Multi-atlas tool for automated segmentation of brain gray matter nuclei and quantification of their magnetic susceptibility

Li, Xu; Chen, Lin; Kutten, Kwame S.; Ceritoglu, Can; Li, Yue; Kang, Ningdong; Hsu, John; Qiao, Ye; Wei, Hongjiang; Liu, Chunlei; Miller, Michael I.; Mori, Susumu; Yousem, David M.; Zijl, Peter van; Faria, Andréia V.

doi:10.1016/j.neuroimage.2019.02.016

Cited by 68 publications

(73 citation statements)

References 98 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Third, ROI tracings were done manually which might have induced some unwanted errors when demarcating different DGM nuclei especially around the edges of the structures; this source of error, however, gets substantially reduced when thresholded low susceptibility values are excluded in the regional analysis. Nonetheless, the undesired errors associated with manual ROI tracing in the global analysis could be effectively minimized by using atlas-based automated DGM segmentation techniques (Li et al, 2019). Finally, the fairly low in-plane resolution used in the GRE sequence made it difficult to evaluate the sub-structures, especially the SN pars compacta whose abnormally high iron deposition is believed to be correlated with neuromelanin degeneration in the midbrain (Castellanos et al, 2015; Huddleston et al, 2017; Langley et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

Regional High Iron in the Substantia Nigra Differentiates Parkinson’s Disease Patients From Healthy Controls

Ghassaban

Sethi

et al. 2019

Front. Aging Neurosci.

View full text Add to dashboard Cite

Background: Iron is important in the pathophysiology of Parkinson’s disease (PD) specifically related to degeneration of the substantia nigra (SN). Magnetic resonance imaging (MRI) can be used to measure brain iron in the entire structure but this approach is insensitive to regional changes in iron content. Objective: The goal of this work was to use quantitative susceptibility mapping (QSM) and R2 ∗ to quantify both global and regional brain iron in PD patients and healthy controls (HC) to ascertain if regional changes correlate with clinical conditions and can be used to discriminate patients from controls. Methods: Susceptibility and R2 ∗ maps of 25 PD and 24 HC subjects were reconstructed from data collected on a 3T GE scanner. For the susceptibility maps, three-dimensional regions-of-interest (ROIs) were traced on eight deep gray matter (DGM) structures and an age-based threshold was applied to define regions of high iron content. The same multi-slice ROIs were duplicated on the R2 ∗ maps as well. Mean susceptibility values of both global and regional high iron (RII) content along with global R2 ∗ values were measured and compared not only between the two cohorts, but also to susceptibility and R2 ∗ baselines as a function of age. Finally, clinical features were compared for those PD patients lying above and below the upper 95% regional susceptibility-age prediction intervals. Results: The SN was the only structure showing significantly higher susceptibility in PD patients compared to controls globally ( p < 0.01) and regionally ( p < 0.001). The R2 ∗ values were also higher only in the SN of PD patients compared to the healthy cohort ( p < 0.05). Furthermore, those patients with abnormal susceptibility values lying above the upper 95% prediction intervals had significantly higher united Parkinson’s diagnostic rating scores. R2 ∗ values had larger errors and showed larger dispersion as a function of age than QSM data for global analysis while the dispersion was significantly less for QSM using the RII iron content. Conclusion: Abnormal iron deposition in the SN, especially in RII areas, could serve as a biomarker to distinguish PD patients from HC and to assess disease severity.

show abstract

Section: Discussionmentioning

confidence: 99%

Regional High Iron in the Substantia Nigra Differentiates Parkinson’s Disease Patients From Healthy Controls

Ghassaban

Sethi

et al. 2019

Front. Aging Neurosci.

View full text Add to dashboard Cite

show abstract

“…SWI sequences provide information on venous vasculature, hemorrhage, iron deposits, and very small vascular structures within the central nervous system (including brain tumors) [4,[15][16][17]. Fractal geometry has been demonstrated to offer appropriate tools to quantify irregular-shaped biological objects, including microvascular and SWI patterns [8], that can be used in automated algorithms for counting microbleeds [18], quantitative susceptibility mapping [19], percentage-wise quantification of intratumoral-susceptibility signals [20], local image variance [21] and other quantitative methodologies [16]. The fractal dimension, the most used parameter in fractal geometry, has been shown as a reliable numerical index to objectively quantify geometrical complexity of microvascular patterns in brain tumors [22].…”

Section: Discussionmentioning

confidence: 99%

Advanced computational and statistical multiparametric analysis of Susceptibility-Weighted Imaging to characterize gliomas and brain metastases

Ieva

Russo

Reste³

et al. 2020

Preprint

View full text Add to dashboard Cite

contributed equally to this work.Abstract. Susceptibility-weighted imaging (SWI) is a technique useful for evaluation of the internal structures of brain tumors, including microvasculature and microbleeds. Intratumoral patterns of magnetic susceptibility can be quantified by means of fractal analysis. Here, we propose a radiomics methodological pipeline to merge advanced fractal-based computational modelling with statistical analysis to objectively characterize the fingerprint of gliomas and brain metastases. Forty-seven patients with glioma (grades II-IV, according to the WHO 2016 classification system) and fourteen with brain metastases underwent 3 Tesla MRI using a SWI protocol. All images underwent computational analysis aimed to quantify three Euclidean parameters (related to tumor and SWI volume) and five fractal-based parameters (related to the pixel distribution and geometrical complexity of the SWI patterns). Principal components analysis, linear and quadratic discriminant analysis, K-nearest neighbor and support vector machine methods were used to discriminate between tumor types. The combination of parameters offered an objective evaluation of the SWI pattern in gliomas and brain metastases. The model accurately predicted 88% of glioblastoma, according to the quantification of intratumoral SWI features, failing to discriminate the other types. SWI is not normally used to classify brain tumors, however fractal-based multi-parametric computational analysis can be used to characterize intratumoral SWI patterns to objectively quantify tumors-related features. Specific parameters still have to be identified to provide completely automatic computerized differential diagnosis.

show abstract

“…Incorporating QSM contrast to enhance DGM segmentation can be done in different ways . In this work, we used the Feng et al approach that first merges T1w and QSM contrasts into a single hybrid contrast (HC), then segments this HC using FMRIB’s Integrated Registration & Segmentation Tool (FIRST) .…”

Section: Methodsmentioning

confidence: 99%

“…QSM studies have been employed for aiding DGM segmentation, where improvements have been most significant in the iron‐rich structures like GP. However, QSM sequences are not typically used in standard brain volumetric studies, which typically rely on MPRAGE.…”

Section: Introductionmentioning

confidence: 99%

On the value of QSM from MPRAGE for segmenting and quantifying iron‐rich deep gray matter

Naji

Sun

Wilman

2020

Magnetic Resonance in Med

View full text Add to dashboard Cite

Purpose: Quantitative susceptibility mapping (QSM) has been employed for both iron evaluation and segmentation of deep gray matter (DGM), but QSM sequences are not typically used in standard brain volumetric studies, which use T1-weighted magnetization-prepared rapid acquisition with gradient echo (MPRAGE) with short TE. Here, QSM produced directly from standard MPRAGE phase (QSM MPRAGE ) is evaluated for segmentation and quantification of highly iron-rich DGM regions. Methods: Simulations were used to explore quality and possible limitations. In addition, QSM from a standard multi-echo gradient-echo (QSM GRE ) was compared to QSM MPRAGE in 40 healthy adults at 3T. DGM structures with weak contrast on MPRAGE magnitude were evaluated for improving segmentation with QSM MPRAGE , with focus on the iron-rich globus pallidus (GP). Furthermore, susceptibility quantification was assessed on six DGM nuclei and compared to standard QSM GRE . Results: Limited by TE and signal-to-noise ratio, only iron-rich regions like GP and dentate nucleus produced adequate contrast on QSM MPRAGE , confining applications to such regions. QSM MPRAGE improved GP segmentation with mean Dice scores raised by 9.0%, and mean volumetric differences reduced by 9.7%. Simulations suggested that phase contrast-to-noise ratio (CNR) should be above 3.0 to attain segmentation improvement. For quantification purposes, higher CNR is required, and typical QSM MPRAGE provided comparable estimates to QSM GRE in large iron-rich DGM nuclei. Conclusion: Despite the short TE of standard MPRAGE, QSM MPRAGE can improve GP segmentation over the use of MPRAGE magnitude alone and roughly quantify high-iron regions in DGM. Thus, reconstructing QSM MPRAGE can be a useful addition to volumetric studies that rarely include standard QSM GRE . K E Y W O R D Sdeep gray matter, globus pallidus, MPRAGE, QSM, segmentation | 1487 NAJI et Al.

show abstract

Multi-atlas tool for automated segmentation of brain gray matter nuclei and quantification of their magnetic susceptibility

Cited by 68 publications

References 98 publications

Regional High Iron in the Substantia Nigra Differentiates Parkinson’s Disease Patients From Healthy Controls

Regional High Iron in the Substantia Nigra Differentiates Parkinson’s Disease Patients From Healthy Controls

Advanced computational and statistical multiparametric analysis of Susceptibility-Weighted Imaging to characterize gliomas and brain metastases

On the value of QSM from MPRAGE for segmenting and quantifying iron‐rich deep gray matter

Contact Info

Product

Resources

About