Retrospective correction of MR intensity inhomogeneity by information minimization

T2*-correction in dynamic contrast-enhanced MRI from double-echo acquisitions

PURPOSE

To evaluate the importance of T2*-effects on the arterial input function (AIF) and on the resulting dynamic parameter estimation in dynamic contrast-enhanced (DCE) MRI of high-grade gliomas.

MATERIALS AND METHODS

Seven patients with high-grade gliomas were imaged in total 50 times using a double-echo DCE sequence. Kinetic analysis using the extended Tofts model was performed using AIFs with and without correction for T2*-effects, and the resulting estimates of the transfer constant (K(trans) ), blood plasma volume (vp ), and the rate constant (kep ) were compared. Numerical simulations were done for comparison with clinical results as well as to further investigate the dependency of parameter values on the magnitude of T2*-induced errors.

RESULTS

All kinetic parameters were found to be overestimated if T2*-effects in the AIF were not accounted for; with vp being most severely affected. The relative error in each parameter was dependent on the absolute parameter magnitude, resulting in incorrect parametric tumor distributions in the presence of uncorrected AIF T2*-effects.

CONCLUSION

In DCE, a sufficiently short echo time should be used or corrections for T2*-effects based on double-echo acquisition should be made for correct quantification of kinetic parameters.

Variation in longitudinal trajectories of regional brain volumes of healthy men and women (ages 10 to 85 years) measured with atlas-based parcellation of MRI

Numerous cross-sectional MRI studies have characterized age-related differences in regional brain volumes that differ with structure and tissue type. The extent to which cross-sectional assumptions about change are accurate depictions of actual longitudinal measurement remains controversial. Even longitudinal studies can be limited by the age range of participants, sex distribution of the samples, and scan intervals. To address these issues, we calculated trajectories of regional brain volume changes from T1-weighted (SPGR) MRI data, quantified with our automated, unsupervised SRI24 atlas-based registration and parcellation method. Longitudinal MRIs were acquired at 3T in 17 boys and 12 girls, age 10 to 14 years, and 41 men and 41 women, age 20 to 85 years at first scan. Application of a regression-based correction function permitted merging of data acquired at 3T field strength with data acquired at 1.5T from additional subjects, thereby expanding the sample to a total of 55 men and 67 women, age 20 to 85 years at first scan. Adjustment for individual supratentorial volume removed regional volume differences between men and women due to sex-related differences in head size. Individual trajectories were computed from data collected on 2 to 6 MRIs at a single field strength over a ~1 to 8 year interval. Using linear mixed-effects models, the pattern of trajectories over age indicated: rises in ventricular and Sylvian fissure volumes, with older individuals showing faster increases than younger ones; declines in selective cortical volumes with faster tissue shrinkage in older than younger individuals; little effect of aging on volume of the corpus callosum; more rapid expansion of CSF-filled spaces in men than women after age 60 years; and evidence for continued growth in central white matter through early adulthood with accelerated decline in senescence greater in men than women.

Illumination correction of retinal images using Laplace interpolation

Retinal images are frequently corrupted by unwanted variations in intensity that occur due to general imperfections in the image acquisition process. This inhomogeneous illumination across the retina can limit the useful information accessible within the acquired image. Specifically, this can lead to serious difficulties when performing image processing tasks requiring quantitative analysis of features present on the retina. Given that the spatial frequency content of the shading profile often overlaps with that of retinal features, retrospectively correcting for inhomogeneous illumination while maintaining the radiometric fidelity of the real data can be challenging. This paper describes a simple method for obtaining an estimate of the illumination profile in retinal images, with the particular goal of minimizing its influence upon features of interest. This is achieved by making use of Laplace interpolation and a multiplicative image formation model.

The INIA19 Template and NeuroMaps Atlas for Primate Brain Image Parcellation and Spatial Normalization

The INIA19 is a new, high-quality template for imaging-based studies of non-human primate brains, created from high-resolution, T₁-weighted magnetic resonance (MR) images of 19 rhesus macaque (Macaca mulatta) animals. Combined with the comprehensive cortical and sub-cortical label map of the NeuroMaps atlas, the INIA19 is equally suitable for studies requiring both spatial normalization and atlas label propagation. Population-averaged template images are provided for both the brain and the whole head, to allow alignment of the atlas with both skull-stripped and unstripped data, and thus to facilitate its use for skull stripping of new images. This article describes the construction of the template using freely available software tools, as well as the template itself, which is being made available to the scientific community (http://nitrc.org/projects/inia19/).

Sampling requirements in DCE-MRI based analysis of high grade gliomas: simulations and clinical results

PURPOSE

To investigate the effect of variations in temporal resolution and total measurement times on the estimations of kinetic parameters derived from dynamic contrast-enhanced (DCE) MRI in patients with high-grade gliomas (HGGs).

MATERIALS AND METHODS

DCE-MRI with high temporal resolution (dynamic sampling time (T(s)) = 2.1 s and 3.4 s) and total sampling time (T(acq)) of 5.2 min was acquired in 101 examinations from 15 patients. Using the modified Tofts model K(trans), k(ep) v(e) and v(p) were estimated. The effects of increasing T(s) and reducing T(acq) on the estimated kinetic parameters were estimated through down-sampling and data truncation, and the results were compared with numerical simulations.

RESULTS

There was an overall dependence of all four kinetic parameters on T(s) and T(acq). Increasing T(s) resulted in under-estimation of K(trans) and over-estimation of V(p), whereas k(ep) and V(e) varied in a less predictable manner. Reducing T(acq) resulted in over-estimation of K(trans) and k(ep) and under-estimation of v(p) and v(e). Increasing T(s) and reducing T(acq) resulted in increased relative error for all four parameters.

CONCLUSION

Estimated K(trans), K(ep), and V(e) in HGGs were within 15% of the high sampling rate reference values for T(s) <20 s. Increasing T(s) and reducing T(acq) leads to reduced precision of the estimated values.

Regional brain structural dysmorphology in human immunodeficiency virus infection: effects of acquired immune deficiency syndrome, alcoholism, and age

BACKGROUND

Human immunodeficiency virus (HIV) infection and alcoholism each carries liability for disruption of brain structure and function integrity. Despite considerable prevalence of HIV-alcoholism comorbidity, few studies examined the potentially heightened burden of disease comorbidity.

METHODS

Participants were 342 men and women: 110 alcoholics, 59 with HIV infection, 65 with HIV infection and alcoholism, and 108 healthy control subjects. This design enabled examination of independent and combined effects of HIV infection and alcoholism along with other factors (acquired immune deficiency syndrome [AIDS]-defining events, hepatitis C infection, age) on regional brain volumes derived from T1-weighted magnetic resonance images.

RESULTS

Brain volumes, expressed as Z scores corrected for intracranial volume and age, were measured in 20 tissue and 5 ventricular and sulcal regions. The most profound and consistent volume deficits occurred with alcohol use disorders, notable in the cortical mantle, insular and anterior cingulate cortices, thalamus, corpus callosum, and frontal sulci. The HIV-only group had smaller thalamic and larger frontal sulcal volumes than control subjects. HIV disease-related factors associated with greater volume abnormalities included CD4 cell count nadir, clinical staging, history of AIDS-defining events, infection age, and current age. Longer sobriety and less lifetime alcohol consumption were predictive of attenuated brain volume abnormalities in both alcohol groups.

CONCLUSIONS

Having HIV infection with alcoholism and AIDS had an especially poor outcome on brain structures. That longer periods of sobriety and less lifetime alcohol consumption were predictive of attenuated brain volume abnormalities encourages the inclusion of alcohol recovery efforts in HIV/AIDS therapeutic settings.

Topology-based nonlocal fuzzy segmentation of brain MR image with inhomogeneous and partial volume intensity

PURPOSE

The aim was to automatically segment brain magnetic resonance (MR) image with inhomogeneous and partial volume (PV) intensity for brain and neurophysiology analysis.

METHODS

Rather than assuming the presence of a single bias field over the image data, we first apply a local model to MR image analysis. With the brain topology knowledge, several specific local regions are selected, and typical brain tissues are then extracted for the prior estimation of fuzzy clustering center and member function. A new nonlocal fuzzy labeling scheme is applied to global optimization segmentation based on the block comparison and distance weight, which is robust to noise and inhomogeneous intensity. The nonlocal labeling provides optimized fuzzy member value and local intensity estimation of brain tissues such as cerebrospinal fluid (CSF), white matter (WM), and gray matter (GM). In addition to inhomogeneous intensity, PV may lead to error segmentation. To correct error segmentation because of PV, this article also provides two correction schemes. The first one is to extract CSF in deep sulci, which captures more CSF candidate by intensity comparison and topology shape comparison. The local pure CSF, WM, and GM is then estimated to correct the interfaces of CSF/GM and WM/GM.

RESULTS

The segmentation experiments are performed on both brainweb-simulated images and Internet brain segmentation repository database (IBSR) real images. The experimental results demonstrate the robust and efficient performance of our approach.

CONCLUSIONS

Our approach can be applied to automatic segmentation of the brain MR image.

Quantitative evaluation of MRI-based tracking of ferritin-labeled endogenous neural stem cell progeny in rodent brain

Endogenous neural stem cells have the potential to facilitate therapy for various neurodegenerative brain disorders. To increase our understanding of neural stem and progenitor cell biology in healthy and diseased brain, methods to label and visualize stem cells and their progeny in vivo are indispensable. Iron oxide particle based cell-labeling approaches enable cell tracking by MRI with high resolution and good soft tissue contrast in the brain. However, in addition to important concerns about unspecific labeling and low labeling efficiency, the dilution effect upon cell division is a major drawback for longitudinal follow-up of highly proliferating neural progenitor cells with MRI. Stable viral vector-mediated marking of endogenous stem cells and their progeny with a reporter gene for MRI could overcome these limitations. We stably and efficiently labeled endogenous neural stem/progenitor cells in the subventricular zone in situ by injecting a lentiviral vector expressing ferritin, a reporter for MRI. We developed an image analysis pipeline to quantify MRI signal changes at the level of the olfactory bulb as a result of migration of ferritin-labeled neuroblasts along the rostral migratory stream. We were able to detect ferritin-labeled endogenous neural stem cell progeny into the olfactory bulb of individual animals with ex vivo MRI at 30 weeks post injection, but could not demonstrate reliable in vivo detection and longitudinal tracking of neuroblast migration to the OB in individual animals. Therefore, although LV-mediated labeling of endogenous neural stem and progenitor cells resulted in efficient and stable ferritin-labeling of stem cell progeny in the OB, even with quantitative image analysis, sensitivity remains a limitation for in vivo applications.

Combining atlas-based parcellation of regional brain data acquired across scanners at 1.5 T and 3.0 T field strengths

Longitudinal brain morphometric studies designed for data acquisition at a single MRI field strength can be seriously limited by system replacements from lower to higher field strength. Merging data across field strengths has not been endorsed for a variety of reasons, yet the ability to combine such data would broaden longitudinal investigations. To determine whether structural T1-weighted MRI data acquired across MR field strengths could be merged, parcellations of archival SPGR data acquired in 114 individuals at 1.5 T and at 3.0 T within 3 weeks of each other were compared. The first set of analyses examined 1) the correspondence between regional tissue volumes derived from data collected at 1.5 T and 3.0 T and 2) whether there were systematic differences for which a correction factor could be determined and applied to improve measurement agreement. Comparability of regional volume determination at 1.5 T and 3.0 T was assessed with intraclass correlation (ICC) computed on volumes derived from the automated and unsupervised SRI24 atlas registration and parcellation method. A second set of analyses measured the reliability of the registration and quantification using the same approach on longitudinal data acquired in 69 healthy adults at a single field strength, 1.5 T, at an interval < 2 years. The mainstay of the analyses was based on the SRI24 method; to examine the potential of merging data across field strengths and across image analysis packages, a secondary set of analyses used FreeSurfer instead of the SRI24 method. For both methods, a regression-based linear correction function significantly improved correspondence. The results indicated high correspondence between most selected cortical, subcortical, and CSF-filled spaces; correspondence was lowest in the globus pallidus, a region rich in iron, which in turn has a considerable field-dependent effect on signal intensity. Thus, the application of a regression-based correction function that improved the correspondence in regional volume estimations argues well for the proposition that selected T1-weighted regional anatomical brain data can be reliably combined across 1.5 T and 3.0 T field strengths with the application of an appropriate correction procedure.

MRI non-uniformity correction through interleaved bias estimation and B-spline deformation with a template

We propose a template-based method for correcting field inhomogeneity biases in magnetic resonance images (MRI) of the human brain. At each algorithm iteration, the update of a B-spline deformation between an unbiased template image and the subject image is interleaved with estimation of a bias field based on the current template-to-image alignment. The bias field is modeled using a spatially smooth thin-plate spline interpolation based on ratios of local image patch intensity means between the deformed template and subject images. This is used to iteratively correct subject image intensities which are then used to improve the template-to-image deformation. Experiments on synthetic and real data sets of images with and without Alzheimer's disease suggest that the approach may have advantages over the popular N3 technique for modeling bias fields and narrowing intensity ranges of gray matter, white matter, and cerebrospinal fluid. This bias field correction method has the potential to be more accurate than correction schemes based solely on intrinsic image properties or hypothetical image intensity distributions.

CAIMAN: an online algorithm repository for Cancer Image Analysis

CAIMAN (CAncer IMage ANalysis: http://www.caiman.org.uk) is an online algorithm repository that provides specifically designed algorithms to analyse the images produced by experiments relevant to Cancer Research and Life Sciences, especially vascular biology. CAIMAN is accessed through a user-friendly website where researchers can upload their images and the results are returned by email. CAIMAN does not intend to replace more sophisticated software solutions such as ImageJ, Matlab, or commercial packages, but it will provide a first stop where any researcher can upload images and can obtain quantitative results without having to do any programming at all.

Developmental change in regional brain structure over 7 months in early adolescence: comparison of approaches for longitudinal atlas-based parcellation

Early adolescence is a time of rapid change in neuroanatomy and sexual development. Precision in tracking changes in brain morphology with structural MRI requires image segmentation with minimal error. Here, we compared two approaches to achieve segmentation by image registration with an atlas to quantify regional brain structural development over a 7-month interval in normal, early adolescent boys and girls. Adolescents were scanned twice (average interval=7.3 months), yielding adequate data for analysis in 16 boys (baseline age 10.9 to 13.9 years; Tanner Stage=1 to 4) and 12 girls (baseline age=11.2 to 13.7 years; Tanner Stage=3 to 4). Brain volumes were derived from T1-weighted (SPGR) images and dual-echo Fast Spin-Echo (FSE) images collected on a GE 3T scanner with an 8-channel phased-array head coil and analyzed by registration-based parcellation using the SRI24 atlas. The "independent" method required two inter-subject registrations: both baseline (MRI 1) to atlas and follow-up (MRI 2) to the atlas. The "sequential" method required one inter-subject registration, which was MRI 1 to the atlas, and one intra-subject registration, which was MRI 2 to MRI 1. Gray matter/white matter/CSF were segmented in both MRI-1 and MRI-2 using FSL FAST with tissue priors also based on the SRI24 atlas. Gray matter volumes were derived for 10 cortical regions, gray+white matter volumes for 5 subcortical structures, and CSF volumes for 4 ventricular regions and the cortical sulci. Across the 15 tissue regions, the coefficient of variation (CV) of change scores across individuals was significantly lower for the sequential method (CV=3.02), requiring only one inter-subject registration, than for the independent method (CV=9.43), requiring two inter-subject registrations. Volume change based on the sequential method revealed that total supratentorial and CSF volumes increased, while cortical gray matter volumes declined significantly (p<0.01) in anterior (lateral and medial frontal, anterior cingulate, precuneus, and parietal) but not posterior (occipital, calcarine) cortical regions. These volume changes occurred in all boys and girls who advanced a step in Tanner staging. Subcortical structures did not show consistent changes. Thus, longitudinal MRI assessment using robust registration methods is sufficiently sensitive to identify significant regional brain changes over a 7-month interval in boys and girls in early adolescence. Increasing the temporal resolution of the retest interval in longitudinal developmental studies could increase accuracy in timing of peak growth of regional brain tissue and refine our understanding of the neural mechanisms underlying the dynamic changes in brain structure throughout adolescence.

Evaluation of the specificity and sensitivity of ferritin as an MRI reporter gene in the mouse brain using lentiviral and adeno-associated viral vectors

The development of in vivo imaging protocols to reliably track transplanted cells or to report on gene expression is critical for treatment monitoring in (pre)clinical cell and gene therapy protocols. Therefore, we evaluated the potential of lentiviral vectors (LVs) and adeno-associated viral vectors (AAVs) to express the magnetic resonance imaging (MRI) reporter gene ferritin in the rodent brain. First, we compared the induction of background MRI contrast for both vector systems in immune-deficient and immune-competent mice. LV injection resulted in hypointense (that is, dark) changes of T(2)/T(2)(*) (spin-spin relaxation time)-weighted MRI contrast at the injection site, which can be partially explained by an inflammatory response against the vector injection. In contrast to LVs, AAV injection resulted in reduced background contrast. Moreover, AAV-mediated ferritin overexpression resulted in significantly enhanced contrast to background on T(2)(*)-weighted MRI. Although sensitivity associated with the ferritin reporter remains modest, AAVs seem to be the most promising vector system for in vivo MRI reporter gene imaging.

A web-based brain atlas of the vervet monkey, Chlorocebus aethiops

Vervet monkeys are a frequently studied animal model in neuroscience research. Although equally distantly related to humans, the ancestors of vervets diverged from those of macaques and baboons more than 11 million years ago, antedating the divergence of the ancestors of humans, chimpanzees and gorillas. To facilitate anatomic localization in the vervet brain, two linked on-line electronic atlases are described, one based on registered MRI scans from hundreds of vervets (http://www.loni.ucla.edu/Research/Atlases/Data/vervet/vervetmratlas/vervetmratlas.html) and the other based on a high-resolution cryomacrotome study of a single vervet (http://www.loni.ucla.edu/Research/Atlases/Data/vervet/vervetatlas/vervetatlas.html). The averaged MRI atlas is also available as a volume in Neuroimaging Informatics Technology Initiative format. In the cryomacrotome atlas, various sulcal and subcortical structures have been anatomically labeled and surface rendered views are provided along the primary planes of section. Both atlases simultaneously provide views in all three primary planes of section, rapid navigation by clicking on the displayed images, and stereotaxic coordinates in the averaged MRI atlas space. Despite the extended time period since their divergence, the major sulcal and subcortical landmarks in vervets are highly conserved relative to those described in macaques.

A new bias field correction method combining N3 and FCM for improved segmentation of breast density on MRI

PURPOSE

Quantitative breast density is known as a strong risk factor associated with the development of breast cancer. Measurement of breast density based on three-dimensional breast MRI may provide very useful information. One important step for quantitative analysis of breast density on MRI is the correction of field inhomogeneity to allow an accurate segmentation of the fibroglandular tissue (dense tissue). A new bias field correction method by combining the nonparametric nonuniformity normalization (N3) algorithm and fuzzy-C-means (FCM)-based inhomogeneity correction algorithm is developed in this work.

METHODS

The analysis is performed on non-fat-sat T1-weighted images acquired using a 1.5 T MRI scanner. A total of 60 breasts from 30 healthy volunteers was analyzed. N3 is known as a robust correction method, but it cannot correct a strong bias field on a large area. FCM-based algorithm can correct the bias field on a large area, but it may change the tissue contrast and affect the segmentation quality. The proposed algorithm applies N3 first, followed by FCM, and then the generated bias field is smoothed using Gaussian kernal and B-spline surface fitting to minimize the problem of mistakenly changed tissue contrast. The segmentation results based on the N3+FCM corrected images were compared to the N3 and FCM alone corrected images and another method, coherent local intensity clustering (CLIC), corrected images. The segmentation quality based on different correction methods were evaluated by a radiologist and ranked.

RESULTS

The authors demonstrated that the iterative N3+FCM correction method brightens the signal intensity of fatty tissues and that separates the histogram peaks between the fibroglandular and fatty tissues to allow an accurate segmentation between them. In the first reading session, the radiologist found (N3+FCM > N3 > FCM) ranking in 17 breasts, (N3+FCM > N3 = FCM) ranking in 7 breasts, (N3+FCM = N3 > FCM) in 32 breasts, (N3+FCM = N3 = FCM) in 2 breasts, and (N3 > N3+FCM > FCM) in 2 breasts. The results of the second reading session were similar. The performance in each pairwise Wilcoxon signed-rank test is significant, showing N3+FCM superior to both N3 and FCM, and N3 superior to FCM. The performance of the new N3+FCM algorithm was comparable to that of CLIC, showing equivalent quality in 57/60 breasts.

CONCLUSIONS

Choosing an appropriate bias field correction method is a very important preprocessing step to allow an accurate segmentation of fibroglandular tissues based on breast MRI for quantitative measurement of breast density. The proposed algorithm combining N3+FCM and CLIC both yield satisfactory results.

Fast algorithm for calculation of inhomogeneity gradient in magnetic resonance imaging data

PURPOSE

To develop and implement a new approach for correcting the intensity inhomogeneity in magnetic resonance imaging (MRI) data.

MATERIALS AND METHODS

The algorithm is based on the assumption that intensity inhomogeneity in MR data is multiplicative and smoothly varying. Using a statistically stable method, the algorithm first calculates the partial derivative of the inhomogeneity gradient across the data. The algorithm then solves for the gradient field and fits it to a parametric surface. It was tested on both simulated and real human and animal MRI data.

RESULTS

The algorithm is shown to restore the homogeneity in all images that were tested. On real human brain images the algorithm demonstrated superior or comparable performance relative to some of the commonly used intensity inhomogeneity correction methods such as SPM, BrainSuite, and N3.

CONCLUSION

The proposed algorithm provides an alternative method for correcting the intensity inhomogeneity in MR images. It is shown to be fast and its performance is superior or comparable to algorithms described in the published literature. Due to its generality, this algorithm is applicable to MR images of both humans and animals.

Template-based B₁ inhomogeneity correction in 3T MRI brain studies

Low noise, high resolution, fast and accurate T₁ maps from MRI images of the brain can be performed using a dual flip angle method. However, B₁ field inhomogeneity, which is particularly problematic at high field strengths (e.g., 3T), limits the ability of the scanner to deliver the prescribed flip angle, introducing errors into the T₁ maps that limit the accuracy of quantitative analyses based on those maps. A dual repetition time method was used for acquiring a B₁ map to correct that inhomogeneity. Additional inaccuracies due to misregistration of the acquired T₁-weighted images were corrected by rigid registration, and the effects of misalignment on the T₁ maps were compared to those of B₁ inhomogeneity in 19 normal subjects. However, since B₁ map acquisition takes up precious scanning time and most retrospective studies do not have B₁ map, we designed a template-based correction strategy. B₁ maps from different subjects were aligned using a twelve-parameter affine registration. Recomputed T₁ maps showed an important improvement with respect to the noncorrected maps: histograms of all corrected maps exhibited two peaks corresponding to white and gray matter tissues, while unimodal histograms were observed in all uncorrected maps because of the inhomogeneity. A method to detect the best nonsubject-specific B₁ correction based on a set of features was designed. The optimum set of weighting factors for those features was computed. The best available B₁ correction was detected in almost all subjects while corrections comparable to the T₁ map corrected using the B₁ map from the same subject were detected in the others.

Evaluation of flow measurement from the first pass bolus T1 weighted images using inversion recovery sequence

OBJECTIVE

Previous studies have shown that the organ blood flows (OBFs) calculated using the T(1) weighted MRI technique were lower than the expected values. The aim of this study was a flow measurement comparison between the theoretical and experimental flows based on the technique before and after corrections (coil non-uniformity and inflow) using a flow phantom at two different concentrations (0.8 and 1.2 mmol l(-1)).

METHODS

A flow phantom was designed to produce three different flow rates at the same time. Theoretical flow was calculated by measuring the volumes of the phantom and dividing them by the time taken to fill these volumes. T(1) weighted turbo fast low-angle shot images were used to measure signal intensity (SI) change during the first bolus passage of the contrast medium through the phantom using linear phase-encoding acquisition.

RESULTS

The corrected experimental flow based on the technique shows a good agreement with the theoretical flow, where the flow rate is low at the two concentrations.

CONCLUSION

The T(1) weighted MRI technique after the two correction factors can be used to measure the absolute flow where the flow rate is low, such as in the capillaries. For measuring high flow rate (e.g. artery), additional correction factors should be considered.

Volumetric cerebral perfusion imaging in healthy adults: regional distribution, laterality, and repeatability of pulsed continuous arterial spin labeling (PCASL)

The regional distribution, laterality, and reliability of volumetric pulsed continuous arterial spin labeling (PCASL) measurements of cerebral blood flow (CBF) in cortical, subcortical, and cerebellar regions were determined in 10 normal volunteers studied on two occasions separated by 3 to 7 days. Regional CBF, normalized for global perfusion, was highly reliable when measured on separate days. Several regions showed significant lateral asymmetry; notably, in frontal regions CBF was greater in the right than left hemisphere, whereas left was greater than right in posterior regions. There was considerable regional variability across the brain, whereby the posterior cingulate and central and posterior precuneus cortices had the highest perfusion and the globus pallidus the lowest gray matter perfusion. The latter may be due to iron-induced T1 shortening affecting labeled spins and computed CBF signal. High CBF in the posterior cingulate and posterior and central precuneus cortices in this task-free acquisition suggests high activity in these principal nodes of the "default mode network."

Progression of cerebral atrophy and white matter hyperintensities in patients with type 2 diabetes

OBJECTIVE

Type 2 diabetes is associated with a moderate degree of cerebral atrophy and a higher white matter hyperintensity (WMH) volume. How these brain-imaging abnormalities evolve over time is unknown. The present study aims to quantify cerebral atrophy and WMH progression over 4 years in type 2 diabetes.

RESEARCH DESIGN AND METHODS

A total of 55 patients with type 2 diabetes and 28 age-, sex-, and IQ-matched control participants had two 1.5T magnetic resonance imaging scans with a 4-year interval. Volumetric measurements of total brain, peripheral cerebrospinal fluid (CSF), lateral ventricles, and WMH were performed with k-nearest neighbor-based probabilistic segmentation. All volumes were expressed as percentage of intracranial volume. Linear regression analyses, adjusted for age and sex, were performed to compare brain volumes between the groups and to identify determinants of volumetric change within the type 2 diabetic group.

RESULTS

At baseline, patients with type 2 diabetes had a significantly smaller total brain volume and larger peripheral CSF volume than control participants. In both groups, all volumes showed a significant change over time. Patients with type 2 diabetes had a greater increase in lateral ventricular volume than control participants (mean adjusted between-group difference in change over time [95% CI]: 0.11% in 4 years [0.00 to 0.22], P = 0.047).

CONCLUSIONS

The greater increase in lateral ventricular volume over time in patients with type 2 diabetes compared with control participants shows that type 2 diabetes is associated with a slow increase of cerebral atrophy over the course of years.

Cerebral blood flow in posterior cortical nodes of the default mode network decreases with task engagement but remains higher than in most brain regions

Functional neuroimaging studies provide converging evidence for existence of intrinsic brain networks activated during resting states and deactivated with selective cognitive demands. Whether task-related deactivation of the default mode network signifies depressed activity relative to the remaining brain or simply lower activity relative to its resting state remains controversial. We employed 3D arterial spin labeling imaging to examine regional cerebral blood flow (CBF) during rest, a spatial working memory task, and a second rest. Change in regional CBF from rest to task showed significant normalized and absolute CBF reductions in posterior cingulate, posterior-inferior precuneus, and medial frontal lobes . A Statistical Parametric Mapping connectivity analysis, with an a priori seed in the posterior cingulate cortex, produced deactivation connectivity patterns consistent with the classic "default mode network" and activation connectivity anatomically consistent with engagement in visuospatial tasks. The large task-related CBF decrease in posterior-inferior precuneus relative to its anterior and middle portions adds evidence for the precuneus' heterogeneity. The posterior cingulate and posterior-inferior precuneus were also regions of the highest CBF at rest and during task performance. The difference in regional CBF between intrinsic (resting) and evoked (task) activity levels may represent functional readiness or reserve vulnerable to diminution by conditions affecting perfusion.

The SRI24 multichannel atlas of normal adult human brain structure

This article describes the SRI24 atlas, a new standard reference system of normal human brain anatomy, that was created using template-free population registration of high-resolution magnetic resonance images acquired at 3T in a group of 24 normal control subjects. The atlas comprises anatomical channels (T1, T2, and proton density weighted), diffusion-related channels (fractional anisotropy, mean diffusivity, longitudinal diffusivity, mean diffusion-weighted image), tissue channels (CSF probability, gray matter probability, white matter probability, tissue labels), and two cortical parcellation maps. The SRI24 atlas enables multichannel atlas-to-subject image registration. It is uniquely versatile in that it is equally suited for the two fundamentally different atlas applications: label propagation and spatial normalization. Label propagation, herein demonstrated using diffusion tensor image fiber tracking, is enabled by the increased sharpness of the SRI24 atlas compared with other available atlases. Spatial normalization, herein demonstrated using data from a young-old group comparison study, is enabled by its unbiased average population shape property. For both propagation and normalization, we also report the results of quantitative comparisons with seven other published atlases: Colin27, MNI152, ICBM452 (warp5 and air12), and LPBA40 (SPM5, FLIRT, AIR). Our results suggest that the SRI24 atlas, although based on 3T MR data, allows equally accurate spatial normalization of data acquired at 1.5T as the comparison atlases, all of which are based on 1.5T data. Furthermore, the SRI24 atlas is as suitable for label propagation as the comparison atlases and detailed enough to allow delineation of anatomical structures for this purpose directly in the atlas.

Brain MRI tissue classification based on local Markov random fields

A new method for tissue classification of brain magnetic resonance images (MRI) of the brain is proposed. The method is based on local image models where each models the image content in a subset of the image domain. With this local modeling approach, the assumption that tissue types have the same characteristics over the brain needs not to be evoked. This is important because tissue type characteristics, such as T1 and T2 relaxation times and proton density, vary across the individual brain and the proposed method offers improved protection against intensity non-uniformity artifacts that can hamper automatic tissue classification methods in brain MRI. A framework in which local models for tissue intensities and Markov Random Field (MRF) priors are combined into a global probabilistic image model is introduced. This global model will be an inhomogeneous MRF and it can be solved by standard algorithms such as iterative conditional modes. The division of the whole image domain into local brain regions possibly having different intensity statistics is realized via sub-volume probabilistic atlases. Finally, the parameters for the local intensity models are obtained without supervision by maximizing the weighted likelihood of a certain finite mixture model. For the maximization task, a novel genetic algorithm almost free of initialization dependency is applied. The algorithm is tested on both simulated and real brain MR images. The experiments confirm that the new method offers a useful improvement of the tissue classification accuracy when the basic tissue characteristics vary across the brain and the noise level of the images is reasonable. The method also offers better protection against intensity non-uniformity artifact than the corresponding method based on a global (whole image) modeling scheme.

“Gold standard” data for evaluation and comparison of 3D/2D registration methods

Evaluation and comparison of registration techniques for image-guided surgery is an important problem that has received little attention in the literature. In this paper we address the challenging problem of generating reliable "gold standard" data for use in evaluating the accuracy of 3D/2D registrations. We have devised a cadaveric lumbar spine phantom with fiducial markers and established highly accurate correspondences between 3D CT and MR images and 18 2D X-ray images. The expected target registration errors for target points on the pedicles are less than 0.26 mm for CT-to-X-ray registration and less than 0.42 mm for MR-to-X-ray registration. As such, the "gold standard" data, which has been made publicly available on the Internet (http://lit.fe.uni-lj.si/Downloads/downloads.asp), is useful for evaluation and comparison of 3D/2D image registration methods.

Intensity Standardization Simplifies Brain MR Image Segmentation

Typically, brain MR images present significant intensity variation across patients and scanners. Consequently, training a classifier on a set of images and using it subsequently for brain segmentation may yield poor results. Adaptive iterative methods usually need to be employed to account for the variations of the particular scan. These methods are complicated, difficult to implement and often involve significant computational costs. In this paper, a simple, non-iterative method is proposed for brain MR image segmentation. Two preprocessing techniques, namely intensity inhomogeneity correction, and more importantly MR image intensity standardization, used prior to segmentation, play a vital role in making the MR image intensities have a tissue-specific numeric meaning, which leads us to a very simple brain tissue segmentation strategy.Vectorial scale-based fuzzy connectedness and certain morphological operations are utilized first to generate the brain intracranial mask. The fuzzy membership value of each voxel within the intracranial mask for each brain tissue is then estimated. Finally, a maximum likelihood criterion with spatial constraints taken into account is utilized in classifying all voxels in the intracranial mask into different brain tissue groups. A set of inhomogeneity corrected and intensity standardized images is utilized as a training data set. We introduce two methods to estimate fuzzy membership values. In the first method, called SMG (for simple membership based on a gaussian model), the fuzzy membership value is estimated by fitting a multivariate Gaussian model to the intensity distribution of each brain tissue whose mean intensity vector and covariance matrix are estimated and fixed from the training data sets. The second method, called SMH (for simple membership based on a histogram), estimates fuzzy membership value directly via the intensity distribution of each brain tissue obtained from the training data sets. We present several studies to evaluate the performance of these two methods based on 10 clinical MR images of normal subjects and 10 clinical MR images of Multiple Sclerosis (MS) patients. A quantitative comparison indicates that both methods have overall better accuracy than the k-nearest neighbors (kNN) method, and have much better efficiency than the Finite Mixture (FM) model based Expectation-Maximization (EM) method. Accuracy is similar for our methods and EM method for the normal subject data sets, but much better for our methods for the patient data sets.

Construction of periventricular white matter hyperintensity maps by spatial normalization of the lateral ventricles

Subcortical and periventricular white matter hyperintensities (WMHs) may have different associations with cognition and pathophysiology. The aim of the present study is to develop an automated method for construction of periventricular WMH maps that enables the analysis of between-group differences in WMH location and characteristics in the periventricular region without the requirement of prior boundary definition. To avoid influence of WMHs on spatial normalization, a reference image of the lateral ventricles was constructed based on images of 24 subjects. Construction was not biased to a single subject. WMHs were segmented by k-nearest neighbor-based classification of magnetic resonance inversion recovery and fluid attenuated inversion recovery images. Cerebrospinal fluid segmentations of individual subjects were nonrigidly mapped to the reference image of the lateral ventricles. The subject's WMHs were transformed to the reference space accordingly. Spatial normalization accuracy was validated using measures of overlap and of displacement relative to the boundary of the lateral ventricles. After spatial normalization, the boundaries of the lateral ventricles closely matched the reference image and in an area of approximately 1 cm around the lateral ventricles the relative displacement was less than 1 mm. To illustrate the method, it was applied to 61 patients with Type 2 diabetes and 26 control subjects, whereupon periventricular WMH maps were constructed and compared. The proposed method is particularly suited to analyze WMH distribution differences at the level of the lateral ventricles between large groups of patients.

Automatic segmentation of newborn brain MRI

Quantitative brain tissue segmentation from newborn MRI offers the possibility of improved clinical decision making and diagnosis, new insight into the mechanisms of disease, and new methods for the evaluation of treatment protocols for preterm newborns. Such segmentation is challenging, however, due to the imaging characteristics of the developing brain. Existing techniques for newborn segmentation either achieve automation by ignoring critical distinctions between different tissue types or require extensive expert interaction. Because manual interaction is time consuming and introduces both bias and variability, we have developed a novel automatic segmentation algorithm for brain MRI of newborn infants. The key algorithmic contribution of this work is a new approach for automatically learning patient-specific class-conditional probability density functions. The algorithm achieves performance comparable to expert segmentations while automatically identifying cortical gray matter, subcortical gray matter, cerebrospinal fluid, myelinated white matter and unmyelinated white matter. We compared the performance of our algorithm with a previously published semi-automated algorithm and with expert-drawn images. Our algorithm achieved an accuracy comparable with methods that require undesirable manual interaction.

Improvement of brain segmentation accuracy by optimizing non-uniformity correction using N3

Smoothly varying and multiplicative intensity variations within MR images that are artifactual, can reduce the accuracy of automated brain segmentation. Fortunately, these can be corrected. Among existing correction approaches, the nonparametric non-uniformity intensity normalization method N3 (Sled, J.G., Zijdenbos, A.P., Evans, A.C., 1998. Nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans. Med. Imag. 17, 87-97.) is one of the most frequently used. However, at least one recent study (Boyes, R.G., Gunter, J.L., Frost, C., Janke, A.L., Yeatman, T., Hill, D.L.G., Bernstein, M.A., Thompson, P.M., Weiner, M.W., Schuff, N., Alexander, G.E., Killiany, R.J., DeCarli, C., Jack, C.R., Fox, N.C., 2008. Intensity non-uniformity correction using N3 on 3-T scanners with multichannel phased array coils. NeuroImage 39, 1752-1762.) suggests that its performance on 3 T scanners with multichannel phased-array receiver coils can be improved by optimizing a parameter that controls the smoothness of the estimated bias field. The present study not only confirms this finding, but additionally demonstrates the benefit of reducing the relevant parameter values to 30-50 mm (default value is 200 mm), on white matter surface estimation as well as the measurement of cortical and subcortical structures using FreeSurfer (Martinos Imaging Centre, Boston, MA). This finding can help enhance precision in studies where estimation of cerebral cortex thickness is critical for making inferences.

Intensity correction with a pair of spoiled gradient recalled echo images

Intensity inhomogeneities in magnetic resonance images (MRI) are a frequently occurring artefact, and result in the same tissue class to have vastly different intensities within an image. These inhomogeneities can be modelled by a slowly varying field, which is also called the bias field. Previous phantom-, image- or sequence based approaches suffer from long scan times, post-processing times or do not sufficiently remove the intensity variations. These intensity variations cause problems for quantitative image analysis algorithms (segmentation, registration) as well as clinicians (e.g. by complicating the visual assessment). This paper presents a novel technique (COIN, correction of intensity inhomogeneities) that uses two calibration images (fast spoiled gradient echo) to map a parameter containing the bias field, which is specific to the patient during a particular exam. This parametric map can then be used to correct any other images acquired during the same exam, regardless of the sequence employed. By using a short repetition time (less than 5 ms) for the calibration scans, the additional scan time is reduced to 60 s (max). The subsequent post-processing time is approximately 60 s per 20 slices. We successfully validate our approach on simulated brain MRI as well as real liver and spinal images. These images were acquired with a number of different coils, sequences and weightings. A comparison of our method with an existing, commercially available algorithm by radiologists shows that COIN is superior.

Nonrigid registration of joint histograms for intensity standardization in magnetic resonance imaging

A major disadvantage of magnetic resonance imaging (MRI) compared to other imaging modalities like computed tomography is the fact that its intensities are not standardized. Our contribution is a novel method for MRI signal intensity standardization of arbitrary MRI scans, so as to create a pulse sequence dependent standard intensity scale. The proposed method is the first approach that uses the properties of all acquired images jointly (e.g., T1- and T2-weighted images). The image properties are stored in multidimensional joint histograms. In order to normalize the probability density function (pdf) of a newly acquired data set, a nonrigid image registration is performed between a reference and the joint histogram of the acquired images. From this matching a nonparametric transformation is obtained, which describes a mapping between the corresponding intensity spaces and subsequently adapts the image properties of the newly acquired series to a given standard. As the proposed intensity standardization is based on the probability density functions of the data sets only, it is independent of spatial coherence or prior segmentations of the reference and current images. Furthermore, it is not designed for a particular application, body region or acquisition protocol. The evaluation was done using two different settings. First, MRI head images were used, hence the approach can be compared to state-of-the-art methods. Second, whole body MRI scans were used. For this modality no other normalization algorithm is known in literature. The Jeffrey divergence of the pdfs of the whole body scans was reduced by 45%. All used data sets were acquired during clinical routine and thus included pathologies.

Subject-matched templates for spatial normalization

Spatial normalization of images from multiple subjects is a common problem in group comparison studies, such as voxel-based and deformation-based morphometric analyses. Use of a study-specific template for normalization may improve normalization accuracy over a study-independent standard template (Good et al., NeuroImage, 14(1):21-36, 2001). Here, we develop this approach further by introducing the concept of subject-matched templates. Rather than using a single template for the entire population, a different template is used for every subject, with the template matched to the subject in terms of age, sex, and potentially other parameters (e.g., disease). All subject-matched templates are created from a single generative regression model of atlas appearance, thus providing a priori template-to-template correspondence without registration. We demonstrate that such an approach is technically feasible and significantly improves spatial normalization accuracy over using a single template.

Robust gradient-based 3-D/2-D registration of CT and MR to X-ray images

One of the most important technical challenges in image-guided intervention is to obtain a precise transformation between the intrainterventional patient's anatomy and corresponding preinterventional 3-D image on which the intervention was planned. This goal can be achieved by acquiring intrainterventional 2-D images and matching them to the preinterventional 3-D image via 3-D/2-D image registration. A novel 3-D/2-D registration method is proposed in this paper. The method is based on robustly matching 3-D preinterventional image gradients and coarsely reconstructed 3-D gradients from the intrainterventional 2-D images. To improve the robustness of finding the correspondences between the two sets of gradients, hypothetical correspondences are searched for along normals to anatomical structures in 3-D images, while the final correspondences are established in an iterative process, combining the robust random sample consensus algorithm (RANSAC) and a special gradient matching criterion function. The proposed method was evaluated using the publicly available standardized evaluation methodology for 3-D/2-D registration, consisting of 3-D rotational X-ray, computed tomography, magnetic resonance (MR), and 2-D X-ray images of two spine segments, and standardized evaluation criteria. In this way, the proposed method could be objectively compared to the intensity, gradient, and reconstruction-based registration methods. The obtained results indicate that the proposed method performs favorably both in terms of registration accuracy and robustness. The method is especially superior when just a few X-ray images and when MR preinterventional images are used for registration, which are important advantages for many clinical applications.

Image background inhomogeneity correction in MRI via intensity standardization

An automatic, simple, and image intensity standardization-based strategy for correcting background inhomogeneity in MR images is presented in this paper. Image intensities are first transformed to a standard intensity gray scale by a standardization process. Different tissue sample regions are then obtained from the standardized image by simply thresholding based on fixed intensity intervals. For each tissue region, a polynomial is fitted to the estimated discrete background intensity variation. Finally, a combined polynomial is determined and used for correcting the intensity inhomogeneity in the whole image. The above procedure is repeated on the corrected image iteratively until the size of the extracted tissue regions does not change significantly in two successive iterations. Intensity scale standardization is effected to make sure that the corrected image is not biased by the fitting strategy. The method has been tested on a number of simulated and clinical MR images. These tests and a comparison with the method of non-parametric non-uniform intensity normalization (N3) indicate that the method is effective in background intensity inhomogeneity correction and may have a slight edge over the N3 method.

Bias artifact suppression on MR volumes

RF-inhomogeneity correction is a relevant research topic in the field of magnetic resonance imaging (MRI). A volume corrupted by this artifact exhibits nonuniform illumination both inside a single slice and between adjacent ones. In this work a bias correction technique is presented, which suppresses this artifact on MR volumes scanned from different body parts without any a priori hypothesis on the artifact model. Theoretical foundations of the method are reported together with experimental results and a comparison is presented with both the 2D version of the algorithm and other techniques that are widely used in MRI literature.

Restoration of MRI data for intensity non-uniformities using local high order intensity statistics

MRI at high magnetic fields (>3.0 T) is complicated by strong inhomogeneous radio-frequency fields, sometimes termed the "bias field". These lead to non-biological intensity non-uniformities across the image. They can complicate further image analysis such as registration and tissue segmentation. Existing methods for intensity uniformity restoration have been optimized for 1.5 T, but they are less effective for 3.0 T MRI, and not at all satisfactory for higher fields. Also, many of the existing restoration algorithms require a brain template or use a prior atlas, which can restrict their practicalities. In this study an effective intensity uniformity restoration algorithm has been developed based on non-parametric statistics of high order local intensity co-occurrences. These statistics are restored with a non-stationary Wiener filter. The algorithm also assumes a smooth non-uniformity and is stable. It does not require a prior atlas and is robust to variations in anatomy. In geriatric brain imaging it is robust to variations such as enlarged ventricles and low contrast to noise ratio. The co-occurrence statistics improve robustness to whole head images with pronounced non-uniformities present in high field acquisitions. Its significantly improved performance and lower time requirements have been demonstrated by comparing it to the very commonly used N3 algorithm on BrainWeb MR simulator images as well as on real 4 T human head images.

Robustness of local binary patterns in brain MR image analysis

The aging population in developed countries has shifted considerable research attention to diseases related to age. Because age is one of the highest risk factors for neurodegenerative diseases, the need for automated brain image analysis has significantly increased. Magnetic Resonance Imaging (MRI) is a commonly used modality to image brain. MRI provides high tissue contrast; hence, the existing brain image analysis methods have often preferred the intensity information to others, such as texture. Recently, an easy-to-compute texture descriptor, Local Binary Pattern (LBP), has shown promise in various applications outside the medical field. In this paper, after extensive experiments, we show that rotation-invariant LBP is invariant to some common MRI artifacts that makes it possible to use it in various high-level brain MR image analysis applications.

The SRI24 Multi-Channel Brain Atlas: Construction and Applications

We present a new standard atlas of the human brain based on magnetic resonance images. The atlas was generated using unbiased population registration from high-resolution images obtained by multichannel-coil acquisition at 3T in a group of 24 normal subjects. The final atlas comprises three anatomical channels (T(1)-weighted, early and late spin echo), three diffusion-related channels (fractional anisotropy, mean diffusivity, diffusion-weighted image), and three tissue probability maps (CSF, gray matter, white matter). The atlas is dynamic in that it is implicitly represented by nonrigid transformations between the 24 subject images, as well as distortion-correction alignments between the image channels in each subject. The atlas can, therefore, be generated at essentially arbitrary image resolutions and orientations (e.g., AC/PC aligned), without compounding interpolation artifacts. We demonstrate in this paper two different applications of the atlas: (a) region definition by label propagation in a fiber tracking study is enabled by the increased sharpness of our atlas compared with other available atlases, and (b) spatial normalization is enabled by its average shape property. In summary, our atlas has unique features and will be made available to the scientific community as a resource and reference system for future imaging-based studies of the human brain.

Clinical neonatal brain MRI segmentation using adaptive nonparametric data models and intensity-based Markov priors

This paper presents a Bayesian framework for neonatal brain-tissue segmentation in clinical magnetic resonance (MR) images. This is a challenging task because of the low contrast-to-noise ratio and large variance in both tissue intensities and brain structures, as well as imaging artifacts and partial-volume effects in clinical neonatal scanning. We propose to incorporate a spatially adaptive likelihood model using a data-driven nonparametric statistical technique. The method initially learns an intensity-based prior, relying on the empirical Markov statistics from training data, using fuzzy nonlinear support vector machines (SVM). In an iterative scheme, the models adapt to spatial variations of image intensities via nonparametric density estimation. The method is effective even in the absence of anatomical atlas priors. The implementation, however, can naturally incorporate probabilistic atlas priors and Markov-smoothness priors to impose additional regularity on segmentation. The maximum-a-posteriori (MAP) segmentation is obtained within a graph-cut framework. Cross validation on clinical neonatal brain-MR images demonstrates the efficacy of the proposed method, both qualitatively and quantitatively.

A nonparametric MRI inhomogeneity correction method

Magnetic resonance images are commonly affected by intensity inhomogeneities which make it difficult to obtain any quantitative measures from them. We present a new method of automatically correcting this artifact using a nonparametric coarse to fine approach which allows bias fields to be modeled with different frequency ranges without user supervision. We also propose a new entropy-related cost function based on the combination of intensity and gradient image features for more robust homogeneity measurement. The proposed methodology has been evaluated for both synthetic and real data and compared with state of the art methods, showing the best results in the comparison. The proposed method is fully automatic and has no input parameters, making it very easy to use in a clinical environment.

Restoration of MRI Data for Field Nonuniformities using High Order Neighborhood Statistics

MRI at high magnetic fields (> 3.0 T ) is complicated by strong inhomogeneous radio-frequency fields, sometimes termed the "bias field". These lead to nonuniformity of image intensity, greatly complicating further analysis such as registration and segmentation. Existing methods for bias field correction are effective for 1.5 T or 3.0 T MRI, but are not completely satisfactory for higher field data. This paper develops an effective bias field correction for high field MRI based on the assumption that the nonuniformity is smoothly varying in space. Also, nonuniformity is quantified and unmixed using high order neighborhood statistics of intensity cooccurrences. They are computed within spherical windows of limited size over the entire image. The restoration is iterative and makes use of a novel stable stopping criterion that depends on the scaled entropy of the cooccurrence statistics, which is a non monotonic function of the iterations; the Shannon entropy of the cooccurrence statistics normalized to the effective dynamic range of the image. The algorithm restores whole head data, is robust to intense nonuniformities present in high field acquisitions, and is robust to variations in anatomy. This algorithm significantly improves bias field correction in comparison to N3 on phantom 1.5 T head data and high field 4 T human head data.

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.

Correction of intensity inhomogeneity in MR images of vascular disease

We are involved in a comprehensive program to characterize atherosclerotic disease using multiple MR images having different contrast mechanisms (T1W, T2W, PDW, magnetization transfer, etc.) of human carotid and animal model arteries. We use specially designed intravascular and surface array coils that give high signal-to-noise but suffer from sensitivity inhomogeneity and significant noise. We present here a new non-parametric method for correcting the images without assumption of the number of different tissues. Intensity inhomogeneity is modeled with cubic spline and is locally optimized using an entropy criterion. Validation has been performed on a specially design neck phantom as well as actual MR scans on patient neck. This same algorithm has been successfully applied to the correction of very high resolution, intravascular coil images. The steep bias is corrected sufficiently to aid human interpretation of gray scales. It should also make possible computerized tissue classification.