MR image intensity inhomogeneity correction

Intensity inhomogeneity (IIH) is often encountered in MR imaging, and a number of techniques have been devised to correct this artifact. This paper attempts to review some of the recent developments in the mathematical modeling of IIH field. Low-frequency models are widely used, but they tend to corrupt the low-frequency components of the tissue. Hypersurface models and statistical models can be adaptive to the image and generally more stable, but they are also generally more complex and consume more computer memory and CPU time. They are often formulated together with image segmentation within one framework and the overall performance is highly dependent on the segmentation process. Beside these three popular models, this paper also summarizes other techniques based on different principles. In addition, the issue of quantitative evaluation and comparative study are discussed.

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Automated Estimation of Acute Infarct Volume from Noncontrast Head CT Using Image Intensity Inhomogeneity Correction

International Journal of Biomedical Imaging ◽

10.1155/2019/1720270 ◽

2019 ◽

Vol 2019 ◽

pp. 1-8 ◽

Cited By ~ 2

Author(s):

Keith A. Cauley ◽

Gino J. Mongelluzzo ◽

Samuel W. Fielden

Keyword(s):

Image Analysis ◽

Infarct Volume ◽

Clinical Decision ◽

Subsequent Treatment ◽

Intensity Inhomogeneity ◽

Lesion Volume ◽

Head Ct ◽

Image Intensity ◽

Inhomogeneity Correction ◽

Clinical Cases

Identification of early ischemic changes (EIC) on noncontrast head CT scans performed within the first few hours of stroke onset may have important implications for subsequent treatment, though early stroke is poorly delimited on these studies. Lack of sharp lesion boundary delineation in early infarcts precludes manual volume measures, as well as measures using edge-detection or region-filling algorithms. We wished to test a hypothesis that image intensity inhomogeneity correction may provide a sensitive method for identifying the subtle regional hypodensity which is characteristic of early ischemic infarcts. A digital image analysis algorithm was developed using image intensity inhomogeneity correction (IIC) and intensity thresholding. Two different IIC algorithms (FSL and ITK) were compared. The method was evaluated using simulated infarcts and clinical cases. For synthetic infarcts, measured infarct volumes demonstrated strong correlation to the true lesion volume (for 20% decreased density “infarcts,” Pearson r = 0.998 for both algorithms); both algorithms demonstrated improved accuracy with increasing lesion size and decreasing lesion density. In clinical cases (41 acute infarcts in 30 patients), calculated infarct volumes using FSL IIC correlated with the ASPECTS scores (Pearson r = 0.680) and the admission NIHSS (Pearson r = 0.544). Calculated infarct volumes were highly correlated with the clinical decision to treat with IV-tPA. Image intensity inhomogeneity correction, when applied to noncontrast head CT, provides a tool for image analysis to aid in detection of EIC, as well as to evaluate and guide improvements in scan quality for optimal detection of EIC.

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A new non-parametric method for image intensity inhomogeneity correction using a non-uniform gradient filter and path integrals

10.1117/12.596166 ◽

2005 ◽

Author(s):

Punam K. Saha

Keyword(s):

Path Integrals ◽

Parametric Method ◽

Intensity Inhomogeneity ◽

Image Intensity ◽

Inhomogeneity Correction ◽

Non Parametric

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Kernel Graph Cuts Segmentation for MR Images with Intensity Inhomogeneity Correction

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.333-335.938 ◽

2013 ◽

Vol 333-335 ◽

pp. 938-943

Author(s):

Qing Luo ◽

Wen Jian Qin ◽

Jia Gu

Keyword(s):

Image Data ◽

Graph Cuts ◽

Intensity Inhomogeneity ◽

Mr Images ◽

Mr Image ◽

Fcm Algorithm ◽

The Real ◽

Inhomogeneity Correction ◽

Segmentation Approach ◽

Correction Step

Since the phenomena of intensity inhomogeneity in MR images are prominent and adversely affect quantitative image analysis .In this paper ,we propose a novel magnetic resonance (MR) image segmentation approach based on the kernel graph cuts technique .Because of automatic multiregion segmentation and global energy minimization ,the kernel graph cuts method can be applied to many kinds of images segmentation ,such as MR images and so on . To reduce or eliminate intensity inhomogeneity in MR images ,we add the intensity inhomogeneity correction step which is based on fuzzy c-means (FCM) algorithm before the segment procedure .Firstly , the real MR image data obtained after bias corrected by FCM algorithm .Secondly ,we segment the real MR image data by kernel graph cuts method .Experiments show that the kernel graph cuts method with intensity inhomogeneity correction have a better segment result in accuracy and over-segmentation .

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Liver MRI segmentation with edge-preserved intensity inhomogeneity correction

Signal Image and Video Processing ◽

10.1007/s11760-017-1221-5 ◽

2017 ◽

Vol 12 (4) ◽

pp. 791-798 ◽

Cited By ~ 3

Author(s):

Hui Liu ◽

Pinpin Tang ◽

Dongmei Guo ◽

HaiXia Liu ◽

Yuanjie Zheng ◽

...

Keyword(s):

Intensity Inhomogeneity ◽

Liver Mri ◽

Mri Segmentation ◽

Inhomogeneity Correction

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Volume and surface coil simultaneous reception (VSSR) method for intensity inhomogeneity correction in MRI

Technology and Health Care ◽

10.3233/thc-213149 ◽

2021 ◽

pp. 1-12

Author(s):

Lin Wu ◽

Tian He ◽

Jie Yu ◽

Hang Liu ◽

Shuang Zhang ◽

...

Keyword(s):

Bias Field ◽

Correction Method ◽

Surface Coil ◽

Intensity Inhomogeneity ◽

Acquisition Time ◽

Surface Coils ◽

In Vivo Experiments ◽

Inhomogeneity Correction ◽

Volume Coil

BACKGROUND: Addressing intensity inhomogeneity is critical in magnetic resonance imaging (MRI) because associated errors can adversely affect post-processing and quantitative analysis of images (i.e., segmentation, registration, etc.), as well as the accuracy of clinical diagnosis. Although several prior methods have been proposed to eliminate or correct intensity inhomogeneity, some significant disadvantages have remained, including alteration of tissue contrast, poor reliability and robustness of algorithms, and prolonged acquisition time. OBJECTIVE: In this study, we propose an intensity inhomogeneity correction method based on volume and surface coils simultaneous reception (VSSR). METHODS: The VSSR method comprises of two major steps: 1) simultaneous image acquisition from both volume and surface coils and 2) denoising of volume coil images and polynomial surface fitting of bias field. Extensive in vivo experiments were performed considering various anatomical structures, acquisition sequences, imaging resolutions, and orientations. In terms of correction performance, the proposed VSSR method was comparatively evaluated against several popular methods, including multiplicative intrinsic component optimization and improved nonparametric nonuniform intensity normalization bias correction methods. RESULTS: Experimental results show that VSSR is more robust and reliable and does not require prolonged acquisition time with the volume coil. CONCLUSION: The VSSR may be considered suitable for general implementation.

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