Beyond the Conventional Structural MRI: Clinical Application of Deep Learning Image Reconstruction and Synthetic MRI of the Brain.
Journal
Investigative radiology
ISSN: 1536-0210
Titre abrégé: Invest Radiol
Pays: United States
ID NLM: 0045377
Informations de publication
Date de publication:
20 Aug 2024
20 Aug 2024
Historique:
medline:
19
8
2024
pubmed:
19
8
2024
entrez:
19
8
2024
Statut:
aheadofprint
Résumé
Recent technological advancements have revolutionized routine brain magnetic resonance imaging (MRI) sequences, offering enhanced diagnostic capabilities in intracranial disease evaluation. This review explores 2 pivotal breakthrough areas: deep learning reconstruction (DLR) and quantitative MRI techniques beyond conventional structural imaging. DLR using deep neural networks facilitates accelerated imaging with improved signal-to-noise ratio and spatial resolution, enhancing image quality with short scan times. DLR focuses on supervised learning applied to clinical implementation and applications. Quantitative MRI techniques, exemplified by 2D multidynamic multiecho, 3D quantification using interleaved Look-Locker acquisition sequences with T2 preparation pulses, and magnetic resonance fingerprinting, enable precise calculation of brain-tissue parameters and further advance diagnostic accuracy and efficiency. Potential DLR instabilities and quantification and bias limitations will be discussed. This review underscores the synergistic potential of DLR and quantitative MRI, offering prospects for improved brain imaging beyond conventional methods.
Identifiants
pubmed: 39159333
doi: 10.1097/RLI.0000000000001114
pii: 00004424-990000000-00248
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.
Déclaration de conflit d'intérêts
Conflicts of interest and sources of funding: This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute, funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI22C1723) and National Research Foundation of Korea (RS-2023-00305153).
Références
Hagiwara A, Fujita S, Kurokawa R, et al. Multiparametric MRI: from simultaneous rapid acquisition methods and analysis techniques using scoring, machine learning, radiomics, and deep learning to the generation of novel metrics. Invest Radiol. 2023;58:548–560.
Bond-Taylor S, Leach A, Long Y, et al. Deep generative modelling: a comparative review of VAEs, GANs, normalizing flows, energy-based and autoregressive models. arXiv [csLG]. 2021. Available at: http://arxiv.org/abs/2103.04922.
Jeong G, Kim H, Yang J, et al. All-in-one deep learning framework for MR image reconstruction. arXiv [eessIV]. 2024. Available at: http://arxiv.org/abs/2405.03684.
Kiryu S, Akai H, Yasaka K, et al. Clinical impact of deep learning reconstruction in MRI. Radiographics. 2023;43:e220133.
Lin DJ, Johnson PM, Knoll F, et al. Artificial intelligence for MR image reconstruction: an overview for clinicians. J Magn Reson Imaging. 2021;53:1015–1028.
Yamashita R, Nishio M, Do RKG, et al. Convolutional neural networks: an overview and application in radiology. Insights Imaging. 2018;9:611–629.
Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells WM, et al., eds. Medical Image Computing and Computer-Assisted Intervention—MICCAI 2015. Cham, Switzerland: Springer International Publishing; 2015:234–241.
Arshad M, Qureshi M, Inam O, et al. Transfer learning in deep neural network based under-sampled MR image reconstruction. Magn Reson Imaging. 2021;76:96–107.
Sara U, Akter M, Uddin MS. Image quality assessment through FSIM, SSIM, MSE and PSNR—a comparative study. J Comput Commun. 2019;07:8–18.
Oshima S, Fushimi Y, Miyake KK, et al. Denoising approach with deep learning–based reconstruction for neuromelanin-sensitive MRI: image quality and diagnostic performance. Jpn J Radiol. 2023;41:1216–1225.
Rudie JD, Gleason T, Barkovich MJ, et al. Clinical assessment of deep learning–based super-resolution for 3D volumetric brain MRI. Radiol Artif Intell. 2022;4:e210059.
Jung W, Lee H-S, Seo M, et al. MR-self Noise2Noise: self-supervised deep learning–based image quality improvement of submillimeter resolution 3D MR images. Eur Radiol. 2023;33:2686–2698.
Lee DH, Park JE, Nam YK, et al. Deep learning-based thin-section MRI reconstruction improves tumour detection and delineation in pre- and post-treatment pituitary adenoma. Sci Rep. 2021;11:21302.
Kim M, Kim HS, Park JE, et al. Thin-slice pituitary MRI with deep learning–based reconstruction for preoperative prediction of cavernous sinus invasion by pituitary adenoma: a prospective study. AJNR Am J Neuroradiol. 2022;43:280–285.
Kim M, Kim HS, Kim HJ, et al. Thin-slice pituitary MRI with deep learning–based reconstruction: diagnostic performance in a postoperative setting. Radiology. 2021;298:114–122.
Park H, Nam YK, Kim HS, et al. Deep learning–based image reconstruction improves radiologic evaluation of pituitary axis and cavernous sinus invasion in pituitary adenoma. Eur J Radiol. 2023;158:110647.
Altmann S, Abello Mercado MA, Brockstedt L, et al. Ultrafast brain MRI protocol at 1.5 T using deep learning and multi-shot EPI. Acad Radiol. 2023;30:2988–2998.
Altmann S, Grauhan NF, Brockstedt L, et al. Ultrafast brain MRI with deep learning reconstruction for suspected acute ischemic stroke. Radiology. 2024;310:e231938.
Bash S, Wang L, Airriess C, et al. Deep learning enables 60% accelerated volumetric brain MRI while preserving quantitative performance: a prospective, multicenter, multireader trial. AJNR Am J Neuroradiol. 2021;42:2130–2137.
Hanamatsu S, Murayama K, Ohno Y, et al. Deep learning reconstruction for brain diffusion-weighted imaging: efficacy for image quality improvement, apparent diffusion coefficient assessment, and intravoxel incoherent motion evaluation in in vitro and in vivo studies. Diagn Interv Radiol. 2023;29:664–673.
Suh PS, Park JE, Roh YH, et al. Improving diagnostic performance of MRI for temporal lobe epilepsy with deep learning–based image reconstruction in patients with suspected focal epilepsy. Korean J Radiol. 2024;25:374–383.
Antun V, Renna F, Poon C, et al. On instabilities of deep learning in image reconstruction and the potential costs of AI. Proc Natl Acad Sci U S A. 2020;117:30088–30095.
Eo T, Jun Y, Kim T, et al. KIKI-net: cross-domain convolutional neural networks for reconstructing undersampled magnetic resonance images. Magn Reson Med. 2018;80:2188–2201.
Muckley MJ, Riemenschneider B, Radmanesh A, et al. Results of the 2020 fastMRI challenge for machine learning MR image reconstruction. IEEE Trans Med Imaging. 2021;40:2306–2317.
Edupuganti V, Mardani M, Cheng J, et al. Exploring the hallucination risk of deep generative models in MR image recovery. In: 27th ISMRM Annual Meeting. 2019. Available at: https://archive.ismrm.org/2019/4773.html.
Bigdeli SA, Jin M, Favaro P, et al. Deep mean-shift priors for image restoration. arXiv [csCV]. 2017. Available at: http://arxiv.org/abs/1709.03749.
Rick Chang JH, Li C-L, Poczos B, et al. One network to solve them all—solving linear inverse problems using deep projection models. arXiv [csCV]. 2017. Available at: http://arxiv.org/abs/1703.09912.
Hamilton-Craig CR, Strudwick MW, Galloway GJ. T1 mapping for myocardial fibrosis by cardiac magnetic resonance relaxometry—a comprehensive technical review. Front Cardiovasc Med. 2017;3:49.
Weiskopf N, Suckling J, Williams G, et al. Quantitative multi-parameter mapping of R1, PD*, MT, and R2* at 3 T: a multi-center validation. Front Neurosci. 2013;7:95.
Kvernby S, Warntjes MJB, Haraldsson H, et al. Simultaneous three-dimensional myocardial T1 and T2 mapping in one breath hold with 3D-QALAS. J Cardiovasc Magn Reson. 2014;16:102.
Fujita S, Hagiwara A, Hori M, et al. Three-dimensional high-resolution simultaneous quantitative mapping of the whole brain with 3D-QALAS: an accuracy and repeatability study. Magn Reson Imaging. 2019;63:235–243.
Fujita S, Hagiwara A, Hori M, et al. 3D quantitative synthetic MRI-derived cortical thickness and subcortical brain volumes: scan-rescan repeatability and comparison with conventional T1-weighted images. J Magn Reson Imaging. 2019;50:1834–1842.
Fujita S, Hagiwara A, Takei N, et al. Accelerated isotropic multiparametric imaging by high spatial resolution 3D-QALAS with compressed sensing: a phantom, volunteer, and patient study. Invest Radiol. 2021;56:292–300.
Fujita S, Gagoski B, Hwang K-P, et al. Cross-vendor multiparametric mapping of the human brain using 3D-QALAS: a multicenter and multivendor study. Magn Reson Med. 2024;91:1863–1875.
A G Teixeira RP, Neji R, Wood TC, et al. Controlled saturation magnetization transfer for reproducible multivendor variable flip angle T1 and T2 mapping. Magn Reson Med. 2020;84:221–236.
Warntjes JBM, Leinhard OD, West J, et al. Rapid magnetic resonance quantification on the brain: optimization for clinical usage. Magn Reson Med. 2008;60:320–329.
Murata S, Hagiwara A, Fujita S, et al. Effect of hybrid of compressed sensing and parallel imaging on the quantitative values measured by 3D quantitative synthetic MRI: a phantom study. Magn Reson Imaging. 2021;78:90–97.
Cho J, Gagoski B, Kim TH, et al. Time-efficient, high-resolution 3 T whole-brain relaxometry using 3D-QALAS with wave-CAIPI readouts. Magn Reson Med. 2024;91:630–639.
Bilgic B, Gagoski BA, Cauley SF, et al. Wave-CAIPI for highly accelerated 3D imaging. Magn Reson Med. 2015;73:2152–2162.
Polak D, Setsompop K, Cauley SF, et al. Wave-CAIPI for highly accelerated MP-RAGE imaging. Magn Reson Med. 2018;79:401–406.
Jun Y, Arefeen Y, Cho J, et al. Zero-DeepSub: zero-shot deep subspace reconstruction for rapid multiparametric quantitative MRI using 3D-QALAS. Magn Reson Med. 2024;91:2459–2482.
Jun Y, Cho J, Wang X, et al. SSL-QALAS: self-supervised learning for rapid multiparameter estimation in quantitative MRI using 3D-QALAS. Magn Reson Med. 2023;90:2019–2032.
Ma D, Gulani V, Seiberlich N, et al. Magnetic resonance fingerprinting. Nature. 2013;495:187–192.
Ye H, Cauley SF, Gagoski B, et al. Simultaneous multislice magnetic resonance fingerprinting (SMS-MRF) with direct-spiral slice-GRAPPA (ds-SG) reconstruction. Magn Reson Med. 2017;77:1966–1974.
Ma D, Jiang Y, Chen Y, et al. Fast 3D magnetic resonance fingerprinting for a whole-brain coverage. Magn Reson Med. 2018;79:2190–2197.
Liao C, Bilgic B, Manhard MK, et al. 3D MR fingerprinting with accelerated stack-of-spirals and hybrid sliding-window and GRAPPA reconstruction. Neuroimage. 2017;162:13–22.
Chen Y, Fang Z, Hung S-C, et al. High-resolution 3D MR fingerprinting using parallel imaging and deep learning. Neuroimage. 2020;206:116329.
Cao X, Liao C, Iyer SS, et al. Optimized multi-axis spiral projection MR fingerprinting with subspace reconstruction for rapid whole-brain high-isotropic-resolution quantitative imaging. Magn Reson Med. 2022;88:133–150.
Cauley SF, Setsompop K, Ma D, et al. Fast group matching for MR fingerprinting reconstruction. Magn Reson Med. 2015;74:523–528.
Cohen O, Zhu B, Rosen MS. MR fingerprinting deep RecOnstruction NEtwork (DRONE). Magn Reson Med. 2018;80:885–894.
Wang K, Doneva M, Meineke J, et al. High-fidelity direct contrast synthesis from magnetic resonance fingerprinting. Magn Reson Med. 2023;90:2116–2129.
Ladopoulos T, Matusche B, Bellenberg B, et al. Relaxometry and brain myelin quantification with synthetic MRI in MS subtypes and their associations with spinal cord atrophy. Neuroimage Clin. 2022;36:103166.
Krauss W, Gunnarsson M, Andersson T, et al. Accuracy and reproducibility of a quantitative magnetic resonance imaging method for concurrent measurements of tissue relaxation times and proton density. Magn Reson Imaging. 2015;33:584–591.
Parlak S, Coban G, Gumeler E, et al. Reduced myelin in patients with isolated hippocampal sclerosis as assessed by SyMRI. Neuroradiology. 2022;64:99–107.
Kikuchi K, Togao O, Yamashita K, et al. Comparison of diagnostic performance of radiologist- and AI-based assessments of T2-FLAIR mismatch sign and quantitative assessment using synthetic MRI in the differential diagnosis between astrocytoma, IDH-mutant and oligodendroglioma, IDH-mutant and 1p/19q-codeleted. Neuroradiology. 2024;66:333–341.
Ouellette R, Mangeat G, Polyak I, et al. Validation of rapid magnetic resonance myelin imaging in multiple sclerosis. Ann Neurol. 2020;87:710–724.
Moll NM, Rietsch AM, Thomas S, et al. Multiple sclerosis normal-appearing white matter: pathology–imaging correlations. Ann Neurol. 2011;70:764–773.
Thom M. Review: hippocampal sclerosis in epilepsy: a neuropathology review. Neuropathol Appl Neurobiol. 2014;40:520–543.
Falconer MA, Serafetinides EA, Corsellis JA. Etiology and pathogenesis of temporal lobe epilepsy. Arch Neurol. 1964;10:233–248.
Meiners LC, Witkamp TD, de Kort GA, et al. Relevance of temporal lobe white matter changes in hippocampal sclerosis. Magnetic resonance imaging and histology. Invest Radiol. 1999;34:38–45.
Yu Z, Zhao T, Assländer J, et al. Exploring the sensitivity of magnetic resonance fingerprinting to motion. Magn Reson Imaging. 2018;54:241–248.
Fujita S, Hagiwara A, Takei N, et al. Rigid real-time prospective motion-corrected three-dimensional multiparametric mapping of the human brain. Neuroimage. 2022;255:119176.
Fujita S, Jun Y, Yong X, et al. Motion resolved rapid 3D multiparametric brain mapping with self-navigation. ISMRM. 2024. Available at: https://submissions.mirasmart.com/ISMRM2024/Itinerary/ConferenceMatrixEventDetail.aspx?ses=O-06. Accessed April 25, 2024.
Fujita S, Yokoyama K, Hagiwara A, et al. 3D quantitative synthetic MRI in the evaluation of multiple sclerosis lesions. AJNR Am J Neuroradiol. 2021;42:471–478.
Fujita S, Hagiwara A, Otsuka Y, et al. Deep learning approach for generating MRA images from 3D quantitative synthetic MRI without additional scans. Invest Radiol. 2020;55:249–256.
Ichimura K, Pearson DM, Kocialkowski S, et al. IDH1 mutations are present in the majority of common adult gliomas but rare in primary glioblastomas. Neuro Oncol. 2009;11:341–347.
Patel SH, Poisson LM, Brat DJ, et al. T2-FLAIR mismatch, an imaging biomarker for IDH and 1p/19q status in lower-grade gliomas: a TCGA/TCIA project. Clin Cancer Res. 2017;23:6078–6085.
Broen MPG, Smits M, Wijnenga MMJ, et al. The T2-FLAIR mismatch sign as an imaging marker for non-enhancing IDH-mutant, 1p/19q-intact lower-grade glioma: a validation study. Neuro Oncol. 2018;20:1393–1399.
Badve C, Yu A, Dastmalchian S, et al. MR fingerprinting of adult brain tumors: initial experience. AJNR Am J Neuroradiol. 2017;38:492–499.
Mostardeiro TR, Panda A, Witte RJ, et al. Whole-brain 3D MR fingerprinting brain imaging: clinical validation and feasibility to patients with meningioma. MAGMA. 2021;34:697–706.
Cohen O, Yu VY, Tringale KR, et al. CEST MR fingerprinting (CEST-MRF) for brain tumor quantification using EPI readout and deep learning reconstruction. Magn Reson Med. 2023;89:233–249.
Haubold J, Demircioglu A, Gratz M, et al. Non-invasive tumor decoding and phenotyping of cerebral gliomas utilizing multiparametric 18F-FET PET-MRI and MR fingerprinting. Eur J Nucl Med Mol Imaging. 2020;47:1435–1445.
Ontaneda D, Gulani V, Deshmane A, et al. Magnetic resonance fingerprinting in multiple sclerosis. Mult Scler Relat Disord. 2023;79:105024.
Nagtegaal MA, Hermann I, Weingärtner S, et al. White matter changes measured by multi-component MR fingerprinting in multiple sclerosis. Neuroimage Clin. 2023;40:103528.
Zhou Z, Li Q, Liao C, et al. Optimized three-dimensional ultrashort echo time: magnetic resonance fingerprinting for myelin tissue fraction mapping. Hum Brain Mapp. 2023;44:2209–2223.
Liao C, Wang K, Cao X, et al. Detection of lesions in mesial temporal lobe epilepsy by using MR fingerprinting. Radiology. 2018;288:804–812.
Ma D, Jones SE, Deshmane A, et al. Development of high-resolution 3D MR fingerprinting for detection and characterization of epileptic lesions. J Magn Reson Imaging. 2019;49:1333–1346.
Ding Z, Hu S, Su T-Y, et al. Combining magnetic resonance fingerprinting with voxel-based morphometric analysis to reduce false positives for focal cortical dysplasia detection. Epilepsia. 2024;65:1631–1643.
Ryu KH, Baek HJ, Moon JI, et al. Initial clinical experience of synthetic MRI as a routine neuroimaging protocol in daily practice: a single-center study. J Neuroradiol. 2020;47:151–160.
Tanenbaum LN, Tsiouris AJ, Johnson AN, et al. Synthetic MRI for clinical neuroimaging: results of the magnetic resonance image compilation (MAGiC) prospective, multicenter, multireader trial. AJNR Am J Neuroradiol. 2017;38:1103–1110.
Demir S, Clifford B, Lo W-C, et al. Optimization of magnetization transfer contrast for EPI FLAIR brain imaging. Magn Reson Med. 2022;87:2380–2387.
Deshmane A, McGivney DF, Ma D, et al. Partial volume mapping using magnetic resonance fingerprinting. NMR Biomed. 2019;32:e4082.
Chen Y, Chen M-H, Baluyot KR, et al. MR fingerprinting enables quantitative measures of brain tissue relaxation times and myelin water fraction in the first five years of life. Neuroimage. 2019;186:782–793.
Qiu S, Ma S, Wang L, et al. Direct synthesis of multi-contrast brain MR images from MR multitasking spatial factors using deep learning. Magn Reson Med. 2023;90:1672–1681.
Hagiwara A, Otsuka Y, Hori M, et al. Improving the quality of synthetic FLAIR images with deep learning using a conditional generative adversarial network for pixel-by-pixel image translation. AJNR Am J Neuroradiol. 2019;40:224–230.
van der Weijden CWJ, Biondetti E, Gutmann IW, et al. Quantitative myelin imaging with MRI and PET: an overview of techniques and their validation status. Brain. 2023;146:1243–1266.
Lazari A, Lipp I. Can MRI measure myelin? Systematic review, qualitative assessment, and meta-analysis of studies validating microstructural imaging with myelin histology. Neuroimage. 2021;230:117744.
Lee J, Hyun J-W, Lee J, et al. So you want to image myelin using MRI: an overview and practical guide for myelin water imaging. J Magn Reson Imaging. 2021;53:360–373.
Oh S-H, Bilello M, Schindler M, et al. Direct visualization of short transverse relaxation time component (ViSTa). Neuroimage. 2013;83:485–492.
Liao C, Cao X, Iyer SS, et al. High-resolution myelin-water fraction and quantitative relaxation mapping using 3D ViSTa-MR fingerprinting. Magn Reson Med. 2024;91:2278–2293.
Yaman B, Hosseini SAH, Moeller S, et al. Self-supervised physics-based deep learning MRI reconstruction without fully-sampled data. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). IEEE; 2020:921–925.
Yaman B, Hosseini SAH, Akçakaya M. Zero-shot self-supervised learning for MRI reconstruction. arXiv [eessIV]. 2021. Available at: http://arxiv.org/abs/2102.07737.
Zhou B, Khosla A, Lapedriza À, et al. Learning deep features for discriminative localization. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. 2015:2921–2929.
Selvaraju RR, Cogswell M, Das A, et al. Grad-CAM: visual explanations from deep networks via gradient-based localization. Int J Comput Vis. 2020;128:336–359.
Wang H, Wang Z, Du M, et al. Score-CAM: score-weighted visual explanations for convolutional neural networks. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2019:111–119.
Bach S, Binder A, Montavon G, et al. On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS One. 2015;10:e0130140.
Montavon G, Lapuschkin S, Binder A, et al. Explaining nonlinear classification decisions with deep Taylor decomposition. Pattern Recognit. 2017;65:211–222.
Edupuganti V, Mardani M, Vasanawala S, et al. Uncertainty quantification in deep MRI reconstruction. IEEE Trans Med Imaging. 2021;40:239–250.
Narnhofer D, Effland A, Kobler E, et al. Bayesian uncertainty estimation of learned variational MRI reconstruction. IEEE Trans Med Imaging. 2022;41:279–291.
Zhang Z, Romero A, Muckley M, et al. Reducing uncertainty in undersampled MRI reconstruction with active acquisition. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. 2019:2049–2053.
Luo G, Blumenthal M, Heide M, et al. Bayesian MRI reconstruction with joint uncertainty estimation using diffusion models. Magn Reson Med. 2023;90:295–311.
Küstner T, Hammernik K, Rueckert D, et al. Predictive uncertainty in deep learning–based MR image reconstruction using deep ensembles: evaluation on the fastMRI data set. Magn Reson Med. 2024;92:289–302.
Hu S, Chen Y, Zong X, et al. Improving motion robustness of 3D MR fingerprinting with a fat navigator. Magn Reson Med. 2023;90:1802–1817.
Karakuzu A, Biswas L, Cohen-Adad J, et al. Vendor-neutral sequences and fully transparent workflows improve inter-vendor reproducibility of quantitative MRI. Magn Reson Med. 2022;88:1212–1228.
Zhang J, Nguyen TD, Solomon E, et al. mcLARO: multi-contrast learned acquisition and reconstruction optimization for simultaneous quantitative multi-parametric mapping. Magn Reson Med. 2024;91:344–356.
Ma S, Wang N, Fan Z, et al. Three-dimensional whole-brain simultaneous T1, T2, and T1ρ quantification using MR multitasking: method and initial clinical experience in tissue characterization of multiple sclerosis. Magn Reson Med. 2021;85:1938–1952.