Inverse C-arm positioning for interventional procedures using real-time body part detection.
The automation and speedup of interventional therapy and diagnostic workflows is a crucial issue. One way to improve these work-flows is to accelerate the image acquisition procedures by fully automating the patient setup. This paper describes a system that performs this task without the use of markers or other prior assumptions. It returns metric coordinates of the 3-D body shape in real-time for inverse positioning. This is achieved by the application of an emerging technology, called Time-of-Flight (ToF) sensor. A ToF sensor is a cost-efficient, off-the-shelf camera which provides more than 40,000 3-D points in real-time. The first contribution of this paper is the incorporation of this novel imaging technology (ToF) in interventional imaging. The second contribution is the ability of a C-arm system to position itself with respect to the patient prior to the acquisition. We are using the 3-D surface information of the patient to partition the body into anatomical sections. This is achieved by a fast two-stage classification process. The system computes the ISO-center for each detected region. To verify our system we performed several tests on the ISO-center of the head. Firstly, the reproducibility of the head ISO-center computation was evaluated. We achieved an accuracy of (x: 1.73 +/- 1.11 mm/y: 1.87 +/- 1.31 mm/z: 2.91 +/- 2.62 mm). Secondly, a C-arm head scan of a body phantom was setup. Our system automatically aligned the ISO-center of the head with the C-arm ISO-center. Here we achieved an accuracy of +/- 1 cm, which is within the accuracy of the patient table control.
Schaller
Christian
C
Chair of Pattern Recognition and Erlangen Graduate School in Advanced Optical Technologies (SAOT), Friedrich-Alexander University Erlangen-Nuremberg, Martensstr. 3, 91058 Erlangen, Germany. christian.schaller@informatik.uni-erlangen.de
Rohkohl
Christopher
C
Penne
Jochen
J
Stürmer
Michael
M
Hornegger
Joachim
J
D003199:Computer Systems; D004867:Equipment Design; D019544:Equipment Failure Analysis; D021621:Imaging, Three-Dimensional / Q000295:instrumentation* / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015641:Radiography, Interventional / Q000295:instrumentation* / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000295:instrumentation* / Q000379:methods*; D014159:Transducers