Review and evaluation of MRI nonuniformity corrections for brain tumor response measurements

Recommendations of Choice of Head Coil and Prescan Normalize Filter Depend on Region of Interest and Task

The performance of MRI head coils together with the influence of the prescan normalize filter in different brain regions was evaluated. Functional and structural data were recorded from 26 participants performing motor, auditory, and visual tasks in different conditions: with the 20- and 64-channel Siemens head/neck coil and the prescan normalize filter turned ON or OFF. Data were analyzed with the MRIQC tool to evaluate data quality differences. The functional data were statistically evaluated by comparison of the β estimates and the time-course signal-to-noise ratio (tSNR) in four regions of interest, i.e., the auditory, visual, and motor cortices and the thalamus. The MRIQC tool indicated a better data quality for both functional and structural data with the prescan normalize filter, with an advantage for the 20-channel head coil in functional data and an advantage for the 64-channel head coil in structural measurements. Nevertheless, recommendations for the functional data regarding choice of head coils and prescan normalize filter depend on the brain regions of interest. Higher β estimates and tSNR values occurred in the auditory cortex and thalamus with the prescan normalize filter, whereas the contrary was true for the visual and motor cortices. Due to higher β estimates in the visual cortex in the 64-channel head coil, this head coil is recommended for studies investigating the visual cortex. For most of the research questions, the 20-channel head coil is better suited for functional experiments, with the prescan normalize filter, especially when investigating deep brain areas. For anatomical studies, the 64-channel head coil seemed to be the better choice.

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.

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.

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.

Monte Carlo bias field correction in endorectal diffusion imaging

Prostate cancer is one of the leading causes of cancer death in the male population. The detection of prostate cancer using imaging has been challenging until recently. Multiparametric magnetic resonance imaging (MRI) has been shown to allow accurate localization of the cancers and can help direct biopsies to cancer foci, which is required to plan the treatment. The interpretation of MRI, however, requires a high level of expertise and review of large multiparametric datasets. An endorectal receiver coil is often used to improve signal-to-noise ratio and aid in detection of smaller cancer foci. Moreover, computed high b-value diffusion-weighted imaging show improved delineation of tumors but is subject to strong bias fields near the coil. Here, a nonparametric approach to bias field correction for endorectal diffusion imaging via Monte Carlo sampling is introduced. It will be shown that the delineation between the prostate gland and the background and intensity inhomogeneity may be improved using the proposed approach. High b-value generated results also show improved visualization of tumor regions. The results suggest that Monte Carlo bias correction may have potential as a preprocessing tool for endorectal diffusion images for the prostate cancer detection and localization or segmentation.

Automated image alignment for 2D gel electrophoresis in a high-throughput proteomics pipeline

MOTIVATION

The quest for high-throughput proteomics has revealed a number of challenges in recent years. Whilst substantial improvements in automated protein separation with liquid chromatography and mass spectrometry (LC/MS), aka 'shotgun' proteomics, have been achieved, large-scale open initiatives such as the Human Proteome Organization (HUPO) Brain Proteome Project have shown that maximal proteome coverage is only possible when LC/MS is complemented by 2D gel electrophoresis (2-DE) studies. Moreover, both separation methods require automated alignment and differential analysis to relieve the bioinformatics bottleneck and so make high-throughput protein biomarker discovery a reality. The purpose of this article is to describe a fully automatic image alignment framework for the integration of 2-DE into a high-throughput differential expression proteomics pipeline.

RESULTS

The proposed method is based on robust automated image normalization (RAIN) to circumvent the drawbacks of traditional approaches. These use symbolic representation at the very early stages of the analysis, which introduces persistent errors due to inaccuracies in modelling and alignment. In RAIN, a third-order volume-invariant B-spline model is incorporated into a multi-resolution schema to correct for geometric and expression inhomogeneity at multiple scales. The normalized images can then be compared directly in the image domain for quantitative differential analysis. Through evaluation against an existing state-of-the-art method on real and synthetically warped 2D gels, the proposed analysis framework demonstrates substantial improvements in matching accuracy and differential sensitivity. High-throughput analysis is established through an accelerated GPGPU (general purpose computation on graphics cards) implementation.

AVAILABILITY

Supplementary material, software and images used in the validation are available at http://www.proteomegrid.org/rain/.

A review of methods for correction of intensity inhomogeneity in MRI

Medical image acquisition devices provide a vast amount of anatomical and functional information, which facilitate and improve diagnosis and patient treatment, especially when supported by modern quantitative image analysis methods. However, modality specific image artifacts, such as the phenomena of intensity inhomogeneity in magnetic resonance images (MRI), are still prominent and can adversely affect quantitative image analysis. In this paper, numerous methods that have been developed to reduce or eliminate intensity inhomogeneities in MRI are reviewed. First, the methods are classified according to the inhomogeneity correction strategy. Next, different qualitative and quantitative evaluation approaches are reviewed. Third, 60 relevant publications are categorized according to several features and analyzed so as to reveal major trends, popularity, evaluation strategies and applications. Finally, key evaluation issues and future development of the inhomogeneity correction field, supported by the results of the analysis, are discussed.

A Review on 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.

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

Magnetic resonance imaging is a popular and powerful non-invasive imaging technique. Automated analysis has become mandatory to efficiently cope with the large amount of data generated using this modality. However, several artifacts, such as intensity non-uniformity, can degrade the quality of acquired data. Intensity non-uniformity consists in anatomically irrelevant intensity variation throughout data. It can be induced by the choice of the radio-frequency coil, the acquisition pulse sequence and by the nature and geometry of the sample itself. Numerous methods have been proposed to correct this artifact. In this paper, we propose an overview of existing methods. We first sort them according to their location in the acquisition/processing pipeline. Sorting is then refined based on the assumptions those methods rely on. Next, we present the validation protocols used to evaluate these different correction schemes both from a qualitative and a quantitative point of view. Finally, availability and usability of the presented methods is discussed.

Correction of bias field in MR images using singularity function analysis

A new approach for correcting bias field in magnetic resonance (MR) images is proposed using the mathematical model of singularity function analysis (SFA), which represents a discrete signal or its spectrum as a weighted sum of singularity functions. Through this model, an MR image's low spatial frequency components corrupted by a smoothly varying bias field are first removed, and then reconstructed from its higher spatial frequency components not polluted by bias field. The thus reconstructed image is then used to estimate bias field for final image correction. The approach does not rely on the assumption that anatomical information in MR images occurs at higher spatial frequencies than bias field. The performance of this approach is evaluated using both simulated and real clinical MR images.

Method for bias field correction of brain T1-weighted magnetic resonance images minimizing segmentation error

This work presents a new algorithm (nonuniform intensity correction; NIC) for correction of intensity inhomogeneities in T1-weighted magnetic resonance (MR) images. The bias field and a bias-free image are obtained through an iterative process that uses brain tissue segmentation. The algorithm was validated by means of realistic phantom images and a set of 24 real images. The first evaluation phase was based on a public domain phantom dataset, used previously to assess bias field correction algorithms. NIC performed similar to previously described methods in removing the bias field from phantom images, without introduction of degradation in the absence of intensity inhomogeneity. The real image dataset was used to compare the performance of this new algorithm to that of other widely used methods (N3, SPM'99, and SPM2). This dataset included both low and high bias field images from two different MR scanners of low (0.5 T) and medium (1.5 T) static fields. Using standard quality criteria for determining the goodness of the different methods, NIC achieved the best results, correcting the images of the real MR dataset, enabling its systematic use in images from both low and medium static field MR scanners. A limitation of our method is that it might fail if the bias field is so high that the initial histogram does not show bimodal distribution for white and gray matter.

A semi-automatic method for developing an anthropomorphic numerical model of dielectric anatomy by MRI

Complex permittivity values have a dominant role in the overall consideration of interaction between radiofrequency electromagnetic fields and living matter, and in related applications such as electromagnetic dosimetry. There are still some concerns about the accuracy of published data and about their variability due to the heterogeneous nature of biological tissues. The aim of this study is to provide an alternative semi-automatic method by which numerical dielectric human models for dosimetric studies can be obtained. Magnetic resonance imaging (MRI) tomography was used to acquire images. A new technique was employed to correct nonuniformities in the images and frequency-dependent transfer functions to correlate image intensity with complex permittivity were used. The proposed method provides frequency-dependent models in which permittivity and conductivity vary with continuity--even in the same tissue--reflecting the intrinsic realistic spatial dispersion of such parameters. The human model is tested with an FDTD (finite difference time domain) algorithm at different frequencies; the results of layer-averaged and whole-body-averaged SAR (specific absorption rate) are compared with published work, and reasonable agreement has been found. Due to the short time needed to obtain a whole body model, this semi-automatic method may be suitable for efficient study of various conditions that can determine large differences in the SAR distribution, such as body shape, posture, fat-to-muscle ratio, height and weight.

In vivo molecular imaging for planning radiation therapy of gliomas: an application of 1H MRSI

Gliomas are infiltrative lesions that typically have poorly defined margins on conventional magnetic resonance (MR) and computed tomography (CT) images. This presents a considerable challenge for planning radiation and other forms of focal therapy, and introduces the possibility of both under-treating macroscopic tumor, and over-treating regions of normal brain tissue. New therapy systems are able to deliver radiation more precisely and accurately to irregular three-dimensional target volumes, and have placed a premium on definition of the spatial extent of the lesion. Proton MR spectroscopic imaging (H-MRSI) has been proposed as an in vivo molecular imaging technique that assists in targeting and predicts response to radiation therapy for patients with gliomas. The evidence that supports the use of H-MRSI for planning radiation treatment is reviewed, together with the technical requirements for implementing data acquisition and analysis procedures in a clinical setting. Although there is room for improvement in the spatial resolution and chemical specificity obtained at the conventional field strength of 1.5 T, there are clear benefits to integrating H-MRSI into treatment planning and follow-up examinations. Further work is required to integrate the results of the H-MRSI examination into the treatment planning workstation, and to improve the quality of the data using more sensitive phased array coils and higher field strength magnets.

Retrospective correction of MR intensity inhomogeneity by information minimization

In this paper, the problem of retrospective correction of intensity inhomogeneity in magnetic resonance (MR) images is addressed. A novel model-based correction method is proposed, based on the assumption that an image corrupted by intensity inhomogeneity contains more information than the corresponding uncorrupted image. The image degradation process is described by a linear model, consisting of a multiplicative and an additive component which are modeled by a combination of smoothly varying basis functions. The degraded image is corrected by the inverse of the image degradation model. The parameters of this model are optimized such that the information of the corrected image is minimized while the global intensity statistic is preserved. The method was quantitatively evaluated and compared to other methods on a number of simulated and real MR images and proved to be effective, reliable, and computationally attractive. The method can be widely applied to different types of MR images because it solely uses the information that is naturally present in an image, without making assumptions on its spatial and intensity distribution. Besides, the method requires no preprocessing, parameter setting, nor user interaction. Consequently, the proposed method may be a valuable tool in MR image analysis.

Enhancement of contrast regions in suboptimal ultrasound images with application to echocardiography

In this paper we propose a novel feature-based contrast enhancement approach to enhance the quality of noisy ultrasound (US) images. Our approach uses a phase-based feature detection algorithm, followed by sparse surface interpolation and subsequent nonlinear postprocessing. We first exploited the intensity-invariant property of phase-based acoustic feature detection to select a set of relevant image features in the data. Then, an approximation to the low-frequency components of the sparse set of selected features was obtained using a fast surface interpolation algorithm. Finally, a nonlinear postprocessing step was applied. Results of applying the method to echocardiographic sequences (2-D + T) are presented. The results demonstrate that the method can successfully enhance the intensity of the interesting features in the image. Better balanced contrasted images are obtained, which is important and useful both for manual processing and assessment by a clinician, and for computer analysis of the sequence. An evaluation protocol is proposed in the case of echocardiographic data and quantitative results are presented. We show that the correction is consistent over time and does not introduce any temporal artefacts.

Qualitative and quantitative evaluation of six algorithms for correcting intensity nonuniformity effects

The desire to correct intensity nonuniformity in magnetic resonance images has led to the proliferation of nonuniformity-correction (NUC) algorithms with different theoretical underpinnings. In order to provide end users with a rational basis for selecting a given algorithm for a specific neuroscientific application, we evaluated the performance of six NUC algorithms. We used simulated and real MRI data volumes, including six repeat scans of the same subject, in order to rank the accuracy, precision, and stability of the nonuniformity corrections. We also compared algorithms using data volumes from different subjects and different (1.5T and 3.0T) MRI scanners in order to relate differences in algorithmic performance to intersubject variability and/or differences in scanner performance. In phantom studies, the correlation of the extracted with the applied nonuniformity was highest in the transaxial (left-to-right) direction and lowest in the axial (top-to-bottom) direction. Two of the six algorithms demonstrated a high degree of stability, as measured by the iterative application of the algorithm to its corrected output. While none of the algorithms performed ideally under all circumstances, locally adaptive methods generally outperformed nonadaptive methods.

Automatic morphology-based brain segmentation (MBRASE) from MRI-T1 data

A method called morphology-based brain segmentation (MBRASE) has been developed for fully automatic segmentation of the brain from T1-weighted MR image data. The starting point is a supervised segmentation technique, which has proven highly effective and accurate for quantitation and visualization purposes. The proposed method automates the required user interaction, i.e., defining a seed point and a threshold range, and is based on the simple operations thresholding, erosion, and geodesic dilation. The thresholds are detected in a region growing process and are defined by connections of the brain to other tissues. The method is first evaluated on three computer simulated datasets by comparing the automated segmentations with the original distributions. The second evaluation is done on a total of 30 patient datasets, by comparing the automated segmentations with supervised segmentations carried out by a neuroanatomy expert. The comparison between two binary segmentations is performed both quantitatively and qualitatively. The automated segmentations are found to be accurate and robust. Consequently, the proposed method can be used as a default segmentation for quantitation and visualization of the human brain from T1-weighted MR images in routine clinical procedures.

A modified fuzzy clustering algorithm for operator independent brain tissue classification of dual echo MR images

Methods for brain tissue classification or segmentation of structural magnetic resonance imaging (MRI) data should ideally be independent of human operators for reasons of reliability and tractability. An algorithm is described for fully automated segmentation of dual echo, fast spin-echo MRI data. The method is used to assign fuzzy-membership values for each of four tissue classes (gray matter, white matter, cerebrospinal fluid and dura) to each voxel based on partition of a two dimensional feature space. Fuzzy clustering is modified for this application in two ways. First, a two component normal mixture model is initially fitted to the thresholded feature space to identify exemplary gray and white matter voxels. These exemplary data protect subsequently estimated cluster means against the tendency of unmodified fuzzy clustering to equalize the number of voxels in each class. Second, fuzzy clustering is implemented in a moving window scheme that accommodates reduced image contrast at the axial extremes of the transmitting/receiving coil. MRI data acquired from 5 normal volunteers were used to identify stable values for three arbitrary parameters of the algorithm: feature space threshold, relative weight of exemplary gray and white matter voxels, and moving window size. The modified algorithm incorporating these parameter values was then used to classify data from simulated images of the brain, validating the use of fuzzy-membership values as estimates of partial volume. Gray:white matter ratios were estimated from 20 twenty normal volunteers (mean age 32.8 years). Processing time for each three-dimensional image was approximately 30 min on a 170 MHz workstation. Mean cerebral gray and white matter volumes estimated from these automatically segmented images were very similar to comparable results previously obtained by operator dependent methods, but without their inherent unreliability.

The primate neocortex in comparative perspective using magnetic resonance imaging

In this study we use neuroanatomic data from living anthropoid primate subjects to test the following three hypotheses: (1) that the human neocortex is significantly larger than expected for a primate of our brain size, (2) that the human prefrontal cortex is significantly more convoluted than expected for our brain size, and (3) that increases in cerebral white matter volume outpace increases in neocortical gray matter volume among anthropoid primates. Whole brain MRI scans were obtained from 44 living primate subjects from 11 different species. Image analysis software was used to calculate total brain volume, neocortical gray matter volume, cerebral white matter volume, and the cross sectional area of the spinal cord in each scan. Allometric regression analyses were used to compare the relative size of these brain structures across species, with an emphasis on determining whether human brain proportions correspond with predictions based on nonhuman primate allometric trajectories. All three hypotheses were supported by our analysis. The results of this study provide additional insights into human brain evolution beyond the important observation that brain volume approximately tripled in the hominid lineage by demonstrating that the neocortex was uniquely modified throughout hominid evolution. These modifications may constitute part of the neurobiological substrate that supports some of our species most distinctive cognitive abilities.

Feature extraction for MRI segmentation

Magnetic resonance images (MRIs) of the brain are segmented to measure the efficacy of treatment strategies for brain tumors. To date, no reproducible technique for measuring tumor size is available to the clinician, which hampers progress of the search for good treatment protocols. Many segmentation techniques have been proposed, but the representation (features) of the MRI data has received little attention. A genetic algorithm (GA) search was used to discover a feature set from multi-spectral MRI data. Segmentations were performed using the fuzzy c-means (FCM) clustering technique. Seventeen MRI data sets from five patients were evaluated. The GA feature set produces a more accurate segmentation. The GA fitness function that achieves the best results is the Wilks's lambda statistic when applied to FCM clusters. Compared to linear discriminant analysis, which requires class labels, the same or better accuracy is obtained by the features constructed from a GA search without class labels, allowing fully operator independent segmentation. The GA approach therefore provides a better starting point for the measurement of the response of a brain tumor to treatment.

MRI measurement of brain tumor response: comparison of visual metric and automatic segmentation

An automatic magnetic resonance imaging (MRI) multispectral segmentation method and a visual metric are compared for their effectiveness to measure tumor response to therapy. Automatic response measurements are important for multicenter clinical trials. A visual metric such as the product of the largest diameter and the largest perpendicular diameter of the tumor is a standard approach, and is currently used in the Radiation Treatment Oncology Group (RTOG) and the Eastern Cooperative Oncology Group (EGOG) clinical trials. In the standard approach, the tumor response is based on the percentage change in the visual metric and is categorized into cure, partial response, stable disease, or progression. Both visual and automatic methods are applied to six brain tumor cases (gliomas) of varying levels of segmentation difficulty. The analyzed data were serial multispectral MR images, collected using MR contrast enhancement. A fully automatic knowledge guided method (KG) was applied to the MRI multispectral data, while the visual metric was taken from the MRI films using the T1 gadolinium enhanced image, with repeat measurements done by two radiologists and two residents. Tumor measurements from both visual and automatic methods are compared to "ground truth," (GT) i.e., manually segmented tumor. The KG method was found to slightly overestimate tumor volume, but in a consistent manner, and the estimated tumor response compared very well to hand-drawn ground truth with a correlation coefficient of 0.96. In contrast, the visually estimated metric had a large variation between observers, particularly for difficult cases, where the tumor margins are not well delineated. The inter-observer variation for the measurement of the visual metric was only 16%, i.e., observers generally agreed on the lengths of the diameters. However, in 30% of the studied cases no consensus was found for the categorical tumor response measurement, indicating that the categories are very sensitive to variations in the diameter measurements. Moreover, the method failed to correctly identify the response in half of the cases. The data demonstrate that automatic 3D methods are clearly necessary for objective and clinically meaningful assessment of tumor volume in single or multicenter clinical trials.