Learned spatiotemporal correlation priors for CEST image denoising using incorporated global-spectral convolution neural network.

PURPOSE: To develop a deep learning-based method, dubbed Denoising CEST Network (DECENT), to fully exploit the spatiotemporal correlation prior to CEST image denoising. METHODS: DECENT is composed of two parallel pathways with different convolution kernel sizes aiming to extract the global and spectral features embedded in CEST images. Each pathway consists of a modified U-Net with residual Encoder-Decoder network and 3D convolution. Fusion pathway with 1 × 1 × 1 convolution kernel is utilized to concatenate two parallel pathways, and the output of DECENT is noise-reduced CEST images. The performance of DECENT was validated in numerical simulations, egg white phantom experiments, and ischemic mouse brain and human skeletal muscle experiments in comparison with existing state-of-the-art denoising methods. RESULTS: Rician noise was added to CEST images to mimic a low SNR situation for numerical simulation, egg white phantom experiment, and mouse brain experiments, while human skeletal muscle experiments were of inherently low SNR. From the denoising results evaluated by peak SNR (PSNR) and structural similarity index (SSIM), the proposed deep learning-based denoising method (DECENT) can achieve better performance compared to existing CEST denoising methods such as NLmCED, MLSVD, and BM4D, avoiding complicated parameter tuning or time-consuming iterative processes. CONCLUSIONS: DECENT can well exploit the prior spatiotemporal correlation knowledge of CEST images and restore the noise-free images from their noisy observations, outperforming state-of-the-art denoising methods.

Rapid high-fidelity T 2 * mapping using single-shot overlapping-echo acquisition and deep learning reconstruction.

PURPOSE: To develop and evaluate a single-shot quantitative MRI technique called GRE-MOLED (gradient-echo multiple overlapping-echo detachment) for rapid T 2 * $$ {T}_2^{\ast } $$ mapping. METHODS: In GRE-MOLED, multiple echoes with different TEs are generated and captured in a single shot of the k-space through MOLED encoding and EPI readout. A deep neural network, trained by synthetic data, was employed for end-to-end parametric mapping from overlapping-echo signals. GRE-MOLED uses pure GRE acquisition with a single echo train to deliver T 2 * $$ {T}_2^{\ast } $$ maps less than 90 ms per slice. The self-registered B0 information modulated in image phase was utilized for distortion-corrected parametric mapping. The proposed method was evaluated in phantoms, healthy volunteers, and task-based FMRI experiments. RESULTS: The quantitative results of GRE-MOLED T 2 * $$ {T}_2^{\ast } $$ mapping demonstrated good agreement with those obtained from the multi-echo GRE method (Pearson's correlation coefficient = 0.991 and 0.973 for phantom and in vivo brains, respectively). High intrasubject repeatability (coefficient of variation <1.0%) were also achieved in scan-rescan test. Enabled by deep learning reconstruction, GRE-MOLED showed excellent robustness to geometric distortion, noise, and random subject motion. Compared to the conventional FMRI approach, GRE-MOLED also achieved a higher temporal SNR and BOLD sensitivity in task-based FMRI. CONCLUSION: GRE-MOLED is a new real-time technique for T 2 * $$ {T}_2^{\ast } $$ quantification with high efficiency and quality, and it has the potential to be a better quantitative BOLD detection method.

IMPULSED model based cytological feature estimation with U-Net: Application to human brain tumor at 3T.

PURPOSE: This work introduces and validates a deep-learning-based fitting method, which can rapidly provide accurate and robust estimation of cytological features of brain tumor based on the IMPULSED (imaging microstructural parameters using limited spectrally edited diffusion) model fitting with diffusion-weighted MRI data. METHODS: The U-Net was applied to rapidly quantify extracellular diffusion coefficient (Dex ), cell size (d), and intracellular volume fraction (vin ) of brain tumor. At the training stage, the image-based training data, synthesized by randomizing quantifiable microstructural parameters within specific ranges, was used to train U-Net. At the test stage, the pre-trained U-Net was applied to estimate the microstructural parameters from simulated data and the in vivo data acquired on patients at 3T. The U-Net was compared with conventional non-linear least-squares (NLLS) fitting in simulations in terms of estimation accuracy and precision. RESULTS: Our results confirm that the proposed method yields better fidelity in simulations and is more robust to noise than the NLLS fitting. For in vivo data, the U-Net yields obvious quality improvement in parameter maps, and the estimations of all parameters are in good agreement with the NLLS fitting. Moreover, our method is several orders of magnitude faster than the NLLS fitting (from about 5 min to <1 s). CONCLUSION: The image-based training scheme proposed herein helps to improve the quality of the estimated parameters. Our deep-learning-based fitting method can estimate the cell microstructural parameters fast and accurately.

Preoperative Subtyping of WHO Grade 1 Meningiomas Using a Single-Shot Ultrafast MR T2 Mapping.

BACKGROUND: Meningioma subtype is crucial in treatment planning and prognosis delineation, for grade 1 meningiomas. T2 relaxometry could provide detailed microscopic information but is often limited by long scanning times. PURPOSE: To investigate the potential of T2 maps derived from multiple overlapping-echo detachment imaging (MOLED) for predicting meningioma subtypes and Ki-67 index, and to compare the diagnostic efficiency of two different region-of-interest (ROI) placements (whole-tumor and contrast-enhanced, respectively). STUDY TYPE: Prospective. PHANTOM/SUBJECTS: A phantom containing 11 tubes of MnCl2 at different concentrations, eight healthy volunteers, and 75 patients with grade 1 meningioma. FIELD STRENGTH/SEQUENCE: 3 T scanner. MOLED, T2-weighted spin-echo sequence, T2-dark-fluid sequence, and postcontrast T1-weighted gradient echo sequence. ASSESSMENT: Two ROIs were delineated: the whole-tumor area (ROI1) and contrast-enhanced area (ROI2). Histogram parameters were extracted from T2 maps. Meningioma subtypes and Ki-67 index were reviewed by a neuropathologist according to the 2021 classification criteria. STATISTICAL TESTS: Linear regression, Bland-Altman analysis, Pearson's correlation analysis, independent t test, Mann-Whitney U test, Kruskal-Wallis test with Bonferroni correction, and multivariate logistic regression analysis with the P-value significance level of 0.05. RESULTS: The MOLED T2 sequence demonstrated excellent accuracy for phantoms and volunteers (Meandiff = -1.29%, SDdiff = 1.25% and Meandiff = 0.36%, SDdiff = 2.70%, respectively), and good repeatability for volunteers (average coefficient of variance = 1.13%; intraclass correlation coefficient = 0.877). For both ROI1 and ROI2, T2 variance had the highest area under the curves (area under the ROC curve = 0.768 and 0.761, respectively) for meningioma subtyping. There was no significant difference between the two ROIs (P = 0.875). Significant correlations were observed between T2 parameters and Ki-67 index (r = 0.237-0.374). DATA CONCLUSION: MOLED T2 maps can effectively differentiate between meningothelial, fibrous, and transitional meningiomas. Moreover, T2 histogram parameters were significantly correlated with the Ki-67 index. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 2.

Free-breathing and instantaneous abdominal T2 mapping via single-shot multiple overlapping-echo acquisition and deep learning reconstruction.

OBJECTIVES: To develop a real-time abdominal T2 mapping method without requiring breath-holding or respiratory-gating. METHODS: The single-shot multiple overlapping-echo detachment (MOLED) pulse sequence was employed to achieve free-breathing T2 mapping of the abdomen. Deep learning was used to untangle the non-linear relationship between the MOLED signal and T2 mapping. A synthetic data generation flow based on Bloch simulation, modality synthesis, and randomization was proposed to overcome the inadequacy of real-world training set. RESULTS: The results from simulation and in vivo experiments demonstrated that our method could deliver high-quality T2 mapping. The average NMSE and R2 values of linear regression in the digital phantom experiments were 0.0178 and 0.9751. Pearson's correlation coefficient between our predicted T2 and reference T2 in the phantom experiments was 0.9996. In the measurements for the patients, real-time capture of the T2 value changes of various abdominal organs before and after contrast agent injection was realized. A total of 33 focal liver lesions were detected in the group, and the mean and standard deviation of T2 values were 141.1 ± 50.0 ms for benign and 63.3 ± 16.0 ms for malignant lesions. The coefficients of variance in a test-retest experiment were 2.9%, 1.2%, 0.9%, 3.1%, and 1.8% for the liver, kidney, gallbladder, spleen, and skeletal muscle, respectively. CONCLUSIONS: Free-breathing abdominal T2 mapping is achieved in about 100 ms on a clinical MRI scanner. The work paved the way for the development of real-time dynamic T2 mapping in the abdomen. KEY POINTS: • MOLED achieves free-breathing abdominal T2 mapping in about 100 ms, enabling real-time capture of T2 value changes due to CA injection in abdominal organs. • Synthetic data generation flow mitigates the issue of lack of sizable abdominal training datasets.

Single-shot multi-parametric mapping based on multiple overlapping-echo detachment (MOLED) imaging.

Multi-parametric quantitative magnetic resonance imaging (mqMRI) allows the characterization of multiple tissue properties non-invasively and has shown great potential to enhance the sensitivity of MRI measurements. However, real-time mqMRI during dynamic physiological processes or general motions remains challenging. To overcome this bottleneck, we propose a novel mqMRI technique based on multiple overlapping-echo detachment (MOLED) imaging, termed MQMOLED, to enable mqMRI in a single shot. In the data acquisition of MQMOLED, multiple MR echo signals with different multi-parametric weightings and phase modulations are generated and acquired in the same k-space. The k-space data is Fourier transformed and fed into a well-trained neural network for the reconstruction of multi-parametric maps. We demonstrated the accuracy and repeatability of MQMOLED in simultaneous mapping apparent proton density (APD) and any two parameters among T2, T2*, and apparent diffusion coefficient (ADC) in 130-170 ms. The abundant information delivered by the multiple overlapping-echo signals in MQMOLED makes the technique potentially robust to system imperfections, such as inhomogeneity of static magnetic field or radiofrequency field. Benefitting from the single-shot feature, MQMOLED exhibits a strong motion tolerance to the continuous movements of subjects. For the first time, it captured the synchronous changes of ADC, T2, and T1-weighted APD in contrast-enhanced perfusion imaging on patients with brain tumors, providing additional information about vascular density to the hemodynamic parametric maps. We expect that MQMOLED would promote the development of mqMRI technology and greatly benefit the applications of mqMRI, including therapeutics and analysis of metabolic/functional processes.

A simultaneous multi-slice T2 mapping framework based on overlapping-echo detachment planar imaging and deep learning reconstruction.

PURPOSE: Quantitative MRI (qMRI) is of great importance to clinical medicine and scientific research. However, most qMRI techniques are time-consuming and sensitive to motion, especially when a large 3D volume is imaged. To accelerate the acquisition, a framework is proposed to realize reliable simultaneous multi-slice T2 mapping. METHODS: The simultaneous multi-slice T2 mapping framework is based on overlapping-echo detachment (OLED) planar imaging (dubbed SMS-OLED). Multi-slice overlapping-echo signals were generated by multiple excitation pulses together with echo-shifting gradients. The signals were excited and acquired with a single-channel coil. U-Net was used to reconstruct T2 maps from the acquired overlapping-echo image. RESULTS: Single-shot double-slice and two-shot triple-slice SMS-OLED scan schemes were designed according to the framework for evaluation. Simulations, water phantom, and in vivo rat brain experiments were carried out. Overlapping-echo signals were acquired, and T2 maps were reconstructed and compared with references. The results demonstrate the superior performance of our method. CONCLUSION: Two slices of T2 maps can be obtained in a single shot within hundreds of milliseconds. Higher quality multi-slice T2 maps can be obtained via multiple shots. SMS-OLED provides a lower specific absorption rate scheme compared with conventional SMS methods with a coil with only a single receiver channel. The new method is of potential in dynamic qMRI and functional qMRI where temporal resolution is vital.

Ultrafast water-fat separation using deep learning-based single-shot MRI.

PURPOSE: To present a deep learning-based reconstruction method for spatiotemporally encoded single-shot MRI to simultaneously obtain water and fat images. METHODS: Spatiotemporally encoded MRI is an ultrafast branch that can encode chemical shift information due to its special quadratic phase modulation. A deep learning approach using a 2D U-Net was proposed to reconstruct spatiotemporally encoded signal and obtain water and fat images simultaneously. The training data for U-Net were generated by MRiLab software (version 1.3) with various synthetic models. Numerical simulations and experiments on ex vivo pork and in vivo rats at a 7.0 T Varian MRI system (Agilent Technologies, Santa Clara, CA) were performed, and the deep learning results were compared with those obtained by state-of-the-art algorithms. The structural similarity index and signal-to-ghost ratio were used to evaluate the residual artifact of different reconstruction methods. RESULTS: With a well-trained neural network, the proposed deep learning approach can accomplish signal reconstruction within 0.46 s on a personal computer, which is comparable with the conjugate gradient method (0.41 s) and much faster than the state-of-the-art super-resolved water-fat image reconstruction method (30.31 s). The results of numerical simulations, ex vivo pork experiments, and in vivo rat experiments demonstrate that the deep learning approach can achieve better fidelity and higher spatial resolution compared to the other 2 methods. The deep learning approach also has a great advantage in artifact suppression, as indicated by the signal-to-ghost ratio results. CONCLUSION: Spatiotemporally encoded MRI with deep learning can provide ultrafast water-fat separation with better performance compared to the state-of-the-art methods.

8DEstablishment and validation of a hypoxia-related signature predicting prognosis in hepatocellular carcinoma.

BACKGROUND: Hypoxia plays a crucial role in immunotherapy of hepatocellular carcinoma (HCC) by changing the tumor microenvironment. Until now the association between hypoxia genes and prognosis of HCC remains obscure. We attempt to construct a hypoxia model to predict the prognosis in HCC. RESULTS: We screened out 3 hypoxia genes (ENO1, UGP2, TPI1) to make the model, which can predict prognosis in HCC. And this model emerges as an independent prognostic factor for HCC. A Nomogram was drawn to evaluate the overall survival in a more accurate way. Furthermore, immune infiltration state and immunosuppressive microenvironment of the tumor were detected in high-risk patients. CONCLUSION: We establish and validate a risk prognostic model developed by 3 hypoxia genes, which could effectively evaluate the prognosis of HCC patients. This prognostic model can be used as a guidance for hypoxia modification in HCC patients undergoing immunotherapy.

Fast chemical exchange saturation transfer imaging based on PROPELLER acquisition and deep neural network reconstruction.

PURPOSE: To develop a method for fast chemical exchange saturation transfer (CEST) imaging. METHODS: The periodically rotated overlapping parallel lines enhanced reconstruction (PROPELLER) sampling scheme was introduced to shorten the acquisition time. Deep neural network was employed to reconstruct CEST contrast images. Numerical simulation and experiments on a creatine phantom, hen egg, and in vivo tumor rat brain were performed to test the feasibility of this method. RESULTS: The results from numerical simulation and experiments show that there is no significant difference between reference images and CEST-PROPELLER reconstructed images under an acceleration factor of 8. CONCLUSION: Although the deep neural network is trained entirely on synthesized data, it works well on reconstructing experimental data. The proof of concept study demonstrates that the combination of the PROPELLER sampling scheme and the deep neural network enables considerable acceleration of saturated image acquisition and may find applications in CEST MRI.

A genomic-clinicopathologic nomogram for predicting overall survival of hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is a common digestive tumor with great heterogeneity and different overall survival (OS) time, causing stern problems for selecting optimal treatment. Here we aim to establish a nomogram to predict the OS in HCC patients. METHODS: International Cancer Genome Consortium (ICGC) database was searched for the target information in our study. Lasso regression, univariate and multivariate cox analysis were applied during the analysis process. And a nomogram integrating model scoring and clinical characteristic was drawn. RESULTS: Six mRNAs were screened out by Lasso regression to make a model for predicting the OS of HCC patients. And this model was proved to be an independent prognostic model predicting OS in HCC patients. The area under the ROC curve (AUC) of this model was 0.803. TCGA database validated the significant value of this 6-mRNA model. Eventually a nomogram including 6-mRNA risk score, gender, age, tumor stage and prior malignancy was set up to predict the OS in HCC patients. CONCLUSIONS: We established an independent prognostic model of predicting OS for 1-3 years in HCC patients, which is available to all populations. And we developed a nomogram on the basis of this model, which could be of great help to precisely individual treatment measures.

Fast quantitative susceptibility reconstruction via total field inversion with improved weighted L0 norm approximation.

Quantitative susceptibility mapping (QSM) is a meaningful MRI technique owing to its unique relation to actual physical tissue magnetic properties. The reconstruction of QSM is usually decomposed into three sub-problems, which are solved independently. However, this decomposition does not conform to the causes of the problems, and may cause discontinuity of parameters and error accumulation. In this paper, a fast reconstruction method named fast TFI based on total field inversion was proposed. It can accelerate the total field inversion by using a specially selected preconditioner and advanced solution of the weighted L0 regularization. Due to the employment of an effective model, the proposed method can efficiently reconstruct the QSM of brains with lesions, where other methods may encounter problems. Experimental results from simulation and in vivo data verified that the new method has better reconstruction accuracy, faster convergence ability and excellent robustness, which may promote clinical application of QSM.

Motion-tolerant diffusion mapping based on single-shot overlapping-echo detachment (OLED) planar imaging.

PURPOSE: A new diffusion-mapping method based on single-shot overlapping-echo detachment (DM-OLED) planar-imaging sequence, along with a corresponding separation algorithm, is proposed to achieve reliable quantitative diffusion mapping in a single shot. The method can resist the effects of motion and help in detecting the quick variation of diffusion under different physiological status. METHODS: The echo-planar imaging method is combined with two excitation pulses with small flip angle to gain overlapping-echo signal in a single shot. Then the overlapping signals are separated by a separation algorithm and used for diffusion computation. Numerical simulation, phantom, and in vivo rat experiments were performed to verify the efficiency, accuracy, and motion tolerance of DM-OLED. RESULTS: The DM-OLED sequence could obtain reliable diffusion maps within milliseconds in numerical simulation, phantom, and in vivo experiments. Compared with conventional diffusion mapping with spin-echo echo-planar imaging, DM-OLED has higher time resolution and fewer motion-incurred errors in the apparent diffusion coefficient maps. CONCLUSIONS: As a reliable fast diffusion measurement tool, DM-OLED shows promise for real-time dynamic diffusion mapping and functional magnetic resonance imaging. Magn Reson Med 80:200-210, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Single-shot T2 mapping using overlapping-echo detachment planar imaging and a deep convolutional neural network.

PURPOSE: An end-to-end deep convolutional neural network (CNN) based on deep residual network (ResNet) was proposed to efficiently reconstruct reliable T2 mapping from single-shot overlapping-echo detachment (OLED) planar imaging. METHODS: The training dataset was obtained from simulations that were carried out on SPROM (Simulation with PRoduct Operator Matrix) software developed by our group. The relationship between the original OLED image containing two echo signals and the corresponding T2 mapping was learned by ResNet training. After the ResNet was trained, it was applied to reconstruct the T2 mapping from simulation and in vivo human brain data. RESULTS: Although the ResNet was trained entirely on simulated data, the trained network was generalized well to real human brain data. The results from simulation and in vivo human brain experiments show that the proposed method significantly outperforms the echo-detachment-based method. Reliable T2 mapping with higher accuracy is achieved within 30 ms after the network has been trained, while the echo-detachment-based OLED reconstruction method took approximately 2 min. CONCLUSION: The proposed method will facilitate real-time dynamic and quantitative MR imaging via OLED sequence, and deep convolutional neural network has the potential to reconstruct maps from complex MRI sequences efficiently.

Changes in brain iron concentration after exposure to high-altitude hypoxia measured by quantitative susceptibility mapping.

Hypoxia can induce physiological changes. This study aims to explore effects of high-altitude (HA) hypoxia on cerebral iron concentration. Twenty-nine healthy sea-level participants were tested shortly before and after approximately 4-week adaptation to the HA environment at fQinghai-Tibet Plateau (4200m), and were re-investigated after re-adaptation to the sea-level environment one year later. Iron concentration was quantified with quantitative susceptibility mapping (QSM), and the results were compared with transverse relaxation rate (R*2) measurements. The variations of magnetic susceptibility indicate that the iron concentration in gray matter regions, especially in basal ganglia, including caudate nucleus, putamen, globus pallidus and substantia nigra, increases significantly after HA exposure. This increase appears consistent with the conclusion from R*2 value variations. However, unlike QSM, the R*2 value fails to demonstrate the statistical difference of iron content in red nucleus. The re-investigation results show that most variations are recovered after sea-level re-adaptation for one year. Additionally, hemisphere- and gender-related differences in iron concentration changes were analyzed among cerebral regions. The results show greater possibilities in the right hemisphere and females. Further studies based on diffusion tensor imaging (DTI) suggest that the fractional anisotropy increases and the mean diffusivity decreases after HA exposure in six deep gray matter nuclei, with linear dependence on iron concentration only in putamen. In conclusion, the magnetic susceptibility value can serve as a quantitative marker of brain iron, and variations of regional susceptibility reported herein indicate that HA hypoxia can result in significant iron deposition in most deep gray matter regions. Additionally, the linear dependence of DTI metrics on iron concentration in putamen indicates a potential relationship between ferritin and water diffusion.

A fast chemical exchange saturation transfer imaging scheme based on single-shot spatiotemporal encoding.

PURPOSE: To design a new approach that can not only keep the spatial and temporal resolution but also have better built-in immunity to magnetic field inhomogeneity and chemical shift effects than the single-shot echo planar imaging (EPI) for chemical exchange saturation transfer (CEST) MRI. METHOD: The single-shot spatiotemporally encoded (SPEN) MRI sequence was combined with a continuous wave saturation pulse for fast CEST MRI (CEST-SPEN MRI). The resulting images were super-resolved reconstructed by a hybrid method that solves the l1 norm minimization together with total variation (TV) regularization. Partial Lorentzian fitting was used to analyze the subsequent Z-spectra. RESULTS: Experimental results of a creatine phantom and in vivo tumor rat brains show that CEST-SPEN MRI has good capability in providing CEST-based and NOE-based contrast images. CONCLUSIONS: Compared with CEST-EPI, CEST-SPEN MRI has better immunity to magnetic field inhomogeneity and provides better contrast images within identical acquisition time, especially under an identical inhomogeneous field. Magn Reson Med 77:1786-1796, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Imaging with referenceless distortion correction and flexible regions of interest using single-shot biaxial spatiotemporally encoded MRI.

Owing to its intrinsic characteristics, spatiotemporally encoded (SPEN) imaging is less sensitive to adverse effects due to field inhomogeneity in comparison with echo planar imaging, a feature highly desired for functional, diffusion, and real-time MRI. However, the quality of images obtained with SPEN MRI is still degraded by geometric distortions when field inhomogeneity exists. In this study, a single-shot biaxial SPEN (bi-SPEN) pulse sequence is implemented, utilizing a 90° and a 180° chirp pulse incorporated with two orthogonal gradients. A referenceless geometric-distortion correction based on the single-shot bi-SPEN sequence is then proposed. The distorted image acquired with the single-shot bi-SPEN sequence is corrected by iterative super-resolved reconstruction involving the field gradients estimated from a field map, which in turn is obtained from its own super-resolved data after a phase-unwrapping procedure without additional scans. In addition, the distortion correction method is applied to improve the quality of the multiple region-of-interest images obtained with single-shot bi-SPEN sequence.

Water-fat separation from a single spatiotemporally encoded echo based on nominal k-space peaking and joint regularized estimation.

PURPOSE: To present a new high-resolution single-point water-fat separation algorithm based on the spatiotemporally encoded chemical shift imaging technique. THEORY: Identifying water and fat peaks on the ensemble of the nominal k-space profiles of all spatiotemporally encoded lines enables evaluation of the mean off-resonance frequencies of the two components. With utilization of the spatial smoothness and filtering regularizations, the water/fat profiles can be discriminated with twice joint linear least squares estimations line-by-line. METHODS: The effectiveness of the proposed algorithm was assessed by experiments on oil-water phantoms and in vivo in rats at 7T using a spatiotemporally encoded variant of the multishot spin-echo sequence. The results were compared with those obtained from previously proposed 1-point Dixon, 2-point Dixon, and 3-point IDEAL methods. RESULTS: The results demonstrate that the new technique can achieve high-quality water-fat separations, comparable in signal-to-noise ratio and contrast to the multipoint methods and is more robust in cases when large areas of low signals or motion artifacts jeopardize the results from the 1-point Dixon method. CONCLUSIONS: The proposed technique is potentially a new viable alternative for single-point water-fat separation.

Observation of true and pseudo NOE signals using CEST-MRI and CEST-MRS sequences with and without lipid suppression.

PURPOSE: To investigate the characteristics of nuclear Overhauser enhancement (NOE) imaging signals in the brain at 7T. METHODS: Fresh hen eggs, as well as six healthy, and six C6 glioma-bearing Wistar rats were scanned using chemical exchange saturation transfer-magnetic resonance imaging (CEST-MRI) and chemical exchange saturation transfer-magnetic resonance spectroscopy (CEST-MRS) sequences (saturation duration 3 s, power 1.47 µT) with and without lipid suppression. CEST data were acquired over an offset range of -6 to +6 ppm relative to the water resonance in 0.5 ppm steps. RESULTS: The water signals were not disrupted by other protons during the CEST-MRS sequences, and true NOE signals could be observed. Using the CEST-MRI sequence without lipid suppression, pseudo NOE imaging signals were observed in the lipid-containing regions (egg yolk, scalp, and even white matter). These pseudo NOE signals were almost (but incompletely) removed with the lipid suppression. Egg yolk results indicated the presence of the NOE to olefinic protons overlapping with the water signal. In vivo experiments showed that the amide proton transfer signal was larger in the tumor, whereas the NOE signal was larger in the normal white matter. CONCLUSIONS: True NOE signals can be detected using MRS sequences, and considerable pseudo NOE imaging signals may be observed using MRI sequences.

[Recent progress of research and applications of fractal and its theories in medicine].

Fractal, a mathematics concept, is used to describe an image of self-similarity and scale invariance. Some organisms have been discovered with the fractal characteristics, such as cerebral cortex surface, retinal vessel structure, cardiovascular network, and trabecular bone, etc. It has been preliminarily confirmed that the three-dimensional structure of cells cultured in vitro could be significantly enhanced by bionic fractal surface. Moreover, fractal theory in clinical research will help early diagnosis and treatment of diseases, reducing the patient's pain and suffering. The development process of diseases in the human body can be expressed by the fractal theories parameter. It is of considerable significance to retrospectively review the preparation and application of fractal surface and its diagnostic value in medicine. This paper gives an application of fractal and its theories in the medical science, based on the research achievements in our laboratory.