The DOI matches its Crossref record; 25 of the 26 references it cites were found in a registry or an index.
PET/MR in Dementia and Other Neurodegenerative Diseases
Abstract
The spectrum of neurodegenerative diseases covers the dementias, parkinsonian syndromes, Huntington disease, amyotrophic lateral sclerosis, and prion diseases. In these entities, brain MRI is often used in clinical routine to exclude other pathologies and to demonstrate specific atrophy patterns. [18F]FDG PET delivers early and sensitive readouts of neural tissue loss, and more specific PET tracers currently in use clinically target β-amyloid plaques or dopaminergic deficiency. The recent integration of PET into MR technology offers a new chance to improve early and differential diagnosis of many neurodegenerative diseases. Initial evidence in the literature is available to support this notion. New emerging PET tracers, such as tracers that bind to tau or α-synuclein aggregates, as well as MR techniques, like diffusion-tensor imaging, resting-state functional MRI, and arterial spin labeling, have the potential to broaden the diagnostic capabilities of combined PET/MRI to image dementias, Parkinson disease, and other neurodegenerative diseases. The ultimate goal is to establish combined PET/MRI as a first-line imaging technique to provide, in a one-stop-shop fashion with improved patient comfort, all biomarker information required to increase diagnostic confidence toward specific diagnoses. The technical challenge of accurate PET data attenuation correction within PET/MRI systems needs yet to be solved. Apart from the projected clinical routine applications, future research would need to answer the questions of whether combined brain PET/MRI is able to improve basic research of neurodegenerative diseases and antineurodegeneration drug testing.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Diagnosis of neurodegenerative diseases is usually based on subjective cognitive tests in combination with neuroimaging [131–133], but the current techniques are not able to successfully diagnose these diseases in their earlier stages, when the pathology is already present but does not cause symptoms yet.”
From Brain-Derived Neurotrophic Factor in Brain Disorders: Focus on Neuroinflammation (2019) · cites this paper as [131]
Citations
Cites
McKhann, G. M., Knopman, D. S., Chertkow, H., Hyman, B. T., Jack, C. R., Kawas, C. H., Klunk, W. E., Koroshetz, W. J., Manly, J. J., Mayeux, R., et al. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.03.005 · Cited by 18,657
Albert, M. S., DeKosky, S. T., Dickson, D., Dubois, B., Feldman, H. H., Fox, N. C., Gamst, A., Holtzman, D. M., Jagust, W. J., Petersen, R. C., et al. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.03.008 · Cited by 10,152 · cited 1×
“This led to the stratification of 33%, 48%, and 19% of the MCI subjects as unlikely owing to AD, intermediate, and high likelihood of MCI due to AD, respectively, according to established diagnostic criteria.(19) For the remaining 41 patients with AD dementia, 59% had pathologic PET and 44%had pathologic PETþMR images.”
Albert, M., DeKosky, S. T., Dickson, D. W., Dubois, B., Feldman, H., Fox, N. C., Gamst, A. C., Holtzman, D. M., Jagust, W. J., Petersen, R. C., et al. (2013) · FOCUS The Journal of Lifelong Learning in Psychiatry · doi:10.1176/appi.focus.11.1.96 · Cited by 2,643
Simultaneous PET-MRI: a new approach for functional and morphological imaging
Judenhofer, M. S., Wehrl, H. F., Newport, D. F., Catana, C., Siegel, S. B., Becker, M., Thielscher, A., Kneilling, M., Lichy, M. P., Eichner, M., et al. (2008) · Nature Medicine · doi:10.1038/nm1700 · Cited by 983
Simultaneous MR/PET Imaging of the Human Brain: Feasibility Study
Schlemmer, H.-P., Pichler, B. J., Schmand, M. J., Burbar, Z., Michel, C. J., Ladebeck, R., Jattke, K., Townsend, D. W., Nahmias, C., Jacob, P. K., et al. (2008) · Radiology · doi:10.1148/radiol.2483071927 · Cited by 473
Molecular Basis of the Neurodegenerative Disorders
Martin, J. B. (1999) · New England Journal of Medicine · doi:10.1056/nejm199906243402507 · Cited by 422
Johnson, K. A., Minoshima, S., Bohnen, N. I., Donohoe, K. J., Foster, N. L., Herscovitch, P., Karlawish, J. H., Rowe, C. C., Carrillo, M. C., Hartley, D. M., et al. (2013) · Journal of Nuclear Medicine · doi:10.2967/jnumed.113.120618 · Cited by 329
In Vivo Quantitative Susceptibility Mapping (QSM) in Alzheimer's Disease
Acosta‐Cabronero, J., Williams, G. B., Cárdenas‐Blanco, A., Arnold, R. J., Lupson, V. C., & Nestor, P. J. (2013) · PLoS ONE · doi:10.1371/journal.pone.0081093 · Cited by 302
PET/MRI for Neurologic Applications
Catana, C., Drzezga, A. E., Heiss, W., & Rosen, B. R. (2012) · Journal of Nuclear Medicine · doi:10.2967/jnumed.112.105346 · Cited by 292
Riedl, V., Kurcyus, K., Strobel, C., Tahmasian, M., Grimmer, T., Förster, S., Friston, K. J., Sorg, C., & Drzezga, A. E. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.0492-14.2014 · Cited by 193
Cited by
Sorted by citations
Brain-Derived Neurotrophic Factor in Brain Disorders: Focus on Neuroinflammation
Lima Giacobbo, B., Doorduin, J., Klein, H. C., Dierckx, R. A. J. O., Bromberg, E., & de Vries, E. F. J. (2019) · Molecular Neurobiology · doi:10.1007/s12035-018-1283-6 · Cited by 750
Test–retest measurements of dopamine D1-type receptors using simultaneous PET/MRI imaging
Kaller, S., Rullmann, M., Patt, M., Becker, G.-A., Luthardt, J., Girbardt, J., Meyer, P. M., Werner, P., Barthel, H., Bresch, A., et al. (2017) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-017-3645-0 · Cited by 152
Self-citationTalwar, P., Kushwaha, S. S., Chaturvedi, M., & Mahajan, V. (2021) · Clinical Neuroradiology · doi:10.1007/s00062-021-01057-7 · Cited by 142
Deep learning-based PET image denoising and reconstruction: a review
Hashimoto, F., Onishi, Y., Ote, K., Tashima, H., Reader, A. J., & Yamaya, T. (2024) · Radiological Physics and Technology · doi:10.1007/s12194-024-00780-3 · Cited by 113
MR/PET Imaging of the Cardiovascular System
Robson, P. M., Dey, D., Newby, D. E., Berman, D. S., Li, D., Fayad, Z. A., & Dweck, M. R. (2017) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2017.07.008 · Cited by 96
Initial experience with a SiPM-based PET/CT scanner: influence of acquisition time on image quality
Sonni, I., Baratto, L., Park, S., Hatami, N., Srinivas, S., Davidzon, G., Gambhir, S. S., & Iagaru, A. (2018) · EJNMMI Physics · doi:10.1186/s40658-018-0207-x · Cited by 74
Broski, S. M., Goenka, A. H., Kemp, B. J., & Johnson, G. B. (2018) · American Journal of Roentgenology · doi:10.2214/ajr.18.20001 · Cited by 74
PET/MR Imaging: New Frontier in Alzheimer's Disease and Other Dementias
Zhang, X. Y., Yang, Z. L., Lu, G., Yang, G. F., & Zhang, L. J. (2017) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2017.00343 · Cited by 75
FDG PET/CT in cancer: comparison of actual use with literature-based recommendations
on behalf of the PET/CT Task Force of the Region of Southern Denmark, Petersen, H., Holdgaard, P. C., Madsen, P. H., Knudsen, L. M., Gad, D., Gravergaard, A. E., Rohde, M., Godballe, C., Engelmann, B. E., et al. (2015) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-015-3217-0 · Cited by 63
Khalili‐Mahani, N., Rombouts, S. A. R. B., van Osch, M. J. P., Duff, E. P., Carbonell, F., Nickerson, L. D. H., Becerra, L. R., Dahan, A., Evans, A. C., Soucy, J., et al. (2017) · Human Brain Mapping · doi:10.1002/hbm.23516 · Cited by 63
Bailey, D. L., Pichler, B. J., Gückel, B., Antoch, G., Barthel, H., Bhujwalla, Z. M., Biskup, S., Biswal, S., Bitzer, M., Boellaard, R., et al. (2017) · Molecular Imaging and Biology · doi:10.1007/s11307-017-1123-5 · Cited by 60
Self-citationHu, X., Sun, X., Hu, F., Liu, F., Ruan, W., Wu, T., An, R. F., & Lan, X. (2021) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-021-05325-z · Cited by 56
Sharma, V., Singh, T. G., Kaur, A., Mannan, A., & Dhiman, S. (2022) · Neurochemical Research · doi:10.1007/s11064-022-03755-1 · Cited by 58
Application of Deep Learning for Prediction of Alzheimer’s Disease in PET/MR Imaging
Zhao, Y., Guo, Q., Zhang, Y., Zheng, J., Yang, Y., Du, X., Feng, H., & Zhang, S. (2023) · Bioengineering · doi:10.3390/bioengineering10101120 · Cited by 48
Generalization of deep learning models for ultra-low-count amyloid PET/MRI using transfer learning
Chen, K. T., Schürer, M., Ouyang, J., Koran, M. E. I., Davidzon, G., Mormino, E. C., Tiepolt, S., Hoffmann, K., Sabri, O., Zaharchuk, G., et al. (2020) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-020-04897-6 · Cited by 48
Self-citationSpatial adaptive and transformer fusion network (STFNet) for low‐count PET blind denoising with MRI
Zhang, L., Xiao, Z., Zhou, C., Yuan, J., He, Q., Yang, Y., Liu, X., Liang, D., Zheng, H., Fan, W., et al. (2021) · Medical Physics · doi:10.1002/mp.15368 · Cited by 46
Current radiotracers to image neurodegenerative diseases
Tiepolt, S., Patt, M., Aghakhanyan, G., Meyer, P., Hesse, S., Barthel, H., & Sabri, O. (2019) · EJNMMI Radiopharmacy and Chemistry · doi:10.1186/s41181-019-0070-7 · Cited by 46
Self-citationTeuho, J., Torrado-Carvajal, Á., Herzog, H. R., Anazodo, U., Klén, R., Iida, H., & Teräs, M. (2020) · Frontiers in Physics · doi:10.3389/fphy.2019.00243 · Cited by 40
Bonvento, G., Valette, J., Flament, J., Mochel, F., & Brouillet, E. (2017) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x17697989 · Cited by 38
Workflow in Simultaneous PET/MRI
Barbosa, F. D. G., von Schulthess, G. K., & Veit‐Haibach, P. (2015) · Seminars in Nuclear Medicine · doi:10.1053/j.semnuclmed.2015.03.007 · Cited by 35
PET/MRI of central nervous system: current status and future perspective
Yang, Z. L., & Zhang, L. J. (2016) · European Radiology · doi:10.1007/s00330-015-4202-5 · Cited by 35
The role of magnetic resonance imaging in the diagnosis of Parkinson's disease: a review
Al‐Radaideh, A., & Rababah, E. M. (2016) · Clinical Imaging · doi:10.1016/j.clinimag.2016.05.006 · Cited by 37
Lew, C. O., Zhou, L., Mazurowski, M. A., Doraiswamy, P. M., Petrella, J. R., & Initiative, A. D. N. (2023) · Radiology · doi:10.1148/radiol.222441 · Cited by 34
Protein and Gene Delivery Systems for Neurodegenerative Disorders: Where Do We Stand Today?
Siafaka, P. I., Okur, M. E., Dilsiz, P., Çağlar, E. Ş., Özgenç, E., Gündoğdu, E., Köprülü, R. E. P., Karantas, I. D., & Okur, N. Ü. (2022) · Pharmaceutics · doi:10.3390/pharmaceutics14112425 · Cited by 34
Ladefoged, C. N., Andersen, F. L., Kjær, A., Højgaard, L., & Law, I. G. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00453 · Cited by 31
Performance Study of a Radio-Frequency Field-Penetrable PET Insert for Simultaneous PET/MRI
Chang, C., Lee, B. J., Grant, A. M., Groll, A. N., & Levin, C. S. (2018) · IEEE Transactions on Radiation and Plasma Medical Sciences · doi:10.1109/trpms.2018.2852686 · Cited by 30
Clinical PET/MR Imaging in Dementia and Neuro-Oncology
Henriksen, O. M., Marner, L., & Law, I. G. (2016) · PET Clinics · doi:10.1016/j.cpet.2016.05.003 · Cited by 30
The Advents of Hybrid Imaging Modalities: A New Era in Neuroimaging Applications
Padmanabhan, P., Nedumaran, A. M., Mishra, S., Pandarinathan, G., Archunan, G., & Gulyás, B. Z. (2017) · Advanced Biosystems · doi:10.1002/adbi.201700019 · Cited by 32
The Additional Value of PET in the Assessment of Cerebral Small Vessel Disease
Heiss, W. (2018) · Journal of Nuclear Medicine · doi:10.2967/jnumed.118.214270 · Cited by 29
Florek, L., Tiepolt, S., Schroeter, M. L., Berrouschot, J., Saur, D., Hesse, S., Jochimsen, T. H., Luthardt, J., Sattler, B., Patt, M., et al. (2018) · Journal of Alzheimer s Disease · doi:10.3233/jad-180522 · Cited by 28
Self-citationOkazawa, H., Ikawa, M., Jung, M., Maruyama, R., Tsujikawa, T., Mori, T., Rahman, M. G. M., Makino, A., Kiyono, Y., & Kosaka, H. (2020) · EJNMMI Research · doi:10.1186/s13550-020-00619-z · Cited by 28
Kumari, R., Nigam, A., & Pushkar, S. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07076-w · Cited by 26
Lee, J., Renslo, J., Wong, K., Clifford, T. G., Beutler, B. D., Kim, P. E., & Gholamrezanezhad, A. (2024) · Diagnostics · doi:10.3390/diagnostics14060585 · Cited by 25
Develop a diagnostic tool for dementia using machine learning and non-imaging features
Wang, H., Sheng, L. Q., Xu, S., Jin, Y., Jin, X., Qiao, S., Chen, Q., Xing, W., Zhao, Z., Yan, J., et al. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.945274 · Cited by 27
Yu, B., Ozdemir, S., Dong, Y., Shao, W., Pan, T., Shi, K., & Gong, K. (2025) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-025-07122-4 · Cited by 24
Ostapchenko, V. G., Snir, J. A., Suchý, M., Fan, J., Cobb, M. R., Chronik, B. A., Kovacs, M. S., Prado, V. F., Hudson, R. H. E., Pasternak, S., et al. (2019) · Contrast Media & Molecular Imaging · doi:10.1155/2019/6403274 · Cited by 24
Sgard, B., Khalifé, M., Bouchut, A., Fernandez, B., Soret, M., Giron, A., Zaslavsky, C., Delso, G., Habert, M., & Kas, A. (2019) · European Radiology · doi:10.1007/s00330-019-06514-z · Cited by 23
Hybrid PET/MR Imaging in Neurology: Present Applications and Prospects for the Future
Heiss, W. (2016) · Journal of Nuclear Medicine · doi:10.2967/jnumed.116.175208 · Cited by 23
Kaltoft, N. S., Marner, L., Larsen, V. A., Hasselbalch, S. G., Law, I. G., & Henriksen, O. M. (2019) · PLoS ONE · doi:10.1371/journal.pone.0216409 · Cited by 21
Posterior Cingulate Cortex Hypometabolism in Non-Amnestic Variants of Alzheimer’s Disease
Bergeron, D., Beauregard, J., Jean-Guimond, Soucy, J., Verret, L., Poulin, S. P., Matías‐Guiu, J. A., Cabrera‐Martín, M. N., Bouchard, R. W., & Laforce, R. J. (2020) · Journal of Alzheimer s Disease · doi:10.3233/jad-200567 · Cited by 20
Multi-technique hybrid imaging in PET/CT and PET/MR: what does the future hold?
Barbosa, F. D. G., Delso, G., ter Voert, E. E. G. W., Huellner, M. W., Herrmann, K., & Veit‐Haibach, P. (2016) · Clinical Radiology · doi:10.1016/j.crad.2016.03.013 · Cited by 20
Porous Materials for Early Diagnosis of Neurodegenerative Diseases
Arghavani, P., Daneshgar, H., Sojdeh, S., Edrisi, M., Moosavi‐Movahedi, A. A., & Rabiee, N. (2025) · Advanced Healthcare Materials · doi:10.1002/adhm.202404685 · Cited by 19
Hybrid PET-MRI Applications in Movement Disorders
Tondo, G., Esposito, M., Dervenoulas, G., Wilson, H., Politis, M., & Pagano, G. (2018) · International review of neurobiology · doi:10.1016/bs.irn.2018.10.003 · Cited by 18
Clinical Value of Hybrid PET/MR Imaging
Kas, A., Rozenblum, L., & Pyatigorskaya, N. (2023) · Magnetic Resonance Imaging Clinics of North America · doi:10.1016/j.mric.2023.06.004 · Cited by 17
Measurement Challenges in Research With Individuals With Cognitive Impairment
Kolanowski, A. M., Gilmore‐Bykovskyi, A. L., Hill, N. L., Massimo, L. M., & Mogle, J. A. (2019) · Research in Gerontological Nursing · doi:10.3928/19404921-20181212-06 · Cited by 19
Clinical and Instrumental Characterization of Patients With Late-Onset Epilepsy
DiFrancesco, J. C., Labate, A., Romoli, M., Chipi, E., Salvadori, N., Galimberti, C. A., Perani, D., Ferrarese, C., & Costa, C. (2022) · Frontiers in Neurology · doi:10.3389/fneur.2022.851897 · Cited by 17
Rigid-body motion correction in hybrid PET/MRI using spherical navigator echoes
Johnson, P. M., Taylor, R., Whelan, T. J., Thiessen, J. D., Anazodo, U., & Drangova, M. (2019) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ab10b2 · Cited by 16
Ito, H., Kubo, H., Takahashi, K., Nishijima, K., Ukon, N., Nemoto, A., Sugawara, S., Yamakuni, R., Ibaraki, M., & Ishii, S. (2021) · Annals of Nuclear Medicine · doi:10.1007/s12149-021-01578-8 · Cited by 15
In vivo whole brain, cellular and molecular imaging in nonhuman primate models of neuropathology
Huang, L., Merson, T. D., & Bourne, J. A. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.04.009 · Cited by 15
FDG PET/MR Imaging in Major Neurocognitive Disorders
Mainta, I., Perani, D., Delattre, B. M. A., Assal, F., Haller, S., Vargas, M. I., Zekry, D., Frisoni, G. B., Zaidi, H., Ratib, O. M., et al. (2016) · Current Alzheimer Research · doi:10.2174/1567205013666160620115130 · Cited by 14
Design and initial results of an RF-penetrable TOF PET insert dedicated for brain PET/MRI
Chang, C., Lee, B. J., Sacco, I., Watkins, R. D., Dong, Q., & Levin, C. S. (2017) · IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) · doi:10.1109/nssmic.2017.8533057 · Cited by 13
van Waalwijk van Doorn, L. J. C., Gispert, J. D., Kuiperij, H. B., Claassen, J. A. H. R., Arighi, A., Baldeiras, I., Blennow, K., Bozzali, M., Castelo-Branco, M., Cavedo, E., et al. (2017) · Journal of Alzheimer’s Disease · doi:10.3233/jad-160668 · Cited by 13
Øen, S. K., Keil, T. M., Berntsen, E. M., Aanerud, J., Schwarzlmüller, T., Ladefoged, C. N., Karlberg, A. M., & Eikenes, L. (2019) · EJNMMI Research · doi:10.1186/s13550-019-0553-2 · Cited by 12
A Role of PET/MR Imaging in Dementia?
Dupont, P. J. (2021) · Seminars in Nuclear Medicine · doi:10.1053/j.semnuclmed.2021.01.003 · Cited by 11
Mannheim, J. G., Cheng, J.-C. K., Vafai, N. M., Shahinfard, E., English, C., McKenzie, J., Zhang, J., Barlow, L. R., & Sossi, V. (2021) · EJNMMI Physics · doi:10.1186/s40658-020-00349-0 · Cited by 11
Dose Reduction in Brain [18F]FDG PET/MRI: Give It Half a Chance
Soret, M., Piekarski, E., Yeni, N., Giron, A., Maisonobe, J.-A., Khalifé, M., Zaslavsky, C., Bertaux, M., Habert, M., & Kas, A. (2019) · Molecular Imaging and Biology · doi:10.1007/s11307-019-01398-3 · Cited by 11
Takeuchi, W., Suzuki, A., Shiga, T., Kubo, N., Morimoto, Y., Ueno, Y., Kobashi, K., Umegaki, K., & Tamaki, N. (2016) · EJNMMI Physics · doi:10.1186/s40658-016-0147-2 · Cited by 10
PET/MRI: A luxury or a necessity?
Carreras-Delgado, J. L., Pérez-Dueñas, V., Riola-Parada, C., & García‐Cañamaque, L. (2016) · Revista Española de Medicina Nuclear e Imagen Molecular (English Edition) · doi:10.1016/j.remnie.2016.07.002 · Cited by 10
Queiroz, M. A., Barbosa, F. D. G., Buchpiguel, C. A., & Cerri, G. G. (2018) · Revista da Associação Médica Brasileira · doi:10.1590/1806-9282.64.01.71 · Cited by 10
Leung, I. H. K., & Strudwick, M. W. (2024) · EJNMMI Reports · doi:10.1186/s41824-024-00194-9 · Cited by 9
Magnetic Resonance Fingerprinting: Implications and Opportunities for PET/MR
Ropella‐Panagis, K., Seiberlich, N., & Gulani, V. (2019) · IEEE Transactions on Radiation and Plasma Medical Sciences · doi:10.1109/trpms.2019.2897425 · Cited by 9
Apostolova, I. I., Lange, C., Roberts, A., Igel, H. J., Maeurer, A., Liese, S., Estrella, M., Prasad, V., Stechl, E., Lämmler, G., et al. (2016) · Journal of Alzheimer s Disease · doi:10.3233/jad-160797 · Cited by 9
Synergy in Neuroimaging: PET-CT and MRI Fusion for Enhanced Characterization of Brain Pathology
Radder, N., Sonar, S., Nanivadekar, A., & Radder, S. (2024) · Cureus · doi:10.7759/cureus.74353 · Cited by 8
Thientunyakit, T., Muangpaisan, W., & Minoshima, S. (2025) · Seminars in Nuclear Medicine · doi:10.1053/j.semnuclmed.2025.04.007 · Cited by 8
Depression, Dementia, and Pseudodementia
Pozzoli, S., Carlo, V. D., & Madonna, D. (2018) · Clinical Cases in Psychiatry: Integrating Translational Neuroscience Approaches · doi:10.1007/978-3-319-91557-9_10 · Cited by 2
Bisdas, S., la Fougère, C. J., & Ernemann, U. (2015) · Clinical Neuroradiology · doi:10.1007/s00062-015-0427-6 · Cited by 7
Seyedin, S. N., Bassalow, R., Mawlawi, O. R., Turner, L. M., Patel, R. R., Mazin, S. R., Oderinde, O. M., Voronenko, Y., Wages, C. A., Olcott, P. D., et al. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.921473 · Cited by 7
Neuroimaging in Alzheimer's disease: preclinical challenges toward clinical efficacy
Dustin, D. J., Hall, B. M., Annapragada, A., & Pautler, R. G. (2016) · Translational research · doi:10.1016/j.trsl.2016.03.005 · Cited by 7
Molecular Imaging with PET in the Assessment of Vascular Dementia and Cerebrovascular Disease
Patil, S., Patel, D., Kata, R., Teichner, E. M., Subtirelu, R. C., Ayubcha, C., Werner, T. J., & Alavi, A. A. (2024) · PET Clinics · doi:10.1016/j.cpet.2024.09.001 · Cited by 6
Yu, B., Ozdemir, S., Dong, Y., Shao, W., Shi, K., & Gong, K. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72104-5_52 · Cited by 6
Sossi, V. (2018) · International review of neurobiology · doi:10.1016/bs.irn.2018.07.034 · Cited by 6
Cognitive impact of exposure to airborne particles captured by brain imaging
Azmoun, S., Diaz, Y. F., Tang, C. Y., Horton, M. K., Clouston, S., Luft, B. J., Bromet, E. J., Gandy, S., Placidi, D., Ambrosi, C., et al. (2022) · Advances in neurotoxicology · doi:10.1016/bs.ant.2022.05.002 · Cited by 6
Hashimoto, F., & Ote, K. (2024) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ad40f6 · Cited by 6
Sekine, T., Buck, A., Delso, G., Kemp, B. J., ter Voert, E. E. G. W., Huellner, M. W., Veit‐Haibach, P., Kaushik, S. S., Wiesinger, F., Warnock, G., et al. (2020) · PLoS ONE · doi:10.1371/journal.pone.0233886 · Cited by 6
Vitrio, B., Destefani, V. C., & Destêfani, A. C. (2024) · Brazilian Journal of Implantology and Health Sciences · doi:10.36557/2674-8169.2024v6n8p3962-3983 · Cited by 6
Imaging in Neurodegeneration: Movement Disorders
Sossi, V., Cheng, J.-C. K., & Klyuzhin, I. S. (2018) · IEEE Transactions on Radiation and Plasma Medical Sciences · doi:10.1109/trpms.2018.2871760 · Cited by 5
Beuthien‐Baumann, B. (2018) · Der Radiologe · doi:10.1007/s00117-018-0355-y · Cited by 5
PET/RM: ¿un lujo o una necesidad?
Carreras-Delgado, J. L., Pérez-Dueñas, V., Riola-Parada, C., & García‐Cañamaque, L. (2016) · Revista Española de Medicina Nuclear e Imagen Molecular · doi:10.1016/j.remn.2016.05.007 · Cited by 5
von Schulthess, G. K., & Veit‐Haibach, P. (2015) · Seminars in Nuclear Medicine · doi:10.1053/j.semnuclmed.2014.12.006 · Cited by 5
Dondi, F., Latronico, N., Bellini, P., Lucchini, S., Camoni, L., Cossandi, M., Viganò, G. L., Santo, G., & Bertagna, F. (2025) · Diagnostics · doi:10.3390/diagnostics15111406 · Cited by 4
Comparative Study of Relevant Methods for MRI/X Brain Image Registration
Abderrahim, M., Baâzaoui, A., & Barhoumi, W. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-51517-1_30 · Cited by 4
Hori, A., Ai, T., Isshiki, M., Motoi, Y., Yano, K., Tabe, Y., Hattori, N., & Miida, T. (2021) · Geriatrics · doi:10.3390/geriatrics6010014 · Cited by 4
Guide à la lecture et interprétation de l’imagerie PET des plaques amyloïdes
Denis, A., Dietemann, S., Noirot, C., Andryszak, P., Trombella, S., Frisoni, G. B., Mainta, I., & Garibotto, V. (2017) · Médecine Nucléaire · doi:10.1016/j.mednuc.2017.06.008 · Cited by 3
Imaging biomarkers in Alzheimer's disease
Sudre, C. H., Cardoso, M. J., Marc Modat, A., & Sebastien Ourselin, A. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-816176-0.00020-x · Cited by 3
Aptamer Detection of Neurodegenerative Disease Biomarkers
Xi, H., & Zhang, Y. (2021) · Neuromethods · doi:10.1007/978-1-0716-1712-0_15 · Cited by 3
Sousa, J. M., Appel, L., Engström, M., Papadimitriou, S., Nyholm, D., Ahlström, H. K., & Lubberink, M. (2022) · Physica Medica · doi:10.1016/j.ejmp.2022.03.012 · Cited by 3
Sousa, J. M., Appel, L., Engström, M., Nyholm, D., Ahlström, H. K., & Lubberink, M. (2023) · Physica Medica · doi:10.1016/j.ejmp.2023.103185 · Cited by 2
Salvador-Ribés, C., Pons, C. S., García, M. J. S., Fechter, T., Olivas, C., Torres-Espallardo, I., Pérez‐Calatayud, J., Baltas, D., Mix, M. D., Martí‐Bonmatí, L., et al. (2025) · EJNMMI Physics · doi:10.1186/s40658-025-00741-8 · Cited by 1
Binary Classification of Alzheimer's Disease Using Siamese Neural Network for Early Stage Diagnosis
Tekin, R., & Onur, T. Ö. (2025) · Celal Bayar Üniversitesi Fen Bilimleri Dergisi · doi:10.18466/cbayarfbe.1529546 · Cited by 1
Precise MRI-histology coregistration of paraffin-embedded tissue with blockface imaging
Wang, Y., Ho, W. H. D., Huszár, I. N., DiGiacomo, P. S., Taghavi, H. M., Tao, L., Choi, M., Nguyen, N. T., Leventis, S., Camarillo, D. B., et al. (2025) · Imaging Neuroscience · doi:10.1162/imag.a.106 · Cited by 1
Yao, J., Ma, J., Li, P., Liao, X., Zan, J., Hu, L., & Li, G. (2026) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2026.1727812 · Cited by 1
Neuroimaging in Cerebral Small Vessel Disease
Heiss, W. (2018) · Journal of Neurology and Neuromedicine · doi:10.29245/2572.942x/2018/1.1171 · Cited by 1
Rovera, C., & Pigoni, A. (2018) · Springer eBooks · doi:10.1007/978-3-319-91557-9_3 · Cited by 1
Calabria, F. F., Cimini, A., Bagnato, A., Gullà, D., Cascini, G. L., Urbano, N., & Schillaci, O. (2019) · Springer eBooks · doi:10.1007/978-3-030-27779-6_1 · Cited by 1
PET/MR: Basics and New Developments
Khalil, M. M. (2016) · Springer eBooks · doi:10.1007/978-3-319-40070-9_9 · Cited by 1
Amyotrophic Lateral Sclerosis (ALS)
(2016) · Elsevier eBooks · doi:10.1016/b978-0-323-44781-2.50200-x · Cited by 1
Positron Emission Tomography/Magnetic Resonanse Imaging Applications in Neurologic Imaging
Akdemir, Ü. Ö., Karabacak, N. İ., & Atay, L. Ö. (2017) · Nuclear Medicine Seminars · doi:10.4274/nts.005 · Cited by 1
Amyotrophic Lateral Sclerosis (ALS)
(2016) · Elsevier eBooks · doi:10.1016/b978-0-323-37754-6.50289-x · Cited by 1
Sathekge, M. M., & Bouchelouche, K. (2021) · Seminars in Nuclear Medicine · doi:10.1053/j.semnuclmed.2021.01.004 · Cited by 1
Recent Developments in Combined PET/MRI
Flavell, R. R., Wilson, D. M., & Behr, S. C. (2016) · Current Radiology Reports · doi:10.1007/s40134-016-0149-9
Metadata
- Authors
- Barthel, H., Schroeter, M. L., Hoffmann, K.-T., & Sabri, O.
- Affiliations
- University Hospital Leipzig (DE)
- Max Planck Institute for Human Cognitive and Brain Sciences (DE)
- Leipzig University (DE)
- Corresponding author
- Barthel, Henryk
- Venue
- Seminars in Nuclear Medicine
- Publisher
- Elsevier BV
- Published
- 2015-05
- Volume
- 45
- Issue
- 3
- Pages
- 224-233
- ISSN
- 0001-2998 (ISSN-L)
- OpenAlex
- W1987273610
- Cited by
- 123
- References
- 26
- Citations by year
- 2026: 3 · 2025: 9 · 2024: 10 · 2023: 6 · 2022: 10 · 2021: 12 · 2020: 7 · 2019: 12 · 2018: 16 · 2017: 13 · 2016: 20 · 2015: 5
- FWCI
- 14.60
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Seminars in Nuclear Medicine (publishedVersion)
- PubMed (publishedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- PET/MRI, neurodegenerative diseases, dementia, Parkinson's disease, Huntington's disease, amyotrophic lateral sclerosis, prion disease, FDG-PET, amyloid PET tracers, tau PET tracers, diffusion tensor imaging, resting-state fMRI, arterial spin labeling, PET attenuation correction, neurodegeneration biomarkers, differential diagnosis
- MeSH terms
- Dementia, Humans, Magnetic Resonance Imaging, Parkinsonian Disorders, Positron-Emission Tomography, Multimodal Imaging
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review