The aim of this study was to evaluate the feasibility of a new imaging modality for HE assessment, based on their intrinsic polarization properties in PS-OCT imaging in patients with diabetic maculopathy. Images obtained by using PS-OCT were systematically compared to CF, the existing gold standard for imaging HEs. Thickness maps of intraretinal depolarizing structures of the posterior pole were generated by using three-dimensional PS-OCT data sets and correlated with corresponding CF. Results of statistical image analysis showed a marked correlation between HEs in CF and intraretinal depolarizing structures. The findings suggest that these depolarizing structures represent HEs, and that PS-OCT may provide a novel method for assessing and quantifying HEs.
Automated assessment and segmentation of HEs is a difficult task in both CF and OCT. Automated HE detection in CF is aggravated by uneven illumination, changing contrast, and color variation of the retinal images. Several attempts have been made to segment HEs from the retinal background, based on grey level thresholding,
34 homogeneity of HE illumination,
35 and edge detection,
15 or mixture models and clustering algorithms.
16 Since these techniques are based on fundus photography assessment, quantifying the extent of HEs is limited to two-dimensional information. However, as known from histology and SD OCT, HEs are found throughout multiple retinal layers and tend to cluster and overlay each other. Recent studies have presented detailed analyses of the presence and distribution of HEs, based on high-resolution SD-OCT imaging. Bolz et al.
9 first described HEs secondary to DME as hyperreflective lesions in SD-OCT mainly located at the border of the ONL and OPL. Additionally, clinically invisible precursors of HEs, microfoci, were detected in all retinal layers before condensing to larger, clinically visible aggregates over the course of time. Other studies have correlated the presence of such microfoci with decreased visual acuity
36 or manually quantified and described the presence and course of HEs and precursors following treatments of DME.
37,38 All of these studies underline the importance of OCT imaging of HEs and precursors for determining the extent of lipid extravasation resulting from blood–retina barrier breakdown. Further, the need for software-based demarcations and calculations is emphasized by these authors owing to the difficulties and limitations of the currently required manual grading.
38
The findings of our study suggest that PS-OCT overcomes the limitations of two-dimensional CF and intensity-based SD-OCT imaging for assessment of HEs. Based on their intrinsic property to change the state of polarized light, HEs could be automatically detected and segmented. By raster scanning the macular region of interest, thickness maps of HEs were generated from 128 individual B-scans. Good agreement with clinical findings of HEs (in this study represented by CF) was achieved with the new method.
The remaining discrepancies between the results obtained by CF and PS-OCT can be attributed to the following reasons: First, accuracy and precision of OCT imaging in general requires clear optical media, as well as sufficient compliance of the patient, to avoid imaging artifacts due to poor image quality or eye motion. This also affects PS-OCT. In this study, images of insufficient quality were eliminated by defining media opacities as exclusion criteria, and scans with evident motion artifacts were repeated until acceptable quality was achieved. Nonetheless, superimposing CF and PS-OCT images often did not reach perfect accuracy of retinal landmarks (e.g., vessels). This is likely caused by residual motion artifacts and can lead to a loss of exact matching between the compared imaging modalities. Such a slight mismatch has a large influence on the pixel-to-pixel correlation of small features such as tiny HEs but should not influence the total count of HEs within the images, which is the relevant diagnostic quantity. Nevertheless, further improvement of PS-OCT image acquisition speed,
39–42 as well as using a retinal tracker,
43 could likely overcome this limitation in future generations of PS-OCT instruments.
Furthermore, errors in the segmentation algorithm may be caused by lesions such as cotton-wool spots, profound RPE atrophy, or blood vessels. In our cohort, four data sets contained noticeable segmentation artifacts. After manual correction of these data sets, linear correlation between the two modalities improved. However, neither means of detected fields nor interrater agreement of pixelwise HE detection differed significantly from the uncorrected data. This indicates a rather negligible effect of segmentation errors on the overall performance of HE detection in PS-OCT imaging.
Another explanation for some of the observed discrepancies might be found in a detailed breakdown of individual PS-OCT B-scans: In an analysis of all acquired B-scans, we found that HEs showed depolarizing properties, as did the previously described clinically invisible precursors of HEs.
9,36–38 However, the depolarization behavior of these precursors (defined as small hyperreflective lesions in SD-OCT) was heterogeneous. While some hyperreflective lesions showed depolarizing signals, others did not. This was observed to be independent of size, degree of reflectivity in the intensity image, and location within the retinal layers (
Figs. 1D,
1G,
4C,
5C). There are two possible explanations: First, the size of these precursors might be too small to provide a sufficient number of independent data points within the DOPU evaluation window to be picked up by the algorithm, which measures the variance of polarization states among neighboring image pixels. Second, different types of hyperreflective microfoci might exist: Using immunohistology, Cusick et al.
8 have found and imaged not only a dense concentration of apolipoprotein B deposits surrounding retinal vessels (as in hyperreflective lesions), but also cellular components such as foam cells and leukocytes. Further studies will be required to describe and categorize these hyperreflective microfoci in more detail.
Since PS-OCT detects at least part of these precursors, it would be expected to have an increased sensitivity for HE detection, as compared to CF imaging. This was in accordance with the significantly higher rates of detected HE-containing fields in PS-OCT in 77.3% of cases as compared to the individual graders as well as the means of the graders (
P = 0.02) (
Table;
Figs. 3,
5). Therefore, the total sensitivity of PS-OCT to detect any HE is indeed higher than that of CF.
Another advantage of PS-OCT for HE assessment is the three-dimensional component of the detection. Hard exudates were automatically segmented throughout all retinal layers. Therefore, their distribution within individual retinal layers as well as their volume can be obtained, quantities that are inaccessible to CF imaging. This might add valuable information to the understanding of DME and its underlying pathomechanisms.
Given the prototype state of our PS-OCT instrument, the preliminary segmentation algorithms, and the limited study population, our data provide satisfactory accuracy to prove the presented principle. Our results strongly suggest that the presented pattern of depolarizing structures in the retina represent HEs. Further studies are underway to assess the reliability and reproducibility of volumetric assessment of HEs by PS-OCT in order to provide a new tool in the analysis of DME and treatment responses.