Biomedical Physics & Engineering Express最新文献

Background. Propagation-based imaging computed tomography (PBI-CT) has been recently emerging for visualizing low-density materials due to its excellent image contrast and high resolution. Based on this, PBI-CT with a helical acquisition mode (PBI-HCT) offers superior imaging quality (e.g., fewer ring artifacts) and dose uniformity, making it ideal for biomedical imaging applications. However, the excessive radiation dose associated with high-resolution PBI-HCT may potentially harm objects or hosts being imaged, especially in live animal imaging, raising a great need to reduce radiation dose.Methods. In this study, we strategically integrated Sparse2Noise (a deep learning approach) with PBI-HCT imaging to reduce radiation dose without compromising image quality. Sparse2Noise uses paired low-dose noisy images with different photon fluxes and projection numbers for high-quality reconstruction via a convolutional neural network (CNN). Then, we examined the imaging quality and radiation dose of PBI-HCT imaging using Sparse2Noise, as compared to when Sparse2Noise was used in low-dose PBI-CT imaging (circular scanning mode). Furthermore, we conducted a comparison study on the use of Sparse2Noise versus two other state-of-the-art low-dose imaging algorithms (i.e., Noise2Noise and Noise2Inverse) for imaging low-density materials using PBI-HCT at equivalent dose levels.Results. Sparse2Noise allowed for a 90% dose reduction in PBI-HCT imaging while maintaining high image quality. As compared to PBI-CT imaging, the use of Sparse2Noise in PBI-HCT imaging shows more effective by reducing additional radiation dose (30%-36%). Furthermore, helical scanning mode also enhances the performance of existing low-dose algorithms (Noise2Noise and Noise2Inverse); nevertheless, Sparse2Noise shows significantly higher signal-to-noise ratio (SNR) value compared to Noise2Noise and Noise2Inverse at the same radiation dose level.Conclusions and significance. Our proposed low-dose imaging strategy Sparse2Noise can be effectively applied to PBI-HCT imaging technique and requires lower dose for acceptable quality imaging. This would represent a significant advance imaging for low-density materials imaging and for future live animals imaging applications.

As adaptive radiotherapy workflows and deep learning model training rise in popularity, the need for repeated applications of a rapid dose calculation algorithm increases. In this work we evaluate the feasibility of a simple algorithm that can calculate dose directly from MLC positions in near real-time. Given the necessary machine parameters, the intensity modulated radiation therapy (IMRT) doses are calculated and can be used in optimization, deep learning model training, or other cases where fast repeated segment dose calculations are needed. The algorithm uses normalized beamlets to modify a pre-calculated patient specific open field into any MLC segment shape. This algorithm was validated on 91 prostate IMRT plans as well as 20 lung IMRT plans generated for the Elekta Unity MR-Linac. IMRT plans calculated using the proposed method were found to match reference Monte Carlo calculated dose within98.02±0.84%and96.57±2.41%for prostate and lung patients respectively with a 3%/2 mm gamma criterion. After the patient-specific open field calculation, the algorithm can calculate the dose of a 9-field IMRT plan in 1.016 ± 0.284 s for a single patient or 0.264 ms per patient for a parallelized batch of 24 patients relevant for deep learning training. The presented algorithm demonstrates an alternative rapid IMRT dose calculator that does not rely on training a deep learning model while still being competitive in terms of speed and accuracy making it a compelling choice in cases where repetitive dose calculation is desired.

This study proposed a microfluidic chip for the detection and quantification of NSE proteins, aimed at developing a rapid point-of-care testing system for early lung cancer diagnosis. The proposed chip structure integrated an electrochemical biosensor within a straight PDMS microchannel, enabling a significant reduction in sample volume. Additionally, a method was developed to deposit silver and silver chloride layers onto the reference electrode. Following fabrication, the working electrode was modified to immobilize NSE antibodies on its surface, facilitating specific protein detection. Electrochemical impedance spectroscopy (EIS) measurements were utilized to investigate the alterations in surface impedance resulting from the specific binding of anti-NSE on the electrode surface across varying concentrations of NSE, ranging from 10 ng ml^-1to 1000 ng ml^-1. The experimental results demonstrated a direct correlation between NSE concentration and surface impedance. Specifically, the charge transfer resistance exhibited an increase from 24.54 MΩ to 89.18 MΩ as the NSE concentration varied from 10 ng ml^-1to 1000 ng ml^-1. Moreover, the concentration of NSE can be quantified by relating it to the charge transfer resistance, which follows a logarithmic equation. The limit of detection (LoD) of the chip was evaluated to be approximately 1.005 ng ml^-1. The proposed chip lays a crucial foundation for developing a Lab-on-a-chip platform dedicated to diagnosing NSE testing and lung cancer.

In transcranial magnetic stimulation (TMS), pulsed magnetic fields are applied to the brain, typically requiring high-power stimulators with high voltages and low series impedance. TMS pulse generators for small animal coils, are underexplored, with limited dedicated circuits and simulation models. Here, we present a new design for a high-power TMS pulse generator for small animals, utilizing a pre-charged supercapacitor that is sufficient to produce repeated pulses for TMS applications without the need for recharging. This approach eliminates the need for expensive high-voltage components and a high-voltage power supply. In this paper, we detail the design approach and basic block diagrams of a supercapacitor (SC) based TMS pulse generator, along with its experimental results. The findings indicate that the new circuit enables a complete test using just a single charge of an SC module. The proposed circuit functions as a versatile pulse-shaping device, where the MOSFET is treated as a dynamically varying resistor element rather than a traditional switch; allowing pulse parameter variations. We analyze a novel circuit for generating and controlling TMS pulses in small animal coils, and demonstrate its effectiveness through experimental results.

Developing an efficient and cost-effective wound-healing substance to treat wounds and regenerate skin is desperately needed in the current world. The present study evaluatedin vivowound healing andin vitroantioxidant, antibacterial, anti-inflammatory activities of propolis mediated silver nanoparticles. Extract of Bee propolis from northeast Punjab, Pakistan, has been prepared via maceration and subjected to chemical identification. The results revealed that it is rich in phenolic contents (88 ± 0.004 mg GAE ml^-1, 34 ± 0.1875 mg QE ml^-1) hence, employed as a reducer and capping agent to afford silver nanoparticles (AgNPs) by green approach. The prepared nanoparticles have been characterized by UV-visible (UV-vis), Fourier transform infrared spectroscopy (FTIR), Scanning electron microscopy (SEM), x-ray diffraction (XRD). The propolis mediated AgNPs possess cubic face center with spherical shape and measured 50-60 nm in size. Moreover, propolis mediated silver nanoparticles have been studied for various biological activities. The results showed excellent antioxidant (0.4696 μg ml^-1), anti-inflammatory (0.3996 μg ml^-1) and antibacterial activities againstStaphylococcus aureus(MIC 0.462 μg ml^-1) andProteus mirabilis(MIC 0.659 μg ml^-1) bacterium. An ointment was prepared by mixing AgNPs with polymeric gels for burn wound treatment in rabbits. We found rapid wound healing and higher collagen deposition in AgNPs treated wounds than in control group. Our data suggest that AgNPs from propolis ameliorate excision wounds, and hence, these AgNPs could be potential therapeutic agents for the treatment of burns.

Purpose. To investigate the effect of the position and orientation of the detector and its influence on the determination of output factors (OF) for small fields for a linear accelerator (MR-linac) integrated with 1.5 T magnetic resonance following the TRS-483 formalism.Methods. OF were measured for small fields in the central axis following the recommendations of the manufacturer and at the dose maximum following the TRS-483 formalism. OF were determined using a microDiamond (MD), a Semiflex (SF) 31021 ionization chamber, Gafchromic EBT3 film and were calculated in Monaco treatment planning system (TPS). Additionally, the orientation response of SF was evaluated, placing it in parallel and perpendicular direction to the radiation beam. The values were compared taking film measurements as reference. The corrected factors,ΩQclinical,msrfclinical,msr, required the use of output correction factorkQclinical,msrfclinical,msrtaken from previous reports. Finally, there are proposed experimentalkQclinical,msrfclinical,msrfor SF and MD, following the measured values in this work.Results. In fields smaller than 4 cm, the positioning of the SF and MD in the central axis or at the point of dose maximum affects the reading significantly with differences of up to 6% and 4%, respectively. For the data calculated in the TPS, the maximum difference of the OF between MD and TPS for fields greater than 2 cm was 0.6% and below this field size the TPS underestimates the OF up to 10.6%. The orientation (parallel or perpendicular) of the SF regarding the radiation beam has a considerable impact on the OF for fields smaller than 3 cm, showing a variation up to 10% for the field of 0.5 cm.Conclusion. This study provides valuable information on the challenges and limitations of measuring output factors in small fields. The outcomes have important implications for the practice of radiosurgery, underscoring the need for accuracy in detector placement and orientation, as well as the importance of using more advanced technologies and more robust measurement methods.

Attenuation correction of PET data is commonly conducted through the utilization of a secondary imaging technique to produce attenuation maps. The customary approach to attenuation correction, which entails the employment of CT images, necessitates energy conversion. However, the present study introduces a novel deep learning-based method that obviates the requirement for CT images and energy conversion. This study employs a residual Pix2Pix network to generate attenuation-corrected PET images using the 4033 2D PET images of 37 healthy adult brains for train and test. The model, implemented in TensorFlow and Keras, was evaluated by comparing image similarity, intensity correlation, and distribution against CT-AC images using metrics such as PSNR and SSIM for image similarity, while a 2D histogram plotted pixel intensities. Differences in standardized uptake values (SUV) demonstrated the model's efficiency compared to the CTAC method. The residual Pix2Pix demonstrated strong agreement with the CT-based attenuation correction, the proposed network yielding MAE, MSE, PSNR, and MS-SSIM values of 3 × 10^-3, 2 × 10^-4, 38.859, and 0.99, respectively. The residual Pix2Pix model's results showed a negligible mean SUV difference of 8 × 10^-4(P-value = 0.10), indicating its accuracy in PET image correction. The residual Pix2Pix model exhibits high precision with a strong correlation coefficient of R² = 0.99 to CT-based methods. The findings indicate that this approach surpasses the conventional method in terms of precision and efficacy. The proposed residual Pix2Pix framework enables accurate and feasible attenuation correction of brain F-FDG PET without CT. However, clinical trials are required to evaluate its clinical performance. The PET images reconstructed by the framework have low errors compared to the accepted test reliability of PET/CT, indicating high quantitative similarity.

Objective. Previous work has that deep learning (DL)-enhanced 4D cone beam computed tomography (4D-CBCT) images improve motion modeling and subsequent motion-compensated (MoCo) reconstruction for 4D-CBCT. However, building the motion model at treatment time via conventional deformable image registration (DIR) methods is not temporally feasible. This work aims to improve the efficiency of 4D-CBCT MoCo reconstruction using DL-based registration for the rapid generation of a motion model prior to treatment.Approach.An artifact-reduction DL model was first used to improve the initial 4D-CBCT reconstruction by reducing streaking artifacts. Based on the artifact-reduced phase images, a groupwise DIR employing DL was used to estimate the inter-phase motion model. Two DL DIR models using different learning strategies were employed: (1) a patient-specific one-shot DIR model which was trained from scratch only using the images to be registered, and (2) a population DIR model which was pre-trained using collected 4D-CT images from 35 patients. The registration accuracy of two DL DIR models was assessed and compared to a conventional groupwise DIR approach implemented in the Elastix toolbox using the publicly available DIR-Lab dataset, a Monte Carlo simulation dataset from the SPARE challenge, and two clinical cases.Main results.The patient-specific DIR model and the population DIR model demonstrated registration accuracy comparable to the conventional state-of-the-art methods on the DIR-Lab dataset. No significant difference in image quality was observed between the final MoCo reconstructions using the patient-specific model and population model for motion modeling, compared to using the conventional approach. The average runtime (hh:mm:ss) of the entire MoCo reconstruction on SPARE dataset was reduced from 01:37:26 using conventional DIR method to 00:10:59 using patient-specific model and 00:01:05 using the pre-trained population model.Significance.DL-based registration methods can improve the efficiency in generating motion models for 4D-CBCT without compromising the performance of final MoCo reconstruction.

The primary objective of this study was to compare organ doses measured using optically stimulated luminescent dosimeters (OSLDs) with those estimated by the CT-EXPO software for common CT protocols. An anthropomorphic ATOM phantom was employed to measure organ doses across head, chest, and abdominal CT scans performed on a Hitachi Supria 16-slice CT scanner. These OSLD measurements were then compared to the estimates provided by the widely used CT-EXPO software. Organ doses were assessed using OSLDs placed in an adult anthropomorphic phantom, with calibration performed through a comprehensive process involving multiple tube potentials and sensitivity corrections. Results from three CT acquisitions per protocol were compared to estimates provided by CT-EXPO software. Findings reveal significant discrepancies between measured and estimated organ doses, with p-values consistently below 0.05 across all organs. For head CT, measured eye lens doses averaged 33.51 mGy, 6.0% lower than the estimated 35.65 mGy. In chest CT, the thyroid dose was 9.82 mGy, 13.5% higher than the estimated 8.65 mGy. For abdominal CT, the liver dose measured 12.11 mGy, 9.6% higher than the estimated 11.05 mGy. Measured doses for the rest of organs were generally lower than those predicted by CT-EXPO, showing some limitations in current estimation models and the importance of precise dosimetry. This study highlights the potential of OSLD measurements as a complementary method for organ dose assessment in CT imaging, emphasizing the need for more accurate organ dose measurement to optimize patient care.

Smartphone-assisted urine analyzers estimate the urinary albumin by quantifying color changes at sensor pad of test strips. These strips yield color variations due to the total protein present in the sample, making it difficult to relate to color changes due to specific analyte. We have addressed it using a Lateral Flow Assay (LFA) device for automatic detection and quantification of urinary albumin. LFAs are specific to individual analytes, allowing color changes to be linked to the specific analyte, minimizing the interference. The proposed reader performs automatic segmentation of the region of interest (ROI) using YOLOv5, a deep learning-based model. Concentrations of urinary albumin in clinical samples were classified using customized machine learning algorithms. An accuracy of 96% was achieved on the test data using the k-Nearest Neighbour (k-NN) algorithm. Performance of the model was also evaluated under different illumination conditions and with different smartphone cameras, and validated using standard nephelometer.