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CT iterative reconstruction algorithms: a task-based image quality assessment

Abstract : To assess the dose performance in terms of image quality of filtered back projection (FBP) and two generations of iterative reconstruction (IR) algorithms developed by the most common CT vendors. Materials and methods: We used four CT systems equipped with a hybrid/statistical IR (H/SIR) and a full/partial/advanced model-based IR (MBIR) algorithms. Acquisitions were performed on an ACR phantom at five dose levels. Raw data were reconstructed using a standard soft tissue kernel for FBP and one iterative level of the two IR algorithm generations. The noise power spectrum (NPS) and the task-based transfer function (TTF) were computed. A detectability index (d') was computed to model the detection task of a large mass in the liver (large feature; 120 HU and 25-mm diameter) and a small calcification (small feature; 500 HU and 1.5-mm diameter). Results: With H/SIR, the highest values of d' for both features were found for Siemens, then for Canon and the lowest values for Philips and GE. For the large feature, potential dose reductions with MBIR compared with H/SIR were - 35% for GE, - 62% for Philips, and - 13% for Siemens; for the small feature, corresponding reductions were - 45%, - 78%, and - 14%, respectively. With the Canon system, a potential dose reduction of - 32% was observed only for the small feature with MBIR compared with the H/SIR algorithm. For the large feature, the dose increased by 100%. Conclusion: This multivendor comparison of several versions of IR algorithms allowed to compare the different evolution within each vendor. The use of d' is highly adapted and robust for an optimization process.
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https://hal.umontpellier.fr/hal-03353682
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Soumis le : vendredi 24 septembre 2021 - 11:37:57
Dernière modification le : mardi 15 mars 2022 - 09:39:03

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J. Greffier, J. Frandon, A. Larbi, J. Beregi, F. Pereira. CT iterative reconstruction algorithms: a task-based image quality assessment. European Radiology, Springer Verlag, 2020, 30 (1), pp.487-500. ⟨10.1007/s00330-019-06359-6⟩. ⟨hal-03353682⟩

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