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Purpose: This study explores the possibility of a new method for x-ray computed tomography (CT) calibration by means of cross-calibration with proton CT (pCT) data. The proposed method aims at a more accurate conversion of CT Hounsfield Units (HU) into proton stopping power ratio (SPR) relative to water to be used in proton-therapy treatment planning.
Methods: X-ray CT scan was acquired on a synthetic anthropomorphic phantom, composed of different tissue equivalent materials (TEMs). A pCT apparatus was instead adopted to obtain a reference three-dimensional distribution of the phantom's SPR values. After rigid registration, the x-ray CT was artificially blurred to the same resolution of pCT. Then a scatter plot showing voxel-by-voxel SPR values as a function of HU was employed to link the two measurements and thus obtaining a cross-calibrated x-ray CT calibration curve. The cross-calibration was tested at treatment planning system and then compared with a conventional calibration based on exactly the same TEMs constituting the anthropomorphic phantom.
Results: Cross-calibration provided an accurate SPR mapping, better than by conventional TEMs calibration. The dose distribution of single beams optimized on the reference SPR map was recomputed on cross-calibrated CT, showing, with respect to conventional calibration, minor deviation at the dose fall-off (lower than 1%).
Conclusions: The presented data demonstrated that, by means of reference pCT data, a heterogeneous phantom can be used for CT calibration, paving the way to the use of biological samples, with their accurate description of patients' tissues. This overcomes the limitations of conventional CT calibration requiring homogenous samples, only available by synthetic TEMs, which fail in accurately mimicking the properties of biological tissues. Once a heterogeneous biological sample is provided with its corresponding reference SPR maps, a cross-calibration procedure could be adopted by other PT centers, even when not equipped with a pCT system.
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http://dx.doi.org/10.1002/mp.14698 | DOI Listing |
JMIR Form Res
September 2025
Department of Critical Care Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangdong Provincial Geriatrics Institute, No. 106, Zhongshaner Rd, Guangzhou, 510080, China, 86 15920151904.
Background: Point-of-care ultrasonography has become a valuable tool for assessing diaphragmatic function in critically ill patients receiving invasive mechanical ventilation. However, conventional diaphragm ultrasound assessment remains highly operator-dependent and subjective. Previous research introduced automatic measurement of diaphragmatic excursion and velocity using 2D speckle-tracking technology.
View Article and Find Full Text PDFAnal Chem
September 2025
College of Chemistry and Chemical Engineering, Central South University, Hunan, Changsha 410083, China.
While deep learning-enhanced Raman spectroscopy enables rapid sample analysis, model portability among spectrometers remains hindered by systematic interdevice variations. In this study, a Low-Rank Adaptation-based Calibration Transfer method (LoRA-CT) is proposed to perform parameter-efficient fine-tuning of deep learning models across spectrometers. By decomposing weight updates into low-rank matrices, LoRA-CT achieves superior calibration transfer with minimal samples, reducing trainable parameters by 600× compared to full parameter fine-tuning.
View Article and Find Full Text PDFJ Formos Med Assoc
September 2025
Department of Emergency Medicine, National Taiwan University Hospital Hsin-Chu Branch, Hsinchu, Taiwan; Department of Emergency Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan. Electronic address:
Background: Accurately predicting the neurological outcomes in out-of-hospital cardiac arrest (OHCA) survivors is crucial. Conventional prediction scores should be validated across different settings. Additionally, machine learning (ML) models may provide improved predictive performance.
View Article and Find Full Text PDFPLoS One
September 2025
Department of design fundamentals, Faculty of Mechanical Engineering, Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam.
The slider-crank mechanism (SCM) is fundamental to various mechanical systems. However, optimizing its dynamic performance remains a pressing challenge due to excessive torque, joint reactions, and energy consumption. This study introduces two key innovations to address these challenges: (1) the integration of springs into SCM to optimize dynamic performance and (2) a novel hybrid optimization approach combining the Conjugate Direction with Orthogonal Shift (CDOS) method and Parameter Space Investigation (PSI).
View Article and Find Full Text PDFACS Chem Neurosci
September 2025
Chemical and Biomolecular Engineering Dept, University of California, Los Angeles, Los Angeles, California 90095, United States.
Simulations in three dimensions and time provide guidance on implantable, electroenzymatic glutamate sensor design; relative placement in planar sensor arrays; feasibility of sensing synaptic release events; and interpretation of sensor data. Electroenzymatic sensors based on the immobilization of oxidases on microelectrodes have proven valuable for the monitoring of neurotransmitter signaling in deep brain structures; however, the complex extracellular milieu featuring slow diffusive mass transport makes rational sensor design and data interpretation challenging. Simulations show that miniaturization of the disk-shaped device size below a radius of ∼25 μm improves sensitivity, spatial resolution, and the accuracy of glutamate concentration measurements based on calibration factors determined .
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