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Toward Super-Resolution Reconstruction of Diffusion–Relaxation MRI Using Slice Excitation With Random Overlap (SERO)

Mortensen, Felix LU ; Jurek, Jakub LU ; Sjölund, Jens ; Vis, Geraline LU ; Wirestam, Ronnie LU orcid ; Molendowska, Malwina LU ; Materka, Andrzej and Szczepankiewicz, Filip LU orcid (2026) In Magnetic Resonance in Medicine
Abstract

Purpose: Diffusion MRI probes tissue microstructure, but low SNR and limited resolution hinder detection of features and parameter estimates. We introduce slice excitation with random overlap (SERO), which enables variable repetition times (TRs) and diffusion weighting within a single shot. This acquisition supports super-resolution reconstruction of baseline signal ((Formula presented.)), diffusivity ((Formula presented.)), diffusional variance ((Formula presented.)), and longitudinal relaxation ((Formula presented.)) maps. Methods: We implemented a diffusion-weighted spin-echo sequence in Pulseq that excites thick slices at random positions. Across shots, pseudo-random overlap produces inter- and intra-slice TR variation (0.15–21.9 s)... (More)

Purpose: Diffusion MRI probes tissue microstructure, but low SNR and limited resolution hinder detection of features and parameter estimates. We introduce slice excitation with random overlap (SERO), which enables variable repetition times (TRs) and diffusion weighting within a single shot. This acquisition supports super-resolution reconstruction of baseline signal ((Formula presented.)), diffusivity ((Formula presented.)), diffusional variance ((Formula presented.)), and longitudinal relaxation ((Formula presented.)) maps. Methods: We implemented a diffusion-weighted spin-echo sequence in Pulseq that excites thick slices at random positions. Across shots, pseudo-random overlap produces inter- and intra-slice TR variation (0.15–21.9 s) with b-values up to 1.4 ms/μm2. The (Formula presented.) -weighting enables through-slice super-resolution and allows (Formula presented.) estimation. Accuracy and precision were evaluated in numerical phantoms across variable SNR. SERO was compared with slice-shifting super-resolution and conventional high-resolution imaging. Feasibility was demonstrated in healthy brain in vivo at 1.5-mm isotropic resolution in 2:30 min. Results: In simulations SERO improved accuracy of (Formula presented.), (Formula presented.), and (Formula presented.) while maintaining voxel-wise precision comparable to direct sampling across SNRs. Regularized SERO achieved RMSE ≈ 0.5 μm2/ms ((Formula presented.)) and ≈ 0.5 μm4/ms2 ((Formula presented.)) at SNR = 3, whereas direct sampling required SNR ≥ 7–10; root-mean–variance decreased by > 50% versus an unregularized fit. In vivo, SERO yielded sharp tissue boundaries and smooth parameter maps. Conclusion: Random slice overlap enriches encoding diversity, improving accuracy and precision of diffusion and relaxation parameters without longer scan time. SERO offers a novel path to high-resolution microstructural imaging, especially at low SNR.

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author
; ; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
epub
subject
keywords
diffusion MRI, diffusion–relaxation imaging, diffusion–weighted imaging, pulse sequence design, super-resolution reconstruction, T mapping
in
Magnetic Resonance in Medicine
publisher
John Wiley & Sons Inc.
external identifiers
  • pmid:41621822
  • scopus:105029042464
ISSN
0740-3194
DOI
10.1002/mrm.70282
language
English
LU publication?
yes
id
cb58dd17-df02-4f79-8b5a-8bea79e15df7
date added to LUP
2026-02-23 11:01:22
date last changed
2026-02-24 03:00:02
@article{cb58dd17-df02-4f79-8b5a-8bea79e15df7,
  abstract     = {{<p>Purpose: Diffusion MRI probes tissue microstructure, but low SNR and limited resolution hinder detection of features and parameter estimates. We introduce slice excitation with random overlap (SERO), which enables variable repetition times (TRs) and diffusion weighting within a single shot. This acquisition supports super-resolution reconstruction of baseline signal ((Formula presented.)), diffusivity ((Formula presented.)), diffusional variance ((Formula presented.)), and longitudinal relaxation ((Formula presented.)) maps. Methods: We implemented a diffusion-weighted spin-echo sequence in Pulseq that excites thick slices at random positions. Across shots, pseudo-random overlap produces inter- and intra-slice TR variation (0.15–21.9 s) with b-values up to 1.4 ms/μm<sup>2</sup>. The (Formula presented.) -weighting enables through-slice super-resolution and allows (Formula presented.) estimation. Accuracy and precision were evaluated in numerical phantoms across variable SNR. SERO was compared with slice-shifting super-resolution and conventional high-resolution imaging. Feasibility was demonstrated in healthy brain in vivo at 1.5-mm isotropic resolution in 2:30 min. Results: In simulations SERO improved accuracy of (Formula presented.), (Formula presented.), and (Formula presented.) while maintaining voxel-wise precision comparable to direct sampling across SNRs. Regularized SERO achieved RMSE ≈ 0.5 μm<sup>2</sup>/ms ((Formula presented.)) and ≈ 0.5 μm<sup>4</sup>/ms<sup>2</sup> ((Formula presented.)) at SNR = 3, whereas direct sampling required SNR ≥ 7–10; root-mean–variance decreased by &gt; 50% versus an unregularized fit. In vivo, SERO yielded sharp tissue boundaries and smooth parameter maps. Conclusion: Random slice overlap enriches encoding diversity, improving accuracy and precision of diffusion and relaxation parameters without longer scan time. SERO offers a novel path to high-resolution microstructural imaging, especially at low SNR.</p>}},
  author       = {{Mortensen, Felix and Jurek, Jakub and Sjölund, Jens and Vis, Geraline and Wirestam, Ronnie and Molendowska, Malwina and Materka, Andrzej and Szczepankiewicz, Filip}},
  issn         = {{0740-3194}},
  keywords     = {{diffusion MRI; diffusion–relaxation imaging; diffusion–weighted imaging; pulse sequence design; super-resolution reconstruction; T mapping}},
  language     = {{eng}},
  publisher    = {{John Wiley & Sons Inc.}},
  series       = {{Magnetic Resonance in Medicine}},
  title        = {{Toward Super-Resolution Reconstruction of Diffusion–Relaxation MRI Using Slice Excitation With Random Overlap (SERO)}},
  url          = {{http://dx.doi.org/10.1002/mrm.70282}},
  doi          = {{10.1002/mrm.70282}},
  year         = {{2026}},
}