Evaluation of an edge-based registration method: application to magnetic resonance first-pass myocardial perfusion data

Purpose

Quantification of cardiac magnetic resonance (CMR) myocardial perfusion remains time consuming since it requires manual interventions to compensate for motion. Thus, the aim of this study was to evaluate a semiautomated registration method.

Materials and Methods

A rigid edge-based registration algorithm was applied on 10 patients who had rest and stress CMR acquisitions on three slice levels (apical, midventricular and basal slices). Registration efficiency was assessed qualitatively by evaluating the quality of k-means maps in terms of symmetry and heart structures identification before and after registration and quantitatively by estimating noise amplitude within the myocardium. Finally, residual registration errors were manually estimated.

Results

Before registration, k-means maps were satisfactory for 15 of 30 slices at rest and for only 5 of 30 slices during stress. After registration, the k-means maps quality was satisfactory for 29 of 30 slices at rest and for 30 of 30 slices during stress. Moreover, registration reduced noise amplitude from 49±26 to 29±11 at rest (P<.01) and from 52±14 to 30±10 during stress (P<.01). The residual horizontal and vertical shifts were 0.06±0.12 and 0.04±0.08 mm at rest and 0.32±0.69 and 0.28±0.53 mm at stress.

Conclusion

The registration was successfully tested on rest and stress CMR perfusion data. It provides a valuable basis for quantitative evaluation of myocardial perfusion.     

An automatic cerebellum extraction method in T1-weighted brain MR images using an active contour model with a shape prior

Purpose

The objective of this paper was to automatically segment the cerebellum from T1-weighted human brain magnetic resonance (MR) images.

Materials and Methods

The proposed method constructs a cerebellum template using five sets of 3-T MR imaging (MRI) data, which are used to determine the initial position and the shape prior of the cerebellum for the active contour model. Our formulation includes the active contour model with shape prior, which thereby maintains the shape of the template. The proposed active contour model is sequentially applied to sagittal-, coronal- and transverse-view images. To evaluate the proposed method, it is applied to BrainWeb data and a 3-T MRI data set and compared with FreeSurfer with respect to performance assessment metrics.

Results

The segmented cerebellum was compared with the results from FreeSurfer. Using the manually segmented cerebellum as reference, we measured the average Jaccard coefficients of the proposed method, which were 0.882 and 0.885 for the BrainWeb data and 3-T MRI data set, respectively.

Conclusion

We presented the active contour model with shape prior for extracting the cerebellum from T1-weighted brain MR images. The proposed method yielded a robust and accurate segmentation result.     

On-line detection of fatigue cracks using an automatic mode tracking technique

Experimental fatigue tests usually require large testing times. In addition to the resulting increased time-to-market, the large fatigue test time also implies that any structural health monitoring technique that is used should be automatic. When using the modal parameters as damage indicators, an important amount of user interaction is still needed to separate physical poles from computational ones. In this paper, an experimental framework will be developed to automatically track the health of the structure on-line with the performance of fatigue tests. The modal parameters are tracked using a combination of the maximum likelihood estimator and an auto-regressive model. Since confidence levels on the modal parameter are available it is possible to detect if damage is present. In addition, the quasi-static stiffness with computed confidence levels is also used as a damage indicator. The proposed techniques are demonstrated on a steel beam with a propagating fatigue crack.     

Assessment of myocardial perfusion using multisection first-pass MRI and color-coded parameter maps: a comparison to 99mTc Sesta MIBI SPECT and systolic myocardial wall thickening analysis

The most recently reported magnetic resonance first-pass myocardial perfusion studies were restricted to single slice imaging or a data analysis based on interactively placed regions of interest. This study was designed to investigate a new saturation recovery TurboFLASH sequence for multisection myocardial perfusion imaging and to develop a pixel-based software tool to calculate qualitative perfusion parameters. The findings of perfusion imaging were compared to percent systolic myocardial wall thickening analysis and 99mTc Sesta MIBI SPECT. Six healthy volunteers and twelve patients with proven coronary artery disease (CAD) or chronic myocardial infarction were examined. Diagnostic images were acquired for all volunteers and patients with the multisection saturation recovery TurboFLASH sequence. Perfusion defects could be visualized on parameter maps for signal intensity increase over baseline and signal intensity upslope. Sensitivity and specificity were 76.9% and 97.1% for first-pass perfusion MRI, and respectively 84.6% and 94.3% for CINE imaging. All perfusion defects determined with 99mTc Sesta MIBI SPECT were identified by the combined analysis of myocardial perfusion and wall thickening. The presented software demonstrated a pixel-based analysis of first-pass perfusion studies and simplified image interpretation in a clinical setting. The combination of perfusion and wall motion imaging provided complementary information for the treatment of patients suffering from CAD.     

Image feature based automatic correction of low-frequency spatial intensity variations in MR images

An automatic method of compensating for low-frequency variations in magnetic resonance images is presented. Small variations within a tissue type are modelled and a correction function is generated. The methods is based completely on image features and does not need a phantom or user interaction to generate the compensation function. This image correction simplifies digital image analysis and may enhance clinical evaluation. As a result, the correction technique reduces inhomogeneity and improves contrast. Our results show that the radiofrequency response variation of coils can be reduced. The segmentation process, even with a simple threshold method, produces more reliable results when corrected images are used. The presented method is most useful for images acquired in the sagital and coronal planes with circular local coils, or using surface coils, e.g., spine coils.     

The registration of MR images using multiscale robust methods

Acquisition of MR images involves their registration against some prechosen reference image. Motion artifacts and misregistration can seriously flaw their interpretation and analysis. This article provides a global registration method that is robust in the presence of noise and local distortions between pairs of images. It uses a two-stage approach, comprising an optional Fourier phase-matching method to carry out preregistration, followed by an iterative procedure. The iterative stage uses a prescribed set of registration points, defined on the reference image, at which a robust nonlinear regression is computed from the squared residuals at these points. The method can readily accommodate general linear, or even nonlinear, registration transformations on the images. The algorithm was tested by recovering the registration transformation parameters when a 256 × 256 pixel T₂¹-weighted human brain image was scaled, rotated, and translated by prescribed amounts, and to which different amounts of Gaussian noise had been added. The results show subpixel accuracy of recovery when no noise is present, and graceful degradation of accuracy as noise is added. When 40% noise is added to images undergoing small shifts, the recovery errors are less than 3 pixels. The same tests applied to the Woods algorithm gave slightly inferior accuracy for these images, but failed to converge to the correct parameters in some cases of large-scale-shifted images with 10% added noise.     

Joint reconstruction of multiecho MR images using correlated sparsity

This works addresses the problem of reconstructing multiple T1- or T2-weighted images of the same anatomical cross section from partially sampled K-space data. Previous studies in reconstructing magnetic resonance (MR) images from partial samples of the K-space used compressed sensing (CS) techniques to exploit the spatial correlation of the images (leading to sparsity in wavelet domain). Such techniques can be employed to reconstruct the individual T1- or T2-weighted images. However, in the current context, the different images are not really independent; they are images of the same cross section and, hence, are highly correlated. We exploit the correlation between the images, along with the spatial correlation within the images to achieve better reconstruction results than exploiting spatial correlation only.For individual MR images, CS-based techniques lead to a sparsity-promoting optimization problem in the wavelet domain. In this article, we show that the same framework can be extended to incorporate correlation between images leading to group/row sparsity-promoting optimization. Algorithms for solving such optimization problems have already been developed in the CS literature. We show that significant improvement in reconstruction accuracy can be achieved by considering the correlation between different T1- and T2-weighted images. If the reconstruction accuracy is considered to be constant, our proposed group sparse formulation can yield the same result with 33% less K-space samples compared with simple sparsity-promoting reconstruction. Moreover, the reconstruction time by our proposed method is about two to four times less than the previous method.     

Estimation of myocardial perfusion using deuterium nuclear magnetic resonance.

A technique to estimate regional and/or global myocardial perfusion in-vivo was developed using deuterium nuclear magnetic resonance measurement of perdeuterated saline washout from the myocardium of 9 dogs. Washout data were fitted to a one-component plus baseline Kety-Schmidt exponential model. To assess the ability of this technique to reliably measure changes in perfusion during change in myocardial workload, norepinephrine (1 microgram/kg/min) was infused and hypoxia was induced (by increasing the inspired ratio of N2/O2 to obtain a PAO2 of 20-30 mmHg) in separate interventions. Myocardial work, as determined by heart rate X systolic blood pressure, increased during both interventions. To support this increased workload, myocardial perfusion increased during both physiological interventions. These data indicate that myocardial perfusion can now be reliably estimated with a non-radioactive, non-toxic (in the concentrations used) tracer, perdeuterated saline. The technique can be used for repeated real-time measurements of perfusion during sequential physiological interventions.     

Reliability of brain structure morphometry in hydrocephalic children using MR images

To assess the ability of human operators to make decisions about region boundaries in significantly malformed brains, we performed a study of the reliability of morphometric measurements of specific brain structures from MRI in children with hydrocephalus and controls. Cross-sectional area measures of the corpus callosum, internal capsules and centrum semiovale, and volumes of the lateral ventricles were made in 50 children. Independent measurements were made by two raters on T₁ and T₂-weighted MR images. Pearson's correlation coefficients (r) and intraclass correlation coefficients (ICC) between the two rater's sets of measures were computed for each structure across all subjects. ICCs ranged from a low of 0.7502 to a high of 0.9895. All ICCs were significant at the p < .0001 level and were generally less than or equal to the corresponding Pearson's r value in every case. Therefore, the Pearson's r may overestimate the reliability. The results of this study support the claim that the ICC should be used rather than the Pearson's r when assessing interater reliability in situations where large between-group differences are present. In addition, the results show that brains malformed by disorders, such as hydrocephalus, can be reliably assessed using morphometric measures of MR images.     

Multi-exponential analysis of magnitude MR images using a quantitative multispectral edge-preserving filter

A solution for discrete multi-exponential analysis of T(2) relaxation decay curves obtained in current multi-echo imaging protocol conditions is described. We propose a preprocessing step to improve the signal-to-noise ratio and thus lower the signal-to-noise ratio threshold from which a high percentage of true multi-exponential detection is detected. It consists of a multispectral nonlinear edge-preserving filter that takes into account the signal-dependent Rician distribution of noise affecting magnitude MR images. Discrete multi-exponential decomposition, which requires no a priori knowledge, is performed by a non-linear least-squares procedure initialized with estimates obtained from a total least-squares linear prediction algorithm. This approach was validated and optimized experimentally on simulated data sets of normal human brains.     

Corners detection on finger vein images using the improved Harris algorithm

An improved Harris corner detection algorithm is proposed based on Barron operator, since Harris corner detection algorithm has a poor accuracy in positioning complex corner detection and may miss certain real corners. Firstly, the image gradient is calculated by using Barron operator to reduce the calculation errors from Prewitt operator or Sobel operator. Secondly, the centre B-spline function is used to smooth image, filter noise, and retain the corners information better. Thirdly, a non-maximal inhibition and corners sieving method is used to determine whether the detected corners are real corners or not. A square window is centered at the pixel and eliminate the corner if the value of the corner response function is non-maximal in the window. And then divide the test image into several blocks so as to process each block independently, and use a cyclic iterative method to determine the threshold value to make sure that the real corners are accurately selected. Finally, experiments indicate the algorithm has relatively great noise proof ability and is able to extract complex corners effectively.     

Sensitive detection of drug vapors using an ion funnel interface for secondary electrospray ionization mass spectrometry

In this study, we use an ion funnel (IF) at ambient pressure to enhance the sensitivity of secondary electrospray ionization (SESI). Atenolol, salbutamol and cocaine as test compounds are delivered to the SESI interface in the gas phase and are charged with three nano electrosprays. In our experiments, we show that the compounds can be detected at concentrations in the low pptv range, which is an increase of two orders of magnitude compared with the results without the IF. With a standard SESI interface, the compounds could not be detected at all. With the use of the SESI IF interface for the headspace analysis of bananas and limes, we can detect many more compounds and at higher intensities than with a standard SESI interface.     

Wavelet-based enhancement for detection of left ventricular myocardial boundaries in magnetic resonance images

MRI is noninvasive and generates clear images, giving it great potential as a diagnostic instrument. However, current methods of image analysis are too time-consuming for dynamic systems such as the cardiovascular system. Since dynamic imagery generate a huge number of images, a computer aided machine vision diagnostic tool is essential for implementing MRI-based measurement. In this paper, a wavelet-based image technique is applied to enhance left ventricular endocardial and epicardial profiles as the preprocessor for a dynamic programming-based automatic border detection algorithm. Statistical tests are conducted to verify the performance of the enhancement technique by comparing borders manually drawn with 1. borders generated from the enhanced images, and 2. borders generated for the original images.     

MR detection of mechanical vibrations using a radiofrequency field gradient

A new method for NMR characterization of mechanical waves, based upon radiofrequency field gradient for motion encoding, is proposed. A binomial B1 gradient excitation scheme was used to visualize the mobile spins undergoing a periodic transverse mechanical excitation. A simple model was designed to simulate the NMR signal as a function of the wave frequency excitation and the periodicity of the NMR pulse sequence. The preliminary results were obtained on a gel phantom at low vibration frequencies (0-200 Hz) by using a ladder-shaped coil generating a nearly constant RF field gradient along a specific known direction. For very small displacements and/or B1 gradients, the NMR signal measured on a gel phantom was proportional to the vibration amplitude and the pulse sequence was shown to be selective with respect to the vibration frequency. A good estimation of the direction of vibrations was obtained by varying the angle between the motion direction and the B1 gradient. The method and its use in parallel to more conventional MR elastography techniques are discussed. The presented approach might be of interest for noninvasive investigation of elastic properties of soft tissues and other materials.     

Automatic segmentation of white matter lesions on magnetic resonance images of the brain by using an outlier detection strategy

White matter lesions (WMLs) are commonly observed on the magnetic resonance (MR) images of normal elderly in association with vascular risk factors, such as hypertension or stroke. An accurate WML detection provides significant information for disease tracking, therapy evaluation, and normal aging research. In this article, we present an unsupervised WML segmentation method that uses Gaussian mixture model to describe the intensity distribution of the normal brain tissues and detects the WMLs as outliers to the normal brain tissue model based on extreme value theory. The detection of WMLs is performed by comparing the probability distribution function of a one-sided normal distribution and a Gumbel distribution, which is a specific extreme value distribution. The performance of the automatic segmentation is validated on synthetic and clinical MR images with regard to different imaging sequences and lesion loads. Results indicate that the segmentation method has a favorable accuracy competitive with other state-of-the-art WML segmentation methods.     

A circle similarity algorithm for an automatic circular decomposition of blood cell images

This paper presents an iterative algorithm for circular decomposition which investigates the separation of overlapped circular particles of a binary image, in order to locate their center coordinates and to estimate their radii. Since this algorithm is based on the measure of a circle similarity of an object in an image to execute a search for concavities, object segmentation and circle recognition, its implementation is simpler than the algorithm based on polygonal approximation. In this work we compare the accuracy and robustness of the proposed circle similarity algorithm with a polygonal approximation based algorithm using synthetic images and real blood cell images. Both the algorithms are able to decompose connected blood cells. However, the input parameters of the polygonal approximation method shows high sensitivity to the shape or agglomeration of an object, while the proposed algorithm demonstrates more stable performance.     

基于高光谱成像的苹果多品质参数同时检测

利用高光谱空间散射曲线的3个洛伦兹拟合参数对苹果的品质(硬度、可溶性固溶物含量)进行同时检测。采用偏最小二乘,逐步多元线性回归和BP神经网络3种方法,对归一化处理和未归一化处理的3个洛伦兹参数组合分别建立苹果品质的预测模型。结果表明:采用偏最小二乘法对未归一化处理参数的组合建立硬度的预测模型其预测结果最好,校正组相关系数Rc=0.93,校正标准差SEC=0.56,验证组相关系数Rv=0.84,验证标准差SEV=0.94。采用偏最小二乘法对归一化处理参数的组合建立可溶性固形物的预测模型其预测结果最好,Rc=0.95,SEC=0.29,Rv=0.83,SEV=0.63。研究结果表明:利用高光谱空间散射曲线的多拟合参数组合可以同时检测苹果的多品质参数。     

Material interface and response stability of the MR sensor

The relation between the material interface of a magnetoresistive (MR) sensor and the stability of its response is investigated. The cause of the hysteresis in the response of an MR sensor to an applied field is investigated using numerical models (a micromagnetic slope model and a micromagnetic step model). Vortex formation is found in the MR sensor. This vortex formation is investigated further by means of an analytical method (Ginzburg–Landau model).