Incorporation of rician noise in the analysis of biexponential transverse relaxation in cartilage using a multiple gradient echo sequence at 3 and 7 tesla

Abstract

Purpose Previous work has evaluated the quality of different analytic methods for extracting relaxation times from magnitude imaging data exhibiting Rician noise. However, biexponential analysis of relaxation in tissue, including cartilage, and materials, is of increasing interest. We therefore analyzed biexponential transverse relaxation decay in the presence of Rician noise and assessed the accuracy and precision of several approaches to determining component fractions and apparent transverse relaxation times. Methods Comparisons of four different voxel-by-voxel fitting methods were performed using Monte Carlo simulations, and phantom and ex-vivo bovine nasal cartilage (BNC) experiments. In each case, preclinical and clinical imaging field strengths of 7T and 3T, respectively, and parameters, were investigated across a range of signal-to-noise ratios (SNR). Results were compared to Cramér-Rao lower bound calculations. Results As expected, at high SNR, all methods performed well. At lower SNR, fits explicitly incorporating the analytic form of the Rician noise maintained performance. The much more efficient correction scheme of Gudbjartsson and Patz performed almost as well in many cases. Ex-vivo experiments on phantoms and BNC were consistent with simulation results. Conclusion Explicit incorporation of Rician noise greatly improves accuracy and precision in the analysis of biexponential transverse decay data.

Assessment of different fitting methods for in-vivo bi-component T2* analysis of human patellar tendon in magnetic resonance imaging

“…The sequence has had successful applications in sodium imaging 42,43 and ex-vivo Achilles tendon imaging 24 . In our study, the 3D cone UTE sequence provided complete anatomic coverage of the patellar tendon in human subjects at 3.0T with clinically feasible spatial resolutions and scan times and with an SNR of 54.5, which has been shown to be adequate to accurately estimate bi-component T2* parameters from previous literature 28 and the results of our simulation study. Recent studies have demonstrated that bi-component exponential signal models are better suited than single-component models for evaluating tendon 19,[23][24][25] .…”

“…Furthermore, previous studies have found a significant increase in short T2* in pathologic tendon when compared to normal tendon, which is specifically due to disruption of the highly organized collagen fiber network, but no significant difference in long T2* 25,39 . Accurate estimations of bi-component T2* parameters requires sufficient SNR and proper noise modeling which is particularly important when estimating pixel-by-pixel spatial variations across a tissue sample 28 . Both our simulation and in-vivo studies showed that T*2, l in tendon has much greater spatial variation, as indicated by the standard deviation of image voxels, and much higher sensitivity to noise when compared to T*2,s and fs.…”

“…Bi-component T2 measurements have been shown to be strongly influenced by experimental details such as the field strength, gradient system, pulse sequence, imaging parameters, and specimen preparation 26,27 . Bouhrara et al also demonstrated that accurate estimation of bi-component T2 parameters requires sufficient signal-to-noise (SNR) ratio and proper noise modeling which is particularly important when estimating pixel-by-pixel spatial variations across a tissue sample 28 . In their study, simulations and imaging experiments on ex vivo phantoms and bovine nasal cartilage specimens were used to compare multiple model fitting methods and to investigate the sensitivity and robustness of bicomponent T2* measurements at different SNR levels.…”

“…Bouhrara et.al. 28 recently introduced a fitting scheme, M4, with the minimization of |S -SR| for where and I1 is the modified first order Bessel function of the first kind. In all the fitting methods, the image SNR was estimated for the first echo signal and defined as 28,29 where the noise standard deviation s was determined as the mean signal of background regions of all images acquired at each TE divided by 31 .…”

“…(1) for the real part of the signal. To simulate the Rician noise, the imaginary part of signal was created using only the zero mean Gaussian noise with the same noise standard deviation as the real part, and the magnitude of the complex signal was used as the final theoretical signal 28 . The fi- nal signal was fitted with the four different fitting methods and corresponding bi-component T2* parameters were estimated for each individual method.…”