Abstract
PurposeChemical exchange saturation transfer (CEST) is an MRI technique sensitive to the presence of low‐concentration solute protons exchanging with water. However, magnetization transfer (MT) effects also arise when large semisolid molecules interact with water, which biases CEST parameter estimates if quantitative models do not account for macromolecular effects. This study establishes under what conditions this bias is significant and demonstrates how using an appropriate model provides more accurate quantitative CEST measurements.MethodsCEST and MT data were acquired in phantoms containing bovine serum albumin and agarose. Several quantitative CEST and MT models were used with the phantom data to demonstrate how underfitting can influence estimates of the CEST effect. CEST and MT data were acquired in healthy volunteers, and a two‐pool model was fit in vivo and in vitro, whereas removing increasing amounts of CEST data to show biases in the CEST analysis also corrupts MT parameter estimates.ResultsWhen all significant CEST/MT effects were included, the derived parameter estimates for each CEST/MT pool significantly correlated (P < .05) with bovine serum albumin/agarose concentration; minimal or negative correlations were found with underfitted data. Additionally, a bootstrap analysis demonstrated that significant biases occur in MT parameter estimates (P < .001) when unmodeled CEST data are included in the analysis.ConclusionsThese results indicate that current practices of simultaneously fitting both CEST and MT effects in model‐based analyses can lead to significant bias in all parameter estimates unless a sufficiently detailed model is utilized. Therefore, care must be taken when quantifying CEST and MT effects in vivo by properly modeling data to minimize these biases.
Highlights
Chemical exchange saturation transfer (CEST) is a MRI method based on the exchange of magnetization between solutes and water
We illustrate this by (1) expanding Chappell et al’s method[32] to incorporate a lineshape function, (2) demonstrating this bias in simulations of a seven-pool model of CEST and magnetization transfer (MT), (3) fully fitting the model to in vitro data by including all observable CEST pools, and (4) showing that these biases will influence the parameter estimates of the MT effect using in vitro and in vivo data in a CEST+MT analysis by fitting a two-pool Quantitative MT (qMT) model with varying amounts of CEST information added to the estimation
Two methods to potentially alleviate the biases introduced by underfitting CEST data are shown in Figures 3 and 4, where a four pool model estimation is performed using the CEST+MT. The differences between these methods can be seen in more detail in Figure 5, which shows the correlation of each moiety with bovine serum albumin (BSA) and agarose concentration, and in Table 1, which displays the significance levels for these correlations
Summary
Chemical exchange saturation transfer (CEST) is a MRI method based on the exchange of magnetization between solutes and water. The protons associated with small, mobile solutes resonate at specific frequency offsets to water, but are difficult to detect directly using MRI, some are in constant, direct chemical exchange with water.[1] CEST contrast is generated by selectively saturating the labile protons of mobile solutes using narrow-bandwidth radiofrequency (RF) irradiation. We demonstrate that the biases described above are largely removed when all detectable CEST effects are sufficiently modeled We illustrate this by (1) expanding Chappell et al’s method[32] to incorporate a lineshape function, (2) demonstrating this bias in simulations of a seven-pool model of CEST and MT, (3) fully fitting the model to in vitro data by including all observable CEST pools, and (4) showing that these biases will influence the parameter estimates of the MT effect using in vitro and in vivo data in a CEST+MT analysis by fitting a two-pool qMT model with varying amounts of CEST information added to the estimation.
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