Publications [#324789] of Ehsan Samei

Papers Published
  1. Zhang, Y; Smitherman, C; Samei, E, Size-specific optimization of CT protocols based on minimum detectability., Medical physics, vol. 44 no. 4 (April, 2017), pp. 1301-1311 [doi] .

    Abstract:
    To develop a comprehensive model of task-based performance of CT across a broad library of CT protocols, so that radiation dose and image quality can be optimized within a large multivendor clinical facility.Eighty adult CT protocols from the Duke University Medical Center were grouped into 23 protocol groups with similar acquisition characteristics. A size-based image quality phantom (Duke Mercury Phantom 2.0) was imaged using these protocol groups for a range of clinically relevant dose levels on two CT manufacturer platforms (Siemens SOMATOM Definition Flash and GE CT750 HD). For each protocol group, phantom size, and dose level, the images were analyzed to extract task-based image quality metrics, the task transfer function (TTF), and the noise power spectrum (NPS). The TTF and NPS were further combined with generalized models of lesion task functions to predict the detectability of the lesions in terms of areas under the receiver operating characteristic curve (Az ). A graphical user interface (GUI) was developed to present Az as a function of lesion size and contrast, dose, patient size, and protocol, as well as to derive the necessary dose to achieve a detection threshold for a targeted lesion.The GUI provided the prediction of Az values modeling detection confidence for a targeted lesion, patient size, and dose. As an example, an abdomen pelvis exam for one scanner, with a reference task size/contrast of 5-mm/50-HU, and an Az of 0.9 indicated a dose requirement of 4.0, 8.9, and 16.9 mGy for patient diameters of 25, 30, and 35 cm, respectively. For a constant patient diameter of 30 cm and 50-HU lesion contrast, the minimum detected lesion size at those dose levels were predicted to be 8.4, 5.0, and 3.9 mm, respectively.A CT protocol optimization platform was developed by combining task-based detectability calculations with a GUI that demonstrates the tradeoff between dose and image quality. The platform can be used to improve individual protocol dose efficiency, as well as to improve protocol consistency across various patient sizes and CT scanners.