Clinical characteristics and follow-up of complex arrhythmias associated with RYR2 gene mutations in children.

Objective: The aim of this study was to analyze the diagnosis, treatment, and follow-up of six cases of complex arrhythmias associated with RYR2 gene mutations in children. Method: A retrospective analysis was conducted on six children diagnosed with complex arrhythmias associated with RYR2 gene mutations. The study included an analysis of the age of onset, initial symptoms, electrocardiographic characteristics, genetic results, treatment course, and follow-up outcomes. Results: Among the six cases included in the study, there were four males and two females, with an average age of 3.5 ± 0.5 years. The average time from initial symptoms to diagnosis was 2.7 ± 1.3 years. The most common clinical manifestation was syncope, with exercise and emotions being the main triggers. All six children had de novo missense mutations in the RYR2 gene identified through whole-exome sequencing. In Holter electrocardiogram, atrial arrhythmias and sinoatrial node dysfunction were commonly observed in younger children. Four patients underwent exercise stress testing, with two experiencing bidirectional ventricular premature contractions and two experiencing bidirectional ventricular tachycardia and polymorphic ventricular tachycardia. Initial treatment involved oral propranolol or metoprolol. If arrhythmias persisted, flecainide or propafenone was added as adjunctive therapy. Two patients received permanent cardiac pacemaker treatment (single chamber ventricular pacemaker, VVI). All patients survived, with three experiencing occasional syncope during treatment. The follow-up period ranged from 12 to 37 months, with an average follow-up time of 24.3 ± 3.7 months. Conclusion: Complex arrhythmias associated with RYR2 gene mutations in children can present with various clinical manifestations. Atrial arrhythmias combined with sinoatrial node dysfunction are commonly observed in younger children, and the combination of pharmacological therapy and cardiac pacemaker treatment yields favourable treatment outcomes.

Combined PET/CT and Autoantibody Detection in Lung Nodules Diagnosis.

Objective: To assess the usefulness of combining positron emission tomography/computed tomography (PET/CT) with lung cancer autoantibody detection in identifying and managing lung nodules. Methods: The researchers identified 160 patients with pulmonary nodules admitted to their hospital between January 2018 and January 2021. These patients were designated as the experimental group. Additionally, 60 healthy individuals without pulmonary nodules were admitted to the hospital during the same period. The individuals constituted the control group. All study participants underwent digital PET/CT detection and had their lung cancer autoantibody levels determined through enzyme-linked immunosorbent assay. Further testing, such as puncture or surgical pathology, was performed for patients with lung nodules. The aim was to evaluate the significance of combining PET/CT with autoantibody detection in diagnosing and treating lung nodules. Results: The study found that testing multiple autoantibodies together increased sensitivity and accuracy compared to testing individual autoantibodies. Combining PET/CT screening with autoantibody detection improved the diagnostic rate for identifying lung nodules, including benign and suspected malignant ones. Several autoantibodies were significantly higher in the experimental group compared to the control group. Testing for multiple autoantibodies showed higher sensitivity and accuracy than testing for one. Pathological examination confirmed 129 benign nodules and 31 malignant nodules. The median SUVmax values were measured at 0.7 for benign nodules and 4.8 for malignant nodules. The diagnostic efficacy of PET/CT combined with autoantibodies was determined through comparison with pathology testing and was as follows: PET/CT combined with autoantibody detection > PET/CT > autoantibody detection. Conclusion: Combining PET/CT with the detection of autoantibodies enhances the positive diagnostic rate and accuracy of lung nodules in the case of lung cancer. The SUVmax also shows excellent potential as a supplement in diagnosing both benign and malignant lung nodules, providing valuable guidance in determining the pathological types.

A Rapid Localization Method Based on Super Resolution Magnetic Array Information for Unknown Number Magnetic Sources.

A rapid method that uses super-resolution magnetic array data is proposed to localize an unknown number of magnets in a magnetic array. A magnetic data super-resolution (SR) neural network was developed to improve the resolution of a magnetic sensor array. The approximate 3D positions of multiple targets were then obtained based on the normalized source strength (NSS) and magnetic gradient tensor (MGT) inversion. Finally, refined inversion of the position and magnetic moment was performed using a trust region reflective algorithm (TRR). The effectiveness of the proposed method was examined using experimental field data collected from a magnetic sensor array. The experimental results showed that all the targets were successfully captured in multiple trials with three to five targets with an average positioning error of less than 3 mm and an average time of less than 300 ms.

Underlying effect of SMAD4 gene polymorphism on risk prediction of papillary thyroid carcinoma in Chinese population.

Objective: This research intends to explore how variations in the SMAD4 gene impact papillary thyroid carcinoma (PTC) among patients in China. Methods: The rs10502913 and rs12968012 polymorphisms were genotyped in 405 subjects using SNP-scan high-throughput technology. Differential mRNA expression of SMAD4 was analyzed using data from TCGA and GSE33630, and protein level expression differences were analyzed using immunohistochemistry. Results: The results showed that SMAD4 mRNA expression was lower in thyroid cancer (THCA) tissues than in normal tissues. Immunohistochemical results showed that the expression level of SMAD4 in normal tissue, thyroid papillary carcinoma tissue and poorly differentiated tissue was significantly different. We found that SMAD4 mismatch variants (rs10502913 and rs12968012) were associated with PTC susceptibility. Specifically, the SMAD4-rs10502913 genotypes (GA and AA) showed a notable correlation with a lower likelihood of PTC in comprehensive and segmented studies (genotype GA: OR (95% CI) = 0.270 (0.077-0.950), p = 0.041; genotype AA: OR (95% CI) = 0.103 (0.025-0.416), p = 0.001). We categorized the immunohistochemical results according to genotype and found that rs10502913-GG protein level was expressed at the lowest level, and both GA and AA were higher than GG (GG vs. AA, P < 0.05), and rs12968012-CG protein level was expressed at the lowest level, and both GG and CC were higher than CG (GG vs. CG, P < 0.01). Conclusion: Two missense variants of SMAD4 (rs10502913 and rs12968012) are associated with reduced risk of papillary thyroid carcinoma, possibly by reducing protein expression leading to susceptibility to papillary thyroid carcinoma.

Correlation Analysis of NAMPT rs61330082 Polymorphism in Type 2 Diabetes Mellitus Combined with Hypertension.

Introduction: This study aimed to investigate the association of Nicotinamide phosphoribosyl transferase (NAMPT) rs61330082 polymorphism with co-morbid hypertension (HTN) and the progression of hypertension in Chinese patients with type 2 diabetes mellitus (T2DM). Methods: A total of 453 T2DM patients were genotyped for the polymorphism of rs61330082 using SNP-scan high-throughput technology. These patients were divided into T2DM group (261 patients) and T2DM combined with hypertension group (T2MH, 192 patients). The T2MH group was further categorized into Grade I, Grade II, and Grade III based on the results of the Hypertension Grade Score. Peripheral blood plasma urea, plasma creatinine, renin-angiotensin system (RAS) indexes, and lipid biochemistry indexes were measured in patients and analyzed in relation to NAMTP polymorphisms. Results: We found that the presence of the NAMPT rs61330082-AA genotype was associated with a significantly increased risk of developing higher-grade hypertension in patients with T2MH. In addition, the A allele of the NAMPT rs61330082 gene displayed more associated in developing a higher grade of hypertension compared to the G allele. Also, the level of total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-c) increased with hypertension grade in the NAMPT rs61330082-GG genotype. Conclusion: NAMPT rs61330082 polymorphism was significantly associated with the progression of hypertension grade in T2MH patients and also affected plasma creatinine and LDL-c levels.

Simultaneous photoacoustic and ultrasound imaging: A review.

Photoacoustic imaging (PAI) is an emerging biomedical imaging technique that combines the advantages of optical and ultrasound imaging, enabling the generation of images with both optical resolution and acoustic penetration depth. By leveraging similar signal acquisition and processing methods, the integration of photoacoustic and ultrasound imaging has introduced a novel hybrid imaging modality suitable for clinical applications. Photoacoustic-ultrasound imaging allows for non-invasive, high-resolution, and deep-penetrating imaging, providing a wealth of image information. In recent years, with the deepening research and the expanding biomedical application scenarios of photoacoustic-ultrasound bimodal systems, the immense potential of photoacoustic-ultrasound bimodal imaging in basic research and clinical applications has been demonstrated, with some research achievements already commercialized. In this review, we introduce the principles, technical advantages, and biomedical applications of photoacoustic-ultrasound bimodal imaging techniques, specifically focusing on tomographic, microscopic, and endoscopic imaging modalities. Furthermore, we discuss the future directions of photoacoustic-ultrasound bimodal imaging technology.

Computational Imaging: The Next Revolution for Biophotonics and Biomedicine.

This Editorial is the preface for the topical collection of "Computational Imaging for Biophotonics and Biomedicine", which collates the 12 contributions listed in Table 1 [...].

Machine learning algorithm to predict postoperative bleeding complications after lateral decubitus percutaneous nephrolithotomy.

Bleeding is a serious complication following percutaneous nephrolithotomy (PCNL). This study establishes a predictive model based on machine learning algorithms to forecast the occurrence of postoperative bleeding complications in patients with renal and upper ureteral stones undergoing lateral decubitus PCNL. We retrospectively collected data from 356 patients with renal stones and upper ureteral stones who underwent lateral decubitus PCNL in the Department of Urology at Peking University First Hospital-Miyun Hospital, between January 2015 and August 2022. Among them, 290 patients had complete baseline data. The data was randomly divided into a training group (n = 232) and a test group (n = 58) in an 8:2 ratio. Predictive models were constructed using Logistic Regression, Random Forest, and Extreme Gradient Boosting (XGBoost). The performance of each model was evaluated using Accuracy, Precision, F1-Score, Receiver Operating Characteristic curves, and Area Under the Curve (AUC). Among the 290 patients, 35 (12.07%) experienced postoperative bleeding complications after lateral decubitus PCNL. Using postoperative bleeding as the outcome, the Logistic model achieved an accuracy of 73.2%, AUC of 0.605, and F1 score of 0.732. The Random Forest model achieved an accuracy of 74.5%, AUC of 0.679, and F1 score of 0.732. The XGBoost model achieved an accuracy of 68.3%, AUC of 0.513, and F1 score of 0.644. The predictive model for postoperative bleeding after lateral decubitus PCNL, established based on machine learning algorithms, is reasonably accurate. It can be utilized to predict postoperative stone residue and recurrence, aiding urologists in making appropriate treatment decisions.

Physics-driven universal twin-image removal network for digital in-line holographic microscopy.

Digital in-line holographic microscopy (DIHM) enables efficient and cost-effective computational quantitative phase imaging with a large field of view, making it valuable for studying cell motility, migration, and bio-microfluidics. However, the quality of DIHM reconstructions is compromised by twin-image noise, posing a significant challenge. Conventional methods for mitigating this noise involve complex hardware setups or time-consuming algorithms with often limited effectiveness. In this work, we propose UTIRnet, a deep learning solution for fast, robust, and universally applicable twin-image suppression, trained exclusively on numerically generated datasets. The availability of open-source UTIRnet codes facilitates its implementation in various DIHM systems without the need for extensive experimental training data. Notably, our network ensures the consistency of reconstruction results with input holograms, imparting a physics-based foundation and enhancing reliability compared to conventional deep learning approaches. Experimental verification was conducted among others on live neural glial cell culture migration sensing, which is crucial for neurodegenerative disease research.

Multilayer analysis of dynamic network reconfiguration in pediatric posttraumatic stress disorder.

Neuroimage studies have reported functional connectome abnormalities in posttraumatic stress disorder (PTSD), especially in adults. However, these studies often treated the brain as a static network, and time-variance of connectome topology in pediatric posttraumatic stress disorder remain unclear. To explore case-control differences in dynamic connectome topology, resting-state functional magnetic resonance imaging data were acquired from 24 treatment-naïve non-comorbid pediatric posttraumatic stress disorder patients and 24 demographically matched trauma-exposed non-posttraumatic stress disorder controls. A graph-theoretic analysis was applied to construct time-varying modular structure of whole-brain networks by maximizing the multilayer modularity. Network switching rate at the global, subnetwork, and nodal levels were calculated and compared between posttraumatic stress disorder and trauma-exposed non-posttraumatic stress disorder groups, and their associations with posttraumatic stress disorder symptom severity and sex interactions were explored. At the global level, individuals with posttraumatic stress disorder exhibited significantly lower network switching rates compared to trauma-exposed non-posttraumatic stress disorder controls. This difference was mainly involved in default-mode and dorsal attention subnetworks, as well as in inferior temporal and parietal brain nodes. Posttraumatic stress disorder symptom severity was negatively correlated with switching rate in the global network and default mode network. No significant differences were observed in the interaction between diagnosis and sex/age. Pediatric posttraumatic stress disorder is associated with dynamic reconfiguration of brain networks, which may provide insights into the biological basis of this disorder.

4D spectral-spatial computational photoacoustic dermoscopy.

Photoacoustic dermoscopy (PAD) is an emerging non-invasive imaging technology aids in the diagnosis of dermatological conditions by obtaining optical absorption information of skin tissues. Despite advances in PAD, it remains unclear how to obtain quantitative accuracy of the reconstructed PAD images according to the optical and acoustic properties of multilayered skin, the wavelength and distribution of excitation light, and the detection performance of ultrasound transducers. In this work, a computing method of four-dimensional (4D) spectral-spatial imaging for PAD is developed to enable quantitative analysis and optimization of structural and functional imaging of skin. This method takes the optical and acoustic properties of heterogeneous skin tissues into account, which can be used to correct the optical field of excitation light, detectable ultrasonic field, and provide accurate single-spectrum analysis or multi-spectral imaging solutions of PAD for multilayered skin tissues. A series of experiments were performed, and simulation datasets obtained from the computational model were used to train neural networks to further improve the imaging quality of the PAD system. All the results demonstrated the method could contribute to the development and optimization of clinical PADs by datasets with multiple variable parameters, and provide clinical predictability of photoacoustic (PA) data for human skin.

Risk factor analysis for infection and bleeding after lateral decubitus percutaneous nephrolithotomy.

This study aimed to explore the risk factors for infection and bleeding after lateral decubitus percutaneous nephrolithotomy procedures to prevent their occurrence and improve surgical outcomes. A retrospective analysis was conducted on 356 patients who underwent lateral decubitus percutaneous nephrolithotomy for the treatment of kidney stones and upper ureteral stones from January 2015 to August 2022. Among them, 290 patients had complete clinical data. General clinical data, perioperative data, and stone characteristics were collected for each patient. Univariate and multivariate logistic regression analyses were performed to identify risk factors for infection and bleeding after lateral decubitus percutaneous nephrolithotomy. The postoperative infection rate after lateral decubitus percutaneous nephrolithotomy was 19.31%, and the postoperative bleeding rate was 12.07%. Independent risk factors for postoperative infection were multiple stones (P < .001), stone size (P < .001), and stone co-infection (P = .012). Independent risk factors for postoperative bleeding were multiple stones (P = .008) and stone size (P = .014). Multiple stones, stone size, and stone co-infection are independent risk factors for postoperative infection after lateral decubitus percutaneous nephrolithotomy. Multiple stones and stone size are independent risk factors for postoperative bleeding after lateral decubitus percutaneous nephrolithotomy.

Distinct pre-COVID brain structural signatures in COVID-19-related post-traumatic stress symptoms and post-traumatic growth.

Post-traumatic stress symptoms and post-traumatic growth are common co-occurring psychological responses following exposure to traumatic events (such as COVID-19 pandemic), their mutual relationship remains unclear. To explore this relationship, structural magnetic resonance imaging data were acquired from 115 general college students before the COVID-19 pandemic, and follow-up post-traumatic stress symptoms and post-traumatic growth measurements were collected during the pandemic. Voxel-based morphometry was conducted and individual structural covariance networks based on gray matter volume were further analyzed using graph theory and partial least squares correlation. Behavioral correlation found no significant relationship between post-traumatic stress symptoms and post-traumatic growth. Voxel-based morphometry analyses showed that post-traumatic stress symptoms were positively correlated with gray matter volume in medial prefrontal cortex/dorsal anterior cingulate cortex, and post-traumatic growth was negatively correlated with gray matter volume in left dorsolateral prefrontal cortex. Structural covariance network analyses found that post-traumatic stress symptoms were negatively correlated with the local efficiency and clustering coefficient of the network. Moreover, partial least squares correlation showed that post-traumatic stress symptoms were correlated with pronounced nodal properties patterns in default mode, sensory and motor regions, and a marginal correlation of post-traumatic growth with a nodal property pattern in emotion regulation-related regions. This study advances our understanding of the neurobiological substrates of post-traumatic stress symptoms and post-traumatic growth, and suggests that they may have different neuroanatomical features.

Predicting lung adenocarcinoma prognosis, immune escape, and pharmacomic profile from arginine and proline-related genes.

Lung adenocarcinoma (LUAD) is a highly heterogeneous disease that ranks first in morbidity and mortality. Abnormal arginine metabolism is associated with inflammatory lung disease and may influence alterations in the tumor immune microenvironment. However, the potential role of arginine and proline metabolic patterns and immune molecular markers in LUAD is unclear. Gene expression, somatic mutations, and clinicopathological information of LUAD were downloaded from The Cancer Genome Atlas (TCGA) database. Univariate Cox regression analysis was performed to identify metabolic genes associated with overall survival (OS). Unsupervised clustering divided the sample into two subtypes with different metabolic and immunological profiles. Gene set enrichment analysis (GESA) and gene set variation analysis (GSVA) were used to analyze the underlying biological processes of the two subtypes. Drug sensitivity between subtypes was also predicted; then prognostic features were developed by multivariate Cox regression analysis. In addition, validation was obtained in the GSE68465, and GSE50081 dataset. Then, gene expression, and clinical characterization of hub genes CPS1 and SMS were performed; finally, in vitro validation experiments for knockdown of SMS were performed in LUAD cell lines. In this study, we first identified 12 arginine and proline-related genes (APRGs) significantly associated with OS and characterized the clinicopathological features and tumor microenvironmental landscape of two different subtypes. Then, we established an arginine and proline metabolism-related scoring system and identified two hub genes highly associated with prognosis, namely CPS1, and SMS. In addition, we performed CCK8, transwell, and other functional experiments on SMS to obtain consistent results. Our comprehensive analysis revealed the potential molecular features and clinical applications of APRGs in LUAD. A model based on 2 APRGs can accurately predict survival outcomes in LUAD, improve our understanding of APRGs in LUAD, and pave a new pathway to guide risk stratification and treatment strategy development for LUAD patients.

Shared and distinct abnormalities of brain magnetization transfer ratio in schizophrenia and major depressive disorder: a comparative voxel-based meta-analysis.

BACKGROUND: Patients with schizophrenia (SCZ) and major depressive disorder (MDD) share significant clinical overlap, although it remains unknown to what extent this overlap reflects shared neural profiles. To identify the shared and specific abnormalities in SCZ and MDD, we performed a whole-brain voxel-based meta-analysis using magnetization transfer imaging, a technique that characterizes the macromolecular structural integrity of brain tissue in terms of the magnetization transfer ratio (MTR). METHODS: A systematic search based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was conducted in PubMed, EMBASE, International Scientific Index (ISI) Web of Science, and MEDLINE for relevant studies up to March 2022. Two researchers independently screened the articles. Rigorous scrutiny and data extraction were performed for the studies that met the inclusion criteria. Voxel-wise meta-analyses were conducted using anisotropic effect size-signed differential mapping with a unified template. Meta-regression was used to explore the potential effects of demographic and clinical characteristics. RESULTS: A total of 15 studies with 17 datasets describing 365 SCZ patients, 224 MDD patients, and 550 healthy controls (HCs) were identified. The conjunction analysis showed that both disorders shared higher MTR than HC in the left cerebellum ( P =0.0006) and left fusiform gyrus ( P =0.0004). Additionally, SCZ patients showed disorder-specific lower MTR in the anterior cingulate/paracingulate gyrus, right superior temporal gyrus, and right superior frontal gyrus, and higher MTR in the left thalamus, precuneus/cuneus, posterior cingulate gyrus, and paracentral lobule; and MDD patients showed higher MTR in the left middle occipital region. Meta-regression showed no statistical significance in either group. CONCLUSIONS: The results revealed a structural neural basis shared between SCZ and MDD patients, emphasizing the importance of shared neural substrates across psychopathology. Meanwhile, distinct disease-specific characteristics could have implications for future differential diagnosis and targeted treatment.

On-Chip Mirror Enhanced Multiphoton Upconversion Super-Resolution Microscopy.

Multiphoton upconversion super-resolution microscopy (MPUM) is a promising imaging modality, which can provide increased resolution and penetration depth by using nonlinear near-infrared emission light through the so-called transparent biological window. However, a high excitation power is needed to achieve emission saturation, which increases phototoxicity. Here, we present an approach to realize the nonlinear saturation emission under a low excitation power by a simply designed on-chip mirror. The interference of the local electromagnetic field can easily confine the point spread function to a specific area to increase the excitation efficiency, which enables emission saturation under a lower excitation power. With no additional complexity, the mirror assists to decrease the excitation power by 10-fold and facilities the achievement of a lateral resolution around 35 nm, 1/28th of the excitation wavelength, in imaging of a single nanoparticle on-chip. This method offers a simple solution for super-resolution enhancement by a predesigned on-chip device.

Optimal vaccination strategies for COVID-19 in population migration between two regions scenario.

Population movements had a significant impact on the spread of COVID-19, and vaccination is considered the most effective means for humans to face viral infections. This study identifies the optimal control strategy for COVID-19 prevention and control, and explores the impact of short-term and long-term migration on the optimal proportion of vaccine allocation between two regions. We proposed to establish the SIR (Susceptible-Infectious-Recovered) model and determine the stability by calculating the disease free equilibrium and Jacobi matrix of the model. We then established the vaccine optimization model, solved the optimal vaccine distribution strategy by gradient descent method and explored the impact of short-term and long-term migration on the optimal vaccine allocation ratio. The stability analysis revealed that the virus could not be eliminated only by reducing the migration rates and infection rates. we introduced the vaccine methods and obtained the optimal vaccine allocation ratio in Shenzhen and Hong Kong as p1:p2=0.000341: 0.001739, and the daily vaccination rate we need to impose in each region as p1:p2=0.00068:0.001901. The presence or absence of short-term migration had no greater impact on the distribution of the vaccine, whereas Rv with long-term migration had a greater effect than no migration. We found that migration rates could not eliminate the outbreak in both regions and that adopting an effective vaccine distribution strategy could be more effective in eliminating the outbreak. And for different allocation scenarios with limited vaccine supply, we obtained the optimal allocation most favorable to control the epidemic.

Parallel synthetic aperture transport-of-intensity diffraction tomography with annular illumination.

Transport-of-intensity diffraction tomography (TIDT) is a recently developed label-free computational microscopy technique that retrieves high-resolution three-dimensional (3D) refractive index (RI) distribution of biological specimens from 3D intensity-only measurements. However, the non-interferometric synthetic aperture in TIDT is generally achieved sequentially through the acquisition of a large number of through-focus intensity stacks captured at different illumination angles, resulting in a very cumbersome and redundant data acquisition process. To this end, we present a parallel implementation of a synthetic aperture in TIDT (PSA-TIDT) with annular illumination. We found that the matched annular illumination provides a mirror-symmetric 3D optical transfer function, indicating the analyticity in the upper half-plane of the complex phase function, which allows for recovery of the 3D RI from a single intensity stack. We experimentally validated PSA-TIDT by conducting high-resolution tomographic imaging of various unlabeled biological samples, including human breast cancer cell lines (MCF-7), human hepatocyte carcinoma cell lines (HepG2), Henrietta Lacks (HeLa) cells, and red blood cells (RBCs).

Focusing light with a metal film coated patchy particle.

Microsphere-assisted super-resolution imaging is a promising technique that can significantly enhance the resolution of conventional optical microscopes. The focus of a classical microsphere is called photonic nanojet, which is a symmetric high-intensity electromagnetic field. Recently, patchy microspheres have been reported to have superior imaging performance than pristine microspheres, and coating microspheres with metal films leads to the formation of photonic hooks, which can enhance the imaging contrast of microspheres. Understanding the influence of metal patches on the near-field focusing of patchy particles is important for the rational design of a nanostructured microlens. In this work, we theoretically and experimentally showed that the light waves can be focused and engineered using patchy particles. When coating dielectric particles with Ag films, light beams with a hook-like structure or S-shaped structure can be generated. Simulation results show that the waveguide ability of metal films and the geometric asymmetry of patchy particles cause the formation of S-shaped light beams. Compared with classical photonic hooks, S-shaped photonic hooks have a longer effective length and a smaller beam waist at far-field region. Experiments were also carried out to demonstrate the generation of classical and S-shaped photonic hooks from patchy microspheres.

Unifying temporal phase unwrapping framework using deep learning.

Temporal phase unwrapping (TPU) is significant for recovering an unambiguous phase of discontinuous surfaces or spatially isolated objects in fringe projection profilometry. Generally, temporal phase unwrapping algorithms can be classified into three groups: the multi-frequency (hierarchical) approach, the multi-wavelength (heterodyne) approach, and the number-theoretic approach. For all of them, extra fringe patterns of different spatial frequencies are required for retrieving the absolute phase. Due to the influence of image noise, people have to use many auxiliary patterns for high-accuracy phase unwrapping. Consequently, image noise limits the efficiency and the measurement speed greatly. Further, these three groups of TPU algorithms have their own theories and are usually applied in different ways. In this work, for the first time to our knowledge, we show that a generalized framework using deep learning can be developed to perform the TPU task for different groups of TPU algorithms. Experimental results show that benefiting from the assistance of deep learning the proposed framework can mitigate the impact of noise effectively and enhance the phase unwrapping reliability significantly without increasing the number of auxiliary patterns for different TPU approaches. We believe that the proposed method demonstrates great potential for developing powerful and reliable phase retrieval techniques.