Share：
Share this content in WeChat
X
[Chinese] [PDF] 5 1
• Reviews •
Diagnostic value and clinical application progress of multimodal PET-MRI in the prodromal stage of Parkinson's disease
YU Haotian  LIU Zhewei  ZHONG Meimeng  QI Xin  YANG Chao 

Cite this article as: YU H T, LIU Z W, ZHONG M M, et al. Diagnostic value and clinical application progress of multimodal PET-MRI in the prodromal stage of Parkinson's disease[J]. Chin J Magn Reson Imaging, 2026, 17(3): 149-154. DOI:10.12015/issn.1674-8034.2026.03.021.


[Abstract] Parkinson's disease (PD) is an age-related neurodegenerative disorder; early diagnosis and treatment are critical to slowing its progression. Although its prodromal stage offers a critical intervention window, conventional MRI lacks specificity, and no imaging gold standard exists for diagnosis. Accurate identification of prodromal PD is essential for precise clinical management, with multimodal imaging features playing a key role in its detection and personalized treatment. This paper examines the clinicopathological features of prodromal PD, analyzes current diagnostic frameworks and risk assessment strategies, describes multimodal imaging characteristics, and reviews artificial intelligence (AI) applications in its diagnosis and prognostic prediction. It also identifies limitations of current research and proposes future directions, to provide radiologists with imaging references for enhancing early diagnosis, treatment optimization and personalized risk assessment of prodromal PD.
[Keywords] Parkinson's disease, prodromal stage；positron emission tomography；magnetic resonance imaging；multimodal imaging；early diagnosis

YU Haotian   LIU Zhewei   ZHONG Meimeng   QI Xin   YANG Chao*  

Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian 116100, China

Corresponding author: YANG C, E-mail: dryangchao@163.com

Conflicts of interest   None.

Received  2025-10-20
Accepted  2026-02-14
DOI: 10.12015/issn.1674-8034.2026.03.021
Cite this article as: YU H T, LIU Z W, ZHONG M M, et al. Diagnostic value and clinical application progress of multimodal PET-MRI in the prodromal stage of Parkinson's disease[J]. Chin J Magn Reson Imaging, 2026, 17(3): 149-154. DOI:10.12015/issn.1674-8034.2026.03.021.

[1]
MALAGUTI C M, LONGO C, MORONI M, et al. Machine Learning Predicts Risk of Falls in Parkison's Disease Patients in a Multicenter Observational Study[J/OL]. Eur J Neurol, 2025, 32(5): e70118 [2025-10-20]. https://pubmed.ncbi.nlm.nih.gov/40304115/. DOI: 10.1111/ENE.70118.
[2]
CUI J Q, YU Q E, ZHANG T J. Progress in Functional MRI Radiomics Research on Parkinson's Disease[J]. Chin J Magn Reson Imaging, 2025, 16(6): 132-138. DOI: 10.12015/issn.1674-8034.2025.06.020.
[3]
SZATMARI S, ILLIGENS B M, SIEPMANN T, et al. Neuropsychiatric symptoms in untreated Parkinson's disease[J]. Neuropsychiatr Dis Treat, 2022, 13: 815-826. DOI: 10.2147/NDT.S130997.
[4]
RUSZ J, KRACK P, TRIPOLITI E. From prodromal stages to clinical trials: The promise of digital speech biomarkers in Parkinson's disease[J/OL]. Neurosci Biobehav Rev, 2024, 167: 105922 [2025-10-20]. https://dx.doi.org/10.1016/j.neubiorev.2024.105922. DOI: 10.1016/j.neubiorev.2024.105922.
[5]
ZHU Z, WEN J, DUANMU X, et al. Identifying brain degeneration patterns in early-stage Parkinson's disease: a multimodal MRI study[J/OL]. NPJ Parkinsons Dis, 2025, 11(1): 93 [2025-10-20]. https://dx.doi.org/10.1038/S41531-025-00975-4. DOI: 10.1038/S41531-025-00975-4.
[6]
FRANZISKA A, TOMMASO B, JANE N, et al. FDG-PET hypometabolism is more sensitive than MRI atrophy in Parkinson's disease: A whole-brain multimodal imaging meta-analysis[J/OL]. Neuroimage Clin, 2018, 21: 101594 [2025-10-20]. https://dx.doi.org/10.1016/j.nicl.2018.11.004. DOI: 10.1016/j.nicl.2018.11.004.
[7]
JEE L, ANTONIO M, ANE M, et al. Multimodal brain and retinal imaging of dopaminergic degeneration in Parkinson disease[J]. Nat Rev Neurol, 2022, 18(4): 203-220. DOI: 10.1038/s41582-022-00618-9.
[8]
RUSZ J, KRACK P, TRIPOLITI E. From Prodromal Stages to Clinical Trials: The Promise of Digital Speech Biomarkers in Parkinson's Disease[J/OL]. Neurosci Biobehav Rev, 2024, 167: 105922 [2025-10-20]. https://dx.doi.org/10.1016/J.NEUBIOREV.2024.105922. DOI: 10.1016/J.NEUBIOREV.2024.105922.
[9]
BRAAK H, GHEBREMEDHIN E, RÜB U, et al. Stages in the development of Parkinson's disease-related pathology[J]. Cell Tissue Res, 2004, 318(1): 121-134. DOI: 10.1007/s00441-004-0956-9..
[10]
PARK H C, YOON U, LEE H P, et al. Brain structural MRI marker for predicting conversion to Parkinson's disease in individuals with prodromal symptoms[J/OL]. Front Aging Neurosci, 2025, 17: 1579326 [2025-10-20]. https://dx.doi.org/10.3389/FNAGI.2025.1579326. DOI: 10.3389/FNAGI.2025.1579326.
[11]
BERG D, POSTUMA R B, ADLER C H, et al. MDS research criteria for prodromal Parkinson's disease[J]. Mov Disord, 2015, 30(12): 1600-1611. DOI: 10.1002/mds.26431.
[12]
JEE L, ANTONIO M, ANE M, et al. Multimodal brain and retinal imaging of dopaminergic degeneration in Parkinson disease[J]. Nat Rev Neurol, 2022, 18(4): 203-220. DOI: 10.1038/s41582-022-00618-9.
[13]
MARECEK S, ROTTOVA V, NEPOZITEK J, et al. Exploring glymphatic system alterations in iRBD and Parkinson's disease using automated DTI-ALPS analysis[J/OL]. NPJ Park Dis, 2025, 11(1): 76 [2025-10-20]. https://dx.doi.org/10.1038/s41531-025-00921-4. DOI: 10.1038/s41531-025-00921-4.
[14]
LANGKAMMER C, PIRPAMER L, SEILER S, et al. Quantitative susceptibility mapping in Parkinson's disease[J/OL]. PLoS One, 2016, 11(9): e0162460 [2025-10-20]. https://dx.doi.org/10.1371/journal.pone.0162460. DOI: 10.1371/journal.pone.0162460.
[15]
SHANG S, LI D, TIAN Y, et al. Hybrid PET-MRI for early detection of dopaminergic dysfunction and microstructural degradation involved in Parkinson's disease[J/OL]. Commun Biol, 2021, 4(1): 1162 [2025-10-20]. https://doi.org/10.1038/s42003-021-02705-x. DOI: 10.1038/s42003-021-02705-x..
[16]
FU J, TANG Y, PAN L, et al. Neuromelanin-MRI identifies locus coeruleus and substantia nigra degeneration as key differentiators in isolated rapid eye movement sleep behavior disorder[J]. Brain Imaging Behav, 2024, 19(1): 1-10. DOI: 10.1007/s11682-024-00926-2.
[17]
PÉROT S, BAEUMLIN E, MANGIAVACCHI L, et al. Longitudinal neuromelanin changes in prodromal and early Parkinson's disease in humans and rat model[J/OL]. bioRxiv, 2025 [2025-10-20]. https://dx.doi.org/10.1093/braincomms/fcaf204. DOI: 10.1093/braincomms/fcaf204.
[18]
YAN Y, ZHANG M, REN W, et al. Neuromelanin-sensitive magnetic resonance imaging: possibilities and promises as an imaging biomarker for Parkinson's disease[J]. Eur J Neurosci, 2024, 59(10): 2616-2627. DOI: 10.1111/ejn.16296.
[19]
ZHANG D, YU J, SUN J, et al. Quantitative Susceptibility Mapping and Free Water Imaging of Substantia Nigra in Parkinson's Disease[J]. J Park Dis, 2022, 12(8): 2469-2478. DOI: 10.3233/JPD-223499.
[20]
CHEN M, WANG Y, ZHANG C, et al. Free water and iron content in the substantia nigra at different stages of Parkinson's disease[J/OL]. Eur J Radiol, 2023, 167: 111030 [2025-10-20]. https://dx.doi.org/10.1016/j.ejrad.2023.111030. DOI: 10.1016/j.ejrad.2023.111030.
[21]
ALUSHAJ E, JONES H N, KUURSTRA A, et al. Increased iron in the substantia nigra pars compacta identifies patients with early Parkinson's disease: a 3T and 7T MRI study[J/OL]. Neuroimage Clin, 2024, 41: 103577 [2025-10-20]. https://dx.doi.org/10.1016/j.nicl.2024.103577. DOI: 10.1016/j.nicl.2024.103577.
[22]
RIKKEN M R, GIESSEN D V E, BRUMBERG J, et al. Imaging pro-inflammatory microglia in Parkinson's disease using [11C]SMW139 PET: a multi-center study[J/OL]. Alzheimers Dement, 2025, 21(S7): e108572 [2025-10-20]. https://dx.doi.org/10.2967/jnumed.125.269671. DOI: 10.2967/jnumed.125.269671.
[23]
YE P, ZHANG Y, ZHANG X, et al. Activated Microglia in the Early Stage of a Rat Model of Parkinson's Disease: Revealed by PET-MRI Imaging by [18F]DPA-714 Targeting TSPO[J]. ACS Chem Neurosci, 2023, 14(11): 2183-2192. DOI: 10.1021/acschemneuro.3c00202.
[24]
DÉGLON F, PACCARD L, GARCIA M, et al. Parkinson's disease multimodal imaging: F-DOPA PET, neuromelanin-sensitive and quantitative iron-sensitive MRI[J/OL]. NPJ Park Dis, 2021, 7(1): 57 [2025-10-20]. https://dx.doi.org/10.1038/s41531-021-00199-2. DOI: 10.1038/s41531-021-00199-2.
[25]
REDDY A, REDDY R P, ROGHANI A K, et al. Artificial intelligence in Parkinson's disease: early detection and diagnostic advancements[J/OL]. Ageing Res Rev, 2024, 99: 102410 [2025-10-20]. https://dx.doi.org/10.1016/j.arr.2024.102410. DOI: 10.1016/j.arr.2024.102410.
[26]
POSTUMA R B, IRANZO A, HU M, et al. Risk and predictors of dementia and parkinsonism in idiopathic REM sleep behaviour disorder: a multicentre study[J]. Brain, 2019, 142(3): 744-759. DOI: 10.1093/brain/awz030.
[27]
ZHU Y, WANG F, NING P, et al. Multimodal neuroimaging-based prediction of Parkinson's disease with mild cognitive impairment using machine learning technique[J/OL]. NPJ Park Dis, 2024, 10: 218 [2025-10-20]. https://dx.doi.org/10.1038/s41531-024-00828-6. DOI: 10.1038/s41531-024-00828-6.
[28]
GIOVANNI P, SARA G, GABRIELE B, et al. Dopamine Transporter Imaging, Current Status of a Potential Biomarker: A Comprehensive Review[J]. Int J Mol Sci, 2021, 22(20): 11234-11234. DOI: 10.3390/IJMS222011234.
[29]
VARGA Z, NEPOZITEK J, HLAVNICKA J, et al. Susceptibility and diffusion MRI biomarkers predict development of Parkinsonism in iRBD[J/OL]. NPJ Park Dis, 2025, 11(1): 332 [2025-10-20]. https://dx.doi.org/10.1038/s41531-025-01174-x. DOI: 10.1038/s41531-025-01174-x.
[30]
SONG'AN S, DAIXIN L, YOUYONG T, et al. Hybrid PET-MRI for early detection of dopaminergic dysfunction and microstructural degradation involved in Parkinson's disease[J]. Commun Biol, 2021, 4(1): 1162-1162. DOI: 10.1038/S42003-021-02705-X.
[31]
DEPIERREUX F, PARMENTIER E, MACKELS L, et al. Parkinson's disease multimodal imaging: F-DOPA PET, neuromelanin-sensitive and quantitative iron-sensitive MRI[J/OL]. NPJ Parkinsons Dis, 2021, 7(1): 57 [2025-10-20]. https://dx.doi.org/10.1038/s41531-021-00199-2. DOI: 10.1038/s41531-021-00199-2.
[32]
SABINA L, PHILIPPE L, ANTOINE P, et al. Disease stage-dependent changes in brain levels and neuroprotective effects of neuroactive steroids in Parkinson's disease[J/OL]. Neurobiol Dis, 2023, 183: 106169 [2025-10-20]. https://dx.doi.org/10.1016/J.NBD.2023.106169. DOI: 10.1016/J.NBD.2023.106169.
[33]
LEE J Y, YOON E J, KIM Y K, et al. Nonmotor and dopamine transporter change in REM sleep behavior disorder by olfactory impairment[J/OL]. J Mov Disord, 2019, 12(2): 103 [2025-10-20]. https://dx.doi.org/10.14802/jmd.18061. DOI: 10.14802/jmd.18061.
[34]
PORCU M, COCCO L, MARROSU F, et al. Hippocampus and olfactory impairment in Parkinson disease: a comparative exploratory combined volumetric/functional MRI study[J]. Neuroradiology, 2024, 66: 1941-1953. DOI: 10.1007/s00234-024-03436-6.
[35]
BABA T, KIKUCHI A, HIRAYAMA K, et al. Severe olfactory dysfunction is a prodromal symptom of dementia associated with Parkinson's disease: a 3-year longitudinal study[J]. Brain, 2012, 135: 161-169. DOI: 10.1093/brain/awr321.
[36]
JENNINGS D, SIDEROWF A, STERN M, et al. Conversion to Parkinson disease in the PARS hyposmic and dopamine transporter-deficit prodromal cohort[J]. JAMA Neurol, 2017, 74(8): 933-940. DOI: 10.1001/jamaneurol.2017.0985.
[37]
SIMUNI T, BRUMM M C, URIBE L, et al. Parkinson's Progression Markers Initiative Investigators. Clinical and Dopamine Transporter Imaging Characteristics of Leucine Rich Repeat Kinase 2 (LRRK2) and Glucosylceramidase Beta (GBA) Parkinson's Disease Participants in the Parkinson's Progression Markers Initiative: A Cross-Sectional Study[J]. Mov Disord, 2020, 35(5): 833-844. DOI: 10.1002/mds.27989.
[38]
CHE L, WANG J, ZHOU Y, et al. Multimodal federated learning: a survey[J/OL]. Sensors, 2023, 23(15): 6986 [2025-10-20]. https://dx.doi.org/10.3390/s23156986. DOI: 10.3390/s23156986.
[39]
YAN Y, ZHANG M, REN W, et al. Neuromelanin-sensitive magnetic resonance imaging: Possibilities and promises as an imaging biomarker for Parkinson's disease[J]. Eur J Neurosci, 2024, 59(10): 2616-2627. DOI: 10.1111/ejn.16296.
[40]
WANG Y, HE N, ZHANG C, et al. An automatic interpretable deep learning pipeline for accurate Parkinson's disease diagnosis using quantitative susceptibility mapping and T1-weighted images[J]. Hum Brain Mapp, 2023, 44(18): 8380-8394. DOI: 10.1002/hbm.26399.
[41]
BIONDETTI E, SANTIN M D, VALABRÈGUE R, et al. The spatiotemporal changes in dopamine, neuromelanin and iron characterizing Parkinson's disease[J]. Brain, 2021, 144(10): 3114-3125. DOI: 10.1093/brain/awab191.
[42]
NALLS M A, BLAUWENDRAAT C, VALLERGA C L, et al. Multimodal biomarker analysis of LRRK2-linked Parkinson's disease across SAA subtypes[J/OL]. Processes, 2025, 13(11): 3448 [2025-10-20]. https://dx.doi.org/10.3390/PR13113448. DOI: 10.3390/PR13113448.
[43]
MINJUNG S, SEONGBEOM P, HEE Y S, et al. Diagnostic performance of a high-spatial-resolution voxelwise analysis of neuromelanin-sensitive imaging in early-stage idiopathic Parkinson's disease[J/OL]. BMC Med Imaging, 2023, 23(1): 64 [2025-10-20]. https://dx.doi.org/10.1186/s12880-023-01018-1. DOI: 10.1186/s12880-023-01018-1.
[44]
FORSMAN A O, SJÖSTRÖM H, SVENNINGSSON P, et al. Combined MR quantitative susceptibility mapping and multi-shell diffusion in Parkinson's disease[J]. J Neuroimaging, 2024, 34(5): 603-611. DOI: 10.1111/jon.13222.
[45]
CARLI G, MELES S K, JANZEN A, et al. Occipital hypometabolism in iRBD predicts short time to phenoconversion[J]. Eur J Nucl Med Mol Imaging, 2023, 50(11): 3290-3301. DOI: 10.1007/s00259-023-06289-y.
[46]
FU J, TANG Y, PAN L, et al. Neuromelanin-MRI identifies locus coeruleus and substantia nigra degeneration as key differentiators in isolated rapid eye movement sleep behavior disorder[J]. Brain Imaging Behav, 2024, 19(1): 1-10. DOI: 10.1007/s11682-024-00926-2.
[47]
HAIDER A, ELGHAZAWY N H, DAWOUD A, et al. Translational molecular imaging and drug development in Parkinson's disease[J/OL]. Mol Neurodegener, 2023, 18(1): 11 [2025-10-20]. https://pubmed.ncbi.nlm.nih.gov/36759912/. DOI: 10.1186/S13024-023-00600-Z.
[48]
BOOTH T C, NATHAN M, WALDMAN A D, et al. The role of functional dopamine-transporter SPECT imaging in parkinsonian syndromes[J]. Am J Neuroradiol, 2015, 36: 229-235. DOI: 10.3174/ajnr.A3971.
[49]
TOSHIHARU N, AKIRA N, HIROHISA W, et al. Neuromelanin in Parkinson's Disease: Tyrosine Hydroxylase and Tyrosinase[J/OL]. Int J Mol Sci, 2022, 23(8): 4176 [2025-10-20]. https://pubmed.ncbi.nlm.nih.gov/35456994/. DOI: 10.3390/IJMS23084176.
[50]
VRENKEN H, JENKINSON M, HORSFIELD M A, et al. Recommendations to improve imaging and analysis of brain lesion load and atrophy in longitudinal studies of multiple sclerosis[J]. J Neurol, 2013, 260(10): 2458-2471. DOI: 10.1007/s00415-012-6762-5.
[51]
HIROTO T, NOBUO K, ATSUKO A, et al. Imaging of the nigrostriatal system for evaluating the preclinical phase of Parkinson's disease development: the utility of neuromelanin, diffusion MRI, and DAT-SPECT[J/OL]. Br J Radiol, 2021, 95(1130): 20210837 [2025-10-20]. https://pubmed.ncbi.nlm.nih.gov/34808066/. DOI: 10.1259/BJR.20210837.
[52]
ALUSHAJ E, JONES H N, KUURSTRA A, et al. Increased iron in the substantia nigra pars compacta identifies patients with early Parkinson's disease: A 3T and 7T MRI study[J/OL]. Neuroimage Clin, 2024, 41: 103577 [2025-10-20]. https://pubmed.ncbi.nlm.nih.gov/38377722/. DOI: 10.1016/J.NICL.2024.103577.
[53]
SUN J, CONG C, LI X, et al. Identification of Parkinson's disease and multiple system atrophy using multimodal PET/MRI radiomics. Eur Radiol, 2024, 34(1): 662-672. https://doi.org/10.1007/s00330-023-10003-9. DOI: 10.1007/s00330-023-10003-9.
[54]
JIGNA H, RAJ P, NISARG P, et al. Convolutional Neural Network-Based Parkinson Disease Classification Using SPECT Imaging Data[J/OL]. Mathematics, 2022, 10(15): 2566 [2025-10-20]. https://dx.doi.org/10.3390/MATH10152566. DOI: 10.3390/MATH10152566.

PREV Advances in magnetic resonance imaging research on post-stroke depression
NEXT Application and progress of nigral imaging in the early diagnosis of Parkinson<sup><sup>,</sup></sup>s disease
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn