Name: pulmonary structural mri using free-breathing, self-gated ultra-short echo time imaging
Uploaded: 2024-09-06T14:55:00.000+00:00
Duration: 5 min 7 s
Description: High quality MRI of the lungs is challenged by low tissue density, fast MRI signal relaxation, and respiratory and cardiac motion. For these reasons, ...

The development of this protocol and the images shown as representative results were supported by the National Scleroderma Foundation.

All human subject imaging was performed with approval from the KUMC IRB. Written informed consent was obtained from all participants. Images in this study were obtained under a generic technical development protocol, and the inclusion/exclusion criteria were deliberately broad. Inclusion Criteria: Age &#8805; 18. Exclusion Criteria: MRI contraindicated based on responses to the MRI screening questionnaire, and pregnancy. The accessories and the equipment used for this study are listed in the Table of Materials.
1. UTE image acquisition
<ol>
	<li>Prepare imaging sequence. Prepare the imaging sequence one time and use this same sequence for all participants.
	<ol>
		<li>Set parameters according to Table 1.</li>
		<li>Place an MRI phantom at the center of the MRI and run the imaging sequence. 
		NOTE: Because this sequence requires fast gradient performance and many RF pulses, it is important to verify that the protocol setup can be run prior to testing in a human.</li>
	</ol>
	</li>
	<li>Prepare the participant for MRI. Use institutional-standard MRI safety screening to ensure the participant can safely enter the MRI.</li>
	<li>Position the participant on the MRI bed and place a chest coil over the participant&#39;s torso. Position the coil close to the participant&#39;s chin in order to ensure full coverage of the lung apices.</li>
	<li>Move the participant into the MRI scanner. Place the positioning landmark just below the sternum of the participant.</li>
	<li>Collect a localizer scan to ensure that the participant&#39;s lungs are within the field of view for the UTE scan. Do not move the geometry of the UTE scan. If the participant&#39;s lungs are not within the field of view, move the participant and collect additional localizer scans until the lungs are fully within the field of view.</li>
	<li>Run the UTE sequence. During this sequence, the participant can breathe normally.</li>
	<li>Export the raw data from the scanner. Depending on the imaging sequence used, the scanner may or may not reconstruct images on the scanner. For the proposed retrospective gating reconstruction, raw imaging data is required to determine whether or not images are generated on the scanner. Note the raw data will be large (&#62;10 GB).</li>
	<li>Export or calculate k-space trajectories (i.e., the location in k-space of every raw data point). 
	NOTE: For some imaging sequences, k-space trajectories may be stored alongside raw data on the MRI scanner and can be directly exported. For other imaging sequences, the k-space trajectories will need to be calculated based on imaging parameters.</li>
</ol>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Parameter</td>
			<td>Generic Recommended Settings</td>
			<td>Settings Implemented Herein</td>
		</tr>
		<tr>
			<td>Imaging Sequence</td>
			<td>3D Non-Cartesian with Center-out k-space trajectories</td>
			<td>3D Radial with Golden Means Projection ordering</td>
		</tr>
		<tr>
			<td>Field-of-View</td>
			<td>400 x 400 x 400 mm3</td>
			<td>400 x 400 x 400 mm3</td>
		</tr>
		<tr>
			<td>Matrix Size</td>
			<td>As desired for target resolution</td>
			<td>320 x 320 x 320 (1.25 mm isotropic resolution)</td>
		</tr>
		<tr>
			<td>Bandwidth</td>
			<td>As needed for readout duration &#60; 1.0 ms</td>
			<td>888 Hz/Pixel</td>
		</tr>
		<tr>
			<td>TE</td>
			<td>&#60; 0.1 ms</td>
			<td>0.07 ms</td>
		</tr>
		<tr>
			<td>TR</td>
			<td>Minimum (Target 3 &#8211; 4 ms)</td>
			<td>3.5 ms</td>
		</tr>
		<tr>
			<td>Flip Angle</td>
			<td>Approximately 5&#176;</td>
			<td>4.8&#176;</td>
		</tr>
		<tr>
			<td>Number of Projections</td>
			<td>Minimum 100,000</td>
			<td>1,35,386</td>
		</tr>
		<tr>
			<td>Image Duration</td>
			<td>Minimum 5 min</td>
			<td>7 min, 54 s</td>
		</tr>
	</tbody>
</table>
Table 1: Recommended settings for UTE imaging. Generic recommended settings are provided that can be used to guide protocol setup. Specific recommended settings that were used for the data are also provided, as shown as representative results. Parameter specifications are generic across vendors, except for bandwidth. Some major MRI vendors specify bandwidth as Hz/Pixel. Other major MRI vendors specify absolute bandwidth. The recommended bandwidth (888 Hz/Pixel) corresponds to an absolute bandwidth of 284,160 Hz.
2. UTE image reconstruction using image-based respiratory soft-gating
NOTE: MATLAB code to complete the following steps is provided at&#160;https://github.com/pniedbalski3/UTE_Reconstruction.
<ol>
	<li>Import data and k-space trajectories into MATLAB. Code for importing raw MRI data is available for all of the major MRI vendors.</li>
	<li>Discard the first 1000 projections to ensure that data is at steady-state magnetization. 
	NOTE: If the imaging sequence used includes dummy scans prior to data acquisition, this step can be skipped.</li>
	<li>Reconstruct a low-resolution image using a very small subset of data.
	<ol>
		<li>Reconstruct the image using a non-uniform fast Fourier transform to a matrix size of 96 x 96 x 96.</li>
		<li>Use approximately 200 projections, corresponding to 0.6 s to 0.8 s worth of data.</li>
		<li>Reconstruct and store images from all coil elements as well as a final, coil-combined image.</li>
	</ol>
	</li>
	<li>In the resulting coil-combined image, select a coronal slice that clearly shows the diaphragm. 
	NOTE: The provided code will prompt the user to select a slice containing the diaphragm.</li>
	<li>Once this slice has been selected, view the individual coil images for this slice and select one or two coil elements that best show the diaphragm (Figure 2). 
	NOTE: The provided code will prompt the user to select coil elements.</li>
	<li>Reconstruct images using a sliding window to generate images with ~0.5 s temporal resolution (Figure 2).
	<ol>
		<li>Reconstruct only the data from the coil elements selected in step 2.4. 
		NOTE: While all coil elements can be reconstructed, only the elements closest to the diaphragm are needed to visualize the diaphragm for the purposes of respiratory self-gating. By reconstructing only the coil elements closest to the diagram, the reconstruction time and computational burden is drastically reduced.</li>
		<li>Use the first 200 projections to reconstruct an image using a non-uniform fast Fourier transform (Figure 2). Store only the slice showing the diaphragm (as found in step 2.4). 
		NOTE: Ultimately, up to 1500 images will be generated; only a 2D slice is needed to visualize the diaphragm position, and storing 3D images for each of the sliding window steps would be prohibitive.</li>
		<li>Shift by 100 projections (i.e., the first image is reconstructed using projections 1-200. The second is reconstructed using projections 101 - 300) and reconstruct an additional image, storing the slice selected in step 2.4.</li>
		<li>Continue until all projections have been used to generate images.</li>
	</ol>
	</li>
	<li>Select a line over the diaphragm in the first of the sliding window images. Ensure that the line is long enough to extend into the lungs by 5-10 voxels and into the diaphragm by 5-10 voxels.</li>
	<li>Visualize respiratory motion by viewing this respiratory navigator for all projections.</li>
	<li>Determine the location of the diaphragm for all respiratory navigators. There are a variety of ways to do this, but a straightforward method is to use Otsu&#39;s method25 to divide the darker side (lung) from the brighter side (diaphragm).</li>
	<li>Use the diaphragm location to label projections as belonging to a given respiratory bin. If a given respiratory navigator shows the diaphragm at &#34;position 1&#34;, then the 200 projections used to generate the image for that navigator would belong to &#34;bin 1&#34;. 
	NOTE: Because images were generated using a sliding window with a 100-projection overlap, some projections may be labeled as belonging to multiple bins. The coarse spatial resolution of sliding window images leads to a total of ~4-6 bins that cover the full range of inspiration to expiration.</li>
	<li>Select the bin to reconstruct by determining which bin has the greatest number of projections, which should correspond to end expiration.
	<ol>
		<li>Alternatively, reconstruct images for the desired respiratory phases based on visual inspection of the respiratory navigator.</li>
	</ol>
	</li>
	<li>Generate weights for soft-gating14.
	<ol>
		<li>Use an exponential filter to provide a weight of 1 to projections within the primary bin and a sharply reducing weight to projections within different respiratory bins.</li>
	</ol>
	</li>
	<li>Use the Berkely Advanced Reconstruction Toolbox (BART; https://mrirecon.github.io/bart/)26,27&#160;to reconstruct a high-resolution image at the desired respiratory bin. 
	&#8203;NOTE: BART is a freely available toolbox for MRI image reconstruction.
	<ol>
		<li>Calculate density compensation weights using iterative density combination.</li>
		<li>Scale the density compensation weights by the soft-gating weights.</li>
		<li>Scale data based on density compensation and soft-gating weights</li>
		<li>Perform a basic non-uniform fast Fourier transform (NUFFT) to facilitate coil combination.</li>
		<li>Convert the NUFFT image into gridded k-space to be used for coil combination.</li>
		<li>Generate a coil combination matrix and use it to combine coils for both the raw data and the gridded k-space.</li>
		<li>Estimate coil sensitivities.</li>
		<li>Perform parallel imaging compressed sense reconstruction using the weighted density compensation, coil combined data, and coil sensitivity maps.</li>
	</ol>
	</li>
	<li>Save the final image. The NIFTI format is easily implemented. If the image is to be uploaded to a PACs system, a DICOM format may be required.</li>
</ol>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/67294/67294fig02.jpg" /> 
Figure 2: Image-based self gating. (1) Using a low-resolution image reconstructed from a small number of projections (for computational efficiency), identify a coronal slice that clearly shows the diaphragm. (2) By examining images from individual coil elements, select the coil elements that are closest to the diaphragm. (3) Performing a sliding window reconstruction only of the coil elements closest to the diaphragm (for computational efficiency). Images can be generated from subsets of 200 projections (corresponding to ~0.8 s); by overlapping projections, a pseudo-temporal resolution of ~0.5 s can be achieved in images. (4) Identifying a line that is perpendicular to the diaphragm to be used as a respiratory navigator. (5) Visualizing the image data on this line shows respiratory motion, which can be used to bin images. <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/67294/67294fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
3. UTE image reconstruction using k-space-based respiratory soft-gating
<ol>
	<li>Complete steps 2.1-2.4 so that the coil element closest to the diaphragm can be identified.</li>
	<li>Generate a k0 time series trace by using the absolute value of the first point on the projection for all projections for the selected coil element. This will provide a visualization of a respiratory waveform.</li>
	<li>In steps of 5000 projections, normalize k0 by the mean signal intensity of those same k0 points28. This mitigates signal intensity drift over time and provides an improved ability to quantitatively bin projections.</li>
	<li>Label each k0 point as occurring during inspiration or expiration.
	<ol>
		<li>Smooth the k0 time series and take the derivative to assess the slope for every point on the gating trace.</li>
		<li>Label inspiration points based on the sign of the slope. A positive slope corresponds to expiration, while a negative slope corresponds to inspiration.</li>
	</ol>
	</li>
	<li>Bin projections based on signal intensity. Because the depth of breathing can be variable, bin projections are based on signal amplitude rather than location in the respiratory phase. 
	NOTE: A simple and rapid method by which to accomplish this is to implement k-means clustering to identify different signal intensity levels.</li>
	<li>For bins intermediate between end-inspiration and end-expiration, identify projections as occurring during inspiration and expiration based on step 3.4.</li>
	<li>Complete image reconstruction following the steps provided in step 2.10 through step 2.13.</li>
	<li>If desired, reconstruct images for all respiratory bins rather than only at end-expiration.</li>
</ol>

Protocol for High-Resolution Lung Imaging Using 3T UTE MRI with Self-Gating

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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>

Peter Niedbalski receives research funding from the National Scleroderma Foundation, the American Heart Association, and the NIH. He is a consultant for Polarean Imaging Plc., a company that develops hyperpolarized 129Xe MRI technology.

When performing UTE imaging of the lungs, many variations of both acquisition and reconstruction can be used to generate images of the lungs. This protocol focuses on ease of implementation and computational efficiency. Imaging using 3D radial UTE is relatively simple, with imaging sequences generally available from the major MRI vendors. MATLAB-based tools are provided for data handling and self-gating. Because most academic institutions have access to MATLAB licenses, this code should be broadly useable and easily impl...

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://jove.unicartagenaproxy.elogim.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.

<table><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 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.

Representative results (Figure 3) were generated using the settings shown in Table 1. The imaging duration used provides high-quality images that are tolerable by most participants.
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/67294/67294fig03.jpg" /> 
Figure 3: Representative UTE images generated. Coronal, sagittal, and axial slices of ima...

Author Spotlight: Optimized Lung MRI Protocol with Computationally Efficient Reconstruction Methods

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.

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

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

Universidad de Cartagena UDEC

Research

JoVE Journal

Medicine

264 Views.  University of Kansas Medical Center. 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. 

Video: Author Spotlight: Optimized Lung MRI Protocol with Computationally Efficient Reconstruction Methods

Contrast Enhanced Vessel Imaging using MicroCT

Microscopic computed tomography (microCT) offers high-resolution volumetric imaging of the anatomy of living small animals. However, the contrast between different soft tissues and body fluids is inherently poor in micro-CT images 1. Under these circumstances, visualization of blood vessels becomes a nearly impossible task. To overcome this and to improve the visualization of blood vessels exogenous contrast agents can be used. Herein, we present a methodology for visualizing the vascular network in a rodent model. By using a long-acting aqueous colloidal polydisperse iodinated blood-pool contrast agent, eXIA 160XL, we optimized image acquisition parameters and volume-rendering techniques for finding blood vessels in live animals. Our findings suggest that, to achieve a superior contrast between bone and soft tissue from vessel, multiple-frames (at least 5-8/ frames per view), and 360-720 views (for a full 360&deg; rotation) acquisitions were mandatory. We have also demonstrated the use of a two-dimensional transfer function (where voxel color and opacity was assigned in proportion to CT value and gradient magnitude), in visualizing the anatomy and highlighting the structure of interest, the blood vessel network. This promising work lays a foundation for the qualitative and quantitative assessment of anti-angiogenesis preclinical studies using transgenic or xenograft tumor-bearing mice.

Contrast enhanced small animal vessel imaging by microCT is a rapid, cost-effective and high-throughput technique for serial in situ examination for tumor development, for analyzing the network of blood vessels that nourish them, and for following the response of tumors to preclinical therapeutic intervention(s).

Microscopic computed tomography (microCT) offers high-resolution volumetric imaging of the anatomy of living small animals. However, the contrast between ...

Magnetic Resonance Imaging Quantification of Pulmonary Perfusion using Calibrated Arterial Spin Labeling

This demonstrates a MR imaging method to measure the spatial distribution of pulmonary blood flow in healthy subjects 
during normoxia (inspired O2, fraction (FIO2) = 0.21) hypoxia (FIO2 = 0.125), and hyperoxia 
(FIO2 = 1.00). In addition, the physiological responses of the subject are monitored in the MR scan environment. MR images 
were obtained on a 1.5 T GE MRI scanner during a breath hold from a sagittal slice in the right lung at functional residual capacity. An arterial 
spin labeling sequence (ASL-FAIRER) was used to measure the spatial distribution of pulmonary blood flow 1,2 and a multi-echo fast 
gradient echo (mGRE) sequence 3 was used to quantify the regional proton (i.e. H2O) density, allowing the quantification 
of density-normalized perfusion for each voxel (milliliters blood per minute per gram lung tissue). 
With a pneumatic switching valve and facemask equipped with a 2-way non-rebreathing valve, different oxygen concentrations 
were introduced to the subject in the MR scanner through the inspired gas tubing. A metabolic cart collected expiratory gas via expiratory tubing. Mixed expiratory O2 and CO2 concentrations, oxygen consumption, carbon dioxide production, respiratory exchange ratio, 
respiratory frequency and tidal volume were measured. Heart rate and oxygen saturation were monitored using pulse-oximetry. 
Data obtained from a normal subject showed that, as expected, heart rate was higher in hypoxia (60 bpm) than during normoxia (51) or hyperoxia (50) and the arterial oxygen saturation (SpO2) was reduced during hypoxia to 86%. Mean ventilation was 8.31 L/min BTPS during hypoxia, 7.04 L/min during normoxia, and 6.64 L/min during hyperoxia. Tidal volume was 0.76 L during hypoxia, 0.69 L during normoxia, and 0.67 L during hyperoxia. 
Representative quantified ASL data showed that the mean density normalized perfusion was 8.86 ml/min/g during hypoxia, 8.26 ml/min/g during normoxia and 8.46 ml/min/g during hyperoxia, respectively. In this subject, the relative dispersion4, an index of global heterogeneity, was increased in hypoxia (1.07 during hypoxia, 0.85 during normoxia, and 0.87 during hyperoxia) while the fractal dimension (Ds), another index of heterogeneity reflecting vascular branching structure, was unchanged (1.24 during hypoxia, 1.26 during normoxia, and 1.26 during hyperoxia). 
Overview. This protocol will demonstrate the acquisition of data to measure the distribution of pulmonary perfusion noninvasively under conditions of normoxia, hypoxia, and hyperoxia using a magnetic resonance imaging technique known as arterial spin labeling (ASL). 
Rationale: Measurement of pulmonary blood flow and lung proton density using MR technique offers high spatial resolution images which can be quantified and the ability to perform repeated measurements under several different physiological conditions. In human studies, PET, SPECT, and CT are commonly used as the alternative techniques. However, these techniques involve exposure to ionizing radiation, and thus are not suitable for repeated measurements in human subjects.

A MR imaging method to study the distribution of pulmonary blood flow under a variety of physiological conditions, in this case exposure to three different inspired oxygen concentrations: hypoxia, normoxia, and hyperoxia, is described. This technique utilizes human pulmonary physiology research techniques in an MR scanning environment.

This demonstrates a MR imaging method to measure the spatial distribution of pulmonary blood flow in healthy subjects 
during normoxia (inspired O2, ...

Evaluation of Nanoparticle Uptake in Tumors in Real Time Using Intravital Imaging

Current technologies for tumor imaging, such as ultrasound, MRI, PET and CT, are unable to yield high-resolution images for the assessment of nanoparticle uptake in tumors at the microscopic level1,2,3, highlighting the utility of a suitable xenograft model in which to perform detailed uptake analyses. Here, we use high-resolution intravital imaging to evaluate nanoparticle uptake in human tumor xenografts in a modified, shell-less chicken embryo model. The chicken embryo model is particularly well-suited for these in vivo analyses because it supports the growth of human tumors, is relatively inexpensive and does not require anesthetization or surgery 4,5. Tumor cells form fully vascularized xenografts within 7 days when implanted into the chorioallantoic membrane (CAM) 6. The resulting tumors are visualized by non-invasive real-time, high-resolution imaging that can be maintained for up to 72 hours with little impact on either the host or tumor systems. Nanoparticles with a wide range of sizes and formulations administered distal to the tumor can be visualized and quantified as they flow through the bloodstream, extravasate from leaky tumor vasculature, and accumulate at the tumor site. We describe here the analysis of nanoparticles derived from Cowpea mosaic virus (CPMV) decorated with near-infrared fluorescent dyes and/or polyethylene glycol polymers (PEG) 7, 8, 9,10,11. Upon intravenous administration, these viral nanoparticles are rapidly internalized by endothelial cells, resulting in global labeling of the vasculature both outside and within the tumor7,12. PEGylation of the viral nanoparticles increases their plasma half-life, extends their time in the circulation, and ultimately enhances their accumulation in tumors via the enhanced permeability and retention (EPR) effect 7, 10,11. The rate and extent of accumulation of nanoparticles in a tumor is measured over time using image analysis software. This technique provides a method to both visualize and quantify nanoparticle dynamics in human tumors.

We present a novel approach to quantify nanoparticle localization in the vasculature of human xenografted tumors using dynamic, real-time intravital imaging in an avian embryo model.

Current technologies for tumor imaging, such as ultrasound, MRI, PET and CT, are unable to yield high-resolution images for the assessment of nanoparticle ...

MRI-guided Disruption of the Blood-brain Barrier using Transcranial Focused Ultrasound in a Rat Model

Focused ultrasound (FUS) disruption of the blood-brain barrier (BBB) is an increasingly investigated technique for circumventing the BBB1-5. The BBB is a significant obstacle to pharmaceutical treatments of brain disorders as it limits the passage of molecules from the vasculature into the brain tissue to molecules less than approximately 500 Da in size6. FUS induced BBB disruption (BBBD) is temporary and reversible4 and has an advantage over chemical means of inducing BBBD by being highly localized. FUS induced BBBD provides a means for investigating the effects of a wide range of therapeutic agents on the brain, which would not otherwise be deliverable to the tissue in sufficient concentration. While a wide range of ultrasound parameters have proven successful at disrupting the BBB2,5,7, there are several critical steps in the experimental procedure to ensure successful disruption with accurate targeting. This protocol outlines how to achieve MRI-guided FUS induced BBBD in a rat model, with a focus on the critical animal preparation and microbubble handling steps of the experiment.

Microbubble-mediated focused ultrasound disruption of the blood-brain barrier (BBB) is a promising technique for non-invasive targeted drug delivery in the brain1-3. This protocol outlines the experimental procedure for MRI-guided transcranial BBB disruption in a rat model.

Focused ultrasound (FUS) disruption of the blood-brain barrier (BBB) is an increasingly investigated technique for circumventing the BBB1-5. The BBB is a ...

Quantitative Mapping of Specific Ventilation in the Human Lung using Proton Magnetic Resonance Imaging and Oxygen as a Contrast Agent

Specific ventilation imaging (SVI) is a functional magnetic resonance imaging technique capable of quantifying specific ventilation &#8213; the ratio of the fresh gas entering a lung region divided by the region&#8217;s end-expiratory volume &#8213; in the human lung, using only inhaled oxygen as a contrast agent. Regional quantification of specific ventilation has the potential to help identify areas of pathologic lung function. Oxygen in solution in tissue shortens the tissue&#8217;s longitudinal relaxation time (T1), and thus a change in tissue oxygenation can be detected as a change in T1-weighted signal with an inversion recovery acquired image. Following an abrupt change between two concentrations of inspired oxygen, the rate at which lung tissue within a voxel equilibrates to a new steady-state reflects the rate at which resident gas is being replaced by inhaled gas. This rate is determined by specific ventilation. To elicit this sudden change in oxygenation, subjects alternately breathe 20-breath blocks of air (21% oxygen) and 100% oxygen while in the MRI scanner. A stepwise change in inspired oxygen fraction is achieved through use of a custom three-dimensional (3D)-printed flow bypass system with a manual switch during a short end-expiratory breath hold. To detect the corresponding change in T1, a global inversion pulse followed by a single shot fast spin echo sequence was used to acquire two-dimensional T1-weighted images in a 1.5&#160;T MRI scanner, using an eight-element torso coil. Both single slice and multi-slice imaging are possible, with slightly different imaging parameters. Quantification of specific ventilation is achieved by correlating the time-course of signal intensity for each lung voxel with a library of simulated responses to the air/oxygen stimulus. SVI estimations of specific ventilation heterogeneity have been validated against multiple breath washout and proved to accurately determine the heterogeneity of the specific ventilation distribution.

Specific ventilation imaging is a functional magnetic resonance imaging technique that allows for quantification of regional specific ventilation in the human lung, using inhaled oxygen as a contrast agent. Here, we present a protocol to collect and analyze specific ventilation imaging data.

Specific ventilation imaging (SVI) is a functional magnetic resonance imaging technique capable of quantifying specific ventilation &#8213; the ratio of ...

Assessing Lung Function with Hyperpolarized Xenon-129 MRI

Quantitative Measure of Lung Structure and Function Obtained from Hyperpolarized Xenon Spectroscopy

Hyperpolarized Xenon-129 (HXe) magnetic resonance imaging (MRI) provides tools for obtaining 2- or 3-dimensional maps of lung ventilation patterns, gas diffusion, Xenon uptake by lung parenchyma, and other lung function metrics. However, by trading spatial for temporal resolution, it also enables tracing of pulmonary Xenon gas exchange on a ms timescale. This article describes one such technique, chemical shift saturation recovery (CSSR) MR spectroscopy. It illustrates how it can be used to assess capillary blood volume, septal wall thickness, and the surface-to-volume ratio in the alveoli. The flip angle of the applied radiofrequency pulses (RF) was carefully calibrated. Single-dose breath-hold and multi-dose free-breathing protocols were employed for administering the gas to the subject. Once the inhaled Xenon gas reached the alveoli, a series of 90&#176; RF pulses was applied to ensure maximum saturation of the accumulated Xenon magnetization in the lung parenchyma. Following a variable delay time, spectra were acquired to quantify the regrowth of the Xenon signal due to gas exchange between the alveolar gas volume and the tissue compartments of the lung. These spectra were then analyzed by fitting complex pseudo-Voigt functions to the three dominant peaks. Finally, the delay time-dependent peak amplitudes were fitted to a one-dimensional analytical gas-exchange model to extract physiological parameters.

Author Spotlight: Advancing Lung Disease Research with Free-Breathing Hyperpolarized Xenon-129 MRI

The manuscript presents a detailed protocol for using hyperpolarized Xenon-129 chemical shift saturation recovery (CSSR) to trace pulmonary gas exchange, assess the apparent alveolar septal wall thickness, and measure the surface-to-volume ratio. The method has the potential to diagnose and monitor lung diseases.

Hyperpolarized Xenon-129 (HXe) magnetic resonance imaging (MRI) provides tools for obtaining 2- or 3-dimensional maps of lung ventilation patterns, gas ...

A Standardized Workflow for Hyperpolarized 129Xe MRI to Study Pulmonary Function

Acquiring Hyperpolarized 129Xe Magnetic Resonance Images of Lung Ventilation

Hyperpolarized 129Xe MRI comprises a unique array of structural and functional lung imaging techniques. Technique standardization across sites is increasingly important given the recent FDA approval of 129Xe as an MR contrast agent and as interest in 129Xe MRI increases among research and clinical institutions. Members of the 129Xe MRI Clinical Trials Consortium (Xe MRI CTC) have agreed upon best practices for each of the key aspects of the 129Xe MRI workflow, and these recommendations are summarized in a recent publication. This work provides practical information to develop an end-to-end workflow for collecting 129Xe MR images of lung ventilation according to the Xe MRI CTC recommendations. Preparation and administration of 129Xe for MR studies will be discussed and demonstrated, with specific topics including choice of appropriate gas volumes for entire studies and for individual MR scans, preparation&#160;and delivery of individual 129Xe doses, and best practices for monitoring subject safety and 129Xe tolerability during studies. Key MR technical considerations will also be covered, including pulse sequence types and optimized parameters, calibration of 129Xe flip angle and center frequency, and 129Xe MRI ventilation image analysis.

Author Spotlight: Standardization and Best Practices for Advancing Lung Imaging Using 129Xe MRI

Hyperpolarized 129Xe magnetic resonance imaging (MRI) is a method for studying regionally resolved aspects of pulmonary function. This work presents an end-to-end standardized workflow for hyperpolarized 129Xe MRI of lung ventilation, with specific attention to pulse sequence design, 129Xe dose preparation, scan workflow, and best practices for subject safety monitoring.

Hyperpolarized 129Xe MRI comprises a unique array of structural and functional lung imaging techniques. Technique standardization across sites is ...

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Hyperpolarized 129Xe gas MRI is an emerging technique to evaluate and measure regional lung function including pulmonary gas distribution and gas exchange. Chest computed tomography (CT) still remains the clinical gold standard for imaging of the lungs, though, in part due to the rapid CT protocols that acquire high-resolution images in seconds and the widespread availability of CT scanners. Quantitative approaches have enabled the extraction of structural lung parenchymal, airway and vascular measurements from chest CT that have been evaluated in many clinical research studies. Together, CT and 129Xe MRI provide complementary information that can be used to evaluate regional lung structure and function, resulting in new insights into lung health and disease. 129Xe MR-CT image registration can be performed to measure regional lung structure-function to better understand lung disease pathophysiology, and to perform image-guided pulmonary interventions. Here, a method for 129Xe MRI-CT registration is outlined to support implementation in research or clinical settings. Registration methods and applications that have been employed to date in the literature are also summarized, and suggestions are provided for future directions that may further overcome technical challenges related to 129Xe MR-CT image registration and facilitate broader implementation of regional lung structure-function evaluation.

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

CT and 129Xe MRI provide complementary lung structure-function information that can be exploited for regional analysis using image registration. Here, we provide a protocol that builds from the existing literature for 129Xe MR to CT image registration using open-source platforms.

Hyperpolarized 129Xe gas MRI is an emerging technique to evaluate and measure regional lung function including pulmonary gas distribution and gas ...

Dynamic Lung Ventilation Assessment with 3D Phase Resolved Functional Lung MRI

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Pulmonary magnetic resonance imaging (MRI) offers a variety of radiation-free techniques tailored to assess regional lung ventilation or its surrogates. These techniques encompass direct measurements, exemplified by hyperpolarized gas MRI and fluorinated gas MRI, as well as indirect measurements facilitated by oxygen-enhanced MRI and proton-based Fourier decomposition (FD) MRI. In recent times, there has been substantial progress in the field of FD MRI, which involved improving spatial/temporal resolution, refining sequence design and postprocessing, and developing a comprehensive whole-lung approach.
The two-dimensional (2D) phase-resolved functional lung (PREFUL) MRI stands out as an FD-based approach developed for the comprehensive assessment of regional ventilation and perfusion dynamics, all within a single MR acquisition. Recently, a new advancement has been made with the development of 3D PREFUL to assess dynamic ventilation of the entire lung using 8 min exam with a self-gated sequence.
The 3D PREFUL acquisition involves employing a stack-of-stars spoiled-gradient-echo sequence with a golden angle increment. Following the compressed sensing image reconstruction of approximately 40 breathing phases, all the reconstructed respiratory-resolved images undergo registration onto a fixed breathing phase. Subsequently, the ventilation parameters are extracted from the registered images.
In a study cohort comprising healthy volunteers and patients with chronic obstructive pulmonary disease, the 3D PREFUL ventilation parameters demonstrated strong correlations with measurements obtained from pulmonary function tests. Additionally, the interscan repeatability of the 3D PREFUL technique was deemed to be acceptable, indicating its reliability for repeated assessments of the same individuals.
In summary, 3D PREFUL ventilation MRI provides a whole lung coverage and captures ventilation dynamics with enhanced spatial resolution compared to 2D PREFUL. 3D PREFUL technique offers a cost-effective alternative to hyperpolarized 129Xe MRI, making it an attractive option for patient-friendly evaluation of pulmonary ventilation.

Three-dimensional (3D) phase-resolved functional lung (PREFUL) is a functional magnetic resonance imaging (MRI) technique that allows for quantification of regional ventilation of the whole human lung volume, using tidal breathing and contrast-agent free acquisition for 8 min. Here, we present an MR protocol to collect and analyze 3D PREFUL imaging data.

Pulmonary magnetic resonance imaging (MRI) offers a variety of radiation-free techniques tailored to assess regional lung ventilation or its surrogates. ...

Advanced Lung Imaging for Disease Detection and Monitoring

Phase-Resolved Functional Lung MRI for Pulmonary Ventilation and Perfusion (V/Q) Assessment

Fourier decomposition is a contrast agent-free 1H MRI method for lung perfusion (Q) and ventilation (V) assessment. After image registration, the time series of each voxel is analyzed with regard to the cardiac and breathing frequency components.
Using a standard 2D spoiled gradient-echo sequence with a temporal resolution of ~300 ms, an image-sorting algorithm was developed to produce phase-resolved functional lung imaging (PREFUL) with an increased temporal resolution. Thus, it is feasible to evaluate regional flow volume loops (FVL) during tidal volume breathing and depict the propagation of the pulse wave during the cardiac cycle. This method can be applied at 1.5T or 3T with standard MR hardware without the necessity for sequence programming, as the described protocol can be implemented with the default SPGRE sequence on most systems.
PREFUL ventilation MRI has been validated using 129Xe and 19F gas imaging with good regional agreement. Perfusion-weighted PREFUL MRI has been validated using SPECT as well as dynamic contrast enhanced (DCE) MRI. PREFUL has been tested in a dual center dual vendor setting and is currently applied in several ongoing multicenter trials. Furthermore, it is feasible across a range of field strengths (0.55T-3T) and different age groups, including newborns.
Quantitative V/Q PREFUL MRI has been used in patients with cystic fibrosis, chronic obstructive pulmonary disease, chronic thromboembolic pulmonary hypertension, and corona virus disease-2019 to quantify disease and monitor treatment change after therapy. Furthermore, PREFUL V/Q imaging has been shown to predict transplant loss due to chronic lung allograft dysfunction in patients after lung transplantation. In summary, PREFUL MRI is a validated technique for quantitative ventilation and pulmonary pulse wave/perfusion imaging for regional pulmonary disease detection, quantification, and treatment monitoring with potential added value to the current clinical routine.

Author Spotlight: Enhancing Diagnostic Strategies and Biomarker Development for Comprehensive Lung Function Analysis

Here, we describe the implementation of phase-resolved functional lung MRI as a contrast-agent-free proton MR technique for the assessment of pulmonary ventilation and perfusion dynamics. Validated and applicable across different field strengths and age groups, it could enhance clinical decision-making in the future by aiding in disease quantification and therapy monitoring.

Fourier decomposition is a contrast agent-free 1H MRI method for lung perfusion (Q) and ventilation (V) assessment. After image registration, the time ...

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

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