Purpose: Intravascular optical coherence tomography (OCT) is widely used for analysis of the coronary artery disease. Its high spatial resolution allows for visualization of arterial tissue components in detail. There are different OCT systems on the market, each of which produces data characterized by its own intensity range and distribution. These differences should be taken into account for the development of image processing algorithms. In order to overcome this difference in the intensity range and distribution, we developed a framework for matching intensities based on the exact histogram matching technique. Methods: In our method, the key step for using the exact histogram matching is to determine the target histogram. For this, we proposed two schemes: a global scheme that uses a single histogram as the target histogram for all the pullbacks, and a local scheme that selects for each single image a target histogram from a predefined database. These two schemes are compared on a unique dataset containing pairs of pullbacks that were acquired shortly after each other with systems from two vendors, St. Jude and Terumo. Pullbacks were aligned according to anatomical landmarks, and a database of matched histogram pairs was created. A leave-one-out cross validation was used to compare performance of the two schemes. The matching accuracy was evaluated by comparing: (a) histograms using Euclidean (d_x2) and Kolmogorov–Smirnov (d_KS) distances, and (b) median intensity level within anatomical regions of interest. Results: Leave-one-out validation indicated that both matching schemes yield comparably high accuracies across the entire validation dataset. The local scheme outperforms the global scheme with marginally lower dissimilarities at both histogram level and intensity level. High visual similarity was observed when comparing the matched images to their aligned counterparts. Conclusion: Both local and global schemes are robust and produce accurate intensity matching. While local scheme performs marginally better than the global scheme, it requires a predefined histogram dataset and is more time consuming. Thus, for offline standardization of the images, the local scheme should be preferred for being more accurate. For online standardization or when another system is involved, the global scheme can be used as a simple and nearly-as-accurate alternative.

Currently two commercial intravascular optical coherence tomography (IVOCT) systems are available: Illumien Optis from St. Jude Medical (SJM) and Lunawave from Terumo. Both systems store the light intensity data in a raw vendor specic polar format. However, whereas SJM uses 16-bits per pixel Terumo uses 8-bits meaning the intensity values are in dierent ranges. This complicates quantitative light intensity based analysis when comparing results based on data from both systems. Therefore, this work aims to find an intensity transformation function from Terumo's 8-bit OFDI data to SJM's 16-bit range. The data consists of 8 pullbacks, 4 acquired with each system in the same arteries of 2 dierent patents pre- and post-stenting implantation. A total of 133 matching sections without stent struts from the two sets of pullbacks were identied based on landmarks such as side-branches and calcied regions. Since the main region of interest in the image is the tissue region only the pixels within 2mm behind the lumen border are used. In order to match the SJM data range, the Terumo data was rescaled and cumulative distribution functions (CDF) were calculated based on the histogram distributions. Comparing these CDFs, the transformation function can be determined. Application of this transformation function not only improves the visual similarity of matching slices it can also be used for further quantitative analysis.