Deep learning method for reducing metal artifacts in dental cone-beam CT using supplementary information from intra-oral scan.

Objective.Recently, dental cone-beam computed tomography (CBCT) methods have been improved to significantly reduce radiation dose while maintaining image resolution with minimal equipment cost. In low-dose CBCT environments, metallic inserts such as implants, crowns, and dental fillings cause severe artifacts, which result in a significant loss of morphological structures of teeth in reconstructed images. Such metal artifacts prevent accurate 3D bone-teeth-jaw modeling for diagnosis and treatment planning. However, the performance of existing metal artifact reduction (MAR) methods in handling the loss of the morphological structures of teeth in reconstructed CT images remains relatively limited. In this study, we developed an innovative MAR method to achieve optimal restoration of anatomical details.Approach.The proposed MAR approach is based on a two-stage deep learning-based method. In the first stage, we employ a deep learning network that utilizes intra-oral scan data as side-inputs and performs multi-task learning of auxiliary tooth segmentation. The network is designed to improve the learning ability of capturing teeth-related features effectively while mitigating metal artifacts. In the second stage, a 3D bone-teeth-jaw model is constructed with weighted thresholding, where the weighting region is determined depending on the geometry of the intra-oral scan data.Main results.The results of numerical simulations and clinical experiments are presented to demonstrate the feasibility of the proposed approach.Significance.We propose for the first time a MAR method using radiation-free intra-oral scan data as supplemental information on the tooth morphological structures of teeth, which is designed to perform accurate 3D bone-teeth-jaw modeling in low-dose CBCT environments.

Enhanced intestinal lymphatic absorption of saquinavir through supersaturated self-microemulsifying drug delivery systems.

The therapeutic potential of saquinavir, a specific inhibitor of human immunodeficiency virus (HIV)-1 and HIV-2 protease enzymes, has been largely limited because of a low solubility and consequnt low bioavailability. Thus, we aimed to design a supersaturated self-microemulsifying drug delivery system (S-SMEDDS) that can maintain a high concentration of saquinavir in gastro-intestinal fluid thorugh inhibiting the drug precipitation to enhance the lymphatic transport of saquinavir and to increase the bioavailability of saquinavir considerably. Solubilizing capacity of different oils, surfactants, and cosurfactants for saquinavir was evaluated to select optimal ingredients for preparation of SMEDDS. Through the construction of pseudo-ternary phase diagram, SMEDDS formulations were established. A polymer as a precipitation inhibitor was selected based on its viscosity and drug precipitation inhibiting capacity. The S-SMEDDS and SMEDDS designed were administered at an equal dose to rats. At predetermined time points, levels of saquinavir in lymph collected from the rats were assessed. SMEDDS prepared presented a proper self-microemulsification efficiency and dispersion stability. The S-SMEDDS fabricated using the SMEDDS and hydroxypropyl methyl cellulose 2910 as a precipitation inhibitor exhibited a signficantly enhanced solubilizing capacity for saquinavir. The drug concentration in a simulated intestinal fluid evaluated with the S-SMEDDS was also maintained at higher levels for prolonged time than that examined with the SMEDDS. The S-SMEDDS showed a considerably enhanced lymphatic absoprtion of saquinavir in rats compared to the SMEDDS. Therefore, the S-SMEDDS would be usefully exploited to enhance the lymphatic absorption of hydrophobic drugs that need to be targeted to the lymphatic system.