23 February 2010 Registration of ultrasound to CT angiography of kidneys: a porcine phantom study
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3D ultrasound (US) to computed tomography (CT) registration is a topic of significant interest because it can potentially improve many minimally invasive procedures such as laparoscopic partial nephrectomy. Partial nephrectomy patients often receive preoperative CT angiography, which helps define the important structures of the kidney such as the vasculature. Intraoperatively, dynamic real-time imaging information can be captured using ultrasound and compared with the preoperative data. Providing accurate registration between the two modalities would enhance navigation and guidance for the surgeon. However, one of the major problems of developing and evaluating registration techniques is obtaining sufficiently accurate and realistic phantom data especially for soft tissue. We present a detailed procedure for constructing tissue phantoms using porcine kidneys, which incorporates contrast agent into the tissue such that the kidneys appear representative of in vivo human CT angiography. These phantoms are also imaged with US and resemble US images from human patients. We then perform registration on corresponding CT and US datasets using a simulation-based algorithm. The method simulates an US image from the CT, generating an intermediate modality that resembles ultrasound. This simulated US is then registered to the original US dataset. Embedded fiducial markers provide a gold standard for registration. Being able to test our registration method on realistic datasets facilitates the development of novel CT to US registration techniques such that we can generate an effective method for human studies.
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Jing Xiang, Jing Xiang, Sean Gill, Sean Gill, Christopher Nguan, Christopher Nguan, Purang Abolmaesumi, Purang Abolmaesumi, Robert N. Rohling, Robert N. Rohling, } "Registration of ultrasound to CT angiography of kidneys: a porcine phantom study", Proc. SPIE 7625, Medical Imaging 2010: Visualization, Image-Guided Procedures, and Modeling, 762518 (23 February 2010); doi: 10.1117/12.844594; https://doi.org/10.1117/12.844594

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