Bias Field Correction for MRI Images

Juntu, Jaber; Sijbers, Jan; Dyck, D. Van; Gielen, Jan

doi:10.1007/3-540-32390-2_64

Cited by 81 publications

(54 citation statements)

References 6 publications

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“…In those cases, the left breast was assessed. The density calculation was performed with 3D T1-weighted axial breast MRI images without fat saturation, using the NIH Fiji (ImageJ) image processing software, with the following steps as shown in Figure 2: 1) Image intensity bias field artefacts were corrected [31], using a 3D Gaussian filter approach to compensate for the low-frequency spatial intensity variation due to magnetic field heterogeneity prior to the segmentation process [32]; 2) Chest regions were removed from the image using a mask drawn along the pectoral muscle; 3) The breast area was cropped; 4) The entire breast was segmented using the Fiji (ImageJ) minimum/auto threshold method interactively; and 5) The bright fatty tissue was segmented using the Fiji moment/auto threshold method interactively. All the above calculations were performed throughout the entire stack of 3D breast MRI images using automated macros.…”

Section: Methodsmentioning

confidence: 99%

Association between breast cancer, breast density, and body adiposity evaluated by MRI

et al. 2015

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Objective Despite the lack of reliable methods with which to measure breast density from 2D mammograms, numerous studies have demonstrated a positive association between breast cancer and breast density. The goal of this study was to study the association between breast cancer and body adiposity, as well as breast density quantitatively assessed from 3D MRI breast images. Methods Breast density was calculated from 3D T1-weighted MRI images. The thickness of the upper abdominal adipose layer was used as a surrogate marker for body adiposity. We evaluated the correlation between breast density, age, body adiposity, and breast cancer. Results Breast density was calculated for 410 patients with unilateral invasive breast cancer, 73 patients with DCIS, and 361 controls without breast cancer. Breast density was inversely related to age and the thickness of the upper abdominal adipose layer. Breast cancer was only positively associated with body adiposity and age. Conclusion Age and body adiposity are predictive of breast density. Breast cancer was not associated with breast density; however, it was associated with the thickness of the upper abdominal adipose layer, a surrogate marker for body adiposity. Our results based on a limited number of patients warrant further investigations.

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Section: Methodsmentioning

confidence: 99%

Association between breast cancer, breast density, and body adiposity evaluated by MRI

et al. 2015

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“…T 2 WI obtained with an ERC are often affected by bias field artifacts, which were corrected using a method described previously. 27 With this method, the bias field is first estimated from acquired image data and is later subtracted from the acquired scan. Additionally, intensity drift artifacts arising from intrapatient variability in MRI can cause image intensities to lack in tissue-specific meaning.…”

Section: Mri Preprocessingmentioning

confidence: 99%

Radiomic features from pretreatment biparametric MRI predict prostate cancer biochemical recurrence: Preliminary findings

Shiradkar

Ghose

Jambor

et al. 2018

Magnetic Resonance Imaging

110

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“…The signal changes the intensity values of image pixels so that the same tissue has a different distribution of grayscale intensities across the image. 9 We applied an estimated bias field correction on the T 1 and T 2 MRIs using FMRIB Software Library (FSL) FAST ( Figure 2), 10 which will be further described in Section 2.3.…”

Section: Data Acquisitionmentioning

confidence: 99%

A High-Resolution Head and Brain Computer Model for Forward and Inverse EEG Simulation

Tate

Burton

et al. 2019

Preprint

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To conduct computational forward and inverse EEG studies of brain electrical activity, researchers must construct realistic head and brain computer models, which is both challenging and time consuming. The availability of realistic head models and corresponding imaging data is limited in terms of imaging modalities and patient diversity. In this paper, we describe a detailed head modeling pipeline and provide a high-resolution, multimodal, open-source, female head and brain model. The modeling pipeline specifically outlines image acquisition, preprocessing, registration, and segmentation; three-dimensional tetrahedral mesh generation; finite element EEG simulations; and visualization of the model and simulation results. The dataset includes both functional and structural images and EEG recordings from two high-resolution electrode configurations. The intermediate results and software components are also included in the dataset to facilitate modifications to the pipeline. This project will contribute to neuroscience research by providing a high-quality dataset that can be used for a variety of applications and a computational pipeline that may help researchers construct new head models more efficiently.

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Bias Field Correction for MRI Images

Cited by 81 publications

References 6 publications

Association between breast cancer, breast density, and body adiposity evaluated by MRI

Association between breast cancer, breast density, and body adiposity evaluated by MRI

Radiomic features from pretreatment biparametric MRI predict prostate cancer biochemical recurrence: Preliminary findings

A High-Resolution Head and Brain Computer Model for Forward and Inverse EEG Simulation

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