arXiv:2511.03876v1 Announce Type: cross 
Abstract: Background: Non-invasive imaging-based assessment of blood flow plays a critical role in evaluating heart function and structure. Computed Tomography (CT) is a widely-used imaging modality that can robustly evaluate cardiovascular anatomy and function, but direct methods to estimate blood flow velocity from movies of contrast evolution have not been developed.
  Purpose: This study evaluates the impact of CT imaging on Physics-Informed Neural Networks (PINN)-based flow estimation and proposes an improved framework, SinoFlow, which uses sinogram data directly to estimate blood flow.
  Methods: We generated pulsatile flow fields in an idealized 2D vessel bifurcation using computational fluid dynamics and simulated CT scans with varying gantry rotation speeds, tube currents, and pulse mode imaging settings. We compared the performance of PINN-based flow estimation using reconstructed images (ImageFlow) to SinoFlow.
  Results: SinoFlow significantly improved flow estimation performance by avoiding propagating errors introduced by filtered backprojection. SinoFlow was robust across all tested gantry rotation speeds and consistently produced lower mean squared error and velocity errors than ImageFlow. Additionally, SinoFlow was compatible with pulsed-mode imaging and maintained higher accuracy with shorter pulse widths.
  Conclusions: This study demonstrates the potential of SinoFlow for CT-based flow estimation, providing a more promising approach for non-invasive blood flow assessment. The findings aim to inform future applications of PINNs to CT images and provide a solution for image-based estimation, with reasonable acquisition parameters yielding accurate flow estimates.

تسلط دراسة حديثة الضوء على التقدم في التصوير القلبي الوعائي من خلال استخدام التصوير المقطعي المحوسب (CT) والشبكات العصبية المستندة إلى الفيزياء. يحسن هذا النهج المبتكر تقدير تدفق الدم، وهو أمر حاسم لتقييم وظيفة القلب وبنيته. من خلال استخدام تدريب قائم على السينوغرام، أظهر الباحثون أن التصوير غير الجراحي يمكن أن يوفر تقييمات أكثر دقة، مما قد يؤدي إلى نتائج أفضل للمرضى. هذا التطور مهم لأنه يمهد الطريق لأدوات تشخيصية محسنة في مجال أمراض القلب.

Un estudio reciente destaca los avances en la imagen cardiovascular a través del uso de tomografía computarizada (CT) y redes neuronales informadas por la física. Este enfoque innovador mejora la estimación del flujo sanguíneo, que es crucial para evaluar la función y estructura del corazón. Al utilizar un entrenamiento basado en sinogramas, los investigadores han demostrado que la imagen no invasiva puede proporcionar evaluaciones más precisas, lo que podría llevar a mejores resultados para los pacientes. Este desarrollo es significativo ya que allana el camino para herramientas de diagnóstico mejoradas en cardiología.

Une étude récente met en lumière les avancées dans l'imagerie cardiovasculaire grâce à l'utilisation de la tomographie par ordinateur (CT) et des réseaux de neurones informés par la physique. Cette approche innovante améliore l'estimation du flux sanguin, ce qui est crucial pour évaluer la fonction et la structure cardiaques. En utilisant un entraînement basé sur le sinogramme, les chercheurs ont démontré que l'imagerie non invasive peut fournir des évaluations plus précises, ce qui pourrait conduire à de meilleurs résultats pour les patients. Ce développement est significatif car il ouvre la voie à des outils de diagnostic améliorés en cardiologie.

A recent study highlights the advancements in cardiovascular imaging through the use of computed tomography (CT) and physics-informed neural networks. This innovative approach improves the estimation of blood flow, which is crucial for assessing heart function and structure. By utilizing sinogram-based training, researchers have demonstrated that non-invasive imaging can provide more accurate evaluations, potentially leading to better patient outcomes. This development is significant as it paves the way for enhanced diagnostic tools in cardiology.

Computed Tomography (CT)-derived Cardiovascular Flow Estimation Using Physics-Informed Neural Networks Improves with Sinogram-based Training: A Simulation Study

arXiv:2601.08455v1 Announce Type: new 
Abstract: Objectives: High-grade serous ovarian carcinoma (HGSOC) is typically diagnosed at an advanced stage with extensive peritoneal metastases, making treatment challenging. Neoadjuvant chemotherapy (NACT) is often used to reduce tumor burden before surgery, but about 40% of patients show limited response. Radiomics, combined with machine learning (ML), offers a promising non-invasive method for predicting NACT response by analyzing computed tomography (CT) imaging data. This study aimed to improve response prediction in HGSOC patients undergoing NACT by integration different feature selection methods. Materials and methods: A framework for selecting robust radiomics features was introduced by employing an automated randomisation algorithm to mimic inter-observer variability, ensuring a balance between feature robustness and prediction accuracy. Four response metrics were used: chemotherapy response score (CRS), RECIST, volume reduction (VolR), and diameter reduction (DiaR). Lesions in different anatomical sites were studied. Pre- and post-NACT CT scans were used for feature extraction and model training on one cohort, and an independent cohort was used for external testing. Results: The best prediction performance was achieved using all lesions combined for VolR prediction, with an AUC of 0.83. Omental lesions provided the best results for CRS prediction (AUC 0.77), while pelvic lesions performed best for DiaR (AUC 0.76). Conclusion: The integration of robustness into the feature selection processes ensures the development of reliable models and thus facilitates the implementation of the radiomics models in clinical applications for HGSOC patients. Future work should explore further applications of radiomics in ovarian cancer, particularly in real-time clinical settings.

أظهرت دراسة حديثة تطوير نماذج راديو ميكولوجيا تنبؤية وموثوقة تهدف إلى تقييم استجابة العلاج الكيميائي لدى المرضى المصابين بسرطان المبيض الحاد عالي الدرجة (HGSOC)، وهو سرطان يتم تشخيصه عادة في مراحل متقدمة. تستخدم الدراسة تقنيات التعلم الآلي لتحليل بيانات التصوير المقطعي المحوسب، مما يعزز من دقة التنبؤ باستجابة العلاج الكيميائي المساعد.

Un estudio reciente ha desarrollado modelos de radiómica predictivos y robustos destinados a evaluar la respuesta a la quimioterapia en pacientes con carcinoma seroso de ovario de alto grado (HGSOC), un cáncer que generalmente se diagnostica en una etapa avanzada. La investigación utiliza técnicas de aprendizaje automático para analizar datos de imágenes por tomografía computarizada, mejorando así la predicción de la respuesta a la quimioterapia neoadyuvante.

Une étude récente a développé des modèles de radiomique prédictifs et robustes visant à évaluer la réponse à la chimiothérapie chez les patientes atteintes de carcinome ovarien séreux de haut grade (HGSOC), un cancer généralement diagnostiqué à un stade avancé. La recherche utilise des techniques d'apprentissage automatique pour analyser les données d'imagerie par tomodensitométrie, améliorant ainsi la prédiction de la réponse à la chimiothérapie néoadjuvante.

A recent study has developed predictive and robust radiomics models aimed at assessing chemotherapy response in patients with high-grade serous ovarian carcinoma (HGSOC), a cancer typically diagnosed at an advanced stage. The research utilizes machine learning techniques to analyze computed tomography imaging data, enhancing the prediction of neoadjuvant chemotherapy response.

Developing Predictive and Robust Radiomics Models for Chemotherapy Response in High-Grade Serous Ovarian Carcinoma

arXiv:2511.11048v2 Announce Type: replace-cross 
Abstract: 4D flow magnetic resonance imaging (MRI) is a reliable, non-invasive approach for estimating blood flow velocities, vital for cardiovascular diagnostics. Unlike conventional MRI focused on anatomical structures, 4D flow MRI requires high spatiotemporal resolution for early detection of critical conditions such as stenosis or aneurysms. However, achieving such resolution typically results in prolonged scan times, creating a trade-off between acquisition speed and prediction accuracy. Recent studies have leveraged physics-informed neural networks (PINNs) for super-resolution of MRI data, but their practical applicability is limited as the prohibitively slow training process must be performed for each patient. To overcome this limitation, we propose PINGS-X, a novel framework modeling high-resolution flow velocities using axes-aligned spatiotemporal Gaussian representations. Inspired by the effectiveness of 3D Gaussian splatting (3DGS) in novel view synthesis, PINGS-X extends this concept through several non-trivial novel innovations: (i) normalized Gaussian splatting with a formal convergence guarantee, (ii) axes-aligned Gaussians that simplify training for high-dimensional data while preserving accuracy and the convergence guarantee, and (iii) a Gaussian merging procedure to prevent degenerate solutions and boost computational efficiency. Experimental results on computational fluid dynamics (CFD) and real 4D flow MRI datasets demonstrate that PINGS-X substantially reduces training time while achieving superior super-resolution accuracy. Our code and datasets are available at https://github.com/SpatialAILab/PINGS-X.

تم تقديم إطار عمل جديد يسمى PINGS-X لتحسين كفاءة الدقة الفائقة في التصوير بالرنين المغناطيسي بتدفق 4D، وهو أمر حاسم لتقدير سرعات تدفق الدم بدقة في تشخيص القلب والأوعية الدموية. تستخدم هذه الطريقة تمثيلات غاوسية موحدة مستندة إلى الفيزياء مع محاذاة المحاور لمعالجة التحديات المتعلقة بأوقات المسح المطولة والتوازن بين سرعة الاكتساب ودقة التنبؤ.

Se ha introducido un nuevo marco llamado PINGS-X para mejorar la eficiencia de la super-resolución en la IRM 4D, que es crucial para la estimación precisa de las velocidades del flujo sanguíneo en el diagnóstico cardiovascular. Este enfoque utiliza un splatting gaussiano normalizado informado por la física con alineación de ejes para abordar los desafíos de los tiempos de escaneo prolongados y el compromiso entre la velocidad de adquisición y la precisión de las predicciones.

Un nouveau cadre nommé PINGS-X a été introduit pour améliorer l'efficacité de la super-résolution en IRM 4D, qui est cruciale pour une estimation précise des vitesses de flux sanguin dans le diagnostic cardiovasculaire. Cette approche utilise un splatting gaussien normalisé informé par la physique avec alignement des axes pour relever les défis des temps de scan prolongés et du compromis entre la vitesse d'acquisition et la précision des prédictions.

A novel framework named PINGS-X has been introduced to enhance the efficiency of super-resolution in 4D flow MRI, which is crucial for accurate blood flow velocity estimation in cardiovascular diagnostics. This approach utilizes physics-informed normalized Gaussian splatting with axes alignment to address the challenges of prolonged scan times and the trade-off between acquisition speed and prediction accuracy.

Computed Tomography (CT)-derived Cardiovascular Flow Estimation Using Physics-Informed Neural Networks Improves with Sinogram-based Training: A Simulation Study

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