ultra-short-echo (ute) image reconstruction using image-based respiratory soft-gating

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

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.

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

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

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.

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.

ultra-short-echo-ute-image-reconstruction-using-image-based

Research

JoVE Journal

Medicine

10 visualizações. Para começar, adquira imagens de ressonância magnética de tempo de eco ultracurto do pulmão durante a respiração livre. Importe os dados e as trajetórias com espaçamento k para o MATLAB. Descarte as primeiras 1000 projeções para garantir que os dados atinjam uma magnetização de estado estacionário.Em seguida, execute a reconstrução da imagem usando uma transformada de Fourier rápida não uniforme para um tamanho de matriz de 96 por 96 p...