Photon Attenuation Correction Research Articles

A Method to Locate Radio-Frequency Coils Using a CT-Based Template for a More Accurate Photon Attenuation Correction in PET/MRI

A Method to Locate Radio-Frequency Coils Using a CT-Based Template for a More Accurate Photon Attenuation Correction in PET/MRI

Automatic attenuation map estimation from SPECT data only for brain perfusion scans using convolutional neural networks

In clinical brain SPECT, correction for photon attenuation in the patient is essential to obtain images which provide quantitative information on the regional activity concentration per unit volume (kBq.). This correction generally requires an attenuation map ( map) denoting the attenuation coefficient at each voxel which is often derived from a CT or MRI scan. However, such an additional scan is not always available and the method may suffer from registration errors. Therefore, we propose a SPECT-only-based strategy for map estimation that we apply to a stationary multi-pinhole clinical SPECT system (G-SPECT-I) for 99mTc-HMPAO brain perfusion imaging. The method is based on the use of a convolutional neural network (CNN) and was validated with Monte Carlo simulated scans. Data acquired in list mode was used to employ the energy information of both primary and scattered photons to obtain information about the tissue attenuation as much as possible. Multiple SPECT reconstructions were performed from different energy windows over a large energy range. Locally extracted 4D SPECT patches (three spatial plus one energy dimension) were used as input for the CNN which was trained to predict the attenuation coefficient of the corresponding central voxel of the patch. Results show that Attenuation Correction using the Ground Truth maps (GT-AC) or using the CNN estimated maps (CNN-AC) achieve comparable accuracy. This was confirmed by a visual assessment as well as a quantitative comparison; the mean deviation from the GT-AC when using the CNN-AC is within 1.8% for the standardized uptake values in all brain regions. Therefore, our results indicate that a CNN-based method can be an automatic and accurate tool for SPECT attenuation correction that is independent of attenuation data from other imaging modalities or human interpretations about head contours.

Accuracy and precision of zero-echo-time, single- and multi-atlas attenuation correction for dynamic [11C]PE2I PET-MR brain imaging

BackgroundA valid photon attenuation correction (AC) method is instrumental for obtaining quantitatively correct PET images. Integrated PET/MR systems provide no direct information on attenuation, and novel methods for MR-based AC (MRAC) are still under investigation. Evaluations of various AC methods have mainly focused on static brain PET acquisitions. In this study, we determined the validity of three MRAC methods in a dynamic PET/MR study of the brain.MethodsNine participants underwent dynamic brain PET/MR scanning using the dopamine transporter radioligand [11C]PE2I. Three MRAC methods were evaluated: single-atlas (Atlas), multi-atlas (MaxProb) and zero-echo-time (ZTE). The 68Ge-transmission data from a previous stand-alone PET scan was used as reference method. Parametric relative delivery (R1) images and binding potential (BPND) maps were generated using cerebellar grey matter as reference region. Evaluation was based on bias in MRAC maps, accuracy and precision of [11C]PE2I BPND and R1 estimates, and [11C]PE2I time-activity curves. BPND was examined for striatal regions and R1 in clusters of regions across the brain.ResultsFor BPND, ZTE-MRAC showed the highest accuracy (bias < 2%) in striatal regions. Atlas-MRAC exhibited a significant bias in caudate nucleus (− 12%) while MaxProb-MRAC revealed a substantial, non-significant bias in the putamen (9%). R1 estimates had a marginal bias for all MRAC methods (− 1.0–3.2%). MaxProb-MRAC showed the largest intersubject variability for both R1 and BPND. Standardized uptake values (SUV) of striatal regions displayed the strongest average bias for ZTE-MRAC (~ 10%), although constant over time and with the smallest intersubject variability. Atlas-MRAC had highest variation in bias over time (+10 to − 10%), followed by MaxProb-MRAC (+5 to − 5%), but MaxProb showed the lowest mean bias. For the cerebellum, MaxProb-MRAC showed the highest variability while bias was constant over time for Atlas- and ZTE-MRAC.ConclusionsBoth Maxprob- and ZTE-MRAC performed better than Atlas-MRAC when using a 68Ge transmission scan as reference method. Overall, ZTE-MRAC showed the highest precision and accuracy in outcome parameters of dynamic [11C]PE2I PET analysis with use of kinetic modelling.

Tomographic 99mTc radioactivity quantification in three-dimensional printed polymeric phantoms with bioinspired geometries

ObjectiveTo characterize quantitatively a single-photon emission computed tomography/computed tomography (SPECT/CT) system through experiments carried out on three-dimensional (3D) printed phantoms with bioinspired anatomic geometries. Materials and methodsThe sensitivity factor of the SPECT/CT system was obtained after correcting the image quality degrading effects. Photon attenuation correction was carried out through CT attenuation maps. Scattering was corrected by 2 methods. The first method introduces a compensation factor based on photopeak and scatter window widths, which corrects the 3D image during iterative reconstruction algorithm. The second estimates scatter within a photopeak window, which is applied projection by projection before the 3D reconstruction. Partial volume effect was corrected using a calibration method based on spheres of different sizes. A liver-like and a multisphere phantom were designed and 3D printed. SPECT/CT images of the designed phantoms filled with different activities were acquired and quantified. A correlation coefficient based on linear regression was calculated with the aim of comparing proposed methods. ResultsThe quantitative SPECT methodology allowed quantification with percentage errors between 1.01-8.96%. The activity measurements validated the 99mTc quantitative performance of the SPECT/CT system. The statistical analysis showed correlation coefficients of 1.00 for the first method and 0.99 for the second one. ConclusionThe implementation of two alternative methodologies for the calculation of sensitivity factors, allowed to correct image quality degrading effects, satisfying all the suggested stages in imaging quantification. The designed phantoms allowed to assess the characterization of a SPECT/CT system. This study demonstrates the effectiveness of 3D printing to develop bioinspired phantoms for the quantitative assessment of 99mTc imaging.

Pseudo CT Image Synthesis and Bone Segmentation From MR Images Using Adversarial Networks With Residual Blocks for MR-Based Attenuation Correction of Brain PET Data

For photon attenuation correction, current positron emission tomography systems combined with magnetic resonance imaging (PET/MR) imaging systems typically use methods based on MR image segmentation with subsequent assignment of empirical attenuation coefficients in PET image reconstruction. Delineation of bone in MR images has been challenging, especially in the head and neck areas, due to the difficulty of separating bone from air. In this article, we study deep learning techniques that assist the MR-based attenuation correction (MRAC) process for PET/MR systems, with focus on the brain region. We use a generative adversarial network (GAN) with residual blocks in a conditional setting for this task. We studied the performance of the designed network on image translation and segmentation tasks, which are essential for MRAC. For both tasks, the network generates pseudo CT images that resemble real CT images with normalized pixel value difference of around 5% and structural similarity (SSIM) index of around 0.8.

Mixture model based joint-MAP reconstruction of attenuation and activity maps in TOF-PET

A challenge to have quantitative positron emission tomography (PET) images is to provide an accurate and patient-specific photon attenuation correction. In PET/MR scanners, the nature of MR signals and hardware limitations have led to a real challenge on the attenuation map extraction. Except for a constant factor, the activity and attenuation maps from emission data on TOF-PET system can be determined by the maximum likelihood reconstruction of attenuation and activity approach (MLAA) from emission data. The aim of the present study is to constrain the joint estimations of activity and attenuation approach for PET system using a mixture model prior based on the attenuation map histogram. This novel prior enforces non-negativity and its hyperparameters can be estimated using a mixture decomposition step from the current estimation of the attenuation map. The proposed method can also be helpful on the solving of scaling problem and is capable to assign the predefined regional attenuation coefficients with some degree of confidence to the attenuation map similar to segmentation-based attenuation correction approaches. The performance of the algorithm is studied with numerical and Monte Carlo simulations and a phantom experiment and was compared with MLAA algorithm with and without the smoothing prior. The results demonstrate that the proposed algorithm is capable of producing the cross-talk free activity and attenuation images from emission data. The proposed approach has potential to be a practical and competitive method for joint reconstruction of activity and attenuation maps from emission data on PET/MR and can be integrated on the other methods.

Improved scatter correction with factor analysis for planar and SPECT imaging.

Quantitative nuclear medicine imaging is an increasingly important frontier. In order to achieve quantitative imaging, various interactions of photons with matter have to be modeled and compensated. Although correction for photon attenuation has been addressed by including x-ray CT scans (accurate), correction for Compton scatter remains an open issue. The inclusion of scattered photons within the energy window used for planar or SPECT data acquisition decreases the contrast of the image. While a number of methods for scatter correction have been proposed in the past, in this work, we propose and assess a novel, user-independent framework applying factor analysis (FA). Extensive Monte Carlo simulations for planar and tomographic imaging were performed using the SIMIND software. Furthermore, planar acquisition of two Petri dishes filled with 99mTc solutions and a Jaszczak phantom study (Data Spectrum Corporation, Durham, NC, USA) using a dual head gamma camera were performed. In order to use FA for scatter correction, we subdivided the applied energy window into a number of sub-windows, serving as input data. FA results in two factor images (photo-peak, scatter) and two corresponding factor curves (energy spectra). Planar and tomographic Jaszczak phantom gamma camera measurements were recorded. The tomographic data (simulations and measurements) were processed for each angular position resulting in a photo-peak and a scatter data set. The reconstructed transaxial slices of the Jaszczak phantom were quantified using an ImageJ plugin. The data obtained by FA showed good agreement with the energy spectra, photo-peak, and scatter images obtained in all Monte Carlo simulated data sets. For comparison, the standard dual-energy window (DEW) approach was additionally applied for scatter correction. FA in comparison with the DEW method results in significant improvements in image accuracy for both planar and tomographic data sets. FA can be used as a user-independent approach for scatter correction in nuclear medicine.

Scatter correction for planar and SPECT imaging with factor analysis

Introduction In order to achieve quantitative data in planar and SPECT imaging, various interactions of photons with matter have to be modelled and compensated. Although correction for photon attenuation has been addressed by including X-ray CT scans, accurate correction for Compton scattering remains an open issue. Purpose In this work we propose and assess a novel, user-independent framework applying factor analysis (FA). Materials and methods Extensive Monte Carlo simulations for planar and tomographic imaging were performed using the SIMIND software. Furthermore a Jaszczak phantom study (Data Spectrum Corporation, Durham, NC, USA) and ten clinical 99 m Tc MDP bone studies were performed using a large-field of view scintillation camera (General Electric’s Infinia Hawkeye). In order to use FA for scatter correction, we subdivided the applied energy window into a number of sub-windows, serving as input data. FA results in two factor images (photo-peak, scatter) and two corresponding factor curves (energy spectra). Results The data obtained by FA showed good agreement with the energy spectra, photo-peak and scatter images obtained in all Monte Carlo simulated data sets. For the Jaszczak phantom, without scatter correction, the cold sphere contrast ranged from −47.7 to −4.75, while with FA scatter correction, it ranged from −51.5 to −7.52. The cold sphere sector contrast ranged from −16.6 to −0.18 for non-scatter corrected data, and for the FA data, from −16.6 to −0.23. Conclusion Factor analysis can be used as a user-independent approach for scatter correction in quantitative nuclear medicine imaging. Disclosure Nothing to disclose.

Dixon Sequence with Superimposed Model-Based Bone Compartment Provides Highly Accurate PET/MR Attenuation Correction of the Brain.

Simultaneous PET/MR of the brain is a promising technology for characterizing patients with suspected cognitive impairment or epilepsy. Unlike CT, however, MR signal intensities do not correlate directly with PET photon attenuation correction (AC), and inaccurate radiotracer SUV estimation can limit future PET/MR clinical applications. We tested a novel AC method that supplements standard Dixon-based tissue segmentation with a superimposed model-based bone compartment. We directly compared SUV estimation between MR-based AC and reference CT AC in 16 patients undergoing same-day PET/CT and PET/MR with a single (18)F-FDG dose for suspected neurodegeneration. Three Dixon-based MR AC methods were compared with CT: standard Dixon 4-compartment segmentation alone, Dixon with a superimposed model-based bone compartment, and Dixon with a superimposed bone compartment and linear AC optimized specifically for brain tissue. The brain was segmented using a 3-dimensional T1-weighted volumetric MR sequence, and SUV estimations were compared with CT AC for whole-image, whole-brain, and 91 FreeSurfer-based regions of interest. Modifying the linear AC value specifically for brain and superimposing a model-based bone compartment reduced the whole-brain SUV estimation bias of Dixon-based PET/MR AC by 95% compared with reference CT AC (P < 0.05), resulting in a residual -0.3% whole-brain SUVmean bias. Further, brain regional analysis demonstrated only 3 frontal lobe regions with an SUV estimation bias of 5% or greater (P < 0.05). These biases appeared to correlate with high individual variability in frontal bone thickness and pneumatization. Bone compartment and linear AC modifications result in a highly accurate MR AC method in subjects with suspected neurodegeneration. This prototype MR AC solution appears equivalent to other recently proposed solutions and does not require additional MR sequences and scanning time. These data also suggest that exclusively model-based MR AC approaches may be adversely affected by common individual variations in skull anatomy.

MR-based attenuation correction for PET/MRI neurological studies with continuous-valued attenuation coefficients for bone through a conversion from R2* to CT-Hounsfield units

AimMR-based correction for photon attenuation in PET/MRI remains challenging, particularly for neurological applications requiring quantitation of data. Existing methods are either not sufficiently accurate or are limited by the computation time required. The goal of this study was to develop an MR-based attenuation correction method that accurately separates bone tissue from air and provides continuous-valued attenuation coefficients for bone. Materials and methodsPET/MRI and CT datasets were obtained from 98 subjects (mean age [±SD]: 66yrs [±9.8], 57 females) using an IRB-approved protocol and with informed consent. Subjects were injected with 352±29MBq of 18F-Florbetapir tracer, and PET acquisitions were begun either immediately or 50min after injection. CT images of the head were acquired separately using a PET/CT system. Dual echo ultrashort echo-time (UTE) images and two-point Dixon images were acquired. Regions of air were segmented via a threshold of the voxel-wise multiplicative inverse of the UTE echo 1 image. Regions of bone were segmented via a threshold of the R2* image computed from the UTE echo 1 and UTE echo 2 images. Regions of fat and soft tissue were segmented using fat and water images decomposed from the Dixon images. Air, fat, and soft tissue were assigned linear attenuation coefficients (LACs) of 0, 0.092, and 0.1cm−1, respectively. LACs for bone were derived from a regression analysis between corresponding R2* and CT values. PET images were reconstructed using the gold standard CT method and the proposed CAR-RiDR method. ResultsThe RiDR segmentation method produces mean Dice coefficient±SD across subjects of 0.75±0.05 for bone and 0.60±0.08 for air. The CAR model for bone LACs greatly improves accuracy in estimating CT values (28.2%±3.0 mean error) compared to the use of a constant CT value (46.9%±5.8, p<10−6). Finally, the CAR-RiDR method provides a low whole-brain mean absolute percent-error (MAPE±SD) in PET reconstructions across subjects of 2.55%±0.86. Regional PET errors were also low and ranged from 0.88% to 3.79% in 24 brain ROIs. ConclusionWe propose an MR-based attenuation correction method (CAR-RiDR) for quantitative PET neurological imaging. The proposed method employs UTE and Dixon images and consists of two novel components: 1) accurate segmentation of air and bone using the inverse of the UTE1 image and the R2* image, respectively and 2) estimation of continuous LAC values for bone using a regression between R2* and CT-Hounsfield units. From our analysis, we conclude that the proposed method closely approaches (<3% error) the gold standard CT-scaled method in PET reconstruction accuracy.

Characterization of Uranium in Bituminized Radioactive Waste Drums by&lt;newline/&gt;Self-Induced X-Ray Fluorescence

This paper reports the experimental qualification of an original uranium characterization method based on fluorescence X-rays induced by the spontaneous gamma emission of bituminized radioactive waste drums. The main 661.7 keV gamma ray following the 137Cs decay produces by Compton scattering in the bituminized matrix an intense photon continuum around 100 keV, i.e. in the uranium X-ray fluorescence region. “Self-induced” X-rays produced without using an external source allow a quantitative assessment of uranium as 137Cs and uranium are homogeneously mixed and distributed in the bituminized matrix. The paper presents the experimental qualification of the method with real waste drums, showing a detection limit well below 1 kg of uranium in 20 min acquisitions while the usual gamma rays of 235U (185 keV) or 238U (1001 keV of 234mPa in the radioactive decay chain) are not detected. The relative uncertainty on the uranium mass assessed by self-induced X-ray fluorescence (SXRF) is about 50%, with a 95% confidence level, taking into account the correction of photon attenuation in the waste matrix. This last indeed contains high atomic numbers elements like uranium, but also barium, in quantities which are not known for each drum. Attenuation is estimated thanks to the peak-to-Compton ratio to limit the corresponding uncertainty. The SXRF uranium masses measured in the real drums are in good agreement with long gamma-ray spectroscopy measurements (1001 keV peak) or with radiochemical analyses.

Quantitative Measurement of In Vivo Tracer Concentration in Rats with Multiplexed Multi-Pinhole SPECT

The goal of this study is to evaluate the quantitative accuracy of measuring in vivo tracer concentrations using multiplexed multi-pinhole microSPECT with CT-based attenuation correction (AC) and simple methods for scatter compensation. Phantom and in vivo rat cardiac images were acquired and reconstructed with no photon AC, with AC only, with attenuation and dual-energy window scatter correction, and using a reduced attenuation coefficient to compensate for scatter. Absolute calibration was also acquired using small sources to minimize self-attenuation and scatter. The phantom tracer concentrations measured with SPECT were compared to dose calibrator measurements. The rats were sacrificed and the cardiac activity measured in vivo was compared to well counter measurements of cardiac activity. With no correction, the tracer concentrations measured in phantoms was as much as 30% below the true value for the largest phantom (52 mm diameter). AC improved the accuracy of quantification, but overestimated activity concentrations by up to 5% for the larger phantoms. With DEW scatter correction, activity concentration measured with SPECT agrees with the dose calibrator measurements to within -2 ±2% and with a reduced attenuation coefficient, the agreement was better than -1 ±1%. In rats injected with <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">99m</sup> Tc-tetrofosmin, SPECT measurements of total cardiac activity were 24±2% below well counter measurements with no corrections, 5±3% above well counter measurements with only attenuation correction, 3±3% below with DEW scatter correction, and 1±3% below with reduced attenuation correction.

Practical estimation method of the optimal scanning protocol for 180° data acquisition in parallel SPECT imaging

In Single Photon Emission Computed Tomography (SPECT) projections over 2π with non-uniform attenuation contain redundant information thus the scanning angle can be reduced to π which can be used to improve the imaging quality. The precondition of this reduction is the application of correction for gamma photon attenuation and the distance dependent spatial resolution of the detector during reconstruction. Despite the existing redundancy in the projections in opposite directions they cannot be treated identically because of statistical differences of the corresponding views. In this work we propose a new approximation method to select an optimal set of views over π with the best statistical properties. Using this set of views during the data acquisition the image quality can be improved. The paper also contains the verification and evaluation results of the proposed approximation method using a mathematical and two realistic physical phantoms.

Classification of bones from MR images in torso PET-MR imaging using a statistical shape model

There have been exclusive features for hybrid PET/MRI systems in comparison with its PET/CT counterpart in terms of reduction of radiation exposure, improved soft-tissue contrast and truly simultaneous and multi-parametric imaging capabilities. However, quantitative imaging on PET/MR is challenged by attenuation of annihilation photons through their pathway. The correction for photon attenuation requires the availability of patient-specific attenuation map, which accounts for the spatial distribution of attenuation coefficients of biological tissues. However, the lack of information on electron density in the MR signal poses an inherent difficulty to the derivation of the attenuation map from MR images. In other words, the MR signal correlates with proton densities and tissue relaxation properties, rather than with electron density and, as such, it is not directly related to attenuation coefficients. In order to derive the attenuation map from MR images at 511keV, various strategies have been proposed and implemented on prototype and commercial PET/MR systems. Segmentation-based methods generate an attenuation map by classification of T1-weighted or high resolution Dixon MR sequences followed by assignment of predefined attenuation coefficients to various tissue types. Intensity-based segmentation approaches fail to include bones in the attenuation map since the segmentation of bones from conventional MR sequences is a difficult task. Most MR-guided attenuation correction techniques ignore bones owing to the inherent difficulties associated with bone segmentation unless specialized MR sequences such as ultra-short echo (UTE) sequence are utilized. In this work, we introduce a new technique based on statistical shape modeling to segment bones and generate a four-class attenuation map. Our segmentation approach requires a torso bone shape model based on principle component analysis (PCA). A CT-based training set including clearly segmented bones of the torso region of 20 clinical studies was designed. Using this training set, a bone atlas was trained taking advantage of PCA analysis. Our active shape segmentation technique uses the trained shape model to segment bones from user defined initial seed points. The segmentation algorithm was evaluated using 10 clinical datasets (aligned MR and CT pairs). The resulting attenuation maps were compared to corresponding attenuation maps derived from CT resulting in a mean relative difference less than 7%.

Tissue Classification for PET/MRI Attenuation Correction Using Conditional Random Field and Image Fusion

Photon attenuation correction is a challenging task in the emerging hybrid PET/MRI medical imaging techniques because of the missing link between tissue attenuation coefficient and MRI signal. MRI-based tissue classification methods for attenuation correction have difficulties caused by the significantly different abilities of photon absorption in tissues with similar MRI signal, such as bone and air. We proposed a novel method of integrating the information from MRI and PET emission data to increase the tissue classification accuracy. A classifier based on conditional random field was trained using features extracted from fused MRI and uncorrected PET images. The efficacy of the proposed method was validated quantitatively on synthetic datasets. It was found that the inclusion of PET data improved the classifier’s performance in terms of classification accuracy and PET image reconstruction quality.

Importance of Attenuation Correction (AC) for Small Animal PET Imaging.

The purpose of this study was to investigate whether a correction for annihilation photon attenuation in small objects such as mice is necessary. The attenuation recovery for specific organs and subcutaneous tumors was investigated. A comparison between different attenuation correction methods was performed. Methods: Ten NMRI nude mice with subcutaneous implantation of human breast cancer cells (MCF-7) were scanned consecutively in small animal PET and CT scanners (MicroPETTM Focus 120 and ImTek’s MicroCATTM II). CT-based AC, PET-based AC and uniform AC methods were compared. Results: The activity concentration in the same organ with and without AC revealed an overall attenuation recovery of 9–21% for MAP reconstructed images, i.e., SUV without AC could underestimate the true activity at this level. For subcutaneous tumors, the attenuation was 13 ± 4% (9–17%), for kidneys 20 ± 1% (19–21%), and for bladder 18 ± 3% (15–21%). The FBP reconstructed images showed almost the same attenuation levels as the MAP reconstructed images for all organs. Conclusions: The annihilation photons are suffering attenuation even in small subjects. Both PET-based and CT-based are adequate as AC methods. The amplitude of the AC recovery could be overestimated using the uniform map. Therefore, application of a global attenuation factor on PET data might not be accurate for attenuation correction.

Image Quality and Quantitative Accuracy of Small Animal PET Images: Role of Physical Effects Correction and Reconstruction Algorithm

Positron emission tomography (PET) provides important metabolic information about tracer kinetics. Photon attenuation, scattered radiation, spatial resolution limitation and reconstruction algorithm parameters are variables that influence image quality and quantitative accuracy. The objective of this work was to examine the impact of the combined correction of photon attenuation and scatter on the quality and quantitative accuracy of small animal PET images reconstructed using the analytic filtered back projection algorithm (FBP) and the iterative maximum a posteriori probability (MAP) technique at different values of the smoothing parameter β.Methods. A homogenous water phantomandthree different configurations of NEMA image quality phantom were employed using a small animal PET scanner (InveonTM, Siemens medical solutions, Inc). A 20 min PET/CT scans were performed for all phantoms using a list mode acquisition. All Data were sorted into 3D sinograms and reconstructed using 2D FBP after Fourier rebinning and also reconstructed using OSEM3D/MAP (2 iteration OSEM3D and 18 iterations using MAP). Figures of merit used were error of tracer estimate, recovery coefficient, percentage standard deviation, spillover ratio and image noise. Results. Based on object geometry and size, FBP with attenuation and scatter correction provides accurate tracer concentration within the range of 5% and possible improvement can be achieved using MAP reconstruction. Without correction, errors of activity quantitation is significantly high, on average 34.5±7.2% Recovery coefficient was also found to improve with proper tuning of the β value using MAP reconstruction. Spillover ratio indicated some variations between FBP and MAP in air and water compartments, which might be attributed to the performance of the scatter correction. Noise measurements were superior with MAP reconstruction especially at high β values, but this occurs with edge artifacts and slight distortion of some geometric structures. Very small values of the beta have better resolution characteristics but high values have increased smoothing effect and blurring appearance. Conclusion: Attenuation correction is important in small animal PET and allows for reliable tracer estimate. Spatial resolution is improved by MAP technique and low β values provide high resolution capabilities and recovery coefficients but high values improve noise levels. For a given application, proper selection and employment of the smoothing parameter β should be followed. Scatter correction need to be improved especially in large objects or simultaneous acquisition of multiple numbers of animals.

Optimal scanning protocol for 180° data acquisition in parallel SPECT imaging

In Single Photon Emission Computed Tomography (SPECT) projections over 2π with non-uniform attenuation contains redundant information thus the scanning angle can be reduced to π which can be used to improve the imaging quality The precondition of this reduction is the application of correction for gamma photon attenuation and the distance dependent spatial resolution of the detector during reconstruction. Despite the existing redundancy in the projections in opposite directions they cannot be treated identically because of statistical differences of the corresponding views. In this work we propose a new method to select an optimal set of views over π with the best statistical properties. Using this set of views during the data acquisition the image quality can be improved.

595 Evaluation of [18F]PyKYNE-losartan in rats using pet imaging

595 Evaluation of [18F]PyKYNE-losartan in rats using pet imaging

Angular dependence of bremsstrahlung produced by 17.5-keV electrons incident on thick Ag

The bremsstrahlung produced by 17.5-keV electrons incident on a 2415 \ensuremath{\mu}g/cm${}^{2}$ Ag target has been measured at forward angles in the range 0\ifmmode^\circ\else\textdegree\fi{} to 55\ifmmode^\circ\else\textdegree\fi{} relative to the electron beam. The intensity of the thick-target bremsstrahlung produced is presented on an absolute scale at angles in this range, with corrections for detector efficiency, photon attenuation within the target itself, and other factors. After correction for photon attenuation within the target, the results indicate that bremsstrahlung is essentially isotropic. The experimental data are in approximate agreement with both the predictions of a modified version of the Kramers formula and the Monte Carlo program, penelope.