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Investigating the Sensitivity of the Diffusion MRI Signal to Magnetization Transfer and Permeability via Monte-Carlo Simulations.

Code ↔ Paper

9 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 9 matches
  1. [1] § Methods › Monte‐Carlo Simulation › General Simulation Parameters › Sequence Parameters ↔ Example_simulation.ipynb, lines 134–188 · score 0.83 · spin echo, gradient duration, gradient strengths, diffusion weighting, sequence parameters, 1200 mT
  2. [2] § Methods › Monte‐Carlo Simulation › General Simulation Parameters › Substrate ↔ Example_simulation.ipynb, lines 134–188 · score 0.80 · cylinder walls, parallel cylinders, MCMRSimulator, outside, distributed diameter, distance
  3. [3] § Methods › Monte‐Carlo Simulation › Implementation of MT and Permeability › MT › Parameter Selection ↔ distributed_diameter/create_distributed_cylinders.ipynb, lines 15–40 · score 0.73 · surface density, cylinder radius, distributed cylinder, bound pool, volume ratio, dwell
  4. [4] § Methods › Monte‐Carlo Simulation › Implementation of MT and Permeability › MT › Modeling Approach ↔ Example_simulation.ipynb, lines 25–47 · score 0.69 · isochromat density, surface density, bound pool, volume ratio, probability, dwell
  5. [5] § Methods › Monte‐Carlo Simulation › Implementation of MT and Permeability › MT › Parameter Selection ↔ fixed_diameter/create_cylinders.ipynb, lines 16–46 · score 0.67 · surface density, cylinder radius, bound pool, volume ratio, dwell, 150 ms
  6. [6] § Methods › Monte‐Carlo Simulation › Implementation of MT and Permeability › MT › Modeling Approach ↔ distributed_diameter/simulate_permeability.jl, lines 43–92 · score 0.61 · free water, diffusion weighted, MCMRSimulator, repetition, transverse, sequences
  7. [7] § Methods › Two‐Compartment Model Fitting ↔ distributed_diameter/local_optim_fit.py, lines 11–53 · score 0.59 · diffusion weighted signal, simulated signal, compartment model, attenuation, fit, density
  8. [8] § Methods › Two‐Compartment Model Fitting ↔ fixed_diameter/local_optim_fit.py, lines 10–52 · score 0.59 · diffusion weighted signal, simulated signal, compartment model, attenuation, fit, density
  9. [9] § Methods › Monte‐Carlo Simulation › General Simulation Parameters › Monte‐Carlo Simulation Setup ↔ distributed_diameter/simulate_mt.jl, lines 43–94 · score 0.51 · intrinsic diffusivity, intra axonal, speed, tissue, weighted, ms

Paper

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The authors' code

Jupyter notebook · 194 lines · 16 KB · Apache-2.0 · 3 matches

  1. # %% [markdown]
  2. # # Demo for generating substrates and running a single simulation
  3. # This notebook is intended as a demo that reproduces a single simulation with generic parameters for the substrate geometry and the sequence applied. Readers are free to alter the parameters and use this as a base example for their own explorations. For a more general introduction to MCMRSimulator, see [its dedicated tutorial](https://open.win.ox.ac.uk/pages/ndcn0236/mcmrsimulator.jl/v0.11/). Note this demo uses the latest stable version of MCMRSimulator as of 2025-1, v0.11.0 and in our paper we used v0.9.0.
  5. # %% [markdown]
  6. # ## Set up environment and import packages
  8. # %%
  9. using Pkg
  10. # Activate the specific environment with the correct packages and version
  11. Pkg.activate("julia-envs/modern_mcmr")
  12. Pkg.instantiate()
  13. # Import packages
  14. using MCMRSimulator
  15. using MRIBuilder
  16. using CairoMakie
  17. using DelimitedFiles
  18. using Printf
  19. using JLD2
  21. # %%
  22. # Create output directory if it does not exist
  23. mkpath("example_output")
  25. # %% [markdown]
  26. # ## Create substrates
  27. # To generate parallel cylinder substrates for our simulation, we will call `random_positions_radii()` for generating positions and radius values for a given volume density value and radius distribution (Gamma distribution characterised by mean and variance) and call `Cylinders()` to create the substrate object in simulation.
  29. # %%
  31. # Set various parameters for the substrate
  32. T2 = 100 # effective T2 caused by MT (ms)
  33. rho = 0.65 # cylinder volume density
  34. r = 1 # cylinder radius (μm)
  35. rep = r*20 # (half of) the distance over which the substrate repeats itself spatially
  37. # Set MT (surface relaxivity) and permeability
  38. T2_bound = 1e-2 # Bound pool T2 to model surface relaxivity (ms)
  39. t_dwell = 30 # Dwell time of isochromats in the bound pool, controls the strength of surface relaxivity
  40. svratio = 2/r # Calculate the surface to volume ratio that is used to calculate the surface density later
  41. surf_dens = 1/svratio*t_dwell/T2 # Surface density of isochromats that achieves the given T2, it's the ratio of isochromat density on the substrate (cylinder) and isochromat density in the volume of interest
  42. perm = 0.005 # permeability, probability for an isochromat to pass through the cylinder wall when they encounter
  44. # Create a substrate with parallel cylinders of fixed diameters
  45. res = MCMRSimulator.random_positions_radii([rep, rep], rho, 2, mean=r, variance=0, max_iter=1000, repulsion_strength=3e-3) # generate cylinder center positions based on slightly larger radius to avoid overlapping
  46. fixed_d_cyl = Cylinders(position=res[1], radius=r, dwell_time=t_dwell, density=surf_dens, permeability=perm, repeats=[rep, rep], R2_surface=1/T2_bound) # create the cylinders object with refined positions
  47. MCMRSimulator.write_geometry("example_output/cylinders_MT_"* string(T2) *"_sus_0_perm_"*@sprintf("%.3f",perm)*"_rmean_"*@sprintf("%.2f",r)*"_density_0.65.json", fixed_d_cyl) # it's possible to save the object as a .json file to be read by MCMRSimulator later
  50. # %% [markdown]
  51. # Similarly, we can also create substrates containing parallel cylinders with Gamma-distributed diameters, note this may take a bit longer than the fixed diameter case.
  53. # %%
  55. # Set various parameters for the substrate
  56. T2 = 100 # effective T2 caused by MT (ms)
  57. rho = 0.65 # cylinder volume density
  58. r = 1 # mean cylinder radius (μm)
  59. r_var = 1 # variance of cylinder radius (μm^2)
  60. rep = r*20 # (half of) the distance over which the substrate repeats itself spatially
  62. # Set MT (surface relaxivity) and permeability
  63. T2_bound = 1e-2 # Bound pool T2 to model surface relaxivity (ms)
  64. t_dwell = 30 # Dwell time of isochromats in the bound pool, controls the strength of surface relaxivity
  65. svratio = 2/r # Calculate the surface to volume ratio that is used to calculate the surface density later
  66. surf_dens = 1/svratio*t_dwell/T2 # Surface density of isochromats that achieves the given T2, it's the ratio of isochromat density on the substrate (cylinder) and isochromat density in the volume of interest
  67. perm = 0.005 # permeability, probability for an isochromat to pass through the cylinder wall when they encounter
  69. # Create a substrate with parallel cylinders of distributed diameters
  70. res = MCMRSimulator.random_positions_radii([rep, rep], rho, 2, mean=r, variance=r_var, max_iter=1000, repulsion_strength=1e-2) # generate cylinder center positions and radii following the mean and variance
  71. distr_d_cyl = Cylinders(position=res[1], radius=res[2], dwell_time=t_dwell, density=surf_dens, permeability=perm, repeats=[rep, rep], R2_surface=1/T2_bound) # create the cylinders object with refined positions
  72. MCMRSimulator.write_geometry("example_output/cylinders_MT_"* string(T2) *"_sus_0_perm_"*@sprintf("%.3f",perm)*"_rmean_"*@sprintf("%.2f",r)*"_rvar_"*@sprintf("%.2f", r_var)*"_density_0.65.json", distr_d_cyl) # it's possible to save the object as a .json file to be read by MCMRSimulator later
  75. # %% [markdown]
  76. # It is possible to plot the geometry to visually check it
  78. # %%
  79. f = plot(PlotPlane(size=50), fixed_d_cyl)
  81. # %%
  82. f = plot(PlotPlane(size=50), distr_d_cyl)
  84. # %% [markdown]
  85. # ## Create diffusion-weighted spin-echo sequence
  86. # This step involves using the `MRIBuilder` package, a new package compatable with MCMRSimulator, designed for building generic sequences using optimisation tools. Here we will just use the `DiffusionSpinEcho()` function to create a DWSE sequence. For more generic use, see [its documentation](https://open.win.ox.ac.uk/pages/ndcn0236/mribuilder.jl/dev/) for more details.
  87. #
  88. # This step just uses built-in function `dwi()` of MCMRSimulator. It allows the user to set many parameters of the DWSE sequence. In our case, gradient_duration, gradient_strength, diffusion_time, TE, and TR were set. For a more detailed explanation and general use of the function, check the [MCMRSimulator tutorial](https://open.win.ox.ac.uk/pages/ndcn0236/mcmrsimulator.jl/v0.9/sequence/#sequence). Note in the later versions of MCMRSimulator, a new package `MRIBuilder` was created for building generic sequences using optimisation tools. See [its documentation](https://open.win.ox.ac.uk/pages/ndcn0236/mribuilder.jl/dev/) for more details.
  90. # %%
  91. # Set sequence parameters
  92. axcaliber7T = Scanner(B0=7., gradient=1460, slew_rate=1e10, units=:Tesla) # Define the scanner hardware limits, gradient means maximum gradient strength (mT/m), set a very high slew rate so the ramp time is almost zero.
  93. TR=3000 # ms
  94. TE=166 # ms
  95. delta=2.5 # gradient duration (ms)
  96. g=1200 # gradient strength (mT/m)
  97. gamma=0.00004257638476 # gyromagnetic ratio for proton (kHz/mT ̇1e-6), needed as dwi() takes gradient strength with kHz/μm unit
  98. Delta=20 # diffusion time (ms)
  100. # Create the sequence using DiffusionSpinEcho()
  101. seq = DiffusionSpinEcho(echo_time=TE, diffusion_time=Delta, gradient=(gradient_strength=[g*gamma, 0., 0.], duration=delta), scanner=axcaliber7T)
  102. write_sequence("example_output/seq_difftime_"*@sprintf("%.1f",Delta)*"_gdur_"*@sprintf("%.1f",delta)*".jld2", seq, format=:serialize) # the sequence object can be saved for future uses
  105. # %% [markdown]
  106. # It is also possible to plot the sequence diagram, note the diffusion-encoding gradients are not symmetric around the refocusing pulse because the MRIBuilder is set to maximise the time between the end of 2nd gradient and the readout to minimise the eddy current effects.
  108. # %%
  109. f = plot_sequence(seq)
  110. f
  112. # %% [markdown]
  113. # ## Run the simulation and generate signal
  114. # Now we have both the substrate and sequence, we can run the simulation by calling the `Simulation()` constructor and get a readout of the final signal level using the `readout()` function. For more details about `readout()`, check the [MCMRSimulator tutorial](https://open.win.ox.ac.uk/pages/ndcn0236/mcmrsimulator.jl/v0.11/tutorial_julia/#Simple-signal-readouts).
  116. # %%
  117. # Construct the simulation object with Simulation() and our sequence (seq) and substrate (fixed_d_cyl) objects as inputs, diffusivity value can be changed depending on the substrate
  118. d = 2.3 # intrinsic diffusivity (μm^2/ms)
  119. sim = Simulation(seq, diffusivity=d, geometry=fixed_d_cyl) # fixed_d_cyl can be replaced by distr_d_cyl
  121. # Use readout() to obtain the signal level given a certain number of isochromats, the bounding box defines the region of interest, an appropriate size is set to be relevant to the repetition scale of the substrate (rep) without including too many cylinders and significantly slowing down the simulation. The subset argument allows us to obtain signals from different parts of the substrate geometry (inside cylinders, outside cylinders, total signal).
  122. sz = rep*2 # Scale of bounding box (i.e. volume of interest), here the volume is cubic so only one number is needed to characterise it.
  123. bbox = BoundingBox([-sz,-sz,-sz],[sz,sz,sz]) # Create the bounding box (cuboid) by setting its lower and upper vertices along the diagonal.
  124. n_isochromat = 10000 # number of isochromats, larger number can lead to lower noise floor level but will slow down the simulation.
  125. sig = readout(n_isochromat, sim, bounding_box=bbox, subset=[Subset(inside=true), Subset(inside=false), Subset()]) # Unless specified otherwise, readout() gives the signal at the time of TE with zero readout duration.
  127. # The output signal can be written to a csv file and analysed later in python or other language/software, note transverse() is applied to the simulated signal as we can only read the transverse signal in reality.
  128. println("Intraaxonal diffusion-weighted signal: ", transverse.(sig)[1])
  129. println("Extraaxonal diffusion-weighted signal: ", transverse.(sig)[2])
  130. println("Total diffusion-weighted signal: ", transverse.(sig)[3])
  131. println("Total initial (b=0) signal: ", n_isochromat)
  132. writedlm("example_output/signal_MT_"*@sprintf("%d",T2)*"_sus_0_perm_"*@sprintf("%.3f",perm)*"_rmean_"*@sprintf("%.2f",r)*"_density_0.65.csv", transverse.(sig), ',')
  134. # %% [markdown]
  135. # Create a function that takes MT, permeability and b values as input and returns the signal and write it to a file
  137. # %% [markdown]
  138. # # Exploring MT and permeability's effect on the signal level
  139. # We can now incorporate all the above into a function `simulate_mt_perm()` that runs a single simulation for a given pair of MT and permeability values and print the resulting trasverse signal. MT strength is defined by the effective T2 (in ms) of the additional relaxation it induces and permeability is defined by probability (0-1) for an isochromat to pass through the cylinder wall when they encounter. Feel free to play around with different MT and permeability values or even change the sequence parameters.
  141. # %%
  142. function simulate_mt_perm(mt=Inf, # Effective T2 (ms), positive, Inf means no MT
  143. permeability=0; # probability (0-1) for an isochromat to pass through the cylinder wall when they encounter
  144. diffusivity = 2.3, # intrinsic diffusivity (μm^2/ms)
  145. TR=3000, # ms
  146. TE=166, # ms
  147. delta=2.5, # gradient duration (ms)
  148. g=1200, # gradient strength (mT/m)
  149. gamma=0.00004257638476, # gyromagnetic ratio for proton (kHz/mT ̇1e-6), needed as dwi() takes gradient strength with kHz/μm unit
  150. Delta=20, # diffusion time (ms)
  151. rho = 0.65, # cylinder volume density
  152. r = 1, # mean cylinder radius (μm)
  153. r_var = 0 # variance of cylinder radius (μm^2)
  154. )
  155. @assert mt>0 "MT effective T2 needs to positive!"
  156. @assert permeability>=0 && permeability<=1 "Permeability needs to be between 0 and 1!"
  157. rep = r*20 # (half of) the distance over which the substrate repeats itself spatially
  158. T2_bound = 1e-2 # Bound pool T2 to model surface relaxivity (ms)
  159. t_dwell = 30 # Dwell time of isochromats in the bound pool, controls the strength of surface relaxivity
  160. svratio = 2/r # Calculate the surface to volume ratio that is used to calculate the surface density later
  161. surf_dens = 1/svratio*t_dwell/mt # Surface density of isochromats that achieves the given T2, it's the ratio of isochromat density on the substrate (cylinder) and isochromat density in the volume of interest
  163. # Create a substrate with parallel cylinders of distributed diameters
  164. res = MCMRSimulator.random_positions_radii([rep, rep], rho, 2, mean=r, variance=r_var, max_iter=1000, repulsion_strength=1e-2) # generate cylinder center positions and radii following the mean and variance
  165. distr_d_cyl = Cylinders(position=res[1], radius=res[2], dwell_time=t_dwell, density=surf_dens, permeability=perm, repeats=[rep, rep], R2_surface=1/T2_bound) # create the cylinders object with refined positions
  166. MCMRSimulator.write_geometry("example_output/cylinders_MT_"* string(T2) *"_sus_0_perm_"*@sprintf("%.3f",perm)*"_rmean_"*@sprintf("%.2f",r)*"_density_0.65.json", distr_d_cyl) # save the object as a .json file to be read by MCMRSimulator later
  167. # Create the DWSE sequence
  168. axcaliber7T = Scanner(B0=7., gradient=1460, slew_rate=1e10, units=:Tesla) # Define the scanner hardware limits, gradient means maximum gradient strength (mT/m).
  169. seq = DiffusionSpinEcho(echo_time=TE, diffusion_time=Delta, gradient=(gradient_strength=[g*gamma, 0., 0.], duration=delta), scanner=axcaliber7T)
  170. write_sequence("example_output/seq_difftime_"*@sprintf("%.2f",Delta)*"_gdur_"*@sprintf("%.2f",delta)*".jld2", seq, format=:serialize) # save the sequence object for future uses
  172. # Form the simulation object
  173. sim = Simulation(seq, diffusivity=diffusivity, geometry=distr_d_cyl)
  175. # Use readout() to obtain the signal level given a certain number of isochromats, the bounding box defines the region of interest, an appropriate size is set to be relevant to the repetition scale of the substrate (rep) without including too many cylinders and significantly slowing down the simulation. The subset argument allows us to obtain signals from different parts of the substrate geometry (inside cylinders, outside cylinders, total signal).
  176. sz = rep*2 # Scale of bounding box (i.e. volume of interest), here the volume is cubic so only one number is needed to characterise it.
  177. bbox = BoundingBox([-sz,-sz,-sz],[sz,sz,sz]) # Create the bounding box (cuboid) by setting its lower and upper vertices along the diagonal.
  178. n_isochromat = 10000 # number of isochromats, larger number can lead to lower noise floor level but will slow down the simulation.
  179. sig = readout(n_isochromat, sim, bounding_box=bbox, subset=[Subset(inside=true), Subset(inside=false), Subset()]) # Unless specified otherwise, readout() gives the signal at the time of TE with zero readout duration.
  181. # The output signal can be written to a csv file and analysed later in python or other language/software, note transverse() is applied to the simulated signal as we can only read the transverse signal in reality.
  182. # Print the diffusion weighted attenuation for the intra-axonal, extra-axonal compartments and the total attenuation.
  183. println("Intraaxonal diffusion-weighted signal: ", transverse.(sig)[1])
  184. println("Extraaxonal diffusion-weighted signal: ", transverse.(sig)[2])
  185. println("Total diffusion-weighted signal: ", transverse.(sig)[3])
  186. println("Total initial (b=0) signal: ", n_isochromat)
  187. writedlm("example_output/signal_MT_"*@sprintf("%d",T2)*"_sus_0_perm_"*@sprintf("%.3f",perm)*"_rmean_"*@sprintf("%.2f",r)*"_density_0.65.csv", transverse.(sig), ',')
  188. end
  190. # %%
  191. simulate_mt_perm(100, 0.001)
  193. # %%

Example_simulation.ipynb at commit 65bfa23, under Apache-2.0 · at the source

Overview

Authors: Zhiyu Zheng1, Karla L Miller1, Benjamin C Tendler1, Michiel Cottaar1
ORCID iDs: Zhiyu Zheng, Benjamin C Tendler, Michiel Cottaar
  1. Oxford Centre for Integrative Neuroimaging (OxCIN), FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
Institutions: University of Oxford (United Kingdom); Wellcome Centre for Integrative Neuroimaging (United Kingdom)
Journal: Magnetic resonance in medicine, volume 96, issue 2, pages 960-974
Dates: received 26 August 2025; accepted 27 March 2026; published online 12 April 2026; in print August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1002/mrm.70378 · PMID 41968369 · PMCID PMC13269245 · OpenAlex W7153898259
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), computational (subfield)
Keywords: diffusion modeling, exchange, magnetization transfer, microstructure, Monte‐Carlo simulation
MeSH: Brain*, Diffusion Magnetic Resonance Imaging*, Image Interpretation, Computer-Assisted*, White Matter*, Algorithms, Computer Simulation, Humans, Monte Carlo Method, Permeability, Reproducibility of Results, Sensitivity and Specificity (* major topic)
Topic: Advanced Neuroimaging Techniques and Applications (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Funding: Wellcome Trust (215573/Z/19/Z, 203139/A/16/Z, 203139/Z/16/Z, 222829/Z/21/Z, 224573/Z/21/Z); NIHR Oxford Health Biomedical Research Centre (NIHR203316); China Scholarship Council; University of Oxford; National Institute for Health Research (NIHR) (NIHR203316)
Citations: not cited yet (Europe PMC); 77 references in the paper

Abstract

Purpose: Magnetization transfer (MT) and water exchange via permeability operate on a similar spatiotemporal scale to water diffusion. In this study, we use a simulation‐based approach to characterize how MT and permeability impact (1) diffusion‐weighted MRI (dMRI) measurements from cylindrical substrates and (2) parameter estimation using a two‐compartment model of white matter.

Methods: We used Monte‐Carlo simulations to model the dMRI signal inside and outside axons by simulating signals from parallel cylinders with different diameters and volume densities. We subsequently introduced membrane permeability and MT at the cylinder walls to investigate their impact on the dMRI signal. We fitted a two‐compartment model to the simulated signal to produce estimates of the cylinder diameter and density. We evaluated the impact of MT and permeability by comparing the fitted diameter and density to the simulated ground truth.

Results: Permeability leads to underestimation (up to 100%) of cylinder diameter and density. Specifically, by enabling isochromats to escape from restrictions and diffuse more freely, permeability makes the overall displacement profile closer to the extra‐axonal displacement profile. MT had limited effects on diameter estimation but caused substantial bias (20%–50%) in volume density estimates depending on the ratio of the intra‐axonal and extra‐axonal volume fraction. This is due to the intra‐axonal and extra‐axonal space having different surface‐to‐volume ratios and therefore different surface relaxation rates.

Conclusion: Permeability and MT can considerably influence the dMRI signal. They increase the relative contribution from larger cylinders to the dMRI signal and bias microstructural parameter estimates derived from dMRI data.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 9 matches between paragraphs and lines of code.

zhiyuzheng1769/mt-and-permeability-effect-on-two-compartment-dmri-wm-model

License: Apache-2.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 65bfa230be49fcb472cc9325a81efaf8876d7772, 27 February 2026
Languages: Jupyter (8), Julia (5), Shell (4), Python (3)
Size: 47 files, 20 scripts
Software Heritage: not archived
Found in: the text, “Impact”
Holds: README, license file, environment (Manifest.toml, pixi.lock, pixi.toml, Project.toml, julia-envs/modern_mcmr/Manifest.toml, julia-envs/modern_mcmr/Project.toml, julia-envs/old_mcmr/Manifest.toml, julia-envs/old_mcmr/Project.toml), 8 notebooks
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (8 files), pandas (7 files), Matplotlib (5 files), FSL (4 files), SciPy (4 files), Makie (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
22 files
At the source: github.com/zhiyuzheng1769/MT-and-permeability-effect-on-two-compartment-dMRI-WM-model/tree/main

The paper's code and data availability statement is in the Data section.

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Data

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Data Availability Statement

All the scripts and raw data that are used to obtain the following results are available online at https://github.com/zhiyuzheng1769/MT‐and‐permeability‐effect‐on‐two‐compartment‐dMRI‐WM‐model (https://github.com/zhiyuzheng1769/MT-and-permeability-effect-on-two-compartment-dMRI-WM-model). The SHA‐1 hash for the version used is 96b6466de1e743e828d3b3f7aad31a3d8a6 ac1c3.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 5 keywords, 11 MeSH terms, 5 funders, 71 references.

Cite

This paper

Zheng, Z., Miller, K. L., Tendler, B. C., & Cottaar, M. (2026). Investigating the Sensitivity of the Diffusion MRI Signal to Magnetization Transfer and Permeability via Monte-Carlo Simulations. Magnetic resonance in medicine, 96(2), 960-974. https://doi.org/10.1002/mrm.70378

BibTeX

@article{zheng2026investigating,
author = {Zheng, Zhiyu and Miller, Karla L and Tendler, Benjamin C and Cottaar, Michiel},
title = {{Investigating the Sensitivity of the Diffusion MRI Signal to Magnetization Transfer and Permeability via Monte-Carlo Simulations}},
journal = {Magnetic resonance in medicine},
year = {2026},
month = apr,
volume = {96},
number = {2},
pages = {960--974},
publisher = {Wiley},
issn = {0740-3194},
doi = {10.1002/mrm.70378},
url = {https://doi.org/10.1002/mrm.70378},
pmid = {41968369},
pmcid = {PMC13269245}
}

RIS

TY - JOUR
AU - Zheng, Zhiyu
AU - Miller, Karla L
AU - Tendler, Benjamin C
AU - Cottaar, Michiel
TI - Investigating the Sensitivity of the Diffusion MRI Signal to Magnetization Transfer and Permeability via Monte-Carlo Simulations
T2 - Magnetic resonance in medicine
J2 - Magn Reson Med
PY - 2026
DA - 2026/04/12
VL - 96
IS - 2
SP - 960
EP - 974
SN - 0740-3194
PB - Wiley
DO - 10.1002/mrm.70378
UR - https://doi.org/10.1002/mrm.70378
LA - en
ER -

CSL-JSON

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"container-title": "Magnetic resonance in medicine",
"author": [
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"family": "Zheng",
"given": "Zhiyu"
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{
"family": "Miller",
"given": "Karla L"
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{
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"container-title-short": "Magn Reson Med",
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"page": "960-974",
"DOI": "10.1002/mrm.70378",
"PMID": "41968369",
"PMCID": "PMC13269245",
"ISSN": "0740-3194",
"publisher": "Wiley",
"URL": "https://doi.org/10.1002/mrm.70378",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
12
]
]
}
}

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