Improving the spectral resolution and spectral fitting of (1) H MRSI data from human calf muscle by the SPREAD technique

Accelerated MR spectroscopic imaging-a review of current and emerging techniques

Over more than 30 years in vivo MR spectroscopic imaging (MRSI) has undergone an enormous evolution from theoretical concepts in the early 1980s to the robust imaging technique that it is today. The development of both fast and efficient sampling and reconstruction techniques has played a fundamental role in this process. State-of-the-art MRSI has grown from a slow purely phase-encoded acquisition technique to a method that today combines the benefits of different acceleration techniques. These include shortening of repetition times, spatial-spectral encoding, undersampling of k-space and time domain, and use of spatial-spectral prior knowledge in the reconstruction. In this way in vivo MRSI has considerably advanced in terms of spatial coverage, spatial resolution, acquisition speed, artifact suppression, number of detectable metabolites and quantification precision. Acceleration not only has been the enabling factor in high-resolution whole-brain 1 H-MRSI, but today is also common in non-proton MRSI (31 P, 2 H and 13 C) and applied in many different organs. In this process, MRSI techniques had to constantly adapt, but have also benefitted from the significant increase of magnetic field strength boosting the signal-to-noise ratio along with high gradient fidelity and high-density receive arrays. In combination with recent trends in image reconstruction and much improved computation power, these advances led to a number of novel developments with respect to MRSI acceleration. Today MRSI allows for non-invasive and non-ionizing mapping of the spatial distribution of various metabolites' tissue concentrations in animals or humans, is applied for clinical diagnostics and has been established as an important tool for neuro-scientific and metabolism research. This review highlights the developments of the last five years and puts them into the context of earlier MRSI acceleration techniques. In addition to 1 H-MRSI it also includes other relevant nuclei and is not limited to certain body regions or specific applications.

B0 shimming for in vivo magnetic resonance spectroscopy: Experts' consensus recommendations

Magnetic resonance spectroscopy (MRS) and spectroscopic imaging (MRSI) allow the chemical analysis of physiological processes in vivo and provide powerful tools in the life sciences and for clinical diagnostics. Excellent homogeneity of the static B₀ magnetic field over the object of interest is essential for achieving high-quality spectral results and quantitative metabolic measurements. The experimental minimization of B₀ variation is performed in a process called B₀ shimming. In this article, we summarize the concepts of B₀ field shimming using spherical harmonic shimming techniques, specific strategies for B₀ homogenization and crucial factors to consider for implementation and use in both brain and body. In addition, experts' recommendations are provided for minimum requirements for B₀ shim hardware and evaluation criteria for the primary outcome of adequate B₀ shimming for MRS and MRSI, such as the water spectroscopic linewidth.

Effect of linewidth on estimation of metabolic concentration when using water lineshape spectral model fitting for single voxel proton spectroscopy at 7 T

Good B₀ field homogeneity is considered an essential requirement to obtain high-quality MRS data. Many commonly used spectral fitting methods assume that all metabolite signals have Lorentzian or Gaussian shapes. However, B₀ inhomogeneity can both broaden the linewidth and modify the lineshape. In this study, it is hypothesized that a realistic metabolite fitting model, which accounts for B₀ homogeneity on the basis of the water lineshape, will improve the accuracy of estimation of metabolite concentrations. In-vivo water suppressed/unsuppressed single voxel spectroscopy signals were acquired under three different B₀ field homogeneity regimes. Individual realistic basis sets were created for each acquisition. Frequency-domain spectral fitting with LCModel was used to quantify the metabolite concentrations with fitting uncertainties given in terms of the Cramer-Rao lower bound. The quantification results obtained using the water lineshape basis set yielded similar concentrations independent of linewidth and showed a larger fitting error as the linewidth increased. The conventional approach, however quantifies metabolite concentrations with greater variations despite showing a supposedly improved fitting quality. The water lineshape basis set achieved single voxel spectroscopy accuracy that is less sensitive to the linewidth compared to the conventional spectral fitting method for the range of linewidths used in this study, but the precision deteriorated with worsening B₀ field inhomogeneity. The beneficial effect was ascribed to a reduction in the number of degrees of freedom when using the water lineshape to generate the basis set.

Diagnosis of Alzheimer's Disease Using Dual-Tree Complex Wavelet Transform, PCA, and Feed-Forward Neural Network

Background. Error-free diagnosis of Alzheimer's disease (AD) from healthy control (HC) patients at an early stage of the disease is a major concern, because information about the condition's severity and developmental risks present allows AD sufferer to take precautionary measures before irreversible brain damage occurs. Recently, there has been great interest in computer-aided diagnosis in magnetic resonance image (MRI) classification. However, distinguishing between Alzheimer's brain data and healthy brain data in older adults (age > 60) is challenging because of their highly similar brain patterns and image intensities. Recently, cutting-edge feature extraction technologies have found extensive application in numerous fields, including medical image analysis. Here, we propose a dual-tree complex wavelet transform (DTCWT) for extracting features from an image. The dimensionality of feature vector is reduced by using principal component analysis (PCA). The reduced feature vector is sent to feed-forward neural network (FNN) to distinguish AD and HC from the input MR images. These proposed and implemented pipelines, which demonstrate improvements in classification output when compared to that of recent studies, resulted in high and reproducible accuracy rates of 90.06 ± 0.01% with a sensitivity of 92.00 ± 0.04%, a specificity of 87.78 ± 0.04%, and a precision of 89.6 ± 0.03% with 10-fold cross-validation.

Three-Dimensional Eigenbrain for the Detection of Subjects and Brain Regions Related with Alzheimer's Disease

BACKGROUND

Considering that Alzheimer's disease (AD) is untreatable, early diagnosis of AD from the healthy elderly controls (HC) is pivotal. However, computer-aided diagnosis (CAD) systems were not widely used due to its poor performance.

OBJECTIVE

Inspired from the eigenface approach for face recognition problems, we proposed an eigenbrain to detect AD brains. Eigenface is only for 2D image processing and is not suitable for volumetric image processing since faces are usually obtained as 2D images.

METHODS

We extended the eigenbrain to 3D. This 3D eigenbrain (3D-EB) inherits the fundamental strategies in either eigenface or 2D eigenbrain (2D-EB). All the 3D brains were transferred to a feature space, which encoded the variation among known 3D brain images. The feature space was named as the 3D-EB, and defined as eigenvectors on the set of 3D brains. We compared four different classifiers: feed-forward neural network, support vector machine (SVM) with linear kernel, polynomial (Pol) kernel, and radial basis function kernel.

RESULTS

The 50x10-fold stratified cross validation experiments showed that the proposed 3D-EB is better than the 2D-EB. SVM with Pol kernel performed the best among all classifiers. Our "3D-EB + Pol-SVM" achieved an accuracy of 92.81% ± 1.99% , a sensitivity of 92.07% ± 2.48% , a specificity of 93.02% ± 2.22% , and a precision of 79.03% ± 2.37% . Based on the most important 3D-EB U1, we detected 34 brain regions related with AD. The results corresponded to recent literature.

CONCLUSIONS

We validated the effectiveness of the proposed 3D-EB by detecting subjects and brain regions related to AD.

Design and test of a double-nuclear RF coil for (1)H MRI and (13)C MRSI at 7T

RF coil operation at the ultrahigh field of 7T is fraught with technical challenges that limit the advancement of novel human in vivo applications at 7T. In this work, a hybrid technique combining a microstrip transmission line and a lumped-element L-C loop coil to form a double-nuclear RF coil for proton magnetic resonance imaging and carbon magnetic resonance spectroscopy at 7T was proposed and investigated. Network analysis revealed a high Q-factor and excellent decoupling between the coils. Proton images and localized carbon spectra were acquired with high sensitivity. The successful testing of this novel double-nuclear coil demonstrates the feasibility of this hybrid design for double-nuclear MR imaging and spectroscopy studies at the ultrahigh field of 7T.

Pathological brain detection in MRI scanning by wavelet packet Tsallis entropy and fuzzy support vector machine

An computer-aided diagnosis system of pathological brain detection (PBD) is important for help physicians interpret and analyze medical images. We proposed a novel automatic PBD to distinguish pathological brains from healthy brains in magnetic resonance imaging scanning in this paper. The proposed method simplified the PBD problem to a binary classification task. We extracted the wavelet packet Tsallis entropy (WPTE) from each brain image. The WPTE is the Tsallis entropy of the coefficients of the discrete wavelet packet transform. The, the features were submitted to the fuzzy support vector machine (FSVM). We tested the proposed diagnosis method on 3 benchmark datasets with different sizes. A ten runs of K-fold stratified cross validation was carried out. The results demonstrated that the proposed WPTE + FSVM method excelled 17 state-of-the-art methods w.r.t. classification accuracy. The WPTE is superior to discrete wavelet transform. The Tsallis entropy performs better than Shannon entropy. The FSVM excels standard SVM. In closing, the proposed method “WPTE + FSVM” is effective in PBD.