A multi-scale method for automatic correction of intensity non-uniformity in MR images

Surface Coil Intensity Correction in Magnetic Resonance Imaging in Spinal Metastases

Objective

To evaluate the clinical application of phased-array surface coil intensity correction in magnetic resonance imaging (MRI) in spinal metastases.

Methods

3 phantoms and 50 patients with a corresponding total number of 80 spinal metastases were included in this study. Fast spin echo T1- and T2- weighted MRI with and without surface coil intensity correction was routinely performed for all phantoms and patients. Phantoms were evaluated by means of variance to mean ratio of signal intensity on both T1- and T2- weighted MRI obtained with and without surface coil intensity correction. Spinal metastases were evaluated by image quality scores; reading time per case on both T1- and T2- weighted MRI obtained with and without surface coil intensity correction.

Results

Spinal metastases were diagnosed more successfully on MRI with surface coil intensity correction than on MRI with conventional surface coil technique. The variance to mean ratio of signal intensity was 53.36% for original T1-weighted MRI and 53.58% for original T2-weighted MRI. The variance to mean ratio of signal intensity was reduced to 18.99% for T1-weighted MRI with surface coil intensity correction and 22.77% for T2-weighted MRI with surface coil intensity correction. The overall image quality scores (interface conspicuity of lesion and details of lesion) were significantly higher than those of the original MRI. The reading time per case was shorter for MRI with surface coil intensity correction than for MRI without surface coil intensity correction.

Conclusions

Phased-array surface coil intensity correction in MRIs of spinal metastases provides improvements in image quality that leads to more successfully detection and assessment of spinal metastases than original MRI.

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

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

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.

Semi-automatic carotid intraplaque hemorrhage detection and quantification on Magnetization-Prepared Rapid Acquisition Gradient-Echo (MP-RAGE) with optimized threshold selection

BACKGROUND

Intraplaque hemorrhage (IPH) is associated with atherosclerosis progression and subsequent cardiovascular events. We sought to develop a semi-automatic method with an optimized threshold for carotid IPH detection and quantification on MP-RAGE images using matched histology as the gold standard.

METHODS

Fourteen patients scheduled for carotid endarterectomy underwent 3D MP-RAGE cardiovascular magnetic resonance (CMR) preoperatively. Presence and area of IPH were recorded using histology. Presence and area of IPH were also recorded on CMR based on intensity thresholding using three references for intensity normalization: the sternocleidomastoid muscle (SCM), the adjacent muscle and the automatically generated local median value. The optimized intensity thresholds were obtained by maximizing the Youden's index for IPH detection. Using leave-one-out cross validation, the sensitivity and specificity for IPH detection based on our proposed semi-automatic method and the agreement with histology on IPH area quantification were evaluated.

RESULTS

The optimized intensity thresholds for IPH detection were 1.0 times the SCM intensity, 1.6 times the adjacent muscle intensity and 2.2 times the median intensity. Using the semi-automatic method with the optimized intensity threshold, the following IPH detection and quantification performance was obtained: sensitivities up to 59, 68 and 80 %; specificities up to 85, 74 and 79 %; Pearson's correlation coefficients (IPH area measurement) up to 0.76, 0.93 and 0.90, respectively, using SCM, the adjacent muscle and the local median value for intensity normalization, after heavily calcified and small IPH were excluded.

CONCLUSIONS

A semi-automatic method with good performance on IPH detection and quantification can be obtained in MP-RAGE CMR, using an optimized intensity threshold comparing to the adjacent muscle. The automatically generated reference of local median value provides comparable performance and may be particularly useful for developing automatic classifiers. Use of the SCM intensity as reference is not recommended without coil sensitivity correction when surface coils are used.

An active contour model for the segmentation of images with intensity inhomogeneities and bias field estimation

Intensity inhomogeneity causes many difficulties in image segmentation and the understanding of magnetic resonance (MR) images. Bias correction is an important method for addressing the intensity inhomogeneity of MR images before quantitative analysis. In this paper, a modified model is developed for segmenting images with intensity inhomogeneity and estimating the bias field simultaneously. In the modified model, a clustering criterion energy function is defined by considering the difference between the measured image and estimated image in local region. By using this difference in local region, the modified method can obtain accurate segmentation results and an accurate estimation of the bias field. The energy function is incorporated into a level set formulation with a level set regularization term, and the energy minimization is conducted by a level set evolution process. The proposed model first appeared as a two-phase model and then extended to a multi-phase one. The experimental results demonstrate the advantages of our model in terms of accuracy and insensitivity to the location of the initial contours. In particular, our method has been applied to various synthetic and real images with desirable results.

Segmentation priors from local image properties: without using bias field correction, location-based templates, or registration

We present a novel approach for generating information about a voxel's tissue class membership based on its signature--a collection of local image textures estimated over a range of neighborhood sizes. The approach produces a form of tissue class priors that can be used to initialize and regularize image segmentation. The signature-based approach is a departure from current location-based methods, which derive tissue class likelihoods based on a voxel's location in standard template space. To use location-based priors, one needs to register the volume in question to the template space, and estimate the image intensity bias field. Two optimizations, over more than a thousand parameters, are needed when high order nonlinear registration is employed. In contrast, the signature-based approach is independent of volume orientation, voxel position, and largely insensitive to bias fields. For these reasons, the approach does not require the use of population derived templates. The prior information is generated from variations in image texture statistics as a function of spatial scale, and an SVM approach is used to associate signatures with tissue types. With the signature-based approach, optimization is needed only during the training phase for the parameter estimation stages of the SVM hyperplanes, and associated PDFs; a training process separate from the segmentation step. We found that signature-based priors were superior to location-based ones aligned under favorable conditions, and that signature-based priors result in improved segmentation when replacing location-based ones in FAST (Zhang et al., 2001), a widely used segmentation program. The software implementation of this work is freely available as part of AFNI http://afni.nimh.nih.gov.

Current techniques for MR imaging of atherosclerosis

Vessel wall imaging of large vessels has the potential to identify culprit atherosclerotic plaques that lead to cardiovascular events. Comprehensive assessment of atherosclerotic plaque size, composition, and biological activity is possible with magnetic resonance imaging (MRI). Magnetic resonance imaging of the atherosclerotic plaque has demonstrated high accuracy and measurement reproducibility for plaque size. The accuracy of in vivo multicontrast MRI for identification of plaque composition has been validated against histological findings. Magnetic resonance imaging markers of plaque biological activity such as neovasculature and inflammation have been demonstrated. In contrast to other plaque imaging modalities, MRI can be used to study multiple vascular beds noninvasively over time. In this review, we compare the status of in vivo plaque imaging by MRI to competing imaging modalities. Recent MR technological improvements allow fast, accurate, and reproducible plaque imaging. An overview of current MRI techniques required for carotid plaque imaging including hardware, specialized pulse sequences, and processing algorithms are presented. In addition, the application of these techniques to coronary, aortic, and peripheral vascular beds is reviewed.

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.

An improved level set method for brain MR images segmentation and bias correction

Intensity inhomogeneities cause considerable difficulty in the quantitative analysis of magnetic resonance (MR) images. Thus, bias field estimation is a necessary step before quantitative analysis of MR data can be undertaken. This paper presents a variational level set approach to bias correction and segmentation for images with intensity inhomogeneities. Our method is based on an observation that intensities in a relatively small local region are separable, despite of the inseparability of the intensities in the whole image caused by the overall intensity inhomogeneity. We first define a localized K-means-type clustering objective function for image intensities in a neighborhood around each point. The cluster centers in this objective function have a multiplicative factor that estimates the bias within the neighborhood. The objective function is then integrated over the entire domain to define the data term into the level set framework. Our method is able to capture bias of quite general profiles. Moreover, it is robust to initialization, and thereby allows fully automated applications. The proposed method has been used for images of various modalities with promising results.

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.

Coil sensitivity estimation for optimal SNR reconstruction and intensity inhomogeneity correction in phased array MR imaging

Magnetic resonance (MR) images can be acquired by multiple receiver coil systems to improve signal-to-noise ratio (SNR) and to decrease acquisition time. The optimal SNR images can be reconstructed from the coil data when the coil sensitivities are known. In typical MR imaging studies, the information about coil sensitivity profiles is not available. In such cases the sum-of-squares (SoS) reconstruction algorithm is usually applied. The intensity of the SoS reconstructed image is modulated by a spatially variable function due to the non-uniformity of coil sensitivities. Additionally, the SoS images also have sub-optimal SNR and bias in image intensity. All these effects might introduce errors when quantitative analysis and/or tissue segmentation are performed on the SoS reconstructed images. In this paper, we present an iterative algorithm for coil sensitivity estimation and demonstrate its applicability for optimal SNR reconstruction and intensity inhomogeneity correction in phased array MR imaging.

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.

MRI intensity nonuniformity correction using simultaneously spatial and gray-level histogram information

A novel approach for correcting intensity nonuniformity in magnetic resonance imaging (MRI) is presented. This approach is based on the simultaneous use of spatial and gray-level histogram information. Spatial information about intensity nonuniformity is obtained using cubic B-spline smoothing. Gray-level histogram information of the image corrupted by intensity nonuniformity is exploited from a frequential point of view. The proposed correction method is illustrated using both physical phantom and human brain images. The results are consistent with theoretical prediction, and demonstrate a new way of dealing with intensity nonuniformity problems. They are all the more significant as the ground truth on intensity nonuniformity is unknown in clinical images.

MRI image intensity correction with extrapolation and smoothing

A class of methods of MR image intensity correction extracts the sensitivity map from the image. This usually causes the edge enhancement artifact in the corrected image. A novel method of extrapolating the image in advance is proposed to reduce this effect significantly. The closest point algorithm is used to perform extrapolation.

Cardiac MRI in mice at 9.4 Tesla with a transmit-receive surface coil and a cardiac-tailored intensity-correction algorithm

PURPOSE

To evaluate the use of a transmit-receive surface (TRS) coil and a cardiac-tailored intensity-correction algorithm for cardiac MRI in mice at 9.4 Tesla (9.4T).

MATERIALS AND METHODS

Fast low-angle shot (FLASH) cines, with and without delays alternating with nutations for tailored excitation (DANTE) tagging, were acquired in 13 mice. An intensity-correction algorithm was developed to compensate for the sensitivity profile of the surface coil, and was tailored to account for the unique distribution of noise and flow artifacts in cardiac MR images.

RESULTS

Image quality was extremely high and allowed fine structures such as trabeculations, valve cusps, and coronary arteries to be clearly visualized. The tag lines created with the surface coil were also sharp and clearly visible. Application of the intensity-correction algorithm improved signal intensity, tissue contrast, and image quality even further. Importantly, the cardiac-tailored properties of the correction algorithm prevented noise and flow artifacts from being significantly amplified.

CONCLUSION

The feasibility and value of cardiac MRI in mice with a TRS coil has been demonstrated. In addition, a cardiac-tailored intensity-correction algorithm has been developed and shown to improve image quality even further. The use of these techniques could produce significant potential benefits over a broad range of scanners, coil configurations, and field strengths.

Manipulation of image intensity distribution at 7.0 T: passive RF shimming and focusing with dielectric materials

PURPOSE

To investigate the effects of high dielectric material padding on RF field distribution in the human head at 7.0 T, and demonstrate the feasibility and effectiveness of RF passive shimming and focusing with such an approach.

MATERIALS AND METHODS

The intensity distribution changes of gradient-recalled-echo (GRE) and spin-echo (SE) images of a human head acquired with water pads (dielectric constant = 78) placed in specified configurations around the head at 7.0 T were evaluated and compared with computer simulation results using the finite difference time domain (FDTD) method. The contributions to the B(1) field distribution change from the displacement current and conductive current of a given configuration of dielectric padding were determined with computer simulations.

RESULTS

MR image intensity distribution in the human head with an RF coil at 7.0 T can be changed drastically by placing water pads around the head. Computer simulations reveal that the high permittivity of water pads results in a strong displacement current that enhances image intensity in the nearby region and alters the intensity distribution of the entire brain.

CONCLUSION

The image intensity distribution in the human head at ultra-high field strengths can be effectively manipulated with high permittivity padding. Utilizing this effect, the B(1) field inside the human head of a given RF coil can be adjusted to reduce the B(1) field inhomogeneity artifact associated with the wave behavior (RF passive shimming) or to locally enhance the signal-to-noise ratio (SNR) in targeted regions of interest (ROIs; RF field focusing).

MRI of atherosclerosis in clinical trials

Magnetic resonance imaging (MRI) of the arterial wall has emerged as a viable technology for characterizing atherosclerotic lesions in vivo, especially within carotid arteries and other large vessels. This capability has facilitated the use of carotid MRI in clinical trials to evaluate therapeutic effects on atherosclerotic lesions themselves. MRI is specifically able to characterize three important aspects of the lesion: size, composition and biological activity. Lesion size, expressed as a total wall volume, may be more sensitive than maximal vessel narrowing (stenosis) as a measure of therapeutic effects, as it reflects changes along the entire length of the lesion and accounts for vessel remodeling. Lesion composition (e.g. lipid, fibrous and calcified content) may reflect therapeutic effects that do not alter lesion size or stenosis, but cause a transition from a vulnerable plaque composition to a more stable one. Biological activity, most notably inflammation, is an emerging target for imaging that is thought to destabilize plaque and which may be a systemic marker of vulnerability. The ability of MRI to characterize each of these features in carotid atherosclerotic lesions gives it the potential, under certain circumstances, to replace traditional trials involving large numbers of subjects and hard end-points--heart attacks and strokes--with smaller, shorter trials involving imaging end-points. In this review, the state of the art in MRI of atherosclerosis is presented in terms of hardware, image acquisition protocols and post-processing. Also, the results of validation studies for measuring lesion size, composition and inflammation will be summarized. Finally, the status of several clinical trials involving MRI of atherosclerosis will be reviewed.

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.

Computation of transmitted and received B1 fields in magnetic resonance imaging

Computation of B1 fields is a key issue for determination and correction of intensity nonuniformity in magnetic resonance images. This paper presents a new method for computing transmitted and received B1 fields. Our method combines a modified MRI acquisition protocol and an estimation technique based on the Levenberg-Marquardt algorithm and spatial filtering. It enables accurate estimation of transmitted and received B1 fields for both homogeneous and heterogeneous objects. The method is validated using numerical simulations and experimental data from phantom and human scans. The experimental results are in agreement with theoretical expectations.

Method to correct intensity inhomogeneity in MR images for atherosclerosis characterization

We are developing methods to characterize atherosclerotic disease in human carotid arteries using multiple MR images having different contrast mechanisms (T1W, T2W, PDW). To enable the use of voxel gray values for interpretation of disease, we created a new method, local entropy minimization with a bicubic spline model (LEMS), to correct the severe (approximately 80%) intensity inhomogeneity that arises from the surface coil array. This entropy-based method does not require classification and robustly addresses some problems that are more severe than those found in brain imaging, including noise, steep bias field, sensitivity of artery wall voxels to edge artifacts, and signal voids near the artery wall. Validation studies were performed on a synthetic digital phantom with realistic intensity inhomogeneity, a physical phantom roughly mimicking the neck, and patient carotid artery images. We compared LEMS to a modified fuzzy c-means segmentation based method (mAFCM), and a linear filtering method (LINF). Following LEMS correction, skeletal muscles in patient images were relatively isointense across the field of view. In the physical phantom, LEMS reduced the variation in the image to 1.9% and across the vessel wall region to 2.5%, a value which should be sufficient to distinguish plaque tissue types, based on literature measurements. In conclusion, we believe that the correction method shows promise for aiding human and computerized tissue classification from MR signal intensities.

3-D reconstruction of tissue components for atherosclerotic human arteries using ex vivo high-resolution MRI

Automatic computer-based methods are well suited for the image analysis of the different components in atherosclerotic plaques. Although several groups work on such analysis some of the methods used are oversimplified and require improvements when used within a computational framework for predicting meaningful stress and strain distributions in the heterogeneous arterial wall under various loading conditions. Based on high-resolution magnetic resonance imaging of excised atherosclerotic human arteries and a series of two-dimensional (2-D) contours we present a segmentation tool that permits a three-dimensional (3-D) reconstruction of the most important tissue components of atherosclerotic arteries. The underlying principle of the proposed approach is a model-based snake algorithm for identifying 2-D contours, which uses information about the plaque composition and geometric data of the tissue layers. Validation of the computer-generated tissue boundaries is performed with 100 MR images, which are compared with the results of a manual segmentation performed by four experts. Based on the Hausdorff distance and the average distance for computer-to-expert differences and the interexpert differences for the outer boundary of the adventitia, the adventitia-media, media-intima, intima-lumen and calcification boundaries are less than 1 pixel (0.234 mm). The percentage statistic shows similar results to the modified Williams index in terms of accuracy. Except for the identification of lipid-rich regions the proposed algorithm is automatic. The nonuniform rational B-spline-based computer-generated 3-D models of the individual tissue components provide a basis for clinical and computational analysis.

Magnetic resonance imaging image intensity correction with extrapolation and adaptive smoothing

A significant problem in magnetic resonance imaging (MRI) is the inhomogeneity of the image resulting from a number of factors that are hardware related. The obtained image can be treated as the true image multiplied by a signal modulator, which is usually smooth across the image. A class of MR image intensity correction methods extracts the slowly varying component from the image with low-pass filtering or smoothing to approximate the signal modulator. This usually causes the edge enhancement artifact in the corrected image. A novel method of extrapolating the image in advance is proposed to reduce this effect significantly. Closest point algorithm is implemented to minimize the calculation time for extrapolation. To remove bright spots caused by nonuniform sensitivity profiles, a gradient-weighted smoothing method is discussed in this work. The partial differential equations based model is applied for locally adaptive smoothing. The filtered gradient of the corrupted image is used as the weight for smoothing. Phantom and clinical data collected on various MRI systems are used for evaluation of our method. These experimental results show that the proposed method solves the edge enhancement and bright spots problem effectively and robustly.

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.

Multislice MR first-pass myocardial perfusion imaging: impact of the receiver coil array

PURPOSE

To compare a new 12-element body phased-array coil with a conventional four-element surface receiver coil array to provide increased signal-to-noise ratios (SNRs) for cardiac steady state free precession (SSFP) perfusion imaging.

MATERIALS AND METHODS

Thirteen consecutive patients were included in the study. Patients were examined both with a four-element surface coil array and a 12-element body coil array. First-pass myocardial perfusion imaging using saturation recovery SSFP was acquired during antecubital injection of Gd-DTPA. Imaging parameters: TR 2.8 msec/TE 1.3 msec, flip angle 50 degrees , bandwidth 960 Hz/pixel and half-Fourier acquisition. SNR was calculated using six regions of interest (ROI) for the myocardial perfusion scans. Calculations of corresponding ROIs using the two different coil setups were compared using analysis of variance (ANOVA). Semiquantitative perfusion parameters were calculated for both groups.

RESULTS

The mean SNR in myocardial perfusion imaging increased by 21% using the 12-element coil setup (P < 0.001) when compared to the four-element coil. ROI comparisons revealed an increased signal inhomogeneity with the 12-element coil when compared to four-element coil experiments. Absolute normal range values of semiquantitative perfusion parameters were consistently higher using the 12-element coil setup (P < 0.001).

CONCLUSION

The 12-element coil array provides higher SNR, but these improvements come with trade-offs in image homogeneity. Increased SNR translates into higher semiquantitative perfusion values and offers the potential for improved detection of perfusion defects.

In vivo method for correcting transmit/receive nonuniformities with phased array coils

Phased array coils are finding widespread applications in both the research and the clinical setting. However, intensity nonuniformities with such coils can reduce the potential benefits of these coils, particularly for applications such as tissue segmentation. In this work, a method is described for correcting the nonuniform signal response based on in vivo measures of both the transmission field of body coil and the reception sensitivity of phased array coils, separately. For a uniform phantom, the reception sensitivity can be calculated using both Bloch equations and transmission field maps. For a heterogeneous object such as a brain, a minimal contrast acquisition must be obtained to map the receiver nonuniformities. This transmit field/receiver sensitivity (TFRS) approach is compared with the standard methods of using the body coil to obtain a reference scan and low-pass filtering. The quantitative comparison results shows that the TFRS approach provides superior results in correcting intensity nonuniformities for a uniform phantom. This approach reduces the ratio between signal intensity SD of an image and its mean intensity from approximately 21% before correction to 13% after correction. Results are also shown demonstrating the utility of this approach in vivo with human brain images. The method is general and can be applied with most pulse sequences, any coil combination for transmission and reception, and in any anatomic region.

Measurement and correction of transmitter and receiver induced nonuniformities in vivo

Signal intensity nonuniformities in high field MR imaging limit the ability of MRI to provide quantitative information and can negatively impact diagnostic scan quality. In this paper, a simple method is described for correcting these effects based on in vivo measurement of the transmission field B1+ and reception sensitivity maps. These maps can be obtained in vivo with either gradient echo (GE) or spin echo (SE) imaging sequences, but the SE approach exhibits an advantage over the GE approach for correcting images over a range of flip angles. In a uniform phantom, this approach reduced the ratio of the signal SD to its mean from around 30% before correction to approximately 6% for the SE approach and 9% for the GE approach after correction. The application of the SE approach for correcting intensity nonuniformities is demonstrated in vivo with human brain images obtained using a conventional spin echo sequence at 3.0 T. Furthermore, it is also shown that this in vivo B1+ and reception sensitivity mapping can be performed using segmented echo planar imaging sequences providing acquisition times of less than 2 min. Although the correction presented here is demonstrated with a simultaneous transmit and receive volume coil, it can be extended to the case of separate transmission and reception coils, including surface and phase array coils.

MRI of atherosclerosis

The emergence of high-resolution, rapid imaging methods has enabled MRI to noninvasively image the fine internal structure of atherosclerotic artery walls. This capability has, in turn, captured the interest of clinicians, who see it as an opportunity to assess disease severity based on the characteristics of atherosclerotic lesions themselves, rather than only their effects on the vessel lumen. MRI of atherosclerosis thus has the potential to be used in medical treatment decisions or to assess the effects of experimental treatment options. Given this potential, a number of research groups have been investigating MRI of atherosclerosis in an effort to establish the ability of MRI to determine atherosclerotic plaque burden, detect plaque composition, and ultimately identify vulnerable plaque before it leads to a clinical event. In this review, the current state of the art is summarized for the three primary vessel targets: the carotid artery, the aorta, and the coronary arteries.

Carotid atherosclerotic wall imaging by MRI

High spatial resolution magnetic resonance imaging (MRI) is one of the most promising modalities for visualizing the carotid atherosclerotic plaque. MR allows direct visualization of the diseased vessel wall, is capable of characterizing plaque morphology, and can potentially monitor progression of the disease. Though ultrasound and angiography have been the principal methods for determining the severity of carotid atherosclerosis and the need for endarterectomy, these methods only measure percentage of vessel stenosis. There is strong evidence that this is not the best indicator for assessing clinical risk. Improved imaging techniques are therefore needed to reliably identify the high-risk plaques that lead to cerebrovascular events. This article focuses on the current state-of-the-art in MR carotid atherosclerotic plaque imaging to evaluate plaque morphology and composition.

In vivo accuracy of multispectral magnetic resonance imaging for identifying lipid-rich necrotic cores and intraplaque hemorrhage in advanced human carotid plaques

BACKGROUND

High-resolution MRI has been shown to be capable of identifying plaque constituents, such as the necrotic core and intraplaque hemorrhage, in human carotid atherosclerosis. The purpose of this study was to evaluate differential contrast-weighted images, specifically a multispectral MR technique, to improve the accuracy of identifying the lipid-rich necrotic core and acute intraplaque hemorrhage in vivo.

METHODS AND RESULTS

Eighteen patients scheduled for carotid endarterectomy underwent a preoperative carotid MRI examination in a 1.5-T GE Signa scanner using a protocol that generated 4 contrast weightings (T1, T2, proton density, and 3D time of flight). MR images of the vessel wall were examined for the presence of a lipid-rich necrotic core and/or intraplaque hemorrhage. Ninety cross sections were compared with matched histological sections of the excised specimen in a double-blinded fashion. Overall accuracy (95% CI) of multispectral MRI was 87% (80% to 94%), sensitivity was 85% (78% to 92%), and specificity was 92% (86% to 98%). There was good agreement between MRI and histological findings, with a value of kappa=0.69 (0.53 to 0.85).

CONCLUSIONS

Multispectral MRI can identify the lipid-rich necrotic core in human carotid atherosclerosis in vivo with high sensitivity and specificity. This MRI technique provides a noninvasive tool to study the pathogenesis and natural history of carotid atherosclerosis. Furthermore, it will permit a direct assessment of the effect of pharmacological therapy, such as aggressive lipid lowering, on plaque lipid composition.