Mingxi Wan

A Robust Post-Processing Workflow for Datasets with Motion Artifacts in Diffusion Kurtosis Imaging

Purpose The aim of this study was to develop a robust post-processing workflow for motion-corrupted datasets in diffusion kurtosis imaging (DKI). Materials and methods The proposed workflow consisted of brain extraction, rigid registration, distortion correction, artifacts rejection, spatial smoothing and tensor estimation. Rigid registration was utilized to correct misalignments. Motion artifacts were rejected by using local Pearson correlation coefficient (LPCC). The performance of LPCC in characterizing relative differences between artifacts and artifact-free images was compared with that of the conventional correlation coefficient in 10 randomly selected DKI datasets. The influence of rejected artifacts with information of gradient directions and b values for the parameter estimation was investigated by using mean square error (MSE). The variance of noise was used as the criterion for MSEs. The clinical practicality of the proposed workflow was evaluated by the image quality and measurements in regions of interest on 36 DKI datasets, including 18 artifact-free (18 pediatric subjects) and 18 motion-corrupted datasets (15 pediatric subjects and 3 essential tremor patients). Results The relative difference between artifacts and artifact-free images calculated by LPCC was larger than that of the conventional correlation coefficient (p<0.05). It indicated that LPCC was more sensitive in detecting motion artifacts. MSEs of all derived parameters from the reserved data after the artifacts rejection were smaller than the variance of the noise. It suggested that influence of rejected artifacts was less than influence of noise on the precision of derived parameters. The proposed workflow improved the image quality and reduced the measurement biases significantly on motion-corrupted datasets (p<0.05). Conclusion The proposed post-processing workflow was reliable to improve the image quality and the measurement precision of the derived parameters on motion-corrupted DKI datasets. The workflow provided an effective post-processing method for clinical applications of DKI in subjects with involuntary movements.

Rapid and reliable tract-based spatial statistics pipeline for diffusion tensor imaging in the neonatal brain: Applications to the white matter development and lesions

PURPOSE The relatively poor image contrast and variation in the neonatal brain size are technical challenges associated with the typical tract-based spatial statistics (TBSS) for the target identification and normalization. This study aimed to develop a rapid and reliable pipeline for the neonatal TBSS. MATERIALS AND METHODS A rapid TBSS strategy was proposed based on the group-wise target choice for fractional anisotropy (FA) derived from diffusion tensor imaging (DTI). The most representative subject of the entire group was identified via (a) initial group-averaged template creation (b) followed by identification of the target with the minimum warp displacement score between the individual and the group-averaged template. The computation time, registration quality, measurement of regional values, and statistical analyses were evaluated in two applications: brain white matter development in normal term neonates, and alterations in preterm neonates with white matter lesions compared to the matched controls. These performances in the proposed pipeline were compared with those in the typical and previous neonatal TBSS workflows. RESULTS Target choice using the proposed strategy is faster, compared with the previous TBSS pipelines, especially with the increase of the sample size. Registration errors between individuals and the target are assessed through warp displacement scores. Smaller warp displacement scores are observed for the proposed method than the typical pipeline. Due to the relatively accurate registration, the proposed method results in lower standard deviations and higher averaged values of FA across subjects. Additionally, more areas with significant changes related to the development and white matter lesions are detected using the proposed method than previous TBSS pipelines. The proposed pipeline provides stronger correlation between FA and gestational age, and larger difference between preterm neonates with white matter lesions and controls. CONCLUSION The proposed TBSS pipeline improves the efficiency and reliability of the DTI analysis in the neonatal brain.

Diffusion kurtosis imaging with tract-based spatial statistics reveals white matter alterations in preschool children

Diffusion kurtosis imaging (DKI), an extension of diffusion tensor imaging (DTI), provides a practical method to describe non-Gaussian water diffusion in neural tissues. The sensitivity of DKI to detect the subtle changes in several chosen brain structures has been studied. However, intuitive and holistic methods to validate the merits of DKI remain to be explored. In this paper, tract-based spatial statistics (TBSS) was used to demonstrate white matter alterations in both DKI and DTI parameters in preschool children (1-6 years; n=10). Correlation analysis was also performed in multiple regions of interest (ROIs). Fractional anisotropy, mean kurtosis, axial kurtosis and radial kurtosis increased with age, while mean diffusivity and radial diffusivity decreased significantly with age. Fractional anisotropy of kurtosis and axial diffusivity were found to be less sensitive to the changes with age. These preliminary findings indicated that TBSS could be used to detect subtle changes of DKI parameters on the white matter tract. Kurtosis parameters, except fractional anisotropy of kurtosis, demonstrated higher sensitivity than DTI parameters. TBSS may be a convenient method to yield higher sensitivity of DKI.

Elasticity imaging of artery walls with intravascular ultrasound

Intravascular ultrasound (IVUS) techniques offering grayscale images of arteries with sub-millimeter resolution are the basis of imaging artery elasticity. The authors developed a rectifying method for transducer off-axis effects and an optical flow estimation method based on genetic algorithms (GAOF) for the estimation of the strain in the artery wall. Furthermore, elasticity profile reconstruction is performed using the strain and equilibrium equations. Combining these methods and carefully operating, the results in vitro demonstrate that elasticity imaging of an artery with sub-millimeter resolution is possible.

Elasticity measurement of soft tissue using shear acoustic wave and real-time movement estimating based on PCA neural network

A novel ultrasound method for remote measurement of tissue elasticity is proposed. The method is based on acoustic shear wave and time delay estimation using PCA neural network. Compared to other shear wave elasticity imaging approaches, this method has advantages in that it can be recombined with current blood flow imaging equipment and estimates the tissue elasticity in real time with high resolution. Experiments are performed on a homogeneous tissue-mimicking phantom and results of tissue internal stain due to shear wave propagation are reported.

A new quantitative assessment method of facial paralysis based on motion estimation

A novel method is developed to assess facial expression motion and paralysis quantitatively using an optical flow technique based on genetic algorithm (GAOF). This method is more reliable than other assessment methods when only small subpixel motions occur. Experimental results demonstrate that this method is very useful to diagnose the site of facial paralysis and assess progression or recovery profiles of patients combined with other diagnosis methods.

Characterization of Extensive Microstructural Variations Associated with Punctate White Matter Lesions in Preterm Neonates

BACKGROUND AND PURPOSE: Punctate white matter lesions are common in preterm neonates. Neurodevelopmental outcomes of the neonates are related to the degree of extension. This study aimed to characterize the extent of microstructural variations for different punctate white matter lesion grades. MATERIALS AND METHODS: Preterm neonates with punctate white matter lesions were divided into 3 grades (from mild to severe: grades I–III). DTI-derived fractional anisotropy, axial diffusivity, and radial diffusivity between patients with punctate white matter lesions and controls were compared with Tract-Based Spatial Statistics and tract-quantification methods. RESULTS: Thirty-three preterm neonates with punctate white matter lesions and 33 matched controls were enrolled. There were 15, 9, and 9 patients, respectively, in grades I, II, and III. Punctate white matter lesions were mainly located in white matter adjacent to the lateral ventricles, especially regions lateral to the trigone, posterior horns, and centrum semiovale and/or corona radiata. Extensive microstructural changes were observed in neonates with grade III punctate white matter lesions, while no significant changes in DTI metrics were found for grades I and II. A pattern of increased axial diffusivity, increased radial diffusivity, and reduced/unchanged fractional anisotropy was found in regions adjacent to punctate white matter lesion sites seen on T1WI and T2WI. Unchanged axial diffusivity, increased radial diffusivity, and reduced/unchanged fractional anisotropy were observed in regions distant from punctate white matter lesion sites. CONCLUSIONS: White matter microstructural variations were different across punctate white matter lesion grades. Extensive change patterns varied according to the distance to the lesion sites in neonates with severe punctate white matter lesions. These findings may help in determining the outcomes of punctate white matter lesions and selecting treatment strategies.

Generalized LASSO with under-determined regularization matrices

This paper studies the intrinsic connection between a generalized LASSO and a basic LASSO formulation. The former is the extended version of the latter by introducing a regularization matrix to the coefficients. We show that when the regularization matrix is even- or under-determined with full rank conditions, the generalized LASSO can be transformed into the LASSO form via the Lagrangian framework. In addition, we show that some published results of LASSO can be extended to the generalized LASSO, and some variants of LASSO, e.g., robust LASSO, can be rewritten into the generalized LASSO form and hence can be transformed into basic LASSO. Based on this connection, many existing results concerning LASSO, e.g., efficient LASSO solvers, can be used for generalized LASSO.

On increasing signal-to-clutter ratio of pseudorandom ultrasound Doppler system

Several methods of increasing the signal-to-clutter ratio (SCR) of a pseudorandom ultrasound Doppler system are introduced, and a crosscorrelation function and a pair of pseudorandom sequences are derived. The authors also provide theory and experimental results for a Doppler flowmeter with a higher SCR based on this crosscorrelation method.<<ETX>>

An optimal method to segment piecewise Poisson distributed signals with application to sequencing data

To analyze the next generation sequencing data, the so-called read depth signal is often segmented with standard segmentation tools. However, these tools usually assume the signal to be a piecewise constant signal and contaminated with zero mean Gaussian noise, and therefore modeling error occurs. This paper models the read depth signal with piecewise Poisson distribution, which is more appropriate to the next generation sequencing mechanism. Based on the proposed model, an opti- mal dynamic programming algorithm with parallel computing is proposed to segment the piecewise signal, and furthermore detect the copy number variation.