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The aim of this study was to improve image quality of statistical reconstruction by using the 2-D system matrix (SM) trained with an artificial neural network (ANN). For training the ANN SM (SMANN), the inputs of ANN, the digital images, were generated by scanning the mini-deluxe cold spot phantom at ten different orientations using an optical scanner (resolution: 0.01 mm/pixel). The desired outputs were generated by acquiring the projection data with the corresponding angles using the micro positron emission tomography (microPET) R4. In the ANN method, the ADALINE network with a bias vector and a momentum term were used. Moreover, a multiline-source phantom and a four-segment phantom were scanned to obtain the spatial resolutions and the quantitative accuracy for comparison, respectively. A rat FDG microPET image was acquired to compare the difference between the results reconstructed by the microPET's built-in 2-D-ordered subsets expectation maximization algorithm (OSEM), OSEMmicroPET, OSEM by Monte Carlo simulated SM, OSEMSMd, and OSEM by the SMANN, OSEMANN. In the multiline-source experiment, the resolutions of OSEMmicroPET measured at center, 10, and 20 mm from the center were 1.61, 1.78, and 2.30 mm, respectively. The resolutions of OSEMSMd and OSEMANN were 1.24, 1.68, and 1.87, and 1.28, 1.62, and 1.72 mm, respectively. In the results of the four-segment phantom, the sum of absolute error of the truth versus the values reconstructed by OSEMmicroPET, OSEMSMd, and OSEMANN were 1085.53, 913.48, and 435.02, respectively. By interpreting the results of the evaluation, the image quality reconstructed by the SMANN is better than that reconstructed by the original SM. The results indicated that SM can be updated toward ideal SM using ANN for statistical reconstruction.

Medical images usually suffer from a partial volume effect (PVE), which may degrade the accuracy of any quantitative information extracted from the images. Our aim was to recreate accurate radioactivity concentration and time-activity curves (TACs) by microPET R4 quantification using ensemble learning independent component analysis (EL-ICA). We designed a digital cardiac phantom for this simulation and in order to evaluate the ability of EL-ICA to correct the PVE, the simulated images were convoluted using a Gaussian function (FWHM = 1-4 mm). The robustness of the proposed method towards noise was investigated by adding statistical noise (SNR = 2-16). During further evaluation, another set of cardiac phantoms were generated from the reconstructed images, and Poisson noise at different levels was added to the sinogram. In real experiments, four rat microPET images and a number of arterial blood samples were obtained; these were used to estimate the metabolic rate of FDG (MR(FDG)). Input functions estimated using the FastICA method were used for comparison. The results showed that EL-ICA could correct PVE in both the simulated and real cases. After correcting for the PVE, the errors for MR(FDG), when estimated by the EL-ICA method, were smaller than those when TACs were directly derived from the PET images and when the FastICA approach was used.

The serotonin (5-hydroxytryptamine, or 5-HT) transporters (5-HTT) are target-sites for commonly used antidepressants. [(123)I] ADAM is a novel radiotracer that selectively binds the 5-HTT of the central nervous system. The aim for this study was to compare four-parameter model (FPM) with three-parameter model (TPM) from non-invasive reference tissue model (RTM) for 5-HTT quantification using the cerebellum as indirect input function. Furthermore, we compared tracer kinetic model with the tissue ratio (TR) method. The binding potential (BP) values derived from both models were almost the same, but ratio of delivery (R(1)) in TPM had smaller standard deviation than the FPM. There was also significant correlation between BP and specific uptake ratio (SUR). In conclusion, simplified reference tissue model (SRTM) was the better choice because of its stability and convenient implementation for non-invasive quantification of brain SPECT studies. The correlation found between BP and SUR supports the use of TR method for quantification of 5-HTT to avoid arterial sampling in dynamic SPECT scan.

We present a system of image co-registration in small animal study. Marker-based registration is chosen because of its considerable advantage that the fiducial feature is independent of imaging modality. We also experimented with different scanning protocols and different fiducial marker sizes to improve registration accuracy. Co-registration was conducted using rat phantom fixed by stereotactic frame. Overall, the co-registration accuracy was in sub-millimeter level and close to intrinsic system error. Therefore, we conclude that the system is an accurate co-registration method to be used in small animal studies.

The objective of this study was to use a mixture of Poisson (MOP) model expectation maximum (EM) algorithm for segmenting microPET images. Simulated rat phantoms with partial volume effect and different noise levels were generated to evaluate the performance of the method. The partial volume correction was performed using an EM deblurring method before the segmentation. The EM–MOP outperforms the EM–MOP in terms of the estimated spatial accuracy, quantitative accuracy, robustness and computing efficiency. To conclude, the proposed EM–MOP method is a reliable and accurate approach for estimating uptake levels and spatial distributions across target tissues in microPET 11 C-raclopride imaging studies.

The term input function usually refers to the tracer plasma time activity curve (pTAC), which is necessary for quantitative positron emission tomography (PET) studies. The purpose of this study was to acquire the pTAC from the independent component analysis (ICA) estimated whole blood time activity curve (wTAC) using our proposed method: FDG blood-cell-two-compartment model (BCM). We also compared published models, which are linear haematocrit (HCT) correction, nonlinear HCT correction, and two-exponential correction. According to the results, the normalized root mean square error (NRMSE) and error of area under curve (EAUC) of BCM estimated pTAC were the smallest. Compartmental and graphic analyses were used to estimate metabolic rate of FDG (MR FDG ). The percentage error of MR FDG (PE MRFDG ) estimated from BCM corrected pTAC was also the smallest. The BCM is a better choice to transfer wTAC to pTAC for quantification.

The term input function usually refers to the tracer plasma time activity curve (pTAC), which is necessary for quantitative positron emission tomography (PET) studies. The purpose of this study was to acquire the pTAC by independent component analysis (ICA) estimation from the whole blood time activity curve (wTAC) using a novel method, namely the FDG blood-cell-two-compartment model (BCM). This approach was compared to a number of published models, including linear haematocrit (HCT) correction, non-linear HCT correction and two-exponential correction. The results of this study show that the normalized root mean square error (NRMSE) and the error of the area under curve (EAUC) for the BCM estimate of the pTAC were the smallest. Compartmental and graphic analyses were used to estimate the metabolic rate of the FDG (MR"F"D"G). The percentage error for the MR"F"D"G (PE"M"R"F"D"G) was estimated from the BCM corrected pTAC and this was also the smallest. It is concluded that the BCM is a better choice when transferring wTAC into pTAC for quantification.

In this work, we validate the application of GATE (Geant4 Application for Tomographic Emission) Monte Carlo simulation software to model a GE eXplore VISTA small animal PET system. GATE was used to build our three-dimensional PET simulation model. The simulated acquired data were stored in a list-mode file and processed by the same reconstruction scheme as that used in real scanner. To validate the simulation results, several phantoms used in performance assessment experiments were modeled for simulating the system sensitivity, spatial resolution, scatter fraction and count rate response. A realistic voxelized mouse phantom was applied to present the overall quality of our model and compared with mouse images obtained from real exam.

The aim of this study was to improve image quality of statistical reconstruction by using the system matrix (SM) which was trained with artificial neural network (ANN). For training the ANN SM, six input and desired output pairs were generated. The inputs, the digital images, were generated by scanning the mini deluxe cold spot phantom at six different orientations using an optical scanner (resolution: 2400 dpi). Furthermore, the desired outputs were generated by acquiring the projection data with the corresponding angles using the microPET R4. The input and output pairs were used for training the ANN SM. In ANN method, the AD ALINE network with a bias vector was used. The SM, which was created by the geometric information, was treated as the initial guess, and the learning rate was set to le-7 to avoid learning error. The moment term (r = 0.95) was included to improve the stability of the optimization. The inputs were sent to the ANN SM which was updated to minimize the mean square error between the outputs and desired outputs. Moreover, a multi-line source phantom was scanned to obtain the spatial resolutions for comparison. A rat FDG microPET image was acquired to compare the difference between the results reconstructed by the original SM and those by the ANN SM. In the experiment of multi-line source phantom, the resolutions of reconstructed image were measured at center, 10 mm, 20 mm and reconstructed FWHMs by the original SM were 1.61,1.82, and 2.32 mm, respectively. The resolutions reconstructed by the ANN SM were 1.27,1.66, and 1.89 mm. The data size of the original SM and the ANN SM were 90.1,180.0 MB, respectively. The time for reconstruction of the original SM was two times faster than that of the ANN SM. The image quality reconstructed by the ANN SM is better than that reconstructed by the original SM. The results suggested that SM can be updated toward ideal SM using ANN for statistical reconstruction.

Current analytical techniques applied to functional MRI (fMRI) data may be generally divided into two parts: univariate and multivariate techniques. It is therefore our attempt to evaluate and intercompare their respective algorithms on simulated and fMRI visual task data sets. In this study, the two representative univariate approaches, including the correlation and the specified-resolution wavelet analytical methods, and three multivariate based independent component analysis (ICA) approaches; including the Infomax ICA, the Fast ICA, and the JADE ICA are used for the purposes. Two simulated spatial sources with different time courses and noise levels and one fMRI dataset with visual task were employed for intercomparisons. Strategies for quantifying the performance of these techniques, the correlation analysis and receiver operating characteristics (ROC) are used to evaluate their respective accuracies on estimated time-courses and spatial layouts from the simulated and the fMRI visual task dataset In our results, it demonstrates that the multivariate techniques generally outperformed the univariate techniques, among which the Fast ICA performs satisfactory well on temporal and spatial accuracy.

In current combined PET/CT systems, high-quality CT images not only increase the diagnostic values by providing anatomic delineation of hyper- and hypo-metabolic tissues but also offer shorter transmission scanning times compared to standard PET imaging. However, this approach potentially introduces radiation burden due to the higher radiation exposure. Ideally operation protocols for routine PET/CT imaging take diagnostic values, image quality, clinical practicability, scanning time and radiation doses into consideration, but the question of how to reconcile all these factors is made debated topic in nuclear medicine today. In this study, the radiation doses delivered from a typical germanium-based and CT-based transmission scan were measured by means of an anthropomorphic Rando Alderson phantom with insertion of thermoluminescent dosimeters and the image effects of the attenuation correction with various CT acquisition protocols were compared. From the results, we found that image properties and attenuation correction data did not differ significantly by adjusted CT acquisition parameters in PET/CT imaging for reduced patient exposure about 1/26 compared to the currently used while maintaining the advantages of short scanning time and clinical practicability.

There are more than one thousand residents who lived in about 140 radiation-contaminated buildings and received the assessed radiation dose equivalent over 5 mSv/year. In this paper, a systematic approach to dose reconstruction is proposed for evaluating radiation dose equivalent to the residents. The approach includes area survey and exposure measurement, source identification and energy spectrum analysis, special designed TLD-embedded badges for residents to wear and organ dose estimation with Rando phantom simulation. From the study, it is concluded that the ionization chamber should still be considered as the primary modality for external dose measurement. However, lacking of accurate daily activity patterns of the residents, the dose equivalent estimation with the chamber measurements would be somehow overestimated. The encountered limitation could be compensated with the use of the TLD badges and Rando phantom simulation that could also provide more information for internal organ dose equivalent estimations. As the radiation patterns in the buildings are highly anisotropic, which strongly depends on the differences of structural and indoor layouts, it is a great demand to develop a mathematical model dealing with the above concerns. Also, further collaborations with studies on biological markers of the residents would make the entire dose equivalent estimation more helpful and reliable.

Hybrid positron emission tomography-computed tomography (PET-CT) system enhances better differentiation of tissue uptake of 18F-fluorodeoxyglucose (18F-FDG) and provides much more diagnostic value in the non-small-cell lung cancer and nasopharyngeal carcinoma (NPC). In PET-CT, high quality CT images not only offer diagnostic value on anatomic delineation of the tissues but also shorten the acquisition time for attenuation correction (AC) compared with PET-alone imaging. The linear accelerators equipped with the X-ray cone-beam computed tomography (CBCT) imaging system for image-guided radiotherapy (IGRT) provides excellent verification on position setup error. The purposes of our study were to optimize the CT acquisition protocols of PET-CT and to integrate the PET-CT and CBCT for IGRT. The CT imaging parameters were modified in PET-CT for increasing the image quality in order to enhance the diagnostic value on tumour delineation. Reproducibility and registration accuracy via bone co-registration algorithm between the PET-CT and CBCT were evaluated by using a head phantom to simulate a head and neck treatment condition. Dose measurement in computed tomography dose index (CTDI) was also estimated. Optimization of the CT acquisition protocols of PET-CT was feasible in this study. Co-registration accuracy between CBCT and PET-CT on axial and helical modes was in the range of 1.06 to 2.08 and 0.99 to 2.05 mm, respectively. In our result, it revealed that the accuracy of the co-registration with CBCT on helical mode was more accurate than that on axial mode. Radiation doses in CTDI were 4.76 to 18.5 mGy and 4.83 to 18.79 mGy on axial and helical modes, respectively. Registration between PET-CT and CBCT is a state-of-the-art registration technology which could provide much information on diagnosis and accurate tumour contouring on radiotherapy while implementing radiotherapy procedures. This novelty technology of PET-CT and cone-beam CT integration for IGRT may have a potential becoming more clinical use in the future.