Measuring chemical exchange saturation transfer exchange rates in the human brain using a particle swarm optimisation algorithm

The z‐spectrum contains many pools with different exchange rates and T 2 values, which can make it difficult to interpret in vivo data and complicates the design of experiments aimed at providing sensitivity to one pool. This work aims to characterise the main pools observable with MRI at 7T in the human brain. To achieve this, we acquired z‐spectra at multiple saturation powers in the human brain at 7T. We used simulations to optimise the use of particle swarm optimisation (PSO) to fit these data, validating this approach using further simulations and creatine phantoms. We then used the PSO to fit data from grey and white matter for the pool size, exchange rate, and T 2 of five proton pools (magnetisation transfer, amides, amines, nuclear Overhauser enhancement NOE−3.5ppm and NOE−1.7ppm in addition to water). We then devised an approach for using PSO to fit z‐spectra while limiting the computational burden, and we investigated the sensitivity of the fit to T 2 and k for three overlapping pools. We used this to measure the exchange rate of creatine and to show that it varied with temperature, as expected. In the brain we measured a significantly larger pool size in white matter than in grey matter for the magnetisation transfer pool and the NOE−3.5ppm pool. For all other parameters we found no significant difference between grey and white matter. We showed that PSO can be used to fit z‐spectra acquired at a range of B1 to provide information about peak position, amplitude, exchange rate, and T 2 in vivo in the human brain. These data could provide more sensitivity to change in some clinical conditions and will also provide key information for further experimental design

The z-spectrum from human blood at 7T

Chemical Exchange Saturation Transfer (CEST) has been used to assess healthy and pathological tissue in both animals and humans. However, the CEST signal from blood has not been fully assessed. This paper presents the CEST and nuclear Overhauser enhancement (NOE) signals detected in human blood measured via z-spectrum analysis. We assessed the effects of blood oxygenation levels, haematocrit, cell structure and pH upon the z-spectrum in ex vivo human blood for different saturation powers at 7T. The data were analysed using Lorentzian difference (LD) model fitting and AREX (to compensate for changes in T1), which have been successfully used to study CEST effects in vivo. Full Bloch-McConnell fitting was also performed to provide an initial estimate of exchange rates and transverse relaxation rates of the various pools. CEST and NOE signals were observed at 3.5 ppm, -1.7ppm and -3.5 ppm and were found to originate primarily from the red blood cells (RBCs), although the amide proton transfer (APT) CEST effect, and NOEs showed no dependence upon oxygenation levels. Upon lysing, the APT and NOE signals fell significantly. Different pH levels in blood resulted in changes in both the APT and NOE (at -3.5ppm), which suggests that this NOE signal is in part an exchange relayed process. These results will be important for assessing in vivo z-spectra

Age-related differences in myeloarchitecture measured at 7 T

We have used the magnetisation transfer (MT) MRI measure as a primary measure of myelination in both the grey matter (GM) of the 78 cortical automated anatomical labelling (AAL) regions of the brain, and the underlying white matter in each region, in a cohort of healthy adults (aged 19 to 62 years old). The results revealed a significant quadratic trend in myelination with age, with average global myelination peaking at 42.9 years old in grey matter, and at 41.7 years old in white matter. We also explored the possibility of using the Nuclear Overhauser Enhancement (NOE) effect, which is acquired in a similar method to MT, as an additional measure of myelination. We found that the MT and NOE signals were strongly correlated in the brain and that the NOE effects displayed similar (albeit weaker) parabolic trends with age. We also investigated differences in cortical thickness with age, and confirmed a previous result of a linear decline of 4.5±1.2μm/year