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Cite this article: Moghiseh M, Aamir R, Panta RK, de Ruiter N, Chernoglazov A, et al. (2016) Discrimination of Multiple High-Z Materials by Multi-Energy Spectral CT– A Phantom Study. JSM Biomed Imaging Data Pap 3(1): 1007.

*Corresponding authorMahdieh Moghiseh, Department of Radiology, University of Otago, Christchurch, 2 Riccarton Avenue, P.O. Box 4345 Christchurch, New Zealand, Tel: +64221602491; Email:

Submitted: 24 October 2016

Accepted: 02 November 2016

Published: 04 November 2016

Copyright© 2016 Moghiseh et al.

OPEN ACCESS

Keywords•Computed tomography (CT)•Spectral Imaging•X-ray detectors•Image reconstructed methods and material

decomposition methods (MD)

Research Article

Discrimination of Multiple High-Z Materials by Multi-Energy Spectral CT– A Phantom StudyMahdieh Moghiseh1*, Raja Aamir1, Raj K. Panta1, Niels de Ruiter1,2, Alex Chernoglazov2, Joe L. Healy3, Anthony PH Butler1,4-6, and Nigel G. Anderson1

1Centre for Bioengineering, Department of Radiology, University of Otago, New Zealand2 HIT Lab NZ, University of Canterbury, New Zealand3Department of Biochemistry, University of Canterbury, New Zealand4European Centre for Nuclear Research (CERN), Geneva, Switzerland5Department of Physics and Astronomy University of Canterbury, New Zealand6MARS Bioimaging Ltd., New Zealand

Abstract

Imaging of biological processes in vivo is the goal of molecular imaging. Multi-energy spectral CT using a photon-counting detector has the potential to enable the imaging and quantification of tissue components, biomarker labels, and pharmaceuticals in order to monitor biological or disease processes. The aim of this study is to provide unprocessed and processed dataset from a spectral CT using Cadmium Telluride (CdTe) assembled Medipix3RX detector in Charge Summing Mode (CSM) so that others can analyse it using their familiar routines. We provide a multi-energy spectral phantom dataset with four energy bins for simultaneous discrimination of various concentrations of four high-Z materials currently used as contrast agents (iodine, gadolinium, and gold and calcium chloride (mimicking bone) in animal and human CT imaging.

ABBREVIATIONSCdTe: Cadmium Telluride; CSM: Charge Summing Mode CT:

Computed Tomography; FOV: Field of View; HU: Hounsfield Unit; mART: MARS Algebraic Reconstruction Technique; MD: Material Decomposition Methods; PACS: Patient Archive Communication System; SDD: Source to Detector Distance; SOD: Source to Object Distance; SPM: Single Pixel Mode

INTRODUCTIONDetecting the multiple contrast agents simultaneously

has application for diagnosis, treatment, tissue labelling and drug delivery. Most current imaging modalities can detect one contrast agent in one scan. Dual energy CT can discriminate two contrast agents [1-6] but only if those contrast materials have significantly dissimilar x-ray attenuation coefficients [7]. MARS spectral CT scanner (MARS Bioimaging Ltd Christchurch New Zealand (MBI)) incorporates a broad spectrum 120 kVp micro-focus x-ray source with a focal spot size of <100 µm. The scanner provides the capability to measure spectral data in the human diagnostic energy range (20–140 keV) with high spatial

resolution (~150 microns). Medipix is a low count rate photon counting detector [8] that provides higher energy resolution due to less intrinsic noise (electronic noise) at the individual pixel level [9]. The Medipix3RX detector bonded to CdTe uses eight energy counters: one arbitration counter, four CSM counters, and three SPM (Single Pixel Mode) counters. The arbitration counter is set above the pixel’s noise level to provide leakage current compensation on a pixel by pixel basis [9]. In CSM mode, a cluster of four neighbouring pixels communicate together to locate the pixel with the highest charge for a quasi-incident interaction, the total charge is then allocated to that pixel. This new feature decreases the effect of charge sharing and increases the overall energy resolution [10]. The active area of the chip is 14 x 14 mm2 with a pixel pitch of 110 μm (matrix of 128 x 128 pixels). The chip can be bonded to a variety of sensor materials [9-11]. Cadmium Telluride (CdTe) semiconductor sensor layer provides high photon detection efficiency in the 20-120keV range [12-14], making it relevant for high-Z contrast agent imaging [15].

Optimal selection of energy thresholds is dependent on the K-edge [16] of each material. We selected four energy bins to

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ensure that each energy range included the K-edge of one of the three high-Z elements, gold, gadolinium and iodine. These high-Z contrast agents can be used as non-targeted agents – depending on the formulation, transit time into tissue and blood half-life, they can be used to image blood vessels [1,17], lymph nodes assessment [18], liver masses [19], angiogenesis [20], and brain [21]. Gadolinium based contrast agents are well-known in magnetic resonance imaging (MRI) [22,23] but have also been used as CT contrast agents [24-26]. Gold nanoparticles depending on size and formulation can be used as blood pool contrast agents or for passive or active targeting of angiogenesis, and tumours [27-29].

In this paper, we present a phantom study to analyse the ability of MARS spectral scanner to distinguish four high-Z materials of clinical relevance in the diagnostic x-ray energy range. The data is published with the intention of allowing interested research groups access to raw, partially processed, and fully processed data produced using the CdTe assembled Medipix3RX camera within the MARS scanner platform. Full access to the data will allow researchers to test and compare their own processing routines.

MATERIALS AND METHODS

Experimental Setup

For this study, 200uL of four high-Z material including Gold Chloride (Salt Lake Metals, Salt Lake City, UT,) (2, 8mg Au/mL), MultiHance (Bracco Diagnostics Inc, Milan) (2, 8mg Gd/mL), Omnipaque 300 (GE Healthcare, Chicago, IL) (18mg I/mL) and Calcium Chloride (BDH Laboratory Supplies, Poole, UK) (140mg Ca/mL) along with water and lipid were placed in a 9 hole PMMA (Polymethyl methacrylate) phantom as shown in Figure (1).

The 720 circular projection over 360º were acquired using a 2mm-thick-CdTe sensor layer bump-bonded at 110µm pitch to Medipix3RX readout chip; and Source-Ray SB-120-350 x-ray tube (SourceRay Inc., Ronkonkoma, NY) with a tungsten anode having 1.8mm of aluminium (equivalent) intrinsic filtration. The focal spot size of the x-rays was ~50μm.

The source to detector distance (SDD) was 250mm and source to object distance (SOD) was 200mm. Five vertical camera positions were used with a translational (vertical) overlap of 3.139mm to cover the 30mm field of view (FOV). The bias voltage for the sensor was -600V. A proprietary camera readout was used for data acquisition [30].

In this study, CT image acquisition was performed with four charge summing counters set at low energy thresholds of 18, 30, 45 and 78keV at a fixed x-ray tube voltage of 118kVp with a tube current and exposure time of 13µA and 300ms respectively. The arbitration counter of the detector was set at 7keV, just above the noise floor of the Medipix3RX chip.

Post processing data

Before the sample scan, MARS system creates a pixel mask by acquiring 20 dark-field (without x-rays) and 200 flat fields (open beam) images and applies a three-step criteria: 1) pixels displaying counts with no incoming x-ray beam (in the dark-field) are identified and labelled as bad pixels and masked, 2) pixels classified as high sensitive or low sensitive pixels (not following Poisson distribution in the flat field images reported elsewhere [31]) are also labelled as bad pixels and masked and 3) there are a few inherent non-functional pixels (in the flat field images) due to sensor layer defects and bump-bonding failures which are also labelled as bad pixels and masked. The pixel mask is then saved by the scanner for the masking or interpolation on dead or noisy pixels on the actual scan data sets.

The raw data in DICOM format transferred from scanner’s server to the inbuilt patient archive communication system (PACS) where an automated image processing was performed. MARS Algebraic Reconstruction Technique (mART) was used to reconstruct CT images of all four energy bins simultaneously into a 480 x 480 mm matrix. The CT reconstruction was performed in subtracted energy bins (18 – 30keV, 30 – 45keV, 45 – 78keV and 78 – 118keV) to reconstruct the difference of the photon counts between the two subsequent counters, providing the number of photons with energies between 18 to 30keV, 30 to 45keV, and 45 to 78keV and 78 to 118keV. The reader is referred to Dicom tags to extract more information about image acquisition like camera position, gantry angle, and others. Table (1) shows an example of some of the Dicom tags associated with each image with their group numbers.

Spectral images–rescaling and data analysis

For data analysis, 16 bit Dicom reconstructed images were converted to 32 bit by multiplying and adding the rescaling slope and intercept values respectively using ImageJ software. The CT attenuation (effective linear attenuation) values for each material in all four energy bins were measured and converted to Hounsfield unit (HU) by normalizing linear attenuation values to

Figure 1 (a) Phantom with nine 6mm inserts (Eppendorf PCR tubes) (top view), (b) One of the Eppendorf tubes filled with water.

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water and air [32,33]. The results were then plotted graphically to evaluate the attenuation trend of each of the given materials at their corresponding K-edge energy bin.

Material decomposition–identification and quantifi-cation

In-house developed material decomposition algorithm (MARS-MD) [34,35], based on constrained linear least square technique, and was applied in the post-reconstructed image domain. For material decomposition basis, effective mass attenuation coefficient for each of the given material was calculated by using the concentration and linear attenuation (obtained from reconstructed images). MARS material decomposition algorithm decomposes the spectral data at the voxel level. One of the main purposes of this paper is to publish processed and unprocessed spectral data so that other groups can test their CT reconstruction and material decomposition tools.

Material identification assessment

Post MD assessment was performed at various material

concentrations of gold, iodine, gadolinium and calcium in the phantom using four spectral measurements in a single acquisition. For quantification and qualitative identification of the decomposed materials, sensitivity (true positive rate) in material image domain (post-MD images) was measured. A quantitative metric in MD images was generated by measuring the percentage of successful voxels (correctly identified as a target material) in a given ROI, comprising of ~900 voxels.

RESULTS AND DISCUSSIONAll concentrations of iodine, gadolinium, gold and calcium

were able to be identified, discriminated, and quantified. Figure 2(b) shows the spectral reconstruction of the phantom for four energy bins. The colour bar represents x-ray attenuation in HU from -1000 to 1500. To assess the x-ray attenuation trend along the rising energy thresholds, the mean values of HU (calculated over ~900 voxels) in each of the given concentrations of high-Z material were measured. Figure (3) shows mean HU value as a function of energy bin. For iodine, gadolinium and gold x-ray, attenuation increased in an energy range that contained the K-edge (figure 3(a), (b) and (c)). For calcium the x-ray attenuation

Table 1: Dicom tags information.0008,103e Series Description 0019, 1306 Energy requested0018,0060 Tube Voltage 0021,1502 Detector Modes0018,1110 SDD 0024,101b Number of Projections0018,1111 SOD 0025,1024 Flat Field Number0018,1150 Exposure Time 0025,1025 Filtration0018,1151 X-ray Current 0025,1103 Energy for CSM10018,7001 Detector Temp 0025,1105 Energy for CSM20019,1001 Gantry Rotation Angle 0025,1107 Energy for CSM30019,1002 Camera Tangential Position 0025,1109 Energy for CSM40019,1003 Sample Axis Position *0025,110b Energy for SPM10019,1201 kV monitor *0025,110d Energy for SPM20019,1202 uA monitor *0025,110f Energy for SPM30019,1203 X-ray Tube Model 0040,a121 Date0019,130A Camera position sequence 0054,0011 Number of Energy Window*SPM counters were not used for data acquisition and image processing.

Figure 2 (a) Schematic of 30mm PMMA phantom with 6mm diameter inserts containing 200µL vials of gold (Au), gadolinium (Gd), calcium chloride (Ca), iodine (I), water and lipid. (b) The transverse CT image (slice#132) with four energy bins, showing the attenuation in relation to energy for the high-Z materials.

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Figure 3 (a) x-ray attenuation of 18mg/mL iodine, showing the k-edge at 30-45keV, (b) x-ray attenuation of 2 and 8mg/mL gadolinium, showing the k-edge at 45-75keV, (c) x-ray attenuation of 2 and 8mg/mL gold, showing the k-edge at 75-118keV, (d) x-ray attenuation of 140mg/mL calcium. The standard error in the measurement of all attenuation is ±2-5HU.

Figure 4 Composite material image of spectral phantom shows classification of the individual element: water (grey), lipid (green), gold (yellow), gadolinium (red), iodine (purple) and calcium (cyan).

decreases with energy (figure 3(d)). For all concentrations of iodine, the gadolinium and gold, maximum attenuations are observed within element’s corresponding K-edge energy bin i.e., 30-45keV, 45-75keV and 75-118keV respectively.

Figure (4) shows the material decomposition image of the phantom, distinguishing different materials assigned by different colors and represents concentration by hue. To calculate the concentrations, material decomposition images were rescaled from 16-bit to 32-bit images as described above. The concentration was measured by taking the mean of approximately 900 voxels in each vial. Figure (5) shows the concentration of each material as measured by material decomposition. There is a good correlation between given and measured concentration for all four materials (Figure 6).

Figure (6) shows that 8mg Au/ml and 2mg Au/ml vials have 95% and 84% of their selected voxels correctly identified as gold

respectively. For gadolinium, 8 mg Gd/mL and 2mg Gd/mL vials show 97% and 37% voxels respectively as correctly identified as gadolinium. For iodine (18mg I/mL) and calcium (140mg Ca/ml), 98% voxels are correctly identified as iodine and calcium respectively. These results are found to be consistent with our previous studies.

CONCLUSIONThis phantom study has shown that multi-energy spectral

CT system has the ability to simultaneously discriminate six materials from each other: gold, gadolinium, and iodine contrast agents; calcium as a major component of bone, lipid, and water as a surrogate for soft tissue. In the spectral CT scanner, the broad x-ray spectrum is split into eight separate energy bins, by the Medipix3RX photon-counting X-ray detector. This energy information is used to differentiate and quantify different materials [36]. For this study four of eight energy bins in CSM

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Figure 5 Measured concentration from MD images versus given concentration, (a) for I 18mg/mL it shows 18 ± 1mg/mL, (b) for Gd 8mg/mL it shows 7.8 ± 2.5mg/mL and for Gd 2mg/mL shows 1.3 ± 1.7mg/mL, (c) for Au 8mg/mL it shows 7.5 ± 2.7mg/mL and for Au 2mg/mL shows 2.3 ± 1.4, (d) for Ca 140 mg/mL it shows 142 ± 10mg/mL.

Figure 6 Identification chart. Yellow shows how much of each material are identified as gold and other colour, red, cyan, purple, grey, green and black represent identified materials as gadolinium, calcium, iodine, water, lipid respectively.

were used to identify and quantify six materials. The limitation of this study is the percentage of correct material identification. For example, 17% and 18% of 2mg Gd/mL, are misidentified as a gold and calcium respectively (Figure 6). Protocol optimization is a key to improving material identification. Parameters like user defined energy thresholds and energy bin size could play a critical role to account for the necessary trade-off between capturing K-edge features in the dataset (so as to differentiate one material from another) and optimizing signal-to-noise ratio (overcoming noise from low photon count rates in the higher energy bands). Optimising the material decomposition algorithm would improve the material identification at lower concentrations. The selection of energy range, filtration, and choice of material decomposition algorithm all influence how well the material is identified. The

availability of raw data in the accompanying dataset extends the range of materials available to researchers from calcium, lipid and water [36] to now include high-Z materials. The results of this study can provide a platform for undertaking spectral CT studies using iodine, gadolinium and gold based contrast agents in biological experiments [37].

ACKNOWLEDGEMENTSThis project was funded by Ministry of Business, Innovation

and Employment (MBIE), New Zealand under contract number UOCX0805. The authors would like to acknowledge Prof Philip Butler, Dr. Christopher Bateman and David Knight; and the Medipix2 and Medipix3 collaboration.

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Moghiseh M, Aamir R, Panta RK, de Ruiter N, Chernoglazov A, et al. (2016) Discrimination of Multiple High-Z Materials by Multi-Energy Spectral CT– A Phantom Study. JSM Biomed Imaging Data Pap 3(1): 1007.

Cite this article

36. Aamir R, Chernoglazov A, Bateman CJ, Butler APH, Butler PH, Anderson NG, et al. MARS spectral molecular imaging of lamb tissue: data collection and image analysis. J Instru. 2014; 9: P02005-P02005.

37. Anderson NG, Butler AP. Clinical applications of spectral molecular imaging: potential and challenges. Contrast Media Mol Imaging. 2014; 9: 3-12.