Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph

Summary

We provide a comprehensive description of the intrinsic retrospective cardiac gating method of the CrumpCAT, a prototype small-animal X-ray computed tomography (CT) scanner designed and constructed at our research institution.

Abstract

The CrumpCAT is a prototype small-animal X-ray computed tomography (CT) scanner developed at our research institution. The CMOS detector with a maximum frame rate of 29 Hz and similar Tungsten X-ray sources with energies ranging from 50 kVp to 80 kVp are widely used across commercially available preclinical X-ray CT instruments. This makes the described work highly relevant to other institutions, despite the generally perceived wisdom that these detectors are not suitable for gating the high heart rates of mice (~600 beats/min). The scanner features medium- (200 µm) and high- (125 µm) resolution imaging, fluoroscopy, retrospective respiratory gating, and retrospective cardiac gating, with iterative or filtered-back projection image reconstruction. Among these features, cardiac gating is the most useful feature for studying cardiac functions in vivo, as it effectively eliminates image blurring caused by respiratory and cardiac motion.

Here, we describe our method for preclinical intrinsic retrospective cardiac-gated CT imaging, aimed at advancing research on in vivo cardiac function and structure analysis. The cardiac-gating method acquires a large number of projections at the shortest practical exposure time (~20 ms) and then retrospectively extracts respiratory and cardiac signals from temporal changes in raw projection sequences. These signals are used to reject projections belonging to the high motion rate inspiration phase of the respiratory cycle and to divide the remaining projections into 12 groups, each corresponding to one phase of the cardiac cycle. Each group is reconstructed independently using an iterative method to produce a volumetric image for each cardiac phase, resulting in a four-dimensional (4D) dataset.

These phase images can be analyzed either collectively or individually, allowing for detailed assessment of cardiac function. We demonstrated the effectiveness of both approaches of the prototype scanner's cardiac-gating feature through representative in vivo imaging results.

Introduction

Small-animal research often employs a combination of non-invasive imaging modalities, with X-ray computed tomography (CT), being a prominent choice due to its maturity, cost-effectiveness, speed^1,2, and ability to provide complementary information alongside other modalities such as positron emission tomography (PET)^2,3 and single-photon emission computed tomography (SPECT)^2,4. However, like other imaging techniques, CT is susceptible to physiological motion artifacts caused by the beating heart or respiration, which introduce blurring and limit the accuracy of the research.

To address this limitation, respiratory and cardiac motion blurring can be mitigated through a technique known as gating^5,6,7,8, where data acquisition is synchronized with specific phases of the cardiac or respiratory cycle (or gates). One approach to achieve this, known as prospective gating^3,6, involves attaching sensors to the animal to provide real-time gating signals to a compatible scanner. While effective, this method is labor-intensive and time-consuming, particularly when attaching sensors to the chest and the paws of small animals like mice, thereby limiting the scale of studies. Alternatively, intrinsic retrospective gating^7,9,10,11 involves acquiring time series data without the use of sensors but by identifying features in the data that allow retrospective sorting of the results based on their phase in the cardiac or respiratory cycle. This approach offers results comparable to prospective gating but without the need for additional hardware or the effort involved in pulse sensor attachment and, therefore, greatly simplifies experimental protocols.

In our method for preclinical cardiac CT imaging, we utilize intrinsic retrospective gating to extract respiratory and cardiac cycles from amplitude variations in regions in X-ray projections that exhibit the most significant changes between successive frames. To facilitate this process, a mouse thorax template is co-registered onto the first posteroanterior projection using Mutual Information¹². Once the template is in place, pixel intensities in a window near the diaphragm are summed to generate a surrogate respiratory signal, while those near the myocardium are summed to derive the surrogate cardiac signal. These signals are then band-pass filtered in the time domain, and each frame in the dataset is assigned a fractional phase number (between 0 and 1) based on its respiratory and cardiac phase. This allows for the selection or rejection of projections according to their phase values. Typically, frames corresponding to the end-expiration phase of the respiratory cycle (0.15 ≤ phase < 0.85) are retained, while those from the inspiration phase, where motion is most pronounced, are discarded. The remaining frames are grouped into 12 cardiac phases, each representing 1/12 (0.083) of the cardiac cycle and are reconstructed into 3D images using an iterative method (Ordered Subset Expectation Maximization [OSEM])^13,14. The whole process is summarized in Figure 1.

Protocol

Animal experimental protocols were reviewed and approved by the Institutional Animal Care and Use Committee of the University of California, Los Angeles (UCLA). C57BL/6J mice (8 weeks old, male, 24-26 g) were used in this protocol. The CT scanner used in this study is the CrumpCAT (Figure 2), a prototype developed at our research institution for preclinical research, providing us with the control and flexibility needed to optimize acquisition and reconstruction protocols. The method assumes that anesthetized mice will have a heart rate no greater than 600 beats/min and a respiration rate between 20 and 180 breaths/min¹⁵.

1. Equipment settings

1. Operate the X-ray source at a peak voltage of 50 kVp with 200 µA continuous current.
2. Set the X-ray camera pixel binning to 2 and the exposure time to its shortest practical value (20 ms exposure time plus 14 ms read time for a total sampling time of 34 ms).
   NOTE: The no-binning option (binning 1) has a slower frame rate and, therefore, cannot be used for gated acquisitions. The maximum frame rate is around 30 frames/s.

2. Animal preparation

1. Anesthetize mice with a mixture of oxygen and isoflurane gas at 2.0% concentration. Apply sterile lubricant to eyes to prevent corneal drying and ulceration.
2. Keep the animals under anesthesia for 10 min before CT imaging, to ensure that vital signs are stable and that animals are relaxed to avoid any body motion during the scan. Judge a mouse's anesthesia depth by the lack of the toe pinch reflex when firmly pinching the webbing between the toes with a fingernail.
3. For the cardiac-gated imaging to visualize heart chambers, intravenously inject 100 µL of CT contrast agent through the tail vein immediately before CT imaging (Figure 3A).
4. Place the anesthetized mouse inside the CT imaging chamber (Figure 3B).

3. Data acquisition

1. Turn on the CrumpCAT software.
2. Non-gated CT imaging with high resolution (125 µm, binning 1) (Figure 4A)
   1. Enter a Study ID on the user interface.
   2. Select Mouse Hi-Res in the Protocol dropdown menu.
   3. Click the Scan button on the user interface to acquire 720 projections with an exposure time of 80 ms/projection.
3. Non-gated CT imaging with medium resolution (200 µm, binning 2) (Figure 4B)
   1. Enter a Study ID on the user interface.
   2. Select Mouse Standard in the Protocol dropdown menu.
   3. Click the Scan button on the user interface to acquire 720 projections with an exposure time of 100 ms/projection.
4. Gated CT imaging (200 µm, binning 2) (Figure 4C)
   1. Enter a Study ID on the user interface.
   2. Select Cardiac Gating in the Protocol dropdown menu.
   3. Click the Scan button on the user interface, and acquire 21,600 projections with an exposure time of 20 ms/projection.

4. Data preprocessing

NOTE: Preprocessing steps are required only for gated acquisitions. All these steps are performed automatically by the reconstruction software and no operator intervention is required.

1. Signal extraction
   1. For thorax template co-registration, let a small image (template) coarsely representing a mouse thorax with the ribs, the heart, the lungs, and the liver be automatically co-registered onto the first X-ray projection, by maximizing the Mutual Information¹² between the projection and template (Figure 5).
      NOTE: Only translation operations are performed on the template and the co-registered template is used to identify regions of interest (ROIs) on all projections.
   2. For respiratory signal extraction, allow a rectangular ROI (ROI-1) to be designated in the template to represent the diaphragm and the respiratory signal to be generated by summing the pixel intensities inside ROI-1 for each projection (Figure 5) .
   3. For cardiac signal extraction, let a second rectangle ROI (ROI-2) be designated in the template near the heart, and the cardiac signal generated by summing the pixel intensities in ROI-2 for each projection (Figure 5).
      NOTE: The thorax template is co-registered only on the first projection to identify which image rows and columns (window) should be used for signal extraction. That window is wide enough to encompass all projections as long as the animal is reasonably centered, as is the case when using our animal bed¹⁶.
2. Band-pass filter
   1. Allow a band-pass filter to be generated by combining two Sinc functions with different cutoff frequencies in the temporal domain¹⁷, which correspond to low-pass filters in the frequency domain as described below.
   2. Let the software apply the band-pass filter on the cardiac signal using a frequency band of [300, 600] min^-1 (Figure 6A).
      NOTE: The unfiltered cardiac signal (Figure 6B) still contains a significant contribution from the respiratory motion.
   3. Similarly, let the respiratory signal be filtered using a frequency band of [20, 300] min^-1. The higher harmonics are preserved to produce a signal that is not simply sinusoidal and more clearly identifies the two main phases: inspiration and expiration.
      NOTE: At this point, both respiratory and cardiac signals can easily be interpolated and considered continuous functions of time for the purpose of calculating the fractional phase number.
3. Phase assignment
   1. Let the software identify the beginning of each cycle (respiratory or cardiac) by finding the zero-crossings of the signal's first derivative. Each zero-crossing corresponds to a maximum in the signal and marks the beginning of a new cycle and the end of the previous one. For an example, see the respiratory signal in Figure 7.
   2. Allow a fractional phase value (between 0 and 1) to be assigned to each projection for each of the two signals (respiratory or cardiac).
      NOTE: By definition, the beginning point has a fractional phase value of zero (for the current cycle) or one (for the previous cycle).
4. Selection masks
   1. Let 12 binary masks (one per cardiac phase) be created to select projections belonging to each phase. Each mask contains 21,600 entries (one per projection) that are either a 0 or a 1, meaning reject or keep that projection, respectively.
   2. For each mask (phase), observe that the software indicates whether a projection is kept (1) or discarded (0) according to its fractional phase number. For phase 0, projections with a cardiac phase value in the interval [0, 1/12] are kept. For phase 1, cardiac phase values in the interval [1/12, 2/12] are kept, and so on.
   3. For each mask (phase), note that any projections with a respiratory phase less than 0.15 or greater than 0.85 are rejected (0 in mask) because they belong to the inspiration phase, which has the largest motion (Figure 8A). Figure 8B shows the cardiac phase assignment to projections for the first 2 s.
      NOTE: No significant bias was introduced with this selection process since the number of projections per phase was relatively constant at 1,800 ± 194.

5. Image reconstruction

1. For non-gated CT scans, reconstruct images at medium (200 µm) or high (125 µm) resolution using the Filtered back-projection (Feldkamp¹⁸) algorithm with a Shepp-Logan filter.
2. For cardiac-gated CT scans, reconstruct each cardiac phase image using the iterative OSEM algorithm¹³, with 12 subsets and 8 iterations, only taking into account selected projections contributing to a given phase (as indicated in a phase selection binary mask).
3. To reduce noise, apply a post reconstruction median filter in the temporal dimension (i.e., across images) and a light 3D-Gaussian filter in the spatial dimension (σ = 38 µm).
   NOTE: For respiratory-gated image reconstruction, use all cardiac phases indistinctly. Beam-hardening correction (water correction) is applied to every reconstruction.

6. Image assessment and left ventricle (LV) volume quantification

1. Open the CT image in a DICOM viewer such as Amide¹⁹ (Figure 9A).
2. Enhance the visible image contrast by setting the CT-value (measured in Hounsfield Unit [HU]) range as [-500, 500] (Figure 9A).
3. Image quality assessment
   1. Draw an ROI for the calcified region, which is defined as the region around the maximum with a CT-value greater or equal to 85% of the maximum HU value (Figure 9B).
   2. Use contrast-to-noise ratio (CNR)^20,21 as a metric to assess image quality and its capacity to identify small structures, such as small calcifications:
      ,
      where I and σ represent the mean intensity and standard deviation of a region: calcification (subscript C) or background (subscript B).
4. Left ventricle volume quantification
   1. Draw a 3D-Freehand ROI to identify the LV in each phase (Figure 10A).
   2. Quantify the LV volume by calculating voxels with a threshold CT-value of 730 HU (Figure 10B). A plot of LV volumes is shown in Figure 10C.
      NOTE: The CT-value threshold is not absolute; it depends on the contrast agent used, animal size, strain, health condition, and the time between injection and CT imaging. Amide is a freely distributed software developed at our institute, but other more sophisticated image viewers are available (e.g., ORS Dragonfly²²).

Results

We first compared non-gated and gated CT images for visualizing cardiac calcification in mice (male, 30-32 g). The murine model of cardiac calcification was created by inducing cardiac injury by rapid freeze-thaw of cardiac tissue (cryo-injury), as described previously²³. With the non-gated CT imaging protocols, cardiac calcifications were more clearly identified on the high-resolution (125 µm, binning 1) image (Figure 11A). The CNR was 3.2 ± 0.3 and 4.0
...

Discussion

The specific hardware implementation described here is a custom-made X-ray CT system unique to our institute, but the specific detector is widely used across commercially available preclinical X-ray CT instruments, making the described work relevant to other institutions. This system is functionally the prototype for two commercially available and widely used in vivo X-ray microCT subsystems embedded
in preclinical PET/CT scanners. These microCT scanners share the detector architecture and performance...

Materials

Name	Company	Catalog Number	Comments
C57BL/6J mice	Jackson Laboratory	664	Male, 8 weeks old, 24-26 g
Dexela camera	Varex	1512	Detector, 20 ms exposure, 74.8/149.6 µm pixel
VivoVist	Nanoprobes	1301-5X0.25ML	CT Contrast agent
X-ray source	Moxtek	TUB00082	50 kV peak, 200 µA, 1.0 mm-thick Al filter