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Treatment Planning

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Hybrid PET/MR Neuroimaging

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Abstract

Pre-surgical and pre-treatment planning is a crucial step toward maximizing the patient’s chances of a successful outcome. The rational use of advanced imaging techniques will help the accurate targeting of the neurological pathology and minimize potential collateral damage to surrounding normal brain parenchyma. This chapter aims to summarize our current understanding of available techniques and help the clinician use them rationally.

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References

  1. Zhang H, Feng Y, Cheng L, Liu J, Li H, Jiang H. Application of diffusion tensor tractography in the surgical treatment of brain tumors located in functional areas. Oncol Lett. 2020;19(1):615–22. https://doi.org/10.3892/ol.2019.11167.

    Article  PubMed  Google Scholar 

  2. Yu Q, Lin K, Liu Y, Li X. Clinical uses of diffusion tensor imaging fiber tracking merged neuronavigation with lesions adjacent to corticospinal tract : a retrospective cohort study. J Korean Neurosurg Soc. 2020;63(2):248–60. https://doi.org/10.3340/jkns.2019.0046.

    Article  PubMed  Google Scholar 

  3. Rahmat R, Saednia K, Haji Hosseini Khani MR, Rahmati M, Jena R, Price SJ. Multi-scale segmentation in GBM treatment using diffusion tensor imaging. Comput Biol Med. 2020;123:103815. https://doi.org/10.1016/j.compbiomed.2020.103815.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Henderson F, Abdullah KG, Verma R, Brem S. Tractography and the connectome in neurosurgical treatment of gliomas: the premise, the progress, and the potential. Neurosurg Focus. 2020;48(2):E6. https://doi.org/10.3171/2019.11.FOCUS19785.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Panesar SS, Abhinav K, Yeh FC, Jacquesson T, Collins M, Fernandez-Miranda J. Tractography for surgical neuro-oncology planning: towards a gold standard. Neurotherapeutics. 2019;16(1):36–51. https://doi.org/10.1007/s13311-018-00697-x.

    Article  PubMed  Google Scholar 

  6. Soni N, Mehrotra A, Behari S, Kumar S, Gupta N. Diffusion-tensor imaging and tractography application in pre-operative planning of intra-axial brain lesions. Cureus. 2017;9(10):e1739. https://doi.org/10.7759/cureus.1739.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Caverzasi E, Hervey-Jumper SL, Jordan KM, Lobach IV, Li J, Panara V, et al. Identifying pre-operative language tracts and predicting postoperative functional recovery using HARDI q-ball fiber tractography in patients with gliomas. J Neurosurg. 2016;125(1):33–45. https://doi.org/10.3171/2015.6.JNS142203.

    Article  PubMed  Google Scholar 

  8. Voets NL, Bartsch A, Plaha P. Brain white matter fibre tracts: a review of functional neuro-oncological relevance. J Neurol Neurosurg Psychiatry. 2017;88(12):1017–25. https://doi.org/10.1136/jnnp-2017-316170.

    Article  PubMed  Google Scholar 

  9. Masjoodi S, Hashemi H, Oghabian MA, Sharifi G. Differentiation of edematous, tumoral and normal areas of brain using diffusion tensor and neurite orientation dispersion and density imaging. J Biomed Phys Eng. 2018;8(3):251–60.

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Morrison MA, Churchill NW, Cusimano MD, Schweizer TA, Das S, Graham SJ. Reliability of task-based fMRI for preoperative planning: a test-retest study in brain tumor patients and healthy controls. PLoS One. 2016;11(2):e0149547. https://doi.org/10.1371/journal.pone.0149547.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Voets NL, Plaha P, Parker Jones O, Pretorius P, Bartsch A. Presurgical localization of the primary sensorimotor cortex in gliomas : when is resting state FMRI beneficial and sufficient? Clin Neuroradiol. 2020. https://doi.org/10.1007/s00062-020-00879-1.

  12. Sparacia G, Parla G, Cannella R, Perri A, Lo Re V, Mamone G, et al. Resting-state functional magnetic resonance imaging for brain tumor surgical planning: feasibility in clinical setting. World Neurosurg. 2019;131:356–63. https://doi.org/10.1016/j.wneu.2019.07.022.

    Article  PubMed  Google Scholar 

  13. Metwali H, Samii A. Seed-based connectivity analysis of resting-state fMRI in patients with brain tumors: a feasibility study. World Neurosurg. 2019;128:e165–e76. https://doi.org/10.1016/j.wneu.2019.04.073.

    Article  PubMed  Google Scholar 

  14. Lee MH, Miller-Thomas MM, Benzinger TL, Marcus DS, Hacker CD, Leuthardt EC, et al. Clinical resting-state fMRI in the pre-operative setting: are we ready for prime time? Top Magn Reson Imaging. 2016;25(1):11–8. https://doi.org/10.1097/RMR.0000000000000075.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Villanueva-Meyer JE, Mabray MC, Cha S. Current clinical brain tumor imaging. Neurosurgery. 2017;81(3):397–415. https://doi.org/10.1093/neuros/nyx103.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Brahimaj BC, Kochanski RB, Pearce JJ, Guryildirim M, Gerard CS, Kocak M, et al. Structural and functional imaging in glioma management. Neurosurgery. 2020. https://doi.org/10.1093/neuros/nyaa360.

  17. Verburg N, de Witt Hamer PC. State-of-the-art imaging for glioma surgery. Neurosurg Rev. 2020. https://doi.org/10.1007/s10143-020-01337-9.

  18. Nabavizadeh SA, Ware JB, Wolf RL. Emerging techniques in imaging of glioma microenvironment. Top Magn Reson Imaging. 2020;29(2):103–14. https://doi.org/10.1097/RMR.0000000000000232.

    Article  PubMed  Google Scholar 

  19. Munshi A, Ganesh T, Gupta RK, Vaishya S, Patir R, Sarkar B, et al. Perfusion magnetic resonance imaging in contouring of glioblastoma patients: preliminary experience from a single institution. J Cancer Res Ther. 2020;16(6):1488–94. https://doi.org/10.4103/jcrt.JCRT_1151_19.

    Article  PubMed  Google Scholar 

  20. Payne GS. Clinical applications of in vivo magnetic resonance spectroscopy in oncology. Phys Med Biol. 2018;63(21):21TR02. https://doi.org/10.1088/1361-6560/aae61e.

    Article  CAS  PubMed  Google Scholar 

  21. Verburg N, Hoefnagels FWA, Barkhof F, Boellaard R, Goldman S, Guo J, et al. Diagnostic accuracy of neuroimaging to delineate diffuse gliomas within the brain: a meta-analysis. AJNR Am J Neuroradiol. 2017;38(10):1884–91. https://doi.org/10.3174/ajnr.A5368.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Chen R, Ravindra VM, Cohen AL, Jensen RL, Salzman KL, Prescot AP, et al. Molecular features assisting in diagnosis, surgery, and treatment decision making in low-grade gliomas. Neurosurg Focus. 2015;38(3):E2. https://doi.org/10.3171/2015.1.FOCUS14745.

    Article  PubMed  Google Scholar 

  23. Yano H, Shinoda J, Iwama T. Clinical utility of positron emission tomography in patients with malignant glioma. Neurol Med Chir (Tokyo). 2017;57(7):312–20. https://doi.org/10.2176/nmc.ra.2016-0312.

    Article  Google Scholar 

  24. Stegmayr C, Stoffels G, Filss C, Heinzel A, Lohmann P, Willuweit A, et al. Current trends in the use of O-(2-[(18)F]fluoroethyl)-L-tyrosine ([(18)F]FET) in neurooncology. Nucl Med Biol. 2020. https://doi.org/10.1016/j.nucmedbio.2020.02.006.

  25. Stegmayr C, Willuweit A, Lohmann P, Langen KJ. O-(2-[18F]-Fluoroethyl)-L-tyrosine (FET) in neurooncology: a review of experimental results. Curr Radiopharm. 2019;12(3):201–10. https://doi.org/10.2174/1874471012666190111111046.

    Article  CAS  PubMed  Google Scholar 

  26. Suchorska B, Albert NL, Tonn JC. Role of amino-tracer PET for decision-making in neuro-oncology. Curr Opin Neurol. 2018;31(6):720–6. https://doi.org/10.1097/WCO.0000000000000616.

    Article  PubMed  Google Scholar 

  27. Miyake K, Ogawa D, Okada M, Hatakeyama T, Tamiya T. Usefulness of positron emission tomographic studies for gliomas. Neurol Med Chir (Tokyo). 2016;56(7):396–408. https://doi.org/10.2176/nmc.ra.2015-0305.

    Article  Google Scholar 

  28. Widhalm G, Traub-Weidinger T, Hainfellner JA, Bienkowski M, Wolfsberger S, Czech T. Bioimaging and surgery of brain tumors. Handb Clin Neurol. 2017;145:535–45. https://doi.org/10.1016/B978-0-12-802395-2.00033-X.

    Article  PubMed  Google Scholar 

  29. Fink JR, Muzi M, Peck M, Krohn KA. Multimodality brain tumor imaging: MR imaging, PET, and PET/MR imaging. J Nucl Med. 2015;56(10):1554–61. https://doi.org/10.2967/jnumed.113.131516.

    Article  CAS  PubMed  Google Scholar 

  30. Tsiouris S, Bougias C, Fotopoulos A. Principles and current trends in the correlative evaluation of glioma with advanced MRI techniques and PET. Hell J Nucl Med. 2019;22(3):206–19.

    PubMed  Google Scholar 

  31. Abouzied MM, Crawford ES, Nabi HA. 18F-FDG imaging: pitfalls and artifacts. J Nucl Med Technol. 2005;33(3):145–55; quiz 62-3.

    PubMed  Google Scholar 

  32. Hoberuck S, Michler E, Zophel K, Platzek I, Kotzerke J, Brogsitter C. Brain metastases of a neuroendocrine tumor visualized by 68Ga-DOTATATE PET/CT. Clin Nucl Med. 2019;44(1):50–2. https://doi.org/10.1097/RLU.0000000000002341.

    Article  PubMed  Google Scholar 

  33. Nguyen NC, Moon CH, Muthukrishnan A, Furlan A. 68Ga-DOTATATE PET/MRI for neuroendocrine tumors: a pictorial review. Clin Nucl Med. 2020;45(9):e406–e10. https://doi.org/10.1097/RLU.0000000000003085.

    Article  PubMed  Google Scholar 

  34. Ivanidze J, Roytman M, Lin E, Magge RS, Pisapia DJ, Liechty B, et al. Gallium-68 DOTATATE PET in the evaluation of intracranial meningiomas. J Neuroimaging. 2019;29(5):650–6. https://doi.org/10.1111/jon.12632.

    Article  PubMed  Google Scholar 

  35. Dadgar H, Norouzbeigi N, Ahmadzadehfar H, Assadi M. 68Ga-DOTATATE and 18F-FDG PET/CT for the Management of Esthesioneuroblastoma of the sphenoclival region. Clin Nucl Med. 2020;45(8):e363–e4. https://doi.org/10.1097/RLU.0000000000003133.

    Article  PubMed  Google Scholar 

  36. Xiao J, Zhu Z, Zhong D, Ma W, Wang R. Improvement in diagnosis of metastatic pituitary carcinoma by 68Ga DOTATATE PET/CT. Clin Nucl Med. 2015;40(2):e129–31. https://doi.org/10.1097/RLU.0000000000000462.

    Article  PubMed  Google Scholar 

  37. Telli T, Lay Ergun E, Volkan Salanci B, Ozgen KP. The complementary role of 68Ga-DOTATATE PET/CT in neuroblastoma. Clin Nucl Med. 2020;45(4):326–9. https://doi.org/10.1097/RLU.0000000000002961.

    Article  PubMed  Google Scholar 

  38. Ito K, Matsuda H, Kubota K. Imaging spectrum and pitfalls of (11)C-methionine positron emission tomography in a series of patients with intracranial lesions. Korean J Radiol. 2016;17(3):424–34. https://doi.org/10.3348/kjr.2016.17.3.424.

    Article  PubMed  PubMed Central  Google Scholar 

  39. de Zwart PL, van Dijken BRJ, Holtman GA, Stormezand GN, Dierckx R, Jan van Laar P, et al. Diagnostic accuracy of PET tracers for the differentiation of tumor progression from treatment-related changes in high-grade glioma: a systematic review and metaanalysis. J Nucl Med. 2020;61(4):498–504. https://doi.org/10.2967/jnumed.119.233809.

    Article  CAS  PubMed  Google Scholar 

  40. Jacobs A. Amino acid uptake in ischemically compromised brain tissue. Stroke. 1995;26(10):1859–66. https://doi.org/10.1161/01.str.26.10.1859.

    Article  CAS  PubMed  Google Scholar 

  41. Becherer A, Karanikas G, Szabo M, Zettinig G, Asenbaum S, Marosi C, et al. Brain tumour imaging with PET: a comparison between [18F]fluorodopa and [11C]methionine. Eur J Nucl Med Mol Imaging. 2003;30(11):1561–7. https://doi.org/10.1007/s00259-003-1259-1.

    Article  CAS  PubMed  Google Scholar 

  42. Fuenfgeld B, Machler P, Fischer DR, Esposito G, Rushing EJ, Kaufmann PA, et al. Reference values of physiological 18F-FET uptake: implications for brain tumor discrimination. PLoS One. 2020;15(4):e0230618. https://doi.org/10.1371/journal.pone.0230618.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Chondrogiannis S, Marzola MC, Al-Nahhas A, Venkatanarayana TD, Mazza A, Opocher G, et al. Normal biodistribution pattern and physiologic variants of 18F-DOPA PET imaging. Nucl Med Commun. 2013;34(12):1141–9. https://doi.org/10.1097/MNM.0000000000000008.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Fernandez P, Zanotti-Fregonara P, Eimer S, Gimbert E, Monteil P, Penchet G, et al. Combining 3'-Deoxy-3′-[18F] fluorothymidine and MRI increases the sensitivity of glioma volume detection. Nucl Med Commun. 2019;40(10):1066–71. https://doi.org/10.1097/MNM.0000000000001056.

    Article  CAS  PubMed  Google Scholar 

  45. Ferdova E, Ferda J, Baxa J, Tupy R, Mracek J, Topolcan O, et al. Assessment of grading in newly-diagnosed glioma using 18F-fluorothymidine PET/CT. Anticancer Res. 2015;35(2):955–9.

    CAS  PubMed  Google Scholar 

  46. Tripathi M, Sharma R, D’Souza M, Jaimini A, Panwar P, Varshney R, et al. Comparative evaluation of F-18 FDOPA, F-18 FDG, and F-18 FLT-PET/CT for metabolic imaging of low grade gliomas. Clin Nucl Med. 2009;34(12):878–83. https://doi.org/10.1097/RLU.0b013e3181becfe0.

    Article  PubMed  Google Scholar 

  47. Wei Y, Zhao W, Huang Y, Yu Q, Zhu S, Wang S, et al. A comparative study of noninvasive hypoxia imaging with 18F-fluoroerythronitroimidazole and 18F-fluoromisonidazole PET/CT in patients with lung cancer. PLoS One. 2016;11(6):e0157606. https://doi.org/10.1371/journal.pone.0157606.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Mahvash M, Boettcher I, Petridis AK, Besharati TL. Image guided surgery versus conventional brain tumor and craniotomy localization. J Neurosurg Sci. 2017;61(1):8–13. https://doi.org/10.23736/S0390-5616.16.03142-8.

    Article  PubMed  Google Scholar 

  49. Stopa BM, Senders JT, Broekman MLD, Vangel M, Golby AJ. Pre-operative functional MRI use in neurooncology patients: a clinician survey. Neurosurg Focus. 2020;48(2):E11. https://doi.org/10.3171/2019.11.FOCUS19779.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Vysotski S, Madura C, Swan B, Holdsworth R, Lin Y, Rio AMD, et al. Preoperative FMRI associated with decreased mortality and morbidity in brain tumor patients. Interdiscip Neurosurg. 2018;13:40–5. https://doi.org/10.1016/j.inat.2018.02.001.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Meyer EJ, Gaggl W, Gilloon B, Swan B, Greenstein M, Voss J, et al. The impact of intracranial tumor proximity to white matter tracts on morbidity and mortality: a retrospective diffusion tensor imaging study. Neurosurgery. 2017;80(2):193–200. https://doi.org/10.1093/neuros/nyw040.

    Article  PubMed  Google Scholar 

  52. Lorenzen A, Groeschel S, Ernemann U, Wilke M, Schuhmann MU. Role of pre-surgical functional MRI and diffusion MR tractography in pediatric low-grade brain tumor surgery: a single-center study. Childs Nerv Syst. 2018;34(11):2241–8. https://doi.org/10.1007/s00381-018-3828-4.

    Article  PubMed  Google Scholar 

  53. Dubey A, Kataria R, Sinha VD. Role of diffusion tensor imaging in brain tumor surgery. Asian J Neurosurg. 2018;13(2):302–6. https://doi.org/10.4103/ajns.AJNS_226_16.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Brennan NP, Peck KK, Holodny A. Language mapping using fMRI and direct cortical stimulation for brain tumor surgery: the good, the bad, and the questionable. Top Magn Reson Imaging. 2016;25(1):1–10. https://doi.org/10.1097/RMR.0000000000000074.

    Article  PubMed  PubMed Central  Google Scholar 

  55. D’Andrea G, Trillo G, Picotti V, Raco A. Functional magnetic resonance imaging (fMRI), pre-intraoperative tractography in neurosurgery: the experience of Sant’ Andrea Rome University Hospital. Acta Neurochir Suppl. 2017;124:241–50. https://doi.org/10.1007/978-3-319-39546-3_36.

    Article  PubMed  Google Scholar 

  56. Zhang J, Zhuang DX, Yao CJ, Lin CP, Wang TL, Qin ZY, et al. Metabolic approach for tumor delineation in glioma surgery: 3D MR spectroscopy image-guided resection. J Neurosurg. 2016;124(6):1585–93. https://doi.org/10.3171/2015.6.JNS142651.

    Article  PubMed  Google Scholar 

  57. Grunert M, Kassubek R, Danz B, Klemenz B, Hasslacher S, Stroh S, et al. Radiation and brain tumors: an overview. Crit Rev Oncog. 2018;23(1–2):119–38. https://doi.org/10.1615/CritRevOncog.2018025927.

    Article  PubMed  Google Scholar 

  58. Ajithkumar T, Horan G, Padovani L, Thorp N, Timmermann B, Alapetite C, et al. SIOPE - brain tumor group consensus guideline on craniospinal target volume delineation for high-precision radiotherapy. Radiother Oncol. 2018;128(2):192–7. https://doi.org/10.1016/j.radonc.2018.04.016.

    Article  PubMed  Google Scholar 

  59. Cordova JS, Kandula S, Gurbani S, Zhong J, Tejani M, Kayode O, et al. Simulating the effect of spectroscopic MRI as a metric for radiation therapy planning in patients with glioblastoma. Tomography. 2016;2(4):366–73. https://doi.org/10.18383/j.tom.2016.00187.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Cordova JS, Shu HK, Liang Z, Gurbani SS, Cooper LA, Holder CA, et al. Whole-brain spectroscopic MRI biomarkers identify infiltrating margins in glioblastoma patients. Neuro Oncol. 2016;18(8):1180–9. https://doi.org/10.1093/neuonc/now036.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Jafari-Khouzani K, Loebel F, Bogner W, Rapalino O, Gonzalez GR, Gerstner E, et al. Volumetric relationship between 2-hydroxyglutarate and FLAIR hyperintensity has potential implications for radiotherapy planning of mutant IDH glioma patients. Neuro Oncol. 2016;18(11):1569–78. https://doi.org/10.1093/neuonc/now100.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Gurbani S, Weinberg B, Cooper L, Mellon E, Schreibmann E, Sheriff S, et al. The Brain Imaging Collaboration Suite (BrICS): a cloud platform for integrating whole-brain spectroscopic MRI into the radiation therapy planning workflow. Tomography. 2019;5(1):184–91. https://doi.org/10.18383/j.tom.2018.00028.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Rahmat R, Brochu F, Li C, Sinha R, Price SJ, Jena R. Semi-automated construction of patient individualised clinical target volumes for radiotherapy treatment of glioblastoma utilising diffusion tensor decomposition maps. Br J Radiol. 2020;93(1108):20190441. https://doi.org/10.1259/bjr.20190441.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Hathout L, Patel V. Estimating subthreshold tumor on MRI using a 3D-DTI growth model for GBM: an adjunct to radiation therapy planning. Oncol Rep. 2016;36(2):696–704. https://doi.org/10.3892/or.2016.4878.

    Article  PubMed  Google Scholar 

  65. Duffau H. Why brain radiation therapy should take account of the individual structural and functional connectivity: toward an irradiation “a la carte”. Crit Rev Oncol Hematol. 2020;154:103073. https://doi.org/10.1016/j.critrevonc.2020.103073.

    Article  PubMed  Google Scholar 

  66. Yahya N, Manan HA. Utilisation of diffusion tensor imaging in intracranial radiotherapy and radiosurgery planning for white matter dose optimization: a systematic review. World Neurosurg. 2019;130:e188–e98. https://doi.org/10.1016/j.wneu.2019.06.027.

    Article  PubMed  Google Scholar 

  67. Scranton RA, Hsiao KY, Sadrameli SS, Wang HC, Thong Y, Garcia Luzardo P, et al. Combinatorial anatomic and functional neural tract mapping for stereotactic radiosurgery planning. Cureus. 2019;11(11):e6161. https://doi.org/10.7759/cureus.6161.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Liu F, Yadav P, Baschnagel AM, McMillan AB. MR-based treatment planning in radiation therapy using a deep learning approach. J Appl Clin Med Phys. 2019;20(3):105–14. https://doi.org/10.1002/acm2.12554.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Lipkova J, Angelikopoulos P, Wu S, Alberts E, Wiestler B, Diehl C, et al. Personalized radiotherapy design for glioblastoma: integrating mathematical tumor models, multimodal scans, and Bayesian inference. IEEE Trans Med Imaging. 2019;38(8):1875–84. https://doi.org/10.1109/TMI.2019.2902044.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Florez E, Nichols T, Parker EE, Lirette ST, Howard CM, Fatemi A. Multiparametric magnetic resonance imaging in the assessment of primary brain tumors through radiomic features: a metric for guided radiation treatment planning. Cureus. 2018;10(10):e3426. https://doi.org/10.7759/cureus.3426.

    Article  PubMed  PubMed Central  Google Scholar 

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Authors and Affiliations

  1. Harvard Medical School, Boston, MA, USA

    Otto Rapalino

  2. Massachusetts General Hospital, Boston, MA, USA

    Otto Rapalino

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  1. Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, The Feinstein Institutes for Medical Research, Manhasset, NY, USA

    Ana M. Franceschi

  2. Stony Brook University Hospital, Stony Brook, NY, USA

    Dinko Franceschi

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Rapalino, O. (2022). Treatment Planning. In: Franceschi, A.M., Franceschi, D. (eds) Hybrid PET/MR Neuroimaging. Springer, Cham. https://doi.org/10.1007/978-3-030-82367-2_49

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