Skip to content
2000
  1. Home
  2. A-Z Publications
  3. Current Medical Imaging
  4. Volume 20, Issue 1
  5. Article
Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
file format pdf download PDF
side by side viewer icon HTML

Abstract

Background

Three-dimensional (3D) whole-heart magnetic resonance imaging (MRI) is an excellent tool to check the heart anatomy of patients with congenital and acquired heart disease. However, most 3D whole-heart MRI acquisitions take a long time to perform, and the sequence used is susceptible to banding artifacts.

Purpose

To validate an unsupervised neural network that can reduce acquisition time and improve image quality for 3D whole-heart MRI by super-resolving low-resolution images.

Methods

The results of the super-resolution neural network (SRNN) were compared with bilinear interpolation, a state-of-the-art method known as AdapSR, and the ground truth high-resolution images qualitatively and quantitatively. Thirty pediatric patients with varying congenital and acquired heart diseases were included in this study. Results from the SRNN without a ground truth image were compared qualitatively with the contrast-enhanced whole-heart images. Signal-to-noise ratio (SNR) was used to quantitatively compare each of the methods and the high-resolution ground truth.

Results

As confirmed by both the quantitative and qualitative results, the SRNN improves image quality. Furthermore, because it only requires a low-resolution acquisition, the use of the SRNN reduces acquisition time.

Conclusion

The SRNN lessens noise and eliminates artifacts while maintaining correct anatomical structure in the images.

© 2024 The Author(s). Published by Bentham Science.
This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Loading

Article metrics loading...

/content/journals/cmir/10.2174/0115734056328337241002072721
2024-01-01
2025-04-13

  • From This Site

    /content/journals/cmir/10.2174/0115734056328337241002072721
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
The full text of this item is not currently available.

References

  1. GreilG. TandonA.A. Silva VieiraM. HussainT. 3D whole heart imaging for congenital heart disease.Front Pediatr.201753610.3389/fped.2017.0003628289674
     [Google Scholar]
  2. CruzG. AtkinsonD. HenningssonM. BotnarR.M. PrietoC. Highly efficient nonrigid motion-corrected 3D whole-heart coronary vessel wall imaging.Magn. Reson. Med.20177751894190810.1002/mrm.2627427221073
     [Google Scholar]
  3. BustinA. RashidI. CruzG. HajhosseinyR. CorreiaT. NejiR. RajaniR. IsmailT.F. BotnarR.M. PrietoC. 3D whole-heart isotropic sub-millimeter resolution coronary magnetic resonance angiography with non-rigid motion-compensated PROST.J. Cardiovasc. Magn. Reson.20202212410.1186/s12968‑020‑00611‑532299445
     [Google Scholar]
  4. PoskaiteP. PammingerM. KranewitterC. KremserC. ReindlM. ReiterG. PicciniD. DumfarthJ. HenningerB. TillerC. HolzknechtM. ReinstadlerS.J. KlugG. MetzlerB. MayrA. Self-navigated 3D whole-heart MRA for non-enhanced surveillance of thoracic aortic dilation: A comparison to CTA.Magn. Reson. Imaging20217612313010.1016/j.mri.2020.12.00333309920
     [Google Scholar]
  5. NamS. AkçakayaM. BashaT. StehningC. ManningW.J. TarokhV. NezafatR. Compressed sensing reconstruction for whole‐heart imaging with 3D radial trajectories: A graphics processing unit implementation.Magn. Reson. Med.20136919110210.1002/mrm.2423422392604
     [Google Scholar]
  6. MoyéD.M. HussainT. BotnarR.M. TandonA. GreilG.F. DyerA.K. HenningssonM. Dual-phase whole-heart imaging using image navigation in congenital heart disease.BMC Med. Imaging20181813610.1186/s12880‑018‑0278‑030326847
     [Google Scholar]
  7. HussainT. LossnitzerD. Bellsham-RevellH. Three-dimensional dual-phase whole-heart MR imaging: Clinical implications for congenital heart disease.Radiology201226325475410.1148/radiol.12111700.
     [Google Scholar]
  8. LiD. KirbergerM. QiaoJ. GuiZ. XueS. PuF. JiangJ. XuY. TanS. SalarianM. IbhaguiO. HekmatyarK. YangJ.J. Protein MRI contrast agents as an effective approach for precision molecular imaging.Invest. Radiol.202459217018610.1097/RLI.000000000000105738180819
     [Google Scholar]
  9. WahsnerJ. GaleE.M. Rodríguez-RodríguezA. CaravanP. Chemistry of MRI contrast agents: current challenges and new frontiers.Chem. Rev.20191192957105710.1021/acs.chemrev.8b0036330350585
     [Google Scholar]
  10. KostevšekN. A review on the optimal design of magnetic nanoparticle-based T2 MRI contrast agents.Magnetochemistry2020611110.3390/magnetochemistry6010011
     [Google Scholar]
  11. BoujanT. NeubergerU. PfaffJ. NagelS. HerwehC. BendszusM. MöhlenbruchM.A. Value of contrast-enhanced MRA versus time-of-flight MRA in acute ischemic stroke MRI.AJNR Am. J. Neuroradiol.20183991710171610.3174/ajnr.A577130115678
     [Google Scholar]
  12. YouS.H. KimB. YangK.S. KimB.K. WooS. ParkS.E. Development and validation of visual grading system for stenosis in intracranial atherosclerotic disease on time-of-flight magnetic resonance angiography.Eur. Radiol.20223242781279010.1007/s00330‑021‑08319‑534839372
     [Google Scholar]
  13. VillaG. RinggaardS. HermannI. NobleR. BrambillaP. KhatirD.S. ZöllnerF.G. FrancisS.T. SelbyN.M. RemuzziA. CaroliA. Phase-contrast magnetic resonance imaging to assess renal perfusion: A systematic review and statement paper.MAGMA202033132110.1007/s10334‑019‑00772‑031422518
     [Google Scholar]
  14. TurskiP. ScaranoA. HartmanE. ClarkZ. SchubertT. RiveraL. WuY. WiebenO. JohnsonK. Neurovascular 4DFlow MRI (Phase Contrast MRA): Emerging clinical applications.Neurovasc. Imaging201621810.1186/s40809‑016‑0019‑0
     [Google Scholar]
  15. PandaA. FrancoisC.J. BookwalterC.A. ChaturvediA. CollinsJ.D. LeinerT. RajiahP.S. Non-Contrast magnetic resonance angiography: Techniques, principles, and applications.Magn. Reson. Imaging Clin. N. Am.202331333736010.1016/j.mric.2023.04.00137414465
     [Google Scholar]
  16. TanE.J. ZhangS. TirukondaP. ChongL.R. REACT – A novel flow-independent non-gated non-contrast MR angiography technique using magnetization-prepared 3D non-balanced dual-echo dixon method: Preliminary clinical experience.Eur. J. Radiol. Open2020710023810.1016/j.ejro.2020.10023832548214
     [Google Scholar]
  17. KukukG.M. HittatiyaK. SprinkartA.M. EggersH. GiesekeJ. BlockW. MoellerP. WillinekW.A. SpenglerU. TrebickaJ. FischerH.P. SchildH.H. TräberF. Comparison between modified Dixon MRI techniques, MR spectroscopic relaxometry, and different histologic quantification methods in the assessment of hepatic steatosis.Eur. Radiol.201525102869287910.1007/s00330‑015‑3703‑625903702
     [Google Scholar]
  18. Van ReethE. ThamI.W.K. TanC.H. PohC.L. Super‐resolution in magnetic resonance imaging: A review.Concepts Magn. Reson. Part A Bridg. Educ. Res.201240A630632510.1002/cmr.a.21249
     [Google Scholar]
  19. Eskreis-WinklerS. ZhouD. LiuT. GuptaA. GauthierS.A. WangY. SpincemailleP. On the influence of zero-padding on the nonlinear operations in Quantitative Susceptibility Mapping.Magn. Reson. Imaging20173515415910.1016/j.mri.2016.08.02027587225
     [Google Scholar]
  20. DasS. AdhikaryA. LaghariA.A. MitraS. Eldo-care: EEG with Kinect sensor based telehealthcare for the disabled and the elderly.Neurosci Inform20233210013010.1016/j.neuri.2023.100130
     [Google Scholar]
  21. SaeedU. KumarK. KhuhroM.A. LaghariA.A. ShaikhA.A. RaiA. DeepLeukNet—A CNN based microscopy adaptation model for acute lymphoblastic leukemia classification.Multimedia Tools Appl.2023837210192104310.1007/s11042‑023‑16191‑2
     [Google Scholar]
  22. LaghariA.A. SunY. AlhusseinM. AurangzebK. AnwarM.S. RashidM. Deep residual-dense network based on bidirectional recurrent neural network for atrial fibrillation detection.Sci. Rep.20231311510910.1038/s41598‑023‑40343‑x
     [Google Scholar]
  23. FengC. FuH. YuanS. XuY. Multi-contrast MRI Super-Resolution via a Multi-stage Integration Network.Medical Image Computing and Computer Assisted Intervention – MICCAI202110.1007/978‑3‑030‑87231‑1_14
     [Google Scholar]
  24. FengC. YanY. FuH. ChenL. XuY. Task transformer network for joint MRI reconstruction and super-resolution.Medical Image Computing and Computer Assisted Intervention – MICCAI202110.1007/978‑3‑030‑87231‑1_30
     [Google Scholar]
  25. HuangB. XiaoH. LiuW. ZhangY. WuH. WangW. YangY. YangY. MillerG.W. LiT. CaiJ. MRI super-resolution via realistic downsampling with adversarial learning.Phys. Med. Biol.2021662020500410.1088/1361‑6560/ac232e34474407
     [Google Scholar]
  26. LiY. SixouB. PeyrinF. A review of the deep learning methods for medical images super resolution problems.IRBM202142212013310.1016/j.irbm.2020.08.004
     [Google Scholar]
  27. UlyanovD. VedaldiA. LempitskyV. Deep Image Prior.Int. J. Comput. Vis.202012871867188810.1007/s11263‑020‑01303‑4
     [Google Scholar]
  28. ChiabaiO. Van NieuwenhoveS. VekemansM.C. TombalB. PeetersF. WutsJ. TriqueneauxP. OmoumiP. KirchgesnerT. MichouxN. LecouvetF.E. Whole-body MRI in oncology: Can a single anatomic T2 Dixon sequence replace the combination of T1 and STIR sequences to detect skeletal metastasis and myeloma?Eur. Radiol.202233124425710.1007/s00330‑022‑09007‑835925384
     [Google Scholar]
  29. XuK. ZhangM. JegelkaS. KawaguchiK. Optimization of graph neural networks: Implicit acceleration by skip connections and more depth.arXiv202110.48550/arXiv.2105.04550.
     [Google Scholar]
  30. LillicrapT.P. SantoroA. MarrisL. AkermanC.J. HintonG. Backpropagation and the brain.Nat. Rev. Neurosci.202021633534610.1038/s41583‑020‑0277‑332303713
     [Google Scholar]
  31. MehtaS. PaunwalaC. VaidyaB. CNN based traffic sign classification using Adam Optimizer.International Conference on Intelligent Computing and Control Systems (ICCS)Madurai, India20191293129810.1109/ICCS45141.2019.9065537
     [Google Scholar]
  32. YanT. WangD. KongJ.Z. XiaT. PengZ. XiL. Definition of signal-to-noise ratio of health indicators and its analytic optimization for machine performance degradation assessment.IEEE Trans. Instrum. Meas.20217011610.1109/TIM.2021.3075779
     [Google Scholar]
  33. BorsoiR.A. CostaG.H. BermudezJ.C.M. A new adaptive video super-resolution algorithm with improved robustness to innovations.IEEE Trans. Image Process.201928267368610.1109/TIP.2018.286618130130193
     [Google Scholar]
  34. BenjaminJ. O’LearyC. HurS. GurevichA. KleinW.M. ItkinM. Imaging and interventions for lymphatic and lymphatic-related disorders.Radiology20233073e22023110.1148/radiol.22023136943078
     [Google Scholar]
  35. LaiW.S. HuangJ.B. AhujaN. YangM.H. Deep Laplacian pyramid networks for fast and accurate super-resolution.IEEE Conference on Computer Vision and Pattern Recognition (CVPR)Honolulu, HI, USA20175835584310.1109/CVPR.2017.618
     [Google Scholar]
  36. LedigC. TheisL. HuszarF. Photo-realistic single image super-resolution using a generative adversarial network.IEEE Conference on Computer Vision and Pattern Recognition (CVPR)Honolulu, HI, USA201710511410.1109/CVPR.2017.19
     [Google Scholar]
  37. LaghariAA. EstrelaVV. YinS. How to collect and interpret medical pictures captured in highly challenging environments that range from nanoscale to hyperspectral imaging.Curr Med Imag202420110
     [Google Scholar]
/content/journals/cmir/10.2174/0115734056328337241002072721
Loading
Untrained Network for Super-resolution for Non-contrast-enhanced Whole-heart MRI Acquired using Cardiac-triggered REACT (SRNN-REACT)
Curr. Med. Imaging 20, e15734056328337 (2024); https://doi.org/10.2174/0115734056328337241002072721
/content/journals/cmir/10.2174/0115734056328337241002072721
/content/journals/cmir/10.2174/0115734056328337241002072721
Loading

Data & Media loading...


  • Article Type:     Research Article
Keyword(s): 3D whole-heart MRI; Deep neural network; Non-contrast imaging; Non-contrast magnetic resonance angiograpy; SRNN; Super-resolution; Unsupervised learning

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test