Abstract:

Oligomers with a dimethylsiloxane backbone coated as thin films on different substrate surfaces were thermally as well as photochemically cross-linked. The structure and the degree of cross-linking were examined spectroscopically. Diffusion of different gases in the thin polymer films was measured by time resolved infrared ATR-spectroscopy. The process of diffusion is almost immediately followed by a swelling of the polymer proportional to gas concentration. Therefore diffusion may also be measured by spectral interferometry, giving a very sensitive device for optical sensing of hydrocarbons. Furthermore, diffusion in polymers may be measured very accurately by spatially resolved UV/Vis-spectroscopy. Diffusion coefficients may also be determined indirectly from the equilibrium of monomers and excimers indicated by the fluorescence intensities. This method allows the in situ observation of the cross-linking process.

Abstract:

Introduction: Photogrammetric surface scans provide a radiation-free option to assess and classify craniosynostosis. Due to the low prevalence of craniosynostosis and high patient restrictions, clinical data is rare. Synthetic data could support or even replace clinical data for the classification of craniosynostosis, but this has never been studied systematically. Methods: We test the combinations of three different synthetic data sources: a statistical shape model (SSM), a generative adversarial network (GAN), and image-based principal component analysis for a convolutional neural network (CNN)-based classification of craniosynostosis. The CNN is trained only on synthetic data, but validated and tested on clinical data. Results: The combination of a SSM and a GAN achieved an accuracy of more than 0.96 and a F1-score of more than 0.95 on the unseen test set. The difference to training on clinical data was smaller than 0.01. Including a second image modality improved classification performance for all data sources. Conclusion: Without a single clinical training sample, a CNN was able to classify head deformities as accurate as if it was trained on clinical data. Using multiple data sources was key for a good classification based on synthetic data alone. Synthetic data might play an important future role in the assessment of craniosynostosis.

Abstract:

Optical Coherence Tomography (OCT) is a stan- dard imaging procedure in ophthalmology. OCT Angiography is a promising extension, allowing for fast and non-invasive imaging of the retinal vasculature analyzing multiple OCT scans at the same place. Local variance is examined and highlighted. Despite its introduction in the clinic, unanswered questions remain when it comes to signal generation. Multi- phase fluids like intralipid, milk-water solutions and human blood cells were applied in phantom studies shedding light on some of the mechanisms. The use of hydrogel beads allows for the generation of alternative blood models for OCT and OCT Angiography. Beads were produced in Hannover, their size was measured and their long term stability was assessed. Then, beads were shipped to Karlsruhe, where OCT imaging resulted in first insights. The hydrogel acts as a diffusion barrier, which enables a clear distinction of bead and fluid when scattering particles were added. Further on, the scattering medium be- low the bead showed increased signal intensity. We conclude that the inside of the bead structure shows enhanced transmis- sion compared to the plasma substitute with dissolved TiO2 surrounding it. Beads were found clumped and deformed af- ter shipping, an issue to be addressed in further investigations. Nevertheless, hydrogel beads are promising as a blood model for OCT Angiography investigations, offering tunable optical parameters within the blood substitute solution.

Abstract:

Purpose: To evaluate the impact of lens opacity on the reliability of optical coherence tomog- raphy angiography metrics and to find a vessel caliber threshold that is reproducible in cataract patients.Methods: A prospective cohort study of 31 patients, examining one eye per patient, by applying 33mm macular optical coherence tomography angiography before (18.94±12.22days) and 3 months (111 ± 23.45 days) after uncomplicated cataract surgery. We extracted superficial (SVC) and deep vascular plexuses (DVC) for further analysis and evaluated changes in image contrast, vessel metrics (perfusion density, flow deficit and vessel-diameter index) and foveal avascular area (FAZ). Results: After surgery, the blood flow signal in smaller capillaries was enhanced as image contrast improved. Signal strength correlated to average lens density defined by objective measurement in Scheimpflug images (Pearson’s r: –.40, p: .027) and to flow deficit (r1⁄4 –.70, p<.001). Perfusion density correlated to the signal strength index (r1⁄4.70, p<.001). Vessel metrics and FAZ area, except for FAZ area in DVC, were significantly different after cataract surgery, but the mean change was approximately 3–6%. A stepwise approach in extracting vessels according to their pixel caliber showed a threshold of > 6 pixels caliber ($20–30 mm) was comparable before and after lens removal.Conclusion: In patients with cataract, OCTA vessel metrics should be interpreted with caution. In addition to signal strength, contrast and pixel properties can serve as supplementary quality met- rics to improve the interpretation of OCTA metrics. Vessels with $20–30 mm in caliber seem to be reproducible.

Abstract:

Objective: Diagnosis of craniosynostosis using photogrammetric 3D surface scans is a promising radiation-free alternative to traditional computed tomography. We propose a 3D surface scan to 2D distance map conversion enabling the usage of the first convolutional neural networks (CNNs)-based classification of craniosynostosis. Benefits of using 2D images include preserving patient anonymity, enabling data augmentation during training, and a strong under-sampling of the 3D surface with good classification performance.Methods: The proposed distance maps sample 2D images from 3D surface scans using a coordinate transformation, ray casting, and distance extraction. We introduce a CNNbased classification pipeline and compare our classifier to alternative approaches on a dataset of 496 patients. We investigate into low-resolution sampling, data augmentation, and attribution mapping.Results: Resnet18 outperformed alternative classifiers on our dataset with an F1-score of 0.964 and an accuracy of 98.4 %. Data augmentation on 2D distance maps increased performance for all classifiers. Under-sampling allowed 256-fold computation reduction during ray casting while retaining an F1-score of 0.92. Attribution maps showed high amplitudes on the frontal head.Conclusion: We demonstrated a versatile mapping approach to extract a 2D distance map from the 3D head geometry increasing classification performance, enabling data augmentation during training on 2D distance maps, and the usage of CNNs. We found that low-resolution images were sufficient for a good classification performance.Significance: Photogrammetric surface scans are a suitable craniosynostosis diagnosis tool for clinical practice. Domain transfer to computed tomography seems likely and can further contribute to reducing ionizing radiation exposure for infants.

Abstract:

Background: Craniosynostosis is a condition caused by the premature fusion of skull sutures, leading to irregular growth patterns of the head. Three-dimensional photogrammetry is a radiation-free alternative to the diagnosis using computed tomography. While statistical shape models have been proposed to quantify head shape, no shape-model-based classification approach has been presented yet. Methods: We present a classification pipeline that enables an automated diagnosis of three types of craniosynostosis. The pipeline is based on a statistical shape model built from photogrammetric surface scans. We made the model and pathology-specific submodels publicly available, making it the first publicly available craniosynostosis-related head model, as well as the first focusing on infants younger than 1.5 years. To the best of our knowledge, we performed the largest classification study for craniosynostosis to date. Results: Our classification approach yields an accuracy of 97.8 %, comparable to other state-of-the-art methods using both computed tomography scans and stereophotogrammetry. Regarding the statistical shape model, we demonstrate that our model performs similar to other statistical shape models of the human head. Conclusion: We present a state-of-the-art shape-model-based classification approach for a radiation-free diagnosis of craniosynostosis. Our publicly available shape model enables the assessment of craniosynostosis on realistic and synthetic data.

Abstract:

During cerebral revascularization surgery, it is imperative to examine the perfusion of the treated region to preserve patients from fatal consequences, done by measuring the volume flow in single blood vessels. Weichelt et al. suggested to quantify the volume flow from contact free recorded fluorescence angiography video data, multiplying the vessel cross section by the observed fluorophor velocity. Compared to reference measurements, the method overestimates the volume flow. Depending on the vessel diameter d the deviations range from 7% (given as k = 1,07,d = 1,6mm) to 58% (given as k = 1,58,d = 4mm) [1]. The observed deviations are investigated in recent research. There is a flow velocity profile over the vessel cross section. There are varying amounts of intensity contributing to the video data coming from different depths within the vessel due to radiative transfer in turbid media. These varying amounts should be considered in an optic probability density function. So, one approach integrates the local relative blood velocity, weighted by the optic probability density function over the vessel cross section to approximate k. If the deviations can be explained by a combination of information depth and local blood velocity, the approximated k match the observed ones. In previous work, the optical weighting was obtained applying a Monte Carlo Multi Layer model, analyzing the deepest penetration depth of each photon. This implies many assumptions, especially regarding model geometry, information source and illumination modelling. The approximated k do not match the observations. [2] This work investigates the influence of use of optical weights from a Fluorescence Multi Cylinder Monte Carlo simulation instead of a Monte Carlo Multi Layer to assess the validity of assumptions made by using the optical weighting factors from Monte Carlo Multi Layer. Three aspects of the optic model were reimplemented to obtain the optic weights: 1. the fluorescence location of each photon was assumed to be the source of information given by this photon instead of the deepest penetration location 2. the Multi Layer geometry was changed to a Multi Cylinder geometry 3. homogeneous illumination was simulated instead of single point illumination It was found, that there are clear differences in approximated k-factors, obtained from optical weights from the Fluorescence Multi Cylinder Monte Carlo model, compared to the optical weights from Monte Carlo Multi Layer model. The deviations coming from model geometry, information source interpretation and illumination show a Root Square Mean Error of up to 38%. The assumptions made in previous work are not met.