Adaptive compressed tomography sensing

Oren Barkan, Jonathan Weill, Amir Averbuch, Shai Dekel

نتاج البحث: نشر في مجلةمقالة من مؤنمرمراجعة النظراء


One of the main challenges in Computed Tomography (CT) is how to balance between the amount of radiation the patient is exposed to during scan time and the quality of the CT image. We propose a mathematical model for adaptive CT acquisition whose goal is to reduce dosage levels while maintaining high image quality at the same time. The adaptive algorithm iterates between selective limited acquisition and improved reconstruction, with the goal of applying only the dose level required for sufficient image quality. The theoretical foundation of the algorithm is nonlinear Ridge let approximation and a discrete form of Ridge let analysis is used to compute the selective acquisition steps that best capture the image edges. We show experimental results where for the same number of line projections, the adaptive model produces higher image quality, when compared with standard limited angle, non-adaptive acquisition algorithms.

اللغة الأصليةالإنجليزيّة
رقم المقال6619129
الصفحات (من إلى)2195-2202
عدد الصفحات8
دوريةProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
المعرِّفات الرقمية للأشياء
حالة النشرنُشِر - 2013
منشور خارجيًانعم
الحدث26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013 - Portland, OR, الولايات المتّحدة
المدة: ٢٣ يونيو ٢٠١٣٢٨ يونيو ٢٠١٣


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