Retrospective correction of MR intensity inhomogeneity by information minimization

Data adaptive regularization with reference tissue constraints for liver quantitative susceptibility mapping

PURPOSE

To improve repeatability and reproducibility across acquisition parameters and reduce bias in quantitative susceptibility mapping (QSM) of the liver, through development of an optimized regularized reconstruction algorithm for abdominal QSM.

METHODS

An optimized approach to estimation of magnetic susceptibility distribution is formulated as a constrained reconstruction problem that incorporates estimates of the input data reliability and anatomical priors available from chemical shift-encoded imaging. The proposed data-adaptive method was evaluated with respect to bias, repeatability, and reproducibility in a patient population with a wide range of liver iron concentration (LIC). The proposed method was compared to the previously proposed and validated approach in liver QSM for two multi-echo spoiled gradient-recalled echo protocols with different acquisition parameters at 3T. Linear regression was used for evaluation of QSM methods against a reference FDA-approvedR 2 $$ {R}_2 $$ -based LIC measure andR 2 ∗ $$ {R}_2^{\ast } $$ measurements; repeatability/reproducibility were assessed by Bland-Altman analysis.

RESULTS

The data-adaptive method produced susceptibility maps with higher subjective quality due to reduced shading artifacts. For both acquisition protocols, higher linear correlation with bothR 2 $$ {R}_2 $$ - andR 2 ∗ $$ {R}_2^{\ast } $$ -based measurements were observed for the data-adaptive method (r 2 = 0 . 74 / 0 . 69 $$ {r}^2=0.74/0.69 $$ forR 2 $$ {R}_2 $$ ,0 . 97 / 0 . 95 $$ 0.97/0.95 $$ forR 2 ∗ $$ {R}_2^{\ast } $$ ) than the standard method (r 2 = 0 . 60 / 0 . 66 $$ {r}^2=0.60/0.66 $$ and0 . 79 / 0 . 88 $$ 0.79/0.88 $$ ). For both protocols, the data-adaptive method enabled better test-retest repeatability (repeatability coefficients 0.19/0.30 ppm for the data-adaptive method, 0.38/0.47 ppm for the standard method) and reproducibility across protocols (reproducibility coefficient 0.28 vs. 0.53ppm) than the standard method.

CONCLUSIONS

The proposed data-adaptive QSM algorithm may enable quantification of LIC with improved repeatability/reproducibility across different acquisition parameters as 3T.

Delayed Surgical Closure of the Patent Ductus Arteriosus: Does the Brain Pay the Price?

OBJECTIVE

To investigate the relation between duration of hemodynamically significant patent ductus arteriosus (PDA), cerebral oxygenation, magnetic resonance imaging-determined brain growth, and 2-year neurodevelopmental outcome in a cohort of infants born preterm whose duct was closed surgically.

STUDY DESIGN

Infants born preterm at <30 weeks of gestational age who underwent surgical ductal closure between 2008 and 2018 (n = 106) were included in this observational study. Near infrared spectroscopy-monitored cerebral oxygen saturation during and up to 24 hours after ductal closure and a Bayley III developmental test at the corrected age of 2 years is the institutional standard of care for this patient group. Infants also had magnetic resonance imaging at term-equivalent age.

RESULTS

In total, 90 infants fulfilled the inclusion criteria (median [range]: 25.9 weeks [24.0-28.9]; 856 g [540-1350]. Days of a PDA ranged from 1 to 41. Multivariable linear regression analysis showed that duration of a PDA negatively influenced cerebellar growth and motor and cognitive outcome at 2 years of corrected age.

CONCLUSIONS

Prolonged duration of a PDA in this surgical cohort is associated with reduced cerebellar growth and suboptimal neurodevelopmental outcome.

Jointly estimating bias field and reconstructing uniform MRI image by deep learning

Bias field is one of the main artifacts that degrade the quality of magnetic resonance images. It introduces intensity inhomogeneity and affects image analysis such as segmentation. In this work, we proposed a deep learning approach to jointly estimate bias field and reconstruct uniform image. By modeling the quality degradation process as the product of a spatially varying field and a uniform image, the network was trained on 800 images with true bias fields from 12 healthy subjects. A network structure of bias field estimation and uniform image reconstruction was designed to compensate for the intensity loss. To further evaluate the benefit of bias field correction, a quantitative analysis was made on image segmentation. Experimental results show that the proposed BFCNet improves the image uniformity by 8.3% and 10.1%, the segmentation accuracy by 4.1% and 6.8% on white and grey matter in T2-weighted brain images. Moreover, BFCNet outperforms the state-of-the-art traditional methods and deep learning methods on estimating bias field and preserving image structure, and BFCNet is robust to different levels of bias field and noise.

ABCnet: Adversarial bias correction network for infant brain MR images

Automatic correction of intensity nonuniformity (also termed as the bias correction) is an essential step in brain MR image analysis. Existing methods are typically developed for adult brain MR images based on the assumption that the image intensities within the same brain tissue are relatively uniform. However, this assumption is not valid in infant brain MR images, due to the dynamic and regionally-heterogeneous image contrast and appearance changes, which are caused by the underlying spatiotemporally-nonuniform myelination process. Therefore, it is not appropriate to directly use existing methods to correct the infant brain MR images. In this paper, we propose an end-to-end 3D adversarial bias correction network (ABCnet), tailored for direct prediction of bias fields from the input infant brain MR images for bias correction. The "ground-truth" bias fields for training our network are carefully defined by an improved N4 method, which integrates manually-corrected tissue segmentation maps as anatomical prior knowledge. The whole network is trained alternatively by minimizing generative and adversarial losses. To handle the heterogeneous intensity changes, our generative loss includes a tissue-aware local intensity uniformity term to reduce the local intensity variation in the corrected image. Besides, it also integrates two additional terms to enhance the smoothness of the estimated bias field and to improve the robustness of the proposed method, respectively. Comprehensive experiments with different sizes of training datasets have been carried out on a total of 1492 T1w and T2w MR images from neonates, infants, and adults, respectively. Both qualitative and quantitative evaluations on simulated and real datasets consistently demonstrate the superior performance of our ABCnet in both accuracy and efficiency, compared with popularly available methods.

Normative Analysis of Individual Brain Differences Based on a Population MRI-Based Atlas of Cynomolgus Macaques

The developmental trajectory of the primate brain varies substantially with aging across subjects. However, this ubiquitous variability between individuals in brain structure is difficult to quantify and has thus essentially been ignored. Based on a large-scale structural magnetic resonance imaging dataset acquired from 162 cynomolgus macaques, we create a species-specific 3D template atlas of the macaque brain, and deploy normative modeling to characterize individual variations of cortical thickness (CT) and regional gray matter volume (GMV). We observed an overall decrease in total GMV and mean CT, and an increase in white matter volume from juvenile to early adult. Specifically, CT and regional GMV were greater in prefrontal and temporal cortices relative to early unimodal areas. Age-dependent trajectories of thickness and volume for each cortical region revealed an increase in the medial temporal lobe, and decreases in all other regions. A low percentage of highly individualized deviations of CT and GMV were identified (0.0021%, 0.0043%, respectively, P < 0.05, false discovery rate [FDR]-corrected). Our approach provides a natural framework to parse individual neuroanatomical differences for use as a reference standard in macaque brain research, potentially enabling inferences regarding the degree to which behavioral or symptomatic variables map onto brain structure in future disease studies.

Intensity non-uniformity correction in MR imaging using residual cycle generative adversarial network

Correcting or reducing the effects of voxel intensity non-uniformity (INU) within a given tissue type is a crucial issue for quantitative magnetic resonance (MR) image analysis in daily clinical practice. Although having no severe impact on visual diagnosis, the INU can highly degrade the performance of automatic quantitative analysis such as segmentation, registration, feature extraction and radiomics. In this study, we present an advanced deep learning based INU correction algorithm called residual cycle generative adversarial network (res-cycle GAN), which integrates the residual block concept into a cycle-consistent GAN (cycle-GAN). In cycle-GAN, an inverse transformation was implemented between the INU uncorrected and corrected magnetic resonance imaging (MRI) images to constrain the model through forcing the calculation of both an INU corrected MRI and a synthetic corrected MRI. A fully convolution neural network integrating residual blocks was applied in the generator of cycle-GAN to enhance end-to-end raw MRI to INU corrected MRI transformation. A cohort of 55 abdominal patients with T1-weighted MR INU images and their corrections with a clinically established and commonly used method, namely, N4ITK were used as a pair to evaluate the proposed res-cycle GAN based INU correction algorithm. Quantitatively comparisons of normalized mean absolute error (NMAE), peak signal-to-noise ratio (PSNR), normalized cross-correlation (NCC) indices, and spatial non-uniformity (SNU) were made among the proposed method and other approaches. Our res-cycle GAN based method achieved an NMAE of 0.011 ± 0.002, a PSNR of 28.0 ± 1.9 dB, an NCC of 0.970 ± 0.017, and a SNU of 0.298 ± 0.085. Our proposed method has significant improvements (p < 0.05) in NMAE, PSNR, NCC and SNU over other algorithms including conventional GAN and U-net. Once the model is well trained, our approach can automatically generate the corrected MR images in a few minutes, eliminating the need for manual setting of parameters.

Context Dependent Fuzzy Associated Statistical Model for Intensity Inhomogeneity Correction From Magnetic Resonance Images

In this paper, a novel context-dependent fuzzy set associated statistical model-based intensity inhomogeneity correction technique for magnetic resonance image (MRI) is proposed. The observed MRI is considered to be affected by intensity inhomogeneity and it is assumed to be a multiplicative quantity. In the proposed scheme the intensity inhomogeneity correction and MRI segmentation is considered as a combined task. The maximum a posteriori probability (MAP) estimation principle is explored to solve this problem. A fuzzy set associated Gibbs’ Markov random field (MRF) is considered to model the spatio-contextual information of an MRI. It is observed that the MAP estimate of the MRF model does not yield good results with any local searching strategy, as it gets trapped to local optimum. Hence, we have exploited the advantage of variable neighborhood searching (VNS)-based iterative global convergence criterion for MRF-MAP estimation. The effectiveness of the proposed scheme is established by testing it on different MRIs. Three performance evaluation measures are considered to evaluate the performance of the proposed scheme against existing state-of-the-art techniques. The simulation results establish the effectiveness of the proposed technique.

Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review

In recent years, deep convolutional neural networks (CNNs) have shown record-shattering performance in a variety of computer vision problems, such as visual object recognition, detection and segmentation. These methods have also been utilised in medical image analysis domain for lesion segmentation, anatomical segmentation and classification. We present an extensive literature review of CNN techniques applied in brain magnetic resonance imaging (MRI) analysis, focusing on the architectures, pre-processing, data-preparation and post-processing strategies available in these works. The aim of this study is three-fold. Our primary goal is to report how different CNN architectures have evolved, discuss state-of-the-art strategies, condense their results obtained using public datasets and examine their pros and cons. Second, this paper is intended to be a detailed reference of the research activity in deep CNN for brain MRI analysis. Finally, we present a perspective on the future of CNNs in which we hint some of the research directions in subsequent years.

Simultaneous segmentation and bias field estimation using local fitted images

Level set methods often suffer from boundary leakage and inadequate segmentation when used to segment images with inhomogeneous intensities. To handle this issue, a novel region-based level set method was developed, in which two different local fitted images are used to construct a hybrid region intensity fitting energy functional. This novel method enables simultaneous segmentation of the regions of interest and estimation of the bias fields from inhomogeneous images. Our experiments on both synthetic images and a publicly available dataset demonstrate the feasibility and reliability of the proposed method.

A non-iterative multi-scale approach for intensity inhomogeneity correction in MRI

Intensity inhomogeneity is the prime obstacle for MR image processing like automatic segmentation, registration etc. This complication has strong dependence on the associated acquisition hardware and patient anatomy which recommends retrospective correction. In this paper, a new method is developed for correcting the intensity inhomogeneity using a non-iterative multi-scale approach that doesn't necessitate segmentation and any prior knowledge on the scanner or subject. The proposed algorithm extracts bias field at different scales using a Log-Gabor filter bank followed by smoothing operation. Later, they are combined to fit a third degree polynomial to estimate the bias field. Finally, the corrected image is estimated by performing pixel-wise division of original image and bias field. The performance of the same was tested on BrainWeb simulated data, HCP dataset and is found to provide better performance than the state-of-the-art method, N4. A good agreement between the extracted and ground truth bias field is observed through correlation coefficient on different MR modality images that include T1w, T2w and PD. Significant reduction in coefficient variation and coefficient of joint variation ratios in real data indicate an improved inter-class separation and reduced intra-class intensity variations across white and grey matter tissue regions.

A New Variational Method for Bias Correction and Its Applications to Rodent Brain Extraction

Brain extraction is an important preprocessing step for further analysis of brain MR images. Significant intensity inhomogeneity can be observed in rodent brain images due to the high-field MRI technique. Unlike most existing brain extraction methods that require bias corrected MRI, we present a high-order and L₀ regularized variational model for bias correction and brain extraction. The model is composed of a data fitting term, a piecewise constant regularization and a smooth regularization, which is constructed on a 3-D formulation for medical images with anisotropic voxel sizes. We propose an efficient multi-resolution algorithm for fast computation. At each resolution layer, we solve an alternating direction scheme, all subproblems of which have the closed-form solutions. The method is tested on three T2 weighted acquisition configurations comprising a total of 50 rodent brain volumes, which are with the acquisition field strengths of 4.7 Tesla, 9.4 Tesla and 17.6 Tesla, respectively. On one hand, we compare the results of bias correction with N3 and N4 in terms of the coefficient of variations on 20 different tissues of rodent brain. On the other hand, the results of brain extraction are compared against manually segmented gold standards, BET, BSE and 3-D PCNN based on a number of metrics. With the high accuracy and efficiency, our proposed method can facilitate automatic processing of large-scale brain studies.

In vivo evidence for long-term vascular remodeling resulting from chronic cerebral hypoperfusion in mice

We have characterized both acute and long-term vascular and metabolic effects of unilateral common carotid artery occlusion in mice by in vivo magnetic resonance imaging and positron emission tomography. This common carotid artery occlusion model induces chronic cerebral hypoperfusion and is therefore relevant to both preclinical stroke studies, where it serves as a control condition for a commonly used mouse model of ischemic stroke, and neurodegeneration, as chronic hypoperfusion is causative to cognitive decline. By using perfusion magnetic resonance imaging, we demonstrate that under isoflurane anesthesia, cerebral perfusion levels recover gradually over one month. This recovery is paralleled by an increase in lumen diameter and altered tortuosity of the contralateral internal carotid artery at one year post-ligation as derived from magnetic resonance angiography data. Under urethane/α-chloralose anesthesia, no acute perfusion differences are observed, but the vascular response capacity to hypercapnia is found to be compromised. These hemispheric perfusion alterations are confirmed by water [¹⁵O]-H₂O positron emission tomography. Glucose metabolism ([¹⁸F]-FDG positron emission tomography) or white matter organization (diffusion-weighted magnetic resonance imaging) did not show any significant alterations. In conclusion, permanent unilateral common carotid artery occlusion results in acute and long-term vascular remodeling, which may have immediate consequences for animal models of stroke but also vascular dementia.

Comparative evaluation of performance measures for shading correction in time-lapse fluorescence microscopy

Time-lapse fluorescence microscopy is a valuable technology in cell biology, but it suffers from the inherent problem of intensity inhomogeneity due to uneven illumination or camera nonlinearity, known as shading artefacts. This will lead to inaccurate estimates of single-cell features such as average and total intensity. Numerous shading correction methods have been proposed to remove this effect. In order to compare the performance of different methods, many quantitative performance measures have been developed. However, there is little discussion about which performance measure should be generally applied for evaluation on real data, where the ground truth is absent. In this paper, the state-of-the-art shading correction methods and performance evaluation methods are reviewed. We implement 10 popular shading correction methods on two artificial datasets and four real ones. In order to make an objective comparison between those methods, we employ a number of quantitative performance measures. Extensive validation demonstrates that the coefficient of joint variation (CJV) is the most applicable measure in time-lapse fluorescence images. Based on this measure, we have proposed a novel shading correction method that performs better compared to well-established methods for a range of real data tested.

Noise-Compensated, Bias-Corrected Diffusion Weighted Endorectal Magnetic Resonance Imaging via a Stochastically Fully-Connected Joint Conditional Random Field Model

Diffusion weighted magnetic resonance imaging (DW-MR) is a powerful tool in imaging-based prostate cancer screening and detection. Endorectal coils are commonly used in DW-MR imaging to improve the signal-to-noise ratio (SNR) of the acquisition, at the expense of significant intensity inhomogeneities (bias field) that worsens as we move away from the endorectal coil. The presence of bias field can have a significant negative impact on the accuracy of different image analysis tasks, as well as prostate tumor localization, thus leading to increased inter- and intra-observer variability. Retrospective bias correction approaches are introduced as a more efficient way of bias correction compared to the prospective methods such that they correct for both of the scanner and anatomy-related bias fields in MR imaging. Previously proposed retrospective bias field correction methods suffer from undesired noise amplification that can reduce the quality of bias-corrected DW-MR image. Here, we propose a unified data reconstruction approach that enables joint compensation of bias field as well as data noise in DW-MR imaging. The proposed noise-compensated, bias-corrected (NCBC) data reconstruction method takes advantage of a novel stochastically fully connected joint conditional random field (SFC-JCRF) model to mitigate the effects of data noise and bias field in the reconstructed MR data. The proposed NCBC reconstruction method was tested on synthetic DW-MR data, physical DW-phantom as well as real DW-MR data all acquired using endorectal MR coil. Both qualitative and quantitative analysis illustrated that the proposed NCBC method can achieve improved image quality when compared to other tested bias correction methods. As such, the proposed NCBC method may have potential as a useful retrospective approach for improving the consistency of image interpretations.

Image-based in vivo assessment of targeting accuracy of stereotactic brain surgery in experimental rodent models

Stereotactic neurosurgery is used in pre-clinical research of neurological and psychiatric disorders in experimental rat and mouse models to engraft a needle or electrode at a pre-defined location in the brain. However, inaccurate targeting may confound the results of such experiments. In contrast to the clinical practice, inaccurate targeting in rodents remains usually unnoticed until assessed by ex vivo end-point histology. We here propose a workflow for in vivo assessment of stereotactic targeting accuracy in small animal studies based on multi-modal post-operative imaging. The surgical trajectory in each individual animal is reconstructed in 3D from the physical implant imaged in post-operative CT and/or its trace as visible in post-operative MRI. By co-registering post-operative images of individual animals to a common stereotaxic template, targeting accuracy is quantified. Two commonly used neuromodulation regions were used as targets. Target localization errors showed not only variability, but also inaccuracy in targeting. Only about 30% of electrodes were within the subnucleus structure that was targeted and a-specific adverse effects were also noted. Shifting from invasive/subjective 2D histology towards objective in vivo 3D imaging-based assessment of targeting accuracy may benefit a more effective use of the experimental data by excluding off-target cases early in the study.

Transient CNS responses to repeated binge ethanol treatment

The effects of ethanol (EtOH) on in vivo magnetic resonance (MR)-detectable brain measures across repeated exposures have not previously been reported. Of 28 rats weighing 340.66 ± 21.93 g at baseline, 15 were assigned to an EtOH group and 13 to a control group. Animals were exposed to five cycles of 4 days of intragastric (EtOH or dextrose) treatment and 10 days of recovery. Rats in both groups had structural MR imaging and whole-brain MR spectroscopy (MRS) scans at baseline, immediately following each binge period and after each recovery period (total = 11 scans per rat). Blood alcohol level at each of the five binge periods was ~300 mg/dl. Blood drawn at the end of the experiment did not show group differences for thiamine or its phosphate derivatives. Postmortem liver histopathology provided no evidence for hepatic steatosis, alcoholic hepatitis or alcoholic cirrhosis. Cerebrospinal fluid volumes of the lateral ventricles and cisterns showed enlargement with each binge EtOH exposure but recovery with each abstinence period. Similarly, changes in MRS metabolite levels were transient: levels of N-acetylaspartate and total creatine decreased, while those of choline-containing compounds and the combined resonance from glutamate and glutamine increased with each binge EtOH exposure cycle and then recovered during each abstinence period. Changes in response to EtOH were in expected directions based on previous single-binge EtOH exposure experiments, but the current MR findings do not provide support for accruing changes with repeated binge EtOH exposure.

Quantitative Evaluation of Intensity Inhomogeneity Correction Methods for Structural MR Brain Images

The correction of intensity non-uniformity (INU) in magnetic resonance (MR) images is extremely important to ensure both within-subject and across-subject reliability. Here we tackled the problem of objectively comparing INU correction techniques for T1-weighted images, which are the most commonly used in structural brain imaging. We focused our investigations on the methods integrated in widely used software packages for MR data analysis: FreeSurfer, BrainVoyager, SPM and FSL. We used simulated data to assess the INU fields reconstructed by those methods for controlled inhomogeneity magnitudes and noise levels. For each method, we evaluated a wide range of input parameters and defined an enhanced configuration associated with best reconstruction performance. By comparing enhanced and default configurations, we found that the former often provide much more accurate results. Accordingly, we used enhanced configurations for a more objective comparison between methods. For different levels of INU magnitude and noise, SPM and FSL, which integrate INU correction with brain segmentation, generally outperformed FreeSurfer and BrainVoyager, whose methods are exclusively dedicated to INU correction. Nonetheless, accurate INU field reconstructions can be obtained with FreeSurfer on images with low noise and with BrainVoyager for slow and smooth inhomogeneity profiles. Our study may prove helpful for an accurate selection of the INU correction method to be used based on the characteristics of actual MR data.

Persistent Impact of In utero Irradiation on Mouse Brain Structure and Function Characterized by MR Imaging and Behavioral Analysis

Prenatal irradiation is known to perturb brain development. Epidemiological studies revealed that radiation exposure during weeks 8-15 of pregnancy was associated with an increased occurrence of mental disability and microcephaly. Such neurological deficits were reproduced in animal models, in which rodent behavioral testing is an often used tool to evaluate radiation-induced defective brain functionality. However, up to now, animal studies suggested a threshold dose of around 0.30 Gray (Gy) below which no behavioral alterations can be observed, while human studies hinted at late defects after exposure to doses as low as 0.10 Gy. Here, we acutely irradiated pregnant mice at embryonic day 11 with doses ranging from 0.10 to 1.00 Gy. A thorough investigation of the dose-response relationship of altered brain function and architecture following in utero irradiation was achieved using a behavioral test battery and volumetric 3D T2-weighted magnetic resonance imaging (MRI). We found dose-dependent changes in cage activity, social behavior, anxiety-related exploration, and spatio-cognitive performance. Although behavioral alterations in low-dose exposed animals were mild, we did unveil that both emotionality and higher cognitive abilities were affected in mice exposed to ≥0.10 Gy. Microcephaly was apparent from 0.33 Gy onwards and accompanied by deviations in regional brain volumes as compared to controls. Of note, total brain volume and the relative volume of the ventricles, frontal and posterior cerebral cortex, cerebellum, and striatum were most strongly correlated to altered behavioral parameters. Taken together, we present conclusive evidence for persistent low-dose effects after prenatal irradiation in mice and provide a better understanding of the correlation between their brain size and performance in behavioral tests.

Automatic Segmentation of MR Brain Images With a Convolutional Neural Network

Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages. This paper presents a method for the automatic segmentation of MR brain images into a number of tissue classes using a convolutional neural network. To ensure that the method obtains accurate segmentation details as well as spatial consistency, the network uses multiple patch sizes and multiple convolution kernel sizes to acquire multi-scale information about each voxel. The method is not dependent on explicit features, but learns to recognise the information that is important for the classification based on training data. The method requires a single anatomical MR image only. The segmentation method is applied to five different data sets: coronal T₂-weighted images of preterm infants acquired at 30 weeks postmenstrual age (PMA) and 40 weeks PMA, axial T₂-weighted images of preterm infants acquired at 40 weeks PMA, axial T₁-weighted images of ageing adults acquired at an average age of 70 years, and T₁-weighted images of young adults acquired at an average age of 23 years. The method obtained the following average Dice coefficients over all segmented tissue classes for each data set, respectively: 0.87, 0.82, 0.84, 0.86, and 0.91. The results demonstrate that the method obtains accurate segmentations in all five sets, and hence demonstrates its robustness to differences in age and acquisition protocol.

Intensity Inhomogeneity Correction of Structural MR Images: A Data-Driven Approach to Define Input Algorithm Parameters

Intensity non-uniformity (INU) in magnetic resonance (MR) imaging is a major issue when conducting analyses of brain structural properties. An inaccurate INU correction may result in qualitative and quantitative misinterpretations. Several INU correction methods exist, whose performance largely depend on the specific parameter settings that need to be chosen by the user. Here we addressed the question of how to select the best input parameters for a specific INU correction algorithm. Our investigation was based on the INU correction algorithm implemented in SPM, but this can be in principle extended to any other algorithm requiring the selection of input parameters. We conducted a comprehensive comparison of indirect metrics for the assessment of INU correction performance, namely the coefficient of variation of white matter (CVWM), the coefficient of variation of gray matter (CVGM), and the coefficient of joint variation between white matter and gray matter (CJV). Using simulated MR data, we observed the CJV to be more accurate than CVWM and CVGM, provided that the noise level in the INU-corrected image was controlled by means of spatial smoothing. Based on the CJV, we developed a data-driven approach for selecting INU correction parameters, which could effectively work on actual MR images. To this end, we implemented an enhanced procedure for the definition of white and gray matter masks, based on which the CJV was calculated. Our approach was validated using actual T1-weighted images collected with 1.5 T, 3 T, and 7 T MR scanners. We found that our procedure can reliably assist the selection of valid INU correction algorithm parameters, thereby contributing to an enhanced inhomogeneity correction in MR images.

A subspace-based coil combination method for phased-array magnetic resonance imaging

PURPOSE

Coil-by-coil reconstruction methods are followed by coil combination to obtain a single image representing a spin density map. Typical coil combination methods, such as square-root sum-of-squares and adaptive coil combining, yield images that exhibit spatially varying modulation of image intensity. Existing practice is to first combine coils according to a signal-to-noise criterion, then postprocess to correct intensity inhomogeneity. If inhomogeneity is severe, however, intensity correction methods can yield poor results. The purpose of this article is to present an alternative optimality criterion for coil combination; the resulting procedure yields reduced intensity inhomogeneity while preserving contrast.

THEORY AND METHODS

A minimum mean squared error criterion is adopted for combining coils via a subspace decomposition. Techniques are compared using both simulated and in vivo data.

RESULTS

Experimental results for simulated and in vivo data demonstrate lower bias, higher signal-to-noise ratio (about 7×) and contrast-to-noise ratio (about 2×), compared to existing coil combination techniques.

CONCLUSION

The proposed coil combination method is noniterative and does not require estimation of coil sensitivity maps or image mask; the method is particularly suited to cases where intensity inhomogeneity is too severe for existing approaches.

An efficient level set method for simultaneous intensity inhomogeneity correction and segmentation of MR images

Intensity inhomogeneity (bias field) is a common artefact in magnetic resonance (MR) images, which hinders successful automatic segmentation. In this work, a novel algorithm for simultaneous segmentation and bias field correction is presented. The proposed energy functional allows for explicit regularization of the bias field term, making the model more flexible, which is crucial in presence of strong inhomogeneities. An efficient minimization procedure, attempting to find the global minimum, is applied to the energy functional. The algorithm is evaluated qualitatively and quantitatively using a synthetic example and real MR images of different organs. Comparisons with several state-of-the-art methods demonstrate the superior performance of the proposed technique. Desirable results are obtained even for images with strong and complicated inhomogeneity fields and sparse tissue structures.

Rough Sets and Stomped Normal Distribution for Simultaneous Segmentation and Bias Field Correction in Brain MR Images

The segmentation of brain MR images into different tissue classes is an important task for automatic image analysis technique, particularly due to the presence of intensity inhomogeneity artifact in MR images. In this regard, this paper presents a novel approach for simultaneous segmentation and bias field correction in brain MR images. It integrates judiciously the concept of rough sets and the merit of a novel probability distribution, called stomped normal (SN) distribution. The intensity distribution of a tissue class is represented by SN distribution, where each tissue class consists of a crisp lower approximation and a probabilistic boundary region. The intensity distribution of brain MR image is modeled as a mixture of finite number of SN distributions and one uniform distribution. The proposed method incorporates both the expectation-maximization and hidden Markov random field frameworks to provide an accurate and robust segmentation. The performance of the proposed approach, along with a comparison with related methods, is demonstrated on a set of synthetic and real brain MR images for different bias fields and noise levels.

The L0 Regularized Mumford-Shah Model for Bias Correction and Segmentation of Medical Images

We propose a new variant of the Mumford-Shah model for simultaneous bias correction and segmentation of images with intensity inhomogeneity. First, based on the model of images with intensity inhomogeneity, we introduce an L0 gradient regularizer to model the true intensity and a smooth regularizer to model the bias field. In addition, we derive a new data fidelity using the local intensity properties to allow the bias field to be influenced by its neighborhood. Second, we use a two-stage segmentation method, where the fast alternating direction method is implemented in the first stage for the recovery of true intensity and bias field and a simple thresholding is used in the second stage for segmentation. Different from most of the existing methods for simultaneous bias correction and segmentation, we estimate the bias field and true intensity without fixing either the number of the regions or their values in advance. Our method has been validated on medical images of various modalities with intensity inhomogeneity. Compared with the state-of-art approaches and the well-known brain software tools, our model is fast, accurate, and robust with initializations.

A Variational Approach to Simultaneous Image Segmentation and Bias Correction

This paper presents a novel variational approach for simultaneous estimation of bias field and segmentation of images with intensity inhomogeneity. We model intensity of inhomogeneous objects to be Gaussian distributed with different means and variances, and then introduce a sliding window to map the original image intensity onto another domain, where the intensity distribution of each object is still Gaussian but can be better separated. The means of the Gaussian distributions in the transformed domain can be adaptively estimated by multiplying the bias field with a piecewise constant signal within the sliding window. A maximum likelihood energy functional is then defined on each local region, which combines the bias field, the membership function of the object region, and the constant approximating the true signal from its corresponding object. The energy functional is then extended to the whole image domain by the Bayesian learning approach. An efficient iterative algorithm is proposed for energy minimization, via which the image segmentation and bias field correction are simultaneously achieved. Furthermore, the smoothness of the obtained optimal bias field is ensured by the normalized convolutions without extra cost. Experiments on real images demonstrated the superiority of the proposed algorithm to other state-of-the-art representative methods.

Sensitive biomarkers of alcoholism's effect on brain macrostructure: similarities and differences between France and the United States

Alcohol consumption patterns and recognition of health outcomes related to hazardous drinking vary widely internationally, raising the question whether these national differences are reflected in brain damage observed in alcoholism. This retrospective analysis assessed variability of alcoholism's effects on brain cerebrospinal fluid (CSF) and white matter volumes between France and the United States (U.S.). MRI data from two French sites (Caen and Orsay) and a U.S. laboratory (SRI/Stanford University) were acquired on 1.5T imaging systems in 287 controls, 165 uncomplicated alcoholics (ALC), and 26 alcoholics with Korsakoff's Syndrome (KS). All data were analyzed at the U.S. site using atlas-based parcellation. Results revealed graded CSF volume enlargement from ALC to KS and white matter volume deficits in KS only. In ALC from France but not the U.S., CSF and white matter volumes correlated with lifetime alcohol consumption, alcoholism duration, and length of sobriety. MRI highlighted CSF volume enlargement in both ALC and KS, serving as a basis for an ex vacuo process to explain correlated gray matter shrinkage. By contrast, MRI provided a sensitive in vivo biomarker of white matter volume shrinkage in KS only, suggesting a specific process sensitive to mechanisms contributing to Wernicke's encephalopathy, the precursor of KS. Identified structural brain abnormalities may provide biomarkers underlying alcoholism's heterogeneity in and among nations and suggest a substrate of gray matter tissue shrinkage. Proposed are hypotheses for national differences in interpreting whether the severity of sequelae observe a graded phenomenon or a continuum from uncomplicated alcoholism to alcoholism complicated by KS.

Automated Feature Extraction in Brain Tumor by Magnetic Resonance Imaging Using Gaussian Mixture Models

This paper presents a novel method for Glioblastoma (GBM) feature extraction based on Gaussian mixture model (GMM) features using MRI. We addressed the task of the new features to identify GBM using T1 and T2 weighted images (T1-WI, T2-WI) and Fluid-Attenuated Inversion Recovery (FLAIR) MR images. A pathologic area was detected using multithresholding segmentation with morphological operations of MR images. Multiclassifier techniques were considered to evaluate the performance of the feature based scheme in terms of its capability to discriminate GBM and normal tissue. GMM features demonstrated the best performance by the comparative study using principal component analysis (PCA) and wavelet based features. For the T1-WI, the accuracy performance was 97.05% (AUC = 92.73%) with 0.00% missed detection and 2.95% false alarm. In the T2-WI, the same accuracy (97.05%, AUC = 91.70%) value was achieved with 2.95% missed detection and 0.00% false alarm. In FLAIR mode the accuracy decreased to 94.11% (AUC = 95.85%) with 0.00% missed detection and 5.89% false alarm. These experimental results are promising to enhance the characteristics of heterogeneity and hence early treatment of GBM.

Brain development in heavy-drinking adolescents

OBJECTIVE

Heavy alcohol use during adolescence may alter the trajectory of normal brain development. The authors measured within-subject changes in regional brain morphometry over longer intervals and in larger samples of adolescents than previously reported and assessed differences between adolescents who remained nondrinkers and those who drank heavily during adolescence as well as differences between the sexes.

METHOD

The authors examined gray and white matter volume trajectories in 134 adolescents, of whom 75 transitioned to heavy drinking and 59 remained light drinkers or nondrinkers over roughly 3.5 years. Each underwent MRI scanning two to six times between ages 12 and 24 and was followed for up to 8 years. The volumes of the neocortex, allocortex, and white matter structures were measured using atlas-based parcellation with longitudinal registration. Linear mixed-effects models described differences in trajectories of heavy drinkers and nondrinkers over age; secondary analyses considered the contribution of other drug use to identified alcohol use effects.

RESULTS

Heavy-drinking adolescents showed accelerated gray matter reduction in cortical lateral frontal and temporal volumes and attenuated white matter growth of the corpus callosum and pons relative to nondrinkers. These results were largely unchanged when use of marijuana and other drugs was examined. Male and female drinkers showed similar patterns of development trajectory abnormalities.

CONCLUSIONS

Longitudinal analysis enabled detection of accelerated typical volume decline in frontal and temporal cortical volumes and attenuated growth in principal white matter structures in adolescents who started to drink heavily. These results provide a call for caution regarding heavy alcohol use during adolescence, whether heavy drinking is the sole cause or one of several in these alterations in brain development.

A modified method for MRF segmentation and bias correction of MR image with intensity inhomogeneity

Markov random field (MRF) model is an effective method for brain tissue classification, which has been applied in MR image segmentation for decades. However, it falls short of the expected classification in MR images with intensity inhomogeneity for the bias field is not considered in the formulation. In this paper, we propose an interleaved method joining a modified MRF classification and bias field estimation in an energy minimization framework, whose initial estimation is based on k-means algorithm in view of prior information on MRI. The proposed method has a salient advantage of overcoming the misclassifications from the non-interleaved MRF classification for the MR image with intensity inhomogeneity. In contrast to other baseline methods, experimental results also have demonstrated the effectiveness and advantages of our algorithm via its applications in the real and the synthetic MR images.

Accelerated aging of selective brain structures in human immunodeficiency virus infection: a controlled, longitudinal magnetic resonance imaging study

Advances in treatment have transformed human immunodeficiency virus (HIV) infection from an inexorable march to severe morbidity and premature death to a manageable chronic condition, often marked by good health. Thus, infected individuals are living long enough that there is a potential for interaction with normal senescence effects on various organ systems, including the brain. To examine this interaction, the brains of 51 individuals with HIV infection and 65 uninfected controls were studied using 351 magnetic resonance imaging and a battery of neuropsychological tests collected 2 or more times over follow-up periods ranging from 6 months to 8 years. Brain tissue regions of interest showed expected age-related decrease in volume; cerebrospinal fluid-filled spaces showed increase in volume for both groups. Although HIV-infected individuals were in good general health, and free of clinically-detectable dementia, several brain regions supporting higher-order cognition and integration of functions showed acceleration of the normal aging trajectory, including neocortex, which extended from the frontal and temporal poles to the parietal lobe, and the thalamus. Beyond an anticipated increase in lateral ventricle and Sylvian fissure volumes and decrease in tissue volumes (specifically, the frontal and sensorimotor neocortices, thalamus, and hippocampus) with longer duration of illness, most regions also showed accelerated disease progression. This accelerated loss of cortical tissue may represent a risk factor for premature cognitive and motor compromise if not dementia. On a more promising note, HIV-infected patients with increasing CD4 counts exhibited slower expansion of Sylvian fissure volume and slower declines of frontal and temporoparietal cortices, insula, and hippocampus tissue volumes. Thus, attenuated shrinkage of these brain regions, likely with adequate pharmacologic treatment and control of further infection, has the potential of abating decline in associated higher-order functions, notably, explicit memory, executive functions, self-regulation, and visuospatial abilities.

A hybrid method based on fuzzy clustering and local region-based level set for segmentation of inhomogeneous medical images

medical images are more affected by intensity inhomogeneity rather than noise and outliers. This has a great impact on the efficiency of region-based image segmentation methods, because they rely on homogeneity of intensities in the regions of interest. Meanwhile, initialization and configuration of controlling parameters affect the performance of level set segmentation. To address these problems, this paper proposes a new hybrid method that integrates a local region-based level set method with a variation of fuzzy clustering. Specifically it takes an information fusion approach based on a coarse-to-fine framework that seamlessly fuses local spatial information and gray level information with the information of the local region-based level set method. Also, the controlling parameters of level set are directly computed from fuzzy clustering result. This approach has valuable benefits such as automation, no need to prior knowledge about the region of interest (ROI), robustness on intensity inhomogeneity, automatic adjustment of controlling parameters, insensitivity to initialization, and satisfactory accuracy. So, the contribution of this paper is to provide these advantages together which have not been proposed yet for inhomogeneous medical images. Proposed method was tested on several medical images from different modalities for performance evaluation. Experimental results approve its effectiveness in segmenting medical images in comparison with similar methods.

Early decrease of type 1 cannabinoid receptor binding and phosphodiesterase 10A activity in vivo in R6/2 Huntington mice

Several lines of evidence imply early alterations in endocannabinoid and phosphodiesterase 10A (PDE10A) signaling in Huntington disease (HD). Using [(18)F]MK-9470 and [(18)F]JNJ42259152 small-animal positron emission tomography (PET), we investigated for the first time cerebral changes in type 1 cannabinoid (CB1) receptor binding and PDE10A levels in vivo in presymptomatic, early symptomatic, and late symptomatic HD (R6/2) mice, in relation to glucose metabolism ([(18)F]FDG PET), brain morphology (magnetic resonance imaging) and motor function. Ten R6/2 and 16 wild-type (WT) mice were investigated at 3 different time points between the age of 4 and 13 weeks. Parametric CB1 receptor and PDE10A images were anatomically standardized to Paxinos space and analyzed voxelwise. Volumetric microMRI imaging was performed to assess HD pathology. In R6/2 mice, CB1 receptor binding was decreased in comparison with WT in a cluster comprising the bilateral caudate-putamen, globus pallidus, and thalamic nucleus at week 5 (-8.1% ± 2.6%, p = 1.7 × 10(-5)). Longitudinal follow-up showed further progressive decline compared with controls in a cluster comprising the bilateral hippocampus, caudate-putamen, globus pallidus, superior colliculus, thalamic nucleus, and cerebellum (late vs. presymptomatic age: -13.7% ± 3.1% for R6/2 and +1.5% ± 4.0% for WT, p = 1.9 × 10(-5)). In R6/2 mice, PDE10A binding potential also decreased over time to reach significance at early and late symptomatic HD (late vs. presymptomatic age: -79.1% ± 1.9% for R6/2 and +2.1% ± 2.7% for WT, p = 1.5 × 10(-4)). The observed changes in CB1 receptor and PDE10A binding were correlated to anomalies exhibited by R6/2 animals in motor function, whereas no correlation was found with magnetic resonance imaging-based striatal volume. Our findings point to early regional dysfunctions in endocannabinoid and PDE10A signaling, involving the caudate-putamen and lateral globus pallidus, which may play a role in the progression of the disease in R6/2 animals. PET quantification of in vivo CB1 and/or PDE10A binding may thus be useful early biomarkers for HD. Our results also provide evidence of subtle motor deficits at earlier stages than previously described.

Multiplicative intrinsic component optimization (MICO) for MRI bias field estimation and tissue segmentation

This paper proposes a new energy minimization method called multiplicative intrinsic component optimization (MICO) for joint bias field estimation and segmentation of magnetic resonance (MR) images. The proposed method takes full advantage of the decomposition of MR images into two multiplicative components, namely, the true image that characterizes a physical property of the tissues and the bias field that accounts for the intensity inhomogeneity, and their respective spatial properties. Bias field estimation and tissue segmentation are simultaneously achieved by an energy minimization process aimed to optimize the estimates of the two multiplicative components of an MR image. The bias field is iteratively optimized by using efficient matrix computations, which are verified to be numerically stable by matrix analysis. More importantly, the energy in our formulation is convex in each of its variables, which leads to the robustness of the proposed energy minimization algorithm. The MICO formulation can be naturally extended to 3D/4D tissue segmentation with spatial/sptatiotemporal regularization. Quantitative evaluations and comparisons with some popular softwares have demonstrated superior performance of MICO in terms of robustness and accuracy.

Chronic alcohol consumption and its effect on nodes of frontocerebellar and limbic circuitry: comparison of effects in France and the United States

Alcohol use disorders present a significant public health problem in France and the United States (U.S.), but whether the untoward effect of alcohol on the brain results in similar damage in both countries remains unknown. Accordingly, we conducted a retrospective collaborative investigation between two French sites (Caen and Orsay) and a U.S. laboratory (SRI/Stanford University) with T1-weighted, structural MRI data collected on a common imaging platform (1.5T, General Electric) on 288 normal controls (NC), 165 uncomplicated alcoholics (ALC), and 26 patients with alcoholic Korsakoff's syndrome (KS) diagnosed at all sites with a common interview instrument. Data from the two countries were pooled, then preprocessed and analyzed together at the U.S. site using atlas-based parcellation. National differences indicated that thalamic volumes were smaller in ALC in France than the U.S. despite similar alcohol consumption levels in both countries. By contrast, volumes of the hippocampus, amygdala, and cerebellar vermis were smaller in KS in the U.S. than France. Estimated amount of alcohol consumed over a lifetime, duration of alcoholism, and length of sobriety were significant predictors of selective regional brain volumes in France and in the U.S. The common analysis of MRI data enabled identification of discrepancies in brain volume deficits in France and the U.S. that may reflect fundamental differences in the consequences of alcoholism on brain structure between the two countries, possibly related to genetic or environmental differences.

A new kernel-based fuzzy level set method for automated segmentation of medical images in the presence of intensity inhomogeneity

Researchers recently apply an integrative approach to automate medical image segmentation for benefiting available methods and eliminating their disadvantages. Intensity inhomogeneity is a challenging and open problem in this area, which has received less attention by this approach. It has considerable effects on segmentation accuracy. This paper proposes a new kernel-based fuzzy level set algorithm by an integrative approach to deal with this problem. It can directly evolve from the initial level set obtained by Gaussian Kernel-Based Fuzzy C-Means (GKFCM). The controlling parameters of level set evolution are also estimated from the results of GKFCM. Moreover the proposed algorithm is enhanced with locally regularized evolution based on an image model that describes the composition of real-world images, in which intensity inhomogeneity is assumed as a component of an image. Such improvements make level set manipulation easier and lead to more robust segmentation in intensity inhomogeneity. The proposed algorithm has valuable benefits including automation, invariant of intensity inhomogeneity, and high accuracy. Performance evaluation of the proposed algorithm was carried on medical images from different modalities. The results confirm its effectiveness for medical image segmentation.

Serial diffusion tensor imaging for early detection of radiation-induced injuries to normal-appearing white matter in high-grade glioma patients

PURPOSE

To study the potential of diffusion tensor imaging (DTI) to serve as a biomarker for radiation-induced brain injury during chemo-radiotherapy (RT) treatment.

MATERIALS AND METHODS

Serial DTI data were collected from 18 high-grade glioma (HGG) patients undergoing RT and 7 healthy controls. Changes across time in mean, standard deviation (SD), skewness, and kurtosis of fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (λa ), and transversal diffusivity (λt ) within the normal-appearing white matter (NAWM) were modeled using a linear mixed-effects model to assess dose dependent changes of five dose bins (0-60 Gy), and global changes compared with a control group.

RESULTS

Mean MD, λa and λt were all significantly increasing in >41 Gy dose regions (0.14%, 0.10%, and 0.18% per week) compared with <12 Gy regions. SD λt had significant dose dependent time evolution of 0.019*dose per week. Mean and SD MD, λa and λt in the global NAWM of the patient group significantly increased (mean; 0.06%, 0.03%, 0.09%, and SD; 0.57%, 0.34%, 0.51 per week) compared with the control group. The changes were significant at week 6 of, or immediately after RT.

CONCLUSION

DTI is not sensitive to acute global NAWM changes during the treatment of HGG, but sensitive to early posttreatment changes.

Rat strain differences in brain structure and neurochemistry in response to binge alcohol

RATIONALE

Ventricular enlargement is a robust phenotype of the chronically dependent alcoholic human brain, yet the mechanism of ventriculomegaly is unestablished. Heterogeneous stock Wistar rats administered binge EtOH (3 g/kg intragastrically every 8 h for 4 days to average blood alcohol levels (BALs) of 250 mg/dL) demonstrate profound but reversible ventricular enlargement and changes in brain metabolites (e.g., N-acetylaspartate (NAA) and choline-containing compounds (Cho)).

OBJECTIVES

Here, alcohol-preferring (P) and alcohol-nonpreferring (NP) rats systematically bred from heterogeneous stock Wistar rats for differential alcohol drinking behavior were compared with Wistar rats to determine whether genetic divergence and consequent morphological and neurochemical variation affect the brain's response to binge EtOH treatment.

METHODS

The three rat lines were dosed equivalently and approached similar BALs. Magnetic resonance imaging and spectroscopy evaluated the effects of binge EtOH on brain.

RESULTS

As observed in Wistar rats, P and NP rats showed decreases in NAA. Neither P nor NP rats, however, responded to EtOH intoxication with ventricular expansion or increases in Cho levels as previously noted in Wistar rats. Increases in ventricular volume correlated with increases in Cho in Wistar rats.

CONCLUSIONS

The latter finding suggests that ventricular volume expansion is related to adaptive changes in brain cell membranes in response to binge EtOH. That P and NP rats responded differently to EtOH argues for intrinsic differences in their brain cell membrane composition. Further, differential metabolite responses to EtOH administration by rat strain implicate selective genetic variation as underlying heterogeneous effects of chronic alcoholism in the human condition.

Automatic segmentation of eight tissue classes in neonatal brain MRI

Purpose

Volumetric measurements of neonatal brain tissues may be used as a biomarker for later neurodevelopmental outcome. We propose an automatic method for probabilistic brain segmentation in neonatal MRIs.

Materials and Methods

In an IRB-approved study axial T1- and T2-weighted MR images were acquired at term-equivalent age for a preterm cohort of 108 neonates. A method for automatic probabilistic segmentation of the images into eight cerebral tissue classes was developed: cortical and central grey matter, unmyelinated and myelinated white matter, cerebrospinal fluid in the ventricles and in the extra cerebral space, brainstem and cerebellum. Segmentation is based on supervised pixel classification using intensity values and spatial positions of the image voxels. The method was trained and evaluated using leave-one-out experiments on seven images, for which an expert had set a reference standard manually. Subsequently, the method was applied to the remaining 101 scans, and the resulting segmentations were evaluated visually by three experts. Finally, volumes of the eight segmented tissue classes were determined for each patient.

Results

The Dice similarity coefficients of the segmented tissue classes, except myelinated white matter, ranged from 0.75 to 0.92. Myelinated white matter was difficult to segment and the achieved Dice coefficient was 0.47. Visual analysis of the results demonstrated accurate segmentations of the eight tissue classes. The probabilistic segmentation method produced volumes that compared favorably with the reference standard.

Conclusion

The proposed method provides accurate segmentation of neonatal brain MR images into all given tissue classes, except myelinated white matter. This is the one of the first methods that distinguishes cerebrospinal fluid in the ventricles from cerebrospinal fluid in the extracerebral space. This method might be helpful in predicting neurodevelopmental outcome and useful for evaluating neuroprotective clinical trials in neonates.

A method for handling intensity inhomogenieties in fMRI sequences of moving anatomy of the early developing brain

This paper presents a method for intensity inhomogeniety removal in fMRI studies of a moving subject. In such studies, subtle changes in signal as the subject moves in the presence of a bias field can be a significant confound for BOLD signal analysis. The proposed method avoids the need for a specific tissue model or assumptions about tissue homogeneity by making use of the multiple views of the underlying bias field provided by the subject's motion. A parametric bias field model is assumed and a regression model is used to estimate the basis function weights of this model. Quantitative evaluation of the effects of motion and noise in motion estimates are performed using simulated data. Results demonstrate the strength and robustness of the new method compared to the state of the art 4D nonparametric bias estimator (N4ITK). We also qualitatively demonstrate the impact of the method on resting state neuroimage analysis of a moving adult brain with simulated motion and bias fields, as well as on in vivo moving fetal fMRI.

Rough sets for bias field correction in MR images using contraharmonic mean and quantitative index

One of the challenging tasks for magnetic resonance (MR) image analysis is to remove the intensity inhomogeneity artifact present in MR images, which often degrades the performance of an automatic image analysis technique. In this regard, the paper presents a novel approach for bias field correction in MR images. It judiciously integrates the merits of rough sets and contraharmonic mean. While the contraharmonic mean is used in low-pass averaging filter to estimate the bias field in multiplicative model, the concept of lower approximation and boundary region of rough sets deals with vagueness and incompleteness in filter structure definition. A theoretical analysis is presented to justify the use of both rough sets and contraharmonic mean for bias field estimation. The integration enables the algorithm to estimate optimum or near optimum bias field. Some new quantitative indexes are introduced to measure intensity inhomogeneity artifact present in a MR image. The performance of the proposed approach, along with a comparison with other approaches, is demonstrated on both simulated and real MR images for different bias fields and noise levels.

Feasibility of semiautomated MR volumetry using gadoxetic acid-enhanced MRI at hepatobiliary phase for living liver donors

PURPOSE

To assess the feasibility of semiautomated MR volumetry using gadoxetic acid-enhanced MRI at the hepatobiliary phase compared with manual CT volumetry.

METHODS

Forty potential live liver donor candidates who underwent MR and CT on the same day, were included in our study. Semiautomated MR volumetry was performed using gadoxetic acid-enhanced MRI at the hepatobiliary phase. We performed the quadratic MR image division for correction of the bias field inhomogeneity. With manual CT volumetry as the reference standard, we calculated the average volume measurement error of the semiautomated MR volumetry. We also calculated the mean of the number and time of the manual editing, edited volume, and total processing time.

RESULTS

The average volume measurement errors of the semiautomated MR volumetry were 2.35% ± 1.22%. The average values of the numbers of editing, operation times of manual editing, edited volumes, and total processing time for the semiautomated MR volumetry were 1.9 ± 0.6, 8.1 ± 2.7 s, 12.4 ± 8.8 mL, and 11.7 ± 2.9 s, respectively.

CONCLUSION

Semiautomated liver MR volumetry using hepatobiliary phase gadoxetic acid-enhanced MRI with the quadratic MR image division is a reliable, easy, and fast tool to measure liver volume in potential living liver donors.