7 research outputs found

    Prepolarized MRI of Hard Tissues and Solid-State Matter

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    [EN] Prepolarized MRI (PMRI) is a long-established technique conceived to counteract the loss in signal-to-noise ratio (SNR) inherent to low-field MRI systems. When it comes to hard biological tissues and solid-state matter, PMRI is severely restricted by their ultra-short characteristic relaxation times. Here we demonstrate that efficient hard-tissue prepolarization is within reach with a special-purpose 0.26 T scanner designed for ex vivo dental MRI and equipped with suitable high-power electronics. We have characterized the performance of a 0.5 T prepolarizer module, which can be switched on and off in 200 mu s. To this end, we have used resin, dental and bone samples, all with T1T1 {\mathbf{T}}_{\mathbf{1}} times of the order of 20 ms at our field strength. The measured SNR enhancement is in good agreement with a simple theoretical model, and deviations in extreme regimes can be attributed to mechanical vibrations due to the magnetic interaction between the prepolarization and main magnets.Agencia Valenciana de la Innovaci~o; European Regional Development Fund; Ministerio de Ciencia e Innovacion; This work was supported by the Ministerio de Ciencia e Innovaci~on of Spain through research grant PID2019-111436RBC21. Action co-financed by the European Union through the Programa Operativo del Fondo Europeo de Desarrollo Regional (FEDER) of the Comunitat Valenciana 2014-2020 (IDIFEDER/2018/022). JMG and JB acknowledge support from the Innodocto program of the Agencia Valenciana de la Innovacion (INNTA3/2020/22 and INNTA3/2021/17); Ministerio de Ciencia e Innovaci~on of Spain, Grant/Award Number: PID2019-111436RB-C21; Programa Operativo del Fondo Europeo de Desarrollo Regional (FEDER) of the Comunitat Valenciana, Grant/Award Number: IDIFEDER/2018/022; Innodocto program of the Agencia Valenciana de la Innovacion, Grant/Award Numbers: INNTA3/2020/22, INNTA3/2021/17Borreguero-Morata, J.; González Hernández, JM.; Pallás Lodeiro, E.; Rigla, JP.; Algarín-Guisado, JM.; Bosch-Esteve, R.; Galve, F.... (2022). Prepolarized MRI of Hard Tissues and Solid-State Matter. NMR in Biomedicine. 35(8):1-10. https://doi.org/10.1002/nbm.473711035

    The UTE and ZTE Sequences at Ultra-High Magnetic Field Strengths: A Survey

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    UTE (Ultrashort Echo Time) and ZTE (Zero Echo Time) sequences have been developed to detect short T2 relaxation signals coming from regions that are unable to be detected by conventional MRI methods. Due to the high dipole-dipole interactions in solid and semi-solid tissues, the echo time generated is simply not enough to produce a signal using conventional imaging method, often leading to void signal coming from the discussed areas. By the application of these techniques, solid and semi-solid areas can be imaged which can have a profound impact in clinical imaging. High and Ultra-high field strength (UHF) provides a vital advantage in providing better sensitivity and specificity of MR imaging. When coupled with the UTE and ZTE sequences, the image can recover void signals as well as a much-improved signal quality. To further this strategy, secondary data from various research tools was obtained to further validate the research while addressing the drawbacks to this approach. It was found that UTE and ZTE sequences coupled with some techniques such as qualitative imaging and new trajectories are very crucial for accurate image depiction of the areas of the musculoskeletal system, neural system, lung imaging and dental imaging

    Principles of the magnetic resonance imaging movie method for articulatory movement : a review

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    Magnetic resonance imaging (MRI) has become a critical tool for dental examination. MRI has many advantages over radiographic examination methods, including the lack of a requirement for patient exposure and the ability to capture high-contrast images of various tissue and organ types. However, MRI also has several limitations, including long examination times and the existence of metallic or motion artifacts. A cardiac imaging method using cine sequences was developed in the 1990s. This technique allows for analysis of heart movement and functional blood flow. Moreover, this method has been applied in dentistry. Recent research involving 3T MRI has led to the achievement of a temporal resolution of <10 ms, surpassing the frame rate of typical video recording. The current review introduces the history and principles of the cine sequence method and its application to the oral and maxillofacial regions

    Simultaneous imaging of hard and soft biological tissues in a low-field dental MRI scanner

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    [EN] Magnetic Resonance Imaging (MRI) of hard biological tissues is challenging due to the fleeting lifetime and low strength of their response to resonant stimuli, especially at low magnetic fields. Consequently, the impact of MRI on some medical applications, such as dentistry, continues to be limited. Here, we present three-dimensional reconstructions of ex-vivo human teeth, as well as a rabbit head and part of a cow femur, all obtained at a field strength of 260 mT. These images are the first featuring soft and hard tissues simultaneously at sub-Tesla fields, and they have been acquired in a home-made, special-purpose, pre-medical MRI scanner designed with the goal of demonstrating dental imaging at low field settings. We encode spatial information with two pulse sequences: Pointwise-Encoding Time reduction with Radial Acquisition and a new sequence we have called Double Radial Non-Stop Spin Echo, which we find to perform better than the former. For image reconstruction we employ Algebraic Reconstruction Techniques (ART) as well as standard Fourier methods. An analysis of the resulting images shows that ART reconstructions exhibit a higher signal-to-noise ratio with a more homogeneous noise distribution.We thank anonymous donors for their tooth samples, Andrew Webb and Thomas O'Reilly (LUMC) for discussions on hardware and pulse sequences, and Antonio Tristan (UVa) for information on reconstruction techniques. This work was supported by the European Commission under Grants 737180 (FET-OPEN: HISTO-MRI) and 481 (ATTRACT: DentMRI). Action co-financed by the European Union through the Programa Operativo del Fondo Europeo de Desarrollo Regional (FEDER) of the Comunitat Valenciana 2014-2020 (IDIFEDER/2018/022). Santiago Aja-Fernandez acknowledges Ministerio de Ciencia e Innovacion of Spain for research grant RTI2018-094569-B-I00.Algarín-Guisado, JM.; Díaz-Caballero, E.; Borreguero-Morata, J.; Galve, F.; Grau-Ruiz, D.; Rigla, JP.; Bosch-Esteve, R.... (2020). Simultaneous imaging of hard and soft biological tissues in a low-field dental MRI scanner. Scientific Reports. 10(1):1-14. https://doi.org/10.1038/s41598-020-78456-2S114101Haacke, E. M. et al. Magnetic Resonance Imaging: Physical Principles and Sequence Design Vol. 82 (Wiley-liss, New York, 1999).Bercovich, E. & Javitt, M. C. Medical imaging: from roentgen to the digital revolution, and beyond. Rambam Maimonides Med. J. 9, e0034. https://doi.org/10.5041/rmmj.10355 (2018).Mastrogiacomo, S., Dou, W., Jansen, J. A. & Walboomers, X. F. Magnetic resonance imaging of hard tissues and hard tissue engineered bio-substitutes. Mol. Imag. Biol. 21, 1003–1019. https://doi.org/10.1007/s11307-019-01345-2 (2019).Duer, M. J. Introduction to Solid-State NMR Spectroscopy (Blackwell, Oxford, 2004).Oatridge, A. et al. Magnetic resonance: magic angle imaging of the achilles tendon. Lancet 358, 1610–1611. https://doi.org/10.1016/S0140-6736(01)06661-2 (2001).Funduk, N. et al. Composition and relaxation of the proton magnetization of human enamel and its contribution to the tooth NMR image. Magnetic Resonance Med.1, 66–75. https://doi.org/10.1002/mrm.1910010108 (1984).Schreiner, L. J. et al. Proton NMR spin grouping and exchange in dentin. Biophys. J . 59, 629–639. https://doi.org/10.1016/S0006-3495(91)82278-0 (1991).Niraj, L. K. et al. MRI in dentistry–a future towards radiation free imaging-systematic review. JCDRhttps://doi.org/10.7860/JCDR/2016/19435.8658 (2016).Shah, N. Recent advances in imaging technologies in dentistry. World J. Radiol. 6, 794. https://doi.org/10.4329/wjr.v6.i10.794 (2014).Newton, C. W., Hoen, M. M., Goodis, H. E., Johnson, B. R. & McClanahan, S. B. Identify and determine the metrics, hierarchy, and predictive value of all the parameters and/or methods used during endodontic diagnosis. J. Endodontics 35, 1635–1644. https://doi.org/10.1016/j.joen.2009.09.033 (2009).Brady, E., Mannocci, F., Brown, J., Wilson, R. & Patel, S. A comparison of cone beam computed tomography and periapical radiography for the detection of vertical root fractures in nonendodontically treated teeth. Int. Endod. J. 47, 735–746. https://doi.org/10.1111/iej.12209 (2014).Idiyatullin, D., Garwood, M., Gaalaas, L. & Nixdorf, D. R. Role of MRI for detecting micro cracks in teeth. Dentomaxillofac. Radiol. 45, 20160150. https://doi.org/10.1259/dmfr.20160150 (2016).Idiyatullin, D. et al. Dental magnetic resonance imaging: making the invisible visible. J. Endodontics 37, 745–752 (2011).Marques, J. P., Simonis, F. F. & Webb, A. G. Low-field MRI: an MR physics perspective. J. Magn. Reson. Imaging 49, 1528–1542. https://doi.org/10.1002/jmri.26637 (2019).Sarracanie, M. et al. Low-cost high-performance MRI. Sci. Rep. 5, 15177. https://doi.org/10.1038/srep15177 (2015).Weiger, M. et al. High-resolution ZTE imaging of human teeth. NMR Biomed. 25, 1144–1151. https://doi.org/10.5041/rmmj.103552 (2012).Grodzki, D. M., Jakob, P. M. & Heismann, B. Ultrashort echo time imaging using pointwise encoding time reduction with radial acquisition (PETRA). Magn. Reson. Med. 67, 510–518. https://doi.org/10.5041/rmmj.103553 (2012).Kaczmarz, S. Angenäherte auflösung von systemen linearer gleichungen. Bull. Int. Acad. Pol. Sci. Let., Cl. Sci. Math. Nat. 35, 355–357 (1937).Gordon, R., Bender, R. & Herman, G. T. Algebraic reconstruction techniques (ART) for three-dimensional electron microscopy and X-ray photography. J. Theor. Biol. 29, 471–481. https://doi.org/10.1016/0022-5193(70)90109-8 (1970).Gower, R. M. & Richtarik, P. Randomized iterative methods for linear systems. SIAM J. Matrix Anal. Appl.36, 1660–1690. 10.1137/15M1025487 (2015). arXiv:1506.03296.Ludwig, U. et al. Dental MRI using wireless intraoral coils. Sci. Rep.6, https://doi.org/10.1038/srep23301 (2016).Maggioni, M., Katkovnik, V., Egiazarian, K. & Foi, A. Nonlocal transform-domain filter for volumetric data denoising and reconstruction. IEEE Trans. Image Process. 22, 119–133. https://doi.org/10.5041/rmmj.103555 (2013).Weiger, M. & Pruessmann, K. P. Short-t2 mri: principles and recent advances. Prog. Nucl. Magn. Reson. Spectrosc. 114–115, 237–270 (2019).Jang, H., Wiens, C. N. & McMillan, A. B. Ramped hybrid encoding for improved ultrashort echo time imaging. Magn. Resonance Med. 76, 814–825 (2016).Wu, Y. et al. Water- and fat-suppressed proton projection mri (waspi) of rat femur bone. Magn. Reson. Med. 57, 554–567 (2007).Carr, H. Y. Steady-state free precession in nuclear magnetic resonance. Phys. Rev. 112, 1693–1701. https://doi.org/10.5041/rmmj.103556 (1958).Waugh, J. S., Huber, L. M. & Haeberlen, U. Approach to high-resolution NMR in solids. Phys. Rev. Lett. 20, 180–182. https://doi.org/10.5041/rmmj.103557 (1968).Waeber, A. M. et al. Pulse control protocols for preserving coherence in dipolar-coupled nuclear spin baths. Nat. Commun. 10, 1–9. https://doi.org/10.1038/s41467-019-11160-6 (2019).Frey, M. A. et al. Phosphorus-31 MRI of hard and soft solids using quadratic echo line-narrowing. Proc. Natl. Acad. Sci. U.S.A. 109, 5190–5195. https://doi.org/10.1073/pnas.1117293109 (2012).Galve, F., Alonso, J., Algarín, J. M. & Benlloch, J. M. Magnetic resonance imaging method with zero echo time and slice selection. ESP202030504 (2020).Cooley, C. Z. et al. A portable brain mri scanner for underserved settings and point-of-care imaging. arXiv2004.13183 (2020).Hills, B. P. & Clark, C. J. Quality Assessment of Horticultural Products by NMRhttps://doi.org/10.1016/S0066-4103(03)50002-3 (2003).Somers, A. E., Bastow, T. J., Burgar, M. I., Forsyth, M. & Hill, A. J. Quantifying rubber degradation using NMR. Polym. Degrad. Stab. 70, 31–37. https://doi.org/10.1007/s11307-019-01345-21 (2000).Tyler, D. J., Robson, M. D., Henkelman, R. M., Young, I. R. & Bydder, G. M. Magnetic resonance imaging with ultrashort TE (UTE) PULSE sequences: technical considerations. J. Magn. Reson. Imaging 25, 279–289. https://doi.org/10.1002/jmri.20851 (2007).Weiger, M., Pruessmann, K. P. & Hennel, F. MRI with zero echo time: hard versus sweep pulse excitation. Magn. Reson. Med. 66, 379–389. https://doi.org/10.1002/mrm.22799 (2011).Rahmer, J., Blume, U. & Börnert, P. Selective 3D ultrashort TE imaging: comparison of “dual-echo” acquisition and magnetization preparation for improving short-T2 contrast. Magn. Resonance Mater. Phys. Biol. Med.20, 83–92. https://doi.org/10.1007/s10334-007-0070-6 (2007).Rasche, V., Holz, D. & Schepper, W. Radial turbo spin echo imaging. Magn. Reson. Med. 32, 629–638 (1994).Fessler, J. A. On NUFFT-based gridding for non-Cartesian MRI. J. Magn. Reson. 188, 191–195. https://doi.org/10.1007/s11307-019-01345-24 (2007).Fessler, J. Model-based image reconstruction for MRI. In IEEE Signal Processing Magazine, vol. 27, 81–89, https://doi.org/10.1109/MSP.2010.936726(Institute of Electrical and Electronics Engineers Inc., 2010).Aja-Fernández, S. & Vegas-Sánchez-Ferrero, G. Statistical Analysis of Noise in MRI (Springer, Berlin, 2016).Aja-Fernández, S., Pieciak, T. & Vegas-Sánchez-Ferrero, G. Spatially variant noise estimation in MRI: a homomorphic approach. Med. Image Anal. 20, 184–197 (2015)

    Deep learning synthesis of cone-beam computed tomography from zero echo time magnetic resonance imaging

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    Cone-beam computed tomography (CBCT) produces high-resolution of hard tissue even in small voxel size, but the process is associated with radiation exposure and poor soft tissue imaging. Thus, we synthesized a CBCT image from the magnetic resonance imaging (MRI), using deep learning and to assess its clinical accuracy. We collected patients who underwent both CBCT and MRI simultaneously in our institution (Seoul). MRI data were registered with CBCT data, and both data were prepared into 512 slices of axial, sagittal, and coronal sections. A deep learning-based synthesis model was trained and the output data were evaluated by comparing the original and synthetic CBCT (syCBCT). According to expert evaluation, syCBCT images showed better performance in terms of artifacts and noise criteria but had poor resolution compared to the original CBCT images. In syCBCT, hard tissue showed better clarity with significantly different MAE and SSIM. This study result would be a basis for replacing CBCT with non-radiation imaging that would be helpful for patients planning to undergo both MRI and CBCT. © 2023. The Author(s).ope

    Imaging tooth enamel using zero echo time (ZTE) magnetic resonance imaging

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