Virtual cone-beam computed tomography simulator with human phantom library and its application to the elemental material decomposition

Shimomura, Taisei; Fujiwara, Daiyu; Inoue, Yuki; Takeya, Atsushi; Ohta, Takeshi; Nozawa, Yuki; Imae, Toshikazu; Nawa, Kanabu; Nakagawa, Keiichi; Haga, Akihiro

doi:10.1016/j.ejmp.2023.102648

Cited by 1 publication

(1 citation statement)

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“…With this developed phantom, we also developed a virtual CBCT system 23 to produce the various 3D CBCT images by sampling the tube voltage for incident X-rays and the noise of the cone-beam projection images on a flat panel detector (FPD). By referring to the typical geometry of a dental CBCT device, the cone-beam X-ray projections passing through the aforementioned digital human phantom are simulated by a material-based forward projection algorithm from the source to each element in an FPD with a size of 2000 × 1000.…”

Section: Introductionmentioning

confidence: 99%

Fine structural human phantom in dentistry and instance tooth segmentation

Takeya,

Watanabe,

Haga

2024

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In this study, we present the development of a fine structural human phantom designed specifically for applications in dentistry. This research focused on assessing the viability of applying medical computer vision techniques to the task of segmenting individual teeth within a phantom. Using a virtual cone-beam computed tomography (CBCT) system, we generated over 170,000 training datasets. These datasets were produced by varying the elemental densities and tooth sizes within the human phantom, as well as varying the X-ray spectrum, noise intensity, and projection cutoff intensity in the virtual CBCT system. The deep-learning (DL) based tooth segmentation model was trained using the generated datasets. The results demonstrate an agreement with manual contouring when applied to clinical CBCT data. Specifically, the Dice similarity coefficient exceeded 0.87, indicating the robust performance of the developed segmentation model even when virtual imaging was used. The present results show the practical utility of virtual imaging techniques in dentistry and highlight the potential of medical computer vision for enhancing precision and efficiency in dental imaging processes.

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Section: Introductionmentioning

confidence: 99%