A Denoising Convolutional Autoencoder for SNR Enhancement in Chemical Exchange Saturation Transfer imaging: (DCAE-CEST).

Kurmi, Yashwant; Viswanathan, Malvika; Zu, Zhongliang

Kurmi, Yashwant; Viswanathan, Malvika; Zu, Zhongliang.

Afiliación

Kurmi Y; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, USA.
Viswanathan M; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, USA.
Zu Z; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, USA.

bioRxiv ; 2024 Jun 21.

Article en En | MEDLINE | ID: mdl-38895366

ABSTRACT

ABSTRACT

Purpose:

To develop a SNR enhancement method for chemical exchange saturation transfer (CEST) imaging using a denoising convolutional autoencoder (DCAE), and compare its performance with state-of-the-art denoising methods.

Method:

The DCAE-CEST model encompasses an encoder and a decoder network. The encoder learns features from the input CEST Z-spectrum via a series of 1D 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.

Results:

In 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 confirms the effectiveness of the DCAE-CEST in denoising the APT and NOE maps when compared to other methods. While no significant difference was observed in APT between tumors and normal tissues, there was a significant difference in NOE, consistent with previous findings.

Conclusion:

The DCAE-CEST can learn the most important features of the CEST Z-spectrum and provide the most effective denoising solution compared to other methods.

Palabras clave

Chemical exchange saturation transfer (CEST); amide proton transfer (APT); deep learning; denoising convolutional autoencoder (DCAE); nuclear Overhauser effect; tumor

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: BioRxiv Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos

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