10 March 2017 Characterization of a CT unit for the detection of low contrast structures
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Abstract
Major technological advances in CT enable the acquisition of high quality images while minimizing patient exposure. The goal of this study was to objectively compare two generations of iterative reconstruction (IR) algorithms for the detection of low contrast structures. An abdominal phantom (QRM, Germany), containing 8, 6 and 5mm-diameter spheres (with a nominal contrast of 20HU) was scanned using our standard clinical noise index settings on a GE CT: “Discovery 750 HD”. Two additional rings (2.5 and 5 cm) were also added to the phantom. Images were reconstructed using FBP, ASIR-50%, and VEO (full statistical Model Based Iterative Reconstruction, MBIR). The reconstructed slice thickness was 2.5 mm except 0.625 mm for VEO reconstructions. NPS was calculated to highlight the potential noise reduction of each IR algorithm. To assess LCD (low Contrast Detectability), a Channelized Hotelling Observer (CHO) with 10 DDoG channels was used with the area under the curve (AUC) as a figure of merit. Spheres contrast was also measured. ASIR-50% allowed a noise reduction by a factor two when compared to FBP without an improvement of the LCD. VEO allowed an additional noise reduction with a thinner slice thickness compared to ASIR-50% but with a major improvement of the LCD especially for the large-sized phantom and small lesions. Contrast decreased up to 10% with the phantom size increase for FBP and ASIR-50% and remained constant with VEO. VEO is particularly interesting for LCD when dealing with large patients and small lesion sizes and when the detection task is difficult.
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Anais Viry, Anais Viry, Damien Racine, Damien Racine, Alexandre Ba, Alexandre Ba, Fabio Becce, Fabio Becce, François O. Bochud, François O. Bochud, Francis R. Verdun, Francis R. Verdun, } "Characterization of a CT unit for the detection of low contrast structures", Proc. SPIE 10136, Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment, 101361C (10 March 2017); doi: 10.1117/12.2250529; https://doi.org/10.1117/12.2250529
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