English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Purpose: Common to most MRSI techniques, the spatial resolution and theminimal scan duration of Deuterium Metabolic Imaging (DMI) are limited by theachievable SNR. This work presents a deep learning method for sensitivityenhancement of DMI.  Methods: A convolutional neural network (CNN) was designed to estimate the2H-labeled metabolite concentrations from low SNR and distorted DMI FIDs. TheCNN was trained with synthetic data that represent a range of SNR levelstypically encountered in vivo. The estimation precision was further improved byfine-tuning the CNN with MRI-based edge-preserving regularization for each DMIdataset. The proposed processing method, PReserved Edge ConvolutIonal neuralnetwork for Sensitivity Enhanced DMI (PRECISE-DMI), was applied to simulationstudies and in vivo experiments to evaluate the anticipated improvements in SNRand investigate the potential for inaccuracies.  Results: PRECISE-DMI visually improved the metabolic maps of low SNRdatasets, and quantitatively provided higher precision than the standardFourier reconstruction. Processing of DMI data acquired in rat brain tumormodels resulted in more precise determination of 2H-labeled lactate andglutamate + glutamine levels, at increased spatial resolution (from >8 to 2$\mu$L) or shortened scan time (from 32 to 4 min) compared to standardacquisitions. However, rigorous SD-bias analyses showed that overuse of theedge-preserving regularization can compromise the accuracy of the results.  Conclusion: PRECISE-DMI allows a flexible trade-off between enhancing thesensitivity of DMI and minimizing the inaccuracies. With typical settings, theDMI sensitivity can be improved by 3-fold while retaining the capability todetect local signal variations.

Preserved Edge Convolutional Neural Network for Sensitivity Enhancement of Deuterium Metabolic Imaging (DMI)