QQ-NET - Using Deep Learning to Solve Quantitative Susceptibility Mapping and Quantitative Blood Oxygen Level Dependent Magnitude (QSM+qBOLD or QQ) Based Oxygen Extraction Fraction (OEF) Mapping

Overview

Journal Magn Reson Med

Publisher Wiley

Specialty Radiology

Date 2021 Oct 31

PMID 34719059

Citations 8

Authors

Junghun Cho

Jinwei Zhang

Pascal Spincemaille

Hang Zhang

Simon Hubertus

Yan Wen

Ramin Jafari

Shun Zhang

Thanh D Nguyen

Alexey V Dimov

Ajay Gupta

Yi Wang

Affiliations

Soon will be listed here.

Abstract

Purpose: To improve accuracy and speed of quantitative susceptibility mapping plus quantitative blood oxygen level-dependent magnitude (QSM+qBOLD or QQ) -based oxygen extraction fraction (OEF) mapping using a deep neural network (QQ-NET).

Methods: The 3D multi-echo gradient echo images were acquired in 34 ischemic stroke patients and 4 healthy subjects. Arterial spin labeling and diffusion weighted imaging (DWI) were also performed in the patients. NET was developed to solve the QQ model inversion problem based on Unet. QQ-based OEF maps were reconstructed with previously introduced temporal clustering, tissue composition, and total variation (CCTV) and NET. The results were compared in simulation, ischemic stroke patients, and healthy subjects using a two-sample Kolmogorov-Smirnov test.

Results: In the simulation, QQ-NET provided more accurate and precise OEF maps than QQ-CCTV with 150 times faster reconstruction speed. In the subacute stroke patients, OEF from QQ-NET had greater contrast-to-noise ratio (CNR) between DWI-defined lesions and their unaffected contralateral normal tissue than with QQ-CCTV: 1.9 ± 1.3 vs 6.6 ± 10.7 (p = 0.03). In healthy subjects, both QQ-CCTV and QQ-NET provided uniform OEF maps.

Conclusion: QQ-NET improves the accuracy of QQ-based OEF with faster reconstruction.

Citing Articles

Multi-Echo Complex Quantitative Susceptibility Mapping and Quantitative Blood Oxygen Level-Dependent Magnitude (mcQSM + qBOLD or mcQQ) for Oxygen Extraction Fraction (OEF) Mapping.

Cho J, Zhang J, Spincemaille P, Zhang H, Nguyen T, Zhang S Bioengineering (Basel). 2024; 11(2).

PMID: 38391617 PMC: 10886243. DOI: 10.3390/bioengineering11020131.

Evaluating Physiological MRI Parameters in Patients with Brain Metastases Undergoing Stereotactic Radiosurgery-A Preliminary Analysis and Case Report.

van Grinsven E, de Leeuw J, Siero J, Verhoeff J, van Zandvoort M, Cho J Cancers (Basel). 2023; 15(17).

PMID: 37686575 PMC: 10487230. DOI: 10.3390/cancers15174298.

Cerebral oxygen metabolism from MRI susceptibility.

Biondetti E, Cho J, Lee H Neuroimage. 2023; 276:120189.

PMID: 37230206 PMC: 10335841. DOI: 10.1016/j.neuroimage.2023.120189.

Liver PDFF estimation using a multi-decoder water-fat separation neural network with a reduced number of echoes.

Meneses J, Arrieta C, Della Maggiora G, Besa C, Urbina J, Arrese M Eur Radiol. 2023; 33(9):6557-6568.

PMID: 37014405 PMC: 10415440. DOI: 10.1007/s00330-023-09576-2.

Assessing Cerebral Oxygen Metabolism Changes in Patients With Preeclampsia Using Voxel-Based Morphometry of Oxygen Extraction Fraction Maps in Magnetic Resonance Imaging.

Zhang Q, Sui C, Cho J, Yang L, Chen T, Guo B Korean J Radiol. 2023; 24(4):324-337.

PMID: 36907593 PMC: 10067693. DOI: 10.3348/kjr.2022.0652.

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