Accuracy and precision in super-resolution MRI : Enabling spherical tensor diffusion encoding at ultra-high b-values and high resolution
Vis, Geraline LU ; Nilsson, Markus LU ; Westin, Carl Fredrik and Szczepankiewicz, Filip LU
(2021)
In NeuroImage
245.
- Abstract
Diffusion MRI (dMRI) can probe the tissue microstructure but suffers from low signal-to-noise ratio (SNR) whenever high resolution is combined with high diffusion encoding strengths. Low SNR leads to poor precision as well as poor accuracy of the diffusion-weighted signal; the latter is caused by the rectified noise floor and can be observed as a positive bias in magnitude signal. Super-resolution techniques may facilitate a beneficial tradeoff between bias and resolution by allowing acquisition at low spatial resolution and high SNR, whereafter high spatial resolution is recovered by image reconstruction. In this work, we describe a super-resolution reconstruction framework for dMRI and investigate its performance with respect to... (More)
Diffusion MRI (dMRI) can probe the tissue microstructure but suffers from low signal-to-noise ratio (SNR) whenever high resolution is combined with high diffusion encoding strengths. Low SNR leads to poor precision as well as poor accuracy of the diffusion-weighted signal; the latter is caused by the rectified noise floor and can be observed as a positive bias in magnitude signal. Super-resolution techniques may facilitate a beneficial tradeoff between bias and resolution by allowing acquisition at low spatial resolution and high SNR, whereafter high spatial resolution is recovered by image reconstruction. In this work, we describe a super-resolution reconstruction framework for dMRI and investigate its performance with respect to signal accuracy and precision. Using phantom experiments and numerical simulations, we show that the super-resolution approach improves accuracy by facilitating a more beneficial trade-off between spatial resolution and diffusion encoding strength before the noise floor affects the signal. By contrast, precision is shown to have a less straightforward dependency on acquisition, reconstruction, and intrinsic tissue parameters. Indeed, we find a gain in precision from super-resolution reconstruction is substantial only when some spatial resolution is sacrificed. Finally, we deployed super-resolution reconstruction in a healthy brain for the challenging combination of spherical b-tensor encoding at ultra-high b-values and high spatial resolution—a configuration that produces a unique contrast that emphasizes tissue in which diffusion is restricted in all directions. This demonstration showcased that super-resolution reconstruction enables a vastly superior image contrast compared to conventional imaging, facilitating investigations that would otherwise have prohibitively low SNR, resolution or require non-conventional MRI hardware.
(Less)
- author
- Vis, Geraline
LU
; Nilsson, Markus
LU
; Westin, Carl Fredrik
and Szczepankiewicz, Filip
LU
- organization
- publishing date
- 2021
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Diffusion magnetic resonance imaging, Noise propagation, Rectified noise floor, Super-resolution reconstruction, Tensor-valued diffusion encoding, Ultra-high b-values
- in
- NeuroImage
- volume
- 245
- article number
- 118673
- publisher
- Elsevier
- external identifiers
-
- scopus:85118353493
- pmid:34688898
- ISSN
- 1053-8119
- DOI
- 10.1016/j.neuroimage.2021.118673
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2021
- id
- a776bfa2-c639-4ccf-8921-64d718004b3f
- date added to LUP
- 2021-11-24 15:17:07
- date last changed
- 2025-04-07 02:05:14
@article{a776bfa2-c639-4ccf-8921-64d718004b3f,
abstract = {{<p>Diffusion MRI (dMRI) can probe the tissue microstructure but suffers from low signal-to-noise ratio (SNR) whenever high resolution is combined with high diffusion encoding strengths. Low SNR leads to poor precision as well as poor accuracy of the diffusion-weighted signal; the latter is caused by the rectified noise floor and can be observed as a positive bias in magnitude signal. Super-resolution techniques may facilitate a beneficial tradeoff between bias and resolution by allowing acquisition at low spatial resolution and high SNR, whereafter high spatial resolution is recovered by image reconstruction. In this work, we describe a super-resolution reconstruction framework for dMRI and investigate its performance with respect to signal accuracy and precision. Using phantom experiments and numerical simulations, we show that the super-resolution approach improves accuracy by facilitating a more beneficial trade-off between spatial resolution and diffusion encoding strength before the noise floor affects the signal. By contrast, precision is shown to have a less straightforward dependency on acquisition, reconstruction, and intrinsic tissue parameters. Indeed, we find a gain in precision from super-resolution reconstruction is substantial only when some spatial resolution is sacrificed. Finally, we deployed super-resolution reconstruction in a healthy brain for the challenging combination of spherical b-tensor encoding at ultra-high b-values and high spatial resolution—a configuration that produces a unique contrast that emphasizes tissue in which diffusion is restricted in all directions. This demonstration showcased that super-resolution reconstruction enables a vastly superior image contrast compared to conventional imaging, facilitating investigations that would otherwise have prohibitively low SNR, resolution or require non-conventional MRI hardware.</p>}},
author = {{Vis, Geraline and Nilsson, Markus and Westin, Carl Fredrik and Szczepankiewicz, Filip}},
issn = {{1053-8119}},
keywords = {{Diffusion magnetic resonance imaging; Noise propagation; Rectified noise floor; Super-resolution reconstruction; Tensor-valued diffusion encoding; Ultra-high b-values}},
language = {{eng}},
publisher = {{Elsevier}},
series = {{NeuroImage}},
title = {{Accuracy and precision in super-resolution MRI : Enabling spherical tensor diffusion encoding at ultra-high b-values and high resolution}},
url = {{http://dx.doi.org/10.1016/j.neuroimage.2021.118673}},
doi = {{10.1016/j.neuroimage.2021.118673}},
volume = {{245}},
year = {{2021}},
}