Introducing fitting models for estimating age-specific dose and effective dose in paediatric patients undergoing head, chest and abdomen-pelvis imaging protocols: a patient study.

INTRODUCTION: Concerns regarding the adverse consequences of radiation have increased due to the expanded application of computed tomography (CT) in medical practice. Certain studies have indicated that the radiation dosage depends on the anatomical region, the imaging technique employed and patient-specific variables. The aim of this study is to present fitting models for the estimation of age-specific dose estimates (ASDE), in the same direction of size-specific dose estimates, and effective doses based on patient age, gender and the type of CT examination used in paediatric head, chest and abdomen-pelvis imaging. METHODS: A total of 583 paediatric patients were included in the study. Radiometric data were gathered from DICOM files. The patients were categorised into five distinct groups (under 15 years of age), and the effective dose, organ dose and ASDE were computed for the CT examinations involving the head, chest and abdomen-pelvis. Finally, the best fitting models were presented for estimation of ASDE and effective doses based on patient age, gender and the type of examination. RESULTS: The ASDE in head, chest, and abdomen-pelvis CT examinations increases with increasing age. As age increases, the effective dose in head and abdomen-pelvis CT scans decreased. However, for chest scans, the effective dose initially showed a decreasing trend until the first year of life; after that, it increases in correlation with age. CONCLUSIONS: Based on the presented fitting model for the ASDE, these CT scan quantities depend on factors such as patient age and the type of CT examination. For the effective dose, the gender was also included in the fitting model. By utilising the information about the scan type, region and age, it becomes feasible to estimate the ASDE and effective dose using the models provided in this study.

Unsupervised deep learning registration model for multimodal brain images.

Multimodal image registration is a key for many clinical image-guided interventions. However, it is a challenging task because of complicated and unknown relationships between different modalities. Currently, deep supervised learning is the state-of-theart method at which the registration is conducted in end-to-end manner and one-shot. Therefore, a huge ground-truth data is required to improve the results of deep neural networks for registration. Moreover, supervised methods may yield models that bias towards annotated structures. Here, to deal with above challenges, an alternative approach is using unsupervised learning models. In this study, we have designed a novel deep unsupervised Convolutional Neural Network (CNN)-based model based on computer tomography/magnetic resonance (CT/MR) co-registration of brain images in an affine manner. For this purpose, we created a dataset consisting of 1100 pairs of CT/MR slices from the brain of 110 neuropsychic patients with/without tumor. At the next step, 12 landmarks were selected by a well-experienced radiologist and annotated on each slice resulting in the computation of series of metrics evaluation, target registration error (TRE), Dice similarity, Hausdorff, and Jaccard coefficients. The proposed method could register the multimodal images with TRE 9.89, Dice similarity 0.79, Hausdorff 7.15, and Jaccard 0.75 that are appreciable for clinical applications. Moreover, the approach registered the images in an acceptable time 203 ms and can be appreciable for clinical usage due to the short registration time and high accuracy. Here, the results illustrated that our proposed method achieved competitive performance against other related approaches from both reasonable computation time and the metrics evaluation.

Introducing radiomics model to predict active plaque in multiple sclerosis patients using magnetic resonance images.

Multiple Sclerosis (MS) is the most common non-traumatic disabling disease in young people. The prediction active plaque has the potential to offer new biomarkers for assessing the activity of MS disease. Consequently it supports patient management in the clinical setting and trials. This study aims to investigate the predictive capability of radiomics features for identifying active plaques in these patients using T2 FLAIR (Fluid Attenuated Inversion Recovery) images. For this purpose, a dataset images from 82 patients with 122 lesions was analyzed. Feature selection was performed using the Least Absolute Shrinkage and Selection Operator (LASSO) method. Six different classifier algorithms, namely K-Nearest Neighbors (KNN), Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest (RF), were employed for modeling. The models were evaluated using 5-fold cross-validation, and performance metrics including sensitivity, specificity, accuracy, area under the curve (AUC), and mean squared error were computed. A total of 107 radiomics features were extracted for each lesion, and 11 robust features were identified through the feature selection process. These features consisted of four shape features (elongation, flatness, major axis length, mesh volume), one first-order feature (energy), one Gray Level Co-occurrence Matrix feature (correlation), two Gray Level Run Length Matrix features (gray level non-uniformity, gray level non-uniformity normalized), and three Gray Level Size Zone Matrix features (low gray level zone emphasis, size zone non-uniformity, small area low gray level emphasis). The NB classifier demonstrated the best performance with an AUC, sensitivity, and specificity of 0.85, 0.82, and 0.66, respectively. The findings indicate the potential of radiomics features in predicting active MS plaques in T2 FLAIR images.

The schedule of ATR inhibitor AZD6738 can potentiate or abolish antitumor immune responses to radiotherapy.

Inhibitors of the DNA damage signaling kinase ATR increase tumor cell killing by chemotherapies that target DNA replication forks but also kill rapidly proliferating immune cells including activated T cells. Nevertheless, ATR inhibitor (ATRi) and radiotherapy (RT) can be combined to generate CD8+ T cell-dependent antitumor responses in mouse models. To determine the optimal schedule of ATRi and RT, we determined the impact of short-course versus prolonged daily treatment with AZD6738 (ATRi) on responses to RT (days 1-2). Short-course ATRi (days 1-3) plus RT caused expansion of tumor antigen-specific, effector CD8+ T cells in the tumor-draining lymph node (DLN) at 1 week after RT. This was preceded by acute decreases in proliferating tumor-infiltrating and peripheral T cells and a rapid proliferative rebound after ATRi cessation, increased inflammatory signaling (IFN-ß, chemokines, particularly CXCL10) in tumors, and an accumulation of inflammatory cells in the DLN. In contrast, prolonged ATRi (days 1-9) prevented the expansion of tumor antigen-specific, effector CD8+ T cells in the DLN, and entirely abolished the therapeutic benefit of short-course ATRi with RT and anti-PD-L1. Our data argue that ATRi cessation is essential to allow CD8+ T cell responses to both RT and immune checkpoint inhibitors.

Incidence risk assessment of secondary cancer due to radiotherapy of women with rectal cancer using BEIR VII, EPA, and ICRP models.

Background: Radiotherapy has a significant side effect known as radiation-induced secondary cancer. This study aims to evaluate the dose and secondary cancer risk for women with rectal cancer treated with three-dimensional conformal radiation therapy (3D-CRT) to the organs at risk (OARs) and some sensitive organs using different types of radiation-induced cancer risk prediction models, including Biological Effects of Ionizing Radiation (BEIRVII), Environmental Protection Agency (EPA) and International Commission on Radiological Protection (ICRP), and compare the results of the different models for same organs. Materials and methods: Thirty female patients with rectal cancer were considered and dose calculations were based on the PCRT-3D treatment planning system, while the radiotherapy of the patients had been performed using Shinva linear accelerator with a total dose of 45 Gy at 25 fractions. Planning target volume (PTV), OARs, and some sensitive organs were contoured, three models were used to evaluate secondary cancer risk (SCR) using the excess relative risk (ERR) and excess absolute risk (EAR). Results: The bladder presents the highest risk, in terms of ERR, and the femur head and uterus in terms of EAR from the three models (BEIR VII, EPA, and ICRP). Conclusion: Based on the obtained results, radiotherapy of rectal cancer is relatively higher for the bladder and femur head, compared to the risk for other organs, the kidney risk is significantly lower. It was observed that the SCR from the ICRP model was higher compared to BEIR VII and EPA models.

Electronic portal image enhancement based on nonuniformity correction in wavelet domain.

PURPOSE: Electronic portal images are one of the most important tools to verify the ongoing radiotherapy treatment through comparison with a reference image generated during treatment planning. In this procedure, two images are geometrically matched by means of visible bone or other landmarks of interest such as implanted fiducials. However, the intrinsically poor contrast and low spatial resolution of portal images can limit image quality. METHODS: In this study, we have provided a multiresolution approach to enhance the quality of portal images acquired from the pelvis treatment fields. The main idea behind this work aims at removing some of the image artifacts that conceal the anatomical information. For this purpose, we have applied the homomorphic filtering on the approximation sub-band of wavelet decomposition to enhance local information. Moreover, in order to sharpen the bone edges, wavelet detail sub-bands were weighted to amplify important image details in the reconstruction of the desired enhanced image. The most appropriate image quality measure was chosen according to the image's characteristics in the spatial domain. By considering the characteristics of portal images as the random and nonperiodic texture, high level of noise, and a nonuniform background, three suitable quality measures of images were assessed: edge content, measure of enhancement, and measure of enhancement by entropy. RESULTS: The higher values of these measures indicate the quality improvement in the processed images through our proposed algorithm. Moreover, the subjective evaluation results indicate that the proposed multiresolution approach significantly enhances the perceived quality of images in comparison with original and the similar approach ( p < 0.001 $p < 0.001$ ). CONCLUSIONS: Our proposed wavelet-based enhancement algorithm successfully reduced image intensity nonuniformity and enhanced anatomical featured information, which drastically improved the objective metrics values. Subjective evaluation of enhanced image confirmed this quality improvement.

Direct Photon-by-Photon Analysis of Time-Resolved Pulsed Excitation Data using Bayesian Nonparametrics.

Lifetimes of chemical species are typically estimated by either fitting time-correlated single-photon counting (TCSPC) histograms or phasor analysis from time-resolved photon arrivals. While both methods yield lifetimes in a computationally efficient manner, their performance is limited by choices made on the number of distinct chemical species contributing photons. However, the number of species is encoded in the photon arrival times collected for each illuminated spot and need not be set by hand a priori. Here, we propose a direct photon-by-photon analysis of data drawn from pulsed excitation experiments to infer, simultaneously and self-consistently, the number of species and their associated lifetimes from a few thousand photons. We do so by leveraging new mathematical tools within the Bayesian nonparametric. We benchmark our method for both simulated and experimental data for 1-4 species.

Pitching single-focus confocal data analysis one photon at a time with Bayesian nonparametrics.

Fluorescence time traces are used to report on dynamical properties of molecules. The basic unit of information in these traces is the arrival time of individual photons, which carry instantaneous information from the molecule, from which they are emitted, to the detector on timescales as fast as microseconds. Thus, it is theoretically possible to monitor molecular dynamics at such timescales from traces containing only a sufficient number of photon arrivals. In practice, however, traces are stochastic and in order to deduce dynamical information through traditional means-such as fluorescence correlation spectroscopy (FCS) and related techniques-they are collected and temporally autocorrelated over several minutes. So far, it has been impossible to analyze dynamical properties of molecules on timescales approaching data acquisition without collecting long traces under the strong assumption of stationarity of the process under observation or assumptions required for the analytic derivation of a correlation function. To avoid these assumptions, we would otherwise need to estimate the instantaneous number of molecules emitting photons and their positions within the confocal volume. As the number of molecules in a typical experiment is unknown, this problem demands that we abandon the conventional analysis paradigm. Here, we exploit Bayesian nonparametrics that allow us to obtain, in a principled fashion, estimates of the same quantities as FCS but from the direct analysis of traces of photon arrivals that are significantly smaller in size, or total duration, than those required by FCS.

Quantitative Kinetic Models from Intravital Microscopy: A Case Study Using Hepatic Transport.

The liver performs critical physiological functions, including metabolizing and removing substances, such as toxins and drugs, from the bloodstream. Hepatotoxicity itself is intimately linked to abnormal hepatic transport, and hepatotoxicity remains the primary reason drugs in development fail and approved drugs are withdrawn from the market. For this reason, we propose to analyze, across liver compartments, the transport kinetics of fluorescein-a fluorescent marker used as a proxy for drug molecules-using intravital microscopy data. To resolve the transport kinetics quantitatively from fluorescence data, we account for the effect that different liver compartments (with different chemical properties) have on fluorescein's emission rate. To do so, we develop ordinary differential equation transport models from the data where the kinetics is related to the observable fluorescence levels by "measurement parameters" that vary across different liver compartments. On account of the steep non-linearities in the kinetics and stochasticity inherent to the model, we infer kinetic and measurement parameters by generalizing the method of parameter cascades. For this application, the method of parameter cascades ensures fast and precise parameter estimates from noisy time traces.