Diagnostic System Research Articles

Characterisation of a Mesophilic Aeromonas salmonicida and the Development of a PCR to Differentiate Atypical and Typical Strains.

This study describes the identification and characterisation of a new mesophilic Aeromonas salmonicida strain, named HMes1 isolated from Atlantic salmon (Salmo salar L.) in Tasmania. Isolates were identified as Aeromonas salmonicida through phenotypic, phylogenetic and genetic characterisation. After characterisation, the diagnostic phenotypic identification system MicroSys A24 was updated and a new multiplex conventional PCR was developed to enable rapid and inexpensive identification of atypical A. salmonicida, and exclusion of the exotic strain, A. salmonicida ssp. salmonicida.

Heterogeneous Online Computational Platform for GEM-Based Plasma Impurity Monitoring Systems

The fusion energy research field presents many intricate challenges that require resolution. Many diagnostic systems employed in experiments are approaching the limits of current technology. Implementing efficient measurements requires using an appropriate set of tools to facilitate the optimal utilization of hardware. Fusion energy measurements must provide low latency processing with the capacity for future improvements and the ability to handle complex data flows efficiently. The presented work addresses these requirements and describes the implementation of a high-performance, low-latency software platform with convenient development for soft X-ray (SXR) plasma impurities emission tracing—the Asynchronous Complex Computation Platform (AC2P). This article presents the architectural design, implementation details, and performance and latency measurements based on the raw data acquired from the WEST tokamak and laboratory tests. AC2P provides the tools to develop low-latency, multi-core, multi-device complex data flow graph scale-up solutions for measuring impurities in hot plasmas. The system has been designed to operate online during experiments, calculate the energy distribution, position and occurrence time of SXR photons, monitor the data stream’s quality and archive any abnormalities for subsequent offline verification and algorithm improvement. This article presents AC2P, which operates as part of the SXR measurement system on the WEST tokamak.

Autism, intelligence, language, and adaptive behavior, disentangling a complex relationship

IntroductionAutism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder characterized by a range of intellectual and language abilities. Its heterogeneity is acknowledged in modern diagnostics, complicating research and necessitating precision medicine and a multidimensional approach for individualized treatment and accurate assessment. Intellectual and language functioning influence adaptive skills and symptomatology. Thus, assessing adaptive functioning in a multidimensional and multi-informant manner is crucial, highlighting the importance of comprehensive evaluations. This study explores the interplay between autistic traits, demographic variables, IQ, adaptive functioning, and the applicability of ICD-11 classifications.MethodsWe analyzed data from the initial global evaluation of 60 diverse autistic children (aged 35 to 120 months; IQ range 16 to 118). Parent-reports using the Vineland Adaptive Behavior Scale (VABS-II) were compared with standardized assessments from the PsychoEducational Profile (PEP-3). Children’s intellectual levels were assessed using Griffiths Scales of Child Development (Griffiths III) and the Autism Diagnostic Observation Schedule (ADOS-2) was used for autistic traits. They were further classified according to the ICD-11 diagnostic system, functional language, and intellectual functioning levels. Correlations among variables, group comparisons, and multivariate analyses were performed.ResultsThe analysis indicates a linear effect of IQ on all adaptive scales and the impact of autistic traits on directly measured adaptive functioning. A factorial effect was observed due to changes concerning specific age, intellectual, and linguistic levels, which do not completely align with ICD-11 categorization. Additionally, a negative correlation between intelligence and measured autistic traits was found. Parental age, education level, and age at childbirth were also found to affect various adaptive scales.DiscussionThe study questions the ICD-11’s proposed distinctions in IQ and language functioning for ASD, advocating for more refined categorization and developmental considerations. It underscores the intricate relationship between autistic traits, IQ, and communication skills, casting doubt on the precision of diagnostic tools across the spectrum. Parental reports and direct assessments are essential for comprehensive evaluation, with parental education and age influencing children’s behaviors and skills. The study calls for a nuanced approach to ASD assessment, integrating various metrics and sources of information for a detailed phenotyping necessary for personalized interventions and biological research.

Diagnosis of solar panels using Fuzzy logic with a recursive method

The article presents an innovative methodology for diagnosing faults in solar panels using an approach that combines fuzzy logic and recursive least squares (RLS). The main objective of the study is to develop a robust and accurate diagnostic system capable of detecting and locating faults in solar panels. The proposed method uses fuzzy logic to model the complex relationships between the parameters of the solar panel, such as brightness, temperature, voltage, current, and the operating state of the solar panel. Recursive least squares (RLS) are integrated into the diagnostic system to dynamically update the fuzzy parameters of the model based on new observations and real data from the solar panel. This approach allows continuous adaptation of the diagnostic model to changes in environmental conditions and the operation of the solar panel. The results demonstrate the effectiveness and accuracy of the proposed diagnostic system, with a high capability to detect and locate different types of faults in solar panels.

SPIKE-Dx

Diagnostic systems are important for many aerospace systems, which are severly limited in available power, like cubesats or UAVs. Therefore, traditional diagnostics systems cannot be used due to their substantial footprint and constraints. In this paper, we present our very low power diagnostic tool SPIKE-Dx. to monitor critical systems with constrained computational and energy resources. This is made possible through spiking neural networks (SNNs), which are executable within optimized simulation environments and further implemented on on cutting-edge neuromorphic hardware. Based upon FMEA (Failure Mode and Effect Analysis) framework, Diagnostic Bayesian Networks (DBNs) can be constructed that provide powerful means for diagnostic reasoning. In this paper, we describe such DBNs and a method to automatically translate the DBN into highly structured networks of spiking neurons for execution in SPIKE-Dx.

Analysis of the sensitivity of high-grade squamous intraepithelial lesion Pap diagnosis and interobserver variability with the Hologic Genius Digital Diagnostics System.

Artificial intelligence (AI)-based systems are transforming cytopathology practice. The aim of this study was to evaluate the sensitivity of high-grade squamous intraepithelial lesion (HSIL) Papanicolaou (Pap) diagnosis assisted by the Hologic Genius Digital Diagnostics System (GDDS). A validation study was performed with 890 ThinPrep Pap tests with the GDDS independently. From this set, a subset of 183 cases originally interpreted as HSIL confirmed histologically were included in this study. The sensitivity for detecting HSIL by three cytopathologists was calculated. Most HSIL cases were classified as atypical glandular cell/atypical squamous cell-high grade not excluded (AGC/ASC-H) and above by all cytopathologists. Of these cases, 11.5% were classified as low-grade squamous intraepithelial lesion (LSIL) by pathologist A (P-A), 6% by pathologist B (P-B), and 5.5% by pathologist C (P-C); 3.8%, 2.7%, and 1.6% of these cases were classified as atypical squamous cell of unknown significance (ASC-US) by P-A, P-B, and P-C, respectively. The sensitivity for detection of cervical intraepithelial neoplasia 2 and above (CIN2+) lesions was 100% if ASC-US and above (ASC-US+) abnormalities were counted among all three pathologists. The sensitivity for detection of CIN2+ lesions was 84.7%, 91.3%, and 92.9% by P-A, P-B, and P-C, respectively, for ASC-H and above abnormalities. The Kendall W coefficient was 0.722, which indicated strong agreement between all pathologists. New-generation AI-assisted Pap test screening systems such as the GDDS have the potential to transform cytology practice. In this study, the GDDS aided in interpreting HSIL in ThinPrep Pap tests, with good sensitivity and agreement between the pathologists who interacted with this system.

Phase Transition of Wax Enabling CRISPR Diagnostics for Automatic At-Home Testing of Multiple Sexually Transmitted Infection Pathogens.

Sexually transmitted infections (STIs) significantly impact women's reproductive health. Rapid, sensitive, and affordable detection of these pathogens is essential, especially for home-based self-testing, which is crucial for individuals who prioritize privacy or live in areas with limited access to healthcare services. Herein, an automated diagnostic system called Wax-CRISPR has been designed specifically for at-home testing of multiple STIs. This system employs a unique strategy by using the solid-to-liquid phase transition of wax to sequentially isolate and mix recombinase polymerase amplification (RPA) and CRISPR assays in a microfluidic chip. By incorporating a home-built controlling system, Wax-CRISPR achieves true one-pot multiplexed detection. The system can simultaneously detect six common critical gynecological pathogens (CT, MG, UU, NG, HPV 16, and HPV 18) within 30min, with a detection limit reaching 10-18 M. Clinical evaluation demonstrates that the system achieves a sensitivity of 96.8% and a specificity of 97.3% across 100 clinical samples. Importantly, eight randomly recruited untrained operators performe a double-blinded test and successfully identified the STI targets in 33 clinical samples. This wax-transition-based one-pot CRISPR assay offers advantages such as low-cost, high-stability, and user-friendliness, making it a useful platform for at-home or field-based testing of multiple pathogen infections.

Development and Validation of Concept of Innovative Method of Computer-Aided Monitoring and Diagnostics of Machine Components

The monitoring and diagnostic system has been suggested as a non-destructive diagnostic method. The structure and operation of the suggested system can be described by the concept of digital shadow (DS). One of the main DS subsystems is a set of sensors properly placed on the monitored object and coupled with a discrete data processing model created in Matlab/Simulink. The discrete model, as another important DS subsystem of the monitored facility, transfers information about its technical condition to the operator based on data recorded by the sensor system. The digital monitoring model processes the recorded data in the form of the object’s response to actions caused by its operating conditions. This work formalized a mathematical model determining the coupling of the digital model with the sensors placed on the monitored object. The formulated method using DS, due to its sensitivity, enables the detection of the damage in the object at an early stage. The tests allowed for detecting the regularities enabling the determination of the area of damage to the shaft and its size.

Cross-calibration and first vertical ECE measurement of electron energy distribution in the TCV tokamak

Abstract This paper describes the first vertical electron cyclotron emission measurement of non-thermal electron distributions in the Tokamak à Configuration Variable. These measurements were conducted in runaway electron scenarios and in the presence of electron cyclotron current drive. Measured intensities of linearly polarized X- and O-mode radiation from fast electrons allow the analysis of the energy distribution. The measurements were made possible through the creation of an operational regime for the diagnostic that is free of thermal background radiation, in relaxed electron density operations. This operational regime notably enables the cross-calibration of the diagnostic system, relying on thermal plasma measurements and modeling with the ray-tracing code SPECE.

Development and Application of an Indirect Enzyme-Linked Immunosorbent Assay Based on a Recombinant Matrix Protein for the Serological Study of Porcine Deltacoronavirus in Mexican Pigs.

Porcine deltacoronavirus (PDCoV) is an infectious disease that causes diarrhoea in pigs of different ages; however, piglets are more susceptible. PDCoV was first reported in 2012 in China and Hong Kong. Later, it was first reported in the USA in 2014 and in Mexico in 2019. Several studies have shown that M protein is highly conserved and, therefore, suitable for diagnostic systems. In this study, for the first time, an indirect enzyme-linked immunosorbent assay (iELISA) based on a recombinant M protein (rM-PDCoV) was developed to evaluate the seroprevalence of PDCoV in four states in Mexico. High sensitivity (83%) and specificity (100%) were observed for the iELISA. The kappa index calculated a nearly perfect agreement (0.8831) compared to the Western blot (gold standard test), suggesting acceptable statistical value support. In this study, 50.38% of the serum samples from backyard pigs were PDCoV-positive. The serological comparison showed that PDCoV/PEDV coinfections occurred in 31.98% of the analysed sera. These results can enrich our understanding of how this virus spreads and enable the evaluation of PDCoV infections. Moreover, it highlights the importance of continually investigating the seroprevalence of PDCoV in Mexico because there is also no information about the current prevalence of the disease.

Machine learning models reveal ARHGAP11A's impact on lymph node metastasis and stemness in NSCLC.

Most patients with non-small cell lung cancer (NSCLC) are diagnosed at an advanced stage of the disease, which complicates treatment due to a heightened risk of metastasis. Consequently, the timely identification of biomarkers associated with lymph node metastasis is essential for improving the clinical management of NSCLC patients. In this research, the WGCNA algorithm was utilized to pinpoint genes linked to lymph node metastasis in NSCLC. A cluster analysis was carried out to investigate how these genes correlate with the prognosis and the outcomes of immunotherapy for NSCLC patients. Following this, diagnostic and prognostic models were created and validated through various machine learning methodologies. The random forest technique highlighted the importance of ARHGAP11A, leading to an in-depth examination of its role in NSCLC. By analyzing 78 tissue chip samples from NSCLC patients, the study confirmed the association between ARHGAP11A expression, patient prognosis, and lymph node metastasis. Finally, the influence of ARHGAP11A on NSCLC cells was assessed through cell function experiments. This research utilized the WGCNA technique to identify 25 genes that are related to lymph node metastasis, clarifying their connections with tumor invasion, growth, and the activation of stemness pathways. Cluster analysis revealed significant associations between these genes and lymph node metastasis in NSCLC, especially concerning immunotherapy and targeted treatments. A diagnostic system that combines various machine learning approaches demonstrated strong efficacy in forecasting both the diagnosis and prognosis of NSCLC. Importantly, ARHGAP11A was identified as a key prognostic gene associated with lymph node metastasis in NSCLC. Molecular docking analyses suggested that ARHGAP11A has a strong affinity for targeted therapies within NSCLC. Additionally, immunohistochemical assessments confirmed that higher levels of ARHGAP11A expression correlate with unfavorable outcomes for NSCLC patients. Experiments on cells showed that reducing ARHGAP11A expression can hinder the proliferation, metastasis, and stemness traits of NSCLC cells. This investigation reveals the novel insight that ARHGAP11A may function as a potential biomarker connected to lymph node metastasis in NSCLC. Moreover, reducing the expression of ARHGAP11A has demonstrated the ability to diminish tumor stemness characteristics, presenting a promising opportunity for improving treatment strategies for this condition.

Leuconostoc pseudomesenteroides Bacteremia in an Immunocompromised Patient with Hematological Comorbidities—Case Report

Leuconostoc pseudomesenteroides is a rare pathogen that can cause bacteremia in immunocompromised patients, particularly those with hematological conditions like acute myeloid leukemia. In this case, a 56-year-old woman developed Leuconostoc bacteremia following chemotherapy and multiple infections, including invasive aspergillosis. Despite broad-spectrum antibiotic treatments, her fever persisted until the blood cultures identified Leuconostoc pseudomesenteroides. Switching to intravenous ampicillin led to the resolution of symptoms. This case highlights the challenges in diagnosing and treating rare, glycopeptide-resistant bacteria in immunosuppressed patients and underscores the importance of repeated blood cultures and automated diagnostic systems. It also highlights the need for more rapid ways of diagnosing and treating such rare infections.

Implementation of Fuzzy Logic Scheme for Assessment of Power Transformer Oil Deterioration Using Imprecise Information

This research aims to analyze the implementation of a fuzzy logic-based approach in improving the diagnosis of power transformer oil deterioration, which is critical for maintaining the efficient performance and operational life of transformers. Traditional diagnoses are based on strict measurements that do not account for the factors of variability and uncertainty of the actual data. In this article, we perform six different types of tests in this regard, and data have been collected during the period of 2021 to 2022 of 188 power transformer failures in the New KotLakhpat Lahore unit, whose voltage range is 132/66 kv and rating capacity is 40/50 MVA. In this case, a fuzzy logic-based scheme is developed based upon the membership function, a rule-based and defuzzification method that works with imprecision and the implementation of uncertainty in assessing the condition of transformer oils. Moisture, acidity, and a dissolved gas analysis indicator, along with other indication approaches such as interfacial tension, viscosity, and tangent delta measurement, are used to analyze the deterioration process in transformer oils. In the visual representation, oil samples with the following properties were first fuzzified: 19.9 mm2/s of viscosity, 0.453 mgKOH/g of acidity, 695 ppm of DGA, 20.8 mg/kg of moisture, 19.98 of IFT, and 4.35 × 100.14 of tangent delta. The output that was generated by software using the values entered into the parameters (HI and Age) after defuzzification is 45. Fuzzy logic serves as a concrete framework for transforming the diagnostics system and deterring the threats to the entire transformer’s health and reliability in the future. By using this technique, various faults were hypothetically and practically analyzed in a transformer to implement early detection technologies with the possibility to reduce maintenance costs and extend operational life up to 45 years. Various case studies indicate the effectiveness of fuzzy logic in comparison to traditional diagnostics.

Assessment of a space and energy resolved diagnostic based on GEM technology on MAST-U

Abstract A Gas Electron Multiplier (GEM)-based detector was utilized for the first
time on a spherical tokamak, MAST-U, during the 2023 campaign to investigate Soft
X-Ray (SXR) radiation (1-20 keV) emitted from the plasma. GEM detectors, chosen
for their resilience to harsh fusion environments and their ability to provide energy-
resolved SXR emission images with sub-millisecond time resolution, are a relatively
new diagnostic compared to standard semiconductor diodes. In this study, the GEM
detector features a pinhole geometry outside the vacuum chamber and observes the
plasma through a beryllium window. Filled with an ArCO2 mixture, the detector
consists of an Aluminized Mylar cathode, three Aluminum-coated GEM foils, and an
anode made of a 16x16 matrix of 6mm2 pads for 2D readout. It employs custom
GEMINI ASICs (Application Specific Integrated Circuits) for signal readout, enabling
photon-counting techniques with Time over Threshold (ToT) analysis on each detector
channel, reaching a total maximum rate of 256 MHz.
Preliminary findings from the 2023 campaign demonstrate the effectiveness of the
GEM detector in complementing existing SXR camera data in terms of spatial and
temporal resolution. Case studies include the identification of Magnetohydrodynamic
(MHD) instabilities, such as the Snake instability, and the exploitation of energy
capabilities for plasma event energy characterization, as shown in the case of Internal
Reconnection Events. Additionally, the GEM detector allows for estimation of the
Electron Temperature of Maxwellian plasmas. These results underscore the potential
of the GEM-based diagnostic system in advancing tokamak research for comprehensive
plasma characterization and control.

Real-Time precision prehospital stroke diagnosis in weightlifting athletes using cutting-edge non-invasive sensors

This research paper presents an investigation into the efficacy of a novel diagnostic technology designed for the real-time monitoring of weightlifting athletes, focusing on precision, time efficiency, and user convenience compared to traditional diagnostic systems. The study introduces an advanced non-invasive sensor system, integrated into a cohesive Internet of Medical Things (IoMT) framework, which facilitates the immediate and accurate assessment of athletes' health parameters. To empirically test the benefits of this new technology, a pedagogical experiment was conducted involving two distinct groups: an experimental group that utilized the proposed technology for medical checkups, and a control group that continued with traditional diagnostic methods. Each group consisted of 30 athletes, and the outcomes were measured across three dimensions: the precision of diagnostic results, the time expended for medical checkups, and the user-reported convenience of the equipment. The findings indicate that the proposed technology not only significantly enhances the precision of health diagnostics but also reduces the time required for medical examinations, thereby increasing overall efficiency. Additionally, the higher convenience scores reported by the experimental group suggest improved user satisfaction and usability. These results demonstrate the potential of the proposed diagnostic system to transform athlete health monitoring by providing more accurate, efficient, and user-friendly medical assessments, suggesting a significant step forward in the application of advanced technologies in sports medicine.

CMOS Point-of-Care Diagnostics Technologies: Recent Advances and Future Prospects

This review provides a comprehensive overview of point-of-care (PoC) devices across several key diagnostic applications, including blood analysis, infectious disease detection, neural interfaces, and commercialized integrated circuits (ICs). In the blood analysis section, the focus is on biomarkers such as glucose, dopamine, and aptamers, and their respective detection techniques. The infectious disease section explores PoC technologies for detecting pathogens, RNA, and DNA, highlighting innovations in molecular diagnostics. The neural interface section reviews advancements in neural recording and stimulation for therapeutic applications. Finally, a survey of commercialized ICs from companies such as Abbott and Medtronic is presented, showcasing existing PoC devices already in widespread clinical use. This review emphasizes the role of complementary metal-oxide-semiconductor (CMOS) technology in enabling compact, efficient diagnostic systems and offers insights into the current and future landscape of PoC devices.

Development of an automated process for monitoring of amiodarone treatment

Abstract Background Amiodarone treatment requires repeated laboratory evaluations of thyroid and liver function due to risk of side effects during long-term administration. Robotic process automation (RPA) represents the use of software robots to automate repetitive and routine tasks typically performed by humans. Purpose We aimed to develop an RPA using a diagnostic classification algorithm and clinical support system for follow-up of amiodarone treatment. Methods Based on international guidelines and clinical specialist advisory we constructed an RPA which was studied in a test environment in parallel with routine amiodarone treatment. Expert advice was integrated through a series of collaborative workshops, ensuring alignment of the algorithm with current best practices in thyroid and liver disease management. The RPA provided recommendations on the time interval to next laboratory testing and suggestions for patient management to the physician, who was serving as a human-in-the-loop responsible for clinical decisions. Results After iterative improvements the RPA was evaluated against physician orders (n=390 paired orders) without technical failures. The recommended time interval to next follow-up was (mean ± SD) 4.5 ± 2.4 vs. 3.1 ± 1.4 months for RPA vs. physician (p<0.0001). While normal laboratory findings resulted in recommended follow-up in 6 months by the RPA (72.2%) this was only ordered in 9.9% by the physician (Figure). Physicians’ orders clustered between 3-4 months (58.5%). All patients diagnosed with new side effects (n=12) were detected and communicated correctly by the RPA, whereas only 8 were correctly detected and responded to by the physician (Table). Conclusions An automated process constitutes a reliable alternative to the current fully manual management of amiodarone follow-up. It may reduce manual labour, frequency of laboratory testing and detect side effects with increased precision, thereby reducing costs and enhancing patient value. While the RPA shows promise, further research is needed to explore its adaptability to other treatment scenarios.

Diagnostic classification systems for disorders of gut-brain interaction should include psychological symptoms.

The group of disorders known as Disorders of Gut Brain Interaction (DGBI) were originally labeled functional GI disorders and were thought to be disorders of the gastrointestinal tract that had several psychological conditions as comorbidities. Despite mounting evidence that psychological morbidity plays an innate role in the etiology and maintenance of DGBI, none of the Rome IV criteria include any measure of psychological symptoms. This study tested the hypothesis that individuals would cluster differently if GI symptoms alone were considered versus GI symptoms combined with measures of psychological symptoms. Data were obtained from the Rome Foundation Global Epidemiology Study measuring Rome IV GI symptoms, psychological measures and demographic characteristics. Latent profile models were used to cluster individuals based on (i) GI symptoms only (GI only) and then (ii) GI and psychological measures (GI + Psych). Individuals clustering into the same group of individuals whether formed via GI only or GI + Psych, ranged from 96% for a 2-class solution (the most simplistic) to 76% with 6 classes (the parsimonious system) and 59% with twenty-two classes (mimicking Rome IV). The generalisability of this finding between six geographic regions was confirmed with agreement varying between 95%-97% for 2 clusters and 71-79% for 6 classes and 51%-63% for 22 classes. These findings were also consistent between DGBI (range 94% with 2 classes to 50% with 22 classes) and non-DGBI (range 97% with 2 clusters to 65% with 22 classes) groups. Our data suggest that considering psychological as well as gastrointestinal symptoms would lead to a different clustering of individuals in more complex, and accurate, classification systems. For this reason, future work on DGBI classification should consider inclusion of psychological traits.

Research and implementation of intelligent diagnostic system for temporomandibular joint disorder

Temporomandibular joint disorder (TMD) is a common oral and maxillofacial disease, which is difficult to detect due to its subtle early symptoms. In this study, a TMD intelligent diagnostic system implemented on edge computing devices was proposed, which can achieve rapid detection of TMD in clinical diagnosis and facilitate its early-stage clinical intervention. The proposed system first automatically segments the important components of the temporomandibular joint, followed by quantitative measurement of the joint gap area, and finally predicts the existence of TMD according to the measurements. In terms of segmentation, this study employs semi-supervised learning to achieve the accurate segmentation of temporomandibular joint, with an average Dice coefficient (DC) of 0.846. A 3D region extraction algorithm for the temporomandibular joint gap area is also developed, based on which an automatic TMD diagnosis model is proposed, with an accuracy of 83.87%. In summary, the intelligent TMD diagnosis system developed in this paper can be deployed at edge computing devices within a local area network, which is able to achieve rapid detecting and intelligent diagnosis of TMD with privacy guarantee.

Dilation framing camera with the dual-pulse excitation technique

In an inertial confinement fusion (ICF) ultrafast diagnostic system that is based on electron beam time-dilation, an ultrafast electrical pulse is used to excite a microstrip photocathode (PC), which generates a varying PC voltage to obtain a photoelectron velocity that varies with emission time. The photoelectron beam achieves time-dilation through the drift process and is then detected by a time-resolved sensor, thereby increasing the temporal resolution of the diagnostic system. A pulse time-dilation diagnostic system is simulated, while the sensor is a gated microchannel plate (MCP) detector with a temporal resolution of 100 ps and an excitation pulse on a PC with a slope of 3 V/ps; the diagnostic system achieves a temporal resolution of 11.12 ps. However, the excitation pulse creates a voltage difference across the PC. A voltage difference of 900 V can be acquired for a PC length of 60 mm, which yields a nonuniform spatial resolution ranging from 30.4 µm to approximately 3000 µm. Furthermore, the voltage difference across the PC also limits the frame size to 2.2 mm along the pulse propagation direction according to the simulation results. To achieve a uniform spatial resolution and a larger frame size, a dual-pulse excitation technique on a PC is presented, which is the technique to symmetrically apply voltage pulses at both ends of the PC microstrip. The theoretical results show that this technique will improve the uniformity of the PC voltage spatial distribution. When the PC pulse slope is 3 V/ps and the dual-pulse excitation technique is employed, the diagnostic system has a temporal resolution of 5.91 ps and a uniform spatial resolution of 30.4 µm. Furthermore, the frame size along the pulse propagation direction is improved to the effective length of the microstrip PC.