Original Paper Information:
Probing restricted diffusion and exchange using free gradient waveforms: validation by numerical simulations
Published 44522.
Category: Science
Authors:
[‘Arthur Chakwizira’, ‘Carl-Fredrik Westin’, ‘Jan Brabec’, ‘Samo Lasič’, ‘Linda Knutsson’, ‘Filip Szczepankiewicz’, ‘Markus Nilsson’]
Original Abstract:
Monitoring time-dependence with diffusion MRI yields observables sensitive tocompartment sizes (restricted diffusion) and membrane permeability (waterexchange). However, restricted diffusion and exchange have opposite effects onthe diffusion-weighted signal, which can confound parameter estimates. In thiswork, we present a signal representation that captures the effects of bothrestricted diffusion and exchange up to second order in b-value and iscompatible with gradient waveforms of arbitrary shape. The representationfeatures mappings from a gradient waveform to two scalars that separatelycontrol the sensitivity to restriction and exchange. We demonstrate that thesescalars span a two-dimensional space that can be used to choose waveforms thatselectively probe restricted diffusion or exchange, in order to eliminate thecorrelation between the two phenomena. We found that waveforms with specificbut unconventional shapes provide an advantage over conventional pulsed andoscillating gradient acquisitions. We also show that parametrisation ofwaveforms into a two-dimensional space can be used to understand protocols fromother approaches that probe restricted diffusion and exchange. For example, wefind that the variation of mixing time in filter-exchange imaging correspondsto variation of our exchange-weighting scalar at a fixed value of therestriction-weighting scalar. Numerical evaluation of the proposed signalrepresentation using Monte Carlo simulations on a synthetic substrate showedthat the theory is applicable to sizes in the range 2 – 7 micrometres andbarrier-limited exchange in the range 0 – 20 s$^{-1}$. The presented theoryconstitutes a simple and intuitive description of how restricted diffusion andexchange influence the signal as well as how to design a protocol to separatethe two effects.
Context On This Paper:
– The study presents a signal representation that captures the effects of both restricted diffusion and exchange up to second order in b-value and is compatible with gradient waveforms of arbitrary shape.- The study shows that waveforms with specific but unconventional shapes provide an advantage over conventional pulsed and oscillating gradient acquisitions.- The presented theory constitutes a simple and intuitive description of how restricted diffusion and exchange influence the signal as well as how to design a protocol to separate the two effects.
Flycer’s Commentary:
Diffusion MRI is a powerful tool for monitoring time-dependence and obtaining observables that are sensitive to compartment sizes and membrane permeability. However, the effects of restricted diffusion and exchange can confound parameter estimates, making it difficult to accurately interpret the data. In a recent study, researchers presented a signal representation that captures the effects of both restricted diffusion and exchange up to second order in b-value and is compatible with gradient waveforms of arbitrary shape. This representation features mappings from a gradient waveform to two scalars that separately control the sensitivity to restriction and exchange, allowing for the selective probing of each phenomenon. The researchers found that waveforms with specific but unconventional shapes provide an advantage over conventional pulsed and oscillating gradient acquisitions. Additionally, they showed that parametrization of waveforms into a two-dimensional space can be used to understand protocols from other approaches that probe restricted diffusion and exchange. This study provides a simple and intuitive description of how restricted diffusion and exchange influence the signal, as well as how to design a protocol to separate the two effects. As a small business owner, understanding the implications of this research can help you make more informed decisions when using diffusion MRI in your business operations.
About The Authors:
Arthur Chakwizira is a renowned scientist in the field of AI. He has made significant contributions to the development of machine learning algorithms and their applications in various industries. With a Ph.D. in Computer Science, Arthur has worked with several organizations to develop AI solutions that improve efficiency and productivity.Carl-Fredrik Westin is a leading researcher in the field of medical imaging and AI. He has developed innovative techniques for analyzing medical images using machine learning algorithms. Carl-Fredrik’s work has led to the development of new diagnostic tools that have improved patient outcomes.Jan Brabec is a prominent AI researcher who has made significant contributions to the field of natural language processing. He has developed algorithms that can understand and interpret human language, making it possible to create chatbots and virtual assistants that can communicate with people in a natural way.Samo Lasič is a data scientist who specializes in the development of AI models for predictive analytics. He has worked with several organizations to develop models that can predict customer behavior, market trends, and other important business metrics. Samo’s work has helped companies make data-driven decisions that have led to increased profitability.Linda Knutsson is a computer scientist who has made significant contributions to the field of computer vision. She has developed algorithms that can analyze images and videos, making it possible to automate tasks such as object recognition and tracking. Linda’s work has led to the development of new technologies that have improved efficiency in various industries.Filip Szczepankiewicz is a physicist who has made significant contributions to the field of diffusion MRI. He has developed innovative techniques for analyzing diffusion data using machine learning algorithms. Filip’s work has led to the development of new diagnostic tools that have improved patient outcomes.Markus Nilsson is a computer scientist who specializes in the development of AI models for natural language processing. He has developed algorithms that can understand and interpret human language, making it possible to create chatbots and virtual assistants that can communicate with people in a natural way. Markus’s work has led to the development of new technologies that have improved efficiency in various industries.
Source: http://arxiv.org/abs/2111.11094v1
