Accurate, image-based planning of joint reduction based on intraoperative cone-beam CT forms the basis for precise robotic assistance and quantitative fluoroscopic guidance. The proposed approach combines statistical shape and pose modeling of the ankle joint to: (1) automatically segment individual bones; and (2) identify the target pose for the dislocated fibula to establish a plan for reduction. Leave-one-out analysis of the atlas members demonstrated accurate segmentation with 0.6 mm mean surface distance error and predicted the fibula pose within 1.6 mm and 1.8°. Future work will expand evaluation and analyze the appropriateness of the contralateral ankle as a patient-specific template.
Adaptive optics scanning laser ophthalmoscopy (AOSLO) has advanced the study of retinal structure and function by enabling in vivo imaging of individual photoreceptors. Most implementations of AOSLOs are large, complex tabletop systems, thereby preventing high quality photoreceptor imaging of patients who are unable to sit upright and/or fixate for an imaging session. We have previously addressed this limitation in the clinical translation of AOSLO by developing the first confocal handheld AOSLO (HAOSLO) capable of cone photoreceptor visualization in adults and infants. However, confocal AOSLO images suffer from imaging artifacts and the inability to detect remnant cone structure, leading to ambiguous or potentially misleading results. Recently, it has been shown that non-confocal split-detection (SD) AOSLO images, created by the collection of multiply backscattered light, enables more reliable studies of retinal photoreceptors by providing images of the cone inner segment. In this paper, we detail the extension of our HAOSLO probe to enable multi-channel light collection resulting in the first ever multimodal handheld AOSLO (M-HAOSLO). Imaging sessions were conducted on two dilated, healthy human adult volunteers, and M-HAOSLO images taken in handheld operation mode reveal the cone photoreceptor mosaic. Aside from being the first miniaturized and portable implementation of a SD AOSLO system, M-HAOSLO relies on sensorless optimization of the wavefront to correct aberrations. Thus, we also show the first ever SD images collected after correction of the eye’s estimated wavefront.
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