Toward truly combined PET/CT imaging using PET detectors and photoncounting CT with iterative reconstruction implementing physicaldetector response

Christian ThibaudeauSherbrooke Molecular Imaging Center, Centre hospitalier universitaire de Sherbrooke, Sherbrooke, QuébecJ1H 5N4, Canada and Department of Electrical and Computer Engineering, Université de Sherbrooke,Sherbrooke, Québec J1K 2R1, Canada

Philippe BérardSherbrooke Molecular Imaging Center and Department of Nuclear Medicine and Radiobiology,Université de Sherbrooke, Sherbrooke, Québec J1H 5N4, Canada

Marc-André Tétrault and Jean-Daniel LerouxDepartment of Electrical and Computer Engineering, Université de Sherbrooke, Sherbrooke,Québec J1K 2R1, Canada

Mélanie BergeronSherbrooke Molecular Imaging Center and Department of Nuclear Medicine and Radiobiology,Université de Sherbrooke, Sherbrooke, Québec J1H 5N4, Canada

Réjean FontaineDepartment of Electrical and Computer Engineering, Université de Sherbrooke, Sherbrooke,Québec J1K 2R1, Canada

Roger Lecomtea)

Sherbrooke Molecular Imaging Center and Department of Nuclear Medicine and Radiobiology,Université de Sherbrooke, Sherbrooke, Québec J1H 5N4, Canada

(Received 12 June 2012; revised 26 July 2012; accepted for publication 2 August 2012;published 30 August 2012)

Purpose: This paper intends to demonstrate the feasibility of truly combined PET/CT imaging andaddresses some of the major challenges raised by this dual modality approach. A method is proposedto retrieve maximum accuracy out of limited resolution computed tomography (CT) scans acquiredwith positron emission tomography (PET) detectors.Methods: A PET/CT simulator was built using the LabPETTM detectors and front-end electronics.Acquisitions of energy-binned data sets were made using this low spatial resolution CT system inphoton counting mode. To overcome the limitations of the filtered back-projection technique, aniterative reconstruction library was developed and tested for the counting mode CT. Constructionof the system matrix is based on a preregistered raster scan from which the experimental detectorresponse is obtained. PET data were obtained sequentially with CT in a conventional manner.Results: A meticulous description of the system geometry and misalignment corrections is imperativeand was incorporated into the matrix definition to achieve good image quality. Using this method,no sinogram precorrection or interpolation is necessary and measured projections can be used asraw input data for the iterative reconstruction algorithm. Genuine dual modality PET/CT images ofphantoms and animals were obtained for the first time using the same detection platform.Conclusions: CT and fused PET/CT images show that LabPETTM detectors can be success-fully used as individual X-ray photon counting devices for low-dose CT imaging of the anatomyin a molecular PET imaging context. © 2012 American Association of Physicists in Medicine.[http://dx.doi.org/10.1118/1.4747265]

Key words: dual modality, positron emission tomography/computed tomography (PET/CT), photoncounting CT, iterative reconstruction, experimental system matrix

I. INTRODUCTION

The concept of using the same detection system for emis-sion and transmission imaging arises naturally from the ba-sic detection process of radiation. There would be substan-tial savings in terms of cost, space, and patient managementif the same detector gantry could be used for X-ray com-puted tomography (CT) and single photon emission computed

tomography (SPECT) or positron emission tomography(PET) imaging. However, the specific requirements and con-straints of each imaging modality have prevented the imple-mentation of combined emission-transmission imaging sys-tems up to now. It was already shown that dual modal-ity is achievable using the same detectors while combiningSPECT with CT.1, 2 By using a mid-range SPECT isotopesuch as 99mTc, both X-rays (∼1−120 keV) and gamma rays

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(140 keV) can be simultaneously measured. Their similarenergy naturally fits in the dynamic range of an appro-priate photon counting device. More recently, a combinedpositron emission tomography (PET) and CT application wasproposed.3 The higher energy of the annihilation photons(511 keV) in PET requires a different type of detector, whichis generally noisier in the low energy range (near-soft X-rayspectrum).

The LabPETTM avalanche photodiode-based detectors andfront-end electronics were originally designed to achieve nearmillimeter spatial resolution in PET,4 but can also be used tomeasure X-rays in photon counting mode.5 Applying a highergain to the charge sensitive preamplifier allows individual X-ray photons to be discriminated from electronic noise andcounted. If these devices appear as leading edge PET de-tectors, they occur as limited spatial resolution CT detectorswhen compared to current dedicated clinical6 or small animalCT scanners.7, 8 The size of the crystals is about one order ofmagnitude greater than the resolution of a clinical CT scan-ner scaled to a 25 g adult mouse (100 μm).8 However, theywould be suitable for low-resolution imaging of the anatomyand attenuation measurement at low-dose in the context ofPET molecular imaging, enabling both PET and CT scans tobe acquired using the same apparatus. Such hardware fusionof the two scanners would enable simultaneous or fast sequen-tial dual-modality imaging of dynamic biological processes.

To investigate the performance of the LabPETTM detectorsin a CT imaging context, a tomographic test bench was as-sembled and transmission measurements were acquired. Thesystem is described by a nonconventional geometry; it hasa very limited number of detection elements, includes sub-stantial dead spaces and is not equilinear nor equiangular.It thus requires important rebinning and interpolation opera-tions of the output sinogram, which degrade resolution andintroduce image artefacts. Consequently, the filtered back-projection (FBP) reconstruction technique leads to very poorimaging performance. While various methods designed to im-prove image quality can be found in the literature,9, 10 most ofthem were validated on large data sets including numerousdetection elements. This paper concentrates on the difficultiesrelated to very low resolution and missing data reconstruc-tion. To mitigate these limitations, a carefully designed sys-tem matrix derived from the experimental detector responseswas used along with a statistical iterative reconstruction al-gorithm. Ultimately, this technology would allow transform-ing simple PET scanners into truly combined PET/CT imag-ing devices (given individual pixel readout is available), asdemonstrated in this feasibility study. The addition of an X-ray source into a full PET detection ring would be an afford-able way to obtain anatomical markups, as well as an attenu-ation map for PET correction.

II. MATERIALS AND METHODS

II.A. Simulator description

A PET/CT simulator reproducing the geometry of theLabPETTM small animal PET scanner was assembled. Madeof two LabPETTM front-end analog boards juxtaposed side

(a)

(b)

FIG. 1. Experimental setup using the LabPETTM detectors and associatedfront-end boards, shown in (a) schematic view, and (b) as actual assembly.During CT experiments, only boards 1 and 2 were used for data acquisition.For PET experiments, only boards 1 and 3 were utilized.

by side with associated digital processing electronics,11 itprovides 32 radial channels opposed to a microfocus X-raysource (model FMT/CBM 65 B-50W, FeinFocus GmbH, Ger-many). Each analog board includes 16 LYSO crystals (2 mmtangentially × 4 mm axially × ∼12 mm radially) individuallycoupled to an avalanche photodiode and enclosed in groupsof four detectors into LabPET-like modules.12 The entrancewindow of the module was replaced by a thin aluminum foilto improve the detector efficiency for low-energy X-rays. Arotating stage lies in between the detector array and the X-ray source, allowing different projections to be taken from aphantom object (Fig. 1). The field of view was limited to adiameter of about 30 mm by the 20◦ aperture cone beam ofthe microfocus tube. A typical scan was made of 360 pro-jections over 360◦, with an X-ray tube current of 30 μA at65 kVp. A 1.2 mm thick Cu plate was inserted at the sourceexit to harden the beam, providing an average 50 keV X-rayenergy. X-ray photons were individually counted by hardwaredigital counters with a pulse-pileup recovery algorithm13 andtheir energy stored in an 8-bit histogram. The system magnifi-cation was set to 2, with 160 mm separating the X-ray sourcefocal spot from the detectors along the system axis (Fig. 1).

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With an X-ray focal spot size of 50 μm in both directions, andaccording to equations given by Paulus et al.,14 the theoreticalsystem spatial resolution should be approximatively 1.18 mmFWHM.

A 1 mm lead slit was inserted in front of the detector ar-ray to define the slice thickness. The source and detector werealso mounted on vertical moving stages, allowing 3D planaracquisitions to be performed. The complete acquisition pro-tocol was written in Python15 and basically interfaces the stepmotors drive along with the LabPETTM control panel. Thedigital counters were implemented directly into the FPGA ofthe digital electronic acquisition boards.

The kovar casing of the LabPETTM modules creates gapsbetween every group of four detectors. According to Zbijew-ski et al.,16 special care must be exercised in distributing thesegaps (that might be considered as missing or dead detectors)asymmetrically from both sides of the system axis. This wasdone by aligning an active detector with the system axis, in-stead of the central hole between the two electronic boards(Fig. 1).

These gaps account for a substantial fraction of the detec-tion surface and their deficient contribution to the field of view(FOV) must be properly dealt with. The source and detec-tors remain static as the object is rotating during a slice scan,this setup hence mimics a 3rd generation acquisition system,which results in circular detector contributions to the FOV. Toavoid interpolation, it was of uttermost importance to alignthe system axis on a living detector to ensure that the centralimage pixels were properly sampled.

In order to complete the combined PET/CT proof of con-cept, an additional detection board was added for PET coin-cidence detection (Fig. 1). This board was positioned under-neath the X-ray source during the CT acquisitions and broughtup to the detection plane (i.e., the same z-level as the opposeddetectors, using a vertical translation stage) prior to each PETacquisition. Only 16 channels were used from the 32 availableon the opposite side during PET experiments (those from ac-quisition board 1), hence 16 channels were facing 16 channelsat any given time. This was done to allow existing software tobe reused without major modifications.

II.B. Energy rescaling

Avalanche photodiode (APD) detectors were manually bi-ased in order to minimize the noise count rate and optimizethe energy resolution of individual detector channels. SinceAPD gain varies with bias, this results in a scattering of theenergy spectra relative to a common energy reference (Fig. 2).Histograms were processed to ensure that the bin-to-energyconversion is uniform among the channels. It then becomespossible to select a common lower-threshold and sum up thevalid counts to generate the sinograms. This threshold waschosen as the mean valley position (lowest energy separat-ing electric noise from signal) for all channels, correspond-ing to 30 keV in the present investigation. The spectral na-ture of the X-ray beam has not yet been integrated into thereconstruction mechanism and will be subjected to furtherinvestigation.

(a)

(b)

FIG. 2. Energy spectra of a 59.5 keV 241Am source before (a) and afterrescaling (b). As different channels have different gain and bias, histogramsneeded to be rescaled in order to properly distribute the detected counts intothe corresponding energy bins.

II.C. Delivered dose

The CT delivered dose was evaluated using an electrome-ter/ion chamber (Radiation Monitor Controller 9010, RadcalCorporation, Monrovia, CA). The probe (model 9060A) wasplaced at the center of the FOV behind a 10 mm axial slitcollimator. The dose was measured for an exposure time of1 s for various values of the tube current and a linear relationof 1.16 × 10−3 μGy/s/μA per axial mm was found. For theanimal experiments, no shutter was used to prevent exposureduring the movement period of the step-and-shoot protocoland no collimation was made to axially confine the photonbeam to the single detection row. Nevertheless, the doses re-ported below will assume those precautions were taken, pro-viding useful doses, or what to be expected from a properlytuned complete system. Mouse doses were also weighted bya geometric factor accounting for the difference in diameterbetween the cylindrical probe and the mean animal thickness(measured from the reconstructed slices).

In PET experiments, the radioactive tracer activity waschosen to be about 74 MBq for both phantoms and small ani-mal acquisitions.

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FIG. 3. Experimental detector responses obtained by a raster scan made us-ing the X-ray source. The gaps at every four detectors are caused by the thick-ness of the kovar casings enclosing the LabPETTM modules.

II.D. CT system matrix construction

The X-ray source was mounted on a separate rotatingstage, providing the bench with the ability to perform a rasterscan. This was done by inserting a narrow 100 μm linear col-limator at the X-ray source exit and microstepping the sourcetangentially. The actual experimental response of all detectorscould thus be recorded. The relative detector position and ge-ometry were then known with great accuracy (Fig. 3).

Assuming shallow penetration before photon interactionwithin the crystal (the mean free path of our LYSO is about380 μm for 50 keV photons), one can determine the detectorresponse as seen by a static source without a great approxima-tion (conversion from θ to φ, in Fig. 4). This transformationis only possible if the source/object distance and the detectorcurvature radius are known.

A CT system matrix should reproduce the trajectory ofthe ballistic photons along their way from the X-ray sourceto the detectors. Using the ray-driven approach, each pixel,if crossed, is weighted by the intersection length between itsCartesian boundary and the source ray.17 Alternate weightingmethods were proposed by Lewitt,18 others by De Man andBasu.19 Given the experimental raster scan data as input, animproved version20 of Siddon’s ray-tracing algorithm21 wasused to generate the matrix. Several additional parameterswere included in the model definition, most of them beingoffsets to adjust the exact position of the source and detec-tors. A threshold was applied to the normalized raster counts

FIG. 4. The raster scan ray paths can be used to calculate the ballistic pho-tons trajectory during a real scan (done by a θ to φ conversion).

FIG. 5. Back-projection of the first projection with different thresholds ap-plied on the normalized raster counts: (a) at threshold = 0.1, overlap occursin adjacent responses; (b) at threshold = 0.9, distinct responses are obtained.Sum of every projections for: (c) a matrix with a system axis aligned ona living detector providing a reasonably well-sampled FOV; and (d) an ill-conditioned matrix aligned on a large gap, showing severely undersampledcentral image pixels and annular regions.

while computing the matrix, so when a value fell below thatthreshold, the corresponding detector was considered not re-sponding and thus no ray was traced (Figs. 3 and 4). Thiswas done primarily to remove noise present in the raster scandata, but also to manually control the effective width of thedetectors.

By summing the pixel contributions for a given projectionangle (i.e., back-projecting the matrix pixel values), one canobserve the distribution of the ray paths over the reconstruc-tion FOV [Figs. 5(a) and 5(b)]. Summing for all projectionsprovides a tool to detect under sampled regions, where statis-tics might be very poor [Figs. 5(c) and 5(d)].

The number of ray lines used to build the system matrixwill greatly affect the quality of the associated reconstructedimages, but also the memory size of the matrix itself. In or-der to create a healthy matrix, several rays must be drawn tofill the section of the FOV seen by a given detector for a cer-tain projection. This is especially true when the ratio of thenumber of detectors to the number of pixels is small, as in thepresent case.

A first solution is to keep each line individually in the ma-trix, hence dividing a given detector into virtual sub-detectors,each of them associated with the same measured counts inthe sinogram [Fig. 6(a)]. This approach is cumbersome sincethe memory size of the matrix then grows linearly with thenumber of lines used. Besides, it implies longer reconstruc-tion time and, as shown in Sec. III, introduces artefacts in theimage. Another method is to sum up the contributions of com-mon pixels between virtual rays impinging a same detector.

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FIG. 6. Weighting factors for building the system matrix, a single detectionelement is shown. (a) No normalization: every pixel from every virtual rayis individually kept; (b) serpentine summed rays: common pixels from everyray are summed and normalized; (c) area overlap: every pixel enclosed by adetector boundaries is area-weighted. Using a large number of virtual rayswith the serpentine summed rays method produces an equivalent to the area-weighting scheme.

As nearby rays cross about the same distance in the objectspace, the summed pixels has to be normalized by the num-ber of lines used to ensure the physics validity of the recon-structed attenuation maps. Doing this will create a serpentineray with an effective length similar to the case of a single line[Fig. 6(b)]. Pixels may then be sorted by their distance fromthe source to accommodate certain reconstruction algorithmssuch as transmission maximum likelihood (TRML).17 In addi-tion, the memory size of the matrix grows asymptotically withthe number of lines used per detector (once every pixel is in-cluded, the matrix stops growing) and the method introducesa gentle smoothing of the pixel values which improves recon-struction quality. Using an infinite (or at least very large) num-ber of lines per detector tends to produce an equivalent to areacalculation [Fig. 6(c)], which attenuates the high-frequencyartefacts present in the back-projection of the matrix pixelsdefining a single projection.19 The longer calculation time as-sociated with this method becomes unimportant if the matrixis precalculated once and loaded only before reconstruction.

To investigate the relation between the quality of the re-constructed image and the number of ray lines traced per de-tector, the standard deviation from a uniform analytical phan-tom was evaluated. It was found that for a geometry sim-ilar to our experimental setup and a 128 × 128 pixelatedmatrix, using more than 10 ray lines per detector is unnec-essary and only time consuming. Moreover, summing up vir-tual rays [Fig. 6(b)] is necessary to achieve satisfactory im-age quality. When individual virtual rays are used [Fig. 6(a)],adding more rays does not improve reconstructed image qual-ity to an acceptable level. As seen in Fig. 7, the memorysize of the normalized-serpentine weighting scheme followsa very asymptotical behavior and reaches an acceptable noiselevel after less than 10 rays are traced for a given detector.This conclusion is, however, particular to the current problemspecification, and would not hold if the geometry, number ofdetectors or number of image pixels were modified.

II.E. PET system matrix construction

Iterative image reconstruction methods have also been pre-ferred over analytical methods for PET reconstruction since

FIG. 7. Noise and system matrix size as a function of the number of ray linesused per detector. Serpentine summed rays normalization was used; whenevery pixel is intercepted, adding more rays does not improve image quality.This happens when the matrix size stops growing.

they can better model the irregular spacing and large gapsbetween each detector modules of our simulator setup. Theformers are also better suited to address the incomplete pro-jection data set measured by the two facing detector gantries(Fig. 1). The small crystal size implies that the effect of crys-tal penetration and detector scatter for 511 keV photons willgreatly impact the response function of the different lines ofresponse (LOR). To include this behavior in the PET im-age reconstruction procedure, an accurate system probabilitymatrix was derived using a Monte Carlo computationmethod22 with the GATE software.23 To speed up the simu-lation, back-to-back 511 keV photons were used in the sim-ulation. No attenuation medium was included in the simula-tion, hence object attenuation and object scatters are not mod-eled in the system matrix. Since these corrections are object-dependent, their inclusion would imply the matrix to be re-computed (or resimulated) each time a new object is scanned.However, scattering events in the detectors were preserved toobtain a more accurate shape of the LORs. Other system char-acteristics such as the energy and time resolution, the energywindow and the coincidence window width were modeled inthe GATE simulation to achieve a realistic system matrix.

II.F. Image reconstruction

All CT images were reconstructed using the ordered sub-sets convex algorithm (OSC) (Ref. 24) over a 128 × 128 pix-elated grid. The TRML (Ref. 17) and gradient algorithms25

were also implemented, but as shown by Lange and Fessler,26

the convergence properties of the convex approach make itthe method of choice. The use of a precomputed system ma-trix with such a small 2D grid allows reconstruction durationin the order of a second for a single slice. As a reasonable ini-tial image estimate, a waterlike uniform value of 0.2 cm−1

was used for all pixels. To reduce the noise level, the ef-fect of filtering during and after the reconstruction process

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was investigated. In agreement with Beekman et al.,27 the ap-proach “over-iterate then filter” was kept, as the online filter-ing yields inferior results. A bilateral Gaussian 3 × 3 movingwindow was applied recursively to the final image estimate.Efforts were made to use finer grids followed by downscal-ing but lead to no appreciable improvement in image qual-ity. This inability to increase the image quality using a finerpixel grid and downscaling might be due to the very limitednumber of independent inputs (32 detectors × 360 projections< 1282 pixels), as this represents an ill-posed mathematicalinversion problem. This approach was, however, proved suc-cessful by Zbijewski and Beekman,9 but with a much densersampled set of simulated data.

The maximum likelihood expectation maximization(MLEM) (Ref. 17) algorithm was chosen for the PET imagereconstruction. The random coincidence estimates werederived from the single event count rates of both coincidentdetectors. Detector efficiency correction factors were ob-tained by performing a scan of a uniformly filled cylindricalphantom covering the whole FOV. The single event countrates on each detector were then used to derive the efficiencyof all LOR detector pairs. Due to the small size of the objectsbeing imaged, attenuation and object scatters were neglected.However, methods for estimating these phenomena from theCT image and the PET measurement could be included infuture works.

II.G. Phantoms and animals preparation

Several phantoms were used in order to evaluate the photoncounting CT and PET imaging capabilities of the simulator.For the animal experiments, ∼20 g mice were euthanized andinserted into a mouse holder just before the acquisition. Thisholder was made of a hollow Plexiglas cylinder with 31 mmouter diameter and 1.6 mm thick wall. When doing a wholebody CT scan, a 1 mm strip collimator was inserted in frontof the detectors to allow thin axial slices (about 0.5 mm, sincemagnification was 2.0) to be obtained. The source/detectorsgantry was then moved up (along the z axis), and several2D slices at a pitch of 1 mm were stacked up to create a3D full body scan. This was necessary because only one rowof detectors was available. Table I reports the experimentalacquisition setup used for all CT images covered in thistext.

FIG. 8. Noise level versus image resolution as a function of the number ofiterations used with the OSC-10 algorithm for reconstruction of an experi-mental water phantom. Noise (in Hounsfield units, Hu) was evaluated from acircular region of interest while resolution was estimated from profiles takenalong the water container edge (see text).

When switching to PET detection mode, only one slicewas acquired per object due to the long acquisition time. Micewere injected with 74 MBq of Na18F or 18FDG 30 min priorto the scan. To partially simulate a complete PET detectionring, the phantom was rotated to 6 different angular positionsduring these acquisitions. The first of these frames was ac-quired during 10 min, and others were time-compensated forthe 18F decay (to collect about the same number of counts ineach frame).

III. RESULTS

III.A. CT image reconstruction

As iterations are carried on, noise and spatial resolution ap-pear as antagonist objectives. In order to determine the num-ber of iterations required to achieve an acceptable level of de-tails without compromising the uniformity, the image of anexperimental water phantom was reconstructed (the mouseholder from Sec. II.G was filled with tap water). By measur-ing the standard deviation from a circular region of interest(ROI) near the center of the phantom, noise (in Hounsfieldunits, Hu) was evaluated (Fig. 8). Resolution was calculatedfrom the full width at half maximum (FWHM) of a line spread

TABLE I. Acquisition parameters for the different CT experiments reported in the present paper. X-ray source voltage was 65 kVp for all experiments.

Projection duration X-ray source currentPhantom Reference in text Number of axial slices Number of projections (ms) (μA)

Water Fig. 8 1 360 2000 20Cold spots Fig. 11(a) 1 360 2000 20Ultra Micro Figs. 11(b) and 14(a) 1 180 1000 40Full mouse Figs. 12 and 13 80 180 500 50Resolution Fig. 14(b) 1 180 1000 30Mouse lungs Fig. 14(c) 1 180 1000 40Mouse heart Fig. 14(d) 1 180 1000 40

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FIG. 9. Images of the Ultra Micro hot spot phantom reconstructed usingdifferent thresholds while computing the system matrix: (a) a too low thresh-old (0.01) will include noise and crosstalk, and lead to a meaningless result;(b and c) for a wide range of thresholds (0.1−0.9), resolution and noise arelittle affected; and (d) a too high threshold (0.99) will include too little of theoriginal detectors response, resulting in a very unstable reconstruction.

function (LSF) obtained from the derivative of the edgespread function (ESF), measured by averaging profiles takenalong the water container edge (Fig. 8). It was determined that10 iterations using 10 subsets of the OSC (OSC-10) algorithmwere sufficient to achieve a resolution close to the theoreticallimit of the system (1.18 mm FWHM), so this was utilizedfor all other CT reconstructions. Using more iterations wouldonly lead to an artificially enhanced spatial resolution, cou-pled with an unacceptable noise level. This noise level wasmainly related to the low acquisition statistics and ring arte-facts present in the image.

Better CT imaging results were obtained using a lowthreshold (0.1) over the normalized raster scan data for com-puting the system matrix. As long as this threshold waschosen above the noise and crosstalk level, there was lit-tle effect on the reconstructed resolution and noise prop-erties of the images. For the threshold range 0.1−0.9, im-age characteristics remained relatively stable, with some faintring artefacts gradually appearing at the higher threshold(Fig. 9 presents reconstructions for selected thresholds). Anexceedingly high threshold led to a very unstable recon-struction, as too much of the original detector responses arediscarded.

Different normalization methods were investigated for thesystem matrix construction and the use of multiple raysper detector was found unavoidable. Using the serpentinesummed rays method [Fig. 6(b)] resulted in fewer artefactsand a less blurry image than the unnormalized method. Theformer method also produced a much smaller system matrix

FIG. 10. Reconstructed images from a simulated water phantom, with a con-figuration similar to the experimental setup. Poisson noise was added to theprojection data (scan with object), corresponding to 1 × 104 counts per detec-tor per projection. In these different reconstructions, only the normalizationstatistics (scan without object) was multiplied by a factor N, relative to the1 × 104 counts. When the calibration statistics is too low (N < 20), ringartefacts are present in the images.

and hence faster reconstruction time. As computation time forthe system matrix was not an issue, 50 virtual rays per detec-tor were utilized to ensure an area-weighted equivalent nor-malization was achieved (Fig. 7 shows this was a very conser-vative choice).

Another criterion deserving attention is the absolute statis-tics collected during the normalization scan (i.e., without ob-ject). As low dose acquisitions simply translate into highernoise in the reconstructed images, a 3rd generation CT sys-tem will also generate ring artefacts if the normalization isstatistics-limited. Given the circular detector contributions tothe FOV, noise present in the calibration data will propagatethrough projections in a very annular manner. To avoid con-fusing statistics and missing-data related artifacts, this effectwas simulated using the analytical CTSIM package28 for aregular equiangular geometry (without dead spaces betweenmodules). By using the same number of detectors as the ex-perimental setup and adding a Poisson noise to the computedsinograms, the statistics-related ring artefacts were shownto be negligible when the total X-ray count is greater than2 × 105 per detector during the normalization. (Fig. 10).

III.B. Photon counting CT imaging results

Photon counting CT images from different phantoms werereconstructed, as shown in Fig. 11. Presence of circular arte-facts was still noticeable, but this effect was reduced by postfiltering the resulting slices. Details of the order of 1 mmare distinguishable in both hot and cold spots phantoms. It

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FIG. 11. Photon counting CT images of PET phantoms in air: (a) cold spotphantom made of 4.7, 3.9, 3.1, 2.3, 1.5, 1.1 mm diameter rods (note the hol-low structure for the 3.1 mm rods and larger); (b) Ultra Micro hot spot phan-tom (2.4, 2.0, 1.7, 1.35, 1.0, 0.75 mm air holes).

is worth noticing that rods are hollow in Fig. 11(a), and thisdetail could be resolved for the larger rods. The apparent dis-tortion appearing at the periphery of the cold spot phantomresults from the fact that this large phantom was not fully cov-ered by the narrow X-ray cone.

A complete mouse was also scanned within the mouseholder, which was later removed from the image by soft-ware (application of a simple cylindrical exclusion region).A decent 3D image was obtained where the main anatomicalfeatures of the animal can be distinguished (Fig. 12). The use-ful dose delivered to the animal was estimated to be 1.4 mGyfor the whole body CT scan. A closer examination of the in-dividual transaxial slices through the mouse head shows thatseveral useful anatomical structures that can be used as land-marks are clearly visible (Fig. 13).

III.C. Dual modality photon counting CT and PETimaging results

Fused PET/CT images were also obtained, showing thatthe same detection apparatus can successfully be used forboth imaging modalities (Fig. 14). Although PET acquisi-tions suffered from noticeable misalignment, 1.35 mm chan-nels can be distinguished in the Ultra Micro hot spot phantom[Fig. 14(a)], while the 1.0 mm hole is clearly visible in theresolution phantom [Fig. 14(b)]. To enhance the contrast inthe CT phantoms reconstructions, the hollow channels wereleft empty (i.e., they contained air) during the transmissionmeasurements. They were filled with 18FDG prior to PET ac-quisitions only. For the mice experiments, the mouse holderwas again removed by software from the CT images, butsubtle remains can still be seen. The use of a bone scintig-raphy agent (Na18F) in Fig. 14(c) presents good agreementbetween PET and CT measurements. The observation of theheart in Fig. 14(d) is greatly facilitated by the PET imag-ing, for the CT counterpart offers limited contrast in thisregion.

IV. DISCUSSION

Physically combining the PET and CT imaging modalitiesbrings about significant challenges, but also offers new op-

(a) (b)

FIG. 12. Photon counting CT whole body scan of a mouse shown in falsecolors: sagittal (a) and coronal (b) summed views. The mouse was scannedwithin a Plexiglas tube which was later removed from the image by software.X-ray tube peak voltage and current were, respectively, 65 kVp and 50 μA.Image is made of 75 slices with 1 mm axial pitch, each slice consisting of180 projections. Every projection was acquired during 500 ms.

portunities to redefine the classical imaging paradigms. Oneof them derives from the use of the same detector for mea-suring PET and CT radiation, which requires the CT data ac-quisition and processing schemes to be fundamentally revis-ited. The detection system must be able to operate in a fast

FIG. 13. CT transaxial images of a mouse head (extracted from the samescan as in Fig. 12). Notice that the skull and the auditory canals are clearlyvisible. Ears are also distinguishable.

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(a)

(b)

(c)

(d)

FIG. 14. Individual and fused PET/CT images obtained with the experimen-tal setup: (a) Ultra Micro hot spot phantom [see Fig. 11(b) for definition]; (b)resolution phantom made of high-density polyethylene (HDPE) with 25 mmouter diameter and holes ranging from 5, 4, 3, 2, 1 to 0.5 mm; (c) mouseinjected with Na18F, scanned at lungs level; and (d) mouse injected with18FDG, scanned at heart level.

pulse counting regime to cope with the high photon flux froman X-ray source and still be compatible with the PET dataacquisition requirements.13 Another obstacle results from thelarger size detectors, by about one order of magnitude, im-posed by the PET detection technology and physics. To coun-terweight the limited spatial resolution and the noncontinu-ous detector configuration, all possible software processingpower is needed. The use of iterative reconstruction meth-ods was preferred over the gold standard analytic FBP to en-able a detailed description of the system geometry and mis-alignment corrections that imperatively needed to be incorpo-rated into the system matrix definition to achieve good imagequality.

Even if the CT system geometry was mostly determinedby the measurement of a raster scan, one part of the specifica-tion was left to the user by the selection of a threshold belowwhich the raster data was ignored (Figs. 3 and 4). This choiceimpacts the effective width of the detector response functionsimplemented in the system matrix and was determined exper-imentally to produce better imaging results when chosen low(0.1). Such a threshold provides a slight overlapping of de-tector responses [Fig. 5(a)] that might better model the actualphotons behavior. Presumably, Compton scattering between

adjacent crystals is mostly responsible for this explanation, asphotons aimed at the edge of a crystal might end up contribut-ing as well to a neighbor.

It is not uncommon to find current flat panel X-ray de-tectors having several thousand detection elements in thetransaxial direction. A major challenge in the current projectwas the reconstruction of well-defined CT images using only32 detection elements. When the ratio of image pixels (inone direction, 128 in this paper) over detection elements (inthe transaxial direction, 32 in this paper) is high, computa-tion of the detector responses for the system matrix cannotbe done by tracing a single ray between the source and agiven detection crystal. Such a case results in an unrealisticsparse sampling in the image domain. Best results were ob-tained by tracing several virtual rays per detection element.As contiguous virtual rays can intersect common image pix-els, these contributions were summed up and thus any givenimage pixel would contribute only once in the stored systemmatrix. By using more than one ray per detection element,the computation of the forward projection leads to overeval-uated line integrals. When dividing the computed responseweights by the number of virtual rays impinging a detectionelement, the computed line integrals are correctly normalizedand lead to quantitative reconstructed images. Using a largenumber of virtual rays is computationally more intensive butproduces an area-weighting equivalent scheme. The distance-driven approach,19 although not producing an area-weightingequivalent, might lead to a similar reconstruction quality fora lesser computational burden. It was not implemented here,for the system matrix was very small and precomputed onlyonce, thus requiring less than a second of calculation time per2D plane.

In low dose photon counting CT, the statistics collectedduring the calibration procedure (i.e., scan without subject)have to be much higher than what is acquired during the in-dividual projections of the object or animal scan. If noise istoo important in the calibration data, it will impact the recon-structed images as circular artefacts in 3rd generation scan-ners. Since the calibration procedure is done without subject,it can be made longer without influencing the administereddose. This calibration was done before each scan, as the APDcharacteristics are temperature dependent. In a temperature-regulated environment, this could be done on a less regularbasis.

The quality of the CT images obtained in this work clearlyshows that the proposed combined PET/CT imaging conceptwill offer an adequate anatomic support for the PET counter-part. One side benefit of implementing the photon countingdetection scheme in CT is a reduction of the dose delivered tothe subject.29–31 Using a lower exposure would greatly bene-fit PET longitudinal studies, avoiding radiation-induced dam-age or cell resistance from repeated scans. The anatomic im-age supplied in this combined PET/CT imaging context mightalso improve the quantitative assessment of the PET counter-part, by providing perfectly coregistered PET and CT imagesand more accurate estimates for attenuation and scatter cor-rections. This would prove useful for larger rodents, and evenmore in a futuristic clinical environment.

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While traditional PET/CT systems double the needed de-tector and electronic hardware, a fully integrated method re-lies on the inclusion of an X-ray source inside a PET detec-tion ring. Due to the inherent low-radiation requirements ofphoton counting CT, a low power, compact X-ray source canbe used. Although this might appear an inexpensive solution,any real-life implementation would still remain an engineer-ing challenge. The limited space between detectors and sub-ject, the need for a wide cone-beam angle, the necessity of areliable X-ray shutter, just to name those, are all difficultiesneeded to be overcome. Once accomplished, the use of thesame apparatus for both modalities would, however, greatlysimplify image fusion and open to a whole new world of ac-quisition protocols.

Many problems found on a test bench would be avoidedusing a full PET detection ring. In the first place, acquisitiontime would greatly decrease due to the absence of movingparts (with the exception of the X-ray source). This would im-prove experiments throughput and reduce time-varying detec-tor characteristics due to temperature shifts. The PET coun-terpart would evidently benefit from a proper system align-ment, as this was a serious issue for the experiments relatedto this work. Another advantage lies in the fact that in acomplete scanner context, the CT geometry will become thatof a 4th generation system. This geometry should be lessprone to induce ring artefacts, as the individual detector re-sponses will not be circular anymore. Furthermore, the X-ray source being included inside the PET ring will also im-prove the system magnification (as compared to the mag-nification of 2 used for this study), which could be easilyincreased to ∼2.5. This would bring a corresponding subtleincrease in CT spatial resolution. The use of a smaller X-raysource spot size would, however, not bring an equivalent ben-efit, as it was already demonstrated that its contribution tothe system modulation transfer function (MTF) is insignifi-cant when PET detector size is considered.5 The possibilityto further reduce the detector size remains as the only othermeans of improving the CT resolution, as well as the PETresolution.32

As photon counting CT acquisition duration is deter-mined by the maximum counting rate capability of the front-end electronics, it might hardly reach the detection speedachievable using current integration-mode detectors. This im-plies a longer scan duration for a given collected statistics,or, equivalently, a lower statistics for a given time. Thisapparent limitation may, however, be found acceptable, asmany recent papers (and references therein) are praising theadvantages of the energy-discriminating counting CT ap-proach in terms of dose effectiveness.33–35 Furthermore, aprolonged CT acquisition could be interposed in a simul-taneous longer PET scan, rendering obsolete this disadvan-tage. The use of smaller detection pixels could also im-prove the maximum manageable X-ray flux intensity, as dead-time being mainly associated to the detector, rather thanelectronics.

For now, the simultaneous acquisition of both PET and CTsignals remains a challenge. In such a case, the needed gainof the electronic chain would be chosen as to accommodate

the PET signal, with a resulting electrical noise overlappingthe X-ray spectrum. This might be alleviated by the use ofmore sophisticated detectors and electronics. Using scintilla-tors with a better light yield and improved light collection,36

or very low capacitive detectors coupled to cleaner pream-plifiers would all be solutions to improve the signal-to-noiseratio of the measured signal. Once accomplished, Comptonscattering of the PET radiation would still affect the CT sig-nal. However, as the count rates for both modalities are verydifferent, this effect might be simply ignored or easily sub-tracted, leaving the simultaneousness a possibility. On theother hand, the simplest sequential approach could still find itsuse. Since a complete CT scan can be acquired quite fast, andsince no bed displacement would be needed between modal-ities in a fully combined approach (assuming sufficient ax-ial coverage), repeated CT scans could be interleaved in anormal PET acquisition. This would allow the observationof some time-varying phenomena, such as peristalsis or fill-ing bladder. Properly incorporated into a 4D PET reconstruc-tion scheme, such dynamic CT information could lead tomore accurate attenuation and scatter corrections. Taken toa next level, the allowed time-stamped collection of eventsin both PET and CT modes would greatly simplify dual-modality gated acquisitions of cardiac or respiratory periodicphenomena.

An alternative to a simultaneous acquisition protocol couldbe a rapid switching between CT and PET acquisition modes.Provided this swap is quick enough, PET segments might beacquired, for example, during the gantry moving phases of aCT step-and-shoot protocol.

V. CONCLUSION

X-ray CT imaging in photon counting mode based on PETdetectors and parallel digital electronics has been demon-strated, using iterative reconstruction to retrieve maximum ac-curacy. The system matrix was constructed based on a rasterscan from which the individual experimental detector re-sponse was obtained, and incorporated a thorough descriptionof the system geometry, including misalignment corrections.Photon counting CT and, for the first time, fused PET/CTimages of phantoms and mice were successfully obtained,demonstrating the feasibility of low-dose CT imaging of theanatomy in a molecular PET imaging context.

ACKNOWLEDGMENTS

The authors would like to thank Mélanie Archambaultand Kim Mongrain for their help with the preparation ofthe animals. This work was supported by Discovery grantsfrom the Natural Sciences and Engineering Research Coun-cil of Canada (NSERC) and by grant MOP-86717 from theCanadian Institutes of Health Research (CIHR). C.T. and P.B.held NSERC Postgraduate Scholarships and M.B. held anAlexander Graham Bell Canada Graduate Scholarship fromNSERC. The Sherbrooke Molecular Imaging Centre is partof the Étienne-Le Bel Clinical Research Center, funded by LeFonds de recherche du Québec - Santé (FRQ-S).

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