Imaging Technology Research Articles

Effective Tomato Spotted Wilt Virus Resistance Assessment Using Non-Destructive Imaging and Machine Learning

There is a growing need to establish a breed reassessment system responding to tomato spotted wilt virus (TSWV) mutations. Conventional visual survey methods allow for assessing TSWV severity and disease incidence, while enzyme-linked Immunosorbent Assay (ELISA) data analysis can replace and validate visual surveys. This study proposes a non-destructive evaluation technique for TSWV using an open software platform based on image processing and machine learning. Many studies have evaluated resistance to the TSWV. However, as strains that destroy TSWV resistance emerge, an evaluation technique that can identify new genetic resources with resistance to the variants is needed. Evaluation techniques based on images and machine learning have the strength to respond quickly and accurately to the emergence of new variants. However, studies on resistance to viruses rely on empirical judgment based on visual surveys. The accuracy of the training model using Support Vector Machine (SVM), Logistic Regression (LR), and neural networks (NNs) was excellent, in the following order: NNs (0.86), LR (0.81), SVM (0.65). Meanwhile, the accuracy of the validation model was good, in the following order NN (0.84), LR (0.79), SVM (0.71). NNs’ prediction performance was verified through ELISA data analysis, showing a causal relationship between the two data sets with an R² of 0.86 with statistical significance. Imaging and NN-based TSWV resistance assessment technologies show significant potential as key tools in genetic resource reassessment systems that ensure a rapid and accurate response to the emergence of new TSWV strains.

Sodium alginate/low methoxyl pectin composite hydrogel beads prepared via gas-shearing technology for enhancing the colon-targeted delivery of probiotics and modulating gut microbiota.

The probiotic encapsulation system has the potential to enhance the prebiotic effects of probiotics. However, challenges arise from the release behavior of this system in vivo and the large size of hydrogel beads. This study aims to address the issues related to the size of previous hydrogel beads and assess the colon-targeted delivery of probiotic polysaccharides composite hydrogel beads (PPHB). PPHB prepared by gas-shearing technique significantly reduced the average particle size and demonstrated a high protective capacity for probiotics (after simulating intestinal conditions for 4 h, the viability of encapsulated probiotics remained at 107 CFU/g). The use of indocyanine green along with near-infrared-II in vivo imaging technology demonstrated the colon-targeted delivery of PPHB in vivo, which also extended the retention time of probiotics in the cecum and colon. Additionally, the colon-targeted delivery of PPHB was also demonstrated by dietary supplementation in vivo. PPHB significantly enhanced the diversity and richness of intestinal microflora species, increased the levels of short-chain fatty acids, raised the relative abundance of beneficial bacteria, and significantly decreased the relative abundance of harmful bacteria. Alginate-based PPHB is more suitable for encapsulating functional ingredients for colon-targeted delivery and modulating gut microbiota.

GenAI synthesis of histopathological images from Raman imaging for intraoperative tongue squamous cell carcinoma assessment

The presence of a positive deep surgical margin in tongue squamous cell carcinoma (TSCC) significantly elevates the risk of local recurrence. Therefore, a prompt and precise intraoperative assessment of margin status is imperative to ensure thorough tumor resection. In this study, we integrate Raman imaging technology with an artificial intelligence (AI) generative model, proposing an innovative approach for intraoperative margin status diagnosis. This method utilizes Raman imaging to swiftly and non-invasively capture tissue Raman images, which are then transformed into hematoxylin-eosin (H&E)-stained histopathological images using an AI generative model for histopathological diagnosis. The generated H&E-stained images clearly illustrate the tissue’s pathological conditions. Independently reviewed by three pathologists, the overall diagnostic accuracy for distinguishing between tumor tissue and normal muscle tissue reaches 86.7%. Notably, it outperforms current clinical practices, especially in TSCC with positive lymph node metastasis or moderately differentiated grades. This advancement highlights the potential of AI-enhanced Raman imaging to significantly improve intraoperative assessments and surgical margin evaluations, promising a versatile diagnostic tool beyond TSCC.

Terahertz Multicolor Imaging of Opaque Objects Using Self-Mixing Interferometry with Quantum-Cascade Lasers

Self-mixing interference in a terahertz quantum-cascade laser has been demonstrated to be suitable for the detection of weak signals scattered or reflected by the target. This technology has achieved the high-sensitivity detection of complex refractive indices, surface/interface morphologies and molecular feature spectra. Here, a set of terahertz quantum-cascade lasers with different lasing frequencies is used to inspect a tiny amount of powder concealed inside a polytetrafluoroethylene tablet by using self-mixing interferometry combined with the penetration properties of terahertz waves. Multicolor spectral images were acquired, which were synthesized by absorption contrast images obtained at different lasing frequencies. They enable the detection of the spatial distribution of hidden objects which are totally opaque in visual light and allow for them to be identified with spectral absorption characteristics. Self-mixing interference technology can also obtain phase information when a terahertz wave interacts with a tablet, showing the difference between the hidden object and surroundings from another dimension. Our research may provide a strategy for the development of terahertz multispectral imaging technology for the inspection of hidden trace residues.

Real-time single-pixel video imaging based on deep learning

The emergence of compressed sensing (CS) theory has enabled the development of single-pixel cameras that achieve high-resolution imaging using a single photodetector. However, traditional CS reconstruction algorithms require significant computational time and face an inherent trade-off between imaging resolution and frame rate, limiting current single-pixel cameras to static scene imaging. A key challenge lies in achieving real-time single-pixel imaging with both high frame rate and high resolution. This paper proposes a real-time single-pixel imaging technology based on deep learning. We design a deep convolutional neural network architecture incorporating residual networks to simulate the measurement and reconstruction process of CS-based single-pixel imaging. The network is trained on an image dataset and subsequently deployed for single-pixel imaging. The trained network can complete image reconstruction at a low sampling rate with minimal latency, enabling real-time single-pixel video capture at 128×128 resolution with 33 frames per second (fps) at a 4\% sampling rate. Furthermore, we implement a four-channel parallel signal processing method to achieve real-time single-pixel imaging video at 256×256 resolution at 33 fps.

Advancing cancer diagnosis and treatment: integrating image analysis and AI algorithms for enhanced clinical practice

Cancer screening and diagnosis with the utilization of innovative Artificial Intelligence tools improved the treatment strategies and patients’ survival. With the rapid development of imaging technologies and the rise of artificial intelligence (AI), there is a significant opportunity to improve cancer diagnostics through the combination of image analysis and AI algorithms. This article provides a comprehensive review of studies that have investigated the application of AI-assisted image processing in cancer diagnosis. We searched the Web of Science and Scopus databases to identify relevant studies published between 2014 and January 2024. The search strategy utilized targeted keywords such as cancer diagnostics, image analysis, artificial intelligence, and advanced imaging techniques. We limited the review to articles written in English and using AI-assisted image processing in cancer diagnosis. The results show that by leveraging machine learning algorithms, including deep learning, computer-aided diagnosis systems have been developed that are efficient in detecting tumors, thereby facilitating early cancer detection. Additionally, various authors have explored the integration of personalized treatment approaches and precision medicine, allowing for the development of treatment plans tailored to individual patient characteristics and needs. The review emphasizes the potential of AI-assisted image processing in revolutionizing cancer diagnostics. The insights gained from this study contribute to the current understanding of the field and pave the way for future research and development aimed at advancing cancer diagnostics using image analysis and artificial intelligence.

Detection of Rock Mass Defects Using Seismic Tomography

Elastic wave tomography is a non-destructive technique used to obtain velocity images of internal structures. Wave velocity tomography has been used to quantitatively describe abnormal structures and identify boundaries in different media and shapes. This research aims to quantitatively describe anomalous structures on a laboratory scale and identify boundaries in different media and shapes. Wave velocity tomography technology was applied to non-empty and empty anomalous areas, and the accuracy of the resulting velocity images was quantified. The reliability of boundary recognition was studied and analyzed. The accuracy of the results was found to relate to the size and shape of the anomalous structures estimated in the prior model, as well as the wave velocity difference between the anomalous and normal regions. In practical engineering, when the presence of abnormal areas cannot be determined, a completely homogeneous prior model is used. The results indicate that, although the inversion velocity may not be accurate, it is sufficient to identify boundaries. The larger the velocity difference, the clearer the boundary to be recognized. Furthermore, to assess the practical efficacy of this physical detection method, the wave velocity tomography technology was used to detect hidden goafs within a tantalum–niobium mine belonging to the Hunan Fuguihengtong Mining Company. Utilizing physical detection, 12 potential goafs and 100 confirmed goafs were identified within the mine, demonstrating the efficacy of the proposed empty anomalous area detection methodology in accurately identifying and delineating the boundaries of hidden goafs.

Optimizing healthcare big data performance through regional computing

The healthcare sector is experiencing a digital transformation propelled by the Internet of Medical Things (IOMT), real-time patient monitoring, robotic surgery, Electronic Health Records (EHR), medical imaging, and wearable technologies. This proliferation of digital tools generates vast quantities of healthcare data. Efficient and timely analysis of this data is critical for enhancing patient outcomes and optimizing care delivery. Real-time processing of Healthcare Big Data (HBD) offers significant potential for improved diagnostics, continuous monitoring, and effective surgical interventions. However, conventional cloud-based processing systems face challenges due to the sheer volume and time-sensitive nature of this data. The migration of large datasets to centralized cloud infrastructures often results in latency, which impedes real-time applications. Furthermore, network congestion exacerbates these challenges, delaying access to vital insights necessary for informed decision-making. Such limitations hinder healthcare professionals from fully leveraging the capabilities of emerging technologies and big data analytics. To mitigate these issues, this paper proposes a Regional Computing (RC) paradigm for the management of HBD. The RC framework establishes strategically positioned regional servers capable of regionally collecting, processing, and storing medical data, thereby reducing dependence on centralized cloud resources, especially during peak usage periods. This innovative approach effectively addresses the constraints of traditional cloud processing, facilitating real-time data analysis at the regional level. Ultimately, it empowers healthcare providers with the timely information required to deliver data-driven, personalized care and optimize treatment strategies.

CDKD-w+: A Keyframe Recognition Method for Coronary Digital Subtraction Angiography Video Sequence Based on w+ Space Encoding

Currently, various deep learning methods can assist in medical diagnosis. Coronary Digital Subtraction Angiography (DSA) is a medical imaging technology used in cardiac interventional procedures. By employing X-ray sensors to visualize the coronary arteries, it generates two-dimensional images from any angle. However, due to the complexity of the coronary structures, the 2D images may sometimes lack sufficient information, necessitating the construction of a 3D model. Camera-level 3D modeling can be realized based on deep learning. Nevertheless, the beating of the heart results in varying degrees of arterial vasoconstriction and vasodilation, leading to substantial discrepancies between DSA sequences, which introduce errors in 3D modeling of the coronary arteries, resulting in the inability of the 3D model to reflect the coronary arteries. We propose a coronary DSA video sequence keyframe recognition method, CDKD-w+, based on w+ space encoding. The method utilizes a pSp encoder to encode the coronary DSA images, converting them into latent codes in the w+ space. Differential analysis of inter-frame latent codes is employed for heartbeat keyframe localization, aiding in coronary 3D modeling. Experimental results on a self-constructed coronary DSA heartbeat keyframe recognition dataset demonstrate an accuracy of 97%, outperforming traditional metrics such as L1, SSIM, and PSNR.

How does career-related parental support benefit career adaptability of medical imaging technology students in Asia-Pacific LMICs? The roles of psychological capital and career values

BackgroundChina, as a low- and middle-income country (LMIC) in the Asia-Pacific region, is advancing its rural medical and health system. Students of Medical Imaging Technology (MIT) in China, who will probably be employed in rural, are facing the pressure from both study and employment. Previous results proposed that career-related parent support and psychological capital could positively influence the level of career adaptability, while career-related parent support affects career values. This study aimed to explore the impact of career-related parental support on career adaptability and the mechanism between them, as well as the role of psychological capital and career values.MethodsA total of 520 (80.8% female) participants were recruited from MIT students in China, using Questionnaires about career-related parental support, psychological capital, career values, career adaptability. Path analysis was conducted using Mplus 8.3.ResultsThere was a significant correlation between career adaptability and career-related parental support, psychological capital and career values. Career-related parental support could positively predict students’ career adaptation. Psychological capital and career values played a parallel mediating role between career-related parental support and career adaptation.ConclusionThe results demonstrate that it is necessary to enhance career-related parental support to improve MIT students’ career adaptability by the enrichment of psychological capital and the refinement of career values, on the basis of the popularization of medical career programs among the public and medical literacy courses in colleges.

Dandelion extract suppresses the stem-like properties of triple-negative breast cancer cells by regulating CUEDC2/β-catenin/OCT4 signaling axis.

Triple-negative breast cancer (TNBC) represents the most aggressive subtype of breast cancer, featuring a high proportion of cancer stem cells (CSCs) and the poorest clinical outcomes. Taraxacum mongolicum Hand. -Mazz., widely recognized as dandelion, is a traditional medicinal herb that has demonstrated promising anti-TNBC potential. However, the efficacy of dandelion in anti-TNBC stem-like properties remains to be elucidated. The aim was to examine the impact of dandelion extract on the stemness properties of TNBC and to delineate the underlying mechanisms. UHPLC-Q-Orbitrap HRMS was employed to characterize the components present in dandelion extract. Network pharmacology was utilized to explore the impact of dandelion-derived compounds on the molecular pathways associated with TNBC. The assessment of TNBC stem-like properties was conducted through mammosphere formation assays and flow cytometry analysis. Western blotting, qRT-PCR, and immunofluorescence were employed to investigate the mechanisms of dandelion extract. 4T1-luc xenograft tumor model was used to assess the anti-tumor effect of dandelion extract in vivo. IVIS imaging technology was used to monitor lung metastasis. In this study, pharmacological network analysis revealed the potential regulatory effects of dandelion extract on TNBC stemness. Dandelion extract disrupts the stem-like properties in MDA-MB-231 and MDA-MB-468 cell lines via reducing ALDH+ cells proportion, impeding mammosphere formation, and downregulating CSC-related markers, including SOX2, SOX9, NANOG, and FOXM1. Furthermore, CUE domain containing protein 2 (CUEDC2) promotes the maintenance of TNBC stemness and contributes to the anti-stemness effects of dandelion extract. Mechanistically, dandelion extract inhibits CUEDC2-mediated nuclear translocation of β-catenin, thereby reducing the transcriptional activity of OCT4. In vivo, dandelion extract suppresses tumor growth, lung metastasis, and decreases the expression of CSC-related markers. These findings suggest that dandelion extract inhibits TNBC stem-like properties via modulating the CUEDC2/β-catenin/OCT4 signaling axis, highlighting its potential as a therapeutic option for TNBC.

A multiplexing method based on multidimensional readout method.


To develop and validate a novel multidimensional readout method that significantly reduces the number of readout channels in PET detectors while maintaining high spatial and energy performance.

Approach:
We arranged a 3×3×4 SiPM array in multiple dimensions and employed row/column/layer summation with a resistor-based splitting circuit. We then applied denoising methods to enhance the peak-to-valley ratio in the decoding map, ensuring accurate crystal-position determination. Additionally, we investigated the system's energy response at 511 keV and evaluated the suitability for both clinical and research PET systems.

Main Results:
The proposed multidimensional readout method achieved a favorable multiplexing ratio, lowering the total number of readout channels without compromising energy resolution at 511 keV. Our tests demonstrated that a SiPM bias voltage of 31 V effectively balances gain and saturation effects, resulting in reliable energy measurements.

Significance:
By reducing system complexity, cost, and power consumption, the multidimensional readout method presents a practical alternative to conventional readout schemes for PET and other large-scale sensor arrays. Additionally, the approach can manage simultaneous multi-layer hits by arranging detector layers and, when needed, uses ICS detection to correct for scatter events. Its adaptable architecture allows scaling to higher dimensions for broader applications (e.g., SPECT, CT, LiDAR). These features make it a valuable contribution toward more efficient, high-performance imaging technologies in both clinical and industrial settings.

Validity of non-traditional measures of obesity compared to total body fat across the life course: A systematic review and meta-analysis.

IntroductionTraditional obesity measures including body mass index, waist circumference, waist-to-hip ratio, and waist-to-height ratio have limitations. The primary objective of this study was to identify and review the validity of non-traditional obesity measures, using measures of total body fat as the reference standard, that have been used across multiple life stages. MethodsWe conducted a systematic review and searched MEDLINE, Embase, and PsycINFO. We included observational studies published from 2013 to October 2023 among "the general population" for any life stage that reported the validity of non-traditional obesity measures compared to total body fat reference standards. Separate meta-analyses were performed to pool correlation coefficients and mean differences for non-traditional obesity measures that were evaluated at multiple life stages. ResultsA total of 123 studies were included, and 55 validated non-traditional obesity measures were identified. Of these, 13 were evaluated at multiple life stages. Two-dimensional (2D) digital imaging technologies, three-dimensional (3D) body scanners, relative fat mass (RFM), and mid-upper arm circumference had high or moderate validity (pooled correlation coefficient >0.70). Pooled mean differences were small (<6%) between total body fat percentage from reference standards and from RFM, 2D digital imaging technologies, 3D body scanners, and the body adiposity index. Heterogeneity (I2) was >75% in most meta-analyses. ConclusionNumerous validated non-traditional obesity measures were identified; relatively few were evaluated at multiple life stages and did not consider health risks associated with adiposity. More research is needed to define valid obesity measures across all life stages that assess health and adiposity.

A Future Picture: A Review of Current Generative Adversarial Neural Networks in Vitreoretinal Pathologies and Their Future Potentials

Machine learning has transformed ophthalmology, particularly in predictive and discriminatory models for vitreoretinal pathologies. However, generative modeling, especially generative adversarial networks (GANs), remains underexplored. GANs consist of two neural networks—the generator and discriminator—that work in opposition to synthesize highly realistic images. These synthetic images can enhance diagnostic accuracy, expand the capabilities of imaging technologies, and predict treatment responses. GANs have already been applied to fundus imaging, optical coherence tomography (OCT), and fluorescein autofluorescence (FA). Despite their potential, GANs face challenges in reliability and accuracy. This review explores GAN architecture, their advantages over other deep learning models, and their clinical applications in retinal disease diagnosis and treatment monitoring. Furthermore, we discuss the limitations of current GAN models and propose novel applications combining GANs with OCT, OCT-angiography, fluorescein angiography, fundus imaging, electroretinograms, visual fields, and indocyanine green angiography.

Schlemm's Bidirectional Fluid Wave and Luminal Blood Reflux as Novel Intra-Operative Signs Confirming Optimal Placement of the iStent.

Trabecular micro-bypass devices (TBDs) such as the iStent series (Glaukos Corporation, Laguna Hills, CA), are effective in reducing intraocular pressure (IOP). However, precise placement of TBDs is crucial in achieving surgical efficacy, as device malpositioning may lead to suboptimal IOP reduction. We demonstrate two novel intra-operative signs to aid confirmation of accurate iStent placement, without reliance on imaging technologies. Surgical technique report with video accompaniment. Two intra-operative clinical signs which confirm optimal iStent placement are described: (1) Luminal Blood Reflux - achieved by gently decompressing the anterior chamber, with or without flushing of the stent lumen with balanced salt solution (BSS), thereby confirming stent positioning in the Schlemm's Canal (SC). (2) Schlemm's Bidirectional Fluid Wave (SBFW) - observable when flushing with BSS, suggesting fluid movement in the canal segments adjacent to the iStent device, thereby confirming stent patency and positioning in the SC. The identified signs are useful, feasible and reproducible indicators of accurate iStent placement. Future studies may evaluate the application of these confirmatory manoeuvres in diverse clinical contexts and further establish correlation with clinical outcomes.

Unlocking the Power of 3D Convolutional Neural Networks for COVID-19 Detection: A Comprehensive Review.

The advent of three-dimensional convolutional neural networks (3D CNNs) has revolutionized the detection and analysis of COVID-19 cases. As imaging technologies have advanced, 3D CNNs have emerged as a powerful tool for segmenting and classifying COVID-19 in medical images. These networks have demonstrated both high accuracy and rapid detection capabilities, making them crucial for effective COVID-19 diagnostics. This study offers a thorough review of various 3D CNN algorithms, evaluating their efficacy in segmenting and classifying COVID-19 across a range of medical imaging modalities. This review systematically examines recent advancements in 3D CNN methodologies. The process involved a comprehensive screening of abstracts and titles to ensure relevance, followed by a meticulous selection and analysis of research papers from academic repositories. The study evaluates these papers based on specific criteria and provides detailed insights into the network architectures and algorithms used for COVID-19 detection. The review reveals significant trends in the use of 3D CNNs for COVID-19 segmentation and classification. It highlights key findings, including the diverse range of networks employed for COVID-19 detection compared to other diseases, which predominantly utilize encoder/decoder frameworks. The study provides an in-depth analysis of these methods, discussing their strengths, limitations, and potential areas for future research. The study reviewed a total of 60 papers published across various repositories, including Springer and Elsevier. The insights from this study have implications for clinical diagnosis and treatment strategies. Despite some limitations, the accuracy and efficiency of 3D CNN algorithms underscore their potential for advancing medical image segmentation and classification. The findings suggest that 3D CNNs could significantly enhance the detection and management of COVID-19, contributing to improved healthcare outcomes.

Non-Destructive Detection of pH Value During Secondary Fermentation of Maize Silage Using Colorimetric Sensor Array Combined with Hyperspectral Imaging Technology

The pH value of maize silage can accurately reflect its quality. In this study, a colorimetric sensor array (CSA) combined with hyperspectral imaging (HSI) was used to predict the pH value of maize silage during secondary fermentation. Seventeen color-sensitive dyes were used to construct the CSA, which was subsequently applied to capture the volatile odor profiles of maize silage samples. Hyperspectral images of the color-sensitive dyes on the CSA were acquired using the HSI technique. Different algorithms were used to preprocess the raw spectral data of each dye, and a partial least squares regression (PLSR) model was built for each dye separately. Subsequently, the adaptive bacterial foraging optimization (ABFO) algorithm was employed to identify three color-sensitive dyes that demonstrated heightened sensitivity to pH variations in maize silage. This study further compared the capabilities of individual dyes, as well as their combinations, in predicting the pH value of maize silage. Additionally, a novel feature wavelength extraction method based on the ABFO algorithm was proposed, which was then compared with two traditional feature extraction algorithms. These methods were combined with PLSR and backpropagation neural network (BPNN) algorithms to construct a quantitative prediction model for the pH value of maize silage. The results show that the quantitative prediction model constructed based on three dyes was more accurate than that constructed based on an individual dye. Among them, the ABFO-BPNN model constructed on the basis of combined dyes had the best prediction performance, with prediction correlation coefficient (RP2), root mean square error of the prediction set (RMSEP), and ratio of performance deviation (RPD) values of 0.9348, 0.3976, and 3.9695, respectively. The aim of this study was to develop a reliable evaluation model to achieve fast and accurate predictions of silage pH.

Potential of echocardiographic assessment of early systolic lengthening in making diagnosis of various forms of coronary heart disease

The occurrence of paradoxical myocardial deformation, which includes early systolic lengthening and post-systolic shortening, was originally described in the 1970s in experimental animal models and in patients with myocardial ischemia at invasive assessment of the left ventricle (LV). Today, new echocardiographic imaging technology demonstrates that these phenomena are much more common than were initially thought. Quite a lot of studies have been conducred to investigate post-systolic shortening (PSS), but the role of early systolic lengthening (ESL) has become increasingly emphasized in the most recent research articles. In this regard, we have made an attempt to describe most completely and accessibly the clinical potential associated with the mechanisms of occurrence of ESL and the significance of its assessment in various forms of coronary heart disease.

Ultra‐Broadband Polarization‐Independent Terahertz Absorber Based on All‐Dielectric GaN Metamaterials

In this article, an ultra‐wideband wide‐angle absorber based on all dielectric gallium arsenide (GaN) is proposed. The proposed absorber comprises a all dielectric square GaN structure and a GaN substrate. Numerical simulation results demonstrate that the proposed absorber achieves over 90% ultra‐wideband absorption in the range of 0.26–1.7 THz, with a relative bandwidth of 146.9%. The ultra‐broadband absorption property of the proposed absorber is further validated by an equivalent circuit model, which agrees well with the full‐wave numerical simulation results obtained using the finite element method. The simulated electromagnetic field distribution indicates that ultra‐wideband absorption primarily originates from the excitation of Fabry–Perot interference modes and electromagnetic resonance. The proposed absorber exhibits wide‐angle incidence properties and polarization insensitivity in both transverse electric and transverse magnetic modes. Thus, the proposed GaN ultra‐wideband terahertz absorber holds significant potential for applications in fields such as imaging technology and biomedicine.

Multi-Baseline Bistatic SAR Three-Dimensional Imaging Method Based on Phase Error Calibration Combining PGA and EB-ISOA

Tomographic synthetic aperture radar (TomoSAR) is an advanced three-dimensional (3D) synthetic aperture radar (SAR) imaging technology that can obtain multiple SAR images through multi-track observations, thereby reconstructing the 3D spatial structure of targets. However, due to system limitations, the multi-baseline (MB) monostatic SAR (MonoSAR) encounters temporal decorrelation issues when observing the scene such as forests, affecting the accuracy of the 3D reconstruction. Additionally, during TomoSAR observations, the platform jitter and inaccurate position measurement will contaminate the MB SAR data, which may result in the multiplicative noise with phase errors, thereby leading to the decrease in the imaging quality. To address the above issues, this paper proposes a MB bistatic SAR (BiSAR) 3D imaging method based on the phase error calibration that combines the phase gradient autofocus (PGA) and energy balance intensity-squared optimization autofocus (EB-ISOA). Firstly, the signal model of the MB one-stationary (OS) BiSAR is established and the 3D imaging principle is presented, and then the phase error caused by platform jitter and inaccurate position measurement is analyzed. Moreover, combining the PGA and EB-ISOA methods, a 3D imaging method based on the phase error calibration is proposed. This method can improve the accuracy of phase error calibration, avoid the vertical displacement, and has the noise robustness, which can obtain the high-precision 3D BiSAR imaging results. The experimental results are shown to verify the effectiveness and practicality of the proposed MB BiSAR 3D imaging method.