Biomedical Physics & Engineering Express最新文献

Objectives. This study investigates the association between cerebral blood flow (CBF) and overall survival (OS) in glioblastoma multiforme (GBM) patients receiving chemoradiation. Identifying CBF biomarkers could help predict patient response to this treatment, facilitating the development of personalized therapeutic strategies.Materials and Methods. This retrospective study analyzed CBF data from dynamic susceptibility contrast (DSC) MRI in 30 newly diagnosed GBM patients (WHO grade IV). Radiomics features were extracted from CBF maps, tested for robustness, and correlated with OS. Kaplan-Meier analysis was used to assess the predictive value of radiomic features significantly associated with OS, aiming to stratify patients into groups with distinct post-treatment survival outcomes.Results. While mean relative CBF and CBV failed to serve as independent prognostic markers for OS, the prognostic potential of radiomic features extracted from CBF maps was explored. Ten out of forty-three radiomic features with highest intraclass correlation coefficients (ICC > 0.9), were selected for characterization. While Correlation and Zone Size Variance (ZSV) features showed significant OS correlations, indicating prognostic potential, Kaplan-Meier analysis did not significantly stratify patients based on these features. Visual analysis of the graphs revealed a predominant association between the identified radiomic features and OS under two years. Focusing on this subgroup, Correlation, ZSV, and Gray-Level Nonuniformity (GLN) emerged as significant, suggesting that a lack of heterogeneity in perfusion patterns may be indicative of a poorer outcome. Kaplan-Meier analysis effectively stratified this cohort based on the features mentioned above. Receiver operating characteristic (ROC) analysis further validated their prognostic value, with ZSV demonstrating the highest sensitivity and specificity (0.75 and 0.85, respectively).Conclusion. Our findings underscored radiomics features sensitive to CBF heterogeneity as pivotal predictors for patient stratification. Our results suggest that these markers may have the potential to identify patients who are unlikely to benefit from standard chemoradiation therapy.

Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are an effective tool for studying cardiac function and disease, and hold promise for screening drug effects on human tissue. Understanding alterations in motion patterns within these cells is crucial for comprehending how the administration of a drug or the onset of a disease can impact the rhythm of the human heart. However, quantifying motion accurately and efficiently from optical measurements using microscopy is currently time consuming. In this work, we present a unified framework for performing motion analysis on a sequence of microscopically obtained images of tissues consisting of hiPSC-CMs. We provide validation of our developed software using a synthetic test case and show how it can be used to extract displacements and velocities in hiPSC-CM microtissues. Finally, we show how to apply the framework to quantify the effect of an inotropic compound. The described software system is distributed as a python package that is easy to install, well tested and can be integrated into any python workflow.

Diabetic retinopathy (DR) is one of the leading causes of vision loss in adults and is one of the detrimental side effects of the mass prevalence of Diabetes Mellitus (DM). It is crucial to have an efficient screening method for early diagnosis of DR to prevent vision loss. This paper compares and analyzes the various Machine Learning (ML) techniques, from traditional ML to advanced Deep Learning models. We compared and analyzed the efficacy of Convolutional Neural Networks (CNNs), Capsule Networks (CapsNet), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), decision trees, and Random Forests. This paper also considers determining factors in the evaluation, including contrast enhancements, noise reduction, grayscaling, etc We analyze recent research studies and compare methodologies and metrics, including accuracy, precision, sensitivity, and specificity. The findings highlight the advanced performance of Deep Learning (DL) models, with CapsNet achieving a remarkable accuracy of up to 97.98% and a high precision rate, outperforming other traditional ML methods. The Contrast Limited Adaptive Histogram Equalization (CLAHE) preprocessing technique substantially enhanced the model's efficiency. Each ML method's computational requirements are also considered. While most advanced deep learning methods performed better according to the metrics, they are more computationally complex, requiring more resources and data input. We also discussed how datasets like MESSIDOR could be more straightforward and contribute to highly evaluated performance and that there is a lack of consistency regarding benchmark datasets across papers in the field. Using the DL models facilitates accurate early detection for DR screening, can potentially reduce vision loss risks, and improves accessibility and cost-efficiency of eye screening. Further research is recommended to extend our findings by building models with public datasets, experimenting with ensembles of DL and traditional ML models, and considering testing high-performing models like CapsNet.

Objective.To investigate the potential of 3D-printable thermoplastics as tissue-equivalent materials to be used in multimodal radiotherapy end-to-end quality assurance (QA) devices.Approach.Six thermoplastics were investigated: Polylactic Acid (PLA), Acrylonitrile Butadiene Styrene (ABS), Polyethylene Terephthalate Glycol (PETG), Polymethyl Methacrylate (PMMA), High Impact Polystyrene (HIPS) and StoneFil. Measurements of mass density (ρ), Relative Electron Density (RED), in a nominal 6 MV photon beam, and Relative Stopping Power (RSP), in a 210 MeV proton pencil-beam, were performed. Average Hounsfield Units (HU) were derived from CTs acquired with two independent scanners. The calibration curves of both scanners were used to predict averageρ,RED and RSP values and compared against the experimental data. Finally, measured data ofρ,RED and RSP was compared against theoretical values estimated for the thermoplastic materials and biological tissues.Main results.Overall, goodρand RSP CT predictions were made; only PMMA and PETG showed differences >5%. The differences between experimental and CT predicted RED values were also <5% for PLA, ABS, PETG and PMMA; for HIPS and StoneFil higher differences were found (6.94% and 9.42/15.34%, respectively). Small HU variations were obtained in the CTs for all materials indicating good uniform density distribution in the samples production. ABS, PLA, PETG and PMMA showed potential equivalency for a variety of soft tissues (adipose tissue, skeletal muscle, brain and lung tissues, differences within 0.19%-8.35% for all properties). StoneFil was the closest substitute to bone, but differences were >10%. Theoretical calculations of all properties agreed with experimental values within 5% difference for most thermoplastics.Significance.Several 3D-printed thermoplastics were promising tissue-equivalent materials to be used in devices for end-to-end multimodal radiotherapy QA and may not require corrections in treatment planning systems' dose calculations. Theoretical calculations showed promise in identifying thermoplastics matching target biological tissues before experiments are performed.

Hand Movement Recognition (HMR) with sEMG is crucial for artificial hand prostheses. HMR performance mostly depends on the feature information that is fed to the classifiers. However, sEMG often captures noise like power line interference (PLI) and motion artifacts. This may extract redundant and insignificant feature information, which can degrade HMR performance and increase computational complexity. This study aims to address these issues by proposing a novel procedure for automatically removing PLI and motion artifacts from experimental sEMG signals. This will make it possible to extract better features from the signal and improve the categorization of various hand movements. Empirical mode decomposition and energy entropy thresholding are utilized to select relevant mode components for artifact removal. Time domain features are then used to train classifiers (kNN, LDA, SVM) for hand movement categorization, achieving average accuracies of 92.36%, 93.63%, and 98.12%, respectively, across subjects. Additionally, muscle contraction efforts are classified into low, medium, and high categories using this technique. Validation is performed on data from ten subjects performing eight hand movement classes and three muscle contraction efforts with three surface electrode channels. Results indicate that the proposed preprocessing improves average accuracy by 9.55% with the SVM classifier, significantly reducing computational time.

The absence of effective extracellular matrix to mimic the natural tumor microenvironment remains a significant obstacle in cancer research. Matrigel, abundant in various biological matrix components, is limited in its application due to its high cost. This has prompted researchers to explore alternative matrix substitutes. Here, we have investigated the effects of the extracellular matrix derived from pig small intestinal submucosa (ECM-SIS) in xenograft tumor modeling. Our results showed that the pig-derived ECM-SIS effectively promotes the establishment of xenograft tumor models, with a tumor formation rate comparable to that of Matrigel. Furthermore, we showed that the pig-derived ECM-SIS exhibited lower immune rejection and fewer infiltrating macrophages than Matrigel. Gene sequencing analysis demonstrated only a 0.5% difference in genes between pig-derived ECM-SIS and Matrigel during the process of tumor tissue formation. These differentially expressed genes primarily participate in cellular processes, biological regulation, and metabolic processes. These findings emphasize the potential of pig-derived ECM-SIS as a cost-effective option for tumor modeling in cancer research.

Magnetic Resonance Imaging (MRI) employs a radiofrequency electromagnetic field to create pictures on a computer. The prospective biological consequences of exposure to radiofrequency electromagnetic fields (RF EMFs) have not yet been demonstrated, and there is not enough evidence on biological hazards to offer a definite response concerning possible RF health dangers. Therefore, it is crucial to research the health concerns in reaction to RF EMFs, considering the entire exposure in terms of patients receiving MRI. Monitoring increases in temperaturein-vivothroughout MRI scan is extremely invasive and has resulted in a rise in the utilization of computational methods to estimate distributions of temperatures. The purpose of this study is to estimate the absorbed power of the brain exposed to RF in patients undergoing brain MRI scan. A three-dimensional Penne's bio-heat equation was modified to computationally analyze the temperature distributions and potential thermal effects within the brain during MRI scans in the 0.3 T to 1.5 T range (12.77 MHz to 63.87 MHz). The instantaneous temperature distributions of thein-vivotissue in the brain temperatures measured at a time, t = 20.62 s is 0.2 °C and t = 30.92 s is 0.4 °C, while the highest temperatures recorded at 1.03 min and 2.06 min were 0.4 °C and 0.6 °C accordingly. From the temperature distributions of thein-vivotissue in the brain temperatures measured, there is heat build-up in patients who are exposed to electromagnetic frequency ranges, and, consequently, temperature increases within patients are difficult to prevent. The study has, however, indicated that lengthier imaging duration appears to be related to increasing body temperature.

This study introduces a novel volume coil design that features two slotted end-plates connected by six rungs, resembling the traditional birdcage coil. The end rings are equipped with six evenly distributed circular slots, inspired by Mansfield's cavity resonator theory, which suggests that circular slots can generate a baseline resonant frequency. One notable advantage of this proposed coil design is its reduced reliance on electronic components compared to other volume coils, making it more efficient. Additionally, the dimensions of the coil can be theoretically computed in advance, enhancing its practicality. To evaluate the performance and safety of the coil, electromagnetic field and specific absorption rate simulations were simulated using a cylindrical saline phantom and the finite element method. Furthermore, a transceiver coil prototype optimized for 7 Tesla and driven in quadrature was constructed, enabling whole-body imaging of rats. The resonant frequency of the coil prototype obtained through experimental measurements closely matched the theoretical frequency derived from Mansfield's theory. To validate the coil design, phantom images were acquired to demonstrate its viability and assess its performance. These images also served to validate the magnetic field simulations. The experimental results aligned well with the simulation findings, confirming the reliability of the proposed coil design. Importantly, the prototype coil showcased significant improvements over a similarly-sized birdcage coil, indicating its potential for enhanced performance. The noise figure was lower in the prototype versus the birdcage coil (NF_birdcage-NF_slotcage= 0.7). Phantom image data were also used to compute the image SNR, giving SNR_slotcage/SNR_birdcage= 34.36/24.34. By proving the feasibility of the coil design through successful rat whole-body imaging, the study provides evidence supporting its potential as a viable option for high-field MRI applications on rodents.