Quantitative 23Na magnetic resonance imaging (MRI) provides tissue sodium concentration (TSC), which is connected to cell viability and vitality. Long acquisition times are one of the most challenging aspects for its clinical establishment. K‐space undersampling is an approach for acquisition time reduction, but generates noise and artifacts. The use of convolutional neural networks (CNNs) is increasing in medical imaging and they are a useful tool for MRI postprocessing. The aim of this study is 23Na MRI acquisition time reduction by k‐space undersampling. CNNs were applied to reduce the resulting noise and artifacts. A retrospective analysis from a prospective study was conducted including image datasets from 46 patients (aged 72 ± 13 years; 25 women, 21 men) with ischemic stroke; the 23Na MRI acquisition time was 10 min. The reconstructions were performed with full dataset (FI) and with a simulated dataset an image that was acquired in 2.5 min (RI). Eight different CNNs with either U‐Net–based or ResNet‐based architectures were implemented with RI as input and FI as label, using batch normalization and the number of filters as varying parameters. Training was performed with 9500 samples and testing included 400 samples. CNN outputs were evaluated based on signal‐to‐noise ratio (SNR) and structural similarity (SSIM). After quantification, TSC error was calculated. The image quality was subjectively rated by three neuroradiologists. Statistical significance was evaluated by Student’s t‐test. The average SNR was 21.72 ± 2.75 (FI) and 10.16 ± 0.96 (RI). U‐Nets increased the SNR of RI to 43.99 and therefore performed better than ResNet. SSIM of RI to FI was improved by three CNNs to 0.91 ± 0.03. CNNs reduced TSC error by up to 15%. The subjective rating of CNN‐generated images showed significantly better results than the subjective image rating of RI. The acquisition time of 23Na MRI can be reduced by 75% due to postprocessing with a CNN on highly undersampled data.

Magnetic resonance imaging has achieved an increasingly important role in the clinical workup of renal diseases such chronic kidney disease (CKD). A large panel of parameters have been proposed to diagnose CKD among them total kidney volume (TKV) which recently qualified as biomarker. Volume estimation in renal MRI is based on image segmentation of the kidney and/or its compartments. Beyond volume estimation renal segmentation supports also the quantification of other MR based parameters such as perfusion or filtration. The aim of the present article is to discuss the recent existing literature on renal image segmentation techniques and show today's limitations of the proposed techniques that might hinder clinical translation. We also provide pointers to open source software related to renal image segmentation.