98%
921
2 minutes
20
The counting efficiency calibration for in vivo measurement is crucial to derive the activity of radionuclides residing inside a monitored subject. Recently, virtual calibration based on computational phantoms has become popular, yet some key questions remain unresolved. Here, we focus on the in vivo measurement of Pb-210 in the skull and systematically examine how virtual calibration compares to those using physical phantoms and how the variety of computational phantoms affects the derived counting efficiency. It is found that the virtually calibrated efficiency based on the MIDA phantom, which characterizes the highest anatomical fidelity, shows reasonable consistency with the experimental counterpart, with a relative bias of approximately 10%. However, in comparison to the case based on the MIDA phantom, those based on the BOMAB and MIRD phantoms show larger deviation, demonstrating underestimations on the counting efficiency by 51% and 42%, respectively. This finding underscores the critical role of computational phantoms in the virtual calibration. This study contributes to the development of techniques for assessing lung cancer risk resulting from chronic radon exposure through in vivo measurement of skeletal Pb-210 activity.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.apradiso.2024.111192 | DOI Listing |
Med Phys
August 2025
GE HealthCare MICT, Stockholm, Sweden.
Background: Photon-counting computed tomography (CT) bears promise to substantially improve spectral and spatial resolution. One reason for the relatively slow evolution of photon-counting detectors in CT-the technology has been used in nuclear medicine and planar radiology for decades-is pulse pileup, that is, the random staggering of pulses, resulting in count loss and spectral distortion, which in turn cause image bias and reduced contrast-to-noise ratio (CNR). The deterministic effects of pileup can be mitigated with a pileup-correction algorithm, but the loss of CNR cannot be recovered, and must be minimized by hardware design.
View Article and Find Full Text PDFJ Mol Cell Cardiol Plus
September 2025
Department of Computer Science, University of Oxford, Oxford, United Kingdom.
Background: Women are under-represented in cardiovascular research, leading to poorer outcomes. Investigating sex-differences in electromechanical function is essential for improving therapy evaluation. This study presents sex-specific human cellular and biventricular electromechanical models for mechanistic investigation of sex-differences in therapeutic response.
View Article and Find Full Text PDFNPJ Syst Biol Appl
September 2025
Pharmacometrics & Systems Pharmacology, Pfizer Research & Development, San Diego, CA, USA.
Elranatamab, an approved bispecific antibody (BsAb) for relapsed/refractory multiple myeloma, forms an immune synapse between the T-cell CD3 marker and B-cell maturation antigen (BCMA) on myeloma cells. Circulating soluble BCMA (sBCMA) is associated with disease burden and may reduce drug exposure, impacting efficacy. A quantitative systems pharmacology model that captures elranatamab's mechanism of action and disease dynamics was developed and calibrated to clinical datasets.
View Article and Find Full Text PDFAcad Radiol
August 2025
Department of Urology, First Affiliated Hospital of Dalian Medical University, Dalian, China (L.L.). Electronic address:
Rationale And Objectives: The research aims to evaluate the effectiveness of a multi-dual-energy CT (DECT) image-based interpretable model that integrates habitat radiomics with a 3D Vision Transformer (ViT) deep learning (DL) for preoperatively predicting muscle invasion in bladder cancer (BCa).
Materials And Methods: This retrospective study analyzed 200 BCa patients, who were divided into a training cohort (n=140) and a test cohort (n=60) in a 7:3 ratio. Univariate and multivariate analyses were performed on the DECT quantitative parameters to identify independent predictors, which were subsequently used to develop a DECT model.
Micromachines (Basel)
July 2025
School of Aerospace, Mechanical and Manufacturing Engineering, RMIT University, East Campus, Melbourne, VIC 3083, Australia.
The increasing utilization of hard-to-cut materials in high-performance sectors such as aerospace and defense has pushed manufacturing systems to be flexible in processing large workpieces with a wide range of materials while also delivering high precision. Recent studies have highlighted the potential of integrating industrial robots (IRs) with electric discharge machining (EDM) to create a non-contact, low-force manufacturing platform, particularly suited for the accurate machining of hard-to-cut materials into complex and large-scale monolithic components. In response to this potential, a novel robotic EDM system has been developed.
View Article and Find Full Text PDF