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      Diffusion processes modeling in magnetic resonance imaging

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          Abstract

          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

          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 2: cyclomethicone and caprylyl methicone. For quantitative assessment of our phantom, we performed DWI on 1.5T magnetic resonance scanner with various fat suppression techniques. We assessed water-in-oil emulsion as an extracorporeal source signal by simultaneously scanning a patient in whole-body DWI sequence.

          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 2/s. The ADC values of polymer solutions are well relevant to the mono-exponential equation with the mean relative difference of 0.91%.

          Conclusion

          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.

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          Most cited references5

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          The Role of DWI in the Assessment of Breast Lesions

          Diffusion-weighted MRI (DWI) holds promise to address some of the shortcomings of routine clinical breast MRI and to expand the capabilities of imaging in breast cancer management. DWI reflects tissue microstructure, and provides unique information to aid in characterization of breast lesions. Potential benefits under investigation include improving diagnostic accuracy and guiding treatment decisions. As a result, DWI is increasingly being incorporated into breast MRI protocols and multicenter trials are underway to validate single institution findings and to establish clinical guidelines. Advancements in DWI acquisition and modeling approaches are helping to improve image quality and extract additional biologic information from breast DWI scans, which may extend diagnostic and prognostic value.
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            An MRI digital brain phantom for validation of segmentation methods.

            Knowledge of the exact spatial distribution of brain tissues in images acquired by magnetic resonance imaging (MRI) is necessary to measure and compare the performance of segmentation algorithms. Currently available physical phantoms do not satisfy this requirement. State-of-the-art digital brain phantoms also fall short because they do not handle separately anatomical structures (e.g. basal ganglia) and provide relatively rough simulations of tissue fine structure and inhomogeneity. We present a software procedure for the construction of a realistic MRI digital brain phantom. The phantom consists of hydrogen nuclear magnetic resonance spin-lattice relaxation rate (R1), spin-spin relaxation rate (R2), and proton density (PD) values for a 24 × 19 × 15.5 cm volume of a "normal" head. The phantom includes 17 normal tissues, each characterized by both mean value and variations in R1, R2, and PD. In addition, an optional tissue class for multiple sclerosis (MS) lesions is simulated. The phantom was used to create realistic magnetic resonance (MR) images of the brain using simulated conventional spin-echo (CSE) and fast field-echo (FFE) sequences. Results of mono-parametric segmentation of simulations of sequences with different noise and slice thickness are presented as an example of possible applications of the phantom. The phantom data and simulated images are available online at http://lab.ibb.cnr.it/.
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              Phantoms for diffusion-weighted imaging and diffusion tensor imaging quality control: a review and new perspectives

              Abstract: Introduction Diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) combine magnetic resonance imaging (MRI) techniques and diffusion measures. In DWI, the contrast is defined by microscopic motion of water protons. Nowadays, DWI has become important for early diagnostic of acute stroke. DTI images are calculated from DWI images acquired in at least six directions, which give information of diffusion directionality, making it possible to reconstruct axonal or muscle fiber images. Both techniques have been applied to study body structures in healthy and pathological conditions. Currently, it is known that these images and derived parameters are quite sensitive to factors related to acquisition and processing. Magnetic field inhomogeneity, susceptibility, chemical shift, radiofrequency (RF) interference, eddy currents and low signal-to-noise ratio (SNR) can have a more harmful effect in diffusion data than in T1- or T2-weighted image data. However, even today there are not reference phantoms and guidelines for DWI or DTI quality control (QC). Review Proposals for construction and use of DWI and DTI QC phantoms can be found in literature. DWI have been evaluated using containers filled by gel or liquid with tissue-like MRI properties, as well as using microfabricated devices. DTI acquisitions also have been checked with these devices or using natural or artificial fiber structures. The head phantom from American College of Radiology (ACR) is also pointed out as an alternative for DTI QC. This article brings a discussion about proposed DWI and DTI phantoms, challenges involved and future perspectives for standardization of DWI and DTI QC.
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                Author and article information

                Contributors
                morozov@npcmr.ru
                Journal
                Insights Imaging
                Insights Imaging
                Insights into Imaging
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                1869-4101
                28 April 2020
                28 April 2020
                December 2020
                : 11
                : 60
                Affiliations
                [1 ]Research and Practical Clinical Center of Diagnostics and Telemedicine Technologies, Department of Health Care of Moscow, 28-1, ul. Srednyaya Kalitnikovskaya, Moscow, 109029 Russia
                [2 ]Hospital center of polyclinics AO, 1-3, ul. Bakuninskaya, Moscow, 105005 Russia
                [3 ]Central Institute of Traumatology and Orthopaedics named after N. N. Priorov, 10, ul. Priorova, Moscow, 127299 Russia
                Article
                863
                10.1186/s13244-020-00863-w
                7188746
                32346809
                ba76e3ef-a56d-490d-988b-a8d2ae86611d
                © The Author(s) 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 31 January 2020
                : 20 March 2020
                Categories
                Original Article
                Custom metadata
                © The Author(s) 2020

                Radiology & Imaging
                magnetic resonance imaging,diffusion-weighted magnetic resonance imaging,water diffusion,emulsion,imaging phantom

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