AI/ML

DeepCEST: 9.4 T Chemical exchange saturation transfer MRI contrast predicted from 3 T data – a proof of concept study.



Related Articles

DeepCEST: 9.4 T Chemical exchange saturation transfer MRI contrast predicted from 3 T data – a proof of concept study.

Magn Reson Med. 2019 Feb 25;:

Authors: Zaiss M, Deshmane A, Schuppert M, Herz K, Glang F, Ehses P, Lindig T, Bender B, Ernemann U, Scheffler K

Abstract
PURPOSE: To determine the feasibility of employing the prior knowledge of well-separated chemical exchange saturation transfer (CEST) signals in the 9.4 T Z-spectrum to separate overlapping CEST signals acquired at 3 T, using a deep learning approach trained with 3 T and 9.4 T CEST spectral data from brains of the same subjects.
METHODS: Highly spectrally resolved Z-spectra from the same volunteer were acquired by 3D-snapshot CEST MRI at 3 T and 9.4 T at low saturation power of B1 = 0.6 µT. The volume-registered 3 T Z-spectra-stack was then used as input data for a three layer deep neural network with the volume-registered 9.4 T fitted parameter stack as target data.
RESULTS: An optimized neural net architecture could be found and verified in healthy volunteers. The gray-/white-matter contrast of the different CEST effects was predicted with only small deviations (Pearson R = 0.89). The 9.4 T prediction was less noisy compared to the directly measured CEST maps, although at the cost of slightly lower tissue contrast. Application to an unseen tumor patient measured at 3 T and 9.4 T revealed that tumorous tissue Z-spectra and corresponding hyper-/hypointensities of different CEST effects can also be predicted (Pearson R = 0.84).
CONCLUSION: The 9.4 T CEST signals acquired at low saturation power can be accurately estimated from CEST imaging at 3 T using a neural network trained with coregistered 3 T and 9.4 T data of healthy subjects. The deepCEST approach generalizes to Z-spectra of tumor areas and might indicate whether additional ultrahigh-field (UHF) scans will be beneficial.

PMID: 30803000 [PubMed – as supplied by publisher]

Source link




Related posts

Hyperspectral imaging for predicting the allicin and soluble solid content of garlic with variable selection algorithms and chemometric models.

Newsemia

A primer on healthcare cybersecurity threats & risks in APAC

Newsemia

Model beats Wall Street analysts in forecasting business financials

Newsemia

This website uses cookies to improve your experience. We'll assume you're ok with this, but you can opt-out if you wish. Accept Read More

Privacy & Cookies Policy