Autostereoscopic-Raman Spectrometry-Based Three-Dimensional Metrology System for Measurements, Tracking and Identification in a Volume

Autostereoscopic 3D Display System for 3D Medical Images

Recent advances in autostereoscopic three-dimensional (3D) display systems have led to innovations in consumer electronics and vehicle systems (e.g., head-up displays). However, medical images with stereoscopic depth provided by 3D displays have yet to be developed sufficiently for widespread adoption in diagnostics. Indeed, many stereoscopic 3D displays necessitate special 3D glasses that are unsuitable for clinical environments. This paper proposes a novel glasses-free 3D autostereoscopic display system based on an eye tracking algorithm and explores its viability as a 3D navigator for cardiac computed tomography (CT) images. The proposed method uses a slit-barrier with a backlight unit, which is combined with an eye tracking method that exploits multiple machine learning techniques to display 3D images. To obtain high-quality 3D images with minimal crosstalk, the light field 3D directional subpixel rendering method combined with the eye tracking module is applied using a user’s 3D eye positions. Three-dimensional coronary CT angiography images were volume rendered to investigate the performance of the autostereoscopic 3D display systems. The proposed system was trialed by expert readers, who identified key artery structures faster than with a conventional two-dimensional display without reporting any discomfort or 3D fatigue. With the proposed autostereoscopic 3D display systems, the 3D medical image navigator system has the potential to facilitate faster diagnoses with improved accuracy.