Publications by authors named "M Van Sambeek"

Photoacoustic imaging (PAI) is a developing image modality that benefits from light-matter interaction and low acoustic attenuation to provide functional information on tissue composition at relatively large depths. Several studies have reported the potential of dichroism-sensitive photoacoustic (DS-PA) imaging to expand PAI capabilities by obtaining morphological information of tissue regarding anisotropy and predominant orientation. However, most of these studies have limited their analysis to superficial scanning of samples, where fluence effects are negligible.

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Photoacoustic imaging creates light-induced ultrasonic signals to provide valuable information on internal body structures and tissue morphology non-invasively. A multi-aperture photoacoustic imaging (MP-PAI) system is an improvement over conventional photoacoustic imaging (PAI) systems in terms of resolution, contrast, and field of view. Previously, a prototype MP-PAI system was introduced based on multiple capacitive micromachined ultrasound transducers (CMUTs) with shared channels, such that each element in a CMUT shares its channel with its counterpart in other CMUTs.

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Currently, abdominal aortic aneurysms (AAAs) are treated based on the diameter of the aorta, however, a more robust patient-specific marker is needed. The mean thickness of the wall is a potential indicator for AAA rupture risk, which varies significantly within and between patients. So far, regional thickness has not been used in previous rupture risk analysis studies, since it is challenging to measure in CT, MRI, and non-invasive ultrasound (US).

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Image-based patient-specific rupture risk analysis for abdominal aortic aneurysms (AAAs) has shown considerable promise. However, clinical translation has been hampered by the use of invasive and costly imaging modalities. Despite being a promising alternative, ultrasound (US) makes a full analysis, including intraluminal thrombus (ILT), not trivial.

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Article Synopsis
  • This study focuses on improving the segmentation of abdominal aortic aneurysms (AAAs) and intraluminal thrombus (ILT) in ultrasound (US) images using a deep learning model called nnU-Net, due to previous challenges with low ILT-blood contrast in US imaging.
  • The nnU-Net model was trained on both ultrasound data and computed tomography (CT) data, with the CT-based training yielding the best results in accurately identifying AAA structures, outperforming traditional methods in metrics like DICE index and Hausdorff distance.
  • Despite the improved segmentation accuracy, the study noted that visibility issues at the lumen-ILT interface limit broader applicability, suggesting that future improvements
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