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Purpose: Dentition defect including edentulism is a problem that deserves attention, which requires precise preoperative planning. The trajectories of the implants can be determined using a pre-made radiographic template, which is adopted for prosthesis-driven oral implantology. However, existing solutions for the registration between the radiographic template and the patient's CBCT still require manual operation and cause inadequate accuracy. In this study, a pre-operative planning system for prosthesis-driven oral implantology is developed with a novel automated registration method.
Methods: Based on threshold segmentation and morphological feature filtering, the potential feature points on two sets of CBCTs are, respectively, recognized. The distance features of the point sets are used to predict the optimal solution for point pair matching, after which the automated registration is implemented. The prosthesis-driven planning can be completed according to the results of registration and multi-planar reconstruction. Then, the surgical templates can be designed and fabricated using 3D printing technology based on the planning results and finally used for intra-operative guidance during implant placement.
Results: Verification of the proposed method was conducted on three clinical cases. The mean Fiducial Registration Error of 0.13 ± 0.01mm was achieved with great efficiency. The average time was 0.15 s for the automatic registration algorithm, and 15.64 s for the whole procedure.
Conclusions: The proposed method proved to be accurate and robust. The results indicate that it can achieve higher efficiency while maintaining a low error level, which will have great potential clinical applications in the future.
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http://dx.doi.org/10.1007/s11548-023-03033-7 | DOI Listing |
Nat Methods
September 2025
Electron Microscopy Science Technology Platform, The Francis Crick Institute, London, UK.
Volume correlative light and electron microscopy (vCLEM) is a powerful imaging technique that enables the visualization of fluorescently labeled proteins within their ultrastructural context. Currently, vCLEM alignment relies on time-consuming and subjective manual methods. This paper presents CLEM-Reg, an algorithm that automates the three-dimensional alignment of vCLEM datasets by leveraging probabilistic point cloud registration techniques.
View Article and Find Full Text PDFBMJ Open
September 2025
Department of Rehabilitation, Shengjing Hospital of China Medical University, Shenyang, Liaoning, Chinax
Objectives: This study evaluated the effects of proximal core training on biomechanical risk factors and strength parameters in individuals at high risk of anterior cruciate ligament (ACL) injury (specifically: those exhibiting pathological movement patterns, neuromuscular deficits or biomechanical risk factors) and compared direct versus indirect interventions. We hypothesised that targeted training enhances dynamic knee stabilisation and hip control during high-risk manoeuvres, with direct approaches providing superior biomechanical benefits through neuromuscular control optimisation.
Design: Systematic review and meta-analysis using the Grading of Recommendation, Assessment, Development and Evaluation (GRADE) approach.
Comput Methods Programs Biomed
August 2025
The Institute of Cancer Research, London, UK. Electronic address:
Background And Objective: Apparent Diffusion Coefficient (ADC) values and Total Diffusion Volume (TDV) from Whole-body diffusion-weighted MRI (WB-DWI) are recognised cancer imaging biomarkers. However, manual disease delineation for ADC and TDV measurements is unfeasible in clinical practice, demanding automation. As a first step, we propose an algorithm to generate fast and reproducible probability maps of the skeleton, adjacent internal organs (liver, spleen, urinary bladder, and kidneys), and spinal canal.
View Article and Find Full Text PDFJ Nucl Med Technol
September 2025
Institute of Nuclear Medicine, First Faculty of Medicine, Charles University and the General University Hospital in Prague, Prague, Czech Republic;
The aim of the study was to validate a new method for semiautomatic subtraction of [Tc]Tc-sestamibi and [Tc]NaTcO SPECT 3-dimensional datasets using principal component analysis (PCA) against the results of parathyroid surgery and to compare its performance with an interactive method for visual comparison of images. We also sought to identify factors that affect the accuracy of lesion detection using the two methods. Scintigraphic data from [Tc]Tc-sestamibi and [Tc]NaTcO SPECT were analyzed using semiautomatic subtraction of the 2 registered datasets based on PCA applied to the region of interest including the thyroid and an interactive method for visual comparison of the 2 image datasets.
View Article and Find Full Text PDFCell Rep Methods
August 2025
Department of Biomedical Engineering and Computational Biology Program, OHSU, Portland, OR, USA; Knight Cancer Institute, OHSU, Portland, OR, USA. Electronic address:
We present UniFORM, a non-parametric, Python-based pipeline for normalizing multiplex tissue imaging (MTI) data at both the feature and pixel levels. UniFORM employs an automated rigid landmark registration method tailored to the distributional characteristics of MTI, with UniFORM operating without prior distributional assumptions and handling both unimodal and bimodal patterns. By aligning the biologically invariant negative populations, UniFORM removes technical variation while preserving tissue-specific expression patterns in positive populations.
View Article and Find Full Text PDF