An MRspec database query and visualization engine with applications as a clinical diagnostic and research tool

Quantification of magnetic resonance spectroscopy data using a combined reference: Application in typically developing infants

Quantification of proton magnetic resonance spectroscopy (¹ H-MRS) data is commonly performed by referencing the ratio of the signal from one metabolite, or metabolite group, to that of another, or to the water signal. Both approaches have drawbacks: ratios of two metabolites can be difficult to interpret because study effects may be driven by either metabolite, and water-referenced data must be corrected for partial volume and relaxation effects in the water signal. Here, we introduce combined reference (CRef) analysis, which compensates for both limitations. In this approach, metabolites are referenced to the combined signal of several reference metabolites or metabolite groups. The approach does not require the corrections necessary for water scaling and produces results that are less sensitive to the variation of any single reference signal, thereby aiding the interpretation of results. We demonstrate CRef analysis using 202 ¹ H-MRS acquisitions from the brains of 140 infants, scanned at approximately 1 and 3 months of age. We show that the combined signal of seven reference metabolites or metabolite groups is highly correlated with the water signal, corrected for partial volume and relaxation effects associated with cerebral spinal fluid. We also show that the combined reference signal is equally or more uniform across subjects than the reference signals from single metabolites or metabolite groups. We use CRef analysis to quantify metabolite concentration changes during the first several months of life in typically developing infants.

Novel imaging technologies for genetic diagnoses in the inborn errors of metabolism

Many inborn errors of metabolism and genetic disorders affect the brain. The brain biochemistry may differ from that in the periphery and is not accessible by simple blood and urine sampling. Therefore, neuroimaging has proven to be a valuable tool to not only evaluate the brain structure, but also biochemistry, blood flow and function. Neuroimaging in patients with inborn errors of metabolism can include additional sequences in addition to T1 and T2-weighted imaging because in early stages, there may be no significant findings on the routine sequnces due to the lack of sensitivity or the evolution of abnormalities lags behind the ability of the imaging to detect it. In addition, findings on T1 and T2-weighted imaging of several inborn errors of metabolism may be non-specific and be seen in other non-genetic conditions. Therefore, additional neuroimaging modalities that have been employed including diffusion tensor imaging (DTI), magnetic resonance spectroscopy, functional MRI (fMRI), functional near infrared spectroscopy (fNIRS), or positron emission tomography (PET) imaging may further inform underlying changes in myelination, biochemistry, and functional connectivity. The use of Magnetic Resonance Spectroscopy in certain disorders may add a level of specificity depending upon the metabolite levels that are abnormal, as well as provide information about the process of brain injury (i.e., white matter, gray matter, energy deficiency, toxic buildup or depletion of key metabolites). It is even more challenging to understand how genetic or metabolic disorders contribute to short and/or long term changes in cognition which represent the downstream effects of IEMs. In order to image “cognition” or the downstream effects of a metabolic disorder on domains of brain function, more advanced techniques are required to analyze underlying fiber tracts or alternatively, methods such as fMRI enable generation of brain activation maps after both task based and resting state conditions. DTI can be used to look at changes in white matter tracks. Each imaging modality can explore an important aspect of the anatomy, physiology or biochemisty of the central nervous system. Their properties, pros and cons are discussed in this article. These imaging modalities will be discussed in the context of several inborn errors of metabolism including Galactosemia, Phenylketonruia, Maple syrup urine disease, Methylmalonic acidemia, Niemann-Pick Disease, type C1, Krabbe Disease, Ornithine transcarbamylase deficiency, Sjogren Larsson syndrome, Pelizeaus-Merzbacher disease, Pyruvate dehydrogenase deficiency, Nonketotic Hyperglycinemia and Fabry disease. Space constraints do not allow mention of all the disorders in which one of these modalities has been investigated, or where it would add value to diagnosis or disease progression.

Evaluating the feasibility of creatine-weighted CEST MRI in human brain at 7 T using a Z-spectral fitting approach

The current study aims to evaluate the feasibility of creatine (Cr) chemical exchange saturation transfer (CEST)-weighted MRI at 7 T in the human brain by optimizing the saturation pulse parameters and computing contrast using a Z-spectral fitting approach. The Cr-weighted (Cr-w) CEST contrast was computed from phantoms data. Simulations were carried out to obtain the optimum saturation parameters for Cr-w CEST with lower contribution from other brain metabolites. CEST-w images were acquired from the brains of four human subjects at different saturation parameters. The Cr-w CEST contrast was computed using both asymmetry analysis and Z-spectra fitting approaches (models 1 and 2, respectively) based on Lorentzian functions. For broad magnetization transfer (MT) effect, Gaussian and Super-Lorentzian line shapes were also evaluated. In the phantom study, the Cr-w CEST contrast showed a linear dependence on concentration in physiological range and a nonlinear dependence on saturation parameters. The in vivo Cr-w CEST map generated using asymmetry analysis from the brain represents mixed contrast with contribution from other metabolites as well and relayed nuclear Overhauser effect (rNOE). Simulations provided an estimate for the optimum range of saturation parameters to be used for acquiring brain CEST data. The optimum saturation parameters for Cr-w CEST to be used for brain data were around B_1rms  = 1.45 μT and duration = 2 seconds. The Z-spectral fitting approach enabled computation of individual components. This also resulted in mitigating the contribution from MT and rNOE to Cr-w CEST contrast, which is a major source of underestimation in asymmetry analysis. The proposed modified z-spectra fitting approach (model 2) is more stable to noise compared with model 1. Cr-w CEST contrast obtained using fitting was 6.98 ± 0.31% in gray matter and 5.45 ± 0.16% in white matter. Optimal saturation parameters reduced the contribution from other CEST effects to Cr-w CEST contrast, and the proposed Z-spectral fitting approach enabled computation of individual components in Z-spectra of the brain. Therefore, it is feasible to compute Cr-w CEST contrast with a lower contribution from other CEST and rNOE.