Comparison of different compressed sensing algorithms for low SNR 19 F MRI applications-Imaging of transplanted pancreatic islets and cells labeled with perfluorocarbons

Abstract

Transplantation of pancreatic islets is a possible treatment option for patients suffering from Type I diabetes. In vivo imaging of transplanted islets is important for assessment of the transplantation site and islet distribution. Thanks to its high specificity, the absence of intrinsic background signal in tissue and its potential for quantification, ¹⁹ F MRI is a promising technique for monitoring the fate of transplanted islets in vivo. In order to overcome the inherent low sensitivity of ¹⁹ F MRI, leading to long acquisition times with low signal-to-noise ratio (SNR), compressed sensing (CS) techniques are a valuable option. We have validated and compared different CS algorithms for acceleration of ¹⁹ F MRI acquisition in a low SNR regime using pancreatic islets labeled with perfluorocarbons both in vitro and in vivo. Using offline simulation on both in vitro and in vivo low SNR fully sampled ¹⁹ F MRI datasets of labeled islets, we have shown that CS is effective in reducing the image acquisition time by a factor of three to four without seriously affecting SNR, regardless of the particular algorithms used in this study, with the exception of CoSaMP. Using CS, signals can be detected that might have been missed by conventional ¹⁹ F MRI. Among different algorithms (SPARSEMRI, OMMP, IRWL1, Two-level and CoSAMP), the two-level l₁ method has shown the best performance if computational time is taken into account. We have demonstrated in this study that different existing CS algorithms can be used effectively for low SNR ¹⁹ F MRI. An up to fourfold gain in SNR/scan time could be used either to reduce the scan time, which is beneficial for clinical and translational applications, or to increase the number of averages, to potentially detect otherwise undetected signal when compared with conventional ¹⁹ F MRI acquisitions. Potential applications in the field of cell therapy have been demonstrated.

Keywords: 19F MRI; cell imaging; cell labeling; compressed sensing; contrast agent; diabetes; pancreatic islets; perfluorocarbon.