Huanjie Li

Functional Connectivity-Based Parcellation of the Thalamus: An Unsupervised Clustering Method and Its Validity Investigation

Node definition or delineating how the brain is parcellated into individual functionally related regions is the first step to accurately map the human connectome. As a result, parcellation of the human brain has drawn considerable attention in the field of neuroscience. The thalamus is known as a relay in the human brain, with its nuclei sending fibers to the cortical and subcortical regions. Functional magnetic resonance imaging techniques offer a way to parcellate the thalamus in vivo based on its connectivity properties. However, the parcellations from previous studies show that both the number and the distribution of thalamic subdivisions vary with different cortical segmentation methods. In this study, we used an unsupervised clustering method that does not rely on a priori information of the cortical segmentation to parcellate the thalamus. Instead, this approach is based on the intrinsic resting-state functional connectivity profiles of the thalamus with the whole brain. A series of cluster solutions were obtained, and an optimal solution was determined. Furthermore, the validity of our parcellation was investigated through the following: (1) identifying specific resting-state connectivity patterns of thalamic parcels with different brain networks and (2) investigating the task activation and psychophysiological interactions of specific thalamic clusters during 8-Hz flashing checkerboard stimulation with simultaneous finger tapping. Together, the current study provides a reliable parcellation of the thalamus and enhances our understating of thalamic. Furthermore, the current study provides a framework for parcellation that could be potentially extended to other subcortical and cortical regions.

Improved adaptive reconstruction of multichannel MR images

PURPOSEnTo improve adaptive reconstruction of multichannel MR images by simultaneously removing nonsmooth phase and signal-loss imaging artifacts.nnnMETHODSnThe improved adaptive reconstruction consists of three steps: (1) modified multichannel images are first derived by dividing raw multichannel images by a reference image (i.e., a normalized single-channel image); (2) the modified multichannel images are smoothed by a low-pass filter; (3) adaptive spatial matched filters determined from the smoothed multichannel images are utilized to obtain multichannel combined images. Numerical simulations, as well as MRI experiments, on phantoms and human subjects are performed to evaluate and compare the effectiveness of this improved adaptive reconstruction approach against traditional coil combination methods.nnnRESULTSnBoth simulation and MRI experimental results demonstrated that the proposed improved adaptive reconstruction method is able to obtain combined images with reduced nonsmooth phase and signal-loss imaging artifacts.nnnCONCLUSIONSnA novel multichannel image reconstruction method is developed that produces high quality multichannel combined images.

Comparison of a non-stationary voxelation-corrected cluster-size test with TFCE for group-Level MRI inference.

Two powerful methods for statistical inference on MRI brain images have been proposed recently, a non‐stationary voxelation‐corrected cluster‐size test (CST) based on random field theory and threshold‐free cluster enhancement (TFCE) based on calculating the level of local support for a cluster, then using permutation testing for inference. Unlike other statistical approaches, these two methods do not rest on the assumptions of a uniform and high degree of spatial smoothness of the statistic image. Thus, they are strongly recommended for group‐level fMRI analysis compared to other statistical methods. In this work, the non‐stationary voxelation‐corrected CST and TFCE methods for group‐level analysis were evaluated for both stationary and non‐stationary images under varying smoothness levels, degrees of freedom and signal to noise ratios. Our results suggest that, both methods provide adequate control for the number of voxel‐wise statistical tests being performed during inference on fMRI data and they are both superior to current CSTs implemented in popular MRI data analysis software packages. However, TFCE is more sensitive and stable for group‐level analysis of VBM data. Thus, the voxelation‐corrected CST approach may confer some advantages by being computationally less demanding for fMRI data analysis than TFCE with permutation testing and by also being applicable for single‐subject fMRI analyses, while the TFCE approach is advantageous for VBM data. Hum Brain Mapp 38:1269–1280, 2017.

3D interslab echo-shifted FLASH sequence for susceptibility weighted imaging.

To develop a novel three‐dimensional (3D) sequence for susceptibility weighted imaging that is able to reduce scan time substantially while maintaining high image signal‐to‐noise ratio (SNR).

A voxelation-corrected non-stationary 3D cluster-size test based on random field theory

Cluster-size tests (CSTs) based on random field theory (RFT) are commonly adopted to identify significant differences in brain images. However, the use of RFT in CSTs rests on the assumption of uniform smoothness (stationarity). When images are non-stationary, CSTs based on RFT will likely lead to increased false positives in smooth regions and reduced power in rough regions. An adjustment to the cluster size according to the local smoothness at each voxel has been proposed for the standard test based on RFT to address non-stationarity, however, this technique requires images with a large degree of spatial smoothing, large degrees of freedom and high intensity thresholding. Recently, we proposed a voxelation-corrected 3D CST based on Gaussian random field theory that does not place constraints on the degree of spatial smoothness. However, this approach is only applicable to stationary images, requiring further modification to enable use for non-stationary images. In this study, we present modifications of this method to develop a voxelation-corrected non-stationary 3D CST based on RFT. Both simulated and real data were used to compare the voxelation-corrected non-stationary CST to the standard cluster-size adjusted non-stationary CST based on RFT and the voxelation-corrected stationary CST. We found that voxelation-corrected stationary CST is liberal for non-stationary images and the voxelation-corrected non-stationary CST performs better than cluster-size adjusted non-stationary CST based on RFT under low smoothness, low intensity threshold and low degrees of freedom.

A high performance 3D cluster-based test of unsmoothed fMRI data

Cluster-size tests (CST) based on random field theory have been widely adopted in fMRI data analysis to detect brain activation. However, most existing approaches can be used appropriately only when the image is highly smoothed in the spatial domain. Unfortunately, spatial smoothing degrades spatial specificity. Recently, a threshold-free cluster enhancement technique was proposed which does not require spatial smoothing, but this method can be used only for group level analysis. Advances in imaging technology now yield high quality high spatial resolution imaging data in single subjects and an inference approach that retains the benefits of greater spatial resolution is called for. In this work, we present a new CST with a correction for voxelation to address this problem. The theoretical formulation of the new approach based on Gaussian random fields is developed to estimate statistical significance using 3D statistical parametric maps without assuming spatial smoothness. Simulated phantom and resting-state fMRI experimental data are then used to compare the voxelation-corrected procedure to the widely used standard random field theory. Unlike standard random field theory approaches, which require heavy spatial smoothing, the new approach has a higher sensitivity for localizing activation regions without the requirement of spatial smoothness.

Combining Multi-Site/Multi-Study MRI Data: Linked-ICA Denoising for Removing Scanner and Site Variability from Multimodal MRI Data

Large multi-site studies that pool magnetic resonance imaging (MRI) data across research sites or studies, or that utilize shared data from imaging repositories, present exceptional opportunities to advance neuroscience and enhance reproducibility of neuroimaging research. However, both scanner and site variability are confounds that hinder pooling data collected across different sites or across different operating systems on the same scanner, even when all acquisition protocols are harmonized. These confounds degrade statistical analyses and can lead to spurious findings. Unfortunately, methods to address this problem are scant. In this study, we propose a novel denoising approach for multi-site multimodal MRI data that implements a data-driven linked independent component analysis (LICA) to efficiently identify scanner/site-related effects for removal. Removing these effects results in denoised data that can then be combined across sites/studies to improve modality-specific statistical processing. We use data from six different studies collected on the same scanner across major hardware (gradient and head coil) and software upgrades to demonstrate our LICA-based denoising approach. The proposed method is superior compared to the existing methods we tested and has great potential for large-scale multi-site studies to produce combined data free from study/site confounds.

SU‐E‐T‐615: SmartArc‐Based Volumetric Modulated Arc Therapy for Endometrial Cancer: A Dosimetric Comparison with Helical Tomotherapy and Intensity‐Modulated Radiation Therapy

PURPOSEnTo investigate the feasibility of volumetric modulated arc therapy with Smart Arc (VMAT-S) for endometrial cancer to achieve equivalent plan quality with higher delivery efficiency, against intensity-modulated radiotherapy (IMRT) and helical tomotherapy (HT).nnnMETHODSnNine patients with endometrial cancer were retrospectively studied. Three plans were generated with VMAT-S, IMRT and HT for each patient. The dose distribution of planning target volume (PTV), organs at risk (OARs) and normal tissue were compared. The monitor units (MUs) and treatment delivery time were also evaluated.nnnRESULTSnThe average homogeneity index was 1.06, 1.10 and 1.07 for VMAT-S, IMRT and HT plans. The V40 of rectum, bladder and pelvis bone decreased 9.0%, 3.0%, and 3.0% in VMAT-S compared with IMRT, respectively. The target coverage and OARs sparing were comparable between VMAT-S and HT. The average MU was 823, 1105 and 8403 for VMAT-S, IMRT and HT. The average delivery time was 2.6 minutes, 8.6 minutes, and 9.5 minutes.nnnCONCLUSIONnVMAT-S provided comparable plan quality with significantly shorter delivery time and less MUs compared with IMRT and HT for endometrial cancer. In addition, more homogeneous PTV coverage and superior OARs sparing in the medium to high dose region were observed in VMAT-S over IMRT. Ruijie Yang was funded by the grant project: National Natural Science Foundation of China (No. 81071237). Junjie Wang, Shouping Xu and Hua Li have no competing interest.