Cardiac magnetic resonance imaging using radial k-space sampling and self-calibrated partial parallel reconstruction

Radial sampling has been demonstrated to be potentially useful in cardiac magnetic resonance imaging because it is less susceptible to motion than Cartesian sampling. Nevertheless, its capability of imaging acceleration remains limited by undersampling-induced streaking artifacts. In this study, a self-calibrated reconstruction method was developed to suppress streaking artifacts for highly accelerated parallel radial acquisitions in cardiac magnetic resonance imaging. Two- (2D) and three-dimensional (3D) radial k-space data were collected from a phantom and healthy volunteers. Images reconstructed using the proposed method and the conventional regridding method were compared based on statistical analysis on a four-point scale imaging scoring. It was demonstrated that the proposed method can effectively remove undersampling streaking artifacts and significantly improve image quality (P<.05). With the use of the proposed method, image score (1–4, 1=poor, 2=good, 3=very good, 4=excellent) was improved from 2.14 to 3.34 with the use of an undersampling factor of 4 and from 1.09 to 2.5 with the use of an undersampling factor of 8. Our study demonstrates that the proposed reconstruction method is effective for highly accelerated cardiac imaging applications using parallel radial acquisitions without calibration data.     

Contrast changes in portal vein and bile duct between centric and linear k-space ordering on three-dimensional segmented true fast imaging with steady-state precession magnetic resonance imaging

Purpose

To compare the degree of visualization of the bile duct and portal vein in terms of the difference in k-space ordering on a three-dimensional (3D) segmented true fast imaging with steady-state precession (trueFISP) sequence.

Materials and Methods

A breath-hold coronal 3D segmented trueFISP sequence was prospectively performed on 14 healthy volunteers. Images obtained with centric and linear k-space ordering in the k(x)–k(y) plane were compared by two independent radiologists qualitatively with depiction scores on a five-point scale (1=not seen to 5=excellent depiction) using the Wilcoxon signed-rank test. Images were also compared quantitatively using relative contrast values for the bile duct and portal vein against the hepatic parenchyma using a paired t-test.

Results

With centric ordering, both the mean depiction scores and relative contrast values for the portal vein were significantly lower than those with linear ordering (1.5 vs. 3.5, P<.01; and 0.08±0.19 vs. 0.51±0.10, P<.01, respectively). However, in the bile duct, there were no significant differences, only slight differences were found among the results obtained with centric and linear ordering (3.9 vs. 3.8, P=.72; and 0.59±0.06 vs. 0.68±0.06, P<.01, respectively).

Conclusion

For visualizing the bile duct, centric k-space ordering on 3D segmented trueFISP sequence is recommended, while linear ordering is recommended for portal vein visualization.     

A new approach to solve correlated k-distribution function

The resorting profile of absorption coefficient is fitted with Voigt function by employing the similarity between it and a single mixed broadened Voigt line type. In this way, simple mathematical fitting formulae are obtained to solve k-distribution (k-D) function at any pressure and temperature. Thus, a new correlated k-D method is proposed on the basis of it. Finally, taking mid-latitude summer atmosphere as an example, longwave cooling rates are calculated for three major gases by the new method, and then compared with the corresponding results by line-by-line integration.     

The effects of the choice of the k-interval number on radiative calculations

The selection of the number of k-interval is a foundation to correlated k-distribution method and the problem of how to do it still remains unsettled. It is pointed out by numerical computation in this work that choosing the number of k-interval is a major factor affecting accuracy and speed in radiative calculation. To increase the number of k-interval is an efficient method to improve the accuracy. However, it is found by this study that there exists a saturation of the accuracy to an increase of the number. The optimal rules on the number of k-interval choosing are proposed in the paper. Then, five versions on atmospheric absorption by gases appropriate for GCMs are given according to them.     

Simple phase method for measurement of magnetic field gradient waveforms

Magnetic field gradients play a fundamental role in MR imaging and localized spectroscopy. The MRI experiment, in particular fast MRI, relies on precise gradient switching, which has become more demanding with the constantly growing number of fast imaging techniques. Here we present a simple MR method to measure the behavior of a magnetic field gradient waveform in an MR scanner. The method employs excitation of a thin slice, followed by application of the studied gradient and simultaneous FID acquisition. Measurements of different gradient waveforms were performed with a spherical phantom filled with doped water and positioned at the isocenter of the gradient set. The presented experiments demonstrate the capability of the technique to measure different gradient waveforms with an estimated error of less than 200 microT/m.     

Numerical explanation for accurate radiative cooling rates resulting from the correlated k-distribution hypothesis

Numerical simulations are conducted to demonstrate that (a) correlation between spectral absorption at two levels decreases with increasing level distance; and (b) the contribution to the flux gained by one layer from another decreases rapidly with the layer separation. The combination of the two facts explains why the existence of poor correlation between distant layers produces insignificant radiative cooling rate error. Therefore, the overall accuracy of cooling rate calculations under the correlated k-distribution hypothesis is high.     

A new k-interval selection technique for fast atmospheric radiance calculation in remote sensing applications

A new technique is proposed to generate the k-interval parameters, including the number of k-intervals, the equivalent absorption coefficients, and the quadrature weights when using the correlated k-distribution method for the computation of spectrally integrated three-dimensional (3D) atmospheric radiance. The main difference between the proposed technique and the traditional exponential sum fitting technique is that only quadrature weights are involved in the optimization process. To avoid the ill-conditioned problem in the proposed technique, the absorption coefficients with high value are dealt with by the delta log(k) (Δlog(k)) technique instead of involving them in the fitting procedure. The performance of the proposed technique is illustrated by radiance calculation results of cloudless and cloudy atmosphere for three different band settings. Results show that there are less relative errors with the proposed optimization technique than with the Δlog(k) technique under the same number of k-intervals. However, as the absorption becomes stronger, the performance of the proposed technique gradually decreases to the Δlog(k) technique. The relative root-mean-square error (RMSE) of radiance for 3D cloudy atmosphere can be controlled in less than 2% when the number of k-intervals is less than 10 particularly for weak absorption band, the RMSEs are less than 1% with only 6 terms.     

Functional magnetic resonance imaging: Comparison between activation maps and computation pipelines in a clinical context

In this study new evaluation strategies for comparing different Statistical Parametric Maps computed from fMRI time-series analysis software tools are proposed. The aim of our work is to assess and quantitatively evaluate the statistical agreement of activation maps. Some pre-processing steps are necessary to compare SPMs (Statistical Parametric Maps), including segmentation and co-registration. The study of the statistical agreement is carried out following two ways. The first way considers SPMs as the result of two classification processes and extracts confusion matrix and Cohen's kappa index to assess agreement. Some considerations will be made on the statistical dependence of classes and a new formulation of kappa index will be used for overcoming this problem. The second way considers SPMs as two 3D images, and computes the similarity of SPMs images with a fuzzy formulation of the Jaccard Index. Several experiments were conducted both to assess the performance of the proposed evaluation tools and to compare activation maps computation pipelines from two widely used software tools in a clinical context.     

Measurement of bone cyst fluid volume using k-means clustering

We designed a semiautomatic segmentation method to easily measure the volume of a bone cyst (simple or aneurysmal) from magnetic resonance imaging (MRI). This method only considers the fluid part of the cyst, even when there are several fluid intensities (fluid-fluid levels) or the cyst is multi-loculated. The nonhomogeneity phenomenon inherent in MRI was handled by a k-means clustering algorithm that classified all of the voxels corresponding to the cyst fluid as the same voxel intensity. Level-set segmentation was expanded into the whole cyst volume and the resulting segmented volume provided the measured cyst volume. The semiautomatic method was compared with the usual manual method (manual contour tracing) in terms of its ability to measure a known volume of water (gold standard) as well as the volume of 29 bone cysts. Both methods were equivalent with regards to the gold standard, but the semiautomatic method was more accurate. In terms of the experimental measurements, the semiautomatic method was more repeatable and reproducible, and less time-consuming and fastidious than the manual method. Our semiautomatic method uses only freeware and can be used routinely whenever measurement of a bone cyst volume is needed.     

Feasibility of using limited-population-based average R10 for pharmacokinetic modeling of osteosarcoma dynamic contrast-enhanced magnetic resonance imaging data

Retrospective analyses of clinical dynamic contrast-enhanced (DCE) MRI studies may be limited by failure to measure the longitudinal relaxation rate constant (R₁) initially, which is necessary for quantitative analysis. In addition, errors in R₁ estimation in each individual experiment can cause inconsistent results in derivations of pharmacokinetic parameters, K^trans and v_e, by kinetic modeling of the DCE-MRI time course data. A total of 18 patients with lower extremity osteosarcomas underwent multislice DCE-MRI prior to surgery. For the individual R₁ measurement approach, the R₁ time course was obtained using the two-point R₁ determination method. For the average R₁₀ (precontrast R₁) approach, the R₁ time course was derived using the DCE-MRI pulse sequence signal intensity equation and the average R₁₀ value of this population. The whole tumor and histogram median K^trans (0.57±0.37 and 0.45±0.32 min⁻¹) and v_e (0.59±0.20 and 0.56±0.17) obtained with the individual R₁ measurement approach are not significantly different (paired t test) from those (K^trans: 0.61±0.46 and 0.44±0.33 min⁻¹; v_e: 0.61±0.19 and 0.55±0.14) obtained with the average R₁₀ approach. The results suggest that it is feasible, as well as practical, to use a limited-population-based average R₁₀ for pharmacokinetic modeling of osteosarcoma DCE-MRI data.     

Data consistency criterion for selecting parameters for k-space-based reconstruction in parallel imaging

k-space-based reconstruction in parallel imaging depends on the reconstruction kernel setting, including its support. An optimal choice of the kernel depends on the calibration data, coil geometry and signal-to-noise ratio, as well as the criterion used. In this work, data consistency, imposed by the shift invariance requirement of the kernel, is introduced as a goodness measure of k-space-based reconstruction in parallel imaging and demonstrated. Data consistency error (DCE) is calculated as the sum of squared difference between the acquired signals and their estimates obtained based on the interpolation of the estimated missing data. A resemblance between DCE and the mean square error in the reconstructed image was found, demonstrating DCE's potential as a metric for comparing or choosing reconstructions. When used for selecting the kernel support for generalized autocalibrating partially parallel acquisition (GRAPPA) reconstruction and the set of frames for calibration as well as the kernel support in temporal GRAPPA reconstruction, DCE led to improved images over existing methods. Data consistency error is efficient to evaluate, robust for selecting reconstruction parameters and suitable for characterizing and optimizing k-space-based reconstruction in parallel imaging.     

An improved treatment of overlapping absorption bands based on the correlated k distribution model for thermal infrared radiative transfer calculations

This paper discusses several schemes for handling gaseous overlapping bands in the context of the correlated k distribution model (CKD). Commonly used methods are generally based on certain spectral correlation assumptions; thus they are either less accurate or less efficient and rarely apply to all overlapping bands. We propose a new treatment, which we developed from the traditional absorber amount weighted scheme and improved for application to various bands. This approach is quite efficient for treating the gaseous mixture as if it were a “single gas.” Numerical experiments demonstrate that the new scheme achieves high accuracy with a fast operating speed. To validate the new scheme, we conducted spectrally integrated calculations and sensitivity experiments in the thermal infrared region. Compared to line-by-line integration results, errors in cooling rates were less than 0.2 K/day below 70 Km and rose to 1 K/day from above 70 Km up to 100 Km; flux differences did not exceed 0.8 W/m² at any altitude. Changes in CO₂ and H₂O concentrations slightly influenced the accuracy of the results.     

Magnetic resonance image enhancement using stochastic resonance in Fourier domain

Objective

In general, low-field MRI scanners such as the 0.5- and 1-T ones produce images that are poor in quality. The motivation of this study was to lessen the noise and enhance the signal such that the image quality is improved. Here, we propose a new approach using stochastic resonance (SR)-based transform in Fourier space for the enhancement of magnetic resonance images of brain lesions, by utilizing an optimized level of Gaussian fluctuation that maximizes signal-to-noise ratio (SNR).

Materials and Methods

We acquired the T1-weighted MR image of the brain in DICOM format. We processed the original MR image using the proposed SR procedure. We then tested our approach on about 60 patients of different age groups with different lesions, such as arteriovenous malformation, benign lesion and malignant tumor, and illustrated the image enhancement by using just-noticeable difference visually as well as by utilizing the relative enhancement factor quantitatively.

Results

Our method can restore the original image from noisy image and optimally enhance the edges or boundaries of the tissues, clarify indistinct structural brain lesions without producing ringing artifacts, as well as delineate the edematous area, active tumor zone, lesion heterogeneity or morphology, and vascular abnormality. The proposed technique improves the enhancement factor better than the conventional techniques like the Wiener- and wavelet-based procedures.

Conclusions

The proposed method can readily enhance the image fusing a unique constructive interaction of noise and signal, and enables improved diagnosis over conventional methods. The approach well illustrates the novel potential of using a small amount of Gaussian noise to improve the image quality.     

Measurement of regional cerebral glucose uptake by magnetic resonance spin-lock imaging

Purpose

The regional uptake of glucose in rat brain in vivo was measured at high resolution using spin-lock magnetic resonance imaging after infusion of the glucose analogue 2-deoxy-d-glucose (2DG). Previous studies of glucose metabolism have used ¹³C-labeled 2DG and NMR spectroscopy, ¹⁸F-labeled fluorodeoxyglucose (FDG) and PET, or chemical exchange saturation transfer (CEST) MRI, all of which have practical limitations. Our goal was to explore the ability of spin-lock sequences to detect specific chemically-exchanging species in vivo and to compare the effects of 2DG in brain tissue on CEST images.

Methods

Numerical simulations of R_1p and CEST contrasts for a variety of sample parameters were performed to evaluate the potential specificity of each method for detecting the exchange contributions of 2DG. Experimental measurements were made in tissue phantoms and in rat brain in vivo which demonstrated the ability of spin-lock sequences for detecting 2DG.

Results

R_1p contrast acquired with appropriate spin-lock sequences can isolate the contribution of exchanging protons in 2DG in vivo and appears to have better sensitivity and more specificity to 2DG–water exchange effects than CEST.

Conclusion

Spin-lock imaging provides a novel approach to the detection and measurement of glucose uptake in brain in vivo.     

The k-moment method for the narrow band modeling of radiative properties of nonuniform gaseous media

Moments of the absorption coefficient distribution function are used for the derivation of statistical narrow band (SNB) model parameters of nonuniform optical paths in gases. The approach yields approximations for the path-averaged first- and second-order k-moments from which equivalent SNB parameters are determined in the frame of the Malkmus model. The approach is assessed through comparisons with LBL data. The nonuniform approximation is shown to enable the computation of transmissivities and radiation intensities with accuracy similar to or higher than those achieved by the Curtis-Godson one.     

Remarks on q-space MR propagator in partially restricted,axially-symmetric,and isotropic environments

The problem of reconstruction of an apparent propagator from a series of diffusion-attenuated magnetic resonance (MR) signals is revisited. In nonimaging acquisitions, the inverse Fourier transform of the MR signal attenuation is consistent with the notion of an ensemble average propagator. However, in image acquisitions where one is interested in quantifying a displacement distribution in every voxel of the image, the propagator derived in the traditional way may lead to a counter-intuitive profile when it is nonsymmetric, which could be a problem in partially restricted environments. By exploiting the reciprocity of the diffusion propagator, an alternative is introduced, which implies a forward Fourier transform of the MR signal attenuations yielding a propagator reflected around the origin. Two simple problems were considered as examples. In the case of diffusion in the proximity of a restricting barrier, the reflected propagator yields a more meaningful result, whereas in the case of curving fibers, the original propagator is more intuitive. In the final section of the article, two more one-dimensional transformations are introduced, which enable the reconstruction of two- and three-dimensional propagators in, respectively, axially symmetric and isotropic environments — in both cases, from one-dimensional q-space MR data.     

Bulk vs. surface effects in ARPES experiment from La2/3Sr1/3MnO3 thin films

Experiments directly probing the electronic states using angle-resolved photoemission (ARPES) were carried out on La_2/3Sr_1/3MnO₃ in order to elucidate its electronic properties. ARPES is a surface sensitive technique where bulk and surface states are usually both present. We present high-resolution ARPES studies in the (1 0 0) and (1 1 0) mirror planes and compare them with simulated ARPES based on GGA + U band structure calculations. In the (1 1 0) mirror plane we identify surface umklapps accounted by surface reconstruction which couple to bulk electronic states. As predicted by the simulated spectra there is additional spectral intensity at the Fermi level detected in ARPES data due to k_⊥-broadening effects in the photoemission final states. We demonstrate that this additional spectral intensity is a convenient spectral marker for determination of the k_F Fermi momenta.     

Evaluation of diethylenetriaminepentaacetic acid-manganese(II) complexes modified by narrow molecular weight distribution of chitosan oligosaccharides as potential magnetic resonance imaging contrast agents

Novel conjugates of narrow molecular weight distribution of chitosan oligosaccharides (CSn; n=6, 8, 11) with manganese-diethylenetriaminepentaacetic acid (Mn-DTPA) as potential magnetic resonance imaging (MRI) contrast agents were synthesized. The structures were characterized by means of Fourier transform infrared spectra, ¹³C nuclear magnetic resonance, size exclusion chromatography and inductively coupled plasma atomic emission spectrometry. The characterization results showed that Mn-DTPA was successfully linked to aminated CSn by an amide function. The magnetic properties were characterized by in vitro and T₁-weighted FLASH image experiments. Relaxivities studies indicated that Mn-DTPA-CSn (n=8, 11) provided higher relaxivity, either in aqueous or bovine serum albumin solution (0.725 mM), than commercial contrast agent Gd-DTPA. The stability results showed that Mn-DTPA-CSn in aqueous were stable enough to prevent Mn^II ions from releasing. The preliminary in vitro and T₁-weighted FLASH image studies suggested that Mn-DTPA-CSn had the advantage of becoming promising MRI contrast agents.     

Magnetic resonance imaging segmentation techniques using batch-type learning vector quantization algorithms

In this article, we propose batch-type learning vector quantization (LVQ) segmentation techniques for the magnetic resonance (MR) images. Magnetic resonance imaging (MRI) segmentation is an important technique to differentiate abnormal and normal tissues in MR image data. The proposed LVQ segmentation techniques are compared with the generalized Kohonen's competitive learning (GKCL) methods, which were proposed by Lin et al. [Magn Reson Imaging 21 (2003) 863-870]. Three MRI data sets of real cases are used in this article. The first case is from a 2-year-old girl who was diagnosed with retinoblastoma in her left eye. The second case is from a 55-year-old woman who developed complete left side oculomotor palsy immediately after a motor vehicle accident. The third case is from an 84-year-old man who was diagnosed with Alzheimer disease (AD). Our comparisons are based on sensitivity of algorithm parameters, the quality of MRI segmentation with the contrast-to-noise ratio and the accuracy of the region of interest tissue. Overall, the segmentation results from batch-type LVQ algorithms present good accuracy and quality of the segmentation images, and also flexibility of algorithm parameters in all the comparison consequences. The results support that the proposed batch-type LVQ algorithms are better than the previous GKCL algorithms. Specifically, the proposed fuzzy-soft LVQ algorithm works well in segmenting AD MRI data set to accurately measure the hippocampus volume in AD MR images.