In Vivo Pentamodal Tomographic Imaging for Small Animals

In vivo pentamodal tomographic imaging for small animals

Abstract: Multimodality molecular imaging emerges as a powerful strategy for correlating multimodal information. We developed a pentamodal imaging system which can perform positron emission tomography, bioluminescence tomography, fluorescence molecular tomography, Cerenkov luminescence tomography and X-ray computed tomography successively. Performance of sub-systems corresponding to different modalities were characterized. In vivo multimodal imaging of an orthotopic hepatocellular carcinoma xenograft mouse model was performed, and acquired multimodal images were fused. The feasibility of pentamodal tomographic imaging system was successfully validated with the imaging application on the mouse model. The ability of integrating anatomical, metabolic, and pharmacokinetic information promises applications of multimodality molecular imaging in precise medicine.

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© 2017 Optical Society of America

OCIS codes: (170.0110) Imaging systems; (170.2655) Functional monitoring and imaging; (170.4580) Optical diagnostics for medicine; (170.6960) Tomography.

References and links

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1. Introduction

Since 1999, molecular imaging proposed by Weissleder [1] has been continuously evolving and contributing to biomedical research and applications [2, 3]. Imaging agents targeting specific biomarkers or molecular processes provide distinctive physiological and pathological information, and they enable imaging modalities and strategies to realize the capability which were impossible several years ago.

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Different imaging modalities probe biological processes via harnessing the power of various vehicles of energy: high-energy gamma photons from radionuclide decays and annihilations of positrons, X-ray photons, visual/near infrared photons of luminescence or excited fluorescence, microwaves, and sound waves [4]. Because of the nature of underlying biophysical interactions, each modality has its own advantages and limitations. High tissue penetrating ability of high-energy gamma photons allows us to collect functional information by locating radiopharmaceuticals in the body with single-photon emission computed tomography (SPECT). Further utilizing coincidences of paired gamma photons from annihilations of positrons, positron emission tomography (PET) significantly enhances the imaging sensitivity to nano-/pico-molar magnitude [5]. As attenuation of X-ray is generally related to tissue density and thickness, X-ray computed tomography (CT) has good resolution on bones, but bad on soft tissues compared with magnetic resonance imaging (MRI) [6]. Optical imaging, including bioluminescence imaging/tomography (BLI/BLT), fluorescence molecular imaging/tomography (FMI/FMT), Cerenkov luminescence imaging/tomography (CLI/CLT), radioluminescence imaging (RLI) and so on, provides highly sensitive, high-throughput, non-invasive targeting imaging in vivo [5, 7]. Optical instrument costs much lower and has higher superficial resolutions than others while penetration depth of visual/near infrared photons is limited [5]. MRI provides high spatial resolution, excellent soft tissue contrast, qualitative and partial quantitative functional measurements, while the instrument costs much higher and safety of metal implants must be evaluated [8]. Ultrasound (US) provides excellent time resolution and its instrument is simple and easy to use, while it is not quantifiable and its imaging quality is greatly affected by air and bones [9]. Detailed comparison of different modalities has been conducted in many comprehensive reviews [4, 8, 10].

Therefore, integrating two and even more modalities becomes the trend of developing new imaging instruments [11]. PET/CT has been applied to oncology, neurology, and cardiology [3, 12] and evolved into a mainstay, replacing PET alone, in clinical imaging. Requirement of anatomical information during interpretation of ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) imaging and attenuation correction enabled by combining PET and CT made PET/CT a success within 20 years [11]. SPECT/CT shares the advantages of combination of anatomical and molecular information and attenuation correction ability with PET/CT. It is playing a more and more important role in endocrinology, infection, musculoskeletal imaging, and pulmonology [3, 13, 14]. Researchers endeavour to combine PET and MRI, seeking to excel PET-CT with simultaneous soft tissue contrast and functional measurement. This challenge is far greater than PET/CT and SPECT/CT as PET and MRI imaging processes can interfere with each other and attenuation map provided by CT is lost. Scientists have proposed several different integration frameworks [15-17] and solutions to attenuation correction with MRI images [18]. Photoacoustic imaging (PAI) detects ultrasound signals emitted from pulse laser excited haemoglobin or contrast agents. Integrations of PAI with US, Optical Coherence Tomography (OCT) and FMI have been explored [19]. Efforts have also been dedicated to CT/MRI [20-22] and combinations of more modalities, such as PET/SPECT/FMI/CT [23-27]. PET combined with optical tomographies (i.e. BLT, FMT, CLT, etc.) supplemented with structural information provided by CT/MRI will render  us to fully capable of imaging a variety of diseases at different scales ranging from molecules level to tissues to whole body at high resolution, sensitivity and specificity [28]. For example: FMT can trace Cy5.5-labelled integrin to assess angiogenesis, BLT can reflect the progression of luciferase-labelled tumor cells, PET can monitor metabolism of tumor via ¹⁸F-FDG, while CLT can evaluate the dosimetry of radionuclide therapy using ⁹⁰Y-labelled tumor targeting peptide. The combination of imaging of these properties provides us a more comprehensive understanding of the theranostic of the tumor.

In this article, we presented a pentamodal tomographic imaging system which can perform PET, CLT, FMT, BLT, and CT sequentially. Performance of subsystems corresponding to different modalities was characterized. In vivo multimodal imaging was performed on an orthotopic hepatocellular carcinoma (HCC) xenograft mouse model. Acquired data were reconstructed with corresponding algorithms and fused data was visualized with our medical imaging processing platform. Lastly, correlation of information obtained from different modalities was analyzed and possible directions of improving the presented system was discussed.

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2. Methods

2.1 Instrumentations of the PET/BLT/FMT/CLT/CT pentamodal imaging system

Fig. 1. System overview of our pentamodal imaging system. (a) CT, optical and PET imaging instruments were arranged on a rotating gantry. (b)The polar coordinate system indicates the arrangement of instruments in the schematic diagram of our imaging system. Different colors are assigned for different sub-systems: yellow for PET sub-system, green for optical tomographies, and blue for CT sub-system. (c) Photograph of the overview of the system is taken before the assembling of the light shield chamber. An MRI system for verification is placed on the opposite of the rotating gantry.

Our pentamodal tomographic imaging system was integrated on a rotating gantry (Fig. 1(a)). In the following, a polar coordinate system was established with its origin locates at the center of the rotating gantry, and the position of each instrument was referred to as polar angle in this system, as shown in Fig. 1(b). An X-ray tube (XTF6011, X-ray Technology, Scotts Valley, CA) was arranged on the direction of 45°. A high-speed, low-noise CMOS X-ray detector (1512, Dexela, London, UK) was placed on the direction of 225°, opposite to the X-ray tube. Four PET block detectors (Raycan Technology, Suzhou, China) [29] were fixed on four electrical linear guideways on the directions of 0°, 90°, 180°, and 270°, respectively. A back-illuminated electron-multiplying charge-coupled device (EMCCD, iXon 888, Andor, Belfast, UK) camera was chosen to collect visual/infrared photons and produce optical images for BLT, FMT, and CLT. The EMCCD camera was coupled to a prime lens (EF 24mm f/1.4L II USM, Canon, Tokyo, Japan) and situated on the direction of 135°. We implemented FMT in trans-illumination mode by positioning an exciting laser source (BWI-780-60E, B&W Tek, Newark, DE) opposite to the EMCCD on the direction of 315°. The laser beam was expanded and collimated to provide a point excitation source in FMT. An animal holder connected to a gas anesthesia vaporizer (Matrx VIP 3000, Midmark, Versailles, OH) could move the imaging subject along the rotation axis of the gantry. A slit was opened in the middle of the animal holder to let the laser beam go through to act as a point excitation source on the back of the mouse. Several holes around the animal holder were drilled for registration of the optical photograph with the CT volume. We made a shield of lead board and lead glass to offer protection to the operators from ionizing radiation. The entire system was set up in a light-shield chamber for removing ambient light to enable accumulation of weak signals and increase signal-to-background ratio (SBR) of optical images. As shown in Fig. 1(c), on the opposite of the rotating gantry, there was an M3 MRI system (Aspect Imaging, Shoham, Israel), for the convenience of conducting MRI successively after other imaging modalities. Here, we used the MRI system as a supplement to our pentamodal system for verification.

2.2 System characterization

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Fig. 2. Spatial resolution characterization of PET and optical sub-systems. (a) The droplet of ¹⁸F-FDG was positioned at the origin of the coordinate system. (b) The photograph shows the experimental setup of PET block detectors. (c) The schematic diagram shows the dimensions of the Derenzo phantom used in the verification of spatial resolution of PET sub-system. (d) The photograph shows the Derenzo phantom. (e) The schematic diagram shows the dimensions of the cubic optical phantom. (f) The schematic diagram shows top view of optical signal acquisition setup. (g) The photograph shows the cubic phantom used in phantom experiment of optical tomographies.

Ex vivo phantom imaging were performed to investigate the spatial resolution, sensitivity, and linearity of PET and optical tomographies, since CT has been characterized in a previous application [30] and MRI system is a commercial instrument. Two PET block detectors were used for spatial resolution testing of PET subsystem. The definition of spatial resolution of PET was adapted from the National Electrical Manufacturers Association (NEMA) NU 4-2008 standard: Resolution is specified as the full width at half maximum (FWHM) or full width at tenth maximum (FWTM) of the point source response, though our measurement was only conducted at the center of the axial field of view (FOV). 10 µCi (0.37 MBq, 0.16 µL) of ¹⁸F-FDG was secreted in a glass capillary of 0.5 mm in diameter and formed approximately a cylinder with a diameter of 0.5 mm and a height of 0.8 mm. The droplet of ¹⁸F-FDG was positioned in the middle of the two detectors and aligned with their centers and the detectors were 50 mm apart, corresponding to the setup in in vivo imaging experiment, as shown in Fig. 2(a) and 2(b). Spatial resolutions on three axes, i.e. x, y, and z, were measured from the acquired PET data. Additionally, we have performed a Derenzo phantom (Fig. 2(c) and 2(d)) study to verify the consistency of the result from the previous test. The Derenzo phantom was filled with a total of 50 μCi (1.85 MBq, 600 µL) of ¹⁸F-FDG, placed at the center of the FOV between the two PET block detectors, and imaged for five minutes. Ordered subset expectation maximization (OSEM) algorithm [31] was used to reconstruct PET scan data of the Derenzo phantom with 10 iterations and 5 subsets. The absolute system sensitivity of PET sub-system was measured using 4 µCi (0.148 MBq, 0.12 µL) of ¹⁸F-FDG, positioned in the middle of four detectors and translated along axial direction in step of 5 mm. At each location, data was acquired with a timing window of 6 ns. Data within an energy window of 250-750 keV were analysed. Counts were corrected for ¹⁸F decay based on acquiring time points. Linearity of PET sub-system was tested with various concentrations of ¹⁸F-FDG (100 µL) filled in strip wells. Each group of wells were imaged for five minutes. Scan data were reconstructed as previously mentioned. Cylinder regions of interest (ROIs) of 6 mm in diameter and 2 mm in height, of the same geometry of ¹⁸F-FDG filled in strip wells, were drawn to calculate statistics of the reconstructed PET value. A cubic polyethylene phantom was used for spatial resolution tests of optical tomographies. The phantom had absorption and scattering coefficients of 0.1 mm^-1 and 9 mm^-1, respectively. As depicted in Fig. 2(e)-2(g), two cylindrical cavities 3 mm apart were drilled on the phantom. Luminescent liquid for BLI (6.28 µL), indocyanine green (ICG) for FMI (2 µg/mL, 6.28 µL), 29 µCi (1.07 MBq, 6.28 µL) of ¹⁸F-FDG for CLI, were injected to the bottom of the cavities, respectively. Therefore, the volumes of the sources were two cylinders with a diameter of 2 mm and a height of 2 mm. As shown in the top view (Fig. 2(f)), optical image was acquired from the front of the phantom. So the depth of the optical sources in the phantom were 2 mm. Displacement of reconstructed source positions was measured to characterize the spatial resolution of the optical imaging subsystem. Data acquisition settings and processing were same as in vivo imaging described in the following. To test the linearity of optical modalities, luciferase labelled mouse breast cancer cells (4T1-luc, 3.3 × 10⁶/mL) for BLI, ICG (16 µM) for FMI, ¹⁸F-FDG (30 µCi, 1.11 MBq, 150 µL) for CLI, were pipetted into 96-well plates to form various concentrations of light sources, respectively. For FMI, each well was excited with laser point source of 1 mW separately. Images were acquired from the top of the 96-well plates. Data acquisition settings and processing were same as in vivo imaging described in the following. The detection limit was determined as the measurement of sensitivity of optical modalities. Several marker pairs were selected in PET, CT and photograph, classic iterative closest point algorithm was used to register images acquired from different sub-systems. Registration errors were determined by calculating root mean square of distances between registered marker pairs.

2.3 Mouse model establishment

An orthotopic HCC mouse model was established for in vivo imaging experiment. Luciferase labelled well-differentiated human HCC cells HepG2-Red-Fluc was purchased (BW134280, PerkinElmer, Waltham, MA) and cultured in Gibco^® Dulbecco’s Modified Eagle’s Medium with high glucose (Life Technologies, Carlsbad, CA) supplemented with 10% fetal bovine serum, 100U/mL penicillin and 10mg/ml streptomycin (Life Technologies, Carlsbad, CA) in a humidified incubator at 37 °C with 5% CO₂. A female 6-week-old BALB/c nude mouse was purchased (Vital River, Beijing, China) and used for establishment of the orthotopic mouse model. All animal experiments were conducted in compliance with the guidelines of the Institutional Animal Care and Use Committee of Chinese PLA General Hospital. HepG2-Red-Fluc cells (3×10⁷) were harvested, suspended in 25 µL of Matrigel^® (Corning, Corning, NY). After anesthetized with 1% isoflurane (Jiupai, Shijiazhuang, China), the mouse received laparotomy. Suspended cells were injected into the left lateral lobe of liver of the mouse with an insulin syringe (BD, Franklin Lakes, NJ). Dissected peritoneum and abdominal skin were stitched separately. Fourteen days later, the mouse was subjected to the in vivo pentamodal tomographic imaging.

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2.4 Data acquisition

The multimodal imaging procedure is consisted of injection of imaging contrast agents, multimodal scan, and sub-system switching overhead (Fig. 3(a)). The mouse was injected with indocyanine green (ICG, 100 mg/L, 100 µL) intravenously 24 h prior to the imaging and fasted overnight. Thirty min before PET imaging, the mouse was injected with 224 µCi (8.288 MBq, 50 µL) ¹⁸F-FDG. PET block detectors were moved to their working positions, and opposite detectors were 5 cm apart. After anesthetized with 1% isoflurane, the mouse was firstly subjected to a PET scan of 15 min. After retraction of PET block detectors along the linear guideways to their idle positions and adjusting the wiring for optical imaging, CLT images were immediately acquired with a 2×2 binning (same for all following optical imaging) and 5-min exposure time setting of EMCCD. Then the laser of 680 nm was turned on and optical image for FMT was acquired with 1 s exposure time and an emission filter of 810 nm (XBPA810, Asahi Spectra, Tokyo, Japan). When FMT imaging was finished and the laser was turned off, the mouse was injected with 75 µL (15 mg/mL) of D-Luciferin (770504, PerkinElmer, Waltham, MA) intraperitoneally and subjected to BLT imaging with 100 s exposure time 10 min later. After all the optical imaging, we turned on the lights in the chamber and acquired a photograph with the EMCCD with 10 ms exposure time. The photograph was used for locating the mouse and helping register optical images with the CT volume. Afterwards, 360 frames of CT projections of the mouse were acquired in cone beam projection mode. Lastly, the mouse was transferred into the imaging bed of MRI and underwent a T2 scan in M3 MRI system to provide soft tissue contrast information and to help verifying the result of functional modalities. The parameters for MRI scan were set as follows: 5578.6 ms of time to repeat (TR) and 66.61 ms of time to echo (TE). Slicing spacing was 1 mm and slice pixel size was 0.23 mm × 0.23 mm. After imaging, we euthanized and dissected the mouse to verify locations of tumors.

Fig. 3. Workflow of data acquisition and optical reconstruction. (a) Workflow of in vivo multimodal imaging is shown with imaging events. Time points begins from starting of PET scan. Injection of contrast agents and corresponding imaging process are indicated with the same color. (b) Reconstruction workflow of optical tomographies (BLT, FMT, and CLT) is shown.

2.5 Reconstruction

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Ordered subset expectation maximization (OSEM) algorithm was used to reconstruct PET scan data with 15 iterations and 20 subsets. Reconstruction of X-ray CT was performed with our previous implementation of fast Katsevich algorithm based on GPU for helical cone-beam computed tomography [32] integrated in our medical imaging processing platform-MITK & 3DMed [33].MRI data were processed with Aspect Imaging software (Aspect Imaging, Shoham, Israel).

As shown in Fig. 3(b), the reconstruction workflow of optical tomographies, including BLT, FMT, and CLT, was as follows: Reconstructed CT data was segmented with semi-automated algorithms integrated in MITK & 3DMed. Segmented CT data was used to reconstruct three-dimensional (3D) mesh data. Incorporated with tissue optical parameters, 3D mesh data was utilized to construct the system equation of radiation transport equation (RTE) for finite element method (FEM). Meanwhile, acquired optical signals were fused with the photograph and registered to reconstructed CT data via markers visible on both modalities. Then we obtained 3D superficial radiance distribution via mapping registered optical signals onto 3D mesh data. Combining the system equation and 3D superficial radiance distribution, we constructed the objective function. Finally, incomplete variables truncated conjugate gradient (IVTCG) method [34] was used to solve the reconstruction problem and 3D spatial radiance distribution was reconstructed.

2.6 Multimodal volume data processing and visualization

Reconstructed optical data was rasterized and mapped onto the CT volume with ITK image processing toolkit [35]. Tetrahedrons consisted of nodes with intensity over 10% of the max intensity in the mesh were rasterized as ellipsoids in the volume [36]. As the voxels of CT data were of single-precision float point type ranging from -1.00 to 1.00, the voxel values of ellipsoids corresponding to BLT, FMT and CLT were -0.75, -0.50 and -0.25, respectively. Reconstructed PET data was mapped to the range from -0.90 to -0.10 in another copy of CT data. For CLT/PET overlay display, PET data was mapped to the range from -0.8 to 0.0, voxels corresponding to CLT were set to -0.9 and rendered as green tetrahedrons to avoid confusion with blue signals in GE color map of PET. Checkerboard overlay was used when CLT and PET rendered the same voxel. GPU full volume rendering module in 3DMed was used to visualize fused volumes. MRI data was displayed in gray and rainbow color map by the slice viewer in 3DMed.

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3. Results

3.1 System characteristics

Fig. 4. Results of performance tests of PET. (a)-(c). PET source intensity profiles along x, y, and z axis res­pectively. (d) Reconstruction of Derenzo phantom. (e) Absolute sensitivity profile along axial direction between the two block detectors. (f) Linear regression of source radioactivity correlated with reconstructed PET values.

Shown in a previous application of the CT subsystem [30], resolution and slice thickness of CT frames were 0.012 mm. By plotting the profiles through the center of reconstructed source in PET image along x, y, and z axis respectively and measuring the full-width at half maximum (FWHM) of the peaks, the resolutions of PET block detectors along x, y, and z axis were 1.139 mm, 1.750 mm, and 2.959 mm, respectively, as shown in Fig. 4(a)-4(c). Full-width at tenth maximum (FWTM) of the peaks along x, y, z axis were 1.914 mm, 3.036 mm, and 6.072 mm. In the reconstructed Derenzo phantom image (Fig. 4(d)), sources of 1.6 mm in diameter could be clearly separated while some sources of 1.2 mm failed to be separated. Peak absolute sensitivity at center of FOV was 67.738 cps/kBq, as shown in Fig. 4(e). PET sub-system showed good linearity (R² = 0.9962) in the range from 0.1 µCi/mL (3.7 kBq/mL) to 225µCi/mL (8.325 MBq/mL) of ¹⁸F-FDG (Fig. 4(f)). The CCD counts of acquired BLI, FMI, and CLI image were mapped onto the frontal surface of the meshed cube (Fig. 5(a), 5(e), and 5(i)), respectively. Reconstructed optical signal above 10% of maximum power was delineated with bounding green polyhedrons, while the magenta cylinders indicated the actual positions of the sources (Fig. 5(b), 5(f), and 5(j)). Slice over the centers of the sources at x = 11 mm (blue plane in Fig. 5(b), 5(f), and 5(j)) was extracted to depict the displacement of the reconstructed sources (Fig. 5(c), 5(g), and 5(k)). Error of reconstruction was measured by both the distances from the centers of the actual sources to the point of the maximum reconstructed power (0.235 mm and 1.145 mm for BLT, 0.235 mm and 1.072 mm for FMT, 0.508 mm and 1.037 mm for CLT) and reconstructed power-weighted centroids [37] (1.145 mm and 0.885 mm for BLT, 0.239 mm and 0.703 mm for FMT, 0.468 mm and 0.939 mm for CLT). All optical tomographies separated sources 3 mm apart at the depth of 2 mm. As shown in Fig. 5(d), 5(h) and 5(l), linearity tests showed BLI was able to detect 1600 /mL to 1.65 × 10⁵ /mL of luciferase-labelled 4T1-luc cells while still maintaining good linearity (R² = 0.9924), FMI could image 0.5 µM to 16 µM of ICG (R² = 0.9776), and CLI observed linear (R² = 0.9869) Cerenkov luminescence from 10 µCi/mL (0.37 MBq/mL) to 150 µCi/mL (5.55 MBq/mL) of ¹⁸F-FDG, respectively. Root mean squares of distances between point pairs of PET and CT, 3D-mapped photograph and CT were calculated to determine registration error of PET-CT and optical tomographies-CT. General performance parameters (including spatial resolution, sensitivity, linearity, and registration error) were summarized in Table 1. The complete specification of the system is presented in Table 2.

Fig. 5. Results of performance tests of optical tomographies. (a, e, i) CCD counts of acquired BLI, FMI, and CLI image in arbitrary units (AU) were mapped on the cubic phantom, respectively.  (b, f, j) BLT/FMT/CLT reconstruction of the two sources in the phantom, respectively. Reconstructed signals above 10% of the maximum reconstructed power is delineated with bounding green polyhedrons. Magenta cylinders center at the positions of the actual sources. (c, g, k) The slice taken from x = 11 mm, i.e. the blue plane in (b, f, j) respectively, shows reconstructed power intensity distribution in AU and displacement from actual sources. Positions of the actual sources are indicated by black circles. (d, h, l) The result of linearity tests of BLI, FMI, and CLI, respectively.

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Table 1. General performance parameters of sub-systems