Enhancing SNR in CEST imaging: A deep learning approach with a denoising convolutional autoencoder

Abstract

AbstractPurposeTo develop a SNR enhancement method for CEST imaging using a denoising convolutional autoencoder (DCAE) and compare its performance with state‐of‐the‐art denoising methods.MethodThe DCAE‐CEST model encompasses an encoder and a decoder network. The encoder learns features from the input CEST Z‐spectrum via a series of one‐dimensional convolutions, nonlinearity applications, and pooling. Subsequently, the decoder reconstructs an output denoised Z‐spectrum using a series of up‐sampling and convolution layers. The DCAE‐CEST model underwent multistage training in an environment constrained by Kullback–Leibler divergence, while ensuring data adaptability through context learning using Principal Component Analysis–processed Z‐spectrum as a reference. The model was trained using simulated Z‐spectra, and its performance was evaluated using both simulated data and in vivo data from an animal tumor model. Maps of amide proton transfer (APT) and nuclear Overhauser enhancement (NOE) effects were quantified using the multiple‐pool Lorentzian fit, along with an apparent exchange‐dependent relaxation metric.ResultsIn digital phantom experiments, the DCAE‐CEST method exhibited superior performance, surpassing existing denoising techniques, as indicated by the peak SNR and Structural Similarity Index. Additionally, in vivo data further confirm the effectiveness of the DCAE‐CEST in denoising the APT and NOE maps when compared with other methods. Although no significant difference was observed in APT between tumors and normal tissues, there was a significant difference in NOE, consistent with previous findings.ConclusionThe DCAE‐CEST can learn the most important features of the CEST Z‐spectrum and provide the most effective denoising solution compared with other methods.