Evaluation of arising exposure of ionizing radiation from computed tomography and the associated health concerns

Healthcare facilities management: A novel data-driven model for predictive maintenance of computed tomography equipment

BACKGROUND

The breakdown of healthcare facilities is a huge challenge for hospitals. Medical images obtained by Computed Tomography (CT) provide information about the patients' physical conditions and play a critical role in diagnosis of disease. To deliver high-quality medical images on time, it is essential to minimize the occurrence frequencies of anomalies and failures of the equipment.

METHODS

We extracted the real-time CT equipment status time series data such as oil temperature, of three equipment, between May 19, 2020, and May 19, 2021. Tube arcing is treated as the classification label. We propose a dictionary-based data-driven model SAX-HCBOP, where the two methods, Histogram-based Information Gain Binning (HIGB) and Coefficient improved Bag of Pattern (CoBOP), are implemented to transform the data into the bag-of-words paradigm. We compare our model to the existing predictive maintenance models based on statistical and time series classification algorithms.

RESULTS

The results show that the Accuracy, Recall, Precision and F1-score of the proposed model achieve 0.904, 0.747, 0.417, 0.535, respectively. The oil temperature is identified as the most important feature. The proposed model is superior to other models in predicting CT equipment anomalies. In addition, experiments on the public dataset also demonstrate the effectiveness of the proposed model.

CONCLUSIONS

The two proposed methods can improve the performance of the dictionary-based time series classification methods in predictive maintenance. In addition, based on the proposed real-time anomaly prediction system, the model assists hospitals in making accurate healthcare facilities maintenance decisions.

Evaluating Effective Dose: A Comparison of Methods Based on Organ Dose Calculations versus Dose-Length Product and Monte Carlo Simulation

Computed tomography (CT) has had a massive impact on diagnostic radiology over the past few decades. Serious concerns exist because of the increase in the effective radiation dose associated with CT scans, which could pose significant health risks. In CT, the effective dose can be estimated by Monte Carlo simulations. The aim of the study was to estimate and compare the effective doses for CT from organ dose-based calculations using the tissue weighting factors of the International Commission on Radiological Protection publications (ICRP 60, 103), Monte Carlo CT-Expo v2.6, and dose-length product (DLP)-based estimates. For 165 CT scans, the effective doses (Ed) of the most common routine radiological investigations were assessed. There were 112 male patients (68%) and 53 female patients (32%). When compared to organ dose-based estimates, the DLP-based estimates of the effective dose produced by applying ICRP 60 coefficients were less than 55–57% (head) and more than 18.1% (chest) and 20% (abdomen). The ICRP 103 values of the Ed were less than 79% (head) and more than 17% (chest), and they changed randomly with the tissue weighting factors for the abdomen. For Monte Carlo CT-Expo, the Ed values were lower by 54% (head), 6% (abdomen), and more than 2% (chest) compared to organ dose-based estimates. Effective doses calculated using the tissue-weighting factors of ICRP 103 values comparable to ICRP 60 differ greatly by an average of 2.3, 2.9, and 4.5 mSv for the head, chest, and abdomen, respectively. In conclusion, all estimates of Ed are subject to the biases inflicted by the assumptions in the methods used; therefore, no significant agreement should be expected. The reason for evaluating ICRP 60 is to make a point that ICRP’s update is indeed more accurate. The variability associated with the use of various methodologies to estimate and compare the effective dose Ed in CT scans was shown to be significant in this study.

Dual-Mode Tumor Imaging Using Probes That Are Responsive to Hypoxia-Induced Pathological Conditions

Hypoxia in solid tumors is associated with poor prognosis, increased aggressiveness, and strong resistance to therapeutics, making accurate monitoring of hypoxia important. Several imaging modalities have been used to study hypoxia, but each modality has inherent limitations. The use of a second modality can compensate for the limitations and validate the results of any single imaging modality. In this review, we describe dual-mode imaging systems for the detection of hypoxia that have been reported since the start of the 21st century. First, we provide a brief overview of the hallmarks of hypoxia used for imaging and the imaging modalities used to detect hypoxia, including optical imaging, ultrasound imaging, photoacoustic imaging, single-photon emission tomography, X-ray computed tomography, positron emission tomography, Cerenkov radiation energy transfer imaging, magnetic resonance imaging, electron paramagnetic resonance imaging, magnetic particle imaging, and surface-enhanced Raman spectroscopy, and mass spectrometric imaging. These overviews are followed by examples of hypoxia-relevant imaging using a mixture of probes for complementary single-mode imaging techniques. Then, we describe dual-mode molecular switches that are responsive in multiple imaging modalities to at least one hypoxia-induced pathological change. Finally, we offer future perspectives toward dual-mode imaging of hypoxia and hypoxia-induced pathophysiological changes in tumor microenvironments.