Purpose; For shoot-through proton treatments, like FLASH radiotherapy, there will be protons exiting the patient which can be used for proton portal imaging (PPI), revealing valuable information for the validation of tumor location in the beam's-eye-view at native gantry angles. However, PPI has poor inherent contrast and spatial resolution. To deal with this issue, we propose a deep-learning-based method to use kV digitally reconstructed radiographs (DRR) to improve PPI image quality. Method; We used a residual generative adversarial network (GAN) framework to learn the nonlinear mapping between PPIs and DRRs. Residual blocks were used to force the model to focus on the structural differences between DRR and PPI. To assess the accuracy of our method, we used 149 images for training and 30 images for testing. PPIs were acquired using a double-scattered proton beam. The DRRs acquired from CT acted as learning targets in the training process and were used to evaluate results from the proposed method using a six-fold cross-validation scheme. Results; Qualitatively, the corrected PPIs showed enhanced spatial resolution and captured fine details present in the DRRs that are missed in the PPIs. The quantitative results for corrected PPIs show average normalized mean error (NME), normalized mean absolute error (NMAE), peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) index of -0.1%, 0.3%, 39.14 dB, and 0.987, respectively. Conclusion; The results indicate the proposed method can generate high quality corrected PPIs and this work shows the potential to use a deep-learning model to make PPI available in proton radiotherapy. This will allow for beam's-eye-view (BEV) imaging with the particle used for treatment, leading to a valuable alternative to orthogonal x-rays or cone-beam CT for patient position verification.The displacement of tumor bed walls during oncoplastic breast surgery (OBS) decreases the accuracy of using surgical clips as the sole surrogate for tumor bed location. This highlights the need for better communication of OBS techniques to radiation oncologists. To facilitate OBS practice and investigate clip placement reliability, a realistic silicone-based breast phantom was constructed with components emulating a breast parenchyma, epidermis, areola, nipple, chest wall, and a tumor. OBS was performed on the phantom and surgical clips were placed to mark the tumor bed. The phantom was imaged with CT, MRI, and ultrasound (US). The parenchyma's signal-to-noise ratio (SNR) and clips to parenchyma's contrast-to-noise ratio (CNR) were measured. The phantom's CT Hounsfield Unit (HU), relative electron density (RED), and mass density were determined. 6 and 10 MV photon beam attenuation measurements were performed in phantom material. The Young's Modulus and ultimate tensile strength (UTS) of the phantom parenchyma and epidermis were measured. https://www.selleckchem.com/products/ly364947.html Results showed that the breast phantom components were visible on all imaging modalities with adequate SNR and CNR. The phantom's HU is 130 ± 10. The RED is 0.983. Its mass density is 1.01 ± 0.01 g cm-3. Photon attenuation measurements in phantom material were within 1% of those in water. The Young's Moduli were 13.4 ± 4.2 kPa (mechanical) and 30.2 ± 4.1 kPa (US elastography) for the phantom parenchyma. The UTS' were 0.05 ± 0.01 MPa (parenchyma) and 0.23 ± 0.12 MPa (epidermis). link2 We conclude that the phantom's imaging characteristics resemble a fibroglandular breast's and allow clear visualization of high-density markers used in radiation therapy. The phantom material is suitable for dose measurements in MV photon beams. Mechanical results confirmed the phantom's similarity to breast tissue. The phantom enables investigation of surgical clip displacements pre- and post-OBS, and is useful for radiation therapy quality assurance applications.To treat cancer, knowledge of mechanical parameters can be essential. This study proposes a new approach for estimating hydraulic conductivity (k) and hydraulic conductivity ratio (α) of a living tissue, based on inverse methods, allowing tissue parameter estimation using only a limited set of measurements. First, two population-based algorithms (Levenberg-Marquardt (LM) method and conjugate gradient (CG) method) and two gradient-based algorithms (genetic algorithm (GA) and particle swarm optimization (PSO) algorithm) are considered, and a comparative study between these different inverse methods is performed to determine which methods have a good performance in terms of convergence rate and stability. CG method is shown to perform well in the case of noise-free input data; however, in the case of noisy input data, it fails to converge. The other three methods (LM, GA, and PSO) converge with estimation errors less then 10% in both noise-free and noisy input data, suggesting their utility to tackle this problem. In the second part, the effectiveness and good accuracy of these robust algorithms (LM, GA, and PSO) are validated with experimental results. The hydraulic conductivity and hydraulic conductivity ratio of a specific living tumor tissue are then estimated for mammary adenocarcinoma (R3230AC). Moreover, assuming measurement of only one-point interstitial pressure inside the tumor, the effect of the location of this one-point on estimation accuracy of hydraulic conductivity is investigated. We show that estimation errors for points measured near the surface and center of the tumor are greater than at other points.Sonography, elastography, sonoelastography are ultrasound imaging techniques commonly used in the clinical practice for the diagnosis of many pathological conditions. These highly reliable, non-invasive methods use high frequency, elastic pressure waves (ultrasounds) to interrogate the internal structure of biological tissues and organs, and the continuum mechanics hypothesis to reconstruct, from the output of the system, the biophysical characteristics of the samples. Nevertheless, continuum mechanics disregards the discrete nature of tissues and organs, resulting in an inability for the model to describe some important tissue biophysical characteristics such as the cell size and their spatial layout. Here, we used the theory of doublet mechanics - a discrete nano-mechanical field theory - to model the propagation of ultrasounds in a multilayered biological tissue. We found that the output of the model exhibits a very high sensitivity to the macro and micro characteristics of the tissue, including cell size. We used results from the model to correlate the internal structure of the samples to the reflection coefficient, i.e. the continuum level response of the system. This model, and its more sophisticated evolutions that will be developed over time, can complement traditional ultrasound imaging, and provide ways to analyze non-invasively living tissues with a resolution inaccessible to conventional techniques of analysis, including positron emission tomography, computer tomography, and magnetic resonance.Although micro-computed tomography (micro-CT) images have high contrast for bone or air, between soft tissues the contrast is typically low. To overcome this inherent issue, attenuating exogenous contrast agents are used to provide contrast enhancement in the vasculature and abdominal organs. The aim of this study is to measure the contrast enhancement time course for a gold nanoparticle blood-pool contrast agent and use it to perform cardiac-gated 4D micro-CT scans of the heart. Six healthy female C57BL/6 mice were anesthetized and imaged after receiving an injected dose of MVivo gold nanoparticle blood-pool contrast agent. Following the injection, we performed micro-CT scans at 0, 0.25, 0.5, 0.75, 1, 2, 4, 8, 24, 48 and 72 h. The mean CT number was measured for 7 different organs. No contrast enhancement was noticed in the bladder, kidneys or muscle during the time-course study. However, it clearly appears that the contrast enhancement is high in both right ventricle and vena cava. To perform cardiac-gated imaging, either the gold nanoparticle agent (n = 3) or an iodine-based (n = 3) contrast agent was introduced and images representing 9 phases of the cardiac cycle were obtained in 6 additional mice. A few typical cardiac parameters were measured or calculated, with similar accuracy between the gold and iodinated agents, but better visualization of structures with the gold agent. The MVivo Au contrast agent can be used for investigations of cardiac or vascular disease with a single bolus injection, with an optimal cardiac imaging window identified during the first hour after injection, demonstrating similar image quality to iodinated contrast agents and excellent measurement accuracy. Furthermore, the long-lasting contrast enhancement of up to 8 h can be very useful for scanning protocols that require longer acquisition times.
Motor imagery can be used as an adjunct to traditional stroke rehabilitation therapies for individuals who have hand and arm impairment resulting from their stroke. The provision of neurofeedback during motor imagery allows individuals to receive real time information regarding their motor imagery-related brain activity. However, the equipment required to administer this feedback is expensive and largely inaccessible to many of the individuals who could benefit from it. Available EEG-based technology provides an accessible, low-cost, wireless alternative to traditional neurofeedback methods, with the tradeoff of lower gain and channel count resulting in reduced signal quality. This study investigated the efficacy of this wireless technology for the provision of motor imagery-related neurofeedback.

Twenty-eight healthy individuals participated in a 2-group, double-blinded study which involved imagining performing a unimanual button pressing task while receiving neurofeedback that is either a direct transfomented in a clinical setting.
Our main findings replicated previous results with research-grade equipment indicating that there is potential for introducing this wireless technology for the provision of neurofeedback. link3 Given the marginal longitudinal effect of neurofeedback in our study, further study is required to address the limitations associated with this technology before our protocol can be implemented in a clinical setting.
A promising application of BCI technology is in the development of personalized therapies that can target neural circuits linked to mental or physical disabilities. Typical BCIs, however, offer limited value due to simplistic designs and poor understanding of the conditions being treated. Building BCIs on more solid grounds may require the characterization of the brain dynamics supporting cognition and behavior at multiple scales, from single-cell and local field potential (LFP) recordings in animals to non-invasive electroencephalography (EEG) in humans. Despite recent efforts, a unifying software framework to support closed-loop studies in both animals and humans is still lacking. The objective of this paper is to develop such a unifying neurotechnological software framework.

Here we develop the Simulink for Brain Signal Interfaces library (SimBSI). Simulink is a mature graphical programming environment within MATLAB that has gained traction for processing electrophysiological data. SimBSI adds to this ecosystem 1) advanced human EEG source imaging, 2) cross-species multimodal data acquisition based on the Lab Streaming Layer library, and 3) a graphical experimental design platform.