Accelerating FLAIR imaging via deep learning reconstruction: potential for evaluating white matter hyperintensities.
Accelerated image
Deep learning reconstruction
FLAIR
White matter hyperintensity
Journal
Japanese journal of radiology
ISSN: 1867-108X
Titre abrégé: Jpn J Radiol
Pays: Japan
ID NLM: 101490689
Informations de publication
Date de publication:
24 Sep 2024
24 Sep 2024
Historique:
received:
28
05
2024
accepted:
16
09
2024
medline:
24
9
2024
pubmed:
24
9
2024
entrez:
24
9
2024
Statut:
aheadofprint
Résumé
To evaluate deep learning-reconstructed (DLR)-fluid-attenuated inversion recovery (FLAIR) images generated from undersampled data, compare them with fully sampled and rapidly acquired FLAIR images, and assess their potential for white matter hyperintensity evaluation. We examined 30 patients with white matter hyperintensities, obtaining fully sampled FLAIR images (standard FLAIR, std-FLAIR). We created accelerated FLAIR (acc-FLAIR) images using one-third of the fully sampled data and applied deep learning to generate DLR-FLAIR images. Three neuroradiologists assessed the quality (amount of noise and gray/white matter contrast) in all three image types. The reproducibility of hyperintensities was evaluated by comparing a subset of 100 hyperintensities in acc-FLAIR and DLR-FLAIR images with those in the std-FLAIR images. Quantitatively, similarities and errors of the entire image and the focused regions on white matter hyperintensities in acc-FLAIR and DLR-FLAIR images were measured against std-FLAIR images using structural similarity index measure (SSIM), regional SSIM, normalized root mean square error (NRMSE), and regional NRMSE values. All three neuroradiologists evaluated DLR-FLAIR as having significantly less noise and higher image quality scores compared with std-FLAIR and acc-FLAIR (p < 0.001). All three neuroradiologists assigned significantly higher frontal lobe gray/white matter visibility scores for DLR-FLAIR than for acc-FLAIR (p < 0.001); two neuroradiologists attributed significantly higher scores for DLR-FLAIR than for std-FLAIR (p < 0.05). Regarding white matter hyperintensities, all three neuroradiologists significantly preferred DLR-FLAIR (p < 0.0001). DLR-FLAIR exhibited higher similarity to std-FLAIR in terms of visibility of the hyperintensities, with 97% of the hyperintensities rated as nearly identical or equivalent. Quantitatively, DLR-FLAIR demonstrated significantly higher SSIM and regional SSIM values than acc-FLAIR, with significantly lower NRMSE and regional NRMSE values (p < 0.0001). DLR-FLAIR can reduce scan time and generate images of similar quality to std-FLAIR in patients with white matter hyperintensities. Therefore, DLR-FLAIR may serve as an effective method in traditional magnetic resonance imaging protocols.
Identifiants
pubmed: 39316286
doi: 10.1007/s11604-024-01666-5
pii: 10.1007/s11604-024-01666-5
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Japan Society for the Promotion of Science
ID : JP22K15837
Informations de copyright
© 2024. The Author(s).
Références
Sati P, George IC, Shea CD, Gaitán MI, Reich DS, FLAIR. FLAIR*: A combined MR contrast technique for visualizing white matter lesions and parenchymal veins. Radiology. 2012;265:926–32. https://doi.org/10.1148/radiol.12120208 .
Simon JH, Li D, Traboulsee A, Coyle PK, Arnold DL, Barkhof F, et al. Standardized MR imaging protocol for multiple sclerosis: Consortium of MS Centers consensus guidelines. AJNR Am J Neuroradiol. 2006;27:455–61. https://www.ajnr.org/content/27/2/455.long
pubmed: 16484429
pmcid: 8148806
Saranathan M, Worters PW, Rettmann DW, Winegar B, Becker J. Physics for clinicians: fluid-attenuated inversion recovery (FLAIR) and double inversion recovery (DIR) Imaging. J Magn Reson Imaging. 2017;46:1590–600. https://doi.org/10.1002/jmri.25737 .
doi: 10.1002/jmri.25737
pubmed: 28419602
Aja-Fernández S, Vegas-Sánchez-Ferrero G, Tristán-Vega A. Noise estimation in parallel MRI: GRAPPA and SENSE. Magn Reson Imaging. 2014;32:281–90. https://doi.org/10.1016/j.mri.2013.12.001 .
doi: 10.1016/j.mri.2013.12.001
pubmed: 24418329
Hoge WS, Brooks DH. On the complimentarity of SENSE and GRAPPA in parallel MR imaging. Conf Proc IEEE Eng Med Biol Soc. 2006;2006:755–8. https://doi.org/10.1109/IEMBS.2006.259697 .
doi: 10.1109/IEMBS.2006.259697
pubmed: 17945998
Lustig M, Donoho D, Pauly JM. Sparse MRI: The application of compressed sensing for rapid MR imaging. Magn Reson Med. 2007;58:1182–95. https://doi.org/10.1002/mrm.21391 .
doi: 10.1002/mrm.21391
pubmed: 17969013
Liang D, Liu B, Wang J, Ying L. Accelerating SENSE using compressed sensing. Magn Reson Med. 2009;62:1574–84. https://doi.org/10.1002/mrm.22161 .
doi: 10.1002/mrm.22161
pubmed: 19785017
Jaspan ON, Fleysher R, Lipton ML. Compressed sensing MRI: a review of the clinical literature. Br J Radiol. 2015;88:20150487. https://doi.org/10.1259/bjr.20150487 .
doi: 10.1259/bjr.20150487
pubmed: 26402216
pmcid: 4984938
Qian Y, Stenger VA, Boada FE. Parallel imaging with 3D TPI trajectory: SNR and acceleration benefits. Magn Reson Imaging. 2009;27:656–63. https://doi.org/10.1016/j.mri.2008.10.008 .
doi: 10.1016/j.mri.2008.10.008
pubmed: 19110392
Smith DS, Gore JC, Yankeelov TE, Welch EB. Real-time compressive sensing MRI reconstruction using GPU computing and split Bregman methods. Int J Biomed Imaging. 2012;2012: 864827. https://doi.org/10.1155/2012/864827 .
doi: 10.1155/2012/864827
pubmed: 22481908
pmcid: 3296267
Kiryu S, Akai H, Yasaka K, Tajima T, Kunimatsu A, Yoshioka N, et al. Clinical impact of deep learning reconstruction in MRI. Radiographics. 2023;43: e220133. https://doi.org/10.1148/rg.220133 .
doi: 10.1148/rg.220133
pubmed: 37200221
Hamabuchi N, Ohno Y, Kimata H, Ito Y, Fujii K, Akino N, et al. Effectiveness of deep learning reconstruction on standard to ultra-low-dose high-definition chest CT images. Jpn J Radiol. 2023;41:1373–88. https://doi.org/10.1007/s11604-023-01470-7 .
doi: 10.1007/s11604-023-01470-7
pubmed: 37498483
pmcid: 10687108
Hosoi R, Yasaka K, Mizuki M, Yamaguchi H, Miyo R, Hamada A, et al. Deep learning reconstruction with single-energy metal artifact reduction in pelvic computed tomography for patients with metal hip prostheses. Jpn J Radiol. 2023;41:863–71. https://doi.org/10.1007/s11604-023-01402-5 .
doi: 10.1007/s11604-023-01402-5
pubmed: 36862290
pmcid: 10366278
Oshima S, Fushimi Y, Miyake KK, Nakajima S, Sakata A, Okuchi S, et al. Denoising approach with deep learning-based reconstruction for neuromelanin-sensitive MRI: image quality and diagnostic performance. Jpn J Radiol. 2023;41:1216–25. https://doi.org/10.1007/s11604-023-01452-9 .
doi: 10.1007/s11604-023-01452-9
pubmed: 37256470
pmcid: 10613599
Tanahashi Y, Kubota K, Nomura T, Ikeda T, Kutsuna M, Funayama S, et al. Improved vascular depiction and image quality through deep learning reconstruction of CT hepatic arteriography during transcatheter arterial chemoembolization. Jpn J Radiol. 2024. https://doi.org/10.1007/s11604-024-01614-3 .
doi: 10.1007/s11604-024-01614-3
pubmed: 38888853
Kim SH, Choi YH, Lee JS, Lee SB, Cho YJ, Lee SH, et al. Deep learning reconstruction in pediatric brain MRI: comparison of image quality with conventional T2-weighted MRI. Neuroradiology. 2023;65:207–14. https://doi.org/10.1007/s00234-022-03053-1 .
doi: 10.1007/s00234-022-03053-1
pubmed: 36156109
Bash S, Wang L, Airriess C, Zaharchuk G, Gong E, Shankaranarayanan A, et al. Deep learning enables 60% accelerated volumetric brain MRI while preserving quantitative performance: a prospective, multicenter, multireader trial. AJNR Am J Neuroradiol. 2021;42:2130–7. https://doi.org/10.3174/ajnr.A7358 .
doi: 10.3174/ajnr.A7358
pubmed: 34824098
pmcid: 8805755
Kidoh M, Shinoda K, Kitajima M, Isogawa K, Nambu M, Uetani H, et al. Deep learning based noise reduction for brain MR imaging: Tests on phantoms and healthy volunteers. Magn Reson Med Sci. 2020;19:195–206. https://doi.org/10.2463/mrms.mp.2019-0018 .
doi: 10.2463/mrms.mp.2019-0018
pubmed: 31484849
Tajima T, Akai H, Yasaka K, Kunimatsu A, Yamashita Y, Akahane M, et al. Usefulness of deep learning-based noise reduction for 1.5 T MRI brain images. Clin Radiol. 2023;78:e13-21. https://doi.org/10.1016/j.crad.2022.08.127 .
doi: 10.1016/j.crad.2022.08.127
pubmed: 36116967
Estler A, Hauser TK, Mengel A, Brunnée M, Zerweck L, Richter V, et al. Deep learning accelerated image reconstruction of fluid-attenuated inversion recovery sequence in brain imaging: reduction of acquisition time and improvement of image quality. Acad Radiol. 2024;31:180–6. https://doi.org/10.1016/j.acra.2023.05.010 .
doi: 10.1016/j.acra.2023.05.010
pubmed: 37280126
Yamamoto T, Lacheret C, Fukutomi H, Kamraoui RA, Denat L, Zhang B, et al. Validation of a denoising method using deep learning-based reconstruction to quantify multiple sclerosis lesion load on fast FLAIR imaging. AJNR Am J Neuroradiol. 2022;43:1099–106. https://doi.org/10.3174/ajnr.A7589 .
doi: 10.3174/ajnr.A7589
pubmed: 35902124
pmcid: 9575422
Suzuki A, Amemiya T, Kaneko Y, Shirai T. Combination of iterative reconstruction and CNN-based denoising for non-uniform noise for parallel imaging in MRI. Med Imaging Technol. 2023;41:37–51.
Samsonov AA, Kholmovski EG, Parker DL, Johnson CR. POCSENSE: POCS-based reconstruction for sensitivity encoded magnetic resonance imaging. Magn Reson Med. 2004;52:1397–406. https://doi.org/10.1002/mrm.20285 .
doi: 10.1002/mrm.20285
pubmed: 15562485
Dong C, Loy CC, He K, Tang X. Image super-resolution using deep convolutional networks. IEEE Trans Pattern Anal Mach Intell. 2016;38:295–307. https://doi.org/10.1109/TPAMI.2015.2439281 .
doi: 10.1109/TPAMI.2015.2439281
pubmed: 26761735
He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016;2016:770–8. https://doi.org/10.1109/CVPR.2016.90 .
doi: 10.1109/CVPR.2016.90
Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: From error visibility to structural similarity. IEEE Trans Image Process. 2004;13:600–12. https://doi.org/10.1109/tip.2003.819861 .
doi: 10.1109/tip.2003.819861
pubmed: 15376593
Koo TK, Li MY. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med. 2016;15:155–63. https://doi.org/10.1016/j.jcm.2016.02.012 .
doi: 10.1016/j.jcm.2016.02.012
pubmed: 27330520
pmcid: 4913118