TubeTK/Intra-operative Ultrasound Registration

Related Works

Surface-to-ultrasound registration

• Evaluation on Similarity Measures of a Surface-to-Image Registration Technique for Ultrasound Images.
  • Wei Shao, Ruoyun Wu, Keck Voon Ling, Choon Hua Thng, Henry Sun Sien Ho, Christopher Wai Sam Cheng, Wan Sing Ng
  • MICCAI (2) 2006: 742-749

Ultrasound simulation

• Advanced training methods using an Augmented Reality ultrasound simulator
  • Blum, T.; Heining, S.M.; Kutter, O.; Navab, N.; Comput. Aided Med. Procedures & Augmented Reality (CAMP), Tech. Univ. Munchen, Munich, Germany
  • This paper appears in: Mixed and Augmented Reality, 2009. ISMAR 2009. 8th IEEE International Symposium on: 177 - 178
  • Ultrasound (US) is a medical imaging modality which is extremely difficult to learn as it is user-dependent, has low image quality and requires much knowledge about US physics and human anatomy. For training US we propose an Augmented Reality (AR) ultrasound simulator where the US slice is simulated from a CT volume. The location of the US slice inside the body is visualized using contextual in-situ techniques. We also propose advanced methods how to use an AR simulator for training.
  • http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5336476
• Registration of 3D ultrasound to computed tomography images of the kidney
  • http://circle.ubc.ca/handle/2429/27817?show=full
  • The integration of 3D computed tomography (CT) and ultrasound (US) is of considerable interest because it can potentially improve many minimally invasive procedures such as robot-assisted laparoscopic partial nephrectomy. Partial nephrectomy patients often receive preoperative CT angiography for diagnosis. The 3D CT image is of high quality and has a large field of view. Intraoperatively, dynamic real-time images are acquired using ultrasound. While US is real-time and safe for frequent imaging, the images captured are noisy and only provide a limited perspective. Providing accurate registration between the two modalities would enhance navigation and image guidance for the surgeon because it can bring the pre-operative CT into a current view of the patient provided by US.
  • The challenging aspect of this registration problem is that US and CT produce very different images. Thus, a recurring strategy is to use preprocessing techniques to highlight the similar elements between the images. The registration technique presented here goes further by dynamically simulating an US image from the CT, and registering the simulated image to the actual US. This is validated on US and CT volumes of porcine phantom data. Validation on realistic phantoms remains an ongoing problem in the development of registration methods. A detailed protocol is presented here for constructing tissue phantoms that incorporate contrast agent into the tissue such that the kidneys appear representative of in vivo human CT angiography. Registration with 3D CT is performed successfully on the reconstructed 3D US volumes, and the mean TREs ranged from 1.8 to 3.5 mm. In addition, the simulation-based algorithm was revised to consider the shape of the US beam by using pre-scan converted US data. The corresponding CT image is iteratively interpolated along the direction of the US beam during simulation. The mean TREs resulting from registering the pre-scan US data and CT data were between 1.4 to 2.6 mm. The results show that both methods yield similar results and are promising for clinical application. Finally, the method is tested on a set of in vivo CT and US

images of a partial nephrectomy patient, and the registration results are discussed.