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Image-based modeling is a popular approach to perform patient-specific biomechanical simulations. Accurate modeling is critical for orthopedic application to evaluate implant design and surgical planning. It has been shown that bone strength can be estimated from the bone mineral density (BMD) and trabecular bone architecture. However, these findings cannot be directly and fully transferred to patient-specific modeling since only BMD can be derived from clinical CT. Therefore, the objective of this study was to propose a method to predict the trabecular bone structure using a µCT atlas and an image registration technique. The approach has been evaluated on femurs and patellae under physiological loading. The displacement and ultimate force for femurs loaded in stance position were predicted with an error of 2.5% and 3.7%, respectively, while predictions obtained with an isotropic material resulted in errors of 7.3% and 6.9%. Similar results were obtained for the patella, where the strain predicted using the registration approach resulted in an improved mean squared error compared to the isotropic model. We conclude that the registration of anisotropic information from of a single template bone enables more accurate patient-specific simulations from clinical image datasets than isotropic model.
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http://dx.doi.org/10.1007/s10439-016-1551-4 | DOI Listing |
Radiol Adv
September 2024
Department of Radiology, Northwestern University and Northwestern Medicine, Chicago, IL, 60611, United States.
Background: In clinical practice, digital subtraction angiography (DSA) often suffers from misregistration artifact resulting from voluntary, respiratory, and cardiac motion during acquisition. Most prior efforts to register the background DSA mask to subsequent postcontrast images rely on key point registration using iterative optimization, which has limited real-time application.
Purpose: Leveraging state-of-the-art, unsupervised deep learning, we aim to develop a fast, deformable registration model to substantially reduce DSA misregistration in craniocervical angiography without compromising spatial resolution or introducing new artifacts.
Comput Struct Biotechnol J
August 2025
Institut de Recherche en Cancérologie de Montpellier (IRCM), Équipe Labellisée Ligue Contre le Cancer, INSERM U1194, Université de Montpellier, Institut régional du Cancer de Montpellier (ICM), Montpellier, France.
Digital twins (DTs) are emerging tools for simulating and optimizing therapeutic protocols in personalized nuclear medicine. In this paper, we present a modular pipeline for constructing patient-specific DTs aimed at assessing and improving dosimetry protocols in PRRT such as therapy. The pipeline integrates three components: (i) an anatomical DT, generated by registering patient CT scans with an anthropomorphic model; (ii) a functional DT, based on a physiologically-based pharmacokinetic (PBPK) model created in SimBiology; and (iii) a virtual clinical trial module using GATE to simulate particle transport, image simulation, and absorbed dose distribution.
View Article and Find Full Text PDFClin Interv Aging
September 2025
Department of Nephrology, Huadong Hospital, Fudan University, Shanghai, People's Republic of China.
Objective: This study evaluates cardiac function in older adults with T2DM and preserved LVEF using two-dimensional speckle-tracking echocardiography to explore the risk factors associated with subclinical left ventricular systolic dysfunction (GLS <18%) in this population.
Methods: All patients (n = 87, aged 60 years and above) and controls (n = 20) underwent clinical assessment and echocardiography, including GLS assessment.
Results: Univariate analysis identified gender (OR 3.
Med Phys
September 2025
Department of Radiology, Keio University School of Medicine, Tokyo, Japan.
Background: Understanding respiratory motions of liver and its surrogate organs is crucial for precise dose delivery in liver cancer radiotherapy. Although these motions have been studied for respiratory motion management in the supine posture, few studies have quantified them and evaluated their correlations in the upright posture.
Purpose: This study quantified the respiratory motions of liver and surrogate organs and evaluated the correlations between the liver motions and surrogate signals for respiratory motion monitoring in both the supine and upright postures.
Med Phys
September 2025
School of Computer, Electronics and Information, Guangxi University, Nanning, China.
Background: Deformable medical image registration is a critical task in medical imaging-assisted diagnosis and treatment. In recent years, medical image registration methods based on deep learning have made significant success by leveraging prior knowledge, and the registration accuracy and computational efficiency have been greatly improved. Models based on Transformers have achieved better performance than convolutional neural network methods (ConvNet) in image registration.
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