Dr. Dávid Szöllősi1,2, Dr. Marcell Gyánó1,3, Dr. Viktor Imre Óriás1,3,4 Dr. Szabolcs Osváth1,2, Dr. Péter Sótonyi3, Dr. Krisztián Szigeti1,2
1: Kinepict Health Ltd, Budapest, Hungary
2: Department of Biophysics and Radiation Biology, Semmelweis University, Budapest, Hungary
3: Heart and Vascular Center, Semmelweis University, Budapest, Hungary
4: Bács-Kiskun County Hospital, Kecskemét, Hungary
Introduction: Digital variance angiography (DVA) is a method based on applying kinetic imaging to X-ray angiographic image series. In pursuit of better patient safety and higher cost-efficiency, image quality is a limiting factor: better image quality (e.g. higher contrast-to-noise ratio (CNR), less artifacts) provides an opportunity X-ray dose and injected contrast media reduction.
Aims: Our purpose was to develop an image processing algorithm that can suppress the motion artifacts and enhance the contrast of DVA images.
Method: An image processing algorithm based on temporal differentiation (DVA+) was developed and implemented in Matlab 2016a (Mathworks). To compare the contrast-to-noise ratio, abdominal and lower extremity X-ray angiographic image series of 30 patients were used to create DVA and DVA+ images. Pairs of vascular and perivascular background regions of interest (ROIs) were selected by hand (n=3283) using an ImageJ macro. CNR was calculated as the ratio of the vessel-background difference and the background standard deviation.
Results: The median SNR of DVA images was 9.26 (Q1: 4.90, Q3: 15.86) while the median SNR of DVA+ images was 14.44 (Q1: 7.14, Q3: 26.00). The median ratio of the SNR values of the same ROI pairs was 1.49 (Q1: 1.12, Q3: 2.02).
Conclusion: DVA+ is an effective algorithm for CNR improvement in X-ray angiography based on kinetic imaging. This improvement may enable the implementation of substantial X-ray and/or contrast media dose reduction clinical protocols.
Doctoral School: Basic and Translational Medicine
Program: Cellular and molecular biophysics
Supervisor: Krisztián Szigeti