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Yayın Micromachined capacitive transducer arrays for intravascular ultrasound(SPIE, 2005) Değertekin, Fahrettin Levent; Güldiken, Rasim Oytun; Karaman, MustafaIntravascular ultrasound (IVUS) imaging has become an essential imaging modality for the effective diagnosis and treatment of cardiovascular diseases during the past decade enabled by innovative applications of piezoelectric transducer technology. The limitations in the manufacture and performance of the same piezoelectric transducers have also impeded the improvement of IVUS for emerging clinically important applications such as forward viewing arrays for guiding interventions and high resolution imaging of arterial structure such as vulnerable plaque and fibrous cap, and also implementation of techniques such as harmonic imaging of the tissue and of the contrast agents. Capacitive micromachined ultrasonic transducer (CMUT) technology shows great potential for transforming IVUS not only to satisfy these clinical needs but also to open up possibilities for low-cost imaging devices integrated to therapeutic tools. We have developed manufacturing processes with a maximum process temperature of 250°C to build CMUTs on the same silicon chip with integrated electronics. Using these processes we fabricated CMUT arrays suitable for forward viewing IVUS in the 10-20MHz range. We characterized these array elements in terms of pulse-echo response, radiation pattern measurements and demonstrated its volumetric imaging capabilities on various imaging targets.Yayın Retinal disease classification using optical coherence tomography angiography images(Institute of Electrical and Electronics Engineers Inc., 2024) Aydın, Ömer Faruk; Nazlı, Muhammet Serdar; Tek, Faik Boray; Turkan, YaseminOptical Coherence Tomography Angiography (OCTA) is a non-invasive imaging modality widely used for the detailed visualization of retinal microvasculature, which is crucial for diagnosing and monitoring various retinal diseases. However, manual interpretation of OCTA images is labor-intensive and prone to variability, highlighting the need for automated classification methods. This study presents an aproach that utilizes transfer learning to classify OCTA images into different retinal disease categories, including age-related macular degeneration (AMD) and diapethic retinopathy (DR). We used the OCTA-500 dataset [1], the largest publicly available retinal dataset that contains images from 500 subjects with diverse retinal conditions. To address the class imbalance, we employed k-fold cross-validation and grouped various other conditions under the 'OTHERS' class. Additionally, we compared the performance of the ResNet50 model with OCTA inputs to that of the ResNet50 and RetFound (Vision Transformer) models with OCT inputs to assess the efficiency of OCTA in retinal condition classification. In the three-class (AMD, D R, Normal) classification, ResNet50-OCTA o utperformed ResNet50-OCT, but slightly underperformed compared to RetFound-OCT, which was pretrained on a large OCT dataset. In the four-class (AMD, DR, Normal, Others) classification, ResNet50-OCTA and RetFound-OCT achieved similar classification a ccuracies. This study establishes a baseline for retinal condition classification using the OCTA-500 dataset and provides a comparison between OCT and OCTA input modalities.
