Interior tomographic imaging of mouse heart in a carbon nanotube micro-CT

Abstract

BACKGROUND: The relatively high radiation dose from micro-CT is a cause for concern in preclinical research involving animal subjects. Interior region-of-interest (ROI) imaging was proposed for dose reduction, but has not been experimentally applied in micro-CT.

OBJECTIVE: Our aim is to implement interior ROI imaging in a carbon nanotube (CNT) x-ray source based micro-CT, and present the ROI image quality and radiation dose reduction for interior cardiac micro-CT imaging of a mouse heart in situ.

METHODS: An aperture collimator was mounted at the source-side to induce a small-sized cone beam (10 mm width) at the isocenter. Interior in situ micro-CT scans were conducted on a mouse carcass and several micro-CT phantoms. A GPU-accelerated hybrid iterative reconstruction algorithm was employed for volumetric image reconstruction. Radiation dose was measured for the same system operated at the interior and global micro-CT modes.

RESULTS: Visual inspection demonstrated comparable image quality between two scan modes. Quantitative evaluation demonstrated high structural similarity index (up to 0.9614) with improved contrast-noise-ratio (CNR) on interior micro-CT mode. Interior micro-CT mode yielded significant reduction (up to 83.9%) for dose length product (DLP).

CONCLUSIONS: This work demonstrates the applicability of using CNT x-ray source based interior micro-CT for preclinical imaging with significantly reduced radiation dose.

Keywords

Micro-CT interior tomography carbon-nanotube x-ray micro-CT

1 Introduction

Mouse models of human diseases have been widely used in preclinical studies [1 –4]. Micro-CT provides biomedical researchers with low-cost, high-throughput, high-resolution and non-invasive evaluation of anatomical structure and physiological function [5]. However, high radiation dose is often required to generate high-resolution micro-CT images [6]. The radiation dose is estimated to be inversely scaled with the fourth power of image voxel size in order to maintain the same image noise level [6, 7]. Both high-dose radiation exposure [8, 9] and longitudinal low-dose radiation exposure [10, 11] could induce a variety of pathological effects to small animals and perturb the experimental outcomes. The radiation-induced biological effects may increase the difficulty of planning preclinical studies using longitudinal micro-CT scans [12 –14]. Therefore, it is important to reduce radiation dose while striving to provide the highest image quality for small animal micro-CT.

Several techniques have been proposed to reduce the radiation dose, such as x-ray flux reduction [15, 16], sparse-view acquisition [17 –19], and interior ROI imaging techniques (including theoretically-exact interior tomography [20 –23] and other approximate-reconstruction-based ROI CT techniques [24 –28]). X-ray flux reduction and sparse-view acquisition are readily applicable to the existing commercial / laboratory micro-CT scanners [29]. Noise and artifacts caused by x-ray flux reduction and sparse-view acquisition can be reduced by advanced reconstruction algorithms such as the ones based on compressive sensing and/or statistical modeling [15 –19]. On the other hand, interior ROI imaging techniques require physical modification of system and/or the scan protocols [23 –28]. Briefly, an internal ROI receives primary exposure delivered through an aperture in a “pre-patient” x-ray collimator [23–25 , 28], while the region outside the targeted ROI receives no primary exposure. Interior ROI imaging techniques have been shown to significantly reduce radiation dose in clinical CT systems. For instance, R. Chityala et al. demonstrated 85.7% total dose reduction in a C-arm system, by shielding 82% of original field-of-view (FOV) [27]. D. Bharkhada et al. demonstrated up to 58% radiation dose reduction in a clinical spiral CT [23]. L. Chen et al. even reported 77.3% dose reduction inside the ROI due to scatter reduction [26]. Moreover, interior ROI imaging techniques can be combined with x-ray flux reduction and sparse-view acquisition to further reduce the radiation dose delivered to the internal ROI [20 , 30]. A number of advanced reconstruction algorithms have been proposed to address the artifacts caused by data truncation in interior ROI imaging [20 , 31–35]. The aforementioned research works suggest that interior ROI imaging may be applicable in micro-CT for radiation dose reduction.

Nevertheless, to the best of our knowledge, no collimator-based interior ROI imaging techniques have been physically implemented in micro-CT systems, and radiation dose reduction from interior micro-CT for preclinical imaging has not been experimentally demonstrated. Some commercial micro-CT scanners (e.g. Xradia scanners) can achieve interior tomography through limited detector FOV by multiple objective lens between the sample and the detector. However, the sample is still fully irradiated by a wide x-ray beam and hence it does not serve the purpose of radiation dose reduction. Previously, we have developed a dynamic micro-CT scanner that provides high temporal-spatial resolutions (15 msec and 6.2 lp/mm) by using a carbon nanotube (CNT) field-emission based x-ray source [36]. The CNT x-ray source is programmable for electronic control of x-ray emission, which enables flexible exposure control for cardiac and respiratory gated micro-CT imaging of free-breathing mice with no wasted radiation exposure [36]. The conventional global micro-CT mode was still used in that scanner. Interior ROI imaging can be applied to restrict the x-ray beam path through a small internal ROI (e.g. heart, lung tumor, etc.) in small animals while significantly reducing radiation exposure to the surrounding tissues. We anticipate that the integration of these two techniques (i.e. CNT dynamic micro-CT and interior ROI imaging) will lead to highest image quality and lowest radiation risk in small animal micro-CT imaging.

In this paper, we describe our work on the implementation of interior ROI imaging in a CNT x-ray source based dynamic micro-CT scanner, and demonstrate the high quality of interior ROI images with greatly reduced radiation dose. We explored several practical issues of CNT x-ray source based interior micro-CT, such as the geometric calibration with a small (10 mm) interior FOV, the high-precision alignment between interior FOV of the scanner and internal ROI of the object, and most importantly, the image quality and radiation dose reduction. This paper is organized as follows. In Section 2, we present the system overview, imaging experiments, and methods for image reconstruction and evaluation. In Section 3, we show the experimental results. In the last section, we discuss the results and conclude this study.

2 Methods

2.1 The CNT source based micro-CT system

The schematic system configuration and the physical system setup are shown in Fig. 1. Interior micro-CT was realized by placing a source collimator (i.e. pre-patient) between x-ray source and objects to reduce the FOV (i.e. from FOV2 to FOV1). The system mainly consists of a flat panel x-ray detector (Hamamatsu, Inc. Japan), a motorized gantry (Huber, Inc. Germany), an XYZ manual sample stage (Newport, Inc. USA), and an in-house-built CNT x-ray source. The detector element size is 50μm. The collimator is a 2 mm thick lead plate (with a circular 3.73 mm aperture) fixed on an XYR stage (Thorlab, Inc. USA). The CNT x-ray source was constructed by using the previous design [36]. The x-ray focal spot size is of 100 μm diameter. The cone angle of x-ray beam was reduced from 30° to 3°, by using the aperture collimator.

During micro-CT scans, the object was stationary while the x-ray source and detector were rotated. The cone-beam circular trajectory was adopted for projection data acquisition. The step-and-shoot scan mode was employed to eliminate the motion blurring caused by continuous mechanical rotation of x-ray source and detector. The x-ray source was operated at 50 kV and filtered by a 0.5 mm aluminum filter. The diameters of the spherical FOV were 32 mm and 10 mm in global and interior micro-CT modes, respectively. The interior FOV is just slightly bigger than an adult mouse heart. The other major parameters of scan protocols and imaging geometry are summarized in Table 1. The system control software was developed under LabVIEW (National Instrument, Inc. USA).

2.2 Imaging experiments

The imaging experiments on two micro-CT phantoms and a mouse carcass were conducted to evaluate the image reconstruction quality (see Sec 2.5.1 for evaluation methods). The two micro-CT phantoms included a water phantom and a “multi-feature” phantom. The water phantom was an acrylic cylinder (25.5 mm diameter and 1.5 mm wall thickness) filled with water, with a sealed polypropylene tube (2.5 mm diameter and 0.15 mm wall thickness) placed in the center (Fig. 2A). Micro-CT images of the water phantom were used for Hounsfield Units (HU) calibration and CNR evaluation. The “multi-feature” phantom (diameter 20 mm) was fabricated by modifying the one used in our previous works [34, 35]. This phantom comprises of various attenuating materials such as acrylic tube, rubber granules, micro-pillar arrays, micro-capillary tubes, multi-bore tubing, electrical tapes and duct tapes (thickness range from 0.25 mm to 5 mm, see Fig. 2B). The “multi-feature” phantom was mainly used to evaluate interior ROI reconstruction with the presence of heterogeneous materials and fine structures. For the animal experiment, a euthanized adult C57 mouse (body length 110 mm and thorax diameter 25 mm) underwent an open-chest surgery to ligate all blood vessels from heart, and then 300μl iodine contrast agent (Omnipaque 350 mgI/ml) was injected into the heart chambers. The mouse carcass was frozen by using dry ice. The C57 mouse was chosen because it is a typical animal model widely-used in cardiovascular research. The ligation was used to retain the contrast agent in mouse heart. Micro-CT scans of the mouse heart in situ were conducted using the CNT interior micro-CT scanner. The interior ROI (9 mm in diameter) was targeted at the mouse heart. Two low-dose “scout view” projections (separated by 90° angular interval) of the mouse carcass were acquired at global micro-CT mode, to align the mouse heart with system isocenter. Another low-dose raw projection was acquired at interior micro-CT mode, to calibrate the aperture collimator and align the interior FOV with mouse heart. Both global and interior micro-CT scans were conducted for each sample. The scanning protocol is listed in Table 1.

2.3 Image reconstruction

2.3.1 Geometric calibration and data preprocessing

System geometric calibration was conducted to identify the geometric parameters for accurate image reconstruction. Moreover, switching from global mode to interior mode could induce slight misalignment of interior FOV with the system isocenter, because of the mechanical tolerance of the aperture collimator. The analytic method proposed by F. Noo et al. [37] was adapted for geometric calibration of interior micro-CT. It is practical to assume that the collimator plane is parallel to system rotation axis and the detector plane. So, the geometric calibration of collimator can be accomplished by quantifying the in-plane offset (termed as “interior detector offset”). Interior detector offset can be calculated using interior projections (i.e. raw projections acquired at interior micro-CT mode). Figure 3 demonstrates the geometric calibration of interior detector offset. The projection of the collimator was not exactly circular because of unsmooth edge (Fig. 3A). Therefore, a circular image mask (as marked by the dashed circle) was applied to the interior projections, and the pixel values outside the mask were set to zeroes (Fig. 3B). P_m is the center of image mask, and P₁ is the intercept point between the detector plane and the “piercing line” (i.e. the line connecting focal spot and isocenter). The offset (d_x, d_y) between P₁ and P_m is defined as the interior detector offset. Raw projections were preprocessed to remove detector defects and then subjected to blank and dark projection corrections.

2.3.2 Reconstructions

As to interior micro-CT mode, we utilized our previously published frequency-split displaced detector (FSDDR) reconstruction technique [34]. Briefly, FSDDR algorithm employs a frequency splitting strategy to combine the post-convolution weighting based filtered back projection method (PCW-FBP) with the weighted simultaneous algebraic reconstruction technique (WSART). PCW-FBP incorporates sinogram reflection, sinogram splicing, cosine extrapolation and offset-detector-weighting function to filtered back projection method [34]. For WSART, the conventional SART algorithm is adapted for detector offset, by multiplying the offset-detector-weighting function [34]. Furthermore, total variation minimization (TVM) and ordered subset (OS) were also combined with WSART to guarantee theoretically accurate solution [20]. Finally, the frequency splitting scheme was applied to combine low frequency components and high frequency components respectively from the reconstruction results of PCW-FBP and WSART. For volumetric reconstruction, a GPU-based acceleration technique was utilized to significantly reduce the computational time [38]. As to global micro-CT mode, the conventional FDK algorithm [39] was employed for image reconstruction (referred to as global-FDK-reference images). The reason is that the FDK algorithm is typically utilized as the surrogate truth in current micro-CT applications [17, 30]. In addition, FSDDR algorithm was also applied in global micro-CT mode (referred to as global-FSDDR-reference images) in order to provide a complementary evaluation of image quality by excluding the influence from different reconstruction algorithms.

2.4 Evaluation methods

2.4.1 Image quality analysis

Interior micro-CT images were compared against the global micro-CT images. All reconstructed images were calibrated to Hounsfield unit (HU) scale by using water phantom datasets. Three image quality metrics were employed for the quantitative assessment, including CNR, root mean square error (RMSE) and mean structural similarity index (MSSIM). Water phantom datasets were used to evaluate CNR. As is shown in Fig. 4, two circular ROIs were respectively selected from air region (i.e. ROI1) and water region (i.e. ROI2) to calculate CNR. The CNR is defined as follows [40, 41]: $CNR = \frac{| μ_{1} - μ_{2} |}{\sqrt{σ_{1}^{2} + σ_{2}^{2}}}$ (1) where μ₁ and μ₂ are respectively the average pixel value from ROI1 and ROI2, $σ_{1}^{2}$ and $σ_{2}^{2}$ are the corresponding variance. RMSE and MSSIM were calculated for all datasets to evaluate reconstruction accuracy. The RMSE is defined as follows: $RMSE = \sqrt{\frac{\sum {| g (x, y) - f (x, y) |}^{2}}{N}}$ (2) where N is the number of image pixels within the entire interior FOV, g (x, y) is a pixel from interior micro-CT images, and f (x, y) is the counterpart from global micro-CT images. The MSSIM is defined as follows [42]: $MSSIM = \frac{1}{M} \sum_{j = 1}^{M} {{[l (x_{j}, y_{j})]}^{α} [c (x_{j}, y_{j})]}^{β} [s {(x_{j}, y_{j})}^{γ}]$ (3) where M is the number of local windows, x_j and y_j stand for the two groups of pixels in the jth local window from global micro-CT images and interior micro-CT images respectively, l (x_j, y_j), c (x_j, y_j), and s (x_j, y_j) are respectively the comparison function for luminance, contrast, and structure. α, β and γ are parameters for adjusting relative importance of three comparison functions. These parameters were fixed at the same values in [42].

2.4.2 Radiation dose measurement

We adapted the methods from the reference [23] to evaluate radiation dose reduction. Due to lack of standard phantom for micro-CT, we fabricated two acrylic micro-CT dose phantoms for radiation dose measurement (Fig. 5A). The diameter and the length of the phantoms were respectively fixed at 30 mm and 110 mm which were the approximate size of the commercial micro-CT dose phantom (QRM, Inc. Germany). One phantom has a peripheral bore hole (i.e. phantom 1) and the other has a central bore hole (i.e. phantom 2). Due to lack of small thermoluminescent dosimeters (TLD) and micro-ion chambers, a calibrated 100 mm pencil ion chamber (Model 10X5-3CT, Radcal, Inc.) was placed in the micro-CT dose phantoms to measure the accumulated radiation exposures at the five positions throughout the scans (Fig. 5B). The scan protocol in Table 1 was also used in the dose measurement. The phantom 2 was used to measure the radiation dose in the central position (i.e. position #5). The phantom 1 was used to measure the radiation dose in peripheral positions (i.e. positions #1 - #4). Note that phantom 1 was rotated to next position after radiation dose measurement was accomplished at current position.

We adopted the concepts of CTDI_a from the references [43 –45] to evaluate the average radiation dose across the nominal beam width. The formula of CTDI_a is defined as follows: ${CTDI}_{a} = \frac{1}{a} \int_{- 50 mm}^{50 mm} f (z) dz$ (4) where a is the nominal beam width along z-axis, and f (z) is the radiation dose profile along z-axis. The weighted CTDI_a is calculated as follows: ${CTDI}_{a, w} = \frac{2}{3} {CTDI}_{a}^{periphery} + \frac{1}{3} {CTDI}_{a}^{central}$ (5) Where ${CTDI}_{a}^{periphery}$ is the average dose at peripheral positions, and ${CTDI}_{a}^{central}$ is the dose at central position. The dose length product (DLP) was employed as the descriptor of total dose [44]: $DLP = {CTDI}_{a, w} \times a$ (6)

3 Experimental results

3.1 Imaging results

Figure 6 demonstrates the comparison between global-FDK-reference images and interior micro-CT images of water phantom. By visual inspection, FSDDR algorithm suppressed the image noise of interior micro-CT images, while FDK algorithm yielded higher image noise in global micro-CT images. This is because FSDDR algorithm employs compressive sensing (CS) based iterative reconstruction technique which enables the noise reduction in piecewise constant type phantom. The image differences mainly demonstrated noise-like pattern. Table 2 demonstrated the value of CNR, RMSE and MSSIM from water phantom datasets. The mean CNR of global micro-CT images was 55% lower than that of interior micro-CT images. The mean RMSE of three slices was 103.5HU. High structural similarity index was observed as one would expect. The mean MSSIM of three slices was 0.9173.

Figure 7 demonstrated the comparison between global-FDK-reference images and interior micro-CT images of “multi-feature” phantom. By visual inspection, the low contrast line-shape structures at the bottom of interior micro-CT images are slightly weakened. This can be attributed to the TVM method which could reduce the image noise at the expense of smoothing out small structures [46]. Nevertheless, the high and low contrast structures of the phantom remain largely consistent in the images from both modes, and no significant artifacts are induced in interior micro-CT images. The image differences also mainly demonstrated noise-like pattern. The line profiles from interior micro-CT images were well-matched to those from global micro-CT images. Table 2 also demonstrated the RMSE and the MSSIM from multi-feature phantom datasets. High structural-similarity-index was still observed. The mean MSSIM of three slices was 0.9294. Despite the heterogeneous inner materials and the sub-millimeter structure, interior micro-CT images still presented high structural similarity to global micro-CT images.

Figure 8 presents the comparison between global-FDK-reference images and interior micro-CT images of mouse carcass. By visual inspection, FDK algorithm yielded visible streak artifacts in global micro-CT images, which was mainly induced by bony structure and iodine contrast agent. The streak artifacts had degraded image contrast. On the contrary, FSDDR algorithm suppressed the artifacts and enhanced the image contrast. The suppression of streak artifacts by the FSDDR algorithm could be attributed to the following reasons. First, CS based iterative reconstruction technique has been demonstrated to suppress streak artifacts [47, 48]. Second, the frequency splitting scheme allows adjusting the contribution of analytic reconstruction (i.e. PCW-FBP) and CS based iterative reconstruction (i.e. WSART). Briefly, it could combine less low frequency components from PCW-FBP with more high frequency components from WSART to constitute the reconstruction results of FSDDR algorithm, by using proper image filtering. More details can be referred to in reference [34]. Therefore, the streak artifacts could be reduced by using frequency-split scheme, although some amount of residual could be present. The image differences and line profiles demonstrated relatively larger discrepancy. Table 2 also demonstrated the RMSE and the MSSIM from mouse carcass datasets. The mean RMSE of three slices was 587.76HU. Despite higher RMSE, the structural similarity index was maintained at a relatively high level. The mean MSSIM of three slices was 0.8624. Figure 9 shows the comparison of other representative axial, coronal, and sagittal slices and volume rendering between interior and global-FDK-reference images. The anatomical structure such as blood vessels (as marked by solid arrows) and heart chambers still demonstrated high visual fidelity.

Figure 10 presents the comparison between global-FSDDR-reference images and interior micro-CT images of the three aforementioned samples. By visual inspection, global and interior micro-CT modes demonstrated comparable image quality in terms of image noise, contrast and structural similarity. In water phantom dataset, the CNR of global-FSDDR-reference image was 18.5, which was close to that of interior micro-CT images (see Table 2). Higher structural similarity index was also observed. The MSSIM was respectively 0.9330, 0.9614 and 0.8939 for water phantom, “multi-feature” phantom and mouse carcass.

3.2 Radiation dose

The results of radiation dose measurement are demonstrated in Table 3. Interior micro-CT mode demonstrated significantly less radiation dose than global micro-CT mode. Significant dose reduction was observed at the peripheral positions (up to 65.9%). As is expected, the percentage dose reduction was higher at the peripheral positions than at the center (up to 8.6%). Unlike the central region, the peripheral region was only partially irradiated by the collimated x-ray cone beam during interior micro-CT scan. The dose reduction in both central and peripheral region leads to overall dose reduction. Compared with global micro-CT mode, interior micro-CT mode demonstrated significant percentage reduction of CTDI_a,w (up to 48%) and DLP (up to 83.9%).

4 Discussion and conclusion

In this study, we used a source collimator to implement interior ROI imaging in a CNT x-ray source based micro-CT system. We conducted global and interior micro-CT scans on two micro-CT phantoms and a sacrificed mouse to evaluate the system performance. An iterative reconstruction algorithm (FSDDR) was employed for interior ROI reconstruction. Visual inspection and quantitative analysis were carried out to evaluate the image quality. Moreover, we experimentally measured and compared the radiation dose from global and interior micro-CT.

The influence of source collimators on interior ROI imaging has been overlooked in previous cone-beam CT studies [49]. As is reported by D. Xue et al. [49], the source collimator can induce non-negligible penumbra effects and reduction of primary x-ray field intensity in a clinical cone-beam CT, which mainly results from the off-focal-spot radiation and the large geometric magnification from collimator plane to detector plane. The similar effects were observed in our interior micro-CT system. Despite the same geometry magnifications for the global and interior micro-CT modes in our system, the central x-ray ray intensity dropped about 10% from the global mode to interior mode. We attributed this reduction of the central ray intensity to the aperture collimator at the source, which can reduce the off-focal-spot radiations. In interior micro-CT, the x-ray intensity of the primary field decreases radially from the center toward the edge (up to about 5%). This can be explained by the penumbra effects caused by the interior collimator. Overall, the changes of primary x-ray field intensity can result in reconstruction error for interior ROI imaging [49]. To eliminate such error, interior raw projections have to be normalized by the corresponding interior blank projections in the preprocessing step.

The concept of CTDI was adopted as a micro-CT dose descriptor, because it has demonstrated linear response to the parametric changes of micro-CT scan protocols [45]. Moreover, CTDI was also used in dose measurement of interior clinical CT by D. Bharkhada et al. [23]. Furthermore, the concept of CTDI_a was utilized instead of conventional CTDI₁₀₀, since our micro-CT system conducts cone-beam circular scans [44]. On the other hand, we have noticed that partial irradiation of ion chamber in interior micro-CT should cause some uncertainty to the measured CTDI_a and DLP. Furthermore, the holes of 9.5 mm diameter may cause some uncertainties on dose measurement in the 20 mm to 30 mm diameter phantoms [50]. Nonetheless, our dose measurement should allow us to gauge the radiation dose reduction in this work.

In conclusion, we have demonstrated that interior ROI imaging can be implemented for radiation dose reduction in micro-CT imaging by mounting a source collimator. Using such a micro-CT scanner, we conducted comprehensive analysis of imaging quality and measured the radiation dose when the micro-CT system was applied for imaging physical phantoms and mouse carcass in both the conventional global micro-CT mode and the interior micro-CT mode. Interior micro-CT has been demonstrated to have comparable image quality to conventional global micro-CT, while significantly reducing radiation dose. We conclude that our CNT x-ray source based interior micro-CT system is applicable for high-quality and low-dose ROI-oriented preclinical imaging.

Footnotes

Acknowledgments

This work was partially supported by Dr. Cao’s new faculty startup package at Virginia Tech, Drs. Cao and He’s seed grants from Institute for Critical Technology and Applied Science at Virginia Tech, and Dr. Cao’s CAREER support from the National Science Foundation (CBET 1351936). The work of Rui Liu and Hengyong Yu was partly supported by Dr. Yu’s NSF CAREER award (CBET No. 1540898).

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