Analysis of synthetic magnetic resonance images by multi-channel segmentation increases accuracy of volumetry in the putamen and decreases mis-segmentation in the dural sinuses

Abstract

Background

Voxel-based morphometry (VBM) using magnetic resonance imaging (MR) has been used to estimate cortical atrophy associated with various diseases. However, there are mis-segmentations of segmented gray matter image in VBM.

Purpose

To study a twofold evaluation of single- and multi-channel segmentation using synthetic MR images: (1) mis-segmentation of segmented gray matter images in transverse and cavernous sinuses; and (2) accuracy and repeatability of segmented gray matter images.

Material and Methods

A total of 13 healthy individuals were scanned with 3D quantification using an interleaved Look–Locker acquisition sequence with a T2 preparation pulse (3D-QALAS) sequence on a 1.5-T scanner. Three of the 13 healthy participants were scanned five consecutive times for evaluation of repeatability. We used SyMRI software to create images with three contrasts: T1-weighted (T1W), T2-weighted (T2W), and proton density-weighted (PDW) images. Manual regions of interest (ROI) on T1W imaging were individually set as the gold standard in the transverse sinus, cavernous sinus, and putamen. Single-channel (T1W) and multi-channel (T1W + T2W, T1W + PDW, and T1W + T2W + PDW imaging) segmentations were performed with statistical parametric mapping 12 software.

Results

We found that mis-segmentations in both the transverse and cavernous sinuses were large in single-channel segmentation compared with multi-channel segmentations. Furthermore, the accuracy of segmented gray matter images in the putamen was high in both multi-channel T1W + PDW and T1W + T2W + PDW segmentations compared with other segmentations. Finally, the highest repeatability of left putamen volumetry was found with multi-channel segmentation T1WI + PDWI.

Conclusion

Multi-channel segmentation with T1WI + PDWI provides good results for VBM compared with single-channel and other multi-channel segmentations.

Keywords

Multi-channel putamen segmentation statistical parametric mapping volumetry

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References

Ashburner

Friston

. Voxel-based morphometry--the methods. Neuroimage 2000;11:805–821.

Matsuda

Mizumura

Nemoto

, et al. 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 2012;33:109–1114.

Goto

Abe

Aoki

, et al. Combining segmented grey and white matter images improves voxel-based morphometry for the case of dilated lateral ventricles. Magn Reson Med Sci 2018;17:293–300.

Keller

Roberts

. Voxel-based morphometry of temporal lobe epilepsy: an introduction and review of the literature. Epilepsia 2008;49:741–757.

Musen

Lyoo

Sparks

, et al. Effects of type 1 diabetes on gray matter density as measured by voxel-based morphometry. Diabetes 2006;55:326–333.

Goto

Kamagata

Hatano

, et al. Depressive symptoms in Parkinson’s disease are related to decreased left hippocampal volume: correlation with the 15-item shortened version of the Geriatric Depression Scale. Acta Radiol 2018;59:341–345.

Hayano

Nakamura

Asami

, et al. Smaller amygdala is associated with anxiety in patients with panic disorder. Psychiatry Clin Neurosci 2009;63:266–276.

Scott-Wittenborn

Karadaghy

Piccirillo

, et al. A methodological assessment of studies that use voxel-based morphometry to study neural changes in tinnitus patients. Hear Res 2017;355:23–32.

Goto

Suzuki

Mizukami

, et al. Repeatability of brain volume measurements made with the atlas-based method from T1-weighted Images acquired using a 0.4 Tesla low field MR scanner. Magn Reson Med Sci 2016;15:365–370.

10.

Goto

Abe

Miyati

, et al. Mis-segmentation in voxel-based morphometry due to a signal intensity change in the putamen. Radiol Phys Technol 2017;10:515–524.

11.

Goto

Abe

Miyati

, et al. Association between iron content and gray matter missegmentation with voxel-based morphometry in basal ganglia. J Magn Reson Imaging 2013;38:958–962.

12.

Goto

Abe

Miyati

, et al. Influence of signal intensity non-uniformity on brain volumetry using an atlas-based method. Korean J Radiol 2012;13:391–402.

13.

Lindig

Kotikalapudi

Schweikardt

, et al. Evaluation of multimodal segmentation based on 3D T1-, T2– and FLAIR-weighted images - the difficulty of choosing. Neuroimage 2018;170:210–221.

14.

Viviani

Pracht

Brenner

, et al. Multimodal MEMPRAGE, FLAIR, and [Formula: see text] segmentation to resolve dura and vessels from cortical gray matter. Front Neurosci 2017;11:58.

15.

Goto

Abe

Miyati

Yamasue

Gomi

Takeda

. Head motion and correction methods in resting-state functional MRI. Magn Reson Med Sci 2016;15:178–186.

16.

Wallaert

Hagiwara

Andica

, et al. The advantage of synthetic MRI for the visualization of anterior temporal pole lesions on double inversion recovery (DIR), phase-sensitive inversion recovery (PSIR), and myelin images in a patient with CADASIL. Magn Reson Med Sci 2018;17:275–276.

17.

Kvernby

Warntjes

Engvall

, et al. Clinical feasibility of 3D-QALAS - single breath-hold 3D myocardial T1- and T2-mapping. Magn Reson Imaging 2017;38:13–20.

18.

Fujita

Hagiwara

Hori

, et al. 3D Quantitative synthetic MRI-derived cortical thickness and subcortical brain volumes: scan-rescan repeatability and comparison with conventional T1-weighted images. J Magn Reson Imaging 2019;50:1834–1842.

19.

Fujita

Hagiwara

Hori

, et al. Three-dimensional high-resolution simultaneous quantitative mapping of the whole brain with 3D-QALAS: an accuracy and repeatability study. Magn Reson Imaging 2019;63:235–243.

20.

Hagiwara

Warntjes

Hori

, et al. SyMRI of the brain: rapid quantification of relaxation rates and proton density, with synthetic MRI, automatic brain segmentation, and myelin measurement. Invest Radiol 2017;52:647–657.

21.

Andica

Hagiwara

Hori

, et al. Automated brain tissue and myelin volumetry based on quantitative MR imaging with various in-plane resolutions. J Neuroradiol 2018;45:164–168.

22.

Goto

Hagiwara

Kato

, et al. Effect of changing the analyzed image contrast on the accuracy of intracranial volume extraction using Brain Extraction Tool 2. Radiol Phys Technol 2020;13:76–82.

23.

Fazekas

Chawluk

Alavi

, et al. MR signal abnormalities at 1.5 T in Alzheimer’s dementia and normal aging. AJR Am J Roentgenol 1987;149:351–356.

24.

Ogg

Steen

. Age-related changes in brain T1 are correlated with iron concentration. Magn Reson Med 1998;40:749–753.

25.

Hallgren

Sourander

. The effect of age on the non-haemin iron in the human brain. J Neurochem 1958;3:41–51.

26.

Haacke

Cheng

House

, et al. Imaging iron stores in the brain using magnetic resonance imaging. Magn Reson Imaging 2005;23:1–25.

27.

Chen

Zhang

, et al. Iron deposition in Parkinson’s disease by quantitative susceptibility mapping. BMC Neurosci 2019;20:23.

28.

Lambert

Lutti

Helms

, et al. Multiparametric brainstem segmentation using a modified multivariate mixture of Gaussians. Neuroimage Clin 2013;2:684–694.

29.

Barron

Tantiwongkosi

, et al. Patterns of gray matter atrophy in atypical parkinsonism syndromes: a VBM meta-analysis. Brain Behav 2015;5:e00329.

30.

Xuan

Guan

Huang

, et al. Different patterns of gray matter density in early- and middle-late-onset Parkinson’s disease: a voxel-based morphometry study. Brain Imaging Behav 2019;13:172–179.

31.

Hagiwara

Otsuka

Hori

, et al. Improving the quality of synthetic FLAIR images with deep learning using a conditional generative adversarial network for pixel-by-pixel image translation. AJNR Am J Neuroradiol 2019;40:224–230.

32.

Ryu

Nam

Gho

, et al. Data-driven synthetic MRI FLAIR artifact correction via deep neural network. J Magn Reson Imaging 2019;50:1413–1423.