Endogenous chemical exchange saturation transfer (CEST) effects are always diluted by competing effects, such as direct water proton saturation (spillover) and semi-solid macromolecular magnetization transfer (MT). This leads to unwanted T2 and MT signal contributions that lessen the CEST signal specificity to the underlying biochemical exchange processes. A spillover correction is of special interest for clinical static field strengths and protons resonating near the water peak. This is the case for all endogenous CEST agents, such as amide proton transfer, –OH-CEST of glycosaminoglycans, glucose or myo-inositol, and amine exchange of creatine or glutamate. All CEST effects also appear to be scaled by the T1 relaxation time of water, as they are mediated by the water pool. This forms the motivation for simple metrics that correct the CEST signal. Based on eigenspace theory, we propose a novel magnetization transfer ratio (MTRRex), employing the inverse Z-spectrum, which eliminates spillover and semi-solid MT effects. This metric can be simply related to Rex, the exchange-dependent relaxation rate in the rotating frame, and ka, the inherent exchange rate. Furthermore, it can be scaled by the duty cycle, allowing for simple translation to clinical protocols. For verification, the amine proton exchange of creatine in solutions with different agar concentrations was studied experimentally at a clinical field strength of 3 T, where spillover effects are large. We demonstrate that spillover can be properly corrected and that quantitative evaluation of pH and creatine concentration is possible. This proves that MTRRex is a quantitative and biophysically specific CEST-MRI metric. Applied to acute stroke induced in rat brain, the corrected CEST signal shows significantly higher contrast between the stroke area and normal tissue, as well as less B1 dependence, than conventional approaches.

Off-resonant radiofrequency irradiation in tissue indirectly lowers the water signal by saturation transfer processes: On the one hand, there are selective chemical exchange saturation transfer (CEST) effects originating from exchanging endogenous protons resonating a few ppm from water; on the other hand, there is the broad semi-solid magnetization transfer (MT) originating from immobile protons associated with the tissue matrix with kHz line-widths. Recently it was shown that endogenous CEST contrasts can be strongly affected by the MT background so that corrections are needed to derive accurate estimates of CEST effects. Herein we show that a full analytical solution of the underlying Bloch-McConnell equations for both MT and CEST provides insights into their interaction and suggests a simple means to isolate their effects. The presented analytical solution, based on the eigenspace solution of the Bloch-McConnell equations, extends previous treatments by allowing arbitrary line-shapes for the semi-solid MT effects and simultaneously describing multiple CEST pools in the presence of a large MT pool for arbitrary irradiation. The structure of the model indicates that semi-solid MT and CEST effects basically add up inversely in determining the steady-state Z-spectrum, as previously shown for direct saturation and CEST effects. Implications for existing previous CEST analyses in the presence of a semi-solid MT are studied and discussed. It turns out that to accurately quantify CEST contrast, a good reference Z-value, the observed longitudinal relaxation rate of water, and the semi-solid MT pool size fraction, must all be known.

Chemical exchange saturation transfer (CEST) provides an indirect means to detect exchangeable protons within tissues through their effects on the water signal. Previous studies have suggested that amide proton transfer (APT) imaging, a specific form of CEST, detects endogenous amide protons with a resonance frequency offset 3.5 ppm downfield from water, and thus may be sensitive to variations of mobile proteins/peptides in tumors. However, since CEST measurements are influenced by various confounding effects, such as spillover saturation, magnetization transfer (MT) and MT asymmetry, the mechanism or degree of increased APT signal in tumors are not certain. In addition to APT, nuclear Overhauser enhancement (NOE) effects upfield from water may also provide distinct information about tissue composition. In the current study, APT, NOE and several other magnetic resonance parameters were measured and compared comprehensively in order to elucidate the origins of APT and NOE contrasts in tumors at 9.4T. In addition to conventional CEST methods, a new intrinsic inverse metric was applied to correct for relaxation and other effects. After corrections for spillover, MT and T1 effects, corrected APT in tumors was found not significantly different from normal tissues, but corrected NOE effects in tumors showed significant decreases compared with normal tissues. Biochemical measurements verified that there is no significant enhancement of protein contents in the tumors studied, consistent with corrected APT measurements and previous literature while qMT data showed decreases in the fractions of immobile macromolecules in tumors. Our results may assist better understanding the contrast depicted by CEST imaging in tumors, and the development of improved APT and NOE measurements for cancer imaging.

Accurate quantification of chemical exchange saturation transfer (CEST) effects, including dipole-dipole mediated relayed nuclear Overhauser enhancements (rNOE) saturation transfer, is important for applications and studies of molecular concentration and transfer rate (and thereby pH or temperature). Although several quantification methods, such as Lorentzian difference (LD) analysis, multiple-pool Lorentzian fits, and the three-point method, have been extensively used in several preclinical and clinical applications, the accuracy of these methods has not been evaluated. Here we simulated multiple-pool Z-spectra containing the pools that contribute to the main CEST and rNOE saturation transfer signals in brain, and numerically fit them using the different methods, and then compared their derived CEST metrics with the known solute concentrations and exchange rates. Our results show that the LD analysis overestimates contributions from amide proton transfer (APT) and intermediate exchanging amine protons; the three-point method significantly underestimates both APT and rNOE saturation transfer at −3.5 ppm (NOE(−3.5)). In contrast, the multiple-pool Lorentzian fit is more accurate than the other two methods, but only at lower irradiation powers (< 1 μT at 9.4 T) within the range of our simulations. At higher irradiation powers, this method is also inaccurate because of the presence of a fast exchanging CEST signal that has a non-Lorentzian lineshape. Quantitative parameters derived from in vivo images of rodent brain tumor obtained using an irradiation power of 1 μT were also compared. Our results demonstrate that all three quantification methods show similar contrasts between tumor and contralateral normal tissue for both APT and the NOE(−3.5). However, the quantified values of the three methods are significantly different. Our work provides insight into the fitting accuracy obtainable in a complex tissue model and provides guidelines for evaluating other newly developed quantification methods.

In this study, we introduce a new method for amide proton transfer imaging based on chemical exchange rotation transfer. It avoids several artifacts that plague conventional chemical exchange saturation transfer approaches by creating label and reference scans based on varying the irradiation pulse rotation angle (π and 2π radians) instead of the frequency offset (3.5 and −3.5 ppm). Specifically, conventional analysis is sensitive to confounding contributions from magnetic field (B0) inhomogeneities and, more problematically, inherently asymmetric macromolecular resonances. In addition, the lipid resonance at −3.5 ppm complicates the interpretation of the reference scan and decreases the resulting contrast. Finally, partial overlap of the amide signal by nearby amines and hydroxyls obscure the results. By avoiding these issues, our new method is a promising approach for imaging endogenous protein and peptide content and mapping pH.

The concepts, theoretical behavior and experimental applications of temporal diffusion spectroscopy are reviewed and illustrated. Temporal diffusion spectra are obtained by using oscillating gradient waveforms in diffusion-weighted measurements, and represent the manner in which various spectral components of molecular velocity correlations vary in different geometrical structures that restrict or hinder free movements. Measurements made at different gradient frequencies reveal information on the scale of restrictions or hindrances to free diffusion, and the shape of a spectrum reveals the relative contributions of spatial restrictions at different distance scales. Such spectra differ from other so-called diffusion spectra which depict spatial frequencies and are defined at a fixed diffusion time. Experimentally, oscillating gradients at moderate frequency are more feasible for exploring restrictions at very short distances, which in tissues correspond to structures smaller than cells. We describe the underlying concepts of temporal diffusion spectra and provide analytical expressions for the behavior of the diffusion coefficient as a function of gradient frequency in simple geometries with different dimensions. Diffusion in more complex model media that mimic tissues has been simulated using numerical methods. Experimental measurements of diffusion spectra have been obtained in suspensions of particles and cells, as well as in vivo in intact animals. An observation of particular interest is the increased contrast and heterogeneity observed in tumors using oscillating gradients at moderate frequency compared to conventional pulse gradient methods, and the potential for detecting changes in tumors early in their response to treatment. Computer simulations suggest that diffusion spectral measurements may be sensitive to intracellular structures such as nuclear size, and that changes in tissue diffusion properties may be measured before there are changes in cell density.

Purpose A temporal diffusion MRI spectroscopy based approach has been developed to quantify cancer cell size and density in vivo. Methods A novel Imaging Microstructural Parameters Using Limited Spectrally Edited Diffusion (IMPULSED) method selects a specific limited diffusion spectral window for an accurate quantification of cell sizes ranging from 10 to 20 μm in common solid tumors. In practice, it is achieved by a combination of a single long diffusion time pulsed gradient spin echo (PGSE) and three low-frequency oscillating gradient spin echo (OGSE) acquisitions. To validate our approach, H&E staining and immunostaining of cell membranes, in concert with whole slide imaging, were used to visualize nuclei and cell boundaries, and hence enabled accurate estimates of cell size and cellularity. Results Based on a two compartment model (incorporating intra- and extracellular spaces), accurate estimates of cell sizes were obtained in vivo for three types of human colon cancers. The IMPULSED-derived apparent cellularities showed a stronger correlation (r=−0.81, p<0.0001) with histology-derived cellularities than conventional ADCs (r=−0.69, p<0.03). Conclusion The IMPULSED approach samples a specific region of temporal diffusion spectra with enhanced sensitivity to length scales of 10–20 μm, and enables measurements of cell sizes and cellularities in solid tumors in vivo.

In the present work, we reported a new nuclear Overhauser enhancement (NOE)-mediated magnetization transfer (MT) signal at around −1.6 ppm (NOE(−1.6)) in rat brain and investigated its application in the detection of acute ischemic stroke in rodent model. Using continuous wave (CW) MT sequence, the NOE(−1.6) is reliably detected in rat brain. The amplitude of this new NOE signal in rat brain was quantified using a 5-pool Lorentzian Z-spectral fitting method. Amplitudes of amide, amine, NOE at −3.5 ppm (NOE(−3.5)), as well as NOE(−1.6) were mapped using this fitting method in rat brain. Several other conventional imaging parameters (R 1 , R 2 , apparent diffusion coefficient (ADC), and semi-solid pool size ratio (PSR)) were also measured. Our results showed that NOE(−1.6), R 1 , R 2 , ADC, and APT signals from stroke lesion have significant changes at 0.5-1 h after stroke. Compared with several other imaging parameters, NOE(−1.6) shows the strongest contrast differences between stroke and contralateral normal tissues and stays consistent over time until 2 h after onset of stroke. Our results demonstrate that this new NOE(−1.6) signal in rat brain is a new potential contrast for assessment of acute stroke in vivo and might provide broad applications in the detection of other abnormal tissues.