$$\rightleftharpoonup{xx}$$
$$\longleftharp{xx}$$,
$$\longrightharp{xx}$$,
Ventilation Images
If animal preparation and ventilation procedures are implemented properly, 3D radial imaging can successfully capture ventilation patterns when data acquisition is performed at either inspiration or expiration (Figure 7). While these images are collected over many breaths, the method described here is similar to the single-breath imaging method used in humans. This is because all lines of k-space are collected at a specific time during the breath cycle (e.g., all at end-inspiration), cumulatively creating an image representative of a single inhalation. In these images, the major airways (trachea and bronchi) are clearly visible, no obvious image artifacts are present, and the lung parenchyma appears fully inflated with high SNR (~15 or higher, depending on initial polarization). While counterintuitive, the images acquired at inspiration (Figure 7A, SNR = 15) display lower SNR than those acquired at expiration (Figure 7B, SNR = 27). This is related to the modest driving pressure and slow flow used when ventilating this mouse. For inspiration images, the HP gas had not yet fully washed into the parenchyma of the lungs, causing the 129Xe (and thus signal) to be concentrated in larger airways. This effect is visually exacerbated by k-space scaling during image reconstruction because the high airway signal dominates the dynamic range of the data. Nonetheless, the analysis should be straightforward if the parenchyma is segmented such that the large airways are omitted.

Figure 7: Representative 3D radial ventilation images in an adult C57BL/6J mouse acquired using previously published methods39. While both images have relatively high SNR, images at (A) inspiration have lower SNR than images at (B) expiration largely because the 129Xe gas had not yet had the time to wash into the lungs. Please click here to view a larger version of this figure.
It is important to distinguish between pathologically obstructed ventilation and other causes of low SNR. In Figure 7A, no ventilation obstruction is expected (i.e., low VDP) because the animal was a healthy, wild-type mouse. If ventilation were obstructed, defects would typically appear as regions with low to no 129Xe signal. Nonpathological causes of low signal commonly include:
(i) Low 129Xe polarization: This causes low SNR in lung parenchyma and airways without accompanying artifacts. Low polarization can typically be attributed to incorrectly following polarization procedures, depolarization during transport, or excessive O2 gas content in the HP 129Xe delivery tubing (e.g., from an incorrectly calibrated ventilator).
(ii) Intubation cannula inserted too deeply: If inserted too deeply into the trachea, the cannula may deflect down the left or right bronchus. If lodged tightly enough, the whole-lung tidal volume may be confined to only a few lobes and airways while the rest of the lung receives none. Should this occur, the hyperinflated lobes could be injured, and it may be necessary to euthanize the animal. Alternatively, the tip of the cannula may come in direct contact with tissue. In this case, 129Xe will be primarily confined to the interior of the cannula, resulting in a bright signal from a visibly straight tube and minimal signal within the parenchyma. In this case, the cannula position should be corrected promptly, and the animal should be monitored for signs of distress (e.g., abnormal, absent, or asynchronous chest motion compared to the ventilator).
(iii) Intubation cannula placed too shallowly: When a bolus of gas is delivered too shallowly in the oropharynx, the resistance to that bolus flowing into the lungs can be stronger than the resistance to that bolus flowing around the exterior of the cannula and out of the mouth. This can result in poor lung inflation. Tracheal SNR may be high, while parenchymal SNR will be low. Motion artifacts may be present due to gas flowing up the trachea during data acquisition. The character of the artifact will vary depending on sequence type (GRE, radial, etc.).
(iv) Incorrect acquisition timing: If RF pulsing and data acquisition occurred during periods of inspiratory or expiratory gas flow, severe motion artifacts will usually be present — particularly when using Cartesian sequences — and parenchymal SNR will be low.
(v) Spontaneous respiration: If the animal is insufficiently sedated during data acquisition, the spontaneous respiratory motion will be superimposed asynchronously on top of the mechanical ventilation pattern. This will result in variable breath-to-breath 129Xe delivery, moderate to severe motion artifacts, and poor parenchymal SNR. Careful tracking of sedative dose and timing is critical.
(vi) Animal death: In rare instances, mice may die (e.g., due to incorrect cannula position, insufficient O2 delivery, sedative overdose, or severe experimental pathology). If this occurs, lung tissue stiffens rapidly, and the lung parenchyma will inflate poorly. The upper trachea may remain bright, but lung parenchyma will be very low in signal.
Gas Exchange Spectroscopy
In human subjects, spectroscopy-derived parameters (e.g., signal intensity, spectral width, chemical shift, and phase) offer insights into cardiopulmonary dynamics and disease state52,53. Unlike in humans86, dogs100, and rats20,101, whole-lung spectra from mice obtained when 129Xe magnetization is confined to the lung parenchyma display a single, broad, dissolved phase resonance corresponding to 129Xe dissolved in the capillary membrane tissue, blood plasma, and RBCs54 (Figure 8A, 197 ppm). When transported by blood flow to more distal tissues, 129Xe produces distinct resonance frequencies from various tissues, including adipose tissue54, muscle, gray matter, and white matter102,103. However, due to relatively long transit times (several seconds) and modest solubility104 (Ostwald solubility = 0.1 - 0.3), these signals are typically weak and only observed during dedicated, careful experiments. The exception is 129Xe dissolved in fatty tissues (Figure 8A, 189 ppm). From these tissues, a 129Xe signal can appear within a few seconds (e.g., over the initial few breaths during animal setup). Moreover, signal intensity can be high, because the Ostwald solubility of 129Xe is nearly 20-fold greater in adipose tissues than in blood plasma (i.e., 1.7 vs 0.094, respectively, at 37 ˚C104). The longitudinal relaxation times are also expected to be relatively long (in vivo T1 is ~20 s in lipids105 vs. 3-8 s in blood106). As a result, the 129Xe signal from adipose tissue can be relatively high compared to other tissues.
Due to differing signal dynamics, 129Xe spectroscopy experiments can be tailored to interrogate adipose tissue (e.g., to target brown adipose tissue thermometry107) or gas uptake in the lungs by selecting the correct acquisition parameters. When 129Xe spectra from the lungs are acquired with the settings described in Table 4 (i.e., TR = 50 ms and a 90˚ flip angle centered on the dissolved phase peak), both gas phase and dissolved phase peaks are observed (Figure 8A,B). Notably, the gas volume in the mouse lung is 2-3x higher than the tissue volume, and the solubility of 129Xe in this tissue is only ~10%104,108,109,110,111. Despite this, the amplitude of the dissolved signal exceeds that of the gas phase by approximately 2-fold because the gas phase excitation is far off-resonance (~15 kHz at 7 T; Figure 8B, SNR = 102 vs. 43, respectively). Therefore, the 20-to-30-fold greater magnetization pool in the gas phase can be approximated as a stable (i.e., non-depleted) reservoir for the dissolve-phase signal during static portions of the breath cycle.
With a new breath of magnetization every ~600 ms and a TR of 50 ms, the signal intensity from both gas phase and dissolved phase 129Xe oscillates. For the gas signal, this is attributable to the change in total gas volume (i.e., high at inspiration and low at expiration). The situation is more complex for the dissolved signal because the source gas magnetization, T2*, and capillary blood volume may fluctuate with the inflation state. As a result, the dissolved-to-gas signal amplitude ratio also oscillates with respect to the breath cycle (Figure 8C, mean dissolved:gas ratio = 1.9 ± 0.39).

Figure 8: Representative dynamic spectroscopy in an adult C57BL/6J mouse. (A) A single pulse spectrum shows signal originating from 129Xe in the gas phase, dissolved in adipose tissue (189 ppm), and dissolved in both capillary membrane and blood tissues (197 ppm). (B) Dynamic spectroscopy showing the time-varying signal intensity of both dissolved and gaseous 129Xe (repetition time = 50 ms). To aid visualization, only five spectra are displayed. Diagonal lines on the time axis represent scale breaks. (C) The ratio of dissolved-to-gas signal amplitude over a 2 s interval (corresponding to the red scale break in panel B shows clear variation with the breathing cycle (0.6 s per breath). Please click here to view a larger version of this figure.
The following is a list of factors that could compromise 129Xe gas exchange spectroscopy results. In each case, the likely result is low SNR, which may make the spectral fitting and resulting metrics (e.g., dissolved-to-gas signal amplitude ratio, chemical shift, spectral width, and phase) inaccurate.
(i) Wrong working frequency: Because of the significantly higher gas volume and low tissue solubility, RF pulses centered on the gas phase frequency will produce no dissolved phase signal. RF pulses should be centered at or near the dissolved phase frequency.
(ii) Too broad of a bandwidth: The excitation bandwidth should be broad enough that some gas phase 129Xe is excited in order to measure the ratio of the dissolved-to-gas signal amplitude. But if the bandwidth is too broad, the excitation pulse will destroy magnetization in the gas phase before it dissolves into the tissue. This can be seen as a very high gas signal and low to no dissolved phase signal despite the pulse being centered on the dissolved phase frequency.
(iii) Unoptimized TR: To interrogate signal dynamics over a breath, Nyquist-Shannon sampling theorem requires the signal to be sampled at a minimum of twice its frequency (here, 100 breaths per minute were sampled every 50 ms, or ~12x per breath). Depending on experimental conditions, further interrogation of cardiopulmonary signal dynamics at a faster sampling rate is possible. The results of these experiments would depend on many factors, including excitation bandwidth, breathing rate, and heart rate. The xenon exchange time (i.e., the time for xenon to dissolve into tissue; ~56 ms54) and pulmonary transit time (i.e., the time for blood to flow from right to left ventricle; ~830 ms for a heart rate of ~436 beats/min112) must also be considered. Sampling too often will result in the early destruction of magnetization and low signal, but it will also reduce the influence of blood flow. Sampling less frequently will allow the magnetization to dissolve further and build up more signal, but it will become more dependent on blood flow and less localized to the gas exchange regions in the alveoli.
Diffusion-Weighted Images
The diffusion-weighted images, used to quantify alveolar-airspace size, have been rigorously validated in small animal models through traditional histomorphometry methods98,113,114. Here, we restricted discussion to 2D, slice-selective imaging to minimize scan time and gas usage while matching previously validated protocols98. A minimum of 2 b-value images is sufficient for calculating the ADC, and 4 or more b-value images are necessary for advanced morphometry calculations (such as mean linear intercept). The acquisition of multiple b-value images offers a significant advantage by enhancing the accuracy of the calculations. Unlike imaging humans during a single breath hold, preclinical imaging over many breaths provides continuously replenished signal. Because of this, it is possible to acquire many high SNR b-value images, limited only by the volume of HP gas available and animal sedation duration. Here, we acquired 7 b-value images, each with SNR >15, using 400 mL of 129Xe gas over 18 min of scan time (Figure 9). The signal in each image decays with increased diffusion gradient strength (b-value). This signal decay is proportional to the ADC of the 129Xe within the lung. Large airways can be distinguished from acinar tissue by comparing b0 and b6 images. In regions that are anatomically likely to contain airways, pixels will appear very bright in the b0 image but appear dark in the b6 image (red arrows, Figure 9). These pixels should be excluded from the analysis of lung parenchyma.

Figure 9: Representative diffusion-weighted images in an adult C57BL/6J mouse. Large airways (red arrows), through which 129Xe freely diffuses while diluted with oxygen, display increased signal attenuation relative to the lung parenchyma. This results in bright airways in the b0 image and low signal airways in the b6 image. Mice and rats have similarly small ADC values relative to humans, but the values will depend on biological and experimental factors like age, tidal volume, pressure, and diffusion parameters. Please click here to view a larger version of this figure.
The ADC of this healthy, adult, wild-type mouse follows the expected result based on the literature, its age, SNR, acquisition parameters, and more. Factors that may deleteriously impact the precision and accuracy of the ADC results include:
(i) Low SNR: Importantly, low SNR in the images bias the ADC calculation towards lower values99,115. For this reason, we exclude voxels with SVNR0 < 2.5 times the image noise97.
(ii) Improper segmentation: Poor segmentation (e.g., using signal thresholding without visual inspection for quality control) can skew results. If the images contain artifacts (like those caused by motion), they may exceed the threshold and be incorrectly categorized as parenchyma. Further, it is particularly important to exclude pixels near large airways, as partial volume effects may bias their ADC toward higher values.
(iii) Miscalibrated ventilator: In general, dilution of 129Xe with lighter gases (e.g., O2 or N2) can shift the ADC toward higher values, so ventilation with known gas mixtures is necessary for quantitative comparisons. If ventilating with a 129Xe/O2 mixture, an increase in oxygen concentration prior to delivery will increase T1 relaxation and reduce 129Xe magnetization in the gas delivery tubing, thus decreasing SNR.
(iv) Lung derecruitment: In anesthetized and mechanically ventilated animals, alveolar collapse (atelectasis) can occur over time. When this happens during constant volume ventilation, the same tidal volume is necessarily redistributed to the alveoli that remain recruited. As moderate to severe atelectasis develops, the rest of the lung can become hyperinflated, causing injury and increased ADC in those regions. Regular recruitment maneuvers and PEEP prevent alveolar collapse, avoid decreased lung compliance, and minimize the risk of lung injury that can skew ADC results.
(v) Poor data fitting: The method of ADC estimation must be taken into consideration. Log-linear fitting is the most computationally efficient but introduces bias in low SNR images. Therefore, it is best applied to high SNR images (>20). The weighted linear method reduces this bias and is appropriate in images with SNR > 15. Bayesian estimation can yield estimates of ADC with low uncertainty even with SNR <10 but is computationally expensive99.
(vi) Suboptimal diffusion time: The optimal diffusion time is influenced by the maximum gradient strength of the MRI scanner and the acinar airway radius in mice (radius ~100 µm49), which is approximately three times smaller than that in humans. To ensure optimal conditions, the diffusion time is set to achieve a diffusion length larger than the average alveolar radius but smaller than the mean length of the alveolar ducts. The optimal diffusion time for the site-specific maximum gradient strength can be determined using the methods outlined by Sukstanskii and Yablonskiy49. Using a suboptimal diffusion time may lead to nonlinear decreases in 129Xe ADC, complicating the interpretation of results.
(vii) Suboptimal b-values: There is a tradeoff between maximizing the SNR and maximizing the contrast-to-noise ratio (CNR) generated across b-value images. If CNR is sacrificed by the use of only low b-values (i.e., diffusive signal decay as if acquiring only the first 3 b-value images in Figure 9), then the ADC estimation will be inaccurate due to poor fitting. If the diffusion weighting is too strong, the resulting SNR will be insufficient, and the ADC will be biased toward lower values.