Background: Deep inspiration breath-hold (DIBH) is known as a radiotherapy method for the treatment of patients with left-sided breast cancer. In this method, patient is under exposure only while he/she is at the end of a deep inspiration cycle and holds his/her breath. In this situation, the volume of the lung tissue is enhanced and the heart tissue is pushed away from the treating breast. Therefore, heart dose of these patients, using DIBH, experiences a considerable decline compared to free breathing treatment. There are a few commercialized systems for implementation of DIBH in invasive or noninvasive manners.
Methods: We present a novel constructed noninvasive DIBH device relied on a manufacturing near-field laser distance meter. This in-house constructed system is composed of a CD22-100AM122 laser sensor combined with a data acquisition system for monitoring the breathing curve. Qt Creator (a cross-platform JavaScript, QML, and C++-integrated development environment that is part of the SDK for development of the Qt Graphical User Interface application framework) and Keil MDK-ARM (a programming software where users can write in C and C++ and assemble for ARM-based microcontrollers) are used for composing computer and microcontroller programs, respectively.
Results: This system could be mounted in treatment or computed tomography (CT) room at suitable cost; it is also easy to use and needs a little training for personnel and patients. The system can assess the location of chest wall or abdomen in real time with high precision and frequency. The performance of CD22-100AM122 demonstrates promise for respiratory monitoring for its fast sampling rate as well as high precision. It can also deliver reasonable spatial and temporal accuracy. The patient observes his/her breathing waveform through a 7" 1024 × 600 liquid crystal display and gets some instructions during treatment and CT sessions by an exploited algorithm called "interaction scenario" in this study. The system is also noninvasive and well sustainable for patients.
Conclusions: The constructed system has true real-time operation and is rapid enough for delivering clear contiguous monitoring. In addition, in this system, we have provided an interaction scenario option between patient and CT or Linac operator. In addition, the constructed system has the capability of sending triggers for turning on and off CT or Linac facilities. In this concern, the system has the superiority of combining a plenty of characteristics.
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http://dx.doi.org/10.4103/jmss.JMSS_35_18 | DOI Listing |
MethodsX
June 2025
Department of Networking & Communications, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai, India.
Forecasting student performance with precision in the educational space is paramount for creating tailor-made interventions capable to boost learning effectiveness. It means most of the traditional student performance prediction models have difficulty in dealing with multi-dimensional academic data, can cause sub-optimal classification and generate a simple generalized insight. To address these challenges of the existing system, in this research we propose a new model Multi-dimensional Student Performance Prediction Model (MSPP) that is inspired by advanced data preprocessing and feature engineering techniques using deep learning.
View Article and Find Full Text PDFRecent Pat Nanotechnol
January 2025
Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Nijenborgh 9, 9747 AG Groningen, The Netherlands.
The increase in computational power demand led by the development of Artificial Intelligence is rapidly becoming unsustainable. New paradigms of computation, which potentially differ from digital computation, together with novel hardware architecture and devices, are anticipated to reduce the exorbitant energy demand for data-processing tasks. Memristive systems with resistive switching behavior are under intense research, given their prominent role in the fabrication of memory devices that promise the desired hardware revolution in our intensive data-driven era.
View Article and Find Full Text PDFMagn Reson Imaging
January 2025
School of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China.
Magnetic resonance imaging (MRI) is a non-invasive medical imaging technique that is widely used for high-resolution imaging of soft tissues and organs. However, the slow speed of MRI imaging, especially in high-resolution or dynamic scans, makes MRI reconstruction an important research topic. Currently, MRI reconstruction methods based on deep learning (DL) have garnered significant attention, and they improve the reconstruction quality by learning complex image features.
View Article and Find Full Text PDFNeuroimage Clin
January 2025
Backgrounds/objective: Deep brain stimulation (DBS) has proved the viability of alleviating depression symptoms by stimulating deep reward-related nuclei. This study aims to investigate the abnormal connectivity profiles among superficial, intermediate, and deep brain regions within the reward circuit in major depressive disorder (MDD) and therefore provides references for identifying potential superficial cortical targets for non-invasive neuromodulation.
Methods: Resting-state functional magnetic resonance imaging data were collected from a cohort of depression patients (N = 52) and demographically matched healthy controls (N = 60).
Nat Commun
January 2025
Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, China.
Compute-in-memory based on resistive random-access memory has emerged as a promising technology for accelerating neural networks on edge devices. It can reduce frequent data transfers and improve energy efficiency. However, the nonvolatile nature of resistive memory raises concerns that stored weights can be easily extracted during computation.
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