arXiv:2506.02093v2 Announce Type: replace-cross 
Abstract: Widely adopted evaluation metrics for sparse-view CT reconstruction--such as Structural Similarity Index Measure and Peak Signal-to-Noise Ratio--prioritize pixel-wise fidelity but often fail to capture the completeness of critical anatomical structures, particularly small or thin regions that are easily missed. To address this limitation, we propose a suite of novel anatomy-aware evaluation metrics designed to assess structural completeness across anatomical structures, including large organs, small organs, intestines, and vessels. Building on these metrics, we introduce CARE, a Completeness-Aware Reconstruction Enhancement framework that incorporates structural penalties during training to encourage anatomical preservation of significant structures. CARE is model-agnostic and can be seamlessly integrated into analytical, implicit, and generative methods. When applied to these methods, CARE substantially improves structural completeness in CT reconstructions, achieving up to +32% improvement for large organs, +22% for small organs, +40% for intestines, and +36% for vessels.

تتناول دراسة حديثة قيود المقاييس التقليدية المعتمدة على البكسل المستخدمة في إعادة بناء التصوير المقطعي المحوسب (CT) من العرض النادر، مثل مقياس التشابه الهيكلي ونسبة الإشارة إلى الضوضاء. غالبًا ما تتجاهل هذه المقاييس اكتمال الهياكل التشريحية الحيوية، خاصةً المناطق الصغيرة. يقترح الباحثون مجموعة جديدة من مقاييس التقييم التي تأخذ في الاعتبار الهياكل التشريحية، والتي تهدف إلى تقديم تقييم أكثر شمولاً للاكتمال الهيكلي. هذه الخطوة مهمة لأنها قد تعزز دقة التصوير المقطعي، مما يؤدي إلى نتائج تشخيصية أفضل.

Un estudio reciente discute las limitaciones de las métricas tradicionales basadas en píxeles utilizadas en la reconstrucción de tomografía computarizada (CT) de vista escasa, como el Índice de Similitud Estructural y la Relación Señal-Ruido. Estas métricas a menudo pasan por alto la completitud de estructuras anatómicas críticas, especialmente las regiones más pequeñas. Los investigadores proponen nuevas métricas de evaluación conscientes de la anatomía que buscan proporcionar una evaluación más completa de la integridad estructural. Este avance es significativo, ya que podría mejorar la precisión de la imagenología CT, llevando a mejores resultados diagnósticos.

Une étude récente aborde les limites des métriques traditionnelles basées sur les pixels utilisées dans la reconstruction par tomographie computée (CT) à vue sparse, telles que l'indice de similarité structurelle et le rapport signal sur bruit. Ces métriques négligent souvent la complétude des structures anatomiques critiques, en particulier les petites régions. Les chercheurs proposent de nouvelles métriques d'évaluation conscientes de l'anatomie qui visent à fournir une évaluation plus complète de la complétude structurelle. Cette avancée est significative car elle pourrait améliorer la précision de l'imagerie CT, conduisant à de meilleurs résultats diagnostiques.

A recent study discusses the limitations of traditional pixel-wise metrics used in sparse-view computed tomography (CT) reconstruction, such as the Structural Similarity Index Measure and Peak Signal-to-Noise Ratio. These metrics often overlook the completeness of critical anatomical structures, especially smaller regions. The researchers propose new anatomy-aware evaluation metrics that aim to provide a more comprehensive assessment of structural completeness. This advancement is significant as it could enhance the accuracy of CT imaging, leading to better diagnostic outcomes.

Are Pixel-Wise Metrics Reliable for Sparse-View Computed Tomography Reconstruction?

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.

Are Pixel-Wise Metrics Reliable for Sparse-View Computed Tomography Reconstruction?

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