Kejia Cai

CEST signal at 2ppm (CEST@2ppm) from Z-spectral fitting correlates with creatine distribution in brain tumor.

In general, multiple components such as water direct saturation, magnetization transfer (MT), chemical exchange saturation transfer (CEST) and aliphatic nuclear Overhauser effect (NOE) contribute to the Z‐spectrum. The conventional CEST quantification method based on asymmetrical analysis may lead to quantification errors due to the semi‐solid MT asymmetry and the aliphatic NOE located on a single side of the Z‐spectrum. Fitting individual contributors to the Z‐spectrum may improve the quantification of each component. In this study, we aim to characterize the multiple exchangeable components from an intracranial tumor model using a simplified Z‐spectral fitting method. In this method, the Z‐spectrum acquired at low saturation RF amplitude (50u2009Hz) was modeled as the summation of five Lorentzian functions that correspond to NOE, MT effect, bulk water, amide proton transfer (APT) effect and a CEST peak located at +2u2009ppm, called CEST@2ppm. With the pixel‐wise fitting, the regional variations of these five components in the brain tumor and the normal brain tissue were quantified and summarized. Increased APT effect, decreased NOE and reduced CEST@2ppm were observed in the brain tumor compared with the normal brain tissue. Additionally, CEST@2ppm decreased with tumor progression. CEST@2ppm was found to correlate with the creatine concentration quantified with proton MRS. Based on the correlation curve, the creatine contribution to CEST@2ppm was quantified. The CEST@2ppm signal could be a novel imaging surrogate for in vivo creatine, the important bioenergetics marker. Given its noninvasive nature, this CEST MRI method may have broad applications in cancer bioenergetics. Copyright

Creatine CEST MRI for Differentiating Gliomas with Different Degrees of Aggressiveness

PurposeCreatine (Cr) is a major metabolite in the bioenergetic system. Measurement of Cr using conventional MR spectroscopy (MRS) suffers from low spatial resolution and relatively long acquisition times. Creatine chemical exchange saturation transfer (CrCEST) magnetic resonance imaging (MRI) is an emerging molecular imaging method for tissue Cr measurements. Our previous study showed that the CrCEST contrast, obtained through multicomponent Z-spectral fitting, was lower in tumors compared to normal brain, which further reduced with tumor progression. The current study was aimed to investigate if CrCEST MRI can also be useful for differentiating gliomas with different degrees of aggressiveness.ProceduresIntracranial 9L gliosarcoma and F98 glioma bearing rats with matched tumor size were scanned with a 9.4xa0T MRI scanner at two time points. CEST Z-spectra were collected using a customized sequence with a frequency-selective rectangular saturation pulse (B1u2009=u200950xa0Hz, durationu2009=u20093xa0s) followed by a single-shot readout. Z spectral data were fitted pixel-wise with five Lorentzian functions, and maps of CrCEST peak amplitude, linewidth, and integral were produced. For comparison, single-voxel proton MR spectroscopy (1H-MRS) was performed to quantify and compare the total Cr concentration in the tumor.ResultsCrCEST contrasts decreased with tumor progression from weeks 3 to 4 in both 9L and F98 phenotypes. More importantly, F98 tumors had significantly lower CrCEST integral compared to 9L tumors. On the other hand, integrals of other Z-spectral components were unable to differentiate both tumor progression and phenotype with limited sample size.ConclusionsGiven that F98 is a more aggressive tumor than 9L, this study suggests that CrCEST MRI may help differentiate gliomas with different aggressiveness.

The feasibility of quantitative MRI of perivascular spaces at 7T

BACKGROUNDnDilated brain perivascular spaces (PVSs) are found to be associated with many conditions, including aging, dementia, and Alzheimers disease (AD). Conventionally, PVS assessment is mainly based on subjective observations of the number, size and shape of PVSs in MR images collected at clinical field strengths (≤3T). This study tests the feasibility of imaging and quantifying brain PVS with an ultra-high 7T whole-body MRI scanner.nnnNEW METHODn3D high resolution T2-weighted brain images from healthy subjects (n=3) and AD patients (n=5) were acquired on a 7T whole-body MRI scanner. To automatically segment the small hyperintensive fluid-filling PVS structures, we also developed a quantitative program based on algorithms for spatial gradient, component connectivity, edge-detection, k-means clustering, etc., producing quantitative results of white matter PVS volume densities.nnnRESULTSnThe 3D maps of automatically segmented PVS show an apparent increase in PVS density in AD patients compared to age-matched healthy controls due to the PVS dilation (8.0±2.1 v/v% in AD vs. 4.9±1.3 v/v% in controls, p<0.05).nnnCOMPARISON WITH EXISTING METHODnWe demonstrated that 7T provides sufficient SNR and resolution for quantitatively measuring PVSs in deep white matter that is challenging with clinical MRI systems (≤3T). Compared to the conventional visual counting and rating for the PVS assessment, the quantitation method we developed is automatic and objective.nnnCONCLUSIONSnQuantitative PVS MRI at 7T may serve as a non-invasive and endogenous imaging biomarker for diseases with PVS dilation.

A Diffusion Tensor Imaging Study on White Matter Abnormalities in Patients with Type 2 Diabetes Using Tract-Based Spatial Statistics

BACKGROUND AND PURPOSE: Patients with type 2 diabetes mellitus have considerably higher risk of developing cognitive impairment and dementia. WM changes in these patients have been reported. Our aim was to demonstrate that gradual and continuous WM change and the associated cognitive decline in patients with type 2 diabetes mellitus can be captured by DTI parameters, which can be used to complement neuropsychological test scores in identifying patients with type 2 diabetes mellitus with and without mild cognitive impairment. MATERIALS AND METHODS: Forty-two patients with type 2 diabetes mellitus, divided into a group with mild cognitive impairment (n = 20) and a group with normal cognition (n = 22), were enrolled with age-, sex-, and education-matched healthy controls (n = 26). 3T DTI followed by Tract-Based Spatial Statistics analysis was used to investigate the differences in fractional anisotropy, mean diffusivity, axial diffusivity (λ1), and radial diffusivity (λ23) among the groups. A receiver operating characteristic analysis assessed the performance of DTI parameters for separating the 2 groups with type 2 diabetes mellitus. RESULTS: The whole-brain Tract-Based Spatial Statistics analysis revealed that 7.3% and 24.9% of the WM exhibited decreased fractional anisotropy and increased mean diffusivity (P < .05), respectively, between the diabetes mellitus with mild cognitive impairment and the diabetes mellitus with normal cognition groups, while considerably larger WM regions showed fractional anisotropy (36.6%) and mean diffusivity (58.8%) changes between the diabetes mellitus with mild cognitive impairment and the healthy control groups. These changes were caused primarily by an elevated radial diffusivity observed in the patients with diabetes mellitus with mild cognitive impairment. Radial diffusivity also exhibited subtle but statistically significant changes between the diabetes mellitus with normal cognition and the healthy control groups. Analyses on individual fiber tracts showed pronounced fractional anisotropy reduction and mean diffusivity elevation in regions related to cognitive functions. The receiver operating characteristic analysis on the right cingulum (hippocampus) showed that fractional anisotropy produced a larger area under the curve (0.832) than mean diffusivity (0.753) for separating mild cognitive impairment from normal cognition among patients with type 2 diabetes mellitus. When fractional anisotropy was combined with mean diffusivity, the area under the curve was further improved to 0.857. CONCLUSIONS: DTI parameters can show a substantial difference between patients with type 2 diabetes mellitus with and without mild cognitive impairment, suggesting their potential use as an imaging marker for detecting cognitive decline in patients with type 2 diabetes mellitus. More important, DTI parameters may capture gradual and continuous WM changes that can be associated with early stages of cognitive decline in patients with type 2 diabetes mellitus before they can be diagnosed clinically by using conventional neuropsychological tests.

Imaging short-lived reactive oxygen species (ROS) with endogenous contrast MRI

To characterize the relaxation properties of reactive oxygen species (ROS) for the development of endogenous ROS contrast magnetic resonance imaging (MRI).

Brain white matter changes in CPAP-treated obstructive sleep apnea patients with residual sleepiness

To investigate white matter (WM) structural alterations using diffusion tensor imaging (DTI) in obstructive sleep apnea (OSA) patients, with or without residual sleepiness, following adherent continuous positive airway pressure (CPAP) treatment. Possible quantitative relationships were explored between the DTI metrics and two clinical assessments of somnolence.

Improved Differentiation of Low-Grade and High-Grade Gliomas and Detection of Tumor Proliferation Using APT Contrast Fitted from Z-Spectrum

PurposeThe purpose of the study is to demonstrate the value of quantitative amide proton transfer (APT) imaging for differentiating glioma grades and detecting tumor proliferation.ProceduresThis study included 32 subjects with 16 low-grade gliomas (LGG) and 16 high-grade gliomas (HGG) confirmed by histopathology. Chemical exchange saturation transfer (CEST) magnetic resonance imaging with APT weighting was performed on a 3xa0T scanner. After B0 correction, Z-spectra were fitted with Lorentzian functions corresponding to the upfield semi-solid magnetization transfer and nuclear overhauser enhancement (MT&NOE) effect, the direct saturation (DS) effect, and the downfield APT effect centered at around −u20091.5, 0, and +u20093.5xa0ppm, respectively. To compute the Z-spectral fitted APT (fitted_APT) in solid tumor tissue, double-peak histogram fitting of pixel MT&NOE effect from the whole tumor was used to remove necrosis regions. The fitted APT was then compared with the conventional APT based on magnetization transfer ratio asymmetry. Receiver operating characteristic (ROC) analysis was used to compare the performance between Z-spectral fitted contrasts and the con_APT for LGG versus HGG differentiation. Additionally, the correlations between the imaging contrasts (fitted_APT, con_APT, and fitted_MT&NOE) and Ki-67 labeling index for tumor proliferation were also evaluated.ResultsZ-spectral fitted_APT shows improved statistical power for differentiating HGG and LGG (7.58u2009±u20090.99 vs. 6.79u2009±u20091.05xa0%, pu2009<u20090.05) than con_APT (4.34u2009±u20090.95 vs. 4.05u2009±u20092.02xa0%, pu2009>u20090.05) in solid tumor tissues. Analyses of whole tumor, on the other hand, have less differentiating power for both fitted_APT (p from 0.032 to 0.08) and con_APT (p from 0.696 to 0.809). Similarly, based on ROC analyses, fitted_APT shows larger area under the curve (AUCu2009=u20090.723) than con_APT (AUCu2009=u20090.543). The combination of fitted APT, DS, and MT&NOE further improved the specificity (75xa0%), diagnostic accuracy (78.2xa0%), and area under the curve (0.758) in differentiating LGG and HGG. Consistently, fitted_APT (ru2009=u20090.451, pu2009=u20090.018) is better correlated with Ki-67 than con_APT (ru2009=u20090.331, pu2009=u20090.092).ConclusionsFitted APT from Z-spectrum improves differentiation of low- and high-grade gliomas and better correlated with tumor proliferation than conventional APT.

Discrimination of Malignant versus Benign Mediastinal Lymph Nodes Using Diffusion MRI with an IVIM Model

AbstractObjectivesTo investigate the value of an intravoxel incoherent motion (IVIM) diffusion model for discriminating malignant versus benign mediastinal lymph nodes (MLN).MethodsThirty-five subjects with enlarged MLN were scanned at 1.5 Tesla. Diffusion-weighted imaging was performed with eight b-values. IVIM parameters D, D*, and f, as well as apparent diffusion coefficient (ADC) from a mono-exponential model were obtained. 91 nodes (49 malignant and 42 benign) were analysed with pathologic (n=90) or radiologic (n=1) confirmations. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance.ResultsThe mean values of D, ADC, and f for the malignant group were significantly lower than those for the benign group (p<0.001), while D* showed no significant difference (p=0.281). In the ROC analysis, the combination of D and f produced the largest area under the curve (0.953) compared to ADC or other individual IVIM parameters, leading to the best specificity (92.9%) and diagnostic accuracy (90.1%).ConclusionThis study demonstrates that the combination of IVIM parameters can improve differentiation between malignant and benign MLN as compared to using ADC alone.Key Points• Diffusion MRI is useful for non-invasively discriminating malignant versus benign lymph nodes.n • A mono-exponential model is not adequate to characterise diffusion process in lymph nodes.n • IVIM model is advantageous over mono-exponential model for assessing lymph node malignancy.n • Combination of IVIM parameters improves differentiation of malignant versus benign lymph nodes.

Mapping brown adipose tissue based on fat water fraction provided by Z-spectral imaging: Mapping BAT Using Fat Water Fraction and Z-Spectral Imaging

Brown adipose tissue (BAT) has a great relevance in metabolic diseases and has been shown to be reduced in obesity and insulin resistance patients. Currently, Dixon MRI is used to calculate fat‐water fraction (FWF) and differentiate BAT from white adipose tissue (WAT). However, it may fail in areas of phase wrapping and introduce fat‐water swapping artifacts.

B1− non-uniformity correction of phased-array coils without measuring coil sensitivity

Parallel imaging can be used to increase SNR and shorten acquisition times, albeit, at the cost of image non-uniformity. B1- non-uniformity correction techniques are confounded by signal that varies not only due to coil induced B1- sensitivity variation, but also the objects own intrinsic signal. Herein, we propose a method that makes minimal assumptions and uses only the coil images themselves to produce a single combined B1- non-uniformity-corrected complex image with the highest available SNR. A novel background noise classifier is used to select voxels of sufficient quality to avoid the need for regularization. Unique properties of the magnitude and phase were used to reduce the B1- sensitivity to two joint additive models for estimation of the B1- inhomogeneity. The complementary corruption of the imaged object across the coil images is used to abate individual coil correction imperfections. Results are presented from two anatomical cases: (a) an abdominal image that is challenging in both extreme B1- sensitivity and intrinsic tissue signal variation, and (b) a brain image with moderate B1- sensitivity and intrinsic tissue signal variation. A new relative Signal-to-Noise Ratio (rSNR) quality metric is proposed to evaluate the performance of the proposed method and the RF receiving coil array. The proposed method has been shown to be robust to imaged objects with widely inhomogeneous intrinsic signal, and resilient to poorly performing coil elements.