<meta charset="utf8"></head><body>פרוטוקול להדמיית ריאות ברזולוציה גבוהה באמצעות 3T UTE MRI עם Gating עצמי

<ol>
	<li>Raju, S., Ghosh, S., Mehta, A. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Chest+ct+signs+in+pulmonary+disease:+A+pictorial+review.">Chest ct signs in pulmonary disease: A pictorial review.</a> Chest. 151 (6), 1356-1374 (2017).</li><li>Biederer, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MRI+of+the+lung+(2/3).+Why,+when,+how.">MRI of the lung (2/3). Why, when, how.</a> Insights Imaging. 3 (4), 355-371 (2012).</li><li>Biederer, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MRI+of+the+lung+(3/3)-current+applications+and+future+perspectives.">MRI of the lung (3/3)-current applications and future perspectives.</a> Insights Imaging. 3 (4), 373-386 (2012).</li><li>Johnson, K. M., Fain, S. B., Schiebler, M. L., Nagle, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optimized+3D+ultrashort+echo+time+pulmonary+MRI.">Optimized 3D ultrashort echo time pulmonary MRI.</a> Magn Reson Med. 70 (5), 1241-1250 (2013).</li><li>Ma, W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ultra-short+echo-time+pulmonary+MRI:+Evaluation+and+reproducibility+in+COPD+subjects+with+and+without+bronchiectasis.">Ultra-short echo-time pulmonary MRI: Evaluation and reproducibility in COPD subjects with and without bronchiectasis.</a> J Magn Reson Imaging. 41 (5), 1465-1474 (2015).</li><li>Roach, D. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ultrashort+echo-time+magnetic+resonance+imaging+is+a+sensitive+method+for+the+evaluation+of+early+cystic+fibrosis+lung+disease.">Ultrashort echo-time magnetic resonance imaging is a sensitive method for the evaluation of early cystic fibrosis lung disease.</a> Ann Am Thorac Soc. 13 (11), 1923-1931 (2016).</li><li>Tibiletti, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multistage+three-dimensional+UTE+lung+imaging+by+image-based+self-gating.">Multistage three-dimensional UTE lung imaging by image-based self-gating.</a> Magn Reson Med. 75 (3), 1324-1332 (2016).</li><li>Weick, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dc-gated+high-resolution+three-dimensional+lung+imaging+during+free-breathing.">Dc-gated high-resolution three-dimensional lung imaging during free-breathing.</a> J Magn Reson Imaging. 37 (3), 727-732 (2013).</li><li>Fischer, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Self-gated+non-contrast-enhanced+functional+lung+imaging+(SENCEFUL)+using+a+quasi-random+fast+low-angle+shot+(FLASH)+sequence+and+proton+MRI.">Self-gated non-contrast-enhanced functional lung imaging (SENCEFUL) using a quasi-random fast low-angle shot (FLASH) sequence and proton MRI.</a> NMR Biomed. 27 (8), 907-917 (2014).</li><li>Tibiletti, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Respiratory+self-gated+3D+UTE+for+lung+imaging+in+small+animal+MRI.">Respiratory self-gated 3D UTE for lung imaging in small animal MRI.</a> Magn Reson Med. 78 (2), 739-745 (2017).</li><li>Jiang, W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Motion+robust+high+resolution+3D+free-breathing+pulmonary+MRI+using+dynamic+3D+image+self-navigator.">Motion robust high resolution 3D free-breathing pulmonary MRI using dynamic 3D image self-navigator.</a> Magn Reson Med. 79 (6), 2954-2967 (2018).</li><li>Higano, N. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Retrospective+respiratory+self-gating+and+removal+of+bulk+motion+in+pulmonary+UTE+MRI+of+neonates+and+adults.">Retrospective respiratory self-gating and removal of bulk motion in pulmonary UTE MRI of neonates and adults.</a> Magn Reson Med. 77 (3), 1284-1295 (2016).</li><li>Metze, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Non-uniform+self-gating+in+2D+lung+imaging.">Non-uniform self-gating in 2D lung imaging.</a> Front Phys. 10, (2022).</li><li>Gandhi, D. B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparison+of+weighting+algorithms+to+mitigate+respiratory+motion+in+free-breathing+neonatal+pulmonary+radial+UTE-MRI.">Comparison of weighting algorithms to mitigate respiratory motion in free-breathing neonatal pulmonary radial UTE-MRI.</a> Biomed Phys Eng Express. 10 (3), 035030 (2024).</li><li>Fauveau, V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Performance+of+spiral+UTE-MRI+of+the+lung+in+post-covid+patients.">Performance of spiral UTE-MRI of the lung in post-covid patients.</a> Magn Reson Imaging. 96, 135-143 (2023).</li><li>Metz, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparison+of+diagnostic+quality+of+3D+ultrashort-echo-time+techniques+for+pulmonary+magnetic+resonance+imaging+in+free-breathing.">Comparison of diagnostic quality of 3D ultrashort-echo-time techniques for pulmonary magnetic resonance imaging in free-breathing.</a> Acta Radiologica. 64 (5), 1851-1858 (2023).</li><li>Periaswamy, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparison+of+ultrashort+TE+lung+MRI+and+HRCT+lungs+for+detection+of+pulmonary+nodules+in+oncology+patients.">Comparison of ultrashort TE lung MRI and HRCT lungs for detection of pulmonary nodules in oncology patients.</a> Indian J Radiol Imaging. 32 (04), 497-504 (2022).</li><li>Dar&#231;ot, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparison+between+magnetic+resonance+imaging+and+computed+tomography+in+the+detection+and+volumetric+assessment+of+lung+nodules:+A+prospective+study.">Comparison between magnetic resonance imaging and computed tomography in the detection and volumetric assessment of lung nodules: A prospective study.</a> Frontiers in Medicine. 9, 858731 (2022).</li><li>Dournes, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=3D+ultrashort+echo+time+MRI+of+the+lung+using+stack-of-spirals+and+spherical+k-space+coverages:+Evaluation+in+healthy+volunteers+and+parenchymal+diseases.">3D ultrashort echo time MRI of the lung using stack-of-spirals and spherical k-space coverages: Evaluation in healthy volunteers and parenchymal diseases.</a> J Magn Reson Imaging. 48 (6), 1489-1497 (2018).</li><li>Campbell-Washburn, A. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=2019+American+Thoracic+Society+Bear+Cage+winning+proposal:+Lung+imaging+using+high-performance+low-field+magnetic+resonance+imaging.">2019 American Thoracic Society Bear Cage winning proposal: Lung imaging using high-performance low-field magnetic resonance imaging.</a> Am J Respir Crit Care Med. 201 (11), 1333-1336 (2020).</li><li>Robison, R. K., Anderson, A. G., Pipe, J. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Three-dimensional+ultrashort+echo-time+imaging+using+a+FLORET+trajectory.">Three-dimensional ultrashort echo-time imaging using a FLORET trajectory.</a> Magn Reson Med. 78 (3), 1038-1049 (2017).</li><li>Willmering, M. M., Robison, R. K., Wang, H., Pipe, J. G., Woods, J. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Implementation+of+the+FLORET+sequence+for+lung+imaging.">Implementation of the FLORET sequence for lung imaging.</a> Magn Reson Med. 82 (3), 1091-1100 (2019).</li><li>Chan, R. W., Ramsay, E. A., Cunningham, C. H., Plewes, D. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Temporal+stability+of+adaptive+3D+radial+MRI+using+multidimensional+golden+means.">Temporal stability of adaptive 3D radial MRI using multidimensional golden means.</a> Magn Reson Med. 61 (2), 354-363 (2009).</li><li>Yu, J., Xue, Y., Song, H. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparison+of+lung+T2*+during+free-breathing+at+1.5+T+and+3.0+T+with+ultrashort+echo+time+imaging.">Comparison of lung T2* during free-breathing at 1.5 T and 3.0 T with ultrashort echo time imaging.</a> Magn Reson Med. 66 (1), 248-254 (2011).</li><li>Otsu, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+threshold+selection+method+from+gray-level+histograms.">A threshold selection method from gray-level histograms.</a> IEEE Trans Syst Man Cybern. 9 (1), 62-66 (1979).</li><li>Martin Uecker, F. O., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Berkely+advanced+reconstruction+toolbox.">Berkely advanced reconstruction toolbox.</a> Proc Intl Soc Magn Reson Med. 23, 2486 (2015).</li><li>. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Bart Toolbox for Computational Magnetic Resonance Imaging. , (2024).</li><li>Munidasa, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Treatment monitoring of pediatric cystic fibrosis lung disease using free breathing lung MRI. , (2024).</li><li>Zhu, X., Chan, M., Lustig, M., Johnson, K. M., Larson, P. E. Z. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Iterative+motion-compensation+reconstruction+ultra-short+TE+(IMOCO+UTE)+for+high-resolution+free-breathing+pulmonary+MRI.">Iterative motion-compensation reconstruction ultra-short TE (IMOCO UTE) for high-resolution free-breathing pulmonary MRI.</a> Magn Reson Med. 83 (4), 1208-1221 (2020).</li><li>Tan, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Motion-compensated+low-rank+reconstruction+for+simultaneous+structural+and+functional+UTE+lung+MRI.">Motion-compensated low-rank reconstruction for simultaneous structural and functional UTE lung MRI.</a> Magn Reson Med. 90 (3), 1101-1113 (2023).</li><li>Bhattacharya, I., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Oxygen-enhanced+functional+lung+imaging+using+a+contemporary+0.55+T+MRI+system.">Oxygen-enhanced functional lung imaging using a contemporary 0.55 T MRI system.</a> NMR Biomed. 34 (8), e4562 (2021).</li><li>Kim, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Feasibility+of+dynamic+T2*-based+oxygen-enhanced+lung+MRI+at+3T.">Feasibility of dynamic T2*-based oxygen-enhanced lung MRI at 3T.</a> Magn Reson Med. 91 (3), 972-986 (2024).</li><li>Klime&#353;, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=3D+phase-resolved+functional+lung+ventilation+MR+imaging+in+healthy+volunteers+and+patients+with+chronic+pulmonary+disease.">3D phase-resolved functional lung ventilation MR imaging in healthy volunteers and patients with chronic pulmonary disease.</a> Magn Reson Med. 85 (2), 912-925 (2021).</li></ol>

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Chest MRI Coil</td>
			<td>Siemens, GE, Philips,, Other Clinical MRI Imaging Coil Vendor</td>
			<td>N/A</td>
			<td>A 26 - 32 channel Chest coil should be used</td>
		</tr>
		<tr>
			<td>High Performance Workstation</td>
			<td>HP, Apple, or other Computer Hardware company</td>
			<td>N/A</td>
			<td>A computer with a minimum of 64 GB of Memory is needed for image reconstruction</td>
		</tr>
		<tr>
			<td>Matlab</td>
			<td>Mathworks</td>
			<td>R2016A or newer</td>
			<td>A Matlab license is needed to run the provided computer code</td>
		</tr>
		<tr>
			<td>MRI Phantom</td>
			<td>Siemens, GE, Philips, or Other MRI Phantom Vendor</td>
			<td>N/A</td>
			<td>Any Phantom can be used to test the MRI sequence prior to its use in human subjects.</td>
		</tr>
		<tr>
			<td>MRI Scanner</td>
			<td>Siemens, GE, Philips, or Other Clinical MRI Scanner Vendor</td>
			<td>N/A</td>
			<td>The protocol was developed on a 3T scanner, but 1.5T or 0.55T would also work with minimal adaptation</td>
		</tr>
	</tbody>
</table>

pulmonary structural mri using free-breathing, self-gated ultra-short echo time imaging 

High-resolution imaging of the pulmonary structure is an essential part of diagnostic work-ups for many pulmonary conditions. Typically, this is performed using Computed Tomography (CT) imaging, which is ideally suited to generate high-resolution images of the lungs1. However, CT imaging delivers a non-trivial dose of ionizing radiation, making it ill-suited for regular repeat imaging, imaging at multiple different respiratory phases, or imaging certain populations (e.g., pediatrics). Magnetic resonance imaging (MRI) does not carry the same risk of ionizing radiation, and thus is amenable to such imaging tasks. However, it is challenging to image the lungs using MRI owing to low tissue density, respiratory and cardiac motion, and very fast signal relaxation2,3,4.
One MRI technique that is able to mitigate these challenges is ultra-short echo time (UTE) MRI4,5,6. In UTE MRI, the MRI signal is sampled immediately following signal excitation, which reduces the impact of fast signal relaxation. Moreover, this technique samples k-space from the center outward, which leads to significant oversampling at the center of k-space. This oversampling at the center of the k-space makes this imaging technique robust to motion. In addition to this inherent robustness to motion, repeated sampling of the center of k-space encodes information about respiratory motion, which enables the self-gating of images7,8,9. This self-gating can be used to generate images at a variety of respiratory phases. Because humans spend the majority of the respiratory phase at expiration, it is common to generate an image for end-expiration, as this phase has the most imaging data acquired.
There are a variety of strategies for respiratory self-gating in pulmonary MRI. The first distinction to be made is image-based vs. k-space-based gating10 (Figure 1). In image-based gating, a set of images with high temporal resolution is generated by reconstructing small temporal subsets of the imaging data. Subsequently, the position of the diaphragm in these images is used to identify the respiratory phase for a given set of image projections10,11. In k-space-based gating, data from the center of k-space (&#34;k0&#34;) is examined8,9,12. The signal intensity of the image is encoded in k0, and thus, the intensity of the k0 point varies with respiration. Projections can thus be binned into different respiratory phases based on the intensity of k0. In both image-based and k-space-based gating, projections with like-respiratory phases are grouped for image reconstruction. It has been suggested that image-based gating provides improved fidelity in estimating the respiratory phase, thereby providing images with reduced blurring10,13.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/67294/67294fig01.jpg" /> 
Figure 1: Image-based and k-space based self gating techniques. (A) In image-based gating, low spatial resolution, high temporal resolution images showing the diaphragm are generated from temporal subsets of the overall data. Using a line over the diaphragm, respiratory motion can be visualized and binned for image reconstruction. (B)&#160;In k-space-based gating, the first point on a center-out k-space projection (&#34;k0&#34;) is used to visualize respiratory motion. After smoothing k0, signal intensity differences based on the respiratory cycle are clearly visible and can be used to identify different respiratory phases. <a href="https://www.jove.com/files/ftp_upload/67294/67294fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Both image and k-space-based gating can be performed using either hard gating or soft gating11,14. In hard gating, only the projections corresponding to the desired respiratory phase are reconstructed. However, this discarding of unwanted projections can lead to reduced image signal-to-noise ratio (SNR) and increased undersampling artifacts. These undesired effects can be mitigated by using soft gating. In soft gating, all projections are used for image reconstruction, but projections from an unwanted respiratory phase are weighted such that they have a lesser impact on the final image. In doing so, images can be reconstructed with minimal artifacts and high SNR while still suppressing the impact of respiratory motion.
Through the combination of UTE MRI acquisition with post-acquisition self-gating, high-quality images can be generated that, while not equivalent to CT, have a contrast and resolution that is approaching that of CT imaging6,15,16,17,18,19. Herein, a simple protocol is provided for collecting and reconstructing UTE MRI images to generate high quality images of pulmonary structure.
This protocol is written primarily for 3T MRI scanners; 3T is the most common field strength used for research MRI. Lower magnetic field strengths such as 1.5T or the recently available 0.55 T20 can provide improved image quality and signal intensity within the lungs, as signal relaxation within the lungs is slower at these field strengths.
While every attempt has been made to provide clarity and simplicity in this protocol and the provided image reconstruction code, the protocol will likely require a dedicated MRI physicist (or similar MRI expert) to establish an appropriate UTE MRI sequence on the MRI scanner. The MRI sequence should implement a 3D non-Cartesian encoding strategy with Center-out k-space trajectories. Examples include 3D radial or 3D spiral (e.g., &#34;FLORET&#34;)21,22 imaging sequences. Importantly, the order of projections should have good temporal stability: Over any given subset of time, the projections should cover the full range of k-space23. Examples of projection ordering strategies with good temporal stability are golden means or Halton-randomized Archimedean spiral. If a projection ordering with poor temporal stability is used, post-acquisition self-gating will omit large regions of k-space, leading to image artifacts. Finally, the sequence should be capable of achieving an echo time (TE) of &#60;100 &#181;s. The T2* relaxation time in the lungs at 3T is &#60;1 ms24, so using a very short TE is essential to generating high-quality images.

<meta charset="utf8"></head><body>Author Spotlight: פרוטוקול MRI ריאה אופטימלי עם שיטות שחזור יעילות מבחינה חישובית

MRI מבני ריאתי באמצעות נשימה חופשית, גידור עצמי הד זמן הד קצר במיוחד 

My research is centered on the development and application of MRI methods for imaging lung structure and function. We want to understand how we can best image the lungs and then use those techniques to understand lung health and disease. Lung MRI is a rapidly evolving field.Researchers are working on new ways to collect MRI of different gas agents and new methods of using conventional MRI to image lung structure and function. Structural MRI has come a long way with various new ways to mitigate motion and improve the SNR of images. Many of the reconstruction methods available for generating high quality lung structure MRI images are either complicated, computationally expensive, or both.This protocol uses relatively computationally efficient reconstruction methods and implements a simple pipeline for post-acquisition respiratory gating.

High quality MRI of the lungs is challenged by low tissue density, fast MRI signal relaxation, and respiratory and cardiac motion. For these reasons, ...

pulmonary-structural-mri-using-free-breathing-self-gated-ultra-short

Research

JoVE Journal

Medicine

Pulmonary Structural MRI using Free-Breathing, Self-Gated Ultra-short Echo Time Imaging 

153 Views.  University of Kansas Medical Center. המחקר שלי מתמקד בפיתוח ויישום של שיטות MRI להדמיית מבנה ותפקוד ריאות. אנו רוצים להבין כיצד אנו יכולים לדמיין את הריאות בצורה הטובה ביותר, ואז להשתמש בטכניקות האלה כדי להבין את בריאות הריאות ואת מחלותיהן. MRI ריאות הוא תחום המתפתח במהירות.חוקרים עובדים על...

Video: Author Spotlight: פרוטוקול MRI ריאה אופטימלי עם שיטות שחזור יעילות מבחינה חישובית

High quality MRI of the lungs is challenged by low tissue density, fast MRI signal relaxation, and respiratory and cardiac motion. For these reasons, structural imaging of the lungs is performed almost exclusively using Computed Tomography (CT). However, CT imaging delivers ionizing radiation, and thus is less well suited for certain vulnerable populations (e.g., pediatrics) or for research applications. As an alternative, MRI using ultra-short echo times (UTE) is attracting interest. This technique can be performed during free-breathing over the course of a ~5-10 min scan. Respiratory motion information is encoded alongside images; this information can be used to "self-gate" images. Self-gating thus removes the requirement of advanced MRI pulse sequence programming or the use of respiratory bellows, which simplifies image acquisition. In this protocol, simple, robust, and computationally efficient acquisition and reconstruction methods for acquiring high quality UTE MRI of the lungs are presented. This protocol was developed for use on a 3T MRI scanner, but the same principles can be implemented at lower magnetic field strength. The protocol includes recommended parameter settings for 3D radial UTE image acquisition as well as directions for self-gated image reconstruction to generate images at distinct respiratory phases. Through the implementation of this protocol, users can generate high-resolution UTE images of the lungs with minimal to minimal-to-no motion artifacts. These images can be used to evaluate pulmonary structure, which can be implemented for research use in a variety of pulmonary conditions.

A protocol is described for generating high-resolution structural images of the lungs using ultra-short-echo time (UTE) Magnetic Resonance Imaging (MRI). This protocol allows for images to be acquired using a simple MRI pulse sequence during free-breathing.

Ultra-Short-Echo (UTE) Image Reconstruction Using Image-Based Respiratory Soft-Gating

To begin, acquire ultrashort echo time MRI images of the lung during free breathing. Import the data and k-spaced trajectories into MATLAB. Discard the first 1000 projections to ensure that the data reaches a steady state magnetization.Next, perform the image reconstruction using a non-uniform fast Fourier transform to a matrix size of 96 by 96 by 96. Use approximately 200 projections corresponding to 0.6 to 0.8 seconds worth of data. Then reconstruct and store images from all coil elements as well as the final coil combined image.In the coil combined image, select a coronal slice that clearly shows the diaphragm. Once the coronal slice is selected, view the individual coil images for this slice and select one or two coil elements that best show the diaphragm. Now reconstruct only the data from the coil elements using a sliding window to generate images with approximately 0.5 second temporal resolution.Use the first 200 projections to reconstruct an image using a non-uniform fast Fourier transform and store only the diaphragm slice. Shift by 100 projections and reconstruct an additional image storing the diaphragm slice. Now, select a line over the diaphragm in the first of the sliding window images.Visualize respiratory motion by viewing this respiratory navigator for all projections. Determine the location of the diaphragm for all respiratory navigators and use this location to label projections as belonging to a given respiratory bin. Then identify the bin with the highest number of projections corresponding to the end expiration and choose it for reconstruction.Use an exponential filter to provide a weight of one to projections within the primary bin and a sharply reducing weight to projections within different respiratory bins. Next, use the Berkeley advanced reconstruction toolbox to reconstruct a high resolution image at the desired respiratory bin. Calculate density compensation weights using iterative density combination.Scale the density compensation weights by the soft-gating weights. Then scale data based on the density compensation and soft-gating weights. Now perform a basic non-uniform fast Fourier transform to facilitate coil combination.Convert the non-uniform fast Fourier transform image into gridded k-space for coil combination. Then generate a coil combination matrix and use it to combine coils for both the raw data and the gridded k-space and estimate coil sensitivities. Afterward using the weighted density compensation, coil combined data and coil sensitivity maps, perform parallel imaging compressed sense reconstruction.Images generated at end expiration using both image-based and k-space based gating showed clear visualization of the diaphragm with image-based gating demonstrating superior motion compensation. Soft-gating enhanced the sharpness of the inspiration images reducing under sampling artifacts compared to hard-gating. Both image-based and k-space-based gating successfully detected respiratory waveforms during regular breathing, with image-based gating yielding clearer results under irregular breathing conditions.