DCE-MRI参数联合IFIT1基因对腰椎间盘突出患者椎间盘退变程度的评估价值

探讨DCE-MRI参数及IFIT1基因对腰椎间盘突出症(LDH)患者椎间盘退变(IDD)程度的评估价值。随机选取确诊的66例LDH患者作为研究对象,根据改良Pfirrmann分级法将患者分为2组:轻中度IDD(Ⅰ~Ⅲ级)组(46例)和重度IDD(Ⅳ~Ⅴ级)组20例。通过DCE-MRI检查考察患者的K-trans值,采用日本骨科学会评分(JOA)评估腰椎功能,采用视觉模拟评分法(VAS)评估腰部疼痛,采用RT-PCR检测外周血单个核细胞(PBMC)中IFIT1的mRNA表达。轻中度IDD组的K-trans值显著低于重度IDD组。轻中度IDD组患者PBMC中的IFIT1 mRNA相对表达量显著低于重度IDD组。K-trans值与PBMC中的IFIT1 mRNA水平和VAS评分正相关,与JOA评分负相关(P<0.05)。PBMC中的IFIT1 mRNA水平与VAS评分正相关,与JOA评分负相关(P<0.05)。K-trans值和PBMC中的IFIT1 mRNA水平联合诊断重度IDD的AUC值、敏感性和特异性分别为0.991、91.3%和100.0%,AUC值和特异性均高于单独诊断。DCE-MRI检查中的K-trans值联合PBMC中的IFIT1 mRNA水平可用于LDH患者IDD程度的诊断。     

Integration of quantitative DCE-MRI and ADC mapping to monitor treatment response in human breast cancer: initial results

PURPOSE: The objective of this study was to assess changes in the water apparent diffusion coefficient (ADC) and in pharmacokinetic parameters obtained from the fast-exchange regime (FXR) modeling of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) during neoadjuvant chemotherapy in breast cancer. MATERIALS AND METHODS: Eleven patients with locally advanced breast cancer underwent MRI examination prior to and after chemotherapy but prior to surgery. A 1.5-T scanner was used to obtain T1, ADC and DCE-MRI data. DCE-MRI data were analyzed by the FXR model returning estimates of K(trans) (volume transfer constant), v(e) (extravascular extracellular volume fraction) and tau(i) (average intracellular water lifetime). Histogram and correlation analyses assessed parameter changes post-treatment. RESULTS: Significant (P < .05) changes or trends towards significance (P < .10) were seen in all parameters except tau(i), although there was qualitative reduction in tau(i) values post-treatment. In particular, there was reduction (P < .035) in voxels with K(trans) values in the range 0.2-0.5 min(-1) and a decrease (P < .05) in voxels with ADC values in the range 0.99 x 10(-3) to 1.35 x 10(-3) mm2/s. ADC and v(e) were negatively correlated (r = -.60, P < .02). Parameters sensitive to water distribution and geometry (T(1), v(e), tau(i) and ADC) correlated with a multivariable linear regression model. CONCLUSION: The analysis presented here is sensitive to longitudinal changes in breast tumor status; K(trans) and ADC are most sensitive to these changes. Relationships between parameters provide information on water distribution and geometry in the tumor environment.     

Diagnosis of suspicious breast lesions using an empirical mathematical model for dynamic contrast-enhanced MRI

The purpose of this study was to test whether an empirical mathematical model (EMM) of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can distinguish between benign and malignant breast lesions. A modified clinical protocol was used to improve the sampling of contrast medium uptake and washout. T(1)-weighted DCE magnetic resonance images were acquired at 1.5 T for 22 patients before and after injection of Gd-DTPA. Contrast medium concentration as a function of time was calculated over a small region of interest containing the most rapidly enhancing pixels. Then the curves were fitted with the EMM, which accurately described contrast agent uptake and washout. Results demonstrate that benign lesions had uptake (P<2.0 x 10(-5)) and washout (P<.01) rates of contrast agent significantly slower than those of malignant lesions. In addition, secondary diagnostic parameters, such as time to peak of enhancement, enhancement slope at the peak and curvature at the peak of enhancement, were derived mathematically from the EMM and expressed in terms of primary parameters. These diagnostic parameters also effectively differentiated benign from malignant lesions (P<.03). Conventional analysis of contrast medium dynamics, using a subjective classification of contrast medium kinetics in lesions as "washout," "plateau" or "persistent" (sensitivity=83%, specificity=50% and diagnostic accuracy=72%), was less effective than the EMM (sensitivity=100%, specificity=83% and diagnostic accuracy=94%) for the separation of benign and malignant lesions. In summary, the present research suggests that the EMM is a promising alternative method for evaluating DCE-MRI data with improved diagnostic accuracy.     

Pixel-by-pixel analysis of DCE MRI curve patterns and an illustration of its application to the imaging of the musculoskeletal system

Dynamic contrast enhanced (DCE) MRI is a widespread method that has found broad application in the imaging of the musculoskeletal (MSK) system. A common way of analyzing DCE MRI images is to look at the shape of the time-intensity curve (TIC) in pixels selected after drawing an ROI in a highly enhanced area. Although often applied to a number of MSK affections, shape analysis has so far not led to a unanimous correlation between these TIC patterns and pathology. We hypothesize that this might be a result of the subjective ROI approach. To overcome the shortcomings of the ROI approach (sampling error and interuser variability, among others), we created a method for a fast and simple classification of DCE MRI where time-curve enhancement shapes are classified pixel by pixel according to their shape. The result of the analysis is rendered in multislice, 2D color-coded images. With this approach, we show not only that differences on a short distance range of the TIC patterns are significant and cannot be appreciated with a conventional ROI analysis but also that the information that shape maps and conventional standard DCE MRI parameter maps convey are substantially different.     

A general dual-bolus approach for quantitative DCE-MRI

Purpose

To present a dual-bolus technique for quantitative dynamic contrast-enhanced MRI (DCE-MRI) and show that it can give an arterial input function (AIF) measurement equivalent to that from a single-bolus protocol.

Methods

Five rabbits were imaged using a dual-bolus technique applicable for high-resolution DCE-MRI, incorporating a time resolved imaging of contrast kinetics (TRICKS) sequence for rapid temporal sampling. AIFs were measured from both the low-dose prebolus and the high-dose main bolus in the abdominal aorta. In one animal, TRICKS and fast spoiled gradient echo (FSPGR) acquisitions were compared.

Results

The scaled prebolus AIF was shown to match the main bolus AIF, with 95% confidence intervals overlapping for fits of gamma-variate functions to the first pass and linear fits to the washout phase, with the exception of one case. The AIFs measured using TRICKS and FSPGR were shown to be equivalent in one animal.

Conclusion

The proposed technique can capture even the rapid circulation kinetics in the rabbit aorta, and the scaled prebolus AIF is equivalent to the AIF from a high-dose injection. This allows separate measurements of the AIF and tissue uptake curves, meaning that each curve can then be acquired using a protocol tailored to its specific requirements.     

Reproducibility of the gadolinium concentration measurements and of the fitting parameters of the vascular input function in the superior sagittal sinus in a patient population

It is widely recognised that the measurement of the arterial input function (AIF) is a key issue and a major source of errors in the pharmacokinetic modelling of dynamic, contrast-enhanced magnetic resonance imaging (DCE-MRI) data, and the modality of the AIF determination is still a matter of debate. In this study we addressed the problem of the intrinsic variability of the AIF within the imaged volume of a DCE-MRI scan by systematically investigating the change in the concentration of contrast agent over time and the fit parameters of the derived vascular input function (VIF) obtained from the superior sagittal sinus (SSS) of a patient population that was scanned longitudinally during treatment for high grade glioma. From a total of 82 scanning sessions, we compared the results obtained with three different DCE-MRI protocols and between two different fitting functions. We applied a correction algorithm to the measured concentration-time curves to minimize the effect of the low temporal resolution on the VIF, and investigated the effect of this algorithm on the reproducibility. Finally, where possible, we compared the signal obtained in the SSS to the signal obtained in the middle cerebral artery. We found a good intrapatient reproducibility of both the measured gadolinium concentrations and VIF parameters, and that the variation of the parameters due to slice location within a patient was significantly lower than the intra patient variation. Intrapatient, interscan differences were significantly less marked than inter-patient differences showing a good intraclass correlation coefficient. We did encounter a MRI protocol dependence of the VIF fitting parameters. The correction algorithm significantly improved the reproducibility of the fitting parameters. These results support the idea that the use of a patient specific measured AIF, not necessarily averaged over a large volume, offers a significant benefit with respect to an external AIF or a measured cohort average AIF.     

A nonrigid registration algorithm for longitudinal breast MR images and the analysis of breast tumor response

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can estimate parameters relating to blood flow and tissue volume fractions and therefore may be used to characterize the response of breast tumors to treatment. To assess treatment response, values of these DCE-MRI parameters are observed at different time points during the course of treatment. We propose a method whereby DCE-MRI data sets obtained in separate imaging sessions can be co-registered to a common image space, thereby retaining spatial information so that serial DCE-MRI parameter maps can be compared on a voxel-by-voxel basis. In performing inter-session breast registration, one must account for patient repositioning and breast deformation, as well as changes in tumor shape and volume relative to other imaging sessions. One challenge is to optimally register the normal tissues while simultaneously preventing tumor distortion. We accomplish this by extending the adaptive bases algorithm through adding a tumor-volume preserving constraint in the cost function. We also propose a novel method to generate the simulated breast magnetic resonance (MR) images, which can be used to evaluate the proposed registration algorithm quantitatively. The proposed nonrigid registration algorithm is applied to both simulated and real longitudinal 3D high resolution MR images and the obtained transformations are then applied to lower resolution physiological parameter maps obtained via DCE-MRI. The registration results demonstrate the proposed algorithm can successfully register breast MR images acquired at different time points and allow for analysis of the registered parameter maps.     

A comparison of two methods for estimating DCE-MRI parameters via individual and cohort based AIFs in prostate cancer: A step towards practical implementation

Multi-parametric Magnetic Resonance Imaging, and specifically Dynamic Contrast Enhanced (DCE) MRI, play increasingly important roles in detection and staging of prostate cancer (PCa). One of the actively investigated approaches to DCE MRI analysis involves pharmacokinetic (PK) modeling to extract quantitative parameters that may be related to microvascular properties of the tissue. It is well-known that the prescribed arterial blood plasma concentration (or Arterial Input Function, AIF) input can have significant effects on the parameters estimated by PK modeling. The purpose of our study was to investigate such effects in DCE MRI data acquired in a typical clinical PCa setting. First, we investigated how the choice of a semi-automated or fully automated image-based individualized AIF (iAIF) estimation method affects the PK parameter values; and second, we examined the use of method-specific averaged AIF (cohort-based, or cAIF) as a means to attenuate the differences between the two AIF estimation methods.     

The precision of DCE-MRI using the tissue homogeneity model with continuous formulation of the perfusion parameters

The present trend in dynamic contrast-enhanced MRI is to increase the number of estimated perfusion parameters using complex pharmacokinetic models. However, less attention is given to the precision analysis of the parameter estimates. In this paper, the distributed capillary adiabatic tissue homogeneity pharmacokinetic model is extended by the bolus arrival time formulated as a free continuous parameter. With the continuous formulation of all perfusion parameters, it is possible to use standard gradient-based optimization algorithms in the approximation of the tissue concentration time sequences. This new six-parameter model is investigated by comparing Monte-Carlo simulations with theoretically derived covariance matrices. The covariance-matrix approach is extended from the usual analysis of the primary perfusion parameters of the pharmacokinetic model to the analysis of the perfusion parameters derived from the primary ones. The results indicate that the precision of the estimated perfusion parameters can be described by the covariance matrix for signal-to-noise ratio higher than ~ 20 dB. The application of the new analysis model on a real DCE-MRI data set is also presented.     

Probability parametric imaging with DCE-MRI data to identify prostate cancer in the peripheral zone

To quantitatively analyze prostate dynamic contrast-enhanced magnetic resonance images (DCE-MRI), a retrospective study was carried out to construct a mathematical model to predict whether a lesion is cancerous or not. With known diagnosis of 52 regions-of-interest (10 malignant, 26 benign and 16 normal) in peripheral zone (PZ) of prostate from 18 patients undergoing needle biopsy and 8 normal volunteers, we combined feature parameters (k1, k2 and tp) from the linear-slope model with logistic regression to determine the statistical probability model. Prostate cancerous probability maps were derived using the model on each subject to examine the correlation between the model-based prediction and the known diagnosis. Compared the model prediction results of 52 regions-of-interest with the histopathologic results, the total agreement rate was 90.4%; the agreement rate was 60% for the malignant and 97.6% for the nonmalignant. The diagnostic results only referencing the probability maps by two radiologists showed a high agreement in tissue characteristics and lesion location with the combining results from other diagnostic techniques such as PSA (prostate specific antigen) level and MRS (magnetic resonance spectroscopy).