Diffusion processes modeling in magnetic resonance imaging.

Diffusion-weighted magnetic resonance imaging Emulsion Imaging phantom Magnetic resonance imaging Water diffusion

Journal

Insights into imaging
ISSN: 1869-4101
Titre abrégé: Insights Imaging
Pays: Germany
ID NLM: 101532453

Informations de publication

Date de publication:
28 Apr 2020
Historique:
received: 31 01 2020
accepted: 20 03 2020
entrez: 30 4 2020
pubmed: 30 4 2020
medline: 30 4 2020
Statut: epublish

Résumé

The paper covers modern approaches to the evaluation of neoplastic processes with diffusion-weighted imaging (DWI) and proposes a physical model for monitoring the primary quantitative parameters of DWI and quality assurance. Models of hindered and restricted diffusion are studied. To simulate hindered diffusion, we used aqueous solutions of polyvinylpyrrolidone with concentrations of 0 to 70%. We created siloxane-based water-in-oil emulsions that simulate restricted diffusion in the intracellular space. To obtain a high signal on DWI in the broadest range of b values, we used silicon oil with high T We developed phantom with control substances for apparent diffusion coefficient (ADC) measurements ranging from normal tissue to benign and malignant lesions: from 2.29 to 0.28 mm The phantom can be used to assess the accuracy of the ADC measurements, as well as the effectiveness of fat suppression. The control substances (emulsions) can be used as a body marker for quality assurance in whole-body DWI with a wide range of b values.

Sections du résumé

BACKGROUND BACKGROUND
The paper covers modern approaches to the evaluation of neoplastic processes with diffusion-weighted imaging (DWI) and proposes a physical model for monitoring the primary quantitative parameters of DWI and quality assurance. Models of hindered and restricted diffusion are studied.
MATERIAL AND METHOD METHODS
To simulate hindered diffusion, we used aqueous solutions of polyvinylpyrrolidone with concentrations of 0 to 70%. We created siloxane-based water-in-oil emulsions that simulate restricted diffusion in the intracellular space. To obtain a high signal on DWI in the broadest range of b values, we used silicon oil with high T
RESULTS RESULTS
We developed phantom with control substances for apparent diffusion coefficient (ADC) measurements ranging from normal tissue to benign and malignant lesions: from 2.29 to 0.28 mm
CONCLUSION CONCLUSIONS
The phantom can be used to assess the accuracy of the ADC measurements, as well as the effectiveness of fat suppression. The control substances (emulsions) can be used as a body marker for quality assurance in whole-body DWI with a wide range of b values.

Identifiants

pubmed: 32346809
doi: 10.1186/s13244-020-00863-w
pii: 10.1186/s13244-020-00863-w
pmc: PMC7188746
doi:

Types de publication

Journal Article

Langues

eng

Pagination

60

Références

PLoS One. 2018 Jun 22;13(6):e0199636
pubmed: 29933396
Med Image Anal. 2011 Jun;15(3):329-39
pubmed: 21317021
Eur Radiol. 2015 Jun;25(6):1541-50
pubmed: 25527431
J Magn Reson Imaging. 2016 Sep;44(3):610-9
pubmed: 26949897
AJNR Am J Neuroradiol. 2018 Apr;39(4):748-755
pubmed: 29449279
Cancer Res. 2014 Sep 1;74(17):4638-52
pubmed: 25183788
Phys Med Biol. 2015 Apr 21;60(8):3389-413
pubmed: 25831194
Neuroimage. 2015 Sep;118:468-83
pubmed: 26091854
Magn Reson Imaging. 2008 Jan;26(1):88-102
pubmed: 17574364
Front Oncol. 2016 Aug 02;6:179
pubmed: 27532028
Top Magn Reson Imaging. 2017 Oct;26(5):201-209
pubmed: 28961569
Invest Radiol. 2011 May;46(5):285-91
pubmed: 21102345
Neuroimage. 2019 Feb 15;187:56-67
pubmed: 29277647

Auteurs

Sergey Morozov (S)

Research and Practical Clinical Center of Diagnostics and Telemedicine Technologies, Department of Health Care of Moscow, 28-1, ul. Srednyaya Kalitnikovskaya, Moscow, 109029, Russia. morozov@npcmr.ru.

Kristina Sergunova (K)

Research and Practical Clinical Center of Diagnostics and Telemedicine Technologies, Department of Health Care of Moscow, 28-1, ul. Srednyaya Kalitnikovskaya, Moscow, 109029, Russia.

Alexey Petraikin (A)

Research and Practical Clinical Center of Diagnostics and Telemedicine Technologies, Department of Health Care of Moscow, 28-1, ul. Srednyaya Kalitnikovskaya, Moscow, 109029, Russia.

Ekaterina Akhmad (E)

Research and Practical Clinical Center of Diagnostics and Telemedicine Technologies, Department of Health Care of Moscow, 28-1, ul. Srednyaya Kalitnikovskaya, Moscow, 109029, Russia.

Stanislav Kivasev (S)

Hospital center of polyclinics AO, 1-3, ul. Bakuninskaya, Moscow, 105005, Russia.

Dmitry Semenov (D)

Research and Practical Clinical Center of Diagnostics and Telemedicine Technologies, Department of Health Care of Moscow, 28-1, ul. Srednyaya Kalitnikovskaya, Moscow, 109029, Russia.

Ivan Blokhin (I)

Research and Practical Clinical Center of Diagnostics and Telemedicine Technologies, Department of Health Care of Moscow, 28-1, ul. Srednyaya Kalitnikovskaya, Moscow, 109029, Russia.

Igor Karpov (I)

Central Institute of Traumatology and Orthopaedics named after N. N. Priorov, 10, ul. Priorova, Moscow, 127299, Russia.

Anton Vladzymyrskyy (A)

Research and Practical Clinical Center of Diagnostics and Telemedicine Technologies, Department of Health Care of Moscow, 28-1, ul. Srednyaya Kalitnikovskaya, Moscow, 109029, Russia.

Alexander Morozov (A)

Central Institute of Traumatology and Orthopaedics named after N. N. Priorov, 10, ul. Priorova, Moscow, 127299, Russia.

Classifications MeSH