Reconstruction of multi-animal PET acquisitions with anisotropically variant PSF.

Among other factors such as random, attenuation and scatter corrections, uniform spatial resolution is key to performing accurate quantitative studies in Positron emission tomography (PET). Particularly in preclinical PET studies involving simultaneous acquisition of multiple animals, the degradation of image resolution due to the depth of interaction (DOI) effect far from the center of the Field of View (FOV) becomes a significant concern. In this work, we incorporated a spatially-variant resolution model into a real time iterative reconstruction code to obtain accurate images of multi-animal acquisition. We estimated the spatially variant point spread function (SV-PSF) across the FOV using measurements and Monte Carlo (MC) simulations. The SV-PSF obtained was implemented in a GPU-based Ordered subset expectation maximization (OSEM) reconstruction code, which includes scatter, attenuation and random corrections. The method was evaluated with acquisitions from two preclinical PET/CT scanners of the SEDECAL Argus family: a Derenzo phantom placed 2 cm off center in the 4R-SuperArgus, and a multi-animal study with 4 mice in the 6R-SuperArgus. The SV-PSF reconstructions showed uniform spatial resolution without significant increase in reconstruction time, with superior image quality compared to the uniform PSF model.

Computed Tomography-Based Biomarker for Longitudinal Assessment of Disease Burden in Pulmonary Tuberculosis.

PURPOSE: Computed tomography (CT) images enable capturing specific manifestations of tuberculosis (TB) that are undetectable using common diagnostic tests, which suffer from limited specificity. In this study, we aimed to automatically quantify the burden of Mycobacterium tuberculosis (Mtb) using biomarkers extracted from x-ray CT images. PROCEDURES: Nine macaques were aerosol-infected with Mtb and treated with various antibiotic cocktails. Chest CT scans were acquired in all animals at specific times independently of disease progression. First, a fully automatic segmentation of the healthy lungs from the acquired chest CT volumes was performed and air-like structures were extracted. Next, unsegmented pulmonary regions corresponding to damaged parenchymal tissue and TB lesions were included. CT biomarkers were extracted by classification of the probability distribution of the intensity of the segmented images into three tissue types: (1) Healthy tissue, parenchyma free from infection; (2) soft diseased tissue, and (3) hard diseased tissue. The probability distribution of tissue intensities was assumed to follow a Gaussian mixture model. The thresholds identifying each region were automatically computed using an expectation-maximization algorithm. RESULTS: The estimated longitudinal course of TB infection shows that subjects that have followed the same antibiotic treatment present a similar response (relative change in the diseased volume) with respect to baseline. More interestingly, the correlation between the diseased volume (soft tissue + hard tissue), which was manually delineated by an expert, and the automatically extracted volume with the proposed method was very strong (R2 ≈ 0.8). CONCLUSIONS: We present a methodology that is suitable for automatic extraction of a radiological biomarker from CT images for TB disease burden. The method could be used to describe the longitudinal evolution of Mtb infection in a clinical trial devoted to the design of new drugs.

Improved quantification for local regions of interest in preclinical PET imaging.

In Positron Emission Tomography, there are several causes of quantitative inaccuracy, such as partial volume or spillover effects. The impact of these effects is greater when using radionuclides that have a large positron range, e.g. (68)Ga and (124)I, which have been increasingly used in the clinic. We have implemented and evaluated a local projection algorithm (LPA), originally evaluated for SPECT, to compensate for both partial-volume and spillover effects in PET. This method is based on the use of a high-resolution CT or MR image, co-registered with a PET image, which permits a high-resolution segmentation of a few tissues within a volume of interest (VOI) centered on a region within which tissue-activity values need to be estimated. The additional boundary information is used to obtain improved activity estimates for each tissue within the VOI, by solving a simple inversion problem. We implemented this algorithm for the preclinical Argus PET/CT scanner and assessed its performance using the radionuclides (18)F, (68)Ga and (124)I. We also evaluated and compared the results obtained when it was applied during the iterative reconstruction, as well as after the reconstruction as a postprocessing procedure. In addition, we studied how LPA can help to reduce the 'spillover contamination', which causes inaccurate quantification of lesions in the immediate neighborhood of large, 'hot' sources. Quantification was significantly improved by using LPA, which provided more accurate ratios of lesion-to-background activity concentration for hot and cold regions. For (18)F, the contrast was improved from 3.0 to 4.0 in hot lesions (when the true ratio was 4.0) and from 0.16 to 0.06 in cold lesions (true ratio = 0.0), when using the LPA postprocessing. Furthermore, activity values estimated within the VOI using LPA during reconstruction were slightly more accurate than those obtained by post-processing, while also visually improving the image contrast and uniformity within the VOI.

Development and validation of an open source quantification tool for DSC-MRI studies.

MOTIVATION: This work presents the development of an open source tool for the quantification of dynamic susceptibility-weighted contrast-enhanced (DSC) perfusion studies. The development of this tool is motivated by the lack of open source tools implemented on open platforms to allow external developers to implement their own quantification methods easily and without the need of paying for a development license. MATERIALS AND METHODS: This quantification tool was developed as a plugin for the ImageJ image analysis platform using the Java programming language. A modular approach was used in the implementation of the components, in such a way that the addition of new methods can be done without breaking any of the existing functionalities. For the validation process, images from seven patients with brain tumors were acquired and quantified with the presented tool and with a widely used clinical software package. The resulting perfusion parameters were then compared. RESULTS: Perfusion parameters and the corresponding parametric images were obtained. When no gamma-fitting is used, an excellent agreement with the tool used as a gold-standard was obtained (R(2)>0.8 and values are within 95% CI limits in Bland-Altman plots). CONCLUSION: An open source tool that performs quantification of perfusion studies using magnetic resonance imaging has been developed and validated using a clinical software package. It works as an ImageJ plugin and the source code has been published with an open source license.

Modification of the TASMIP x-ray spectral model for the simulation of microfocus x-ray sources.

PURPOSE: The availability of accurate and simple models for the estimation of x-ray spectra is of great importance for system simulation, optimization, or inclusion of photon energy information into data processing. There is a variety of publicly available tools for estimation of x-ray spectra in radiology and mammography. However, most of these models cannot be used directly for modeling microfocus x-ray sources due to differences in inherent filtration, energy range and/or anode material. For this reason the authors propose in this work a new model for the simulation of microfocus spectra based on existing models for mammography and radiology, modified to compensate for the effects of inherent filtration and energy range. METHODS: The authors used the radiology and mammography versions of an existing empirical model [tungsten anode spectral model interpolating polynomials (TASMIP)] as the basis of the microfocus model. First, the authors estimated the inherent filtration included in the radiology model by comparing the shape of the spectra with spectra from the mammography model. Afterwards, the authors built a unified spectra dataset by combining both models and, finally, they estimated the parameters of the new version of TASMIP for microfocus sources by calibrating against experimental exposure data from a microfocus x-ray source. The model was validated by comparing estimated and experimental exposure and attenuation data for different attenuating materials and x-ray beam peak energy values, using two different x-ray tubes. RESULTS: Inherent filtration for the radiology spectra from TASMIP was found to be equivalent to 1.68 mm Al, as compared to spectra obtained from the mammography model. To match the experimentally measured exposure data the combined dataset required to apply a negative filtration of about 0.21 mm Al and an anode roughness of 0.003 mm W. The validation of the model against real acquired data showed errors in exposure and attenuation in line with those reported for other models for radiology or mammography. CONCLUSIONS: A new version of the TASMIP model for the estimation of x-ray spectra in microfocus x-ray sources has been developed and validated experimentally. Similarly to other versions of TASMIP, the estimation of spectra is very simple, involving only the evaluation of polynomial expressions.

Dual-exposure technique for extending the dynamic range of x-ray flat panel detectors.

This work presents an approach to extend the dynamic range of x-ray flat panel detectors by combining two acquisitions of the same sample taken with two different x-ray photon flux levels and the same beam spectral configuration. In order to combine both datasets, the response of detector pixels was modelled in terms of mean and variance using a linear model. The model was extended to take into account the effect of pixel saturation. We estimated a joint probability density function (j-pdf) of the pixel values by assuming that each dataset follows an independent Gaussian distribution. This j-pdf was used for estimating the final pixel value of the high-dynamic-range dataset using a maximum likelihood method. The suitability of the pixel model for the representation of the detector signal was assessed using experimental data from a small-animal cone-beam micro-CT scanner equipped with a flat panel detector. The potential extension in dynamic range offered by our method was investigated for generic flat panel detectors using analytical expressions and simulations. The performance of the proposed dual-exposure approach in realistic imaging environments was compared with that of a regular single-exposure technique using experimental data from two different phantoms. Image quality was assessed in terms of signal-to-noise ratio, contrast, and analysis of profiles drawn on the images. The dynamic range, measured as the ratio between the exposure for saturation and the exposure equivalent to instrumentation noise, was increased from 76.9 to 166.7 when using our method. Dual-exposure results showed higher contrast-to-noise ratio and contrast resolution than the single-exposure acquisitions for the same x-ray dose. In addition, image artifacts were reduced in the combined dataset. This technique to extend the dynamic range of the detector without increasing the dose is particularly suited to image samples that contain both low and high attenuation regions.

Positron range estimations with PeneloPET.

Technical advances towards high resolution PET imaging try to overcome the inherent physical limitations to spatial resolution. Positrons travel in tissue until they annihilate into the two gamma photons detected. This range is the main detector-independent contribution to PET imaging blurring. To a large extent, it can be remedied during image reconstruction if accurate estimates of positron range are available. However, the existing estimates differ, and the comparison with the scarce experimental data available is not conclusive. In this work we present positron annihilation distributions obtained from Monte Carlo simulations with the PeneloPET simulation toolkit, for several common PET isotopes ((18)F, (11)C, (13)N, (15)O, (68)Ga and (82)Rb) in different biological media (cortical bone, soft bone, skin, muscle striated, brain, water, adipose tissue and lung). We compare PeneloPET simulations against experimental data and other simulation results available in the literature. To this end the different positron range representations employed in the literature are related to each other by means of a new parameterization for positron range profiles. Our results are generally consistent with experiments and with most simulations previously reported with differences of less than 20% in the mean and maximum range values. From these results, we conclude that better experimental measurements are needed, especially to disentangle the effect of positronium formation in positron range. Finally, with the aid of PeneloPET, we confirm that scaling approaches can be used to obtain universal, material and isotope independent, positron range profiles, which would considerably simplify range correction.

Monte Carlo study of the effects of system geometry and antiscatter grids on cone-beam CT scatter distributions.

PURPOSE: The proliferation of cone-beam CT (CBCT) has created interest in performance optimization, with x-ray scatter identified among the main limitations to image quality. CBCT often contends with elevated scatter, but the wide variety of imaging geometry in different CBCT configurations suggests that not all configurations are affected to the same extent. Graphics processing unit (GPU) accelerated Monte Carlo (MC) simulations are employed over a range of imaging geometries to elucidate the factors governing scatter characteristics, efficacy of antiscatter grids, guide system design, and augment development of scatter correction. METHODS: A MC x-ray simulator implemented on GPU was accelerated by inclusion of variance reduction techniques (interaction splitting, forced scattering, and forced detection) and extended to include x-ray spectra and analytical models of antiscatter grids and flat-panel detectors. The simulator was applied to small animal (SA), musculoskeletal (MSK) extremity, otolaryngology (Head), breast, interventional C-arm, and on-board (kilovoltage) linear accelerator (Linac) imaging, with an axis-to-detector distance (ADD) of 5, 12, 22, 32, 60, and 50 cm, respectively. Each configuration was modeled with and without an antiscatter grid and with (i) an elliptical cylinder varying 70-280 mm in major axis; and (ii) digital murine and anthropomorphic models. The effects of scatter were evaluated in terms of the angular distribution of scatter incident upon the detector, scatter-to-primary ratio (SPR), artifact magnitude, contrast, contrast-to-noise ratio (CNR), and visual assessment. RESULTS: Variance reduction yielded improvements in MC simulation efficiency ranging from ∼17-fold (for SA CBCT) to ∼35-fold (for Head and C-arm), with the most significant acceleration due to interaction splitting (∼6 to ∼10-fold increase in efficiency). The benefit of a more extended geometry was evident by virtue of a larger air gap-e.g., for a 16 cm diameter object, the SPR reduced from 1.5 for ADD = 12 cm (MSK geometry) to 1.1 for ADD = 22 cm (Head) and to 0.5 for ADD = 60 cm (C-arm). Grid efficiency was higher for configurations with shorter air gap due to a broader angular distribution of scattered photons-e.g., scatter rejection factor ∼0.8 for MSK geometry versus ∼0.65 for C-arm. Grids reduced cupping for all configurations but had limited improvement on scatter-induced streaks and resulted in a loss of CNR for the SA, Breast, and C-arm. Relative contribution of forward-directed scatter increased with a grid (e.g., Rayleigh scatter fraction increasing from ∼0.15 without a grid to ∼0.25 with a grid for the MSK configuration), resulting in scatter distributions with greater spatial variation (the form of which depended on grid orientation). CONCLUSIONS: A fast MC simulator combining GPU acceleration with variance reduction provided a systematic examination of a range of CBCT configurations in relation to scatter, highlighting the magnitude and spatial uniformity of individual scatter components, illustrating tradeoffs in CNR and artifacts and identifying the system geometries for which grids are more beneficial (e.g., MSK) from those in which an extended geometry is the better defense (e.g., C-arm head imaging). Compact geometries with an antiscatter grid challenge assumptions of slowly varying scatter distributions due to increased contribution of Rayleigh scatter.

Improved dead-time correction for PET scanners: application to small-animal PET.

Pile-up and dead-time are two main causes of nonlinearity in the response of a PET scanner as a function of activity in the field of view (FOV). For a given scanner and acquisition system, pile-up effects depend on the material and size of the object being imaged and on the distribution of activity inside and outside the FOV, because these factors change the singles-to-coincidences ratio (SCR). Thus, it is difficult to devise an accurate correction that would be valid for any acquisition. In this work, we demonstrate a linear relationship between SCR and effective dead-time, which measures the effects of both dead-time (losses) and pile-up (gains and losses). This relationship allows us to propose a simple method to accurately estimate dead-time and pile-up corrections using only two calibration acquisitions with, respectively, a high and low SCR. The method has been tested with simulations and experimental data for two different scanner geometries: a scanner with large area detectors and no pile-up rejection, and a scanner composed of two full rings of smaller detectors. Our results show that the SCR correction method is accurate within 7%, even for high activities in the FOV, and avoids the bias of the standard single-parameter method.

Massively parallelizable list-mode reconstruction using a Monte Carlo-based elliptical Gaussian model.

PURPOSE: A fully three-dimensional (3D) massively parallelizable list-mode ordered-subsets expectation-maximization (LM-OSEM) reconstruction algorithm has been developed for high-resolution PET cameras. System response probabilities are calculated online from a set of parameters derived from Monte Carlo simulations. The shape of a system response for a given line of response (LOR) has been shown to be asymmetrical around the LOR. This work has been focused on the development of efficient region-search techniques to sample the system response probabilities, which are suitable for asymmetric kernel models, including elliptical Gaussian models that allow for high accuracy and high parallelization efficiency. The novel region-search scheme using variable kernel models is applied in the proposed PET reconstruction algorithm. METHODS: A novel region-search technique has been used to sample the probability density function in correspondence with a small dynamic subset of the field of view that constitutes the region of response (ROR). The ROR is identified around the LOR by searching for any voxel within a dynamically calculated contour. The contour condition is currently defined as a fixed threshold over the posterior probability, and arbitrary kernel models can be applied using a numerical approach. The processing of the LORs is distributed in batches among the available computing devices, then, individual LORs are processed within different processing units. In this way, both multicore and multiple many-core processing units can be efficiently exploited. Tests have been conducted with probability models that take into account the noncolinearity, positron range, and crystal penetration effects, that produced tubes of response with varying elliptical sections whose axes were a function of the crystal's thickness and angle of incidence of the given LOR. The algorithm treats the probability model as a 3D scalar field defined within a reference system aligned with the ideal LOR. RESULTS: This new technique provides superior image quality in terms of signal-to-noise ratio as compared with the histogram-mode method based on precomputed system matrices available for a commercial small animal scanner. Reconstruction times can be kept low with the use of multicore, many-core architectures, including multiple graphic processing units. CONCLUSIONS: A highly parallelizable LM reconstruction method has been proposed based on Monte Carlo simulations and new parallelization techniques aimed at improving the reconstruction speed and the image signal-to-noise of a given OSEM algorithm. The method has been validated using simulated and real phantoms. A special advantage of the new method is the possibility of defining dynamically the cut-off threshold over the calculated probabilities thus allowing for a direct control on the trade-off between speed and quality during the reconstruction.

Misalignments calibration in small-animal PET scanners based on rotating planar detectors and parallel-beam geometry.

Technological advances have improved the assembly process of PET detectors, resulting in quite small mechanical tolerances. However, in high-spatial-resolution systems, even submillimetric misalignments of the detectors may lead to a notable degradation of image resolution and artifacts. Therefore, the exact characterization of misalignments is critical for optimum reconstruction quality in such systems. This subject has been widely studied for CT and SPECT scanners based on cone beam geometry, but this is not the case for PET tomographs based on rotating planar detectors. The purpose of this work is to analyze misalignment effects in these systems and to propose a robust and easy-to-implement protocol for geometric characterization. The result of the proposed calibration method, which requires no more than a simple calibration phantom, can then be used to generate a correct 3D-sinogram from the acquired list mode data.

A method for small-animal PET/CT alignment calibration.

Small-animal positron-emission tomography/computed tomography (PET/CT) scanners provide anatomical and molecular imaging, which enables the joint visualization and analysis of both types of data. A proper alignment calibration procedure is essential for small-animal imaging since resolution is much higher than that in human devices. This work presents an alignment phantom and two different calibration methods that provide a reliable and repeatable measurement of the spatial geometrical alignment between the PET and the CT subsystems of a hybrid scanner. The phantom can be built using laboratory materials, and it is meant to estimate the rigid spatial transformation that aligns both modalities. It consists of three glass capillaries filled with a positron-emitter solution and positioned in a non-coplanar triangular geometry inside the system field of view. The calibration methods proposed are both based on automatic line detection, but with different approaches to calculate the transformation of the lines between both modalities. Our results show an average accuracy of the alignment estimation of 0.39 mm over the whole field of view.

Software architecture for multi-bed FDK-based reconstruction in X-ray CT scanners.

Most small-animal X-ray computed tomography (CT) scanners are based on cone-beam geometry with a flat-panel detector orbiting in a circular trajectory. Image reconstruction in these systems is usually performed by approximate methods based on the algorithm proposed by Feldkamp et al. (FDK). Besides the implementation of the reconstruction algorithm itself, in order to design a real system it is necessary to take into account numerous issues so as to obtain the best quality images from the acquired data. This work presents a comprehensive, novel software architecture for small-animal CT scanners based on cone-beam geometry with circular scanning trajectory. The proposed architecture covers all the steps from the system calibration to the volume reconstruction and conversion into Hounsfield units. It includes an efficient implementation of an FDK-based reconstruction algorithm that takes advantage of system symmetries and allows for parallel reconstruction using a multiprocessor computer. Strategies for calibration and artifact correction are discussed to justify the strategies adopted. New procedures for multi-bed misalignment, beam-hardening, and Housfield units calibration are proposed. Experiments with phantoms and real data showed the suitability of the proposed software architecture for an X-ray small animal CT based on cone-beam geometry.

Fluorescence diffuse optical tomography using the split Bregman method.

PURPOSE: Standard image reconstruction methods for fluorescence Diffuse Optical Tomography (fDOT) generally make use of L2-regularization. A better choice is to replace the L2 by a total variation functional that effectively removes noise while preserving edges. Among the wide range of approaches available, the recently appeared Split Bregman method has been shown to be optimal and efficient. Furthermore, additional constraints can be easily included. We propose the use of the Split Bregman method to solve the image reconstruction problem for fDOT with a nonnegativity constraint that imposes the reconstructed concentration of fluorophore to be positive. METHODS: The proposed method is tested with simulated and experimental data, and results are compared with those yielded by an equivalent unconstrained optimization approach based on Gauss-Newton (GN) method, in which the negative part of the solution is projected to zero after each iteration. In addition, the method dependence on the parameters that weigh data fidelity and nonnegativity constraints is analyzed. RESULTS: Split Bregman yielded a reduction of the solution error norm and a better full width at tenth maximum for simulated data, and higher signal-to-noise ratio for experimental data. It is also shown that it led to an optimum solution independently of the data fidelity parameter, as long as the number of iterations is properly selected, and that there is a linear relation between the number of iterations and the inverse of the data fidelity parameter. CONCLUSIONS: Split Bregman allows the addition of a nonnegativity constraint leading to improve image quality.

Data acquisition electronics for gamma ray emission tomography using width-modulated leading-edge discriminators.

We present a new high-performance and low-cost approach for implementing radiation detection acquisition systems. The basic elements used are charge-integrating ADCs and a set of components encapsulated in an HDL (hardware definition language) library which makes it possible to implement several acquisition tasks such as time pickoff and coincidence detection using a new and simple trigger technique that we name WMLET (width-modulated leading-edge timing). As proof of concept, a 32-channel hybrid PET/SPECT acquisition system based on these elements was developed and tested. This demonstrator consists of a master module responsible for the generation and distribution of trigger signals, 2 x 16-channel ADC cards (12-bit resolution) for data digitization and a 32-bit digital I/O PCI card for handling data transmission to a personal computer. System characteristics such as linearity, maximum transmission rates or timing resolution in coincidence mode were evaluated with test and real detector signals. Imaging capabilities of the prototype were also evaluated using different detector configurations. The performance tests showed that this implementation is able to handle data rates in excess of 600k events s(-1) when acquiring simultaneously 32 channels (96-byte events). ADC channel linearity is >98.5% in energy quantification. Time resolution in PET mode for the tested configurations ranges from 3.64 ns FWHM to 7.88 ns FWHM when signals from LYSO-based detectors are used. The measured energy resolution matched the expected values for the detectors evaluated and single elements of crystal matrices can be neatly separated in the acquired flood histograms.

Design and performance evaluation of a coplanar multimodality scanner for rodent imaging.

This work reports on the development and performance evaluation of the VrPET/CT, a new multimodality scanner with coplanar geometry for in vivo rodent imaging. The scanner design is based on a partial-ring PET system and a small-animal CT assembled on a rotatory gantry without axial displacement between the geometric centers of both fields of view (FOV). We report on the PET system performance based on the NEMA NU-4 protocol; the performance characteristics of the CT component are not included herein. The accuracy of inter-modality alignment and the imaging capability of the whole system are also evaluated on phantom and animal studies. Tangential spatial resolution of PET images ranged between 1.56 mm at the center of the FOV and 2.46 at a radial offset of 3.5 cm. The radial resolution varies from 1.48 mm to 1.88 mm, and the axial resolution from 2.34 mm to 3.38 mm for the same positions. The energy resolution was 16.5% on average for the entire system. The absolute coincidence sensitivity is 2.2% for a 100-700 keV energy window with a 3.8 ns coincident window. The scatter fraction values for the same settings were 11.45% for a mouse-sized phantom and 23.26% for a rat-sized phantom. The peak noise equivalent count rates were also evaluated for those phantoms obtaining 70.8 kcps at 0.66 MBq/cc and 31.5 kcps at 0.11 MBq/cc, respectively. The accuracy of inter-modality alignment is below half the PET resolution, and the image quality of biological specimens agrees with measured performance parameters. The assessment presented in this study shows that the VrPET/CT system is a good performance small-animal imager, while the cost derived from a partial ring detection system is substantially reduced as compared with a full-ring PET tomograph.

Sinogram bow-tie filtering in FBP PET reconstruction.

Low-pass filtering of sinograms in the radial direction is the most common practice to limit noise amplification in filtered back projection (FBP) reconstruction of positron emission tomography studies. Other filtering strategies have been proposed to prevent the loss in resolution due to low-pass radial filters, although results have been diverse. Using the well-known properties of the Fourier transform of a sinogram, the authors defined a binary mask that matches the expected shape of the support region in the Fourier domain of the sinogram ("bow tie"). This mask was smoothed by a convolution with a ten-point Gaussian kernel which not only avoids ringing but also introduces a pre-emphasis at low frequencies. A new filtering scheme for FBP is proposed, comprising this smoothed bow-tie filter combined with a standard radial filter and an axial filter. The authors compared the performance of the bow-tie filtering scheme with that of other previously reported methods: Standard radial filtering, angular filtering, and stackgram-domain filtering. All the quantitative data in the comparisons refer to a baseline reconstruction using a ramp filter only. When using the smallest size of the Gaussian kernel in the stackgram domain, the authors achieved a noise reduction of 33% at the cost of degrading radial and tangential resolutions (14.5% and 16%, respectively, for cubic interpolation). To reduce the noise by 30%, the angular filter produced a larger degradation of contrast (3%) and tangential resolution (46% at 10 mm from the center of the field of view) and showed noticeable artifacts in the form of circular blurring dependent on the distance to the center of the field of view. For a similar noise reduction (33%), the proposed bow-tie filtering scheme yielded optimum results in resolution (gain in radial resolution of 10%) and contrast (1% increase) when compared with any of the other filters alone. Experiments with rodent images showed noticeable image quality enhancement when using the proposed bow-tie filtering scheme.

Assessment of airway distribution of transnasal solutions in mice by PET/CT imaging.

PURPOSE: Transnasal administration is one of the most common routes for allergen challenge in mouse models of airway diseases. Although this technique is widely used, neither the amount of allergen that reaches the lung nor its airway distribution has been well established. We used positron emission tomography (PET) and computed tomography (CT) to examine the anatomical distribution of a solution containing a tracer immediately after transnasal delivery and to determine the possible influence of age and administered volume. PROCEDURES: Forty-six female BALB/c mice were divided into three groups according to instillation volume and age: (A) 15 microl, 8-10 weeks old (N = 10), (B) 30 microl, 8-10 weeks old (N = 20), and (C) 30 microl, 32 weeks old (N = 16). Anesthetized animals underwent a dynamic scan in a dedicated small-animal PET scanner immediately after transnasal administration of a solution containing (18)FDG. Regions of interest were used to obtain quantitative data. Animals were also imaged with a small-animal CT scanner to obtain complementary anatomical information. RESULTS: Mean +/- SD (5.69 +/- 4.51%) of the solution administered reached the lungs in group A, 41.84 +/- 8.03% in group B, and 36.65 +/- 16.15% in group C. A comparable percentage was delivered to the left and right lungs in all the groups. Analysis of variance revealed a significant difference between the groups in the proportion of the solution that reached the lungs depending on the injection volume (P < 0.001), but not depending on animal age. CONCLUSIONS: In this first report on quantitative imaging by PET and CT in small animals, we confirmed the suitability of the transnasal route with an instilled volume of 30 microl delivering fluids into the lower airways, although only about 40% of the dose reaches the lungs.

PeneloPET, a Monte Carlo PET simulation tool based on PENELOPE: features and validation.

Monte Carlo simulations play an important role in positron emission tomography (PET) imaging, as an essential tool for the research and development of new scanners and for advanced image reconstruction. PeneloPET, a PET-dedicated Monte Carlo tool, is presented and validated in this work. PeneloPET is based on PENELOPE, a Monte Carlo code for the simulation of the transport in matter of electrons, positrons and photons, with energies from a few hundred eV to 1 GeV. PENELOPE is robust, fast and very accurate, but it may be unfriendly to people not acquainted with the FORTRAN programming language. PeneloPET is an easy-to-use application which allows comprehensive simulations of PET systems within PENELOPE. Complex and realistic simulations can be set by modifying a few simple input text files. Different levels of output data are available for analysis, from sinogram and lines-of-response (LORs) histogramming to fully detailed list mode. These data can be further exploited with the preferred programming language, including ROOT. PeneloPET simulates PET systems based on crystal array blocks coupled to photodetectors and allows the user to define radioactive sources, detectors, shielding and other parts of the scanner. The acquisition chain is simulated in high level detail; for instance, the electronic processing can include pile-up rejection mechanisms and time stamping of events, if desired. This paper describes PeneloPET and shows the results of extensive validations and comparisons of simulations against real measurements from commercial acquisition systems. PeneloPET is being extensively employed to improve the image quality of commercial PET systems and for the development of new ones.

Detection of visual activation in the rat brain using 2-deoxy-2-[(18)F]fluoro-D: -glucose and statistical parametric mapping (SPM).

PURPOSE: This study was designed to assess changes in brain glucose metabolism in rats after visual stimulation. MATERIALS AND METHODS: We sought to determine whether visual activation in the rat brain could be detected using a small-animal positron emission tomography (PET) scanner and 2-deoxy-2-[(18)F]fluoro-D: -glucose (FDG). Eleven rats were divided into two groups: (a) five animals exposed to ambient light and (b) six animals stimulated by stroboscopic light (10 Hz) with one eye covered. Rats were injected with FDG and, after 45 min of visual stimulation, were sacrificed and scanned for 90 min in a dedicated PET tomograph. Images were reconstructed by a three-dimensional ordered subset expectation maximization algorithm (1.8 mm full width at half maximum). A region-of-interest (ROI) analysis was performed on 14 brain structures drawn on coronal sections. Statistical parametric mapping (SPM) adapted for small animals was also carried out. Additionally, the brains of three rats were sliced into 20-microm sections for autoradiography. RESULTS: Analysis of ROI data revealed significant differences between groups in the right superior colliculus, right thalamus, and brainstem (p < or = 0.05). SPM detected the same areas as the ROI approach. Autoradiographs confirmed the existence of hyperactivation in the left superior colliculus and auditory cortex. CONCLUSIONS: To our knowledge, this is the first report that uses FDG-PET and SPM analysis to show changes in rat brain glucose metabolism after a visual stimulus.