Search

Proton-induced secondary-electron-bremsstrahlung (SEB) imaging is a promising method for estimating the ranges of particle beam. However, SEB images do not directly represent dose distributions of particle beams. In addition, the ranges estimated from measured images were deviated because of limited spatial resolutions of the developed x-ray camera as well as statistical noise in the images. To solve these problems, we proposed a method for predicting high-resolution dose images from SEB images with various count level using a deep learning (DL) approach for range and width verification.In this study, we adopted the double U-Net model, which is a previously proposed deep convolutional network model. The first U-Net model in the double U-Net model was used to denoise the SEB images with various count level. The first U-Net model for denoising was trained on 8000 pairs of SEB images with various count level and noise-free images which were created by a sophisticated in-house developed model function. The second U-Net model for dose prediction was trained using 8000 pairs of denoised SEB images from the first U-Net model and high-resolution dose images generated by Monte Carlo simulation.For both simulation and measurement data, the trained DL model could successfully predict high-resolution dose images which showed a clear Bragg peak and no statistical noise. The difference of the range and width was less than 2.1 mm, even from the SEB images measured with a decrease in the number of irradiated protons to less than 11% of 3.2 × 10High-resolution dose images from measured and simulated SEB images were successfully predicted by using the trained DL model for protons. Our proposed DL model was feasible to predict dose images accurately even with smaller number of irradiated protons.

Purpose: Proton-induced secondary-electron-bremsstrahlung (SEB) imaging is a promising method for estimating the ranges of particle beam. However, SEB images do not directly represent dose distributions of particle beams. In addition, the ranges estimated from measured images were deviated because of limited spatial resolutions of the developed x-ray camera as well as statistical noise in the images. To solve these problems, we proposed a method for predicting high-resolution dose images from SEB images with various count level using a deep learning (DL) approach for range and width verification. Methods: In this study, we adopted the double U-Net model, which is a previously proposed deep convolutional network model. The first U-Net model in the double U-Net model was used to denoise the SEB images with various count level. The first U-Net model for denoising was trained on 8000 pairs of SEB images with various count level and noise-free images which were created by a sophisticated in-house developed model function. The second U-Net model for dose prediction was trained using 8000 pairs of denoised SEB images from the first U-Net model and high-resolution dose images generated by Monte Carlo simulation. Results: For both simulation and measurement data, the trained DL model could successfully predict high-resolution dose images which showed a clear Bragg peak and no statistical noise. The difference of the range and width was less than 2.1 mm, even from the SEB images measured with a decrease in the number of irradiated protons to less than 11% of 3.2 ×10^11 protons. Conclusions: High-resolution dose images from measured and simulated SEB images were successfully predicted by using the trained DL model for protons. Our proposed DL model was feasible to predict dose images accurately even with smaller number of irradiated protons.

Deep learning (DL) has shown great potential in conversions between various imaging modalities. Similarly, DL can be applied to synthesize a high-kV computed tomography (CT) image from its corresponding low-kV CT image. This indicates the feasibility of obtaining dual-energy CT (DECT) images without purchasing a DECT scanner. In this study, we investigated whether a low-to-high kV mapping was better than a high-to-low kV mapping. We used a U-Net model to perform conversions between different kV CT images. Moreover, we proposed a double U-Net model to improve the quality of original single-energy CT images. Ninety-eight patients who underwent brain DECT scans were used to train, validate, and test the proposed DL-based model. The results showed that the low-to-high kV conversion was better than the high-to-low kV conversion. In addition, the DL-based DECT images had better signal-to-noise ratios (SNRs) than the true (original) DECT images, but at the expense of a slight loss in spatial resolution. The mean CT number differences between the true and DL-based DECT images were within $$\pm$$ 1 HU. No statistically significant difference in CT number measurements was found between the true and DL-based DECT images (p > 0.05). The DL-based DECT images with improved SNR could produce low-noise virtual monoenergetic images. Our preliminary results indicate that DL has the potential to generate brain DECT images using single-energy brain CT images.

Imaging of the secondary electron bremsstrahlung (SEB) X-rays emitted during particle-ion irradiation is a promising method for beam range estimation. However, the SEB X-ray images are not directly correlated to the dose images. In addition, limited spatial resolution of the X-ray camera and low-count situation may impede correctly estimating the beam range and width in SEB X-ray images. To overcome these limitations of the SEB X-ray images measured by the X-ray camera, a deep learning (DL) approach was proposed in this work to predict the dose images for estimating the range and width of the carbon-ion beam on the measured SEB X-ray images. To prepare enough data for the DL training efficiently, 10,000 simulated SEB X-ray and dose image pairs were generated by our in-house developed model function for different carbon-ion beam energies and doses. The proposed DL neural network consists of two U-nets for SEB X-ray to dose image conversion and super-resolution. After the network being trained with these simulated X-ray and dose image pairs, the dose images were predicted from simulated and measured SEB X-ray testing images for performance evaluation. For the 500 simulated testing images, the average mean squared error (MSE) was 2.5 × 10^-5 and average structural similarity index (SSIM) was 0.997 while the error of both beam range and width was within 1 mm FWHM. For the three measured SEB X-ray images, the MSE was no worse than 5.5 × 10^-3 and SSIM was no worse than 0.980 while the error of the beam range and width was 2 mm and 5 mm FWHM, respectively. We have demonstrated the advantages of predicting dose images from not only simulated data but also measured data using our deep learning approach.

ObjectiveDual-ended readout depth-encoding detectors based on bismuth germanate (BGO) scintillation crystal arrays are good candidates for high-sensitivity small animal positron emission tomography used for very-low-dose imaging. In this paper, the performance of three dual-ended readout detectors based on 15×15 BGO arrays with three different reflector arrangements and 8×8 silicon photomultiplier arrays were evaluated and compared.ApproachThe three BGO arrays, denoted wo-ILG (without internal light guide), wp-ILG (with partial internal light guide), and wf-ILG (with full internal light guide), share a pitch size of 1.6 mm and thickness of 20 mm. Toray E60 with a thickness of 50μm was used as inter-crystal reflector. All reflector lengths in the wo-ILG and wf-ILG BGO arrays were 20 and 18 mm, respectively; the reflectors in the wp-ILG BGO array were 18 mm at the central region of the array and 20 mm at the edge. By using 18 mm reflectors, part of the crystals in the wp-ILG and wf-ILG BGO arrays worked as internal light guides.Main resultsThe results showed that the detector based on the wo-ILG BGO array provided the best flood histogram. The energy, timing and DOI resolutions of the three detectors were similar. The energy resolutions full width at half maximum (FWHM value) based on the wo-ILG, wp-ILG and wf-ILG BGO arrays were 27.2±3.9%, 28.7±4.6%, and 29.5±4.7%, respectively. The timing resolutions (FWHM value) were 4.7±0.5 ns, 4.9±0.5 ns, and 5.0±0.6 ns, respectively. The DOI resolution (FWHM value) were 3.0±0.2 mm, 2.9±0.2 mm, and 3.0±0.2 mm, respectively. Over all, the wo-ILG detector provided the best performance.

Object-based co-localization of fluorescent signals allows the assessment of interactions between two (or more) biological entities using spatial information. It relies on object identification with high accuracy to separate fluorescent signals from the background. Object detectors using convolutional neural networks (CNN) with annotated training samples could facilitate the process by detecting and counting fluorescent-labeled cells from fluorescence photomicrographs. However, datasets containing segmented annotations of colocalized cells are generally not available, and creating a new dataset with delineated masks is label-intensive. Also, the co-localization coefficient is often not used as a component during training with the CNN model. Yet, it may aid with localizing and detecting objects during training and testing. In this work, we propose to address these issues by using a quantification coefficient for co-localization called Manders overlapping coefficient (MOC)(1) as a single-layer branch in a CNN. Fully convolutional one-state (FCOS)(2) with a Resnet101 backbone served as the network to evaluate the effectiveness of the novel branch to assist with bounding box prediction. Training data were sourced from lab curated fluorescence images of neurons from the rat hippocampus, piriform cortex, somatosensory cortex, and amygdala. Results suggest that using modified FCOS with MOC outperformed the original FCOS model for accuracy in detecting fluorescence signals by 1.1% in mean average precision (mAP). The model could be downloaded from https://github.com/Alphafrey946/Colocalization-MOC.

An avalanche photodiode (APD)-based small animal positron emission tomography (PET)-insert was fully evaluated for its PET performance, as well as potential influences on magnetic resonance imaging (MRI) performance. This PET-insert has an extended axial field of view (FOV) compared with the previous design to increase system sensitivity, as well as an updated cooling and temperature regulation to enable stable and reproducible PET acquisitions. The PET performance was evaluated according to the National Electrical Manufacturers Association NU4-2008 protocol. The energy and timing resolution’s full width at half maximum were 16.1% and 4.7 ns, respectively. The reconstructed radial spatial resolution of the PET-insert was 1.8 mm full width at half maximum at the center FOV using filtered back projection for reconstruction and sensitivity was 3.68%. The peak noise equivalent count rates were 70 kcps for a rat-like and 350 kcps for a mouse-like phantom, respectively. Image quality phantom values and contrast recovery were comparable to state-of-the art PET-inserts and standalone systems. Regarding MR compatibility, changes in the mean signal-to-noise ratio for turbo spin echo and echo-planar imaging sequences were below 8.6%, for gradient echo sequences below 1%. Degradation of the mean homogeneity was below 2.3% for all tested sequences. The influence of the PET-insert on the B 0 maps was negligible and no influence on functional MRI sequences was detected. A mouse and rat imaging study demonstrated the feasibility of in vivo simultaneous PET/MRI.

Purpose Imaging of the secondary electron bremsstrahlung (SEB) x rays emitted during particle-ion irradiation is a promising method for beam range estimation. However, the SEB x-ray images are not directly correlated to the dose images. In addition, limited spatial resolution of the x-ray camera and low-count situation may impede correctly estimating the beam range and width in SEB x-ray images. To overcome these limitations of the SEB x-ray images measured by the x-ray camera, a deep learning (DL) approach was proposed in this work to predict the dose images for estimating the range and width of the carbon ion beam on the measured SEB x-ray images. Methods To prepare enough data for the DL training efficiently, 10,000 simulated SEB x-ray and dose image pairs were generated by our in-house developed model function for different carbon ion beam energies and doses. The proposed DL neural network consists of two U-nets for SEB x ray to dose image conversion and super resolution. After the network being trained with these simulated x-ray and dose image pairs, the dose images were predicted from simulated and measured SEB x-ray testing images for performance evaluation. Results For the 500 simulated testing images, the average mean squared error (MSE) was 2.5 × 10-5 and average structural similarity index (SSIM) was 0.997 while the error of both beam range and width was within 1 mm FWHM. For the three measured SEB x-ray images, the MSE was no worse than 5.5 × 10-3 and SSIM was no worse than 0.980 while the error of the beam range and width was 2 mm and 5 mm FWHM, respectively. Conclusions We have demonstrated the advantages of predicting dose images from not only simulated data but also measured data using our deep learning approach.

PET images often suffer poor signal-to-noise ratio (SNR). Our objective is to improve the SNR of PET images using a deep neural network (DNN) model and MRI images without requiring any higher SNR PET images in training. METHODS: Our proposed DNN model consists of three modified U-Nets (3U-net). The PET training input data and targets were reconstructed using filtered-backprojection (FBP) and maximum likelihood expectation maximization (MLEM), respectively. FBP reconstruction was used because of its computational efficiency so that the trained network not only removes noise, but also accelerates image reconstruction. Digital brain phantoms downloaded from BrainWeb were used to evaluate the proposed method. Poisson noise was added into sinogram data to simulate a 6-minute brain PET scan. Attenuation effect was included and corrected before the image reconstruction. Extra Poisson noise was introduced to the training inputs to improve the network denoising capability. Three independent experiments were conducted to examine the reproducibility. A lesion was inserted into testing data to evaluate the impact of mismatched MRI information using the contrast-to-noise ratio (CNR). The negative impact on noise reduction was also studied when miscoregistration between PET and MRI images occurs. RESULTS: Compared with 1U-net trained with only PET images, training with PET/MRI decreased the mean squared error (MSE) by 31.3% and 34.0% for 1U-net and 3U-net, respectively. The MSE reduction is equivalent to an increase in the count level by 2.5 folds and 2.9 folds for 1U-net and 3U-net, respectively. Compared with the MLEM images, the lesion CNR was improved 2.7 folds and 1.4 folds for 1U-net and 3U-net, respectively. CONCLUSIONS: Our proposed method could improve the PET SNR without having higher SNR PET images.

The Compton camera is an imaging device which has been proposed to detect prompt gammas (PGs) produced by proton–nuclear interactions within tissue during proton beam irradiation. Compton-based PG imaging has been developed to verify proton ranges because PG rays, particularly characteristic ones, have strong correlations with the distribution of the proton dose. However, accurate image reconstruction from characteristic PGs is challenging because the detector efficiency and resolution are generally low. Our previous study showed that point spread functions can be incorporated into the reconstruction process to improve image resolution. In this study, we proposed a low-count reconstruction algorithm to improve the image quality of a characteristic PG emission by pooling information from other characteristic PG emissions. PGs were simulated from a proton beam irradiated on a water phantom, and a two-stage Compton camera was used for PG detection. The results show that the image quality of the reconstructed characteristic PG emission is improved with our proposed method in contrast to the standard reconstruction method using events from only one characteristic PG emission. For the 4.44 MeV PG rays, both methods can be used to predict the positions of the peak and the distal falloff with a mean accuracy of 2 mm. Moreover, only the proposed method can improve the estimated positions of the peak and the distal falloff of 5.25 MeV PG rays, and a mean accuracy of 2 mm can be reached.

This work presents a method to improve the separation of edge crystals in PET block detectors. As an alternative to square-shaped crystal arrays, we used an array of triangular-shaped crystals. This increases the distance between the crystal centres at the detector edges potentially improving the separation of edge crystals. To test this design, we have compared the flood histograms of two 4×4 scintillator arrays in both square and triangular configurations. The quality of the flood histogram was quantified using the fraction of events positioned in the correct crystal based on a 2D Gaussian fit of the segmented flood histograms. In the first study, the two crystal arrays were coupled with the SiPM directly using optical grease, and the flood histogram quality for the edge and corner crystals in the triangular-shaped array were much better than that for those crystals in the square-shaped array. The average light collection efficiency for the triangular-shaped array was 5.9% higher than that for the square-shaped array. The average energy resolution for the triangular and square shape array were 11.6% and 13.2% respectively. In the second study, two light guides with thickness 1 mm and 2 mm were used between the crystal arrays and the SiPM. The thicker lightguide degraded the light collection efficiency and energy resolution due to the light loss introduced by the light guide. However, in the 2-mm thick lightguide case, the flood histogram quality for the edge and corner crystals in the square-shaped array were improved due to better separation of those crystals in the flood histogram. Comparing the performance of the two crystal arrays with three different light guides, the triangular-shaped crystal array with no lightguide gave the best performance.

Simultaneous PET/MR imaging is an emerging hybrid modality for clinical and preclinical imaging. The static magnetic field of the MR imaging device affects the trajectory of the positrons emitted by the PET radioisotopes. This effect translates into an improvement of the spatial resolution in transaxial images. However, because of the elongation of the positron range distribution along the magnetic field, the axial resolution worsens and shine-through artifacts may appear. These artifacts can lead to misinterpretation and overstaging. The aim of this work was to study the relevance of this effect. Methods: Measurements were performed in a 3-tesla PET/MR scanner. A 1-cm2 piece of paper, soaked with a radioisotope and placed in air, was scanned, and the magnitude of the shine-through was quantified from the PET images for various radioisotopes. Additionally, PET/MR and PET/CT images of the lungs and the larynx with trachea of a deceased swine were obtained after injecting a mixture of NiSO4 and 68Ga to simulate hot tumor lesions. Results: For the radioactive paper, shine-through artifacts appeared in the location of the acrylic glass backplane, located 3 cm from the source in the axial direction. The ratio between the activity of the shine-through and the activity reconstructed in the original location ranged from 0.9 (18F) to 5.7 (68Ga). For the larynx-with-trachea images, the magnitude of the artifacts depended on the organ orientation with respect to the magnetic field. The shine-through activity could reach 46% of the reconstructed activity (larynx lesion). The lesion within the trachea produced 2 artifacts, symmetrically aligned with the magnetic field and characterized by artifact-to-lesion volume-of-interest ratios ranging from 21% to 30%. Conclusion: In simultaneous PET/MR imaging, the effect of the magnetic field on positrons may cause severe artifacts in the PET image when the lesions are close to air cavities and high-energy radioisotopes are used. For accurate staging and interpretation, this effect needs to be recognized and adequate compensation techniques should be developed.

Deep neural networks have been widely and successfully used in computer vision and attracted growing interests in medical imaging. In this work, we trained a deep residual convolutional neural network to improve quality of PET images. To train the deep neural network, we augmented real patient data with computer simulated phantom data. Specifically, we first trained the network using simulation data and then fine tuned the network using real data. Results based on simulation and real data show that the proposed method is more effective in removing noise than the traditional Gaussian filtering method.

Brain PET scanners that simultaneously provide high-resolution across the field-of-view and high-sensitivity can be constructed using detectors based on SiPM arrays coupled to both ends of scintillator arrays with finely segmented and long detector elements. To reduce the dead space between detector modules and hence improve the sensitivity of PET scanners, crystal arrays with curved surfaces are proposed. In this paper, the performance of a proof-of-concept detector module with nine detector submodules based on SiPMs coupled to both ends of a curved LYSO array with a pitch size of 1.0 × 1.0 mm2 at the front-end and a length of 30 mm was evaluated. A simple signal multiplexing method using the shared-photodetector readout method was evaluated to identify the crystals. The results showed that all the LYSO elements in the detector module of interest could be clearly resolved. The energy resolution, depth-of-interaction resolution, and timing resolution were 14.6% ± 3.6%, 2.77 ± 0.39 mm, and 1.15 ± 0.07 ns, respectively, obtained at a bias voltage of 28.0 V and a temperature of 16.8 °C ± 0.2 °C.

PET scanners with partial-ring geometry have been proposed for various imaging purposes. The incomplete projection data obtained from this design cause undesirable artifacts in the reconstructed images. In this study, we investigated the performance of a deep learning (DL) based method for the recovery of partial-ring PET images. Twenty digital brain phantoms were used in the Monte Carlo simulation toolkit, SimSET, to simulate 15 min full-ring PET scans. Partial-ring PET data were generated from full-ring PET data by removing coincidence events that hit these specific detector blocks. A convolutional neural network based on the residual U-Net architecture was trained to predict full-ring data from partial-ring data in either the projection or image domain. The performance of the proposed DL-based method was evaluated by comparing with the PET images reconstructed using the full-ring projection data in terms of the mean squared error (MSE), structural similarity (SSIM) index and recovery coefficient (RC). The MSE results showed the superiority of the image-domain approach in reduction of 91.7% in contrast to 14.3% for the projection-domain approach. Therefore, the image-domain approach was used to study the influence of the number of detector block removal. The SSIM results were 0.998, 0.996 and 0.993 for 3, 5 and 7 detector block removals, respectively. The activity of gray and white matters could be fully recovered even with 7 detector block removal, while the RCs of two artificially inserted small lesions (3 pixels in diameter) in the testing data were 94%, 89% and 79% for 3, 5, and 7 detector block removals, respectively. Our simulation results suggest that DL has the potential to recover partial-ring PET images.

Combined positron emission tomography (PET) and magnetic resonance imaging (MRI) is a new tool to study functional processes in the brain. Here we study brain function in response to a barrel-field stimulus simultaneously using PET, which traces changes in glucose metabolism on a slow time scale, and functional MRI (fMRI), which assesses fast vascular and oxygenation changes during activation. We found spatial and quantitative discrepancies between the PET and the fMRI activation data. The functional connectivity of the rat brain was assessed by both modalities: the fMRI approach determined a total of nine known neural networks, whereas the PET method identified seven glucose metabolism-related networks. These results demonstrate the feasibility of combined PET-MRI for the simultaneous study of the brain at activation and rest, revealing comprehensive and complementary information to further decode brain function and brain networks.

This chapter, starting from a seemingly standardized dance promotion in Mandarin pop, one of the dominant music genres in East Asia, attempts to reveal the cultural logics and to denaturalize the corporeal discourses behind what is commonly perceived as the “naturally” spectacular hip movement of a Chinese-American superstar, Coco Lee, whose dance is, in Taiwan, often linked with the idea of “sexiness” and “American-ness.” Calling upon Judith Butler’s idea of performativity (1990) in tandem with Richard Dyer’s notion of star image (1979) and the concept of the dancing body (Thomas 1995; Foster 1996), this chapter, using music video analysis (Vernallis 2004; Beebe and Middleton 2007), delineates Coco’sHip Hop Tonight(2006) to point out the contradictions and reversals of the body in contemporary multimedial context in that “sexiness” is desexualized, “American-ness” is Sinocized, and the meaning of “Chinese-ness” continues to shift according to local cultural and political sensibilities. In this process, music video becomes an intersecting point in which cultural boundaries negotiate and body politics fight to gain the upper hand, revealing a web of complex power struggles in Taiwan where meaning of the body is locally produced yet globally contested.

PET Attenuation Correction (AC) in small animal imaging is generally performed via transmission (TX) scans. In simultaneous PET/MR scanners, no transmission source is available, and MR-based AC (MRAC) or AC from emission data is necessary. However, MR-invisible objects such as the bed and the MR coils cannot be corrected from MR data. We quantified the effects of a Medres warming bed and a local brain coil on AC for small animal PET/MR imaging using a dedicated Inveon PET tomograph. Additionally, the effect of segmentation-based MRAC of rats in brain regions was evaluated using MR images separately acquired on a preclinical 7T MRI system.

In the last decade, there are increasing investigations on lateral field excited (LFE) acoustic wave sensors in biochemical liquid sensing applications due to their high sensitivity and simple fabrication. However, the research on this kind of sensor for gas detection is still awaited. Therefore, we adopted finite element method (FEM) to analyze a LFE acoustic wave gas sensor, and further calculate its sensitivity to the variations of mass density and electrical conductivity of a selective film caused from gas concentration. In the meantime, quartz crystal microbalance (QCM) was also analyzed for comparison. Finally, the geometry of the LFE gas sensor was discussed and optimized to obtain a better sensing sensitivity. Results show that the LFE sensor exhibits larger sensing range and higher sensitivity than the QCM. This is because no shielding electrode exists on sensing surface of the LFE sensor, and hence the electric field can penetrate into the selective film. According to the simulation results, we conclude that a LFE acoustic wave sensor is very suitable to apply for gas detection.

An intelligent travel experience management system, abbreviated as ITEMS, is proposed to help tourists organize and present the digital travel contents in an automatic and efficient manner. Readily available metadata are adopted to reduce the overhead of user intervention and manual annotation. Robust image similarity metrics are also incorporated to utilize the powerful searching capability of WWW search engines. The proposed system automatically identifies the inherent geo-information of personal media, and accordingly integrates media with map and text-based schedule to facilitate travel experience management and presentation. We show several prototype systems in two application scenarios and demonstrate the effectiveness of the proposed methodology.

Distributing video contents via broadcasting network mechanisms has become a promising business opportunity for the entertainment industry. However, since content piracy is always a serious problem, broadcasted contents must be adequately protected. Rather than implementing sophisticate key-management schemes for access control, an animation broadcast system based on colorization techniques is proposed. In the proposed system, gray-level animation video sequences are delivered via broadcast mechanisms, such as multicast, to reduce the overhead in server processing and network bandwidth. Moreover, color seeds labeled with fingerprint codes are delivered to each client through low-bandwidth auxiliary connections and then used to generate high-quality full-color animations with slight differences between versions received by each client-side device. When a user illegally duplicates and distributes the received video, his identity can be easily found out by examining features extracted from the pirated video. The proposed scheme also shows good resistance to collusion attacks where two or more users cooperate to generate an illegal copy in expectation of getting rid of legal responsibility. The proposed scheme exhibits advantages in network bandwidth, system performance and content security.

Purpose: The aim of this study was to develop a prototype PET detector module for a combined small animal positron emission tomography and magnetic resonance imaging (PET/MRI) system. The most important factor for small animal imaging applications is the detection sensitivity of the PET camera, which can be optimized by utilizing longer scintillation crystals. At the same time, small animal PET systems must yield a high spatial resolution. The measured object is very close to the PET detector because the bore diameter of a high field animal MR scanner is limited. When used in combination with long scintillation crystals, these small-bore PET systems generate parallax errors that ultimately lead to a decreased spatial resolution. Thus, we developed a depth of interaction (DoI) encoding PET detector module that has a uniform spatial resolution across the whole field of view (FOV), high detection sensitivity, compactness, and insensitivity to magnetic fields. Methods: The approach was based on Geiger mode avalanche photodiode (G-APD) detectors with cross-strip encoding. The number of readout channels was reduced by a factor of 36 for the chosen block elements. Two 12 × 2 G-APD strip arrays (25μm cells) were placed perpendicular on each face of a 12 × 12 lutetium oxyorthosilicate crystal block with a crystal size of 1.55 × 1.55 × 20 mm. The strip arrays were multiplexed into two channels and used to calculate the x, y coordinates for each array and the deposited energy. The DoI was measured in step sizes of 1.8 mm by a collimated 18F source. The coincident resolved time (CRT) was analyzed at all DoI positions by acquiring the waveform for each event and applying a digital leading edge discriminator. Results: All 144 crystals were well resolved in the crystal flood map. The average full width half maximum (FWHM) energy resolution of the detector was 12.8% ± 1.5% with a FWHM CRT of 1.14 ± 0.02 ns. The average FWHM DoI resolution over 12 crystals was 2.90 ± 0.15 mm. Conclusions: The novel DoI PET detector, which is based on strip G-APD arrays, yielded a DoI resolution of 2.9 mm and excellent timing and energy resolution. Its high multiplexing factor reduces the number of electronic channels. Thus, this cross-strip approach enables low-cost, high-performance PET detectors for dedicated small animal PET and PET/MRI and potentially clinical PET/MRI systems.

Purpose: To develop a diagnostic X-ray image simulator for commonly used out-patient-department examinations including lower extremities, skull, abdomen, pelvis, chest, and spinal cord. This simulator can be used as a computer-assistant-teaching software for training technologists more familiar with the relationship between image quality and operation conditions. Method and Materials: A detailed simulation of a diagnostic X-ray image requires four major inputs: operation conditions, X-ray spectral and spatial distributions, a human model, and cross-section corresponding to different materials. This study has collected commonly used operation conditions such as kVp, mAs, SSD for different OPD examinations. These conditions have been inputted to a Monte Carlo code, BEAMnrc, to build a virtual X-ray machine for calculating X-ray spectral and spatial distributions. After that, a high resolution voxelized human model, VIP-Man constructed from segmented Visible Male Dataset, will be imaged with this virtual X-ray machine. The cross-sections of different materials are generated by PEGS4, a cross-section preparation tool come with EGS4. Results: X-ray spectra simulated using BEAMnrc is almost identical to those listed in literatures, or from a spectra simulator, XCOMP3; except there is about 30% underestimation in characteristic peaks. However, this underestimation will lead to less than 0.1% deviation in our simulation. Heel effects can be observed in our simulated images. The intensity response was calibrated to a real digital X-ray machine, and point spread functions of this machine are measured to degrade our simulated images. Conclusion: We have developed this X-ray image simulator which can benefit the training of technologists. This simulator can be further improved to serve as a platform for studying image quality parameters such as QDE or MTF after we adding more realistic model of image receptor.

In the last decade, there are increasing investigations on lateral field excited (LFE) acoustic wave sensors in biochemical liquid sensing applications due to their high sensitivity and simple fabrication. However, the research on this kind of sensor for gas detection is still awaited. This paper presents a theoretical modeling of the LFE acoustic wave gas sensor with a nanostrustured selective film for the first time. We developed this model by adopting a finite element software, COMSOL. Besides the eigenfrequency and frequency-response analyses, the sensitivities to the variations of mass density and electrical conductivity of the selective film caused from gas concentration were calculated. In the meantime, quartz crystal microbalance (QCM) sensors were also analyzed for comparison. Finally, the effect of geometry of the LFE gas sensor on the sensitivity was discussed. Results show that the LFE sensor exhibits larger sensing range and higher sensitivity to external electrical variation than the QCM sensor. This is because no shielding electrode exists on sensing surface of the LFE sensor, and hence the electric field can penetrate into the selective film. The simulation results provide useful guidelines for designing LFE acoustic wave gas sensors.

Iterative PET image reconstructions can improve quantitation accuracy by explicitly modeling photon-limited nature and physical effects of coincident photons. Geometric model in iterative reconstructions defines the mapping between image and sinogram domains based on scanner's geometry, which affects the accuracy of image results and the convergence rate of reconstruction algorithms. This paper examines the convergence rates of a reconstruction algorithm with three PET geometric models: interpolative, area-based, and solid-angle. The iterative algorithm used in this study is the maximum likelihood expectation-maximization (MLEM) algorithm. Experimental data are generated by the GATE package which simulates the Inveon microPET system. The comparison of convergence rate is based on the plot of log-likelihood value versus iteration number. From the plots of log-likelihood curves, the results from solid-angle model consistently reach the highest values at early iterations. It means that the MLEM algorithm with the solid-angle model will converge faster than the other two models. The experimental results indicate that the solid-angle model is a favorable geometric model for faster iterative PET image reconstruction.