.PubMedCrossRef
92.
Sahiner B, Pezeshk A, Hadjiiski LM, Wang X, Drukker K, Cha KH, et al. Deep learning in medical imaging and radiation therapy. Med Phys. 2019;46:e1–36.PubMedCrossRef
93.
Mecheter I, Alic L, Abbod M, Amira A, Ji J. MR Image-based attenuation correction of brain PET imaging: review of literature on machine learning approaches for segmentation. J Digit Imaging. 2020;33:1224–41.PubMedPubMedCentralCrossRef
94.
Chen L, Shen C, Zhou Z, Maquilan G, Albuquerque K, Folkert MR, Wang J. Automatic PET cervical tumor segmentation by combining deep learning and anatomic prior. Phys Med Biol. 2019;64: 085019.PubMedPubMedCentralCrossRef
95.
Cui R, Chen Z, Wu J, Tan Y, Yu G. A multiprocessing scheme for PET image pre-screening, noise reduction, segmentation and lesion partitioning. IEEE J Biomed Health Inform. 2021;25:1699–711.PubMedCrossRef
96.
Matthews DC, Lerman H, Lukic A, Andrews RD, Mirelman A, Wernick MN, et al. FDG PET Parkinson’s disease-related pattern as a biomarker for clinical trials in early stage disease. Neuroimage Clin. 2018;20:572–9.PubMedPubMedCentralCrossRef
97.
Pedersen F, Bergstrom M, Bengtsson E, Langstrom B. Principal component analysis of dynamic positron emission tomography images. Eur J Nucl Med. 1994;21(12):1285–92.PubMedCrossRef
98.
Li Y, Yao Z, Yu Y, Zou Y, Fu Y, Hu B, Alzheimer’s Disease Neuroimaging I. Brain network alterations in individuals with and without mild cognitive impairment: parallel independent component analysis of AV1451 and AV45 positron emission tomography. BMC Psychiatry. 2019;19(1):165.PubMedPubMedCentralCrossRef
99.
Ji H, Lafata K, Mowery Y, Brizel D, Bertozzi AL, Yin FF, Wang C. Post-radiotherapy PET Image outcome prediction by deep learning under biological model guidance: a feasibility study of oropharyngeal cancer application. Front Oncol. 2022;12: 895544.PubMedPubMedCentralCrossRef
100.
Kang SH, Kim J, Kim JP, Cho SH, Choe YS, Jang H, et al. Harmonisation of PET imaging features with different amyloid ligands using machine learning-based classifier. Eur J Nucl Med Mol Imaging. 2021;49:321–30.PubMedCrossRef
101.
Choi H, Jin KH, Alzheimer’s Disease Neuroimaging I. Predicting cognitive decline with deep learning of brain metabolism and amyloid imaging. Behav Brain Res. 2018;344:103–9.PubMedCrossRef
102.
103.
Sanaat A, Mirsadeghi E, Razeghi B, Ginovart N, Zaidi H. Fast dynamic brain PET imaging using stochastic variational prediction for recurrent frame generation. Med Phys. 2021;48:5059–71.PubMedCrossRef
104.
Cui J, Gong K, Guo N, Wu C, Meng X, Kim K, et al. PET image denoising using unsupervised deep learning. Eur J Nucl Med Mol Imaging. 2019;46:2780–9.PubMedPubMedCentralCrossRef
105.
Choi H. Deep learning in nuclear medicine and molecular imaging: current perspectives and future directions. Nucl Med Mol Imaging. 2018;52:109–18.PubMedCrossRef
106.
Zheng X, Yao Z, Huang Y, Yu Y, Wang Y, Liu Y, et al. Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer. Nat Commun. 2020;11:1236.PubMedPubMedCentralCrossRefADS
107.
Avanzo M, Wei L, Stancanello J, Vallieres M, Rao A, Morin O, et al. Machine and deep learning methods for radiomics. Med Phys. 2020;47:e185–202.PubMedCrossRef
108.
Wang T, Lei Y, Fu Y, Curran WJ, Liu T, Nye JA, Yang X. Machine learning in quantitative PET: a review of attenuation correction and low-count image reconstruction methods. Phys Med. 2020;76:294–306.PubMedPubMedCentralCrossRef
109.
Lindgren Belal S, Sadik M, Kaboteh R, Enqvist O, Ulen J, Poulsen MH, et al. 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. 2019;113:89–95.PubMedCrossRef
110.
Bar-Shalom R, Valdivia AY, Blaufox MD. PET imaging in oncology. Semin Nucl Med. 2000;30:150–85.PubMedCrossRef
111.
Domingues I, Pereira G, Martins P, Duarte H, Santos J, Abreu PH. Using deep learning techniques in medical imaging: a systematic review of applications on CT and PET. Artif Intell Rev. 2020;53:4093–160.CrossRef
112.
Seifert R, Weber M, Kocakavuk E, Rischpler C, Kersting D. Artificial intelligence and machine learning in nuclear medicine: future perspectives. Semin Nucl Med. 2021;51:170–7.PubMedCrossRef
113.
Juarez-Orozco LE, Martinez-Manzanera O, van der Zant FM, Knol RJJ, Knuuti J. Deep learning in quantitative PET myocardial perfusion imaging: a study on cardiovascular event prediction. JACC Cardiovasc Imaging. 2020;13:180–2.PubMedCrossRef
114.
Lodha P, Talele A, Degaonkar K, editors. Diagnosis of Alzheimer’s disease using machine learning. 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA); 2018 16–18 Aug. 2018.
115.
Brix G, Lechel U, Glatting G, Ziegler SI, Munzing W, Muller SP, Beyer T. Radiation exposure of patients undergoing whole-body dual-modality 18F-FDG PET/CT examinations. J Nucl Med. 2005;46:608–13.PubMed
Metadaten
Titel
A Review of Machine Learning Approaches for Brain Positron Emission Tomography Data Analysis
verfasst von
Ke Xu
Hakmook Kang
Publikationsdatum
06.02.2024
Verlag
Springer Nature Singapore
Erschienen in
Nuclear Medicine and Molecular Imaging
Print ISSN: 1869-3474
Elektronische ISSN: 1869-3482
DOI
https://doi.org/10.1007/s13139-024-00845-6