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Suppression of Oscillation and Ghosting in RF-Spoiled Gradient-Echo-Based Dynamic Imaging.

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The authors' code

MATLAB · 161 lines · 5.3 KB · no license

  1. clear; close all;
  3. Nx_voxel = 1; Ny_voxel = 1; SpinperVox = 100;
  4. Nx_spin = SpinperVox*Nx_voxel; Ny_spin = SpinperVox*Ny_voxel;
  6. FA = 5; TR = 5e-3;
  7. N_sample = 64;
  9. t = gpuArray(linspace(1e-3,3e-3,N_sample+1)');
  10. T2 = gpuArray(7 * TR * ones(Ny_spin, Nx_spin, N_sample+1));
  11. t2_exp = exp( -reshape(t,1,1,[]) ./ T2 );
  13. T1s = gpuArray(400 * TR * ones(Ny_spin, Nx_spin));
  14. T2s = gpuArray(7 * TR * ones(Ny_spin, Nx_spin));
  16. t1s_exp = exp( -TR ./ T1s );
  17. t2s_exp = exp( -TR ./ T2s );
  19. sp = 50;
  20. dummy = 2000; line = 64; tp = 100;
  21. main = tp*line;
  23. k = 1:1:20000;
  24. RF_spoiler_all = deg2rad( 0.5 * ((k-1).*k) * sp );
  26. Gm_de = Nx_voxel*pi;
  27. Gm_ro = Nx_voxel*2*pi;
  28. Gm_sp = Nx_voxel*1*pi; % pi 3pi 5pi ...
  30. Gm_pe = Ny_voxel * gpuArray(reshape(repmat(linspace(-1/2 + 1/line,1/2,line),tp,1),1,[]) * 2*pi);
  33. %% =========================
  34. % Precompute static terms
  35. % =========================
  36. FA_rad = deg2rad(FA);
  37. cFA = cos(FA_rad);
  38. sFA = sin(FA_rad);
  40. % base phase grids
  41. rot_de_base = gpuArray(linspace(Gm_de/2,-(Gm_de/2)+(Gm_de/Nx_spin),Nx_spin));
  42. rot_sp_base = gpuArray(linspace(-Gm_sp/2,Gm_sp/2-(Gm_sp/Nx_spin),Nx_spin));
  43. rot_dum_ro_base = gpuArray(linspace(-Gm_ro/2,(Gm_ro/2)-(Gm_ro/Nx_spin),Nx_spin));
  45. rot_ro_base = linspace(0,1,N_sample+1)' * linspace(-Gm_ro/2,(Gm_ro/2)-(Gm_ro/Nx_spin),Nx_spin);
  46. rot_ro_base = gpuArray(permute(rot_ro_base,[3 2 1])); % 1 x Nx x (N_sample+1)
  48. % phase-encode base (Ny x 1)
  49. rot_pe_base = gpuArray(linspace(1/2,-1/2+1/Ny_spin,Ny_spin)'); % Ny x Nx
  51. % all phase-encode exponentials (Ny x main)
  52. E_pe_all = exp(-1i * (rot_pe_base * Gm_pe));
  54. % precompute exponentials
  55. E_de = exp(-1i * ( rot_de_base)); % Ny x Nx
  56. E_sp = exp(-1i * (rot_sp_base)); % Ny x Nx
  57. E_dummy_ro = exp(-1i * (repmat(rot_dum_ro_base,[Ny_spin 1]))); % Ny x Nx
  58. E_ro = exp(-1i * (rot_ro_base)); % Ny x Nx x (N_sample+1)
  60. % signal part only uses samples 2:end
  61. E_ro_sig = reshape(E_ro(:,:,2:end) .* t2_exp(:,:,2:end), [], N_sample); % (Ny*Nx) x N_sample
  62. E_ro_end = E_ro(:,:,end);
  64. RF_spoiler = gpuArray(RF_spoiler_all(1:dummy+main));
  65. Ephi_m = exp(-1i * RF_spoiler); % exp(-i*phi_n)
  66. Ephi_p = conj(Ephi_m); % exp(+i*phi_n)
  69. % preallocate
  70. Mxy_pp = complex(gpuArray.zeros(main,N_sample), gpuArray.zeros(main,N_sample));
  72. % initial magnetization
  73. Mxy_TR = complex(gpuArray.zeros(Ny_spin,Nx_spin), gpuArray.zeros(Ny_spin,Nx_spin));
  74. Mz_TR = gpuArray.ones(Ny_spin,Nx_spin);
  76. for n = 1 : dummy+main
  78. % =======================================
  79. % RF rotation using complex transverse M
  80. % =======================================
  81. Mxy = Mxy_TR .* Ephi_m(n); % Rz(-phi)
  82. Mx = real(Mxy);
  83. My = imag(Mxy);
  85. My_rf = cFA .* My + sFA .* Mz_TR;
  86. Mz = -sFA .* My + cFA .* Mz_TR;
  88. Mxy = (Mx + 1i*My_rf) .* Ephi_p(n);
  90. % save post-RF, receiver-demodulated transverse state
  91. Mxy_mm(n,:) = sum(Mxy .* Ephi_m(n),1)/Ny_spin;
  93. % ==========
  94. % Dephasing
  95. % ==========
  96. if n > dummy
  97. idx = n - dummy;
  99. % Ny x 1
  100. E_pe = E_pe_all(:,idx);
  102. % implicit expansion across x
  103. Mxy_de = Mxy .* (E_de .* E_pe);
  105. % ======================
  106. % readout / ADC signal
  107. % ======================
  108. sig = reshape(Mxy_de,1,[]) * E_ro_sig; % 1 x N_sample
  109. Mxy_pp(idx,:) = sig .* Ephi_m(n) / (Nx_spin*Ny_spin);
  111. % final readout state for next spoiler
  112. Mxy_end = Mxy_de .* E_ro_end;
  114. else
  115. E_pe = 1; % scalar
  116. Mxy_de = Mxy .* E_de;
  117. Mxy_end = Mxy_de .* E_dummy_ro;
  118. end
  120. % =====================
  121. % Spoiler + rephasing
  122. % =====================
  123. Mxy_sp = Mxy_end .* (E_sp .* conj(E_pe));
  125. % =====================
  126. % T1 / T2 relaxation
  127. % =====================
  128. Mxy_TR = t2s_exp .* Mxy_sp;
  129. Mz_TR = t1s_exp .* Mz + (1 - t1s_exp);
  131. end
  133. Mxy_echo = reshape(Mxy_pp,tp,line,N_sample);
  135. image = ifftshift(ifft(fftshift(Mxy_echo,2),[],2),2);
  136. image = (ifftshift(ifft(fftshift(image,3),[],3),3));
  138. image_tot = gather(image);
  139. Mxy_echo_tot = gather(Mxy_echo);
  141. wait(gpuDevice);
  143. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  145. rot = rot_de_base + rot_dum_ro_base + rot_sp_base;
  146. figure; plot( rad2deg(rot), abs(squeeze(Mxy_mm(2001,:))),'Color','k','Linewidth',2 );
  147. xlabel('Precession angle [deg]'); ylabel('Magnitude'); ylim([0 0.15])
  148. set(gcf,'color',[1 1 1]); set(gca,'TickDir','out','Box','off','Color','None','FontSize',15);
  150. figure; plot( rad2deg(rot), abs(squeeze(Mxy_mm(2002,:))),'Color','k','Linewidth',2 );
  151. xlabel('Precession angle [deg]'); ylabel('Magnitude'); ylim([0 0.15])
  152. set(gcf,'color',[1 1 1]); set(gca,'TickDir','out','Box','off','Color','None','FontSize',15);
  154. figure; plot( rad2deg(rot), angle(squeeze(Mxy_mm(2001,:))),'Color','k','Linewidth',2 );
  155. xlabel('Precession angle [deg]'); ylabel('Angle'); ylim([-3.5 3.5])
  156. set(gcf,'color',[1 1 1]); set(gca,'TickDir','out','Box','off','Color','None','FontSize',15);
  158. figure; plot( rad2deg(rot), angle(squeeze(Mxy_mm(2002,:))),'Color','k','Linewidth',2 );
  159. xlabel('Precession angle [deg]'); ylabel('Angle'); ylim([-3.5 3.5])
  160. set(gcf,'color',[1 1 1]); set(gca,'TickDir','out','Box','off','Color','None','FontSize',15);

Figure1.m at commit 6b0f7d2, no license · at the source

Overview

Authors: Jae‐Youn Keum1, Jang‐Yeon Park1,2
ORCID iDs: Jae‐Youn Keum
  1. Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea
  2. Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea
Institutions: Sungkyunkwan University (South Korea)
Journal: Magnetic resonance in medicine, volume 96, issue 5, pages 2177-2187
Dates: received 19 February 2026; accepted 22 June 2026; published online 3 July 2026; in print November 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1002/mrm.70497 · PMID 42397159 · PMCID PMC13527254 · OpenAlex W7167229765
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism)
Keywords: 2D line‐scan, gradient‐echo‐based cine imaging, pseudo‐steady state, RF phase‐cycle‐adapted averaging, RF spoiling, ultrahigh temporal resolution
MeSH: Brain*, Image Processing, Computer-Assisted*, Magnetic Resonance Imaging*, Magnetic Resonance Imaging, Cine*, Algorithms, Artifacts, Computer Simulation, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Phantoms, Imaging, Radio Waves, Reproducibility of Results (* major topic)
Topic: Advanced MRI Techniques and Applications (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Funding: National Research Foundation of Korea (RS-2023-NR077284, RS‐2023‐NR077284)
Citations: not cited yet (Europe PMC); 27 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

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Goldrmat/MRM_RF-phase-cycle-adapted-cine-averaging

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 6b0f7d2b4c7cbad986b9c0c6af46a6a4da8e4214, 18 June 2026
Languages: MATLAB (5)
Size: 10 files, 5 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Parallel Computing Toolbox (3 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
6 files
At the source: github.com/Goldrmat/MRM_RF-phase-cycle-adapted-cine-averaging

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

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Read it in the paper: doi.org/10.1002/mrm.70497.

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Version 2, 28 September 2026

  • Publisher: n/a → Wiley

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 6 keywords, 13 MeSH terms, 1 funder, 22 references.

Cite

This paper

Keum, J., & Park, J. (2026). Suppression of Oscillation and Ghosting in RF-Spoiled Gradient-Echo-Based Dynamic Imaging. Magnetic resonance in medicine, 96(5), 2177-2187. https://doi.org/10.1002/mrm.70497

BibTeX

@article{keum2026suppression,
author = {Keum, Jae‐Youn and Park, Jang‐Yeon},
title = {{Suppression of Oscillation and Ghosting in RF-Spoiled Gradient-Echo-Based Dynamic Imaging}},
journal = {Magnetic resonance in medicine},
year = {2026},
month = jul,
volume = {96},
number = {5},
pages = {2177--2187},
publisher = {Wiley},
issn = {0740-3194},
doi = {10.1002/mrm.70497},
url = {https://doi.org/10.1002/mrm.70497},
pmid = {42397159},
pmcid = {PMC13527254}
}

RIS

TY - JOUR
AU - Keum, Jae‐Youn
AU - Park, Jang‐Yeon
TI - Suppression of Oscillation and Ghosting in RF-Spoiled Gradient-Echo-Based Dynamic Imaging
T2 - Magnetic resonance in medicine
J2 - Magn Reson Med
PY - 2026
DA - 2026/07/03
VL - 96
IS - 5
SP - 2177
EP - 2187
SN - 0740-3194
PB - Wiley
DO - 10.1002/mrm.70497
UR - https://doi.org/10.1002/mrm.70497
LA - en
ER -

CSL-JSON

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"given": "Jang‐Yeon"
}
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"container-title-short": "Magn Reson Med",
"volume": "96",
"issue": "5",
"page": "2177-2187",
"DOI": "10.1002/mrm.70497",
"PMID": "42397159",
"PMCID": "PMC13527254",
"ISSN": "0740-3194",
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"URL": "https://doi.org/10.1002/mrm.70497",
"language": "en",
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"date-parts": [
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]
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