Simulation of an Adaptive X-Ray Control Interface Using Automated Anthropometry and Thermal Motion Detection

Fatahah Dwi Ridhani; Winda Wirasa, Wike Kristianti — Wed, 31 Dec 2025 00:00:00 +0000

Manual determination of radiographic exposure parameters often results in inconsistent radiation dosing due to subjective assessment of patient body habitus. This study presents the design and component-level simulation of an automated control interface that regulates tube voltage (kV) and current-time (mAs) based on patient Body Mass Index (BMI). The system architecture integrates a sensor fusion array comprising a VL53L0X Time-of-Flight sensor for non-contact vertical ranging, a strain gauge load cell for gravimetric acquisition, and an AMG8833 thermal grid to enforce a stillness protocol before measurement. A central microcontroller processes these inputs using a combined linear and quadratic algorithm to derive optimal exposure settings, incorporating a variable correction factor for machine-specific characteristics. The control logic was validated through an electronic simulation of a capacitor discharge X-ray generator. Results demonstrate robust performance, with biometric acquisition achieving gravimetric accuracy exceeding 97% and absolute vertical precision. Furthermore, the simulated high-voltage control loop successfully mapped five distinct BMI categories to their corresponding target voltages (57kV–69kV) with a deviation of less than 2%. This research confirms the analytical feasibility of utilizing automated anthropometry to drive high-voltage circuitry, offering a technological pathway to reduce operator error and standardize radiation protection in diagnostic imaging.

Quantitative Measurement of Signal Intensity in Dynamic Contrast-Enhanced MRI For Time–Intensity Curve Analysis in Prostate Imaging

Thu, 19 Feb 2026 02:29:52 +0000

Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is a contrast-based MRI technique used to evaluate tissue perfusion and vascular characteristics. This technique generates time–intensity curves (TIC) that enable differentiation between normal prostate tissue, benign lesions, and malignant tumours. However, most previous studies have focused on complex pharmacokinetic analyses that are less representative of routine clinical practice. This study aimed to measure DCE-MRI signal intensity in prostate patients and characterize lesions based on TIC patterns using a practical semi-quantitative approach. The study employed a descriptive–analytic design, with signal intensity measurements based on region of interest (ROI) analysis and TIC slope calculation. The research was conducted at Pusat Pertamina Hospital from November to December 2024, involving a sample of five patients. The results demonstrated variability in signal intensity patterns, with one patient exhibiting a Type II (plateau) pattern (−3.22%) and four patients showing Type I (persistent) patterns (12.97–46.11%). This approach supports the practical implementation of prostate DCE-MRI in routine clinical settings.

SANITAS: Jurnal Teknologi dan Seni Kesehatan

Simulation of an Adaptive X-Ray Control Interface Using Automated Anthropometry and Thermal Motion Detection

Quantitative Measurement of Signal Intensity in Dynamic Contrast-Enhanced MRI For Time–Intensity Curve Analysis in Prostate Imaging