Dual threshold input receiver FPGA-only signal digitization method for time-of-flight positron emission tomography.

As silicon photomultiplier (SiPM)-based time-of-flight (TOF) positron emission tomography (PET) becomes popular, the need for sophisticated PET data acquisition (DAQ) systems is increasing. One promising solution to this challenge is the adoption of a field-programmable gate array (FPGA)-only signal digitization method. In this paper, we propose a new approach to efficiently implement an FPGA-only digitizer. We configured the input/output (IO) port of the FPGA to function as a dual-threshold voltage comparator through the use of simple passive circuitry and heterogeneous IO standards. This configuration overcomes the limitations of existing methods by allowing different threshold voltages for adjacent IO pins, effectively reducing routing complexity and lowering manufacturing costs. An FPGA-only digitizer was implemented by integrating the dual-threshold voltage comparator and FPGA-based time-to-digital converter. By combining the dual-threshold time-over-threshold (TOT) method and curve fitting, precise energy information could be obtained. The performance of the FPGA-only digitizer was assessed using a detector setup comprising a 3 × 3 × 20 mm3 LYSO scintillation crystal and a single pixel SiPM. Using the configured evaluation setup, an energy resolution of 12.5% and a time resolution of 146 ± 9 ps were achieved for a 20 mm scintillation crystal. The dual-threshold TOT implemented using the proposed method showed consistent linearity across an energy range of 100 keV to 600 keV. The proposed method is well-suited for the development of cost-effective DAQ systems in highly integrated TOF PET systems.

Emergence and Potential Spread of Rust Disease on Wisteria floribunda and Corydalis incisa Influenced by Climate Change in Korea.

Global climate change influences the emergence, spread, and severity of rust diseases that affect crops and forests. In Korea, the rust diseases that affect Wisteria floribunda and its alternate host Corydalis incisa are rapidly spreading northwards. Through morphological, molecular, phylogenetic, and pathogenicity approaches, Neophysopella kraunhiae was identified as the causal agent, alternating between the two host plants to complete its life cycle. Using the maximum entropy model (Maxent) under shared socioeconomic pathways (SSPs), the results of this study suggest that by the 2050s, C. incisa is likely to extend its range into central Korea owing to climate shifts, whereas the distribution of W. floribunda is expected to remain unchanged nationwide. The generalized additive model revealed a significant positive correlation between the presence of C. incisa and the incidence of rust disease, highlighting the role that climate-driven expansion of this alternate host plays in the spread of N. kraunhiae. These findings highlight the profound influence of climate change on both the distribution of a specific plant and the disease a rust fungus causes, raising concerns about the potential emergence and spread of other rust pathogens with similar host dynamics.

Generating 3D images of VMAT plans for predictive models and activation maps associated with plan deliverability.

BACKGROUND: Intensity modulation with dynamic multi-leaf collimator (MLC) and monitor unit (MU) changes across control points (CPs) characterizes volumetric modulated arc therapy (VMAT). The increased uncertainty in plan deliverability required patient-specific quality assurance (PSQA), which remained inefficient upon Quality Assurance (QA) failure. To prevent waste before QA, plan complexity metrics (PCMs) and machine learning models with the metrics were generated, which were lack of providing CP-specific information upon QA failures. PURPOSE: By generating 3D images from digital imaging and comminications in medicine in radiation therapy (DICOM RT) plan, we proposed a predictive model that can estimate the deliverability of VMAT plans and visualize CP-specific regions associated with plan deliverability. METHODS: The patient cohort consisted of 259 and 190 cases for left- and right-breast VMAT treatments, which were split into 235 and 166 cases for training and 24 cases from each treatment for testing the networks. Three-channel 3D images generated from DICOM RT plans were fed into a DenseNet-based deep learning network. To reflect VMAT plan complexity as an image, the first two channels described MLC and MU variations between two consecutive CPs, while the last channel assigned the beam field size. The network output was defined as binary classified PSQA results, indicating deliverability. The predictive performance was assessed by accuracy, sensitivity, specificity, F1-score, and area under the curve (AUC). The gradient-weighted class activation map (Grad-CAM) highlighted the regions of CPs in VMAT plans associated with deliverability, compared against PCMs by Spearman correlation. RESULTS: The DenseNet-based predictive model yielded AUCs of 92.2% and 93.8%, F1-scores of 97.0% and 93.8% and accuracies of 95.8% and 91.7% for the left- and right-breast VMAT cases. Additionally, the specificity of 87.5% for both cases indicated that the predictive model accurately detected QA failing cases. The activation maps significantly differentiated QA failing-labeled from passing-labeled classes for the non-deliverable cases. The PCM with the highest correlation to the Grad-CAM varied from patient cases, implying that plan deliverability would be considered patient-specific. CONCLUSION: This work demonstrated that the deep learning-based network based on visualization of dynamic VMAT plan information successfully predicted plan deliverability, which also provided control-point specific planning parameter information associated with plan deliverability in a patient-specific manner.

Clinical Performance Evaluation of an Artificial Intelligence-Powered Amyloid Brain PET Quantification Method.

Purpose: This study assesses the clinical performance of BTXBrain-Amyloid, an artificial intelligence-powered software for quantifying amyloid uptake in brain PET images. Methods: 150 amyloid brain PET images were visually assessed by experts and categorized as negative and positive. Standardized uptake value ratio (SUVR) was calculated with cerebellum grey matter as the reference region, and receiver operating characteristic (ROC) and precision-recall (PR) analysis for BTXBrain-Amyloid were conducted. For comparison, same image processing and analysis was performed using Statistical Parametric Mapping (SPM) program. In addition, to evaluate the spatial normalization (SN) performance, mutual information (MI) between MRI template and spatially normalized PET images was calculated and SPM group analysis was conducted. Results: Both BTXBrain and SPM methods discriminated between negative and positive groups. However, BTXBrain exhibited lower SUVR standard deviation (0.06 and 0.21 for negative and positive, respectively) than SPM method (0.11 and 0.25). In ROC analysis, BTXBrain had an AUC of 0.979, compared to 0.959 for SPM, while PR curves showed an AUC of 0.983 for BTXBrain and 0.949 for SPM. At the optimal cut-off, the sensitivity and specificity were 0.983 and 0.921 for BTXBrain and 0.917 and 0.921 for SPM12, respectively. MI evaluation also favored BTXBrain (0.848 vs. 0.823), indicating improved SN. In SPM group analysis, BTXBrain exhibited higher sensitivity in detecting basal ganglia differences between negative and positive groups. Conclusion: BTXBrain-Amyloid outperformed SPM in clinical performance evaluation, also demonstrating superior SN and improved detection of deep brain differences. These results suggest the potential of BTXBrain-Amyloid as a valuable tool for clinical amyloid PET image evaluation.

RicePilaf: a post-GWAS/QTL dashboard to integrate pangenomic, coexpression, regulatory, epigenomic, ontology, pathway, and text-mining information to provide functional insights into rice QTLs and GWAS loci.

BACKGROUND: As the number of genome-wide association study (GWAS) and quantitative trait locus (QTL) mappings in rice continues to grow, so does the already long list of genomic loci associated with important agronomic traits. Typically, loci implicated by GWAS/QTL analysis contain tens to hundreds to thousands of single-nucleotide polmorphisms (SNPs)/genes, not all of which are causal and many of which are in noncoding regions. Unraveling the biological mechanisms that tie the GWAS regions and QTLs to the trait of interest is challenging, especially since it requires collating functional genomics information about the loci from multiple, disparate data sources. RESULTS: We present RicePilaf, a web app for post-GWAS/QTL analysis, that performs a slew of novel bioinformatics analyses to cross-reference GWAS results and QTL mappings with a host of publicly available rice databases. In particular, it integrates (i) pangenomic information from high-quality genome builds of multiple rice varieties, (ii) coexpression information from genome-scale coexpression networks, (iii) ontology and pathway information, (iv) regulatory information from rice transcription factor databases, (v) epigenomic information from multiple high-throughput epigenetic experiments, and (vi) text-mining information extracted from scientific abstracts linking genes and traits. We demonstrate the utility of RicePilaf by applying it to analyze GWAS peaks of preharvest sprouting and genes underlying yield-under-drought QTLs. CONCLUSIONS: RicePilaf enables rice scientists and breeders to shed functional light on their GWAS regions and QTLs, and it provides them with a means to prioritize SNPs/genes for further experiments. The source code, a Docker image, and a demo version of RicePilaf are publicly available at https://github.com/bioinfodlsu/rice-pilaf.

Tuning CuMgAl-Layered Double Hydroxide Nanostructures to Achieve CH4 and C2+ Product Selectivity in CO2 Electroreduction.

Electrochemical CO2 reduction reaction (eCO2RR) over Cu-based catalysts is a promising approach for efficiently converting CO2 into value-added chemicals and alternative fuels. However, achieving controllable product selectivity from eCO2RR remains challenging because of the difficulty in controlling the oxidation states of Cu against robust structural reconstructions during the eCO2RR. Herein, we report a novel strategy for tuning the oxidation states of Cu species and achieving eCO2RR product selectivity by adjusting the Cu content in CuMgAl-layered double hydroxide (LDH)-based catalysts. In this strategy, the highly stable Cu2+ species in low-Cu-containing LDHs facilitated the strong adsorption of *CO intermediates and further hydrogenation into CH4. Conversely, the mixed Cu0/Cu+ species in high-Cu-containing LDHs derived from the electroreduction during the eCO2RR accelerated C-C coupling reactions. This strategy to regulate Cu oxidation states using LDH nanostructures with low and high Cu molar ratios produced an excellent eCO2RR performance for CH4 and C2+ products, respectively.

Incidence and predictors of dorsal comminution in older adults with low-energy distal radius fracture.

PURPOSE: To identify the incidence of dorsal comminution using computed tomography (CT) images and identify predictors of this phenomenon in older adults with low-energy distal radius fractures (DRFs). METHODS: A total of 150 patients aged > 50 years with fall-induced dorsally angulated DRFs were enrolled in this study. Patients were divided into two groups based on the presence of dorsal comminution, defined as a metaphyseal void of greater than one-third of the maximum posterior to anterior depth of the bone on at least three cuts in the sagittal plane on post-reduction CT images. Data on participants' basic demographics, including age, sex, body mass index (BMI), and AO classification of DRFs, were collected. Bone mineral density (BMD) was assessed using T-scores of the femoral neck, and cortical thickness of the distal radius was determined from plain post-reduction radiographs. Radiological parameters and combined ulnar fractures were measured on plain pre-reduction radiographs. RESULTS: Among study participants, 91 (61%) had dorsal comminution, whereas 59 (39%) had no dorsal comminution on CT images. Both patient groups were compared based on presence of dorsal comminution, and showed no significant differences in age, sex, BMI, BMD, or cortical thickness on radiographs. However, all radiological parameters were better in the no dorsal comminution group than in the dorsal comminution group, and the proportion of patients with combined ulnar fractures was higher in the dorsal comminution group. In the multivariate analysis, the presence of combined ulnar fractures was the only significant predictor of dorsal comminution (p = 0.029, odds ratio = 2.267, 95% confidence interval: 1.085-4.736). CONCLUSION: The incidence of dorsal comminution is relatively high in patients with low-energy DRFs aged > 50 years. In particular, the presence of combined ulnar fractures is closely associated with dorsal comminution of DRFs. Thus, surgeons should exercise caution when evaluating this phenomenon.

Estimating entanglement entropy via variational quantum circuits with classical neural networks.

Entropy plays a crucial role in both physics and information science, encompassing classical and quantum domains. In this paper, we present the quantum neural entropy estimator (QNEE), an approach that combines classical neural network (NN) with variational quantum circuits to estimate the von Neumann and Rényi entropies of a quantum state. QNEE provides accurate estimates of entropy while also yielding the eigenvalues and eigenstates of the input density matrix. Leveraging the capabilities of classical NN, QNEE can classify different phases of quantum systems that accompany the changes of entanglement entropy. Our numerical simulation demonstrates the effectiveness of QNEE by applying it to the 1D XXZ Heisenberg model. In particular, QNEE exhibits high sensitivity in estimating entanglement entropy near the phase transition point. We expect that QNEE will serve as a valuable tool for quantum entropy estimation and phase classification.

Enhancing voxel-based dosimetry accuracy with an unsupervised deep learning approach for hybrid medical image registration.

BACKGROUND: Deformable registration is required to generate a time-integrated activity (TIA) map which is essential for voxel-based dosimetry. The conventional iterative registration algorithm using anatomical images (e.g., computed tomography (CT)) could result in registration errors in functional images (e.g., single photon emission computed tomography (SPECT) or positron emission tomography (PET)). Various deep learning-based registration tools have been proposed, but studies specifically focused on the registration of serial hybrid images were not found. PURPOSE: In this study, we introduce CoRX-NET, a novel unsupervised deep learning network designed for deformable registration of hybrid medical images. The CoRX-NET structure is based on the Swin-transformer (ST), allowing for the representation of complex spatial connections in images. Its self-attention mechanism aids in the effective exchange and integration of information across diverse image regions. To augment the amalgamation of SPECT and CT features, cross-stitch layers have been integrated into the network. METHODS: Two different 177 Lu DOTATATE SPECT/CT datasets were acquired at different medical centers. 22 sets from Seoul National University and 14 sets from Sunway Medical Centre are used for training/internal validation and external validation respectively. The CoRX-NET architecture builds upon the ST, enabling the modeling of intricate spatial relationships within images. To further enhance the fusion of SPECT and CT features, cross-stitch layers have been incorporated within the network. The network takes a pair of SPECT/CT images (e.g., fixed and moving images) and generates a deformed SPECT/CT image. The performance of the network was compared with Elastix and TransMorph using L1 loss and structural similarity index measure (SSIM) of CT, SSIM of normalized SPECT, and local normalized cross correlation (LNCC) of SPECT as metrics. The voxel-wise root mean square errors (RMSE) of TIA were compared among the different methods. RESULTS: The ablation study revealed that cross-stitch layers improved SPECT/CT registration performance. The cross-stitch layers notably enhance SSIM (internal validation: 0.9614 vs. 0.9653, external validation: 0.9159 vs. 0.9189) and LNCC of normalized SPECT images (internal validation: 0.7512 vs. 0.7670, external validation: 0.8027 vs. 0.8027). CoRX-NET with the cross-stitch layer achieved superior performance metrics compared to Elastix and TransMorph, except for CT SSIM in the external dataset. When qualitatively analyzed for both internal and external validation cases, CoRX-NET consistently demonstrated superior SPECT registration results. In addition, CoRX-NET accomplished SPECT/CT image registration in less than 6 s, whereas Elastix required approximately 50 s using the same PC's CPU. When employing CoRX-NET, it was observed that the voxel-wise RMSE values for TIA were approximately 27% lower for the kidney and 33% lower for the tumor, compared to when Elastix was used. CONCLUSION: This study represents a major advancement in achieving precise SPECT/CT registration using an unsupervised deep learning network. It outperforms conventional methods like Elastix and TransMorph, reducing uncertainties in TIA maps for more accurate dose assessments.

Unraveling the Life Cycle of Nyssopsora cedrelae: A Study of Rust Diseases on Aralia elata and Toona sinensis.

Rust disease poses a major threat to global agriculture and forestry. It is caused by types of Pucciniales, which often require alternate hosts for their life cycles. Nyssopsora cedrelae was previously identified as a rust pathogen on Toona sinensis in East and Southeast Asia. Although this species had been reported to be autoecious, completing its life cycle solely on T. sinensis, we hypothesized that it has a heteroecious life cycle, requiring an alternate host, since the spermogonial and aecial stages on Aralia elata, a plant native to East Asia, are frequently observed around the same area where N. cedrelae causes rust disease on T. sinensis. Upon collecting rust samples from both A. elata and T. sinensis, we confirmed that the rust species from both tree species exhibited matching internal transcribed spacer (ITS), large subunit (LSU) rDNA, and cytochrome oxidase subunit III (CO3) mtDNA sequences. Through cross-inoculations, we verified that aeciospores from A. elata produced a uredinial stage on T. sinensis. This study is the first report to clarify A. elata as an alternate host for N. cedrelae, thus providing initial evidence that the Nyssopsora species exhibits a heteroecious life cycle.

Subperiosteal delivery of transforming growth factor beta 1 and human growth hormone from mineralized PCL films.

The ability to locally deliver bioactive molecules to distinct regions of the skeleton may provide a novel means by which to improve fracture healing, treat neoplasms or infections, or modulate growth. In this study, we constructed single-sided mineral-coated poly-ε-caprolactone membranes capable of binding and releasing transforming growth factor beta 1 (TGF-ß1) and human growth hormone (hGH). After demonstrating biological activity in vitro and characterization of their release, these thin bioabsorbable membranes were surgically implanted using an immature rabbit model. Membranes were circumferentially wrapped under the periosteum, thus placed in direct contact with the proximal metaphysis to assess its bioactivity in vivo. The direct effects on the metaphyseal bone, bone marrow, and overlying periosteum were assessed using radiography and histology. Effects of membrane placement at the tibial growth plate were assessed via physeal heights, tibial growth rates (pulsed fluorochrome labeling), and tibial lengths. Subperiosteal placement of the mineralized membranes induced greater local chondrogenesis in the plain mineral and TGF-ß1 samples than the hGH. More exuberant and circumferential ossification was seen in the TGF-ß1 treated tibiae. The TGF-ß1 membranes also induced hypocellularity of the bone marrow with characteristics of gelatinous degeneration not seen in the other groups. While the proximal tibial growth plates were taller in the hGH treated than TGF-ß1, no differences in growth rates or overall tibial lengths were found. In conclusion, these data demonstrate the feasibility of using bioabsorbable mineral coated membranes to deliver biologically active compounds subperiosteally in a sustained fashion to affect cells at the insertion site, bone marrow, and even growth plate.

A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising with Neural Network Approaches.

Low-dose emission tomography (ET) plays a crucial role in medical imaging, enabling the acquisition of functional information for various biological processes while minimizing the patient dose. However, the inherent randomness in the photon counting process is a source of noise which is amplified in low-dose ET. This review article provides an overview of existing post-processing techniques, with an emphasis on deep neural network (NN) approaches. Furthermore, we explore future directions in the field of NN-based low-dose ET. This comprehensive examination sheds light on the potential of deep learning in enhancing the quality and resolution of low-dose ET images, ultimately advancing the field of medical imaging.

Simplified image-based dosimetry using planar images and patient-specific S-values.

BACKGROUND: Single time point measurement approach and hybrid dosimetry were proposed to simplify the dosimetry process. It is anticipated that utilizing patient-specific S-value would enable more accurate dosimetry assessment based on imaging compared to using the conventional MIRD S-values. PURPOSE: We performed planar image-based dosimetry scaled with a single SPECT image for the entire treatment cycle using patient-specific S-values (PSS dosimetry) of organs. PSS dosimetry could further simplify the dosimetry procedure compared with a conventional 2D planar/3D SPECT hybrid dosimetry, as PSS dosimetry requires only one SPECT/CT image for the treatment of the entire cycle, whereas the conventional hybrid dosimetry requires a SPECT/CT image for each treatment cycle. METHODS: 177Lu-DOTATATE SPECT/CT and planar image datasets acquired from Seoul National University Hospital (SNUH, Seoul, Republic of Korea) were utilized for the evaluation. Images were acquired 4, 24, 48, and 120 h after patients' intravenous injection of 177Lu-DOTATATE. Dose estimations based on a Monte Carlo (MC) simulation using the Geant4 Application for Emission Tomography (GATE) (v.8.2) were considered as the reference. Planar image-based dosimetry scaled with a single SPECT image was performed using the patient-specific S-value (PSS). Briefly, the CT image was considered as the patient's anatomical reference and PSSs were quantified using the multiple voxel S-value (VSV) method. Then, PSS dosimetry was performed by obtaining activity information from sequential planar images and a scaling factor derived from a single SPECT/planar image pair. Hybrid dosimetry using sequential planar images and a single SPECT image was performed for comparison. The absorbed doses of the kidneys, bone marrow (BM) in the lumbar spine, liver, and spleen calculated using the PSS and hybrid dosimetries were compared with the reference MC results. RESULTS: The mean differences (MDs) of the self-absorption S-values between S-value of OLINDA/EXM and PSS for the kidneys, liver, and spleen were -0.04%, -2.39%, and -2.62%, respectively. However, the differences in the self-absorption S-values were significantly higher for the BM (84.99%) and the remainder of the body (ROB) (280.84%). The absorbed doses estimated by the PSS and hybrid dosimetries showed relatively high errors compared with MC simulation result, regardless of the organ. In contrast, the PSS and hybrid dosimetries produced similar dose estimates. For the entire cycles of the treatment, the MDs of absorbed doses between PSS and hybrid dosimetries were -3.31%, -6.04%, 3.37%, and -2.17% for the kidneys, BM, liver, and spleen, respectively. Through a correlation analysis and the Wilcoxon signed-rank test, we concluded that there was no significant difference between the results obtained by the two dosimetry methods. CONCLUSIONS: As the PSS was derived using CT images with actual anatomical information and organ-specific volume of interest (VOI), PSS dosimetry provided reliable results. PSS dosimetry was robust in estimating the absorbed dose for the later treatment cycles. Therefore, PSS dosimetry outperformed hybrid dosimetry in terms of dose estimation for a greater number of treatment cycles.

Sixty-four-fold data reduction of chest radiographs using a super-resolution convolutional neural network.

OBJECTIVES: To develop and validate a super-resolution (SR) algorithm generating clinically feasible chest radiographs from 64-fold reduced data. METHODS: An SR convolutional neural network was trained to produce original-resolution images (output) from 64-fold reduced images (input) using 128 × 128 patches (n = 127 030). For validation, 112 radiographs-including those with pneumothorax (n = 17), nodules (n = 20), consolidations (n = 18), and ground-glass opacity (GGO; n = 16)-were collected. Three image sets were prepared: the original images and those reconstructed using SR and conventional linear interpolation (LI) using 64-fold reduced data. The mean-squared error (MSE) was calculated to measure similarity between the reconstructed and original images, and image noise was quantified. Three thoracic radiologists evaluated the quality of each image and decided whether any abnormalities were present. RESULTS: The SR-images were more similar to the original images than the LI-reconstructed images (MSE: 9269 ± 1015 vs. 9429 ± 1057; P = .02). The SR-images showed lower measured noise and scored better noise level by three radiologists than both original and LI-reconstructed images (Ps < .01). The radiologists' pooled sensitivity with the SR-reconstructed images was not significantly different compared with the original images for detecting pneumothorax (SR vs. original, 90.2% [46/51] vs. 96.1% [49/51]; P = .19), nodule (90.0% [54/60] vs. 85.0% [51/60]; P = .26), consolidation (100% [54/54] vs. 96.3% [52/54]; P = .50), and GGO (91.7% [44/48] vs. 95.8% [46/48]; P = .69). CONCLUSIONS: SR-reconstructed chest radiographs using 64-fold reduced data showed a lower noise level than the original images, with equivalent sensitivity for detecting major abnormalities. ADVANCES IN KNOWLEDGE: This is the first study applying super-resolution in data reduction of chest radiographs.

CO2 Enrichment Boosts Highly Selective Infrared-Light-Driven CO2 Conversion to CH4 by UiO-66/Co9S8 Photocatalyst.

Photocatalytic CO2 reduction to high-value chemicals is an attractive approach to mitigate climate change, but it remains a great challenge to produce a specific product selectively by IR light. Hence, UiO-66/Co9S8 composite is designed to couple the advantages of metallic photocatalysts and porous CO2 adsorbers for IR-light-driven CO2-to-CH4 conversion. The metallic nature of Co9S8 endows UiO-66/Co9S8 with exceptional IR light absorption, while UiO-66 dramatically enhances its local CO2 concentration, revealed by finite-element method simulations. As a result, Co9S8 or UiO-66 alone does not show observable IR-light photocatalytic activity, whereas UiO-66/Co9S8 exhibits exceptional activity. The CH4 evolution rate over UiO-66/Co9S8 reaches 25.7 µmol g-1 h-1 with ca.100% selectivity under IR light irradiation, outperforming most reported catalysts under similar reaction conditions. The X-ray absorption fine structure spectroscopy spectra verify the presence of two distinct Co sites and confirm the existence of metallic Co─Co bond in Co9S8. Energy diagrams analysis and transient absorption spectra manifest that CO2 reduction mainly occurs on Co9S8 for UiO-66/Co9S8, while density functional theory calculations demonstrate that high-electron-density Co1 sites are the key active sites, possessing lower energy barriers for further protonation of *CO, leading to the ultra-high selectivity toward CH4.

Pushing the limit of BGO-based dual-ended Cherenkov PET detectors through photon transit time correction.

Objective. The high production cost of commonly used lutetium-based fast scintillators and the development of silicon photomultipliers technology have made bismuth germanate (BGO) a promising candidate for time-of-flight positron emission tomography (TOF PET) detectors owing to its generation of prompt Cherenkov photons. However, using BGO as a hybrid scintillator is disadvantageous owing to its low photon statistics and distribution that does not conform well to a single Gaussian. To mitigate this, a proposal was made to increase the likelihood of detecting the first Cherenkov photons by positioning two photosensors in opposition at the entrance and exit faces of the scintillator and subsequently selectively picking an earlier timestamp. Nonetheless, the timing variation arising from the photon transit time remains affected by the entire length of the crystal, thereby presenting a possibility for further enhancement.Approach. In this study, we aimed to improve the timing performance of the dual-ended BGO Cherenkov TOF PET detector by capitalizing on the synergistic advantages of applying depth-of-interaction (DOI) information and crystal surface finishes or reflector properties. A dual-ended BGO detector was implemented using a 3 × 3 × 15 mm3BGO crystal. Coincidence events were acquired against a 3 × 3 × 3 mm3LYSO:Ce:Mg reference detector. The timing performance of the dual-ended BGO detectors was analyzed using conventionally proposed timestamp methods before and after DOI correction.Results. Through a DOI-based correction of photon transit time spread, we demonstrated a further improvement in the timing resolution of the BGO-based Cherenkov TOF PET detector utilizing a dual-ended detector configuration and adaptive arrival time pickoff. We achieved further improvements in timing resolution by correcting the offset spread induced by the fluctuation of timing signal rise time in the dual-ended detector.Significance. Although polishing the crystal surface was still favorable in terms of full-width-half-maximum value, incorporating DOI information from the unpolished crystal to compensate for photon travel time facilitated additional enhancement in the overall timing performance, thereby surpassing that achieved with the polished crystal.

Magnetic resonance imaging analysis of the distribution of cartilage damage in scaphoid nonunion.

This study aimed to evaluate the distribution and severity of cartilage damage using magnetic resonance imaging in patients with scaphoid nonunion without advanced wrist arthritis. We retrospectively analysed MRI of patients who underwent osteosynthesis for scaphoid nonunion. Cartilage damage was assessed in eight wrist regions using a modified Whole-Organ Magnetic Resonance Imaging Score. The frequencies of regions affected by any cartilage damage (Score ≥2.5) depicted on MRI were analysed. The study included 32 patients (31 men and one woman), with a mean age of 32 years (SD 11). The distal radioscaphoid (29/32), scaphotrapeziotrapezoid (STT) (22/32), proximal radioscaphoid (14/32) and scaphocapitate (11/32) joints were most commonly affected by degenerative cartilage damage. The STT and proximal radioscaphoid joint were especially likely to be involved in early-stage scaphoid nonunion advanced collapse. We recommend that surgeons should investigate the cartilage status of these joints during decision-making for scaphoid nonunion surgery.Level of evidence: III.

Optimized biomimetic minerals maintain activity of mRNA complexes after long term storage.

mRNA therapeutics can be readily designed, manufactured, and brought to scale, as demonstrated by widespread global vaccination against COVID-19. However, mRNA therapies require cold chain shipment and storage from manufacturing to administration, which may limit them to affluent communities. This problem could be addressed by mimicking the known ability of mineralized fossils to durably stabilize nucleic acids under extreme conditions. We synthesized and screened 40 calcium-phosphate minerals for their ability to store and maintain the activity of lyophilized mRNA complexes. The optimal mineral formulation incorporated mRNA complexes with high efficiency (77 %), and increased mRNA transfection efficiency by 5.6-fold. Lyophilized mRNA complexes stored with the optimized mineral formulation for 6 months at 25 °C were 3.2-fold more active than those stored with state-of-the-art excipients, but without a mineral. mRNA complexes stored with minerals at room temperature did not decline in transfection efficacy from 3 days to 6 months of storage, indicating that minerals can durably maintain activity of therapeutic mRNA complexes without cold chain storage. STATEMENT OF SIGNIFICANCE: Therapeutic mRNA, such as mRNA COVID-19 vaccines, require extensive cold chain storage that limits their general application. This work screened a library of minerals to maintain the activity of mRNA complexes with freeze-drying. The optimized mineral was able to maintain mRNA activity up to 6 months of storage at room temperature outperforming current methods of freeze-drying therapeutic mRNA complexes.

Empowering Vision Transformer by Network Hyper-Parameter Selection for Whole Pelvis Prostate Planning Target Volume Auto-Segmentation.

U-Net, based on a deep convolutional network (CNN), has been clinically used to auto-segment normal organs, while still being limited to the planning target volume (PTV) segmentation. This work aims to address the problems in two aspects: 1) apply one of the newest network architectures such as vision transformers other than the CNN-based networks, and 2) find an appropriate combination of network hyper-parameters with reference to recently proposed nnU-Net ("no-new-Net"). VT U-Net was adopted for auto-segmenting the whole pelvis prostate PTV as it consisted of fully transformer architecture. The upgraded version (v.2) applied the nnU-Net-like hyper-parameter optimizations, which did not fully cover the transformer-oriented hyper-parameters. Thus, we tried to find a suitable combination of two key hyper-parameters (patch size and embedded dimension) for 140 CT scans throughout 4-fold cross validation. The VT U-Net v.2 with hyper-parameter tuning yielded the highest dice similarity coefficient (DSC) of 82.5 and the lowest 95% Haussdorff distance (HD95) of 3.5 on average among the seven recently proposed deep learning networks. Importantly, the nnU-Net with hyper-parameter optimization achieved competitive performance, although this was based on the convolution layers. The network hyper-parameter tuning was demonstrated to be necessary even for the newly developed architecture of vision transformers.

Thermodynamic trade-off relation for first passage time in resetting processes.

Resetting is a strategy for boosting the speed of a target-searching process. Since its introduction over a decade ago, most studies have been carried out under the assumption that resetting takes place instantaneously. However, due to its irreversible nature, resetting processes incur a thermodynamic cost, which becomes infinite in the case of instantaneous resetting. Here, we take into consideration both the cost and the first passage time (FPT) required for a resetting process, in which the reset or return to the initial location is implemented using a trapping potential over a finite but random time period. An iterative generating function and a counting functional method à la Feynman and Kac are employed to calculate the FPT and the average work for this process. From these results, we obtain an explicit form of the time-cost trade-off relation, which provides the lower bound of the mean FPT for a given work input when the trapping potential is linear. This trade-off relation clearly shows that instantaneous resetting is achievable only when an infinite amount of work is provided. More surprisingly, the trade-off relation derived from the linear potential seems to be valid for a wide range of trapping potentials. In addition, we have also shown that the fixed-time or sharp resetting can further enhance the trade-off relation compared to that of the stochastic resetting.