Qun Zhao

YNIMG Journal 2021 Journal Article

Frequency drift in MR spectroscopy at 3T

• Steve C.N. Hui
• Mark Mikkelsen
• Helge J. Zöllner
• Vishwadeep Ahluwalia
• Sarael Alcauter
• Laima Baltusis
• Deborah A. Barany
• Laura R. Barlow

PURPOSE: field, especially when gradient intensive sequences are used. The aim of the study was to set a benchmark for typical drift encountered during MR spectroscopy (MRS) to assess the need for real-time field-frequency locking on MRI scanners by comparing field drift data from a large number of sites. METHOD: A standardized protocol was developed for 80 participating sites using 99 3T MR scanners from 3 major vendors. Phantom water signals were acquired before and after an EPI sequence. The protocol consisted of: minimal preparatory imaging; a short pre-fMRI PRESS; a ten-minute fMRI acquisition; and a long post-fMRI PRESS acquisition. Both pre- and post-fMRI PRESS were non-water suppressed. Real-time frequency stabilization/adjustment was switched off when appropriate. Sixty scanners repeated the protocol for a second dataset. In addition, a three-hour post-fMRI MRS acquisition was performed at one site to observe change of gradient temperature and drift rate. Spectral analysis was performed using MATLAB. Frequency drift in pre-fMRI PRESS data were compared with the first 5:20 minutes and the full 30:00 minutes of data after fMRI. Median (interquartile range) drifts were measured and showed in violin plot. Paired t-tests were performed to compare frequency drift pre- and post-fMRI. A simulated in vivo spectrum was generated using FID-A to visualize the effect of the observed frequency drifts. The simulated spectrum was convolved with the frequency trace for the most extreme cases. Impacts of frequency drifts on NAA and GABA were also simulated as a function of linear drift. Data from the repeated protocol were compared with the corresponding first dataset using Pearson's and intraclass correlation coefficients (ICC). RESULTS: Of the data collected from 99 scanners, 4 were excluded due to various reasons. Thus, data from 95 scanners were ultimately analyzed. For the first 5:20 min (64 transients), median (interquartile range) drift was 0.44 (1.29) Hz before fMRI and 0.83 (1.29) Hz after. This increased to 3.15 (4.02) Hz for the full 30 min (360 transients) run. Average drift rates were 0.29 Hz/min before fMRI and 0.43 Hz/min after. Paired t-tests indicated that drift increased after fMRI, as expected (p < 0.05). Simulated spectra convolved with the frequency drift showed that the intensity of the NAA singlet was reduced by up to 26%, 44 % and 18% for GE, Philips and Siemens scanners after fMRI, respectively. ICCs indicated good agreement between datasets acquired on separate days. The single site long acquisition showed drift rate was reduced to 0.03 Hz/min approximately three hours after fMRI. DISCUSSION: This study analyzed frequency drift data from 95 3T MRI scanners. Median levels of drift were relatively low (5-min average under 1 Hz), but the most extreme cases suffered from higher levels of drift. The extent of drift varied across scanners which both linear and nonlinear drifts were observed.

YNIMG Journal 2010 Journal Article

Gyral folding pattern analysis via surface profiling

• Kaiming Li
• Lei Guo
• Gang Li
• Jingxin Nie
• Carlos Faraco
• Guangbin Cui
• Qun Zhao
• L. Stephen Miller

Folding is an essential shape characteristic of the human cerebral cortex. Descriptors of cortical folding patterns have been studied for decades. However, many previous studies are either based on local shape descriptors such as curvature, or based on global descriptors such as gyrification index or spherical wavelets. This paper proposes a gyrus-scale folding pattern analysis technique via cortical surface profiling. Firstly, we sample the cortical surface into 2D profiles and model them using a power function. This step provides both the flexibility of representing arbitrary shape by profiling and the compactness of representing shape by parametric modeling. Secondly, based on the estimated model parameters, we extract affine-invariant features on the cortical surface, and apply the affinity propagation clustering algorithm to parcellate the cortex into cortical regions with strict hierarchy and smooth transitions among them. Finally, a second-round surface profiling is performed on the parcellated cortical surface, and the number of hinges is detected to describe the gyral folding pattern. We have applied the surface profiling method to two normal brain datasets and a schizophrenia patient dataset. The experimental results demonstrate that the proposed method can accurately classify human gyri into 2-hinge, 3-hinge and 4-hinge patterns. The distribution of these folding patterns on brain lobes and the relationship between fiber density and gyral folding patterns are further investigated. Results from the schizophrenia dataset are consistent with commonly found abnormality in former studies by others, which demonstrates the potential clinical applications of the proposed technique.

YNIMG Journal 2008 Journal Article

Discriminating brain activity from task-related artifacts in functional MRI: Fractal scaling analysis simulation and application

• Jae-Min Lee
• Jing Hu
• Jianbo Gao
• Bruce Crosson
• Kyung K. Peck
• Christina E. Wierenga
• Keith McGregor
• Qun Zhao

Functional magnetic resonance imaging (fMRI) signal changes can be separated from background noise by various processing algorithms, including the well-known deconvolution method. However, discriminating signal changes due to task-related brain activities from those due to task-related head motion or other artifacts correlated in time to the task has been little addressed. We examine whether three exploratory fractal scaling analyses correctly classify these possibilities by capturing temporal self-similarity; namely, fluctuation analysis, wavelet multi-resolution analysis, and detrended fluctuation analysis (DFA). We specifically evaluate whether these fractal analytic methods can be effective and reliable in discriminating activations from artifacts. DFA is indeed robust for such classification. Brain activation maps derived by DFA are similar, but not identical, to maps derived by deconvolution. Deconvolution explicitly utilizes task timing to extract the signals whereas DFA does not, so these methods reveal somewhat different information from the data. DFA is better than deconvolution for distinguishing fMRI activations from task-related artifacts, although a combination of these approaches is superior to either one taken alone. We also present a method for estimating noise levels in fMRI data, validated with numerical simulations suggesting that Birn's model is effective for simulating fMRI signals. Simulations further corroborate that DFA is excellent at discriminating signal changes due to task-related brain activities from those due to task-related artifacts, under a range of conditions.

YNIMG Journal 2000 Journal Article

fMRI analysis: Distribution divergence measure based on quadratic entropy

• Qun Zhao
• Jose Principe
• Margaret Bradley
• Peter Lang