Can the apparent transverse relaxation rate (R2*) evaluate the efficacy of concurrent chemoradiotherapy in locally advanced nasopharyngeal carcinoma? a preliminary experience.

BACKGROUND: The use of the apparent transverse relaxation rate (R2*) in nasopharyngeal carcinoma (NPC) has not been previously reported in the literature. The aim of this study was to investigate the role of the R2* value in evaluating response to concurrent chemoradiotherapy (CCRT) in patients with NPC. METHODS: Forty-one patients with locoregionally advanced NPC confirmed by pathology were examined by blood oxygenation level-dependent (BOLD) magnetic resonance imaging (MRI) before and after CCRT, and conventional MRI was performed 3 months after the completion of CCRT. All patients were divided into a responding group (RG) and a nonresponding group (NRG), according to MRI findings 3 months after the end of treatment. The R2* values before (R2*preT) and after (R2*postT) CCRT and the ΔR2* (ΔR2*=R2*postT - R2*preT) were calculated in the tumor. RESULTS: Among the 41 patients, 26 were in the RG and 15 were in the NRG. There was no statistical difference in the R2*preT between RG and NRG (P = 0.307); however, there were significant differences in R2*postT and ΔR2* (P < 0.001). The area under the curve of R2*postT and ΔR2* for predicting the therapeutic response of NPC was 0.897 and 0.954, respectively, with cutoff values of 40.95 and 5.50 Hz, respectively. CONCLUSION: The R2* value can be used as a potential imaging indicator to evaluate the therapeutic response of locoregionally advanced NPC.

Biophysically motivated efficient estimation of the spatially isotropic R2* component from a single gradient-recalled echo measurement.

PURPOSE: To propose and validate an efficient method, based on a biophysically motivated signal model, for removing the orientation-dependent part of R2* using a single gradient-recalled echo (GRE) measurement. METHODS: The proposed method utilized a temporal second-order approximation of the hollow-cylinder-fiber model, in which the parameter describing the linear signal decay corresponded to the orientation-independent part of R2* . The estimated parameters were compared to the classical, mono-exponential decay model for R2* in a sample of an ex vivo human optic chiasm (OC). The OC was measured at 16 distinct orientations relative to the external magnetic field using GRE at 7T. To show that the proposed signal model can remove the orientation dependence of R2* , it was compared to the established phenomenological method for separating R2* into orientation-dependent and -independent parts. RESULTS: Using the phenomenological method on the classical signal model, the well-known separation of R2* into orientation-dependent and -independent parts was verified. For the proposed model, no significant orientation dependence in the linear signal decay parameter was observed. CONCLUSIONS: Since the proposed second-order model features orientation-dependent and -independent components at distinct temporal orders, it can be used to remove the orientation dependence of R2* using only a single GRE measurement.

Fast quantitative parameter maps without fitting: Integration yields accurate mono-exponential signal decay rates.

PURPOSE: To develop a computationally fast and accurate algorithm for mono-exponential signal modelling and validate the new technique in the context of R2* mapping for iron overload assessment. METHODS: An algorithm is introduced that directly calculates R2* values from a series of images based on integration of the mono-exponential signal decay curve. The algorithm is fast, because fitting is avoided and only arithmetic computations without iterations are applied. Precision and accuracy of the method is determined in comparison to the conventional log-linear (LL), nonlinear least-squares-based Levenberg-Marquardt (NLM), and squared nonlinear Levenberg-Marquardt (SQNLM) methods, which rely on iterative curve fitting. RESULTS: In simulations, the signal integration based method consistently had the same or better accuracy than the LL, NLM, and SQNLM algorithms for R2* values ranging from 50 s-1 to 1200 s-1 . In phantoms and in vivo (12 participants), this method was robust over a wide range of R2* values and signal-to-noise ratios. Computation times were approximately 100, 1460, and 930 times faster than those of the LL, NLM, and SQNLM methods, respectively. CONCLUSIONS: The fast signal integration method accurately calculates R2* maps. It has the potential to replace conventional, mono-exponential fitting methods for quantitative MRI such as R2* parameter mapping. Magn Reson Med 79:2978-2985, 2018. © 2017 International Society for Magnetic Resonance in Medicine.