Polar regions have significant potential as natural gas hydrate resources and represent crucial strategic energy reserves. The South Shetland continental margin off the Antarctic Peninsula is a promising area for gas hydrate accumulation. During China’s 33rd Antarctic Scientific Expedition, the “Haiyang Liuhao” cruise discovered substantial evidence of gas hydrate presence in this region. To investigate the distribution of hydrates, this study identified bottom-simulating reflectors (BSRs), which are key indicators of gas hydrate presence, in multichannel seismic profiles. Constrained sparse spike inversion was applied to the selected key survey lines, revealing the acoustic impedance characteristics in the BSR-featured zones. The results demonstrated a strong correlation between the BSRs and high-impedance anomalies, confirming the effectiveness of the inversion method in delineating hydrate distribution. This study suggests that deep-sourced methane migrates upward through faults and diapiric structures and accumulates within the shallow sub-seafloor gas hydrate stability zone under low-temperature and high-pressure conditions.
Interstitial lung abnormalities (ILAs) are non-specific interstitial changes incidentally found on CT scans, mainly presenting as ground-glass opacities, reticular structures, or traction bronchiectasis. With the popularization of chest imaging techniques and the aging of the population, the detection rate of ILAs has significantly increased. It is positively correlated with age and smoking, has the risk of progression, and affects patients' health in various ways. Some ILAs may progress to interstitial lung disease (ILD) or be accompanied by a decline in lung function. However, there are still deficiencies and controversies in people's understanding of its diagnostic criteria, occurrence mechanism, evaluation, and management. This article systematically reviews the key progress in ILAs research in recent years.
In sparse-view computed tomography(CT),incomplete projection data often lead to artifacts,edge blurring,and structural distortions in the reconstructed images.To address this challenge,this study proposes a diffusion bridge model based on the Ornstein-Uhlenbeck(OU)process for the conditional completion of missing projection-domain data to enable high-quality sparse-view reconstruction.The proposed method leverages the mean-reverting property of the OU process to establish a physically consistent stochastic diffusion mechanism.The diffusion bridge constraint anchors the sampling trajectory between the reconstruction prior and sparse projections,thereby achieving high-fidelity restoration of the missing data.Furthermore,a multiscale feature fusion module in the wavelet domain is incorporated into the network architecture to effectively integrate low-frequency structural information with high-frequency texture details,thereby enhancing the capability of the model to recover fine image features.Experimental results demonstrate that under the same sparse-view conditions,the proposed method outperforms existing state-of-the-art approaches in terms of both visual quality and quantitative metrics,confirming its effectiveness for sparse-view CT reconstruction tasks.
Objective:To identify potential imaging biomarkers for the early diagnosis of autism spectrum disorder(ASD)and further reveal the possible neuroimaging mechanisms underlying the severity of ASD symptoms,this study investigated brain abnormalities and resting-state functional connectivity(RSFC)in preschool-aged children with ASD compared to typically developing(TD)children using magnetic resonance imaging(MRI).The study also evaluated the correlation between functional connectivity and Childhood Autism Rating Scale(CARS)scores.Methods:This prospective study involved 20 preschool-aged children with ASD(aged 3-6 years)and 20 age-matched TD children,who underwent structural and resting-state functional MRI(rs-fMRI)scans.First,based on the structural MRI data,voxel-based morphometry(VBM)was used to identify the differences in gray matter volume(GMV)between the two groups.Based on the VBM results,the brain region with the most significant GMV difference was used as the seed region of interest for the RSFC analysis.Results from both analyses(Rs-fMRI and seed-based functional connectivity analysis)were used to calculate the RSFC values between significant brain regions and the whole brain.Subsequently,the correlation between RSFC values and CARS scores was assessed.Results:Whole-brain VBM analysis revealed a significantly lower GMV in the right insula in the ASD group than in the TD group.The RSFC results showed that the weaker the FC values between the right insula and the left anterior cingulate cortex,the higher the corresponding clinical ASD symptom score,indicating a negative correlation with the CARS scores(r=-0.758).Conclusion:A lower GMV in the right insula may serve as an imaging biomarker for preschool-aged children with ASD.The difference in functional connectivity between the right insula and left anterior cingulate cortex may further reveal the neural underpinnings associated with ASD symptoms.
Objective: This study aimed to investigate the risk factors for medial meniscus extrusion and to construct a logistic regression model based on radiography and magnetic resonance imaging (MRI). Methods: A retrospective analysis was conducted on 156 patients with medial meniscus injuries admitted from August 2020 to June 2023. Based on an extrusion value of ≥3 mm on non-weight-bearing coronal MRI, the patients were divided into an extrusion group (71 cases) and a non-extrusion group (85 cases). Univariate unconditional logistic regression analysis was initially performed to screen for potential risk factors. Subsequently, multivariate unconditional logistic regression analysis was applied to identify independent risk factors for meniscus extrusion and to establish the logistic regression model. The diagnostic accuracy of the model was evaluated using the receiver operating characteristic(ROC) curve. Results: A univariate analysis revealed that the extrusion group had higher values for age, female proportion, meniscus tears at various locations (anterior horn/body/root), knee Kellgren-Lawrence (K-L) grade ≥Ⅲ, cartilage injury Recht grade > Ⅲ, and presence of genu varum. Multivariate logistic regression showed that based on radiography indicators, the knee K-L grade ≥Ⅲ (OR=0.051) and presence of genu varum (OR=0.152) were independent risk factors for extrusion. The area under the ROC curve of medial meniscus extrusion was 0.902, with a sensitivity of 80.0% and specificity of 100%. After combining MRI indicators, medial meniscus root tear (OR=0.053), knee K-L grade ≥Ⅲ (OR=0.160), cartilage injury Recht grade > Ⅲ (OR=0.184), and presence of genu varum (OR=0.177) were independent risk factors for extrusion. The area under the ROC curve of medial meniscus extrusion was 0.975, with a sensitivity of 100% and specificity of 87.5%. Conclusion: The medial meniscus root tear, knee K-L grade ≥Ⅲ, cartilage injury Recht grade > Ⅲ, and presence of genu varum were the primary risk factors of medial meniscus extrusion. The radiography model can serve as a clinical screening tool, while the model combining MRI offers more accurate assessment of individual risk, supporting the development of personalized intervention strategies (such as meniscus repair combined force line correction).
In recent years, owing to its trade-off simulation accuracy and simulation efficient, the variable-grid method has garnered increasing attention. Based on the traditional variable-grid algorithm, this study proposed a multi-block and multi-stage method. First, we derived the mathematical formula for the false reflection error generated by the variable grid algorithm in the grid change area. By introducing the Lanczos filtering operator to enhance the stability of the algorithm, the error caused by grid changes can be reduced. Second, to simulate small cracks using traditional variable-grid algorithms, a large error and instability are often introduced under ultra-high grid ratio variations. However, by introducing a multi-stage condition to the staggered variable-grid method, we can achieve efficient and precise simulations with ultra-high grid ratios. Finally, to improve the simulation efficiency for an existing number of discrete target areas with different variable grid formats, we presented a multi-block variable grid to conduct different multiples of variable-grid simulations in each region.
Multiple endocrine neoplasia (MEN) refers to the occurrence of tumors in two or more endocrine glands, with low incidence, variable symptoms, and difficult diagnosis. This paper reports a case of MEN1-like syndrome with a negative genetic test. Based on laboratory test results, imaging, and pathological results, the patient was considered to have MEN1, and the whole-exon sequencing (including mitochondria) was negative, so it was diagnosed as MEN1-like syndrome. Pathological immunohistochemistry confirmed a pituitary adenoma, and a parathyroid adenoma was revealed by parathyroid pathology. Subsequent examinations revealed an adrenal adenoma and a mammary adenosis nodule.
Objective: To investigate whether dual-energy CT (DECT) post-processing techniques can enhance the accuracy of ASPECTS in patients with anterior circulation occlusion. Methods: A prospective study was conducted from October 2023 to January 2025. A total of 34 patients with anterior circulation occlusion (within 12 h) participated, with a total of 148 lesions (57 at the basal ganglia level and 91 in the cortical areas). Using diffusion weighted imaging (DWI) as the reference standard, an automated scoring software was used to evaluate post-processed DECT and conventional CT images. Sensitivity, specificity, accuracy, negative predictive value, positive predictive value, and 95% confidence intervals were calculated. Discrepancies between imaging modalities and DWI were statistically analyzed. Results: Conventional CT demonstrated uniform sensitivity (54% global, deep nuclear region and cortical regions), specificity (93%, 92%, and 93%), and accuracy (76%, 76%, and 75%). VNC showed higher sensitivity than NCCT (77%, 70%, and 81%) but lower specificity (68%, 70%, and 69%) and accuracy (73%, 71%, and 75%). Both 70 keV and 75 keV achieved comparable specificity to NCCT, with superior sensitivity (70 keV: 73%, 67%, and 77%; 75 keV: 68%, 67%, and 68%) and accuracy (70 keV: 82%, 80%, and 83%; 75 keV: 78%, 78%, and 78%). Compared with DWI, NCCT exhibited statistically significant differences in all regions. VNC showed significant discrepancies in global and cortical regions but not in deep nuclear regions. For 70 keV, significant differences were observed in global and deep nuclear regions, whereas no difference was indicated in cortical regions. Conversely, 75 keV demonstrated significant differences in global and cortical regions, while no statistical significance was indicated in deep nuclear regions. Conclusion: VNC exhibited higher false-positive rates. Among DECT post-processing techniques, 70 keV and 75 keV improved the accuracy of ASPECTS assessment in patients with anterior circulation occlusion as compared with conventional CT.
Objective:To compare contrast-induced hardening artifacts of the axillary vein during the arterial phase between dual-energy CT (DECT) and single-energy CT (SECT) with different contrast agent injection rates in thoracoabdominal enhanced CT examinations, and to assess their clinical value. Methods: A retrospective study was conducted on 77 cases of chest and abdomen enhanced CT scans that were performed from January to November 2024 at our hospital. These scans were performed on the same subjects using both dual-energy (experimental group) and single-energy (control group) CT techniques. The differences between the two groups were compared in terms of axillary vein contrast agent artifact size, arterial phase enhancement, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), subjective evaluations of artifact severity, and overall image quality. The subjective evaluations of artifact severity and image quality were independently assessed by two radiologists. Results: The objective scores for axillary vein contrast agent artifacts in the experimental group were significantly lower than those in the control group. The arterial CT values in the experimental group were higher than those in the control group. The SNR and CNR in non-artifact-affected regions, such as bone, muscle, and adipose tissue, in the experimental group were not lower than those in the control group, whereas the radiation dose was significantly lower in the experimental group. Subjective scores for hardening artifacts were significantly better in the experimental group, and image quality scores in regions outside the axillary vein hardening artifacts were non-inferior to those of the control group. Interobserver agreement between the two radiologists was high. Conclusion: Dual-energy CT blended images combined with a contrast injection rate of 3 mL/s can effectively reduce contrast-induced hardening artifacts, improve image quality, and lower radiation dose in thoracoabdominal enhanced CT examinations, offering superior clinical applicability.
Objective:Ultralow tube voltage scanning was combined with a deep-learning-based image reconstruction algorithm(ClearInfinity,CI).The impacts of this method on the image quality and organ radiation dose in low-dose chest computed tomography(CT)were investigated.Methods:A Lungman PH-1 chest phantom was scanned using two protocols:120 and 70 kV standard-and low-dose protocols,respectively.Images were reconstructed using three algorithms,filtered back-projection(FBP);adaptive iterative reconstruction(ClearView,CV)at 20%,40%,60%,and 80%strengths;and CI at 20%,40%,60%,and 80%strengths,totaling nine reconstruction schemes.Objective image quality metrics for the soft tissue thoracic regions were evaluated,including the signal-to-noise ratio(SNR)for 100 HU solid nodules,contrast-to-noise ratio(CNR)for nodules with varying densities(100 HU,-630 HU,-800 HU),figure of merit(FOM),and the standard deviation(SD)of the CT values.The nodule detection rates were analyzed using an AI-assisted diagnosis system.Four radiomic texture parameters were extracted for-630 HU ground-glass nodules:sum of squares(SumSquares),difference,contrast,and correlation.Image quality was subjectively assessed using a five-point Likert scale,focusing on noise and clarity in high-artifact regions near the cervicothoracic junction.The doses to the breast,thyroid,and thymus were estimated using Monte Carlo simulations.Image quality metrics were compared using one-or multiway ANOVA or the Scheirer-ray-hare test.Results:CI reconstruction at 60%-80%significantly reduced image noise as well as increased the nodule SNR,CNR,and FOM compared with the baseline method.The performance of CI 80%ranked first overall.The AI model detected 100%of the lung nodules at CI reconstruction strengths of 60%or higher with the 70 kV low-dose protocol.The texture of the nodules was most accurately reconstructed with 80%CI under the 70 kV low-dose protocol,with 18.04%,1.59%,and 13.54%mean deviations in the SumSquares,difference entropy,and contrast,respectively,from those of the 120 kV FBP reference.Correlation restoration was highest at CI 40%,with a mean deviation of-16.36%.The image quality did not subjectively significantly differ between the 70 kV CI and 120 kV FBP images when the CI strength exceeded 40%,and these images were deemed diagnostically acceptable.The doses to the thyroid,breast,and thymus in the 70 kV low-dose protocol were 86.32%,81.79%,and 81.90%lower,respectively,than in the 120 kV standard-dose protocol.Conclusion:CI reconstruction,particularly at 60%-80%strength,with ultralow tube voltage chest CT considerably enhances image quality,reduces noise,increases the accuracy AI-based nodule detection,and preserves radiomic texture features compared with the baseline method.The 70 kV low-dose protocol combined with CI reconstruction≥60%balances image quality and radiation dose,demonstrating the potential for clinical application with low-dose chest CT.
Objective:To investigate the value of a computed tomography(CT)radiomics nomogram for predicting disease-free survival(DFS)in patients with stage I-III colorectal cancer(CRC)after radical surgery.Methods:Overall,324 patients with CRC who underwent radical surgery were retrospectively included and grouped into training and validation cohorts at a 7︰3 ratio.The least absolute shrinkage and selection operator Cox regression algorithm was employed to select the relevant CT radiomics features.Using Cox regression analysis,clinically significant risk factors were identified and combined with radiomics features to develop a comprehensive nomogram.The predictive performance of the nomogram was evaluated using the C-index,calibration curves,and decision curves,with DFS probabilities estimated using the Kaplan-Meier method.Results:A clinical model was constructed based on three clinical risk factors:pathological N stage,perineural invasion,and KRAS mutation.We achieved a C-index of 0.709(95%confidence interval(95%CI:0.678-0.740)in the training cohort and 0.696(95%CI:0.644-0.748)in the validation cohort.A nomogram was subsequently developed using the 15 retained radiomics features along with these three clinical risk factors.The nomogram demonstrated superior predictive performance,with a C-index of 0.820(95%CI:0.799-0.841)in the training cohort and 0.818(95%CI:0.775-0.861)in the validation cohort,thus outperforming the clinical model.Calibration curves indicated good agreement between the predicted and observed DFS.Decision curve analysis further confirmed the greater net benefit of the nomogram in clinical applications.Conclusion:The constructed integrated nomogram demonstrated high discriminative ability,good calibration,and greater net benefit in the individualized prediction of postoperative DFS in patients with stage I-III CRC cancer.It maintained a robust predictive performance in the validation cohort and outperformed the clinical model.
The deep-water area of the southern South China Sea is a crucial strategic successor zone for petroleum resources in China, and its exploration and development hold significant importance in ensuring national energy security. As exploration targets extend to greater depths, the quality of mid- to deep-seismic imaging has become a key constraint on understanding deep geological structures and identifying hydrocarbon reservoirs. Seismic data in this region face challenges such as a low signal-to-noise ratio (SNR) at low frequencies, a narrow effective bandwidth due to ghost-notch effects, and inaccurate velocity modeling in complex structural areas. To address these issues, this study proposes an integrated processing workflow that employs amplitude-preserving broadband noise attenuation technology to suppress low-frequency noise and improve low-frequency signal-to-noise ratio (SNR), applying adaptive de-ghosting technology to eliminate notch effects, broadening the effective bandwidth, and introducing a high-density dual-spectrum velocity analysis to construct an accurate anisotropic velocity model. Application to real data demonstrates that this combination of technologies effectively improves the quality of mid- to deep-seismic imaging. The clarity of deep reflective structures and the continuity of seismic events were significantly enhanced, providing a reliable geophysical data foundation for hydrocarbon exploration in deepwater areas.
Ovarian cystadenofibroma (OCAF) is a rare ovarian tumor of epithelial origin and is composed of Mullerian epithelium and fibrous stromal cells on the surface of the ovary. Its clinical incidence is rare, and serous is the most common. This article reports a case of ovarian seromucinous cystadenofibroma in a 76-year-old female who presented abdominal pain for more than two years and a palpable mass in the lower abdomen for eight months. MRI shows the lesions are cysts of varying sizes with heterogeneous signals,and they demonstrate markedly hypointense signals on T2WI. Imaging diagnosis of cystadenofibroma is performed. The postoperative pathological diagnosis was seromucinous cystadenofibroma, and the patient achieved favorable follow-up outcomes. This article aims to provide an imaging description and summary of the disease to improve its diagnosis accuracy.
Magnetic resonance imaging (MRI) is widely used in brain imaging. To better identify brain tissue and structure, brain MRI image segmentation algorithms have been developed, but most of the existing advanced segmentation models have high computational complexity and long computational times, which make them difficult to use widely. To facilitate clinical use, lightweight algorithms are required to improve efficiency and real-time performance. First, in this paper, the research background and significance of lightweight algorithms, and the development history of lightweight segmentation algorithms, are summarized. Next, lightweight segmentation technology is introduced, and the residual module, deep separable convolution, attention mechanism and hierarchical design method are discussed. The lightweight effect of the above methods is compared and analyzed under the framework of U-net and Transformer. Finally, current challenges of lightweight algorithms are illustrated, and future development direction is proposed.
Spectral computed tomography (Spectral CT) is an emerging imaging technology that enhances material differentiation and clinical diagnostic accuracy by acquiring multi-spectral projection data. However, in sparse-angle spectral CT image reconstruction, noise suppression and edge preservation still face challenges. Regularization methods, such as Directional Probabilistic Total Variation (dTV-p) and tight wavelet frame L0, could introduce prior information to improve sparse-angle spectral CT image reconstruction quality. In this study, we propose a dTV-p and tight wavelet frame L0 regularization-based dual-regularization sparse-angle spectral CT image reconstruction algorithm to address noise suppression and edge preservation. We used the fast iterative shrinkage-thresholding algorithm (FISTA) to accelerate the algorithm. First, the dTV-p regularization is solved in the image domain using proximal mapping and FISTA. Next, the L0 regularization is solved in the wavelet domain using iterative hard thresholding. Finally, the dual variables are solved using FISTA acceleration. The experimental results demonstrated the efficacy of the proposed algorithm, revealing superior performance in edge preservation, noise suppression, and quantitative evaluation metrics compared to comparative algorithms.
Located at the northern margin of the Pearl River Delta,Guangzhou is characterized by complex subsurface structures resulting from the intersection of multiple active faults,leading to elevated geohazard risks.This study derives a high-resolution crustal shear-wave velocity structure by integrating data from two temporary seismic networks and local permanent stations using a joint ambient noise tomography approach that combines synchronous(C1)and asynchronous(C2)cross-correlation techniques.The C1 method extracts short-range ray paths between local stations,whereas the C2 method constructs long-range paths across sub-arrays,significantly improving both path coverage and crossing density within the 1-8 s period band.Using multiple-filter analysis to extract Rayleigh wave dispersion curves,we employ a wavelet-based,sparsity-constrained direct inversion algorithm to construct a three-dimensional shear-wave velocity model down to 7 km depth.The results reveal pronounced spatial correlations between major faults—including the Guangzhou-Conghua(GCF),Shougouling(SF),Guangzhou-Sanshui(SSF),and Zhujiangkou(ZJF)faults—and distinct low-velocity anomalies,that reflect sedimentary basin structures controlled by fault activity.Furthermore,a prominent deep low-velocity anomaly beneath the GCF suggests an ongoing or past tectonic inversion process.This study demonstrates that the combined application of C1 and C2 ambient-noise tomography significantly enhances the imaging resolution of crustal structures in urban environments,providing crucial geophysical constraints for geohazard assessment and resource exploration in the Guangzhou region.
The high-steep complex structural belt in the Sichuan Basin is characterized by “dual complexity” seismic geological conditions with severe surface relief and large variations in the near-surface formation dip angles. Traditional isotropic imaging methods do not meet the high-precision imaging requirements. This study employed the finite-difference wave equation method to numerically simulate near-surface anisotropy, establishing a near-surface velocity model containing Tilted Transverse Isotropy (TTI) media. Using forward modeling simulation data, we applied the topographic Kirchhoff prestack depth migration technique to compare the imaging effects between the isotropic and anisotropic migration methods. The results show that: (1) Near-surface anisotropy significantly affects seismic wave propagation time and path, with wavefront propagation speed increasing notably when anisotropy is considered; (2) Ignoring near-surface anisotropy leads to an inability to flatten common image gathers and severely degrades imaging quality for high-steep structures and underlying formations; (3) Among the anisotropic parameters, the ε parameter and formation dip angle were found to have the most significant impact on imaging quality. Conclusions: This numerical study confirms that accurate near-surface anisotropic modeling is a key factor for improving imaging quality in the high-steep complex structural belts of the Sichuan Basin.
Reverse time migration (RTM) is a core technique for seismic imaging in complex structures. A key challenge in anisotropic RTM is constructing a pure qP-wave equation free of pseudo-shear-wave artifacts. In this study, we compare and analyze the dispersion relation derived from the new acoustic approximation with the accurate qP-wave dispersion relation developed by Li. Based on Li’s more precise dispersion relation, we derive a pure qP-wave equation for vertical transversely isotropic (VTI) media using the elliptic decomposition method. Furthermore, by introducing a self-adjoint differential operator, we establish a first-order velocity-stress wave equation for pure qP-waves in tilted transverse isotropy (TTI) media. Forward modeling based on the high-precision dispersion relation is implemented using a staggered-grid finite-difference method. Additionally, a second-order displacement equation for pure qP-waves in TTI media is derived via the wavenumber rotation approach and solved using a conventional grid finite-difference scheme. Both dispersion analysis and model tests demonstrate that the equations based on the updated dispersion relation achieve higher accuracy than those derived from the new acoustic approximation. When solved using the staggered-grid finite-difference method, the first-order velocity-stress equation effectively mitigates amplitude imbalance induced by the asymptotic approximation while maintaining favorable computational efficiency.
The exploration of Mesozoic strata in the Chaoshan Sag of the Pearl River Mouth Basin in the South China Sea faces challenges such as severe seismic wave energy shielding and strong multiple-wave development. This study introduces a wide-line acquisition and processing approach to perform integrated wide-line processing on two 2D seismic lines acquired in proximity but in different years. Key issues during processing, including common midpoint (CMP) bin definitions, multiple suppressions, and imaging methods, are discussed. Noise suppression, multiple attenuation, and consistency processing for the time shifts, amplitudes, frequencies, and phases were performed. The 2D pre-stack depth migration method was selected for imaging and yielded favorable results. The results indicate that compared to the original 2D data, the processed data after integrated wide-line processing enhanced the effective reflection energy in the middle-deep layers, improved the multiple wave suppression effect, broadened frequency bands with richer low-frequency components, improved the signal-to-noise ratio (SNR), increased accuracy in velocity analysis, clearer wave group characteristics of the target horizons, and better continuity of events. This method provides a valuable reference for leveraging historical data to improve middle-deep layer imaging and explores a new approach to wide-line processing. This represents an economical and practical technology for middle- to deep-layer exploration in offshore areas with abundant 2D seismic data.
Objective: To assess the ability of quantitative plaque parameters derived from coronary computed tomography angiography (CCTA), fractional flow reserve (FFR), and different risk stratifications to evaluate coronary plaque progression, and to evaluate the efficacy of their combination for the prediction of plaque progression. Methods: Clinical data and serial CCTA imaging data were retrospectively analyzed. Patients were stratified into a progression or a non-progression plaque group based on the rate of change of plaque burden, and patients were also stratified into a high-risk or a low-risk plaque group based on morphological characteristics. Within the low-risk and high-risk plaque groups, quantitative plaque features and CTTA-FFR parameters were compared between the progression and the non-progression plaque groups. Predictive models incorporating quantitative plaque features and CTTA-FFR values were constructed, and their predictive performance was evaluated. Results: Compared to the non-progression plaque group, the progression plaque group had more severe stenosis, a smaller minimum lumen area, longer plaque length, larger total plaque volume and non-calcified plaque volume, higher plaque burden, and lower CTTA-FFR values. Logistic regression analysis demonstrated a significant negative correlation between CT-FFR values and plaque progression (odds ratio, 0.922; 95% confidence interval [CI], 0.854-0.997). Receiver operating characteristic (ROC) curve analysis identified the combined model incorporating stenosis severity, quantitative plaque features, and CTTA-FFR values as the optimal predictor (area under the ROC curve, 0.831; 95% CI, 0.73-0.91). Conclusion: Quantitative CCTA-FFR plaque parameters can predict plaque progression. The combination of CTTA-FFR, quantitative plaque analysis, and risk stratification enhances predictive efficacy for assessing the risk of plaque progression.
