Comparison of different compressed sensing algorithms for low SNR ^{1919 F MRI applications—Imaging of transplanted pancreatic islets and cells labeled with perfluorocarbons}

摘要

> 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 l ₁ 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 S