arXiv:2511.14310v1 Announce Type: new 
Abstract: Multi-source stationary computed tomography (CT) has recently attracted attention for its ability to achieve rapid image reconstruction, making it suitable for time-sensitive clinical and industrial applications. However, practical systems are often constrained by ultra-sparse-view sampling, which significantly degrades reconstruction quality. Traditional methods struggle under ultra-sparse-view settings, where interpolation becomes inaccurate and the resulting reconstructions are unsatisfactory. To address this challenge, this study proposes Diffusion-Refined Neural Attenuation Fields (Diff-NAF), an iterative framework tailored for multi-source stationary CT under ultra-sparse-view conditions. Diff-NAF combines a Neural Attenuation Field representation with a dual-branch conditional diffusion model. The process begins by training an initial NAF using ultra-sparse-view projections. New projections are then generated through an Angle-Prior Guided Projection Synthesis strategy that exploits inter view priors, and are subsequently refined by a Diffusion-driven Reuse Projection Refinement Module. The refined projections are incorporated as pseudo-labels into the training set for the next iteration. Through iterative refinement, Diff-NAF progressively enhances projection completeness and reconstruction fidelity under ultra-sparse-view conditions, ultimately yielding high-quality CT reconstructions. Experimental results on multiple simulated 3D CT volumes and real projection data demonstrate that Diff-NAF achieves the best performance under ultra-sparse-view conditions.

لقد جذبت التصوير المقطعي المحوسب الثابت متعدد المصادر مؤخرًا الانتباه لقدرتها على تحقيق إعادة بناء الصور بسرعة، مما يجعلها مناسبة للتطبيقات السريرية والصناعية الحساسة للوقت. ومع ذلك، غالبًا ما يؤدي أخذ العينات ذات العرض النادر للغاية إلى تدهور كبير في جودة إعادة البناء. تكافح الطرق التقليدية في هذه الإعدادات، حيث تصبح الاستيفاءات غير دقيقة وتكون إعادة البناء الناتجة غير مرضية. لمعالجة هذا التحدي، تقترح هذه الدراسة حقول التخفيف العصبي المكررة بواسطة الانتشار (Diff-NAF)، وهو إطار تكراري مصمم للتصوير المقطعي الثابت متعدد المصادر في ظل ظروف العرض النادر للغاية.

La tomografía computarizada (CT) estacionaria de múltiples fuentes ha atraído recientemente la atención por su capacidad para lograr reconstrucciones de imágenes rápidas, adecuadas para aplicaciones clínicas e industriales sensibles al tiempo. Sin embargo, el muestreo ultraesparcido a menudo conduce a una degradación significativa en la calidad de la reconstrucción. Los métodos tradicionales luchan en estas configuraciones, lo que resulta en interpolaciones inexactas y reconstrucciones insatisfactorias. Para abordar este desafío, el estudio propone Campos de Atenuación Neuronal Refinados por D…

La tomographie par ordinateur (CT) stationnaire multi-sources a récemment suscité de l'intérêt en raison de sa capacité à réaliser des reconstructions d'images rapides, adaptées aux applications cliniques et industrielles sensibles au temps. Cependant, l'échantillonnage ultra-sparse dégrade souvent la qualité de reconstruction. Les méthodes traditionnelles peinent dans ces conditions, entraînant des interpolations inexactes et des images insatisfaisantes. Pour surmonter cette limitation, l'étude propose des Champs d'Atténuation Neuronale Raffinés par Diffusion (Diff-NAF), un cadre itératif con…

Multi-source stationary computed tomography (CT) has gained attention for its rapid image reconstruction capabilities, particularly in time-sensitive clinical and industrial applications. However, ultra-sparse-view sampling often leads to significant degradation in reconstruction quality. Traditional methods struggle in these settings, resulting in inaccurate interpolations and unsatisfactory images. To overcome this limitation, the study introduces Diffusion-Refined Neural Attenuation Fields (Diff-NAF), an iterative framework designed for multi-source stationary CT under ultra-sparse-view con…

Iterative Diffusion-Refined Neural Attenuation Fields for Multi-Source Stationary CT Reconstruction: NAF Meets Diffusion Model

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