Speckle Imaging Research Articles

Functional and molecular 3D mapping of angiosarcoma tumor using non-invasive laser speckle, hyperspectral, and photoacoustic imaging

ABSTRACT Purpose The gold standard for skin cancer diagnosis is surgical excisional biopsy and histopathological examination. Several non-invasive diagnostic techniques exist, although they have not yet translated into clinical use. This is a proof-of-concept study to assess the possibility of imaging an angiosarcoma in the periocular area. Methods We use laser speckle, hyperspectral, and photoacoustic imaging to monitor blood perfusion and oxygen saturation, as well as the molecular composition of the tissue. The information obtained from each imaging modality was combined in order to yield a more comprehensive picture of the function, as well as molecular composition of a rapidly growing cutaneous angiosarcoma in the periocular area. Results We found an increase in perfusion coupled with a reduction in oxygen saturation in the angiosarcoma. We could also extract the molecular composition of the angiosarcoma at a depth, depicting both the oxygen saturation and highlighting the presence of connective tissue via collagen. Conclusions We demonstrate the different physiological parameters that can be obtained with the different techniques and how these can be combined to provide detailed 3D maps of the functional and molecular properties of tumors useful in preoperative assessment.

Role of Renal Sonography in the Diagnosis of Chronic Kidney Disease

Aim: to use sonographic imaging in grading CKD and assess the serum creatinine, renal longitudinal size,parenchymal thickness and compare these parameters based upon ultrasonographic grade.Material and Methods: Present cross-sectional; study was conducted at the Department of Radiology, Tertiary Care Teaching Institute of India for the duration of 1 year. There are 120 participants with CKD in the research.All patients received a questionnaire, and a thorough clinical examination was done. Each participant underwent sector curved array transducer of 3.5–5 MHz ultrasound images of their liver and kidneys. Low tissue harmonic and speckle reduction imaging techniques are used to measure the echogenicity of the kidney and liver while reducing interobserver bias. In all the participants, the mean values of both the kidneys renal longitudinal size and parenchymal thickness were calculated. Renal cortical echogenicity was compared and graded with the echogenicity of the liver and renal medulla.Results: The mean age of the CKD patients was found to be 45.23 ± 06.05 years. It was found that 50 patientshad Grade 1, 40 patients had Grade 2, 20 patients had Grade 3 and 10 patients had Grade 4. The results showed that, when the comparison was done, serum creatinine was found to be significant among echogenicity grades with ANOVA. when the comparison was done; the mean longitudinal size was found to be significant among echogenicity. On comparison was found to be mean Parenchymal thickness was found to be significant among echogenicity grades.Conclusion: Renal cortical echogenicity and its grading in relation to longitudinal length, parenchymal thickness, and cortical thickness in CKD patients are the best sonographic parameters that correlate with blood creatinine. It is possible to use renal cortical echogenicity as a metric of renal function since it has the benefit of being irreversible in compared to serum creatinine levels.

Investigation into safflower injection as a prophylactic treatment for retinal vein occlusion in a rabbit model

The study aimed to assess the effect and mechanism of safflower injection in preventing retinal vein thrombosis in rabbits. Twenty healthy adult pigmented rabbits were randomly assigned to either the experimental group, receiving safflower injection, or the control group, receiving normal saline. After two weeks of treatment, blood samples were collected to analyze platelet adhesion and aggregation rates. Photodynamic therapy was applied to induce occlusion in the target retinal vein. Fundus photography and fluorescein angiography were recorded using a dynamic microscopic monitoring system, and laser speckle imaging was employed to assess blood flow in the affected vein. The experimental group exhibited significantly lower rates of platelet adhesion and aggregation compared to the control group. Following the induction of retinal vein occlusion, the experimental group showed a lower complete occlusion rate of the target retinal vein. Although initial blood flow in the target vein was similar between groups, the blood flow at 1, 3, and 5 min post-occlusion was significantly higher in the experimental group. Safflower injection delayed retinal vein thrombosis formation, preserved blood flow in the affected retinal area, and reduced platelet adhesion and aggregation. These effects facilitated vascular reperfusion within a limited timeframe.

Enhancing Diagnostic Accuracy: A Hybrid Approach to Mitigate Speckle and Rician Noise in Medical Images

This research explores the detrimental effects of Rician and speckle noise in medical imaging, particularly within MRI and radiographic modalities. Such noise can significantly obscure critical details, potentially leading to misdiagnoses and inaccurate measurements. Rician noise candidly dangles on the signal strength. Removing this noise affects the origial information in image that makes this noise removal perplexing. On other hand speckle noise is visualised like granular clog in ultrasound imageries. It creates ambiguous details and edges in the images. To address this challenge, the study introduces a novel hybrid method implemented in Java. The method employs a frequency-based decomposition approach for speckle noise and separates images into magnitude and phase components for Rician noise. This allows for targeted filtering, utilizing customized versions of median and Gaussian filters, to effectively remove noise while preserving crucial image features. The proposed method was rigorously assessed using 200 contaminated brain and lung MRIs and radiographs, demonstrating superior performance compared to traditional filters based on crucial system of measurement like Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Mean Squared Error (MSE). This research presents a promising solution for enhancing the quality and reliability of medical images, potentially leading to improved diagnostic accuracy and patient outcomes.

Study on transfer learning-based cross-spectral speckle image reconstruction method

Abstract In recent years, convolutional neural networks (CNNs) have been successfully applied to reconstruct images from speckle patterns generated as objects pass through scattering media. To achieve this objective, a large amount of data is collected for training the CNN. However, in certain cases, the characteristics of light passing through the scattering medium may vary. In such situations, it is necessary to collect a substantial amount of new data to re-train the CNN and achieve image reconstruction. To address this challenge, transfer learning techniques are introduced in this study. Specifically, we propose a novel Residual U-Net Generative Adversarial Network, denoted as ResU-GAN. The network is initially pre-trained using a large amount of data collected from either visible or non-visible light sources, and subsequently fine-tuned using a small amount of data collected from non-visible or visible light sources. Experimental results demonstrate the outstanding reconstruction performance of the ResU-GAN network. Furthermore, by combining transfer learning techniques, the network enables the reconstruction of speckle images across different datasets. The findings presented in this paper provide a more generalized approach for utilizing CNNs in cross-spectral speckle imaging.

ViT-Based Image Regression Model for Shear-Strength Prediction of Transparent Soil

The direct-shear test is the primary method used to test the shear strength of transparent soil, but this experiment is complex and easily influenced by experimental conditions. In order to simplify the process of obtaining the shear strength of transparent soil, an image regression model based on a vision transformer (ViT) is proposed in this paper; this is used to recognize the shear strength of the soil based on images of transparent-soil patches. This model uses a convolutional neural network (CNN) to decompose the transparent-soil images into multiple image patches containing high-order features, utilizes a ViT for feature extraction, and designs a regression network to facilitate the transfer of information between the abstract image features and shear strength. This model solves the problem of boundary blurring and difficult-to-identify features in speckle images. To demonstrate the effectiveness of the proposed model, different parameters related to transparent soil were obtained by controlling the particle size of fused quartz sand and the content of aerosol; in addition, the friction angle and cohesive force of the transparent soil under different proportions were measured using direct-shear tests, serving as two datasets. The results show that the proposed method achieves correlations of 0.93 and 0.94 in the two prediction tasks, thus outperforming existing deep learning models.

Optical image double-encryption using speckle decorrelation properties in spatial and spectral domains

Optical image encryption technique has become increasingly important. Recently, double-encryption is introduced to enhance security. However, most coherent double-cryptosystems suffer from artefact noise, while the incoherent double-cryptosystem is not stable enough. This paper proposes a method for image double-encryption using speckle decorrelation properties in spatial and spectral domains. The plaintext is disguised as several different fake plaintexts that are separately encrypted through rotated random phase masks under incoherent illumination with different wavelengths. Since the speckle decorrelation properties in spatial and spectral domains, the plaintexts contained in the detected speckle images are multiplexed in a ciphertext. The security is enhanced since correct decryption can be achieved only when all the keys with spatial and spectral domains information are obtained. Simulations, experimental demonstrations and attack resistance tests are implemented with a simple incoherent cryptosystem that is easy to achieve in practical applications.

ALI: The adaptive levels of interval method for processing laser speckle images with superior activity extraction and discrimination capabilities

This paper describes the development of a new laser speckle imaging method for extracting speckle patterns' temporal activity information with superior extraction and discrimination capabilities. It is based on a novel mechanism which uses the speckle images, in an unconventional manner, through adapting the levels of their interval, to deal with the scattering medium that contains regions with different activity scales. This mechanism overcomes the discrimination inability of the other processing methods when they deal with such a medium and speeds up the process of activity map calculation. The developed speckle processing method was evaluated using numerical and graphical evaluation tests, the results showing the efficacy of using the adapting mechanism for the levels of interval. Experimental tests were also undertaken on the proposed method to validate its ability with real speckle images, obtained through a series of experiments performed using a laser speckle imaging system. The experimental results show the high discrimination and activity extraction capabilities of this method against the speckle processing methods and validate its ability to deal with multi-spectral real speckle images. The developed method achieved a faster processing performance in comparison with traditional methods when processing temporal information for the speckle images and calculating the map of their activity.

A ghost imaging framework based on laser mode speckle pattern for underwater environments

Due to the complex physical processes found in underwater environments, such as absorption, scattering, and noise, it is challenging to obtain high-quality images using conventional camera-based imaging techniques. Ghost imaging possesses strong anti-interference capabilities and can effectively obtain images in underwater environments. Here, we propose a ghost imaging framework based on a physical model of M2-ordered laser mode patterns and apply it to Ghost Imaging. The simulation results show that the Laser Mode Speckle Ghost Imaging can reconstruct the overall trapped contour even at a low sampling rate, specifically below 0.64%. A high-quality image with a Peak Signal-to-Noise Ratio of 19 dB can be achieved using the Laser Mode Speckle Ghost Imaging when the sampling rate is 5%. Even with a relative random noise of 1.0%–5.0%, the imaging quality of Laser Mode Speckle Ghost Imaging is superior to that of Random speckle pattern Ghost Imaging, Walsh speckle pattern Ghost Imaging, and Haar speckle pattern Ghost Imaging when the sampling rate consistent. Our experimental results in a turbid water environment confirm the conclusions drawn from the simulation results. The proposed Laser Mode Speckle Ghost Imaging can be used as an imaging solution in challenging liquid environments, such as turbid liquids, inclement weather, and biological tissue fluids.

Method of generating speckle patterns for digital image correlation based on modified Conway's Game of Life.

The measurement accuracy of digital image correlation (DIC) is influenced by the quality of the speckle pattern. Although various models for generating random speckle patterns have been well discussed, obtaining appropriate speckle images with isotropic quality and performance could be a challenging issue in DIC. In this paper, we propose a novel (to our knowledge) method for generating speckle patterns based on modified Conway's game of life (GoL). By sequentially assembling the speckle patterns generated from the modified GoL, we produced the GoL speckle image. Then, verification and comparison experiments were conducted through pure in-plane translations. The results show that the generated speckle image which was resized with k s=6& k r=2 processing and subsequently fuzzified using a Gaussian filter, produces the best accuracy for DIC measurement. Furthermore, based on the rigid body in-plane rotation displacement tests in the physical experimental results of three different speckle images, the GoL speckle generated from our proposed method shows the smallest measurement error. This indicates that the proposed speckle patterns generating method could provide a new type of speckle pattern with better quality and accuracy.

Quality assessment of laser speckle image sequences for digital image correlation by Sequence-image Quality Evaluation Index

Digital Image Correlation (DIC) with laser speckle is effective for measuring strain in extreme conditions, but its limited resistance to disturbances and decorrelation issues limit its broader use. Addressing this, the Sequence-image Quality Evaluation Index (SQEI) has been introduced. SQEI evaluates laser speckle image sequences by considering individual image quality and their correlation. It improves upon previous standards by confirming speckle image quality for DIC accuracy and removing decorrelated images, enhancing DIC's precision and durability. Verified through metal thermal strain experiments, SQEI assesses the impact of factors like laser wavefront, temperature, and CCD angle. The results confirm SQEI's efficacy in evaluating laser speckle quality and guiding the optimization of optical devices, thus significantly boosting the accuracy and efficiency of DIC strain measurement with laser speckles.

Amyloid β oligomer induces cerebral vasculopathy via pericyte-mediated endothelial dysfunction

BackgroundAlthough abnormal accumulation of amyloid beta (Aβ) protein is thought to be the main cause of Alzheimer’s disease (AD), emerging evidence suggests a pivotal vascular contribution to AD. Aberrant amyloid β induces neurovascular dysfunction, leading to changes in the morphology and function of the microvasculature. However, little is known about the underlying mechanisms between Aβ deposition and vascular injuries. Recent studies have revealed that pericytes play a substantial role in the vasculopathy of AD. Additional research is imperative to attain a more comprehensive understanding.MethodsTwo-photon microscopy and laser speckle imaging were used to examine cerebrovascular dysfunction. Aβ oligomer stereotactic injection model was established to explain the relationship between Aβ and vasculopathy. Immunofluorescence staining, western blot, and real-time PCR were applied to detect the morphological and molecular alternations of pericytes. Primary cultured pericytes and bEnd.3 cells were employed to explore the underlying mechanisms.ResultsVasculopathy including BBB damage, hypoperfusion, and low vessel density were found in the cortex of 8 to 10-month-old 5xFAD mice. A similar phenomenon accompanied by pericyte degeneration appeared in an Aβ-injected model, suggesting a direct relationship between Aβ and vascular dysfunction. Pericytes showed impaired features including low PDGFRβ expression and increased pro-inflammatory chemokines secretion under the administration of Aβ in vitro, of which supernatant cultured with bEND.3 cells led to significant endothelial dysfunction characterized by TJ protein deficiency.ConclusionsOur results provide new insights into the pathogenic mechanism underlying Aβ-induced vasculopathy. Targeting pericyte therapies are promising to ameliorate vascular dysfunction in AD.

DSCNet: lightweight and efficient self-supervised network via depthwise separable cross convolution blocks for speckle image matching.

Speckle structured light has become a research hotspot due to its ability to acquire target three-dimensional information with single image projection in recent years. To address the challenges of a low number of extracted speckle feature points, high mismatch rate and poor real-time performance in traditional algorithms, as well as the obstacle of requiring expensive annotation data in deep learning-based methods, a lightweight and efficient self-supervised convolutional neural network (CNN) is proposed to achieve high-precision and rapid matching of speckle images. First, to efficiently utilize the speckle projection information, a feature extraction backbone based on the depthwise separable cross convolution blocks is proposed. Second, in the feature detection module, a softargmax detection head is designed to refine the coordinates of speckle feature points to sub-pixel accuracy. In the feature description module, a coarse-to-fine module is presented to further refine matching accuracy. Third, we adopt strategies of transfer learning and self-supervised learning to improve the generalization and feature representation capabilities of the model. Data augmentation and real-time training techniques are used to improve the robustness of the model. The experimental results show that the proposed method achieves a mean matching accuracy of 91.62% for speckle feature points on the pilot's helmet, with mere 0.95% mismatch rate. The full model runs at 42ms for a speckle image pair on an RTX 3060.

Experimental and simulation investigation of stereo-DIC via a deep learning algorithm based on initial speckle positioning technology.

For the deep-learning-based stereo-digital image correlation technique, the initial speckle position is crucial as it influences the accuracy of the generated dataset and deformation fields. To ensure measurement accuracy, an optimized extrinsic parameter estimation algorithm is proposed in this study to determine the rotation and translation matrix of the plane in which the speckle is located between the world coordinate system and the left camera coordinate system. First, the accuracy of different extrinsic parameter estimation algorithms was studied by simulations. Subsequently, the dataset of stereo speckle images was generated using the optimized extrinsic parameters. Finally, the improved dual-branch CNN deconvolution architecture was proposed to output displacements and strains simultaneously. Simulation results indicate that DAS-Net exhibits enhanced expressive capabilities, as evidenced by a reduction in displacement errors compared to previous research. The experimental results reveal that the mean absolute percentage error between the stereo-DIC results and the generated dataset is less than 2%, suggesting that the initial speckle positioning technology effectively minimizes the discrepancy between the images in the dataset and those obtained experimentally. Furthermore, the DAS-Net algorithm accurately measures the displacement and strain fields as well as their morphological characteristics.

A method of liquid refractive index measurement based on image correlation coefficient

According to the difference of speckle image caused by the axial displacement due to the refraction of a transparent medium, the liquid refractive index is measured by using the image correlation coefficient. An optical measurement system based on laser speckle is built into the experiment. Firstly, the CCD camera is adjusted to move along the optical axis in air, and multiple speckle images at different positions are collected, which are correlated with the speckle image at zero position to obtain the image correlation coefficients, and then the exponential calibration relation between the correlation coefficient and the axial displacement can be obtained. Secondly, two speckle images are collected when the laser speckle spreads respectively through the liquid to be measured and water as the internal standard, and the correlation coefficient between them is calculated. Finally, according to the above calibration relation, the corresponding axial displacement between the liquid to be measured and water can be derived, and then the refractive index of the liquid to be measured is calculated using the axial displacement formula. The results show that this method can realize the measurement of liquid refractive index, and the optical structure is simple and easy to operate.

Training With Uncertain Annotations for Semantic Segmentation of Basal Cell Carcinoma From Full-Field OCT Images.

Semantic segmentation of basal cell carcinoma (BCC) from full-field optical coherence tomography (FF-OCT) images of human skin has received considerable attention in medical imaging. However, it is challenging for dermatopathologists to annotate the training data due to OCT's lack of color specificity. Very often, they are uncertain about the correctness of the annotations they made. In practice, annotations fraught with uncertainty profoundly impact the effectiveness of model training and hence the performance of BCC segmentation. To address this issue, we propose an approach to model training with uncertain annotations. The proposed approach includes a data selection strategy to mitigate the uncertainty of training data, a class expansion to consider sebaceous gland and hair follicle as additional classes to enhance the performance of BCC segmentation, and a self-supervised pre-training procedure to improve the initial weights of the segmentation model parameters. Furthermore, we develop three post-processing techniques to reduce the impact of speckle noise and image discontinuities on BCC segmentation. The mean Dice score of BCC of our model reaches 0.503±0.003, which, to the best of our knowledge, is the best performance to date for semantic segmentation of BCC from FF-OCT images.

Leptomeningeal collaterals regulate reperfusion in ischemic stroke and rescue the brain from futile recanalization

Recanalization is the mainstay of ischemic stroke treatment. However, even with timely clot removal, many stroke patients recover poorly. Leptomeningeal collaterals (LMCs) are pial anastomotic vessels with yet-unknown functions. We applied laser speckle imaging, ultrafast ultrasound, and two-photon microscopy in a thrombin-based mouse model of stroke and fibrinolytic treatment to show that LMCs maintain cerebral autoregulation and allow for gradual reperfusion, resulting in small infarcts. In mice with poor LMCs, distal arterial segments collapse, and deleterious hyperemia causes hemorrhage and mortality after recanalization. In silico analyses confirm the relevance of LMCs for preserving perfusion in the ischemic region. Accordingly, in stroke patients with poor collaterals undergoing thrombectomy, rapid reperfusion resulted in hemorrhagic transformation and unfavorable recovery. Thus, we identify LMCs as key components regulating reperfusion and preventing futile recanalization after stroke. Future therapeutic interventions should aim to enhance collateral function, allowing for beneficial reperfusion after stroke.

Evaluating a 3D Ultrasound Imaging Resolution of Single Transmitter/Receiver with Coding Mask by Extracting Phase Information.

We are currently investigating the ultrasound imaging of a sensor that consists of a randomized encoding mask attached to a single lead zirconate titanate (PZT) oscillator for a puncture microscope application. The proposed model was conducted using a finite element method (FEM) simulator. To increase the number of measurements required by a single element system that affects its resolution, the transducer was rotated at different angles. The image was constructed by solving a linear equation of the image model resulting in a poor quality. In a previous work, the phase information was extracted from the echo signal to improve the image quality. This study proposes a strategy by integrating the weighted frequency subbands compound and a super-resolution technique to enhance the resolution in range and lateral direction. The image performance with different methods was also evaluated using the experimental data. The results indicate that better image resolution and speckle suppression were obtained by applying the proposed method.

A new staging system using right atrial strain in patients with immunoglobulin light-chain cardiac amyloidosis.

There are minimal data on the prognostic impact of right atrial strain during the reservoir phase (RASr) in patients with immunoglobulin light-chain (AL) cardiac amyloidosis. Among 78 patients who were diagnosed with AL cardiac amyloidosis at Kumamoto University Hospital from 2007 to 2022, 72 patients with sufficient two-dimensional speckle tracking imaging data without chemotherapy before the diagnosis were retrospectively analysed. During a median follow-up of 403days, 31 deaths occurred. Age and the rate of male sex were not significantly different between the all-cause death group and the survival group (age, 70.4±8.8years vs. 67.0±10.0years, P=0.14, male sex, 65% vs. 66%, P=0.91). The estimated glomerular filtration rate (eGFR) was significantly lower, and B-type natriuretic peptide (BNP) and high sensitivity cardiac troponin T (hs-cTnT) were significantly higher, in the all-cause death group versus the survival group (eGFR, 48.2±21.0mL/min/1.73m2 vs. 59.4±24.4mL/min/1.73m2, P<0.05, BNP, 725 [360-1312] pg/mL vs. 123 [81-310] pg/mL, P<0.01, hs-cTnT, 0.12 [0.07-0.18] ng/mL vs. 0.05 [0.03-0.08] ng/mL, P<0.01). Left ventricular (LV) global longitudinal strain (GLS) (LV-GLS), left atrial strain during the reservoir phase (LASr), right ventricular GLS (RV-GLS), and RASr were significantly lower in the all-cause death group versus the survival group (LV-GLS, 8.5±4.3% vs. 11.8±3.8%, P<0.01, LASr, 8.8±7.1% vs. 14.3±8.1%, P<0.01, RV-GLS, 11.6±5.1% vs. 16.4±3.9%, P<0.01, RASr, 10.2±7.3% vs. 20.7±9.5%, P<0.01). RASr was significantly associated with all-cause death after adjusting for RV-GLS, LV-GLS and LASr (hazard ratio [HR]: 0.91, 95% confidence interval [95% CI]: 0.83-0.99, P<0.05). RASr and log-transformed BNP were significantly associated with all-cause death after adjusting for log-transformed troponin T and eGFR (RASr, HR: 0.93, 95% CI: 0.87-1.00, P<0.05; log-transformed BNP, HR: 2.10, 95% CI: 1.17-3.79, P<0.05). The optimal cut-off values were RASr: 16.4% (sensitivity: 66%, specificity: 84%, area under curve [AUC]: 0.81) and BNP: 311.2pg/mL (sensitivity: 83%, specificity: 78%, AUC: 0.82) to predict all-cause mortality using ROC analysis. Kaplan-Meier analysis revealed that patients with low RASr (<16.4%) or high BNP (>311.2pg/mL) had a significantly high probability of all-cause death (both, P<0.01). We devised a new staging score by adding 1 point if RASr decreased or BNP levels increased more than each cut-off value. The HR for all-cause death using score 0 as a reference was 5.95 (95% CI: 1.19-29.79; P<0.05) for score 1 and 23.29 (95% CI: 5.37-100.98; P<0.01) for score 2. The new staging system using RASr and BNP predicted prognosis in patients with AL cardiac amyloidosis.

Quantitative Analysis of Morphology and Function in the Fetal Heart with Severe Tricuspid Regurgitation by Speckle Tracking Imaging.

Morphology and function in a fetal heart with severe tricuspid regurgitation remains challenging. The aim of this study was to assess cardiac morphology and function in fetuses with severe tricuspid regurgitation by fetal heart quantification (HQ) and to assess the practical value of fetal HQ. Clinical information was analyzed for 63 pregnant women who underwent fetal cardiac ultrasonography. The women were divided into those who had a fetus with severe tricuspid regurgitation (n = 20) and those with a normal fetus (n = 40). The global sphericity index (GSI), fractional area change (FAC), and global longitudinal strain (GLS) of both ventricles and the sphericity index (SI) and fractional shortening (FS) of 24 segments were quantified by fetal HQ using speckle tracking imaging. Fetuses with severe tricuspid regurgitation had a significantly lower GSI (1.14 ± 0.10 vs. 1.26 ± 0.08, p < 0.001) and a higher GSI Z-score (-0.98 ± 1.01 vs. 0.25 ± 0.87, p < 0.001) as well as a significantly lower right ventricular FAC (36.50 ± 7.34% vs. 45.19 ± 3.39%, p < 0.001), FAC Z-score (-1.02 ± 1.41 vs. 0.49 ± 0.74, p < 0.001), and GLS (-21.01 ± 5.66% vs. 45.19 ± 3.49%, p < 0.001). The SI and SI Z-score were significantly lower in segments 1-18 of the right ventricle in fetuses with severe tricuspid regurgitation (p < 0.05); furthermore, FS of segments 1-12 and 19-24 and the FS Z-score of segments 18-24 were significantly lower in fetuses with severe tricuspid regurgitation (p < 0.05). Fetal HQ is useful for evaluation of cardiac morphology and function in fetuses with severe tricuspid regurgitation and can provide important reference information for both clinical diagnosis and treatment.