Feasibility study of YSO/SiPM based detectors for virtual monochromatic image synthesis.

BACKGROUND: The development of photon-counting CT systems has focused on semiconductor detectors like cadmium zinc telluride (CZT) and cadmium telluride (CdTe). However, these detectors face high costs and charge-sharing issues, distorting the energy spectrum. Indirect detection using Yttrium Orthosilicate (YSO) scintillators with silicon photomultiplier (SiPM) offers a cost-effective alternative with high detection efficiency, low dark count rate, and high sensor gain. OBJECTIVE: This work aims to demonstrate the feasibility of the YSO/SiPM detector (DexScanner L103) based on the Multi-Voltage Threshold (MVT) sampling method as a photon-counting CT detector by evaluating the synthesis error of virtual monochromatic images. METHODS: In this study, we developed a proof-of-concept benchtop photon-counting CT system, and employed a direct method for empirical virtual monochromatic image synthesis (EVMIS) by polynomial fitting under the principle of least square deviation without X-ray spectral information. The accuracy of the empirical energy calibration techniques was evaluated by comparing the reconstructed and actual attenuation coefficients of calibration and test materials using mean relative error (MRE) and mean square error (MSE). RESULTS: In dual-material imaging experiments, the overall average synthesis error for three monoenergetic images of distinct materials is 2.53% ±2.43%. Similarly, in K-edge imaging experiments encompassing four materials, the overall average synthesis error for three monoenergetic images is 4.04% ±2.63%. In rat biological soft-tissue imaging experiments, we further predicted the densities of various rat tissues as follows: bone density is 1.41±0.07 g/cm3, adipose tissue density is 0.91±0.06 g/cm3, heart tissue density is 1.09±0.04 g/cm3, and lung tissue density is 0.32±0.07 g/cm3. Those results showed that the reconstructed virtual monochromatic images had good conformance for each material. CONCLUSION: This study indicates the SiPM-based photon-counting detector could be used for monochromatic image synthesis and is a promising method for developing spectral computed tomography systems.

Volumetric visceral fat machine learning phenotype on CT for differential diagnosis of inflammatory bowel disease.

OBJECTIVES: To investigate whether volumetric visceral adipose tissue (VAT) features extracted using radiomics and three-dimensional convolutional neural network (3D-CNN) approach are effective in differentiating Crohn's disease (CD) and ulcerative colitis (UC). METHODS: This retrospective study enrolled 316 patients (mean age, 36.25 ± 13.58 [standard deviation]; 219 men) with confirmed diagnosis of CD and UC who underwent CT enterography between 2012 and 2021. Volumetric VAT was semi-automatically segmented on the arterial phase images. Radiomics analysis was performed using principal component analysis (PCA) and the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm. We developed a 3D-CNN model using VAT imaging data from the training cohort. Clinical covariates including age, sex, modified body mass index, and disease duration that impact VAT were added to the machine learning model for adjustment. The model's performance was evaluated on the testing cohort separating from the model's development process by its discrimination and clinical utility. RESULTS: Volumetric VAT radiomics analysis with LASSO had the highest AUC value of 0.717 (95% CI, 0.614-0.820), though difference of diagnostic performance among the 3D-CNN model (AUC = 0.693; 95% CI, 0.587-0.798) and radiomics analysis with PCA (AUC = 0.662; 95% CI, 0.548-0.776) and LASSO have not reached statistical significance (all p > 0.05). The radiomics score was higher in UC than in CD on the testing cohort (mean ± SD, UC 0.29 ± 1.05 versus CD -0.60 ± 1.25; p < 0.001). The LASSO model with adjustment of clinical covariates reached an AUC of 0.775 (95%CI, 0.683-0.868). CONCLUSION: The developed volumetric VAT-based radiomics and 3D-CNN models provided comparable and effective performance for the characterization of CD from UC. KEY POINTS: • High-output feature data extracted from volumetric visceral adipose tissue on CT enterography had an effective diagnostic performance for differentiating Crohn's disease from ulcerative colitis. • With adjustment of clinical covariates that cause difference in volumetric visceral adipose tissue, adjusted clinical machine learning model reached stronger performance when distinguishing Crohn's disease patients from ulcerative colitis patients.

Multiparametric MRI-Based Radiomic Signature for Preoperative Evaluation of Overall Survival in Intrahepatic Cholangiocarcinoma After Partial Hepatectomy.

BACKGROUND: The clinical outcomes of patients with intrahepatic cholangiocarcinoma (ICC) after partial hepatectomy remain suboptimal. Identifying patients with poor outcomes before surgery is urgently required. PURPOSE: To develop a multiparametric magnetic resonance imaging (MRI)-based radiomic signature to evaluate overall survival (OS) preoperatively and to investigate its incremental value for disease stratification. STUDY TYPE: Retrospective. SUBJECTS: One hundred and sixty-three patients with pathologically defined ICC, divided into training (N = 115) and validation sets (N = 48). SEQUENCE: Three-dimensional T1-weighted gradient-echo sequence with and without contrast agent, T2-weighted fast spin-echo sequence, and diffusion-weighted imaging with single-shot echo-planar sequence at 1.5 T or 3.0 T. ASSESSMENT: OS was defined as the time from the date of surgery to death or last contact. The radiomic signature was built based on the least absolute shrinkage and selection operator regression model. A clinicopathologic-radiographic (CPR) model and a combined model integrating radiomic signature with CPR factors were developed with multivariable Cox regression models. STATISTICAL TESTS: Harrell's concordance index (C-index) was used to compare the discrimination of different models. Net reclassification index (NRI) and integrated discrimination improvement (IDI) were used to quantify the improvement of prognostic accuracy after adding radiomic signature. RESULTS: The high-risk patients of death defined by the radiomic signature showed significantly lower OS compared with low-risk patients in validation set (3-year OS 17.1% vs. 56.4%, P < 0.001). Integrating radiomic signature into tumor, node, and metastasis (TNM) staging system significantly improved the prognostic accuracy compared with TNM stage alone (validation set C-index 0.745 vs. 0.649, P = 0.039, NRI improvement 39.9%-43.8%, IDI improvement 16.1%-19.4%). The radiomic signature showed no significant difference of C-index with postoperative CPR model (validation set, 0.698 vs. 0.674, P = 0.752). Incorporating the radiomic signature into CPR model significantly improved prognostic accuracy (NRI improvement 32.5%-34.3%, IDI improvement 8.1%-12.9%). DATA CONCLUSION: Multiparametric MRI-based radiomic signature is a potential biomarker for preoperative prognostic evaluation of ICC patients. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 4.

Effect of Increased IL-1ß on Expression of HK in Alzheimer's Disease.

Alzheimer's disease (AD) is a neurodegenerative disease characterized by decreased glucose metabolism and increased neuroinflammation. Hexokinase (HK) is the key enzyme of glucose metabolism and is associated with mitochondria to exert its function. Recent studies have demonstrated that the dissociation of HK from mitochondria is enough to activate the NOD-like receptor protein 3 (NLRP3) inflammasome and leads to the release of interleukin-1ß (IL-1ß). However, the effect of increased IL-1ß on the expression of HK is still unclear in AD. In this paper, we used positron emission tomography (PET), Western blotting and immunofluorescence to study the glucose metabolism, and the expression and distribution of HK in AD. Furthermore, we used lipopolysaccharide (LPS), nigericin (Nig), CY-09 and lonidamine (LND) to treat N2a and N2a-sw cells to investigate the link between IL-1ß and HK in AD. The results show decreased expression of HK and the dissociation of HK from mitochondria in AD. Furthermore, a reduction of the expression of IL-1ß could increase the expression of HK in AD. These results suggest that inhibiting inflammation may help to restore glucose metabolism in AD.

Selenoprotein F knockout leads to glucose and lipid metabolism disorders in mice.

Selenoprotein F (Selenof), an endoplasmic reticulum (ER)-resident protein, is considered to be involved in glycoprotein folding and quality control in the ER. However, its function has not yet been thoroughly addressed. In this study, proteomics analysis revealed that Selenof deficiency in mice led to the differential expression of hepatic proteins associated with glucose and lipid metabolism. The phenotype analysis revealed that Selenof knockout mice showed glucose intolerance and insulin reduction, even with a normal diet. Additionally, Selenof knockout exacerbated high-fat diet-induced obesity, hyperglycemia, glucose intolerance, and hepatic steatosis. Furthermore, lipoprotein lipase and carboxylesterase 1D, two glycoproteins involved in lipid metabolism, were significantly decreased in the liver of Selenof knockout mice with a normal or high-fat diet. Collectively, these findings suggested that Selenof deficiency might cause the perturbation of glycoprotein quality control and thus contribute to glucose and lipid metabolism disorders, implying a novel biological function of Selenof.

New Digital Plug and Imaging Sensor for a Proton Therapy Monitoring System Based on Positron Emission Tomography.

One of the most challenging areas of sensor development for nuclear medicine is the design of proton therapy monitoring systems. Sensors are operated in a high detection rate regime in beam-on conditions. We realized a prototype of a monitoring system for proton therapy based on the technique of positron emission tomography. We used the Plug and Imaging (P&I) technology in this application. This sensing system includes LYSO/silicon photomultiplier (SiPM) detection elements, fast digital multi voltage threshold (MVT) readout electronics and dedicated image reconstruction algorithms. In this paper, we show that the P&I sensor system has a uniform response and is controllable in the experimental conditions of the proton therapy room. The prototype of PET monitoring device based on the P&I sensor system has an intrinsic experimental spatial resolution of approximately 3 mm (FWHM), obtained operating the prototype both during the beam irradiation and right after it. The count-rate performance of the P&I sensor approaches 5 Mcps and allows the collection of relevant statistics for the nuclide analysis. The measurement of both the half life and the relative abundance of the positron emitters generated in the target volume through irradiation of 10 10 protons in approximately 15 s is performed with 0.5% and 5 % accuracy, respectively.

Application of CMOS Technology to Silicon Photomultiplier Sensors.

We use the 180 nm GLOBALFOUNDRIES (GF) BCDLite CMOS process for the production of a silicon photomultiplier prototype. We study the main characteristics of the developed sensor in comparison with commercial SiPMs obtained in custom technologies and other SiPMs developed with CMOS-compatible processes. We support our discussion with a transient modeling of the detection process of the silicon photomultiplier as well as with a series of static and dynamic experimental measurements in dark and illuminated environments.

RayPlus: a Web-Based Platform for Medical Image Processing.

Medical image can provide valuable information for preclinical research, clinical diagnosis, and treatment. As the widespread use of digital medical imaging, many researchers are currently developing medical image processing algorithms and systems in order to accommodate a better result to clinical community, including accurate clinical parameters or processed images from the original images. In this paper, we propose a web-based platform to present and process medical images. By using Internet and novel database technologies, authorized users can easily access to medical images and facilitate their workflows of processing with server-side powerful computing performance without any installation. We implement a series of algorithms of image processing and visualization in the initial version of Rayplus. Integration of our system allows much flexibility and convenience for both research and clinical communities.

Generating multi-double-scroll attractors via nonautonomous approach.

It is a common phenomenon that multi-scroll attractors are realized by introducing the various nonlinear functions with multiple breakpoints in double scroll chaotic systems. Differently, we present a nonautonomous approach for generating multi-double-scroll attractors (MDSA) without changing the original nonlinear functions. By using the multi-level-logic pulse excitation technique in double scroll chaotic systems, MDSA can be generated. A Chua's circuit, a Jerk circuit, and a modified Lorenz system are given as designed example and the Matlab simulation results are presented. Furthermore, the corresponding realization circuits are designed. The Pspice results are in agreement with numerical simulation results, which verify the availability and feasibility of this method.

Super-resolution positron emission tomography by intensity modulation: Proof of concept.

We investigate a new approach for increasing the resolution of clinical positron emission tomography (PET). It is inspired by the method of super-resolution (SR) structured illumination microscopy (SIM) for overcoming the intrinsic resolution limit in microscopy due to diffraction of light. For implementing the key idea underlying SIM, we propose using a rotating intensity modulator of the radiation beams in front of the stationary PET detector ring to masquerade above-the-bandwidth signals of the projection data into detectable, lower-frequency ones. Then, an SR image whose resolution is above the system's bandwidth due to instrumentation is computed from several such measurements obtained at several rotational positions of the modulator. We formulated an imaging model that relates the SR image to the measurements and implemented an ordered-subsets expectation-maximization algorithm for solving the model. Based on simulation data produced by using an analytic projector, we showed that 0.9~mm sources can be resolved in the SR image obtained from noise-free data when using 4.2~mm detectors. Noisy data were produced either by adding Poisson noise to the noise-free data and by Monte-Carlo simulation. With noisy data, as expected, the SR performance is diminished but the results remain promising. In particular, 1.2~mm sources were resolvable, and the visibility and quantification of small sources are improved despite considerable sensitivity loss incurred by the modulator. Further studies are needed to better understand the theoretical aspects of the proposed approach and for optimizing the design of the modulator and the reconstruction algorithm in the presence of noise.

Feasibility study of photon-counting CT for material identification based on YSO/SiPM detector: A proof of concept.

BACKGROUND: Current photon-counting computed tomography (CT) systems utilize semiconductor detectors, such as cadmium telluride (CdTe), cadmium zinc telluride (CZT), and silicon (Si), which convert x-ray photons directly into charge pulses. An alternative approach is indirect detection, which involves Yttrium Orthosilicate (YSO) scintillators coupled with silicon photomultipliers (SiPMs). This presents an attractive and cost-effective option due to its low cost, high detection efficiency, low dark count rate, and high sensor gain. OBJECTIVE: This study aims to establish a comprehensive quantitative imaging framework for three-energy-bin proof-of-concept photon-counting CT based on YSO/SiPM detectors developed in our group using multi-voltage threshold (MVT) digitizers and assess the feasibility of this spectral CT for material identification. METHODS: We developed a proof-of-concept YSO/SiPM-based benchtop spectral CT system and established a pipeline for three-energy-bin photon-counting CT projection-domain processing. The empirical A-table method was employed for basis material decomposition, and the quantitative imaging performance of the spectral CT system was assessed. This evaluation included the synthesis errors of virtual monoenergetic images, electron density images, effective atomic number images, and linear attenuation coefficient curves. The validity of employing A-table methods for material identification in three-energy-bin spectral CT was confirmed through both simulations and experimental studies. RESULTS: In both noise-free and noisy simulations, the thickness estimation experiments and quantitative imaging results demonstrated high accuracy. In the thickness estimation experiment using the practical spectral CT system, the mean absolute error for the estimated thickness of the decomposed Al basis material was 0.014 ± 0.010 mm, with a mean relative error of 0.66% ± 0.42%. Similarly, for the decomposed polymethyl methacrylate (PMMA) basis material, the mean absolute error in thickness estimation was 0.064 ± 0.058 mm, with a mean relative error of 0.70% ± 0.38%. Additionally, employing the equivalent thickness of the basis material allowed for accurate synthesis of 70 keV virtual monoenergetic images (relative error 1.85% ± 1.26%), electron density (relative error 1.81% ± 0.97%), and effective atomic number (relative error 2.64% ± 1.26%) of the tested materials. In addition, the average synthesis error of the linear attenuation coefficient curves in the energy range from 40 to 150 keV was 1.89% ± 1.07%. CONCLUSIONS: Both simulation and experimental results demonstrate the accurate generation of 70 keV virtual monoenergetic images, electron density, and effective atomic number images using the A-table method. Quantitative imaging results indicate that the YSO/SiPM-based photon-counting detector is capable of accurately reconstructing virtual monoenergetic images, electron density images, effective atomic number images, and linear attenuation coefficient curves, thereby achieving precise material identification.

PET image reconstruction using weighted nuclear norm maximization and deep learning prior.

The ill-posed Positron emission tomography (PET) reconstruction problem usually results in limited resolution and significant noise. Recently, deep neural networks have been incorporated into PET iterative reconstruction framework to improve the image quality. In this paper, we propose a new neural network-based iterative reconstruction method by using weighted nuclear norm (WNN) maximization, which aims to recover the image details in the reconstruction process. The novelty of our method is the application of WNN maximization rather than WNN minimization in PET image reconstruction. Meanwhile, a neural network is used to control the noise originated from WNN maximization. Our method is evaluated on simulated and clinical datasets. The simulation results show that the proposed approach outperforms state-of-the-art neural network-based iterative methods by achieving the best contrast/noise tradeoff with a remarkable contrast improvement on the lesion contrast recovery. The study on clinical datasets also demonstrates that our method can recover lesions of different sizes while suppressing noise in various low-dose PET image reconstruction tasks. Our code is available athttps://github.com/Kuangxd/PETReconstruction.

In-beam PET monitoring of proton therapy: a method for filtering prompt radiation events.

Objective. In-beam positron emission tomography (PET) is a promising technology for real-time monitoring of proton therapy. Random coincidences between prompt radiation events and positron annihilation photon pairs can deteriorate imaging quality during beam-on operation. This study aimed to improve the PET image quality by filtering out the prompt radiation events.Approach. We investigated a prompt radiation event filtering method based on the accelerator radio frequency phase and assessed its performance using various prompt gamma energy thresholds. An in-beam PET prototype was used to acquire the data when the 70 MeV proton beam irradiated a water phantom and a mouse. The signal-to-background ratio (SBR) indicator was utilized to evaluate the quality of the PET reconstruction image.Main results. The selection of the prompt gamma energy threshold will affect the quality of the reconstructed image. Using the optimal energy threshold of 580 keV can obtain a SBR of 1.6 times for the water phantom radiation experiment and 2.0 times for the mouse radiation experiment compared to those without background removal, respectively.Significance. Our results show that using this optimal threshold can reduce the prompt radiation events, enhancing the SBR of the reconstructed image. This advancement contributes to more accurate real-time range verification in subsequent steps.

Deep denoiser prior driven relaxed iterated Tikhonov method for low-count PET image restoration.

Objective. Low-count positron emission tomography (PET) imaging is an efficient way to promote more widespread use of PET because of its short scan time and low injected activity. However, this often leads to low-quality PET images with clinical image reconstruction, due to high noise and blurring effects. Existing PET image restoration (IR) methods hinder their own restoration performance due to the semi-convergence property and the lack of suitable denoiser prior.Approach. To overcome these limitations, we propose a novel deep plug-and-play IR method called Deep denoiser Prior driven Relaxed Iterated Tikhonov method (DP-RI-Tikhonov). Specifically, we train a deep convolutional neural network denoiser to generate a flexible deep denoiser prior to handle high noise. Then, we plug the deep denoiser prior as a modular part into a novel iterative optimization algorithm to handle blurring effects and propose an adaptive parameter selection strategy for the iterative optimization algorithm.Main results. Simulation results show that the deep denoiser prior plays the role of reducing noise intensity, while the novel iterative optimization algorithm and adaptive parameter selection strategy can effectively eliminate the semi-convergence property. They enable DP-RI-Tikhonov to achieve an average quantitative result (normalized root mean square error, structural similarity) of (0.1364, 0.9574) at the stopping iteration, outperforming a conventional PET IR method with an average quantitative result of (0.1533, 0.9523) and a state-of-the-art deep plug-and-play IR method with an average quantitative result of (0.1404, 0.9554). Moreover, the advantage of DP-RI-Tikhonov becomes more obvious at the last iteration. Experiments on six clinical whole-body PET images further indicate that DP-RI-Tikhonov successfully reduces noise intensity and recovers fine details, recovering sharper and more uniform images than the comparison methods.Significance. DP-RI-Tikhonov's ability to reduce noise intensity and effectively eliminate the semi-convergence property overcomes the limitations of existing methods. This advancement may have substantial implications for other medical IR.

Application of geometric shape-based CT field-of-view extension algorithms in an all-digital positron emission tomography/computed tomography system.

BACKGROUND: Computed tomography (CT)-based positron emission tomography (PET) attenuation correction (AC) is a commonly used method in PET AC. However, the CT truncation caused by the subject's limbs outside the CT field-of-view (FOV) leads to errors in PET AC. PURPOSE: In order to enhance the quantitative accuracy of PET imaging in the all-digital DigitMI 930 PET/CT system, we assessed the impact of FOV truncation on its image quality and investigated the effectiveness of geometric shape-based FOV extension algorithms in this system. METHODS: We implemented two geometric shape-based FOV extension algorithms. By setting the data from different numbers of detector channels on either side of the sinogram to zero, we simulated various levels of truncation. Specific regions of interest (ROI) were selected, and the mean values of these ROIs were calculated to visually compare the differences between truncated CT, CT extended using the FOV extension algorithms, and the original CT. Furthermore, we conducted statistical analyses on the mean and standard deviation of residual maps between truncated/extended CT and the original CT at different levels of truncation. Subsequently, similar data processing was applied to PET images corrected using original CT and those corrected using simulated truncated and extended CT images. This allowed us to evaluate the influence of FOV truncation on the images produced by the DigitMI 930 PET/CT system and assess the effectiveness of the FOV extension algorithms. RESULTS: Truncation caused bright artifacts at the CT FOV edge and a slight increase in pixel values within the FOV. When using truncated CT data for PET AC, the PET activity outside the CT FOV decreased, while the extension algorithm effectively reduced these effects. Patient data showed that the activity within the CT FOV decreased by 60% in the truncated image compared to the base image, but this number could be reduced to at least 17.3% after extension. CONCLUSION: The two geometric shape-based algorithms effectively eliminate CT truncation artifacts and restore the true distribution of CT shape and PET emission data outside the FOV in the all-digital DigitMI 930 PET/CT system. These two algorithms can be used as basic solutions for CT FOV extension in all-digital PET/CT systems.

DOI- and TOF-capable PET array detector using double-ended light readout and stripline-based row and column electronic readout.

We investigate a highly multiplexing readout for depth-of-interaction (DOI) and time-of-flight PET detector consisting of an N×N crystals whose light outputs at the front and back ends are detected by using silicon photomultipliers (SiPM). The front N×N SiPM array is read by using a stripline (SL) configured to support discrimination of the row position of the signal-producing crystal. The back N×N SiPM array is similarly read by an SL for column discrimination. Hence, the detector has only four outputs. We built 4×4 and 8×8 detector modules (DM) by using 3.0×3.0×20 mm3 lutetium-yttrium oxyorthosilicates. The outputs were sampled and processed offline. For both DMs, crystal discrimination was successful. For the 4×4 DM, we obtained an average energy resolution (ER) of 14.1%, an average DOI resolution of 2.5 mm, a non DOI-corrected coincidence resolving time (CRT), measured in coincidence with a single-pixel reference detector, of about 495 ps. For the 8×8 DM, the average ER, average DOI resolution and average CRT were 16.4%, 2.9 mm, and 641 ps, respectively. We identified the intercrystal scattering as a probable cause for the CRT deterioration when the DM was increased from 4×4 to 8×8.

Timing offset calibration for TOF PET using stationary line source scans at multiple positions.

Background. Accurate timing offset calibration is crucial for time-of-flight (TOF) positron emission tomography (PET) to mitigate image artifacts and improve quantitative accuracy. However, existing methods are often time-consuming, complex, or costly.Objective. This paper presents a method for TOF PET timing offset calibration that eliminates the need for costly equipment, phantoms, short-half-life sources, and precise source positioning.Approach. We estimate channel timing offsets using stationary scans of a68Ge line source, typically used for routine quality control, at a minimum of three non-coplanar positions, with each position scanned for two minutes. The line source positions are accurately determined by applying a simple algorithm to their reconstructed images, allowing precise calculation of arrival time differences. Channel timing offsets are estimated by solving a least squares problem. This method is assessed through analyses of phantoms and patient images using a RAYSOLUTION DigitMI 930 scanner.Main results. The estimated timing offsets ranged from -500 ps to 500 ps across all channels. Calibration with a minimum of three scanned positions was sufficient to correct these offsets, achieving less than a 1% discrepancy across various metrics of the image quality (IQ) phantom compared to 12 positions. This calibration significantly reduced edge artifacts in TOF reconstruction of both phantoms and patients. Furthermore, the IQ phantom displayed a 14% increase in average contrast recovery, a 61% reduction in average background variability across all spheres, and a 90% reduction in average residual error. Consistent with the phantom results, patient data revealed enhancements in maximum standardized uptake values (SUVmax) from 14% to 55% for lesions measuring 6 mm to 14 mm. The calibration also improved lesion-to-background contrast and eliminated artifacts caused by the spillover effect of the kidneys and bladder.Significance. The proposed method is fast, user-friendly, and cost-effective, effectively improving lesion detection and diagnostic accuracy.

Proton spot dose estimation based on positron activity distributions with neural network.

BACKGROUND: Positron emission tomography (PET) has been investigated for its ability to reconstruct proton-induced positron activity distributions in proton therapy. This technique holds potential for range verification in clinical practice. Recently, deep learning-based dose estimation from positron activity distributions shows promise for in vivo proton dose monitoring and guided proton therapy. PURPOSE: This study evaluates the effectiveness of three classical neural network models, recurrent neural network (RNN), U-Net, and Transformer, for proton dose estimating. It also investigates the characteristics of these models, providing valuable insights for selecting the appropriate model in clinical practice. METHODS: Proton dose calculations for spot beams were simulated using Geant4. Computed tomography (CT) images from four head cases were utilized, with three for training neural networks and the remaining one for testing. The neural networks were trained with one-dimensional (1D) positron activity distributions as inputs and generated 1D dose distributions as outputs. The impact of the number of training samples on the networks was examined, and their dose prediction performance in both homogeneous brain and heterogeneous nasopharynx sites was evaluated. Additionally, the effect of positron activity distribution uncertainty on dose prediction performance was investigated. To quantitatively evaluate the models, mean relative error (MRE) and absolute range error (ARE) were used as evaluation metrics. RESULTS: The U-Net exhibited a notable advantage in range verification with a smaller number of training samples, achieving approximately 75% of AREs below 0.5 mm using only 500 training samples. The networks performed better in the homogeneous brain site compared to the heterogeneous nasopharyngeal site. In the homogeneous brain site, all networks exhibited small AREs, with approximately 90% of the AREs below 0.5 mm. The Transformer exhibited the best overall dose distribution prediction, with approximately 92% of MREs below 3%. In the heterogeneous nasopharyngeal site, all networks demonstrated acceptable AREs, with approximately 88% of AREs below 3 mm. The Transformer maintained the best overall dose distribution prediction, with approximately 85% of MREs below 5%. The performance of all three networks in dose prediction declined as the uncertainty of positron activity distribution increased, and the Transformer consistently outperformed the other networks in all cases. CONCLUSIONS: Both the U-Net and the Transformer have certain advantages in the proton dose estimation task. The U-Net proves well suited for range verification with a small training sample size, while the Transformer outperforms others at dose-guided proton therapy.

Development and evaluation of a new high-TOF-resolution all-digital brain PET system.

Objective.Time-of-flight (TOF) capability and high sensitivity are essential for brain-dedicated positron emission tomography (PET) imaging, as they improve the contrast and the signal-to-noise ratio (SNR) enabling a precise localization of functional mechanisms in the different brain regions.Approach.We present a new brain PET system with transverse and axial field-of-view (FOV) of 320 mm and 255 mm, respectively. The system head is an array of 6 × 6 detection elements, each consisting of a 3.9 × 3.9 × 20 mm3lutetium-yttrium oxyorthosilicate crystal coupled with a 3.93 × 3.93 mm2SiPM. The SiPMs analog signals are individually digitized using the multi-voltage threshold (MVT) technology, employing a 1:1:1 coupling configuration.Main results.The brain PET system exhibits a TOF resolution of 249 ps at 5.3 kBq ml-1, an average sensitivity of 22.1 cps kBq-1, and a noise equivalent count rate (NECR) peak of 150.9 kcps at 8.36 kBq ml-1. Furthermore, the mini-Derenzo phantom study demonstrated the system's ability to distinguish rods with a diameter of 2.0 mm. Moreover, incorporating the TOF reconstruction algorithm in an image quality phantom study optimizes the background variability, resulting in reductions ranging from 44% (37 mm) to 75% (10 mm) with comparable contrast. In the human brain imaging study, the SNR improved by a factor of 1.7 with the inclusion of TOF, increasing from 27.07 to 46.05. Time-dynamic human brain imaging was performed, showing the distinctive traits of cortex and thalamus uptake, as well as of the arterial and venous flow with 2 s per time frame.Significance.The system exhibited a good TOF capability, which is coupled with the high sensitivity and count rate performance based on the MVT digital sampling technique. The developed TOF-enabled brain PET system opens the possibility of precise kinetic brain PET imaging, towards new quantitative predictive brain diagnostics.

[Design of a pulse oximeter used to low perfusion and low oxygen saturation].

This paper presents a new pulse oximeter used to low perfusion at 0.125% and wide oxygen saturation range from 35% to 100%. In order to acquire the best PPG signals, the variable gain amplifier(VGA) is adopted in hardware. The self-developed auto-correlation modeling method is adopted in software and it can extract pulse wave from low perfusion signals and remove motion artifacts partly.