Method Article

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

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DOI:

10.3791/67294

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September 6th, 2024

1Division of Pulmonary, Critical Care, and Sleep Medicine, Hoglund Biomedical Imaging Center, University of Kansas Medical Center

In This Article

Summary

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.

Abstract

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.

Introduction

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 ("k0") 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.

Sliding window reconstruction, k-space self-gating diagram, signal processing in imaging analysis.
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) In k-space-based gating, the first point on a center-out k-space projection ("k0") 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. Please click here to view a larger version of this figure.

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., "FLORET")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 <100 µs. The T2* relaxation time in the lungs at 3T is <1 ms24, so using a very short TE is essential to generating high-quality images.

Protocol

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 ≥ 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

  1. Prepare imaging sequence. Prepare the imaging sequence one time and use this same sequence for all participants.
    1. Set parameters according to Table 1.
    2. 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.
  2. Prepare the participant for MRI. Use institutional-standard MRI safety screening to ensure the participant can safely enter the MRI.
  3. Position the participant on the MRI bed and place a chest coil over the participant's torso. Position the coil close to the participant's chin in order to ensure full coverage of the lung apices.
  4. Move the participant into the MRI scanner. Place the positioning landmark just below the sternum of the participant.
  5. Collect a localizer scan to ensure that the participant's lungs are within the field of view for the UTE scan. Do not move the geometry of the UTE scan. If the participant'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.
  6. Run the UTE sequence. During this sequence, the participant can breathe normally.
  7. 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 (>10 GB).
  8. 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.
ParameterGeneric Recommended SettingsSettings Implemented Herein
Imaging Sequence3D Non-Cartesian with Center-out k-space trajectories3D Radial with Golden Means Projection ordering
Field-of-View400 x 400 x 400 mm3400 x 400 x 400 mm3
Matrix SizeAs desired for target resolution320 x 320 x 320 (1.25 mm isotropic resolution)
BandwidthAs needed for readout duration < 1.0 ms888 Hz/Pixel
TE< 0.1 ms0.07 ms
TRMinimum (Target 3 – 4 ms)3.5 ms
Flip AngleApproximately 5°4.8°
Number of ProjectionsMinimum 100,0001,35,386
Image DurationMinimum 5 min7 min, 54 s

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 https://github.com/pniedbalski3/UTE_Reconstruction.

  1. Import data and k-space trajectories into MATLAB. Code for importing raw MRI data is available for all of the major MRI vendors.
  2. 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.
  3. Reconstruct a low-resolution image using a very small subset of data.
    1. Reconstruct the image using a non-uniform fast Fourier transform to a matrix size of 96 x 96 x 96.
    2. Use approximately 200 projections, corresponding to 0.6 s to 0.8 s worth of data.
    3. Reconstruct and store images from all coil elements as well as a final, coil-combined image.
  4. 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.
  5. 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.
  6. Reconstruct images using a sliding window to generate images with ~0.5 s temporal resolution (Figure 2).
    1. 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.
    2. 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.
    3. 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.
    4. Continue until all projections have been used to generate images.
  7. 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.
  8. Visualize respiratory motion by viewing this respiratory navigator for all projections.
  9. 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's method25 to divide the darker side (lung) from the brighter side (diaphragm).
  10. Use the diaphragm location to label projections as belonging to a given respiratory bin. If a given respiratory navigator shows the diaphragm at "position 1", then the 200 projections used to generate the image for that navigator would belong to "bin 1".
    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.
  11. Select the bin to reconstruct by determining which bin has the greatest number of projections, which should correspond to end expiration.
    1. Alternatively, reconstruct images for the desired respiratory phases based on visual inspection of the respiratory navigator.
  12. Generate weights for soft-gating14.
    1. 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.
  13. Use the Berkely Advanced Reconstruction Toolbox (BART; https://mrirecon.github.io/bart/)26,27 to reconstruct a high-resolution image at the desired respiratory bin.
    ​NOTE: BART is a freely available toolbox for MRI image reconstruction.
    1. Calculate density compensation weights using iterative density combination.
    2. Scale the density compensation weights by the soft-gating weights.
    3. Scale data based on density compensation and soft-gating weights
    4. Perform a basic non-uniform fast Fourier transform (NUFFT) to facilitate coil combination.
    5. Convert the NUFFT image into gridded k-space to be used for coil combination.
    6. Generate a coil combination matrix and use it to combine coils for both the raw data and the gridded k-space.
    7. Estimate coil sensitivities.
    8. Perform parallel imaging compressed sense reconstruction using the weighted density compensation, coil combined data, and coil sensitivity maps.
  14. 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.

MRI process diagram, diaphragm coil selection, sliding window reconstruction, respiratory analysis.
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. Please click here to view a larger version of this figure.

3. UTE image reconstruction using k-space-based respiratory soft-gating

  1. Complete steps 2.1-2.4 so that the coil element closest to the diaphragm can be identified.
  2. 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.
  3. 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.
  4. Label each k0 point as occurring during inspiration or expiration.
    1. Smooth the k0 time series and take the derivative to assess the slope for every point on the gating trace.
    2. Label inspiration points based on the sign of the slope. A positive slope corresponds to expiration, while a negative slope corresponds to inspiration.
  5. 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.
  6. For bins intermediate between end-inspiration and end-expiration, identify projections as occurring during inspiration and expiration based on step 3.4.
  7. Complete image reconstruction following the steps provided in step 2.10 through step 2.13.
  8. If desired, reconstruct images for all respiratory bins rather than only at end-expiration.

Results

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.

ct scan gating comparison, k0-based vs image-based methods, diagram showing soft and hard gating results
Figure 3: Representative UTE images generated. Coronal, sagittal, and axial slices of images are shown for the same patient with datasets reconstructed using both image and k-space-based gating, as well as hard gating and soft gating. A region in each image (squares) is shown magnified to highlight resolution differences between images. For this participant, image and k-space-based gating perform similarly in mitigating respiratory motion. Soft-gating leads to reduced blurring in images. The approximate parenchymal signal-to-noise ratio in each image is: Image-based hard-gating: 3.2; Image-based soft-gating: 3.6; k0-based hard-gating: 4.3; k0-based soft-gating: 3.2. Please click here to view a larger version of this figure.

Through this protocol, high-resolution MRI images of the lungs at end-expiration can be generated. Following free-breathing image acquisition, respiratory motion can be visualized using either image-based or k-space gating. For optimal image quality, these images are acquired using multi-channel receiver array coils. Thus, it is essential to identify the coil element closest to the diaphragm in order to have the greatest sensitivity to respiratory motion (Figure 2). The index of the coil element closest to the diaphragm can change from scan to scan based on the positioning of the coil. As a result, it is important to view images from individual coil elements for each scan to ensure proper identification of the proper coil element.

If image-based gating is used, sliding window reconstruction is used to generate images of a single slice showing the diaphragm. Following sliding window image reconstruction, the diaphragm should be clearly visible in each of these images. If the diaphragm is not clearly visible, it may be necessary to repeat this reconstruction using different coil elements. A line evaluated over the diaphragm enables the visualization of respiratory motion over time. Similarly, the k0 point when using k-space based gating also enables visualization of respiratory motion.

In a participant who remains still and breathes regularly, both image-based and k-space gating show a consistent respiratory waveform. In a less compliant participant, both gating methods can identify temporal regions where irregular breathing occurs. Image-based and k-space based gating for particularly compliant and particularly non-compliant participants are shown in Figure 4. Similar to previous work7,13, the results shown here suggest that image-based gating may provide improved image fidelity as compared to k0-based gating.

MRI k-space and image-based analysis, comparing regular vs irregular breathing in lung scans.
Figure 4: k0 and Image-based gating traces for a participant with very regular breathing (left) and a participant who breathed shallowly and irregularly (right). Representative images for image-based and k0-based gating for both participants are shown below gating traces, including a magnified region of the image to highlight differences between images. For the participants who breathed regularly, image-based and k0-based gating had similar performance. For the participant who breathed irregularly, both images show relatively poor quality, though the image-based gating had slightly better performance. Please click here to view a larger version of this figure.

Following visualization of respiratory motion, images at an arbitrary number of respiratory phases can be reconstructed. This protocol is primarily aimed toward generating a single image at end-expiration (Figure 3). Images at end-expiration tend to have the best image quality and SNR due to the greater number of projections acquired during expiration and the greater density of the lungs during this respiratory phase.

Because 3D volumetric images require a huge number of projections to be fully sampled, it is common practice to collect undersampled images. In the protocol described in Table 1, approximately 60% sampling is used. 3D spiral and 3D radial imaging methods tend to be robust to undersampling, so this typically is not a significant barrier to collecting high-resolution images. However, retrospective gating reduces the number of projections in these already-undersampled images, so blurring and artifacts can be a concern, particularly in less compliant participants who may not have as regular of a breathing pattern. The use of soft gating can help to mitigate blurring and artifacts due to undersampling, as shown in Figure 3 and Figure 5.

Lung MRI showing gating techniques: expiration, hard-gating, soft-gating, poor weighting results.
Figure 5: Benefits and risks of soft-gating. Expiration images (A) typically are generated using a much larger number of projections, and thus are not as susceptible to image artifacts when using hard-gating. Inspiration images are generated with a much smaller number of projections, and thus, hard gating can lead to reductions in image quality and image artifacts (B). Soft-gating can reduce these artifacts and improve image resolution and quality (C), but care needs to be taken to use appropriate projection weighting. If poor weighting is implemented, images can be blurred and lose fidelity to the target respiratory phase (D). Regions of each image are shown magnified to highlight differences in resolution and diaphragm position. Please click here to view a larger version of this figure.

Figure 3 shows UTE images reconstructed using both image- and k-space based gating, as well as using both soft- and hard-gating. Image and k-space based gating both show an ability to resolve the diaphragm at end-expiration, though, as noted above, several groups have demonstrated superior motion compensation when using image-based gating11,13. Hard gating, in the case of the expiration images shown in Figure 3, does not result in significant image artifacts. However, in inspiration images, which are reconstructed with fewer projections, hard gating results in reduced signal-to-noise and increased undersampling artifacts. Soft gating increases the sharpness of images and, in the case of inspiration images, suppresses image artifacts (Figure 5). Care should be taken with soft-gating, however, because if the undesired projections are not weighted properly, motion fidelity can be lost (Figure 5). Inspiration images are particularly susceptible to diaphragm blurring, given the much larger number of end expiration projections.

Upon reconstruction, features relating to normal lung physiology and pathophysiology can be observed (Figure 6). In a high-quality image with optimized reconstruction, airways down to the 3rd or 4th generation can be viewed. In addition, the larger vasculature is visible, particularly in the central regions of the lungs. In patients with interstitial lung disease, features consistent with CT features are visible, such as ground glass opacities and honeycombing (Figure 6). In some cases, features such as air trapping or emphysema can be inferred from images, but typically, the SNR within the lung parenchyma is too low to confidently identify these features for imaging acquired at 3T.

CT and UTE lung imaging comparison, insets show resolution detail; airway masks in 3D render.
Figure 6: Comparison of CT to UTE MRI. (A) CT imaging (1st column) clearly exceeds UTE MRI (2nd column) in terms of image resolution and quality. Regions of CT images (3rd column) and UTE MRI (4th column) are magnified for easier comparability between images. However, important features, such as large vasculature, large airways, and texture features related to pulmonary pathophysiology, such as honeycombing in (B) and ground glass opacity in (C) can be visualized in UTE MRI. While UTE MRI does not have the same ability to resolve airways as CT, airways to the 3rd or 4th generation can be manually segmented from images (D). Please click here to view a larger version of this figure.

Discussion

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 implemented. The provided MATLAB code is tailored to image acquisition using a homebuilt imaging sequence on a Siemens 3T MRI scanner. Other sequences and MRI platforms may require some editing of the code to properly read the imaging data.

The raw imaging data files for these images are very large (>10 GB), and thus the reconstruction algorithms are computationally expensive. As a result, image reconstruction can be very time-consuming. The Berkely Advanced Reconstruction Toolbox (BART; https://mrirecon.github.io/bart/)26,27 provides high quality image reconstruction tools that are easily implemented and computationally efficient. BART is easily integrated into the provided MATLAB-based tools for self-gating. The provided pipeline implementing image-based retrospective gating and BART requires 20-30 min to generate images from raw data. This is performed on a computer with 128GB of RAM and a 13th-generation Intel i9 3.00 GHz processor. Currently, the protocol does not use a graphics processing unit (GPU) to accelerate computation, though this could be implemented to further reduce the computational time. A computer with at least 64 GB of RAM is recommended in order to ensure adequate memory for handling the large datasets involved in this protocol.

The choice of image- vs. k-space based self-gating can impact workflows and image quality. While k-space based gating is faster, it may resolve the respiratory phases with less accuracy13. Image-based gating requires more time to execute, given the length of time required for sliding window reconstruction (~5 min using the provided Matlab code), but this method can provide better fidelity to diaphragm motion and thus provide images with reduced motion blurring.

A variety of improvements could be made to this protocol at the expense of ease of application, computational efficiency, or image reconstruction time. Images can be acquired with a greater number of projections, which would increase the quality of images but require additional scan duration and additional reconstruction time. Depending on how many projections are added, additional computer memory could also be required to store the very large datasets. Additional sophistication in gating could also be incorporated, such as using higher resolution image reconstruction in the sliding window reconstruction. This, again, would increase the computational burden of this protocol. Additionally, more advanced image reconstruction could be implemented, such as techniques like iMoCo29,30. While these improvements could lead to improved image quality, they come at significantly increased computational time, and thus, there could be diminishing returns.

The present protocol is focused on structural MRI. A variety of changes could be made to this protocol to instead interrogate lung function, such as using oxygen-enhanced MRI31,32 or using PREFUL analysis28,33.

Ultimately, this protocol enables the acquisition of high-resolution MRI images of the lungs using a 5-8 min, free-breathing acquisition strategy. Raw data acquired during image acquisition can be retrospectively gated, generating images at different respiratory phases that are uncorrupted by respiratory motion. This protocol has emphasized ease of implementation and computational efficiency, with an overall image reconstruction time of 20-30 min using a high-performance workstation. Once established, image reconstruction can be performed with minimal user intervention, though the requirements of manually selecting an image slice showing the diaphragm and the most appropriate coil elements prevent the protocol from being totally automated. While MRI of the lungs is not commonly performed in the clinic, increased availability of UTE MRI over time may increase its usage for elucidating pulmonary structure.

Disclosures

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.

Acknowledgements

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

Materials

List of materials used in this article
NameCompanyCatalog NumberComments
Chest MRI CoilSiemens, GE, Philips,, Other Clinical MRI Imaging Coil VendorN/AA 26 - 32 channel Chest coil should be used
High Performance WorkstationHP, Apple, or other Computer Hardware companyN/AA computer with a minimum of 64 GB of Memory is needed for image reconstruction
MatlabMathworksR2016A or newerA Matlab license is needed to run the provided computer code
MRI PhantomSiemens, GE, Philips, or Other MRI Phantom VendorN/AAny Phantom can be used to test the MRI sequence prior to its use in human subjects.
MRI ScannerSiemens, GE, Philips, or Other Clinical MRI Scanner VendorN/AThe protocol was developed on a 3T scanner, but 1.5T or 0.55T would also work with minimal adaptation

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Pulmonary MRIFree Breathing MRISelf Gated ImagingLung Structure ImagingRespiratory Motion Compensation3D Radial UTENon Uniform Fast FourierCoil CombinationParallel Imaging

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