Mobile C-arms are a tool commonly used in trauma and orthopedic surgery. They have many different applications, including spine, knee, and wrist surgery. With the possibility of intra-operative imaging, mobile C-arms are a great enrichment for checking the progress of the surgery or for providing guidance during minimally invasive procedures. However, there is one drawback to using them, and that is the relatively increased radiation exposure of the patient and the surgical staff [1]. Collimation is an option for reducing the radiation exposure. By focusing the x-ray beams only on the region of interest, it becomes possible to reduce the irradiated area and consequently lower radiation doses. Another effect of choosing the field-of-view is enhanced contrast and improved image quality. Enhanced image quality is consistently sought after in surgical procedures, as it can significantly benefit the surgeon and contribute to
better and more efficient surgical outcomes [2]. Therefore, the accurate adjustment of the collimators has a great impact on the outcome of the surgery, the patient and the medical staff involved. Nevertheless, this crucial adjustment is frequently overlooked due to lack of time and insufficient training of the staff. Software that automatically finds the region of interest can
help to properly adjust the collimation without additional effort for the medical staff. Various hardware- and software-based approaches have previously been employed to address this issue. Yap et al.[3] proposed a deep-learning based method to detect the region of interest. They have used a Faster R-CNN to predict axis-aligned boxes covering the region of interest. The experiments focus on the detection of breast lesion and only consider breast ultrasound data. The aim of this master thesis is to use Transformer networks for finding bounding boxes of bones from various anatomical regions to automate the collimation which leads to a reduction of radiation exposure and increase in image quality.
References
[1] Yang-Sub Lee, Hae-Kag Lee, Jae-Hwan Cho, and Ham-Gyum Kim. Analysis of radiation risk to patients
from intra-operative use of the mobile x-ray system (c-arm). J. Res. Med. Sci., 20(1):7–12, January 2015.
[2] Thomas Werncke, Christian von Falck, Matthias Luepke, Georg Stamm, Frank K Wacker, and Bernhard
Christian Meyer. Collimation and image quality of c-arm computed tomography. Invest. Radiol.,
50(8):514–521, August 2015.
[3] Moi Hoon Yap, Manu Goyal, Fatima Osman, Robert Mart´ı, Erika Denton, Arne Juette, and Reyer Zwiggelaar.
Breast ultrasound region of interest detection and lesion localisation. Artificial Intelligence in Medicine,
107:101880, 2020.