基于一种改进的相位校正算法消除MRI运动伪影

背景：近年来,MRI由于具有高的空间分辨率和软组织对比度,在临床上的运用越来越广泛。但是其成像时间较长,所以容易受到患者身体运动的影响,产生运动伪影。 目的：去除MRI图像成像时产生的伪影,改善图像质量。 方法：使用改进的相位矫正算法,并结合水平集算法去除图像伪影。去除伪影后使用模糊增强改善处理后图像的质量。 结果与结论：实验证明使用改进的相位矫正算法得到的图像比使用原始的相位矫正算法得到的图像效果更加理想。     

Treatment of radioactive decay in pharmacokinetic modeling: influence on parameter estimation in cardiac 13N-PET

Radioactive decay during measurement can be accounted for by either a decay correction of the measured data before modeling (DbM) or by direct implementation of decay into the pharmacokinetic model (DiM). The purpose of this study was to quantify the influence of the type of decay correction on the calculated parameters for the example of a three-compartment model used for the calculation of myocardial perfusion with 13N ammonia and positron emission tomography (PET). For a given input function [Ca(t)infinity t exp(-kt), k= 1.72/min] the tissue uptake for two parameter sets of K1, k2, k3, TBV were calculated for 20 frames (12 x 10 s, 4 x 30 s, 3 x 120 s, 1 x 300 s). These values were mathematically deteriorated by various noise levels according to Poisson statistics and fitted by a Levenberg-Marquardt algorithm. Estimated parameter means and coefficients of variation of the fitted parameters were calculated for the DbM and DiM case. The estimated parameter means for both decay correction methods were of comparable quality. The important measure for a single fit is the relative variability of the fitted parameters. This value is up to a factor 1.15 smaller for K1 obtained with DiM and a reasonable noise level of 10%. Therefore, decay correction should be taken into account during modeling to reduce the variability in the fitted parameters.     

PHARM—An interactive graphic program for individual and population pharmacokinetic parameter estimation

This paper describes a new computer program PHARM to estimate individual or population pharmacokinetic parameters in nonlinear models. PHARM is an interactive program which uses graphic facilities to display data and results. The structural model can be defined using differential or integrated equations. The user can also define an error model associated with experimental data. The nonlinear mixed effect model is used to estimate the mean population parameters and their interindividual variability. The maximum likelihood and Bayesian criteria are used to estimate simultaneously the error and structural model parameters.     

Quantifying brain microstructure with diffusion MRI: Theory and parameter estimation

We review, systematize and discuss models of diffusion in neuronal tissue, by putting them into an overarching physical context of coarse‐graining over an increasing diffusion length scale. From this perspective, we view research on quantifying brain microstructure as occurring along three major avenues. The first avenue focusses on transient, or time‐dependent, effects in diffusion. These effects signify the gradual coarse‐graining of tissue structure, which occurs qualitatively differently in different brain tissue compartments. We show that transient effects contain information about the relevant length scales for neuronal tissue, such as the packing correlation length for neuronal fibers, as well as the degree of structural disorder along the neurites. The second avenue corresponds to the long‐time limit, when the observed signal can be approximated as a sum of multiple nonexchanging anisotropic Gaussian components. Here, the challenge lies in parameter estimation and in resolving its hidden degeneracies. The third avenue employs multiple diffusion encoding techniques, able to access information not contained in the conventional diffusion propagator. We conclude with our outlook on future directions that could open exciting possibilities for designing quantitative markers of tissue physiology and pathology, based on methods of studying mesoscopic transport in disordered systems.     

Respiratory liver motion estimation and its effect on scanned proton beam therapy

Proton therapy with active scanning beam delivery has significant advantages compared to conventional radiotherapy. However, so far only static targets have been treated in this way, since moving targets potentially lead to interplay effects. For 4D treatment planning, information on the target motion is needed to calculate time-resolved dose distributions. In this study, respiratory liver motion has been extracted from 4D CT data using two deformable image registration algorithms. In moderately moving patient cases (mean motion range around 6 mm), the registration error was no more than 3 mm, while it reached 7 mm for larger motions (range around 13 mm). The obtained deformation fields have then been used to calculate different time-resolved 4D treatment plans. Averaged over both motion estimations, interplay effects can increase the D?-D?? value for the clinical target volume (CTV) from 8.8% in a static plan to 23.4% when motion is considered. It has also been found that the different deformable registration algorithms can provide different motion estimations despite performing similarly for the selected landmarks, which in turn can lead to differing 4D dose distributions. Especially for single-field treatments where no motion mitigation is used, a maximum (mean) dose difference (averaged over three cases) of 32.8% (2.9%) can be observed. However, this registration ambiguity-induced uncertainty can be reduced if rescanning is applied or if the treatment plan consists of multiple fields, where the maximum (mean) difference can decrease to 15.2% (0.57%). Our results indicate the necessity to interpret 4D dose distributions for scanned proton therapy with some caution or with error bars to reflect the uncertainties resulting from the motion estimation. On the other hand, rescanning has been found to be an appropriate motion mitigation technique and, furthermore, has been shown to be a robust approach to also deal with these motion estimation uncertainties.     

The effect of logarithmic compression on estimation of the Nakagami parameter for ultrasonic tissue characterization: a simulation study

Previous studies have demonstrated that the Nakagami parameter estimated using the envelopes of backscattered ultrasound is useful in detecting variations in the concentration of scatterers in tissues. The signal processing in those studies was linear, whereas nonlinear logarithmic compression is routinely employed in existing ultrasonic scanners. We therefore explored the effect of the logarithmic compression on the estimation of the Nakagami parameter in this study. Computer simulations were used to produce backscattered signals of various scatterer concentrations for the estimation of the Nakagami parameters before and after applying the logarithmic compression on the backscattered envelopes. The simulated results showed that the logarithmic compression would move the statistics of the backscattered envelopes towards post-Rayleigh distributions for most scatterer concentrations. Moreover, the Nakagami parameter calculated using compressed backscattered envelopes is more sensitive than that calculated using uncompressed envelopes in differentiating variations in the scatterer concentration, making the former better at quantifying the scatterer concentration in biological tissues.     

Reliable estimation of brain intravoxel incoherent motion parameters using denoised diffusion‐weighted MRI

In this study, we evaluate whether diffusion‐weighted magnetic resonance imaging (DW‐MRI) data after denoising can provide a reliable estimation of brain intravoxel incoherent motion (IVIM) perfusion parameters. Brain DW‐MRI was performed in five healthy volunteers on a 3 T clinical scanner with 12 different b‐values ranging from 0 to 1000 s/mm². DW‐MRI data denoised using the proposed method were fitted with a biexponential model to extract perfusion fraction (PF), diffusion coefficient (D) and pseudo‐diffusion coefficient (D*). To further evaluate the accuracy and precision of parameter estimation, IVIM parametric images obtained from one volunteer were used to resimulate the DW‐MRI data using the biexponential model with the same b‐values. Rician noise was added to generate DW‐MRI data with various signal‐to‐noise ratio (SNR) levels. The experimental results showed that the denoised DW‐MRI data yielded precise estimates for all IVIM parameters. We also found that IVIM parameters were significantly different between gray matter and white matter (P < 0.05), except for D* (P = 0.6). Our simulation results show that the proposed image denoising method displays good performance in estimating IVIM parameters (both bias and coefficient of variation were <12% for PF, D and D*) in the presence of different levels of simulated Rician noise (SNR_b=0 = 20‐40). Simulations and experiments show that brain DW‐MRI data after denoising can provide a reliable estimation of IVIM parameters.     

Assessment of the effect of haematocrit-dependent arterial input functions on the accuracy of pharmacokinetic parameters in dynamic contrast-enhanced MRI

The detection and prognosis of prostate cancer in its early stages are critically important. It is therefore essential to improve the existing dynamic contrast-enhanced MRI (DCE MRI) techniques commonly used for the assessment of the tumour vascular environment. The goal of this study was to describe a method for the estimation of the arterial input function (AIF) in DCE MRI by measuring R(2) * values in the femoral artery of patients with early-stage prostate cancer. The calculation of contrast agent concentrations was based on calibration curves determined in whole blood samples for a range of normal haematocrit (HCT) values (HCT = 0.35-0.525). Individual AIFs corrected for HCT were compared with individual AIFs calibrated with a mean whole blood [R(2)*-Gd-DTPA-BMA] [Gd-DTPA-BMA, gadolinium diethylenetriaminepentaacetate-bis(methylamide) (gadodiamide)] curve at an assumed HCT = 0.44, as well as a population AIF at an assumed HCT = 0.45. The area under the curve of the first-pass bolus ranged between 0.6 min mM at HCT = 0.53 and 1.3 min mM at HCT = 0.39. Significant differences in magnitude at peak contrast agent concentrations (HCT = 0.36, [Gd-DTPA-BMA](max) = 9 ± 0.4 mM; HCT = 0.46, [Gd-DTPA-BMA](max) = 4.0 ± 0.2 mM) were found. Using model-based simulations, the accuracy of the kinetic parameters estimated using individual AIFs corrected for HCT demonstrated that, for the use of individual calibration curves with HCT values differing by more than 10%, K(trans) and k(ep) values were largely underestimated (up to 60% difference for K(trans)). Moreover, blood volume estimates were severely underestimated. Estimates of kinetic parameters in early-stage prostate cancer patients demonstrated that the efflux rate constant (k(ep)) was influenced significantly by the definition of AIF. Regardless of whether an individually calibrated AIF or a population AIF (average of all individually calibrated AIFs) was used, pixel-by-pixel mapping of k(ep) and v(b) in the prostate gland appeared to be more sensitive than with the usual biexponential approach.     

Comments on 'The effect of logarithmic compression on the estimation of the Nakagami parameter for ultrasonic tissue characterization'

In a recently published paper (Tsui et al 2005 Phys. Med. Biol. 50 3235-44), the authors demonstrated estimation of the Nakagami parameter of the logcompressed envelopes and the application of such an approach in ultrasonic tissue characterization. The comments in this letter suggest that the authors ignored some important statistical properties of logcompressed data leading to serious errors in their studies and results.     

Dynamic contrast-enhanced MRI parametric mapping using high spatiotemporal resolution Golden-angle RAdial Sparse Parallel MRI and iterative joint estimation of the arterial input function and pharmacokinetic parameters

The aim of this work is to develop a data-driven quantitative dynamic contrast-enhanced (DCE) MRI technique using Golden-angle RAdial Sparse Parallel (GRASP) MRI with high spatial resolution and high flexible temporal resolution and pharmacokinetic (PK) analysis with an arterial input function (AIF) estimated directly from the data obtained from each patient. DCE-MRI was performed on 13 patients with gynecological malignancy using a 3-T MRI scanner with a single continuous golden-angle stack-of-stars acquisition and image reconstruction with two temporal resolutions, by exploiting a unique feature in GRASP that reconstructs acquired data with user-defined temporal resolution. Joint estimation of the AIF (both AIF shape and delay) and PK parameters was performed with an iterative algorithm that alternates between AIF and PK estimation. Computer simulations were performed to determine the accuracy (expressed as percentage error [PE]) and precision of the estimated parameters. PK parameters (volume transfer constant [K^trans], fractional volume of the extravascular extracellular space [v_e], and blood plasma volume fraction [v_p]) and normalized root-mean-square error [nRMSE] (%) of the fitting errors for the tumor contrast kinetic data were measured both with population-averaged and data-driven AIFs. On patient data, the Wilcoxon signed-rank test was performed to compare nRMSE. Simulations demonstrated that GRASP image reconstruction with a temporal resolution of 1 s/frame for AIF estimation and 5 s/frame for PK analysis resulted in an absolute PE of less than 5% in the estimation of K^trans and v_e, and less than 11% in the estimation of v_p. The nRMSE (mean ± SD) for the dual temporal resolution image reconstruction and data-driven AIF was 0.16 ± 0.04 compared with 0.27 ± 0.10 (p < 0.001) with 1 s/frame using population-averaged AIF, and 0.23 ± 0.07 with 5 s/frame using population-averaged AIF (p < 0.001). We conclude that DCE-MRI data acquired and reconstructed with the GRASP technique at dual temporal resolution can successfully be applied to jointly estimate the AIF and PK parameters from a single acquisition resulting in data-driven AIFs and voxelwise PK parametric maps.     

脉搏波在运动干预中参数指示作用的Meta分析

目的探究评价脉搏波在运动干预评价体系中作为参数的指示效用,为完善运动干预评价体系提供指导。方法通过网上检索中国知网(CNKI)、Web of Science、Latrobe University Library、PubMed等数据库,搜集运动干预中存在以脉搏波为参数进行指示的随机对照试验。由5名研究者依据Corhrane评价标准对纳入的文献进行严格质量评价和资料提取,对符合要求的文献采用RevMan 5.3软件进行Meta分析。具体分析指标包括 I^2、P 、CI、MD等。结果共纳入16篇文献,涉及312例对象。Meta分析的结果显示,运动干预对PWV[MD=0.72,95%CI(0.46,0.97), P <0.0001]、AIx [MD=1.97,95%CI(0.75,3.19)、 P =0.002]、PP[MD=2.16,95%CI(0.83,3.49), P =0.001]的影响具有统计学意义。结论脉搏波可以加强反映周身血管功能变化的检验效能,起到较好地完善运动干预评价体系的作用。     

The design and implementation of a motion correction scheme for neurological PET

A method is described to monitor the motion of the head during neurological positron emission tomography (PET) acquisitions and to correct the data post acquisition for the recorded motion prior to image reconstruction. The technique uses an optical tracking system, Polaris, to accurately monitor the position of the head during the PET acquisition. The PET data are acquired in list mode where the events are written directly to disk during acquisition. The motion tracking information is aligned to the PET data using a sequence of pseudo-random numbers, which are inserted into the time tags in the list mode event stream through the gating input interface on the tomograph. The position of the head is monitored during the transmission acquisition, and it is assumed that there is minimal head motion during this measurement. Each event, prompt and delayed, in the list mode event stream is corrected for motion and transformed into the transmission space. For a given line of response, normalization, including corrections for detector efficiency, geometry and crystal interference and dead time are applied prior to motion correction and rebinning in the sinogram. A series of phantom experiments were performed to confirm the accuracy of the method: (a) a point source located in three discrete axial positions in the tomograph field of view, 0 mm, 10 mm and 20 mm from a reference point, (b) a multi-line source phantom rotated in both discrete and gradual rotations through +/- 5 degrees and +/- 15 degrees, including a vertical and horizontal movement in the plane. For both phantom experiments images were reconstructed for both the fixed and motion corrected data. Measurements for resolution, full width at half maximum (FWHM) and full width at tenth maximum (FWTM), were calculated from these images and a comparison made between the fixedand motion corrected datasets. From the point source measurements, the FWHM at each axial position was 7.1 mm in the horizontal direction, and increasing from 4.7 mm at the 0 mm position, to 4.8 mm, 20 mm offset, in the vertical direction. The results from the multi-line source phantom with +/- 5 degrees rotations showed a maximum degradation in FWHM, when compared with the stationary phantom, of 0.6 mm, in the horizontal direction, and 0.3 mm in the vertical direction. The corresponding values for the larger rotation, +/- 15 degrees, were 0.7 mm and 1.1 mm, respectively. The performance of the method was confirmed with a Hoffman brain phantom moved continuously, and a clinical acquisition using [11C]raclopride (normal volunteer). A visual comparison of both the motion and non-motion corrected images of the Hoffman brain phantom clearly demonstrated the efficacy of the method. A sample time-activity curve extracted from the clinical study showed irregularities prior to motion correction, which were removed after correction. A method has been developed to accurately monitor the motion of the head during a neurological PET acquisition, and correct for this motion prior to image reconstruction. The method has been demonstrated to be accurate and does not add significantly to either the acquisition or the subsequent data processing.     

The effects of measurement errors in the plasma radioactivity curve on parameter estimation in positron emission tomography

The effects of three error sources in plasma curve measurements on parameter estimation in kinetic analysis of positron emission tomography (PET) fluorodeoxyglucose (FDG) data are investigated by computer simulation. The three error sources are: (1) measurement noise in the radioactivity concentrations of plasma samples; (2) linear interpolation between adjacent plasma sampling points of the plasma time activity curve; and (3) incorrect weights used for the least-squares regression. All three error sources are found to increase the variability of the parameter estimates, with the first one a primary error source in normal PET FDG studies. The performance of five estimation methods which account for the error sources are evaluated. When the noise variances of the plasma and the tissue measurements are not known, an iterative weighting procedure is shown to give accurate and reliable estimates.     

Tomographic motion detection and correction directly in sinogram space

Patient motion, especially respiratory motion, results in various artefacts such as blurring and streaks in tomographic images. The interplay of the movement of the beam aperture and variations of organ anatomy during delivery can create 'hot' and 'cold' spots throughout the field in intensity-modulated radiation therapy (IMRT). Detection and correction of patient motion is extremely important in tomographic imaging and IMRT. Tomographic projection data (sinogram) encode not only the patient anatomy information, but also the intra-scanning motion information. In this paper, we developed an algorithm to detect and correct the in-plane respiratory motion directly in sinogram space. The respiratory motion is modelled as time-varying scaling along the x and y directions. Its effects on the sinogram are discussed. Based on the traces of some nodal points in the sinogram, the intra-scanning motion is determined. The motion correction is also implemented in sinogram space. The motion-corrected sinogram is used for reconstruction by the filtered back-projection (FBP) method. Computer simulations validate the motion detection and correction algorithm. The reconstructed images from the motion-corrected sinogram eliminate the majority of the artefacts. The method could be applied to projection data used in CT and ECT, as well as in tomotherapy delivery modification and dose reconstruction.     

The effect of tibiofemoral loading on proximal tibiofibular joint motion

The human proximal tibiofibular joint (PTFJ) and its relationship to overall knee joint mechanics have been largely unexplored. This study describes force/displacement data from experiments done on four human cadaveric knee specimens and general conclusions obtained with the help of a statistical modeling technique. Specimens were rigidly affixed at the tibia to a force plate and the femur was attached to a custom made device allowing for manual load application. Motion of the fibular head was tracked relative to the tibial plateau by means of reflective markers and a high speed digital camera synchronized with the force plate data stream. Each specimen was subjected to a range of loading conditions and a quadratic regression model was created and then used to predict the specimen's response to standardized loading conditions and compare these across specimens. Statistical analysis was performed with a three-factor analysis of variance with repeated measures. Proximal tibiofibular joint motion was largest in the anterior-posterior direction with translations of 1-3 mm observed during a range of physiological loading conditions. The applied internal-external rotation moment had a significant effect on proximal tibiofibular joint translation (P < 0.05). Effects of varus-valgus loading and flexion angle were seen in some specimens. This study demonstrates that substantial proximal tibiofibular joint motion can occur in physiologic loading states. Preservation of proximal tibiofibular joint function, and anatomical variations which affect this function, may need to be considered when designing surgical procedures for the knee joint.     

Suppressing motion artefacts in MRI using an Inception-ResNet network with motion simulation augmentation

The suppression of motion artefacts from MR images is a challenging task. The purpose of this paper was to develop a standalone novel technique to suppress motion artefacts in MR images using a data-driven deep learning approach. A simulation framework was developed to generate motion-corrupted images from motion-free images using randomly generated motion profiles. An Inception-ResNet deep learning network architecture was used as the encoder and was augmented with a stack of convolution and upsampling layers to form an encoder-decoder network. The network was trained on simulated motion-corrupted images to identify and suppress those artefacts attributable to motion. The network was validated on unseen simulated datasets and real-world experimental motion-corrupted in vivo brain datasets. The trained network was able to suppress the motion artefacts in the reconstructed images, and the mean structural similarity (SSIM) increased from 0.9058 to 0.9338. The network was also able to suppress the motion artefacts from the real-world experimental dataset, and the mean SSIM increased from 0.8671 to 0.9145. The motion correction of the experimental datasets demonstrated the effectiveness of the motion simulation generation process. The proposed method successfully removed motion artefacts and outperformed an iterative entropy minimization method in terms of the SSIM index and normalized root mean squared error, which were 5–10% better for the proposed method. In conclusion, a novel, data-driven motion correction technique has been developed that can suppress motion artefacts from motion-corrupted MR images. The proposed technique is a standalone, post-processing method that does not interfere with data acquisition or reconstruction parameters, thus making it suitable for routine clinical practice.     

Log transformation benefits parameter estimation in microwave tomographic imaging

Microwave tomographic imaging falls under a broad category of nonlinear parameter estimation methods when a Gauss-Newton iterative reconstruction technique is used. A fundamental requirement in using these approaches is evaluating the appropriateness of the regression model. While there have been numerous investigations of regularization techniques to improve overall image quality, few, if any, studies have explored the underlying statistical properties of the model itself. The ordinary least squares (OLS) approach is used most often, but there are other options such as the weighted least squares (WLS), maximum likelihood (ML), and maximum a posteriori (MAP) that may be more appropriate. In addition, a number of variance stabilizing transformations can be applied to make the inversion intrinsically more linear. In this paper, a statistical analysis is performed of the properties of the residual errors from the reconstructed images utilizing actual measured data and it is demonstrated that the OLS algorithm with a log transformation (OLSlog) is clearly advantageous relative to the more commonly used OLS approach by itself. In addition, several high contrast imaging experiments are performed, which demonstrate that different subsets of data are emphasized in each method and may contribute to the overall image quality differences.     

Eye movements influence estimation of time-to-contact in prediction motion

In many situations, it is necessary to predict when a moving object will reach a given target even though the object may be partially or entirely occluded. Typically, one would track the moving object with eye movements, but it remains unclear whether ocular pursuit facilitates accurate estimation of time-to-contact (TTC). The present study examined this issue using a prediction-motion (PM) task in which independent groups estimated TTC in a condition that required fixation on the arrival location as an object approached, or a condition in which participants were instructed to pursue the moving object. The design included 15 TTC ranging from 0.4 to 1.5 s and three object velocities (2.5, 5, 10 deg/s). Both constant error and variable error in TTC estimation increased as a function of actual TTC. However, for the fixation group only, there was a significant effect of object velocity with a relative overestimation of TTC for the slower velocity and underestimation for the faster velocity. Further analysis indicated that the velocity effect exhibited by the fixation group was consistent with participants exhibiting a relatively constant misperception for each level of object velocity. Overall, these findings show that there is an advantage in the PM task to track the moving object with the eyes. We explain the different pattern of TTC estimation error exhibited when fixating and during pursuit with reference to differences in the available retinal and/or extra-retinal input.