Fully automated planning for anatomical fetal brain MRI on 0.55T.
T2* relaxometry
fetal MRI
fetal brain development
motion correction
motion detection
tracking
Journal
Magnetic resonance in medicine
ISSN: 1522-2594
Titre abrégé: Magn Reson Med
Pays: United States
ID NLM: 8505245
Informations de publication
Date de publication:
22 Apr 2024
22 Apr 2024
Historique:
revised:
08
03
2024
received:
18
01
2024
accepted:
02
04
2024
medline:
23
4
2024
pubmed:
23
4
2024
entrez:
23
4
2024
Statut:
aheadofprint
Résumé
Widening the availability of fetal MRI with fully automatic real-time planning of radiological brain planes on 0.55T MRI. Deep learning-based detection of key brain landmarks on a whole-uterus echo planar imaging scan enables the subsequent fully automatic planning of the radiological single-shot Turbo Spin Echo acquisitions. The landmark detection pipeline was trained on over 120 datasets from varying field strength, echo times, and resolutions and quantitatively evaluated. The entire automatic planning solution was tested prospectively in nine fetal subjects between 20 and 37 weeks. A comprehensive evaluation of all steps, the distance between manual and automatic landmarks, the planning quality, and the resulting image quality was conducted. Prospective automatic planning was performed in real-time without latency in all subjects. The landmark detection accuracy was 4.2 Real-time automatic planning of all three key fetal brain planes was successfully achieved and will pave the way toward simplifying the acquisition of fetal MRI thereby widening the availability of this modality in nonspecialist centers.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Deutsche Forschungsgemeinschaft
ID : 502024488
Organisme : EPSRC Research Council DTP
ID : EP/R513064/1
Organisme : Health Services and Delivery Research Programme
ID : NIHR3016640
Organisme : Wellcome Trust Collaboration in Science
ID : WT201526/Z/16/Z
Organisme : UK Research and Innovation
ID : MR/T018119/1
Organisme : Wellcome EPSRC Centre for Medical Engineering
ID : WT203148/Z/16/Z
Organisme : Medical Research Council (MRC)
ID : MR/X010007/1
Informations de copyright
© 2024 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.
Références
Alford RE, Bailey AA, Twickler DM. Fetal central nervous system. In: Masselli G, ed. MRI of Fetal and Maternal Diseases in Pregnancy. Springer; 2016:91‐118. doi:10.1007/978‐3‐319‐21428‐3_6
Prayer D, Brugger PC, Nemec U, Milos RI, Mitter C, Kasprian G. Cerebral malformations. In: Prayer D, ed. Fetal MRI. Medical Radiology. Springer; 2010:287‐308. doi:10.1007/174_2010_117
Epstein KN, Kline‐Fath BM, Zhang B, et al. Prenatal evaluation of intracranial Hemorrhage on Fetal MRI: a retrospective review. Am J Neuroradiol. 2021;42:2222‐2228. doi:10.3174/ajnr.A7320
van der Hoek‐Snieders HEM, van den Heuvel AJML, van Os‐Medendorp H, Kamalski DMA. Diagnostic accuracy of fetal MRI to detect cleft palate: a meta‐analysis. Eur J Pediatr. 2020;179:29‐38. https://link.springer.com/article/10.1007/s00431‐019‐03526‐7
Cornejo P, Feygin T, Vaughn J, et al. Imaging of fetal brain tumors. Pediatr Radiol. 2020;50:1959‐1973. doi:10.1007/s00247‐020‐04777‐z
Kyriakopoulou V, Vatansever D, Davidson A, et al. Normative biometry of the fetal brain using magnetic resonance imaging. Brain Struct Funct. 2017;222:2295‐2307. doi:10.1007/s00429‐016‐1345‐8.
Gholipour A, Rollins CK, Velasco‐Annis C, et al. A normative spatiotemporal MRI atlas of the fetal brain for automatic segmentation and analysis of early brain growth. Sci Rep. 2017;7:476. https://www.nature.com/articles/s41598‐017‐00516‐6
Uus A, Zhang T, Jackson LH, et al. Deformable slice‐to‐volume registration for motion correction of Fetal body and placenta MRI. IEEE Trans Med Imaging. 2020;39:2750‐2759. doi:10.1109/TMI.2020.2972867
Kuklisova‐Murgasova M, Quaghebeur G, Rutherford MA, Hajnal JV, Schnabel JA. Reconstruction of fetal brain MRI with intensity matching and complete outlier removal. Med Image Anal. 2012;16:1550‐1564. doi:10.1016/j.media.2012.06.001
Uus AU, Kyriakopoulou V, Makropoulos A, et al. BOUNTI: brain vOlumetry and aUtomated parcellatioN for 3D feTal MRI. bioRxiv. 2023;27:2023.04.18.537347. doi:10.1101/2023.04.18.537347
Aviles Verdera J, Story L, Hall M, et al. Reliability and feasibility of low‐field‐strength Fetal MRI at 0.55 T during pregnancy. Radiology. 2023;309:e223050. doi:10.1148/radiol.223050
Ponrartana S, Nguyen HN, Cui SX, et al. Low‐field 0.55 T MRI evaluation of the fetus. Pediatr Radiol. 2023;53:1469‐1475. doi:10.1007/s00247‐023‐05151‐8
Payette K, Uus A, Aviles Verdera J, et al. An automated pipeline for quantitative T2* fetal body MRI and segmentation at low field. ArXiv. 2023 https://arxiv.org/abs/2308.02132
Gagoski B, Xu J, Wighton P, et al. Automated detection and reacquisition of motion‐degraded images in fetal HASTE imaging at 3 T. Magn Reson Med. 2022;87:1914‐1922. doi:10.1002/mrm.29106
Faghihpirayesh R, Karimi D, Erdogmus D, Gholipour A. Deep learning framework for real‐time fetal brain segmentation in MRI. arXiv 2022 https://arxiv.org/abs/2205.01675
Salehi SSM, Hashemi SR, Velasco‐Annis C, et al. Real‐time automatic fetal brain extraction in fetal MRI by deep learning. Paper presented at: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018); April. 2018:720‐724. doi:10.1109/ISBI.2018.8363542
Singh A, Salehi SSM, Gholipour A. Deep predictive motion tracking in magnetic resonance imaging: application to Fetal imaging. IEEE Trans Med Imaging. 2020;39:3523‐3534. doi:10.1109/TMI
Neves Silva S, Aviles Verdera J, Tomi‐Tricot R, et al. Real‐time fetal brain tracking for functional fetal MRI. Magn Reson Med. 2023;90:2306‐2320.
Lei K, Syed AB, Zhu X, Pauly JM, Vasanawala SV. Automated MRI field of view prescription from region of interest prediction by intra‐stack attention neural network. Bioengineering (Basel). 2023;10(1):92. doi:10.3390/bioengineering10010005
Xue H, Artico J, Fontana M, Moon JC, Davies RH, Kellman P. Landmark detection in cardiac MRI by using a convolutional neural network. Radiol Artif Intell. 2021;3(5):e200197.
Yang X, Tang WT, Tjio G, Yeo SY, Su Y. Automatic detection of anatomical landmarks in brain MR scanning using multi‐task deep neural networks. Neurocomputing. 2020;396:514‐521. doi:10.1016/j.neucom.2020.04.078
Hoffmann M, Abaci Turk E, Gagoski B, et al. Rapid head‐pose detection for automated slice prescription of fetal‐brain MRI. Int J Imaging Syst Technol. 2021;31:1136‐1154. doi:10.1002/ima.22563
Xu J, Zhang M, Turk EA, et al. Fetal pose estimation in volumetric MRI using a 3D convolution neural network. Med Image Comput Comput Assist Intervent. 2019;2019:403‐410.
Hansen MS, Sørensen TS. Gadgetron: an open source framework for medical image reconstruction. Magn Reson Med. 2013;69:1768‐1776. doi:10.1002/mrm.24383
Uus A, Grigorescu I, van Poppel M, et al. 3D UNet with GAN discriminator for robust localization of the fetal brain and trunk in MRI with partial coverage of the fetal body. bioRxiv. 2021;2021‐2026. doi:10.1101/2021.06.23.449574
Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier‐Hein KH. nnU‐net: a self‐configuring method for deep learning‐based biomedical image segmentation. Nat Methods. 2021;18:203‐211. doi:10.1038/s41592‐020‐01008‐z
Mohseni Salehi SS, Khan S, Erdogmus D, Gholipour A. Real‐time deep pose estimation with geodesic loss for image‐to‐template rigid registration. IEEE Trans Med Imaging. 2019;38:470‐481. doi:10.1109/TMI.2018.2866442
She J, Huang H, Ye Z, et al. Automatic biometry of fetal brain MRIs using deep and machine learning techniques. Sci Rep. 2023;13:17860. doi:10.1038/s41598‐023‐17202‐0