N4ITK: Improved N3 Bias Correction

Tustison, Nicholas J.; Avants, Brian B.; Cook, Philip A.; Zheng, Yuanjie; Egan, A; Yushkevich, Paul A.; Gee, James C.

doi:10.1109/tmi.2010.2046908

Cited by 4,945 publications

(3,394 citation statements)

References 24 publications

Supporting

Mentioning

3,389

Contrasting

Unclassified

Order By: Relevance

“…T1w and T2w images were brain‐extracted (FSL's Brain Extraction Tool; FSL 5.0.8; http://fsl.fmrib.ox.ac.uk/fsl) and corrected for bias field inhomogeneities 21. Each subject's T1w image was aligned to an age‐appropriate template22 using nonlinear registration 23.…”

Section: Methodsmentioning

confidence: 99%

Multimodal image analysis of clinical influences on preterm brain development

Ball

Aljabar

Nongena

et al. 2017

Annals of Neurology

View full text Add to dashboard Cite

ObjectivePremature birth is associated with numerous complex abnormalities of white and gray matter and a high incidence of long‐term neurocognitive impairment. An integrated understanding of these abnormalities and their association with clinical events is lacking. The aim of this study was to identify specific patterns of abnormal cerebral development and their antenatal and postnatal antecedents.MethodsIn a prospective cohort of 449 infants (226 male), we performed a multivariate and data‐driven analysis combining multiple imaging modalities. Using canonical correlation analysis, we sought separable multimodal imaging markers associated with specific clinical and environmental factors and correlated to neurodevelopmental outcome at 2 years.ResultsWe found five independent patterns of neuroanatomical variation that related to clinical factors including age, prematurity, sex, intrauterine complications, and postnatal adversity. We also confirmed the association between imaging markers of neuroanatomical abnormality and poor cognitive and motor outcomes at 2 years.InterpretationThis data‐driven approach defined novel and clinically relevant imaging markers of cerebral maldevelopment, which offer new insights into the nature of preterm brain injury. Ann Neurol 2017;82:233–246

show abstract

Section: Methodsmentioning

confidence: 99%

Multimodal image analysis of clinical influences on preterm brain development

Ball

Aljabar

Nongena

et al. 2017

Annals of Neurology

View full text Add to dashboard Cite

show abstract

“…• N4 bias field correction ( Tustison et al, 2010 ) to remove magnetic field inhomogeneity effect: Bspline grid resolution = 400, number of iteration = 200 (at each grid resolution), convergence threshold = 0.001, Bspline order = 3, Spline distance = 400, number of histograms = 256 and shrink factor = 3.…”

Section: Data Preprocessingmentioning

confidence: 99%

Comparison of atlas-based techniques for whole-body bone segmentation

Arabi

Zaidi

2017

Medical Image Analysis

View full text Add to dashboard Cite

a b s t r a c tWe evaluate the accuracy of whole-body bone extraction from whole-body MR images using a number of atlas-based segmentation methods. The motivation behind this work is to find the most promising approach for the purpose of MRI-guided derivation of PET attenuation maps in whole-body PET/MRI. To this end, a variety of atlas-based segmentation strategies commonly used in medical image segmentation and pseudo-CT generation were implemented and evaluated in terms of whole-body bone segmentation accuracy. Bone segmentation was performed on 23 whole-body CT/MR image pairs via leave-one-out cross validation procedure. The evaluated segmentation techniques include: (i) intensity averaging (IA), (ii) majority voting (MV), (iii) global and (iv) local (voxel-wise) weighting atlas fusion frameworks implemented utilizing normalized mutual information (NMI), normalized cross-correlation (NCC) and mean square distance (MSD) as image similarity measures for calculating the weighting factors, along with other atlas-dependent algorithms, such as (v) shape-based averaging (SBA) and (vi) Hofmann's pseudo-CT generation method. The performance evaluation of the different segmentation techniques was carried out in terms of estimating bone extraction accuracy from whole-body MRI using standard metrics, such as Dice similarity (DSC) and relative volume difference (RVD) considering bony structures obtained from intensity thresholding of the reference CT images as the ground truth. Considering the Dice criterion, global weighting atlas fusion methods provided moderate improvement of whole-body bone segmentation (DSC = 0.65 ± 0.05) compared to non-weighted IA (DSC = 0.60 ± 0.02). The local weighed atlas fusion approach using the MSD similarity measure outperformed the other strategies by achieving a DSC of 0.81 ± 0.03 while using the NCC and NMI measures resulted in a DSC of 0.78 ± 0.05 and 0.75 ± 0.04, respectively. Despite very long computation time, the extracted bone obtained from both SBA (DSC = 0.56 ± 0.05) and Hofmann's methods (DSC = 0.60 ± 0.02) exhibited no improvement compared to non-weighted IA. Finding the optimum parameters for implementation of the atlas fusion approach, such as weighting factors and image similarity patch size, have great impact on the performance of atlas-based segmentation approaches. The voxel-wise atlas fusion approach exhibited excellent performance in terms of cancelling out the non-systematic registration errors leading to accurate and reliable segmentation results. Denoising and normalization of MR images together with optimization of the involved parameters play a key role in improving bone extraction accuracy.

show abstract

“…First, the data were skull‐stripped by using the different MP2RAGE output volumes (UNI, T 1 map and INV2; see Marques et al, 2010). The INV2 (i.e., PDw) image was used to obtain a brain mask, as it provides the best intra‐ and extracranial tissue contrast, especially after removal of any RF bias field using ANTs' (Advanced Normalization Tools) N4BiasFieldRemoval tool (Tustison et al, 2010). Remaining non‐brain tissue was removed using probability maps of the dura mater and arteries.…”

Section: Methodsmentioning

confidence: 99%

The impact of correction on MP2RAGE cortical T₁ and apparent cortical thickness at 7T

Haast

Ivanov

Uludağ

2018

Human Brain Mapping

View full text Add to dashboard Cite

Determination of cortical thickness using MRI has often been criticized due to the presence of various error sources. Specifically, anatomical MRI relying on T1 contrast may be unreliable due to spatially variable image contrast between gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF). Especially at ultra‐high field (≥ 7T) MRI, transmit and receive B1‐related image inhomogeneities can hamper correct classification of tissue types. In the current paper, we demonstrate that residual normalB1+ (transmit) inhomogeneities in the T1‐weighted and quantitative T1 images using the MP2RAGE sequence at 7T lead to biases in cortical thickness measurements. As expected, post‐hoc correction for the spatially varying normalB1+ profile reduced the apparent T1 values across the cortex in regions with low normalB1+, and slightly increased apparent T1 in regions with high normalB1+. As a result, improved contrast‐to‐noise ratio both at the GM‐CSF and GM‐WM boundaries can be observed leading to more accurate surface reconstructions and cortical thickness estimates. Overall, the changes in cortical thickness ranged between a 5% decrease to a 70% increase after normalB1+ correction, reducing the variance of cortical thickness values across the brain dramatically and increasing the comparability with normative data. More specifically, the cortical thickness estimates increased in regions characterized by a strong decrease of apparent T1 after normalB1+ correction in regions with low normalB1+ due to improved detection of the pial surface. The current results suggest that cortical thickness can be more accurately determined using MP2RAGE data at 7T if normalB1+ inhomogeneities are accounted for.

show abstract

N4ITK: Improved N3 Bias Correction

Cited by 4,945 publications

References 24 publications

Multimodal image analysis of clinical influences on preterm brain development

Multimodal image analysis of clinical influences on preterm brain development

Comparison of atlas-based techniques for whole-body bone segmentation

The impact of correction on MP2RAGE cortical T₁ and apparent cortical thickness at 7T

Contact Info

Product

Resources

About

N4ITK: Improved N3 Bias Correction

Cited by 4,945 publications

References 24 publications

Multimodal image analysis of clinical influences on preterm brain development

Multimodal image analysis of clinical influences on preterm brain development

Comparison of atlas-based techniques for whole-body bone segmentation

The impact of correction on MP2RAGE cortical T1 and apparent cortical thickness at 7T

Contact Info

Product

Resources

About

The impact of correction on MP2RAGE cortical T₁ and apparent cortical thickness at 7T