Test-retest variability of brain morphometry analysis: an investigation of sequence and coil effects

Song, Yan; Qian, Tianyi; Maréchal, Bénédicte; Kober, Tobias; Zhang, Xianchang; Zhu, Jinxia; Lei, Jing; Li, Mingli; Jin, Zhengyu

doi:10.21037/atm.2019.11.149

Cited by 22 publications

(15 citation statements)

References 31 publications

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“…Therefore, our method was more reliable in the intra-scanner reliability test. Compared with previous results on the performance of Siemens scanners, the intra-scanner reliability of Qbrain (0.9-1.67%) was much smaller than that of FS (0-16.46%) and another work-in-progress package issued by Yan et al (24) (0-9.89%).…”

Section: Intra-scanner Reproducibilitycontrasting

confidence: 80%

Improving segmentation reliability of multi-scanner brain images using a generative adversarial network

Niu¹,

Li²,

Zhang³

et al. 2022

Quant Imaging Med Surg

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Background: Magnetic resonance (MR) images generated by different scanners generally have inconsistent contrast properties, making it difficult to perform a combined quantitative analysis of images from a range of scanners. In this study, we aimed to develop an automatic brain image segmentation model to provide a more reliable analysis of MR images taken with different scanners. Methods:The spatially localized atlas network tiles-27 (SLANT-27) deep learning model was used to train the automatic segmentation module, based on a multi-center dataset of 1,917 three-dimensional (3D) T1weighted MR images. Subsequently, a framework called Qbrain, consisting of a new generative adversarial network (GAN) image transfer module and the SLANT-27 segmentation module, was developed. Another 3D T1-weighted MRI interscan dataset of 48 participants who were scanned in 3 MRI scanners (1.5T Siemens Avanto, 3T Siemens Trio Tim, and 3T Philips Ingenia) on the same day was used to train and test the Qbrain model. Volumetric T1-weighted images were processed with Qbrain, SLANT-27, and FreeSurfer (FS). The automatic segmentation reliability across the scanners was assessed using test-retest variability (TRV). Results:The reproducibility of different segmentation methods across scanners showed a consistent trend in the greater reliability and robustness of QBrain compared to SLANT-27 which, in turn, showed greater reliability and robustness compared to FS. Furthermore, when the GAN image transfer module was added, the mean segmentation error of the TRV of the 3T Siemens vs. 1.5T Siemens, the 3T Philips vs. 1.5T Siemens, and the 3T Siemens vs. 3T Philips scanners was reduced by 1.57%, 2.01%, and 0.56%, respectively.In addition, the segmentation model improved intra-scanner variability (0.9-1.67%) compared with that of FS (2.47-4.32%).

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Section: Intra-scanner Reproducibilitycontrasting

confidence: 80%

Improving segmentation reliability of multi-scanner brain images using a generative adversarial network

Niu¹,

Li²,

Zhang³

et al. 2022

Quant Imaging Med Surg

Self Cite

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“…Previous reports showed that results of CTh analyses can be in uenced by scanner platform [56,57], eld strength [58][59][60], pulse sequence [58,61,62], the number of coil channels [61], scanner relocation [63], and imaging sites [56,64]. As shown in Table 2, differences in scanner manufacturer and platform (Siemens, Philips, and GE), eld strength (3.0 T and 1.5 T), head coil (8-, 12-, 32-, and 64-channel), MR sequence (MPRAGE, FFE, TFE, FLASH, and FSPGR), TR/TE, and voxel size (from 1.33 × 1.0 × 1.0 to 0.89 × 0.89 × 0.8 mm 3 ) across studies were noted.…”

Section: Discussionmentioning

confidence: 99%

Cortical thickness in migraine: a coordinate-based meta-analysis

Sheng

Shi³

et al. 2020

Preprint

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Background Cortical thickness (CTh) analysis is a popular method to characterize brain morphometry. Many studies have been performed to investigate CTh abnormalities in migraine. However, the results from these studies were not consistent and even conflicting. These divergent results hinder us from obtaining a clear picture of brain morphometry regarding CTh alterations in migraine. Coordinate-based meta-analysis (CBMA) is a promising technique to quantitatively pool individual neuroimaging studies to identify consistent brain areas involved. Methods Electronic databases (PubMed, Embase, Web of Science, China National Knowledge Infrastructure, WanFang, and SinoMed) and other sources (bioRxiv and reference lists of relevant articles and reviews) were systematically searched for studies that compared regional CTh differences between patients with migraine and healthy controls (HCs) up to May 15, 2020. A CBMA was performed using the Seed-based d Mapping with Permutation of Subject Images (SDM-PSI) approach. Results In total, we identified 16 studies with 17 datasets reported that were eligible for the CBMA. The 17 datasets included 872 patients with migraine (average sample size 51.3, mean age 39.6 years, 721 females) and 949 HCs (average sample size 59.3, mean age 44.2 years, 680 females). The CBMA detected no statistically significant consistency of CTh alterations in patients with migraine relative to HCs. Sensitivity analysis and subgroup analysis verified this result to be robust. Meta-regression analyses revealed that this CBMA result was not confounded by age, gender, aura, attack frequency per month, and illness duration. Conclusions Our CBMA adds to the evidence of the replication crisis in neuroimaging research that is increasingly recognized. The current evidence suggests that CTh is not a reliable biomarker of migraine. Many potential confounders, such as underpowered sample size, heterogeneous patient selection criteria, and differences in imaging collection and methodology, may contribute to the inconsistencies of CTh alterations in migraine, which merit attention before planning future research on this topic.

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“…MRI data of most SBM studies included in the CMBA were acquired at a single site except for the Parkinson’s Progression Markers Initiative (PPMI) cohort, a multicenter database [ 68 ]. Several studies have reported the effects of scanner platform [ 83 , 84 ], field strength [ 85 – 87 ], pulse sequence [ 85 , 88 , 89 ], number of coil channels [ 88 ], scanner relocation [ 90 ], imaging sites [ 83 , 91 ], type of computing workstation [ 92 ], and operating systems [ 92 , 93 ] on cerebral CTh measurements. A higher field strength, multi-echo sequence, more coil channels, harmonization of CTh measurements across scanners and sites, use of homogeneous sets of platforms, and constant operating systems would reduce the bias and improve the reproducibility.…”

Section: Discussionmentioning

confidence: 99%

Cortical thickness in Parkinson's disease: a coordinate-based meta-analysis

Sheng

Zhao

et al. 2021

Aging

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Parkinson’s disease (PD) is a common age-related neurodegenerative disease that affects the structural architecture of the cerebral cortex. Cortical thickness (CTh) via surface-based morphometry (SBM) analysis is a popular measure to assess brain structural alterations in the gray matter in PD. However, the results of CTh analysis in PD lack consistency and have not been systematically reviewed. We conducted a comprehensive coordinate-based meta-analysis (CBMA) of 38 CTh studies (57 comparison datasets) in 1,843 patients with PD using the latest seed-based d mapping software. Compared with 1,172 healthy controls, no significantly consistent CTh alterations were found in patients with PD, suggesting CTh as an unreliable neuroimaging marker for PD. The lack of consistent CTh alterations in PD could be ascribed to the heterogeneity in clinical populations, variations in imaging methods, and underpowered small sample sizes. These results highlight the need to control for potential confounding factors to produce robust and reproducible CTh results in PD.

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Test-retest variability of brain morphometry analysis: an investigation of sequence and coil effects

Cited by 22 publications

References 31 publications

Improving segmentation reliability of multi-scanner brain images using a generative adversarial network

Improving segmentation reliability of multi-scanner brain images using a generative adversarial network

Cortical thickness in migraine: a coordinate-based meta-analysis

Cortical thickness in Parkinson's disease: a coordinate-based meta-analysis

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