Intensity non-uniformity correction in MRI: Existing methods and their validation

Belaroussi, Boubakeur; Milles, Julien; Carme, Sabin; Zhu, Yuemin; Benoit-Cattin, Hugues

doi:10.1016/j.media.2005.09.004

Cited by 218 publications

(171 citation statements)

References 76 publications

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“…23,24 The method used in this study is simple to implement and gives a second order correction. However, it requires segmentation of a tissue that is supposed to have a homogenous intensity.…”

Section: Discussionmentioning

confidence: 99%

Automated and reproducible segmentation of visceral and subcutaneous adipose tissue from abdominal MRI

et al. 2007

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Objectives: (1) To develop a fully automated algorithm for segmentation of visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT), excluding intermuscular adipose tissue (IMAT) and bone marrow (BM), from axial abdominal magnetic resonance imaging (MRI) data. (2) To evaluate the algorithm accuracy and total method reproducibility using a semiautomatically segmented reference and data from repeated measurements. Background: MRI is a widely used in adipose tissue (AT) assessment. Manual analysis of MRI data is time consuming and biased by the operator. Automated analysis spares resources and increase reproducibility. Fully automated algorithms have been presented. However, reproducibility analysis has not been performed nor has methods for exclusion of IMAT and BM been presented. Methods: In total, 49 data sets from 31 subjects were acquired using a clinical 1.5 T MRI scanner. Thirteen data sets were used in the derivation of the automated algorithm and 36 were used in the validation. Common image analysis tools such as thresholding, morphological operations and geometrical models were used to segment VAT and SAT. Accuracy was assessed using a semi-automatically created reference. Reproducibility was assessed from repeated measurements. Results: Resulting AT volumes from the automated analysis and the reference were not found to differ significantly (2.0714% and 0.8472.7%, given as mean7s.d., for VAT and SAT, respectively). The automated analysis of the repeated measurements data significantly increased the reproducibility of the VAT measurements. One athletic subject with very small amounts of AT was considered to be an outlier. Conclusions: An automated method for segmentation of VAT and SAT and exclusion of IMAT and BM from abdominal MRI data has been reported. The accuracy and reproducibility of the method has also been demonstrated using a semi-automatically segmented reference and analysis of repeated acquisitions. The accuracy of the method is limited in lean subjects.

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Section: Discussionmentioning

confidence: 99%

Automated and reproducible segmentation of visceral and subcutaneous adipose tissue from abdominal MRI

et al. 2007

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“…The recognized texture attributes such as "smoothness", "roughness", "granularity", "directionality", etc. [4] have their origin in the physical microstructure of tissues and organs that are visualized in the image [2], [1].…”

Section: Problem Definitionmentioning

confidence: 99%

“…These variations may be induced by the nonuniformity of gradient coils, spatially dependent sensitivity of the radiofrequency coils, the image acquisition measurement sequence properties and other factors. The nonuniformities can be corrected prospectively (using phantoms) and/or retrospectively [23], [4]. The image intensity inhomogeneity in MRI is an adverse phenomenon that affects qualitative and quantitative analysis of images.…”

Section: Image Modelmentioning

confidence: 99%

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On The Effect Of Image Brightness And Contrast Nonuniformity On Statistical Texture Parameters

Materka

Strzelecki

2015

Foundations of Computing and Decision Sciences

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Abstract. Computerized texture analysis characterizes spatial patterns of image intensity, which originate in the structure of tissues. However, a number of texture descriptors also depend on local average image intensity and/or contrast. This variations, known as image nonuniformity (inhomogeneity) artefacts often occur, e.g. in MRI. Their presence may lead to errors in tissue description. This unwanted effect is explained in this paper using statistical texture descriptors applied for MRI slices of a normal and fibrotic liver. To reduce the errors, correction of image spatial nonuniformity prior to texture analysis is performed. The issue of sensitivity of popular texture parameters to image nonuniformities is discussed. It is illustrated by classification examples of natural Brodatz textures, digitally modified to account for inhomogeneities -modeled as smooth variations of image intensity and contrast. A set of texture features is identified which represent certain immunity to image inhomogeneities.

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“…These algorithms can be classified into two categories (15): prospective methods (2-9) and retrospective methods (10 -14). Prospective methods are based on prior knowledge.…”

mentioning

confidence: 99%

Projection‐based estimation and nonuniformity correction of sensitivity profiles in phased‐array surface coils

Yun

Kyriakos

Chung

et al. 2007

Magnetic Resonance Imaging

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Purpose: To develop a novel approach for calculating the accurate sensitivity profiles of phased-array coils, resulting in correction of nonuniform intensity in parallel MRI. Materials and Methods:The proposed intensity-correction method estimates the accurate sensitivity profile of each channel of the phased-array coil. The sensitivity profile is estimated by fitting a nonlinear curve to every projection view through the imaged object. The nonlinear curve-fitting efficiently obtains the low-frequency sensitivity profile by eliminating the high-frequency image contents. Filtered back-projection (FBP) is then used to compute the estimates of the sensitivity profile of each channel. The method was applied to both phantom and brain images acquired from the phased-array coil.Results: Intensity-corrected images from the proposed method had more uniform intensity than those obtained by the commonly used sum-of-squares (SOS) approach. With the use of the proposed correction method, the intensity variation was reduced to 6.1% from 13.1% of the SOS. When the proposed approach was applied to the computation of the sensitivity maps during sensitivity encoding (SENSE) reconstruction, it outperformed the SOS approach in terms of the reconstructed image uniformity. Conclusion:The proposed method is more effective at correcting the intensity nonuniformity of phased-array surface-coil images than the conventional SOS method. In addition, the method was shown to be resilient to noise and was successfully applied for image reconstruction in parallel imaging.

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Intensity non-uniformity correction in MRI: Existing methods and their validation

Cited by 218 publications

References 76 publications

Automated and reproducible segmentation of visceral and subcutaneous adipose tissue from abdominal MRI

Automated and reproducible segmentation of visceral and subcutaneous adipose tissue from abdominal MRI

On The Effect Of Image Brightness And Contrast Nonuniformity On Statistical Texture Parameters

Projection‐based estimation and nonuniformity correction of sensitivity profiles in phased‐array surface coils

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