NOE‐weighted imaging in tumors using low‐duty‐cycle2π‐CEST

Abstract

Purpose: Nuclear Overhauser enhancement (NOE)-mediated CEST imaging at −3.5 ppm has shown clinical interest in diagnosing tumors. Multiple-pool Lorentzian fit has been used to quantify NOE, which, however, requires a long scan time. Asymmetric analysis of CEST signals could be a simple and fast method to quantify this NOE, but it has contamination from the amide proton transfer (APT) at 3.5 ppm. This work proposes a new method using an asymmetric analysis of a low-duty-cycle pulsed-CEST sequence with a flip angle of 360 • , termed 2π-CEST, to reduce the contribution from APT.Methods: Simulations were used to evaluate the capability of the 2π-CEST to reduce APT. Experiments on animal tumor models were performed to show its advantages compared with the conventional asymmetric analysis. Samples of reconstituted phospholipids and proteins were used to evaluate the molecular origin of this NOE. Results:The 2π-CEST has reduced contribution from APT. In tumors where we show that the NOE is comparable to the APT effect, reducing the contamination from APT is crucial. The results show that the NOE signal obtained with 2π-CEST in tumor regions appears more homogeneous than that obtained with the conventional method. The phantom study showed that both phospholipids and proteins contribute to the NOE at −3.5 ppm. Conclusion:The NOE at −3.5 ppm has a different contrast mechanism from APT and other CEST/NOE effects. The proposed 2π-CEST is more accurate than the conventional asymmetric analysis in detecting NOE, and requires much less scan time than the multiple-pool Lorentzian fit. K E Y W O R D S chemical exchange saturation transfer (CEST), magnetization transfer (MT), nuclear Overhauser enhancement (NOE), tumor

Assignment of molecular origins of NOE signal at −3.5 ppm in the brain

“…Summarily, the maps of T2 weighted anatomy and the AREXmfit APT display higher intensities in tumor tissues than in normal tissues while the maps of R1w, MT pool size ratio, AREXmfit NOE(-3.5 ppm), and AREXmfit MT show lower intensities; the maps of R1w, MT pool size ratio, AREXmfit NOE(-3.5 ppm), and AREXmfit MT show higher intensities in the corpus callosum than in the caudate putamen. Note that the previous studies have also demonstrated reduced NOE(-3.5 ppm) signals in the cancer tissues(12,17,18,39,(41)(42)(43)(44), which guarantees the reproducibility of our experiments.…”

“…For all phantom imaging, f m was set to 0 due to the low MT pool size ratio. The AREX determined semi-solid MT effect was obtained by R 1 W L MT /(1 – L MT ), where L MT is the Lorentzian line for the MT effect (39). The AREXmfit quantified APT and NOE(−3.5 ppm) values were obtained by choosing the maximum value between 4 ppm and 3 ppm in the fitted APT spectra and between −3 ppm and −4 ppm in the fitted NOE(−3.5 ppm) spectra, respectively.…”

Assignment of molecular origins of NOE signal at −3.5 ppm in the brain

“…Summarily, the maps of T 2 weighted anatomy and the AREX mfit APT display higher intensities in tumor tissues than in normal tissues while the maps of R 1 w , MT pool size ratio, AREX mfit NOE (−3.5 ppm), and AREX mfit MT show lower intensities; the maps of R 1 w , MT pool size ratio, AREX mfit NOE (−3.5 ppm), and AREX mfit MT show higher intensities in the corpus callosum than in the caudate putamen. Note that the previous studies have also demonstrated reduced NOE (−3.5 ppm) signals in the cancer tissues, 12,17,18,39,41–44 which guarantees the reproducibility of our experiments.…”

Radiofrequency labeling strategies in chemical exchange saturation transfer MRI

“…Cui et al also compared CEST signals acquired using CW-and pulsed-saturation schemes to filter out unwanted components. 78 Note that the simulations in Figures 2, 4, and 7 were performed using custom-written scripts in Python and the code can be downloaded from a public repository, https://github.com/ChongxueBie/RF-labeling-techniques-in-CEST-MRI. The simulations in Figures 5 and 6 were performed on MATLAB 2022b using source code downloaded from https://github.com/kherz/pulseq-cest-library.…”

“…For example, Jin et al developed the “average saturation efficiency filter” (ASEF) method, which utilizes these saturation parameters to achieve exchange rate filtering, 76,77 separating out signals of intermediate‐exchanging protons from fast‐exchanging protons and MTC. Cui et al also compared CEST signals acquired using CW‐ and pulsed‐saturation schemes to filter out unwanted components 78 …”