Skip to main content

Advertisement

SpringerLink
Log in

A diagnostic index based on quantitative susceptibility mapping and voxel-based morphometry may improve early diagnosis of Alzheimer’s disease

  • Head and Neck
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

Voxel-based morphometry (VBM) is widely used to quantify the progression of Alzheimer’s disease (AD), but improvement is still needed for accurate early diagnosis. We evaluated the feasibility of a novel diagnosis index for early diagnosis of AD based on quantitative susceptibility mapping (QSM) and VBM.

Methods

Thirty-seven patients with AD, 24 patients with mild cognitive impairment (MCI) due to AD, and 36 cognitively normal (NC) subjects from four centers were included. A hybrid sequence was performed by using 3-T MRI with a 3D multi-echo GRE sequence to obtain both a T1-weighted image for VBM and phase images for QSM. The index was calculated from specific voxels in QSM and VBM images by using a linear support vector machine. The method of voxel extraction was optimized to maximize diagnostic accuracy, and the optimized index was compared with the conventional VBM-based index using receiver operating characteristic analysis.

Results

The index was optimal when voxels were extracted as increased susceptibility (AD > NC) in the parietal lobe and decreased gray matter volume (AD < NC) in the limbic system. The optimized proposed index showed excellent performance for discrimination between AD and NC (AUC = 0.94, p = 1.1 × 10−10) and good performance for MCI and NC (AUC = 0.87, p = 1.8 × 10−6), but poor performance for AD and MCI (AUC = 0.68, p = 0.018). Compared with the conventional index, AUCs were improved for all cases, especially for MCI and NC (p < 0.05).

Conclusions

In this preliminary study, the proposed index based on QSM and VBM improved the diagnostic performance between MCI and NC groups compared with the VBM-based index.

Key Points

• We developed a novel diagnostic index for Alzheimer’s disease based on quantitative susceptibility mapping (QSM) and voxel-based morphometry (VBM).

• QSM and VBM images can be acquired simultaneously in a single sequence with little increasing scan time.

• In this preliminary study, the proposed diagnostic index improved the discriminative performance between mild cognitive impairment and normal control groups compared with the conventional VBM-based index.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Abbreviations

AAL:

Automated anatomical labeling

ASL:

Arterial spin labeling

AD:

Alzheimer’s disease

DSM-5:

Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition

GM:

Gray matter

MCI:

Mild cognitive impairment

MMSE:

Mini-mental state examination

MNI:

Montreal Neurological Institute

NC:

Cognitively normal

QSM:

Quantitative susceptibility mapping

VBM:

Voxel-based morphometry

VOI:

Volume of interest

References

  1. Alzheimer’s Association (2016) 2016 Alzheimer’s disease facts and figures. Alzheimers Dement 12:459–509

    Article  Google Scholar 

  2. Ashburner J, Friston KJ (2000) Voxel-based morphometry: the methods. Neuroimage 11:805–821

    Article  CAS  Google Scholar 

  3. Matsuda H, Mizumura S, Nemoto K et al (2012) Automatic voxel-based morphometry of structural MRI by SPM8 plus diffeomorphic anatomic registration through exponentiated lie algebra improves the diagnosis of probable Alzheimer disease. AJNR Am J Neuroradiol 33:1109–1114

    Article  CAS  Google Scholar 

  4. Binnewijzend MA, Kuijer JP, Benedictus MR et al (2013) Cerebral blood flow measured with 3D pseudocontinuous arterial spin-labeling MR imaging in Alzheimer disease and mild cognitive impairment: a marker for disease severity. Radiology 267:221–230

    Article  Google Scholar 

  5. Collij LE, Heeman F, Kuijer JP et al (2016) Application of machine learning to arterial spin labeling in mild cognitive impairment and Alzheimer disease. Radiology 281:865–875

    Article  Google Scholar 

  6. Wang K, Liang M, Wang L et al (2007) Altered functional connectivity in early Alzheimer’s disease: a resting-state fMRI study. Hum Brain Mapp 28:967–978

    Article  Google Scholar 

  7. Agosta F, Pievani M, Geroldi C, Copetti M, Frisoni GB, Filippi M (2012) Resting state fMRI in Alzheimer’s disease: beyond the default mode network. Neurobiol Aging 33:1564–1578

    Article  Google Scholar 

  8. Zhang Y, Schuff N, Jahng GH et al (2007) Diffusion tensor imaging of cingulum fibers in mild cognitive impairment and Alzheimer disease. Neurology 68:13–19

    Article  CAS  Google Scholar 

  9. Graña M, Termenon M, Savio A et al (2011) Computer aided diagnosis system for Alzheimer disease using brain diffusion tensor imaging features selected by Pearson’s correlation. Neurosci Lett 502:225–229

    Article  Google Scholar 

  10. Bron EE, Smits M, Papma JM et al (2017) Multiparametric computer-aided differential diagnosis of Alzheimer’s disease and frontotemporal dementia using structural and advanced MRI. Eur Radiol 27:3372–3382

    Article  Google Scholar 

  11. Acosta-Cabronero J, Williams GB, Cardenas-Blanco A, Arnold RJ, Lupson V, Nestor PJ (2013) In vivo quantitative susceptibility mapping (QSM) in Alzheimer’s disease. PLoS One 8:e81093. https://doi.org/10.1371/journal.pone.0081093

    Article  PubMed  PubMed Central  Google Scholar 

  12. Moon Y, Han SH, Moon WJ (2016) Patterns of brain iron accumulation in vascular dementia and Alzheimer’s dementia using quantitative susceptibility mapping imaging. J Alzheimers Dis 51:737–745

    Article  CAS  Google Scholar 

  13. van Bergen JM, Li X, Hua J et al (2016) Colocalization of cerebral iron with amyloid beta in mild cognitive impairment. Sci Rep 6:35514. https://doi.org/10.1038/srep35514

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Ayton S, Fazlollahi A, Bourgeat P et al (2017) Cerebral quantitative susceptibility mapping predicts amyloid-β-related cognitive decline. Brain 140:2112–2119

    Article  Google Scholar 

  15. Kim HG, Park S, Rhee HY et al (2017) Quantitative susceptibility mapping to evaluate the early stage of Alzheimer’s disease. Neuroimage Clin 16:429–438

    Article  Google Scholar 

  16. Shmueli K, de Zwart JA, van Gelderen P, Li TQ, Dodd SJ, Duyn JH (2009) Magnetic susceptibility mapping of brain tissue in vivo using MRI phase data. Magn Reson Med 62:1510–1522

    Article  Google Scholar 

  17. Liu T, Spincemaille P, de Rochefort L, Kressler B, Wang Y (2009) Calculation of susceptibility through multiple orientation sampling (COSMOS): a method for conditioning the inverse problem from measured magnetic field map to susceptibility source image in MRI. Magn Reson Med 61:196–204

    Article  Google Scholar 

  18. Liu T, Liu J, de Rochefort L et al (2011) Morphology enabled dipole inversion (MEDI) from a single-angle acquisition: comparison with COSMOS in human brain imaging. Magn Reson Med 66:777–783

    Article  Google Scholar 

  19. Sato R, Shirai T, Taniguchi Y, Murase T, Bito Y, Ochi H (2017) Quantitative susceptibility mapping using the multiple dipole-inversion combination with k-space segmentation method. Magn Reson Med Sci 16:340–350

    Article  CAS  Google Scholar 

  20. Langkammer C, Schweser F, Krebs N et al (2012) Quantitative susceptibility mapping (QSM) as a means to measure brain iron? A post mortem validation study. Neuroimage 62:1593–1599

    Article  Google Scholar 

  21. Sun H, Walsh AJ, Lebel RM et al (2015) Validation of quantitative susceptibility mapping with Perls’ iron staining for subcortical gray matter. Neuroimage 105:486–492

    Article  Google Scholar 

  22. Wang Y, Spincemaille P, Liu Z et al (2017) Clinical quantitative susceptibility mapping (QSM): biometal imaging and its emerging roles in patient care. J Magn Reson Imaging 46:951–971

    Article  Google Scholar 

  23. Deistung A, Schweser F, Reichenbach JR (2017) Overview of quantitative susceptibility mapping. NMR Biomed 30:e3569. https://doi.org/10.1002/nbm.3569

    Article  Google Scholar 

  24. Sato R, Kudo K, Kawata Y et al (2018) Hybrid sequence and analysis of T1-weighted imaging and quantitative susceptibility mapping for early diagnosis of Alzheimer’s diseases. In: Proceedings of AAIC 2018, Chicago.

  25. Kan H, Uchida Y, Arai N et al (2020) Simultaneous voxel-based magnetic susceptibility and morphometry analysis using magnetization-prepared spoiled turbo multiple gradient echo. NMR Biomed 33:e4272. https://doi.org/10.1002/nbm.4272

    Article  PubMed  Google Scholar 

  26. Sun H, Wilman AH (2014) Background field removal using spherical mean value filtering and Tikhonov regularization. Magn Reson Med 71:1151–1157

    Article  Google Scholar 

  27. Shirai T, Sato R, Murase T, Bito Y, Ochi H (2018) Whole brain background field removal using spherical mean value filtering and local polynomial approximation for quantitative susceptibility mapping. In: Proceedings of Joint Annual Meeting ISMRM-ESMRMB 2018, Paris

  28. Shirai T, Sato R, Taniguchi Y, Murase T, Bito Y, Ochi H (2015) Quantitative susceptibility mapping using adaptive edge-preserving filtering. In: Proceedings of the 23rd Annual Meeting of ISMRM, Toronto, Ontario, Canada.

  29. Ashburner J (2007) A fast diffeomorphic image registration algorithm. Neuroimage 38:95–113

    Article  Google Scholar 

  30. Tzourio-Mazoyer N, Landeau B, Papathanassiou D et al (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15:273–289

    Article  CAS  Google Scholar 

  31. Rolls ET, Joliot M, Tzourio-Mazoyer N (2015) Implementation of a new parcellation of the orbitofrontal cortex in the automated anatomical labeling atlas. Neuroimage 122:1–5

    Article  Google Scholar 

  32. Hsu CW, Chang CC, Lin CJ (2003) A practical guide to support vector classification. Technical report, Department of Computer Science, National Taiwan University. Available via https://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf

  33. Carter JV, Pan J, Rai SN, Galandiuk S (2016) ROC-ing along: evaluation and interpretation of receiver operating characteristic curves. Surgery 159:1638–1645

    Article  Google Scholar 

  34. Pemberton HG, Goodkin O, Prados F et al (2021) Automated quantitative MRI volumetry reports support diagnostic interpretation in dementia: a multi-rater, clinical accuracy study. Eur Radiol 31:5312–5323

    Article  Google Scholar 

  35. van Bergen JMG, Li X, Quevenco FC et al (2018) Simultaneous quantitative susceptibility mapping and Flutemetamol-PET suggests local correlation of iron and β-amyloid as an indicator of cognitive performance at high age. Neuroimage 174:308–316

    Article  Google Scholar 

  36. Tao Y, Wang Y, Rogers JT, Wang F (2014) Perturbed iron distribution in Alzheimer’s disease serum, cerebrospinal fluid, and selected brain regions: a systematic review and meta-analysis. J Alzheimers Dis 42:679–690

    Article  CAS  Google Scholar 

  37. van Rooden S, Versluis MJ, Liem MK et al (2014) Cortical phase changes in Alzheimer’s disease at 7 T MRI: a novel imaging marker. Alzheimers Dement 10:e19–e26

    PubMed  Google Scholar 

  38. Zhu W, Zhong W, Wang W et al (2009) Quantitative MR phase-corrected imaging to investigate increased brain iron deposition of patients with Alzheimer disease. Radiology 253:497–504

    Article  Google Scholar 

  39. Nakada T, Matsuzawa H, Igarashi H, Fujii Y, Kwee IL (2008) In vivo visualization of senile-plaque-like pathology in Alzheimer’s disease patients by MR microscopy on a 7 T system. J Neuroimag 18:125–129

    Article  Google Scholar 

  40. Lovell MA, Robertson JD, Teesdale WJ, Campbell JL, Markesbery WR (1998) Copper, iron and zinc in Alzheimer’s disease senile plaques. J Neurol Sci 158:47–52

    Article  CAS  Google Scholar 

  41. Falangola MF, Lee SP, Nixon RA, Duff K, Helpern JA (2005) Histological co-localization of iron in Abeta plaques of PS/APP transgenic mice. Neurochem Res 30:201–205

    Article  CAS  Google Scholar 

  42. Farooq A, Anwar S, Awais M, Rehman S (2017) A deep CNN based multi-class classification of Alzheimer’s disease using MRI. In: Proceedings of 2017 IEEE International Conference on Imaging Systems and Techniques (IST), Beijing. https://doi.org/10.1109/IST.2017.8261460

  43. Hon M, Khan NM (2017) Towards Alzheimer’s disease classification through transfer learning. In: Proceedings of 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Kansas City, Missouri. https://doi.org/10.1109/BIBM.2017.8217822

Download references

Acknowledgements

We would like to thank Dr. Taisuke Harada, Dr. Hiroyuki Kameda, Dr. Akane Miyazaki, Kinya Ishizaka, and Taro Fujiwara (Hokkaido University Hospital) for helpful discussions and their support for this study.

Funding

This research was supported by AMED under Grant Number JP18he1402002.

Author information

Authors and Affiliations

  1. Innovative Technology Laboratory, FUJIFILM Healthcare Corporation, Tokyo, Japan

    Ryota Sato, Tomoki Amemiya, Hisaaki Ochi & Toru Shirai

  2. Department of Diagnostic Imaging, Hokkaido University Graduate School of Medicine, Sapporo, Hokkaido, Japan

    Ryota Sato, Kohsuke Kudo, Akinori Yamaguchi, Yoshitaka Bito, Hisaaki Ochi & Toru Shirai

  3. Department of Psychiatry, Hokkaido University Graduate School of Medicine, Sapporo, Hokkaido, Japan

    Niki Udo

  4. Department of Neurology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan

    Masaaki Matsushima & Ichiro Yabe

  5. Global Center for Biomedical Science and Engineering, Hokkaido University Faculty of Medicine, Sapporo, Hokkaido, Japan

    Khin Khin Tha

  6. Institute for Biomedical Sciences, Iwate Medical University, Morioka, Iwate, Japan

    Makoto Sasaki

  7. Department of Radiology, Tokushima University, Tokushima, Japan

    Masafumi Harada

  8. Department of Neurology, Nagoya City University, Nagoya, Aichi, Japan

    Noriyuki Matsukawa

  9. Radiation Diagnostic Systems Division, FUJIFILM Healthcare Corporation, Tokyo, Japan

    Yasuo Kawata & Yoshitaka Bito

Authors
  1. Ryota Sato
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kohsuke Kudo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Niki Udo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Masaaki Matsushima
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ichiro Yabe
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Akinori Yamaguchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Khin Khin Tha
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Makoto Sasaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Masafumi Harada
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Noriyuki Matsukawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Tomoki Amemiya
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Yasuo Kawata
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Yoshitaka Bito
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Hisaaki Ochi
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Toru Shirai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ryota Sato.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Prof. Kohsuke Kudo.

Conflict of Interest

Ryota Sato, Tomoki Amemiya, Yasuo Kawata, Yoshitaka Bito, Hisaaki Ochi, and Toru Shirai are employees of FUJIFILM Healthcare Corporation. Kohsuke Kudo, Makoto Sasaki, and Masafumi Harada receive research funding from FUJIFILM Healthcare Corporation.

Statistics and Biometry

One of the authors has significant statistical expertise.

No complex statistical methods were necessary for this paper.

Informed Consent

Written informed consent was obtained from all subjects in the prospective cohort.

Ethical Approval

Institutional Review Board approval was obtained.

Methodology

• prospective and retrospective

• cross-sectional study, observational

• multicenter study

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

ESM 1

(DOCX 207 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sato, R., Kudo, K., Udo, N. et al. A diagnostic index based on quantitative susceptibility mapping and voxel-based morphometry may improve early diagnosis of Alzheimer’s disease. Eur Radiol 32, 4479–4488 (2022). https://doi.org/10.1007/s00330-022-08547-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-022-08547-3

Keywords

Search

Navigation