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Ferumoxytol-Enhanced Cardiac Cine MRI Reconstruction Using a Variable-Splitting Spatiotemporal Network.
Gao, Chang; Ming, Zhengyang; Nguyen, Kim-Lien; Pang, Jianing; Bedayat, Arash; Dale, Brian M; Zhong, Xiaodong; Finn, J Paul.
Afiliación
  • Gao C; Department of Physics and Biology in Medicine, University of California Los Angeles, Los Angeles, California, USA.
  • Ming Z; Department of Radiological Sciences, University of California Los Angeles, Los Angeles, California, USA.
  • Nguyen KL; Department of Physics and Biology in Medicine, University of California Los Angeles, Los Angeles, California, USA.
  • Pang J; Department of Radiological Sciences, University of California Los Angeles, Los Angeles, California, USA.
  • Bedayat A; Department of Physics and Biology in Medicine, University of California Los Angeles, Los Angeles, California, USA.
  • Dale BM; Department of Radiological Sciences, University of California Los Angeles, Los Angeles, California, USA.
  • Zhong X; Division of Cardiology, University of California Los Angeles and VA Greater Los Angeles Healthcare System, Los Angeles, California, USA.
  • Finn JP; MR R&D Collaborations, Siemens Medical Solutions USA, Inc., Chicago, Illinois, USA.
J Magn Reson Imaging ; 2024 Mar 04.
Article en En | MEDLINE | ID: mdl-38436994
ABSTRACT

BACKGROUND:

Balanced steady-state free precession (bSSFP) imaging is commonly used in cardiac cine MRI but prone to image artifacts. Ferumoxytol-enhanced (FE) gradient echo (GRE) has been proposed as an alternative. Utilizing the abundance of bSSFP images to develop a computationally efficient network that is applicable to FE GRE cine would benefit future network development.

PURPOSE:

To develop a variable-splitting spatiotemporal network (VSNet) for image reconstruction, trained on bSSFP cine images and applicable to FE GRE cine images. STUDY TYPE Retrospective and prospective.

SUBJECTS:

41 patients (26 female, 53 ± 19 y/o) for network training, 31 patients (19 female, 49 ± 17 y/o) and 5 healthy subjects (5 female, 30 ± 7 y/o) for testing. FIELD STRENGTH/SEQUENCE 1.5T and 3T, bSSFP and GRE. ASSESSMENT VSNet was compared to VSNet with total variation loss, compressed sensing and low rank methods for 14× accelerated data. The GRAPPA×2/×3 images served as the reference. Peak signal-to-noise-ratio (PSNR), structural similarity index (SSIM), left ventricular (LV) and right ventricular (RV) end-diastolic volume (EDV), end-systolic volume (ESV), and ejection fraction (EF) were measured. Qualitative image ranking and scoring were independently performed by three readers. Latent scores were calculated based on scores of each method relative to the reference. STATISTICS Linear mixed-effects regression, Tukey method, Fleiss' Kappa, Bland-Altman analysis, and Bayesian categorical cumulative probit model. A P-value <0.05 was considered statistically significant.

RESULTS:

VSNet achieved significantly higher PSNR (32.7 ± 0.2), SSIM (0.880 ± 0.004), rank (2.14 ± 0.06), and latent scores (-1.72 ± 0.22) compared to other methods (rank >2.90, latent score < -2.63). Fleiss' Kappa was 0.52 for scoring and 0.61 for ranking. VSNet showed no significantly different LV and RV ESV (P = 0.938) and EF (P = 0.143) measurements, but statistically significant different (2.62 mL) EDV measurements compared to the reference.

CONCLUSION:

VSNet produced the highest image quality and the most accurate functional measurements for FE GRE cine images among the tested 14× accelerated reconstruction methods. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY Stage 1.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Magn Reson Imaging Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Magn Reson Imaging Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos