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001     910521
005     20221028130545.0
037 _ _ |a FZJ-2022-03904
100 1 _ |a Lohmann, Philipp
|0 P:(DE-Juel1)145110
|b 0
|e Corresponding author
|u fzj
111 2 _ |a EAN 2022 – Vienna
|c Vienna
|d 2022-06-25 - 2022-06-25
|w Austria
245 _ _ |a Combining advanced MRI and PET
260 _ _ |c 2022
336 7 _ |a Abstract
|b abstract
|m abstract
|0 PUB:(DE-HGF)1
|s 1666942438_8013
|2 PUB:(DE-HGF)
336 7 _ |a Conference Paper
|0 33
|2 EndNote
336 7 _ |a INPROCEEDINGS
|2 BibTeX
336 7 _ |a conferenceObject
|2 DRIVER
336 7 _ |a Output Types/Conference Abstract
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336 7 _ |a OTHER
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520 _ _ |a ABSTRACTNeuroimaging techniques such as magnetic resonance imaging (MRI) and positron emission tomography (PET) provide valuable diagnostic information in the follow-up of patients with glioma. Particularly PET using radiolabeled amino acids, advanced MRI techniques such as perfusion-weighted imaging, diffusion-weighted imaging or MR spectroscopy, and combinations thereof demonstrated their potential for a non-invasive assessment of biological characteristics of brain cancer. Considering the growing complexity of neuroimaging data, advanced statistical methods from the field of artificial intelligence such as radiomics play an important role for clinical decision-making and have the potential to significantly impact diagnosis and response assessment in neuro-oncology. Further, the increasing availability of hybrid PET/MRI systems and the advent of ultra-high field MRI scanners operating at magnetic field strengths of 7 Tesla or more opens new possibilities for improvements in metabolic imaging. The presentation summarizes the status of advanced MRI and PET, highlights the latest developments and methodological advancements, and envisions the future role of advanced neuroimaging in the follow-up of patients with glioma.
536 _ _ |a 5253 - Neuroimaging (POF4-525)
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909 C O |o oai:juser.fz-juelich.de:910521
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910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 0
|6 P:(DE-Juel1)145110
913 1 _ |a DE-HGF
|b Key Technologies
|l Natural, Artificial and Cognitive Information Processing
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|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
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|v Decoding Brain Organization and Dysfunction
|9 G:(DE-HGF)POF4-5253
|x 0
914 1 _ |y 2022
920 1 _ |0 I:(DE-Juel1)INM-4-20090406
|k INM-4
|l Physik der Medizinischen Bildgebung
|x 0
980 _ _ |a abstract
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)INM-4-20090406
980 _ _ |a UNRESTRICTED

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