SEARCH · Searcharxiv

Results for “physics.med-ph”

Search indexed arXiv papers on artificial intelligence, large language models, computer vision and robotics. Read source abstracts and follow links to arXiv.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 343 records · Page 19Linked to original sources

arXiv2018

Non-Invasive Functional-Brain-Imaging with a Novel Magnetoencephalography System

A non-invasive functional-brain-imaging system based on optically-pumped-magnetometers (OPM) is presented. The OPM-based magnetoencephalography (MEG) system features 20 OPM channels conforming to the subject's scalp. Due to proximity (12 mm) of the OPM channels to the brain, it is anticipated that this MEG system offers an enhanced spatial resolution as it can capture finer spatial features compared to traditional MEG systems employing superconducting quantum interference device (SQUID). We have conducted two MEG experiments on three subjects: somatosensory evoked magnetic field (SEF) and auditory evoked magnetic field (AEF) using our OPM-based MEG system and a commercial SQUID-based MEG system. We have cross validated the robustness of our system by calculating the distance between the location of the equivalent current dipole (ECD) yielded by our OPM-based MEG system and the ECD location calculated by the commercial SQUID-based MEG system. We achieved sub-centimeter accuracy for both SEF and AEF responses in all three subjects.

physics.med-ph↗

arXiv2018

Quantification of Morphological Features in Non-Contrast Ultrasound Microvasculature Imaging

Morphological features of small vessels provide invaluable information regarding underlying tissue, especially in cancerous tumors. This paper introduces methods for obtaining quantitative morphological features from microvasculature images obtained by non-contrast ultrasound imaging. Those images suffer from the artifact that limit quantitative analysis of the vessel morphological features. In this paper we introduce processing steps to increase accuracy of the morphological assessment for quantitative vessel analysis in presence of these artifact. Specifically, artificats are reduced by additional filtering and vessel segments obtained by skeletonization of the regularized microvasculature images are further analyzed to satisfy additional constraints, such as diameter, and length of the vessel segments. Measurement of some morphological metrics, such as tortuosity, depends on preserving large vessel trunks that may be broken down into multiple branches. We propose two methods to address this problem. In the first method, small vessel segments are suppressed in the vessel filtering process via adjusting the size scale of the regularization. Hence, tortuosity of the large trunks can be more accurately estimated by preserving longer vessel segments. In the second approach, small connected vessel segments are removed by a combination of morphological erosion and dilation operations on the segmented vasculature images. These methods are tested on representative in vivo images of breast lesion microvasculature, and the outcomes are discussed. This paper provides a tool for quantification of microvasculature image from non-contrast ultrasound imaging may result in potential biomarkers for diagnosis of some diseases.

physics.med-ph↗

arXiv2018

SANTIS: Sampling-Augmented Neural neTwork with Incoherent Structure for MR image reconstruction

Deep learning holds great promise in the reconstruction of undersampled Magnetic Resonance Imaging (MRI) data, providing new opportunities to escalate the performance of rapid MRI. In existing deep learning-based reconstruction methods, supervised training is performed using artifact-free reference images and their corresponding undersampled pairs. The undersampled images are generated by a fixed undersampling pattern in the training, and the trained network is then applied to reconstruct new images acquired with the same pattern in the inference. While such a training strategy can maintain a favorable reconstruction for a pre-selected undersampling pattern, the robustness of the trained network against any discrepancy of undersampling schemes is typically poor. We developed a novel deep learning-based reconstruction framework called SANTIS for efficient MR image reconstruction with improved robustness against sampling pattern discrepancy. SANTIS uses a data cycle-consistent adversarial network combining efficient end-to-end convolutional neural network mapping, data fidelity enforcement and adversarial training for reconstructing accelerated MR images more faithfully. A training strategy employing sampling augmentation with extensive variation of undersampling patterns was further introduced to promote the robustness of the trained network. Compared to conventional reconstruction and standard deep learning methods, SANTIS achieved consistent better reconstruction performance, with lower errors, greater image sharpness and higher similarity with respect to the reference regardless of the undersampling patterns during inference. This novel concept behind SANTIS can particularly be useful towards improving the robustness of deep learning-based image reconstruction against discrepancy between training and evaluation, which is currently an important but less studied open question.

cs.CV↗

arXiv2018

Cone-beam CT for Dental Radiography

The cone beam computed tomography (CBCT) technique was first inserted in dental imaging 15 years ago. Due to this technique 3D imaging in dentistry has been excessively changed. The purpose beyond using CBCT instead of computed tomography (CT) imaging is that CT can not be used in many cases in dentistry, in addition to the higher radiation and cost of CT. Nowadays, CBCT can be applied in diagnostics for several dental specialties because of the variety of services it offers and the specification of use. In contrast to CT, CBCT has shown great benefits in endodontics, implant planning oral and maxillofacial surgery and even in orthodontics.

physics.med-ph↗

arXiv2018

Q-PET: PET with 3rd Eye Quantum Entanglement based Positron Emission Tomography

In the present ongoing study, we are proposing a prototype model for positron emission tomography detection technology by introduction of a new discriminatory window parameter. It can be a new generation PET detection technique. We introduced Polarization Measurement of the annihilation photons(generated from the annihilation of positron and electron) as an additional parameter in proposed prototype, to correlate annihilation photons of a particular annihilation event. The motivation behind this introduction is Quantum Entanglement relation between the two annihilation photons. These two oppositely emitted photons are linearly polarized at right angle to each other. Simulations studies for this research work are undergoing and some preliminary results are presented here.

physics.ins-det↗

arXiv2018

Stochastic Modelling and Dynamic Analysis of Cardiovascular System with Rotary Left Ventricular Assist Devices

The left ventricular assist device (LVAD) has been used for end-stage heart failure patients as a therapeutic option. The aortic valve plays a critical role in heart failure and its treatment with LVAD. The cardiovascular-LVAD model is often used to investigate the physiological demands required by patients and predict the hemodynamic of the native heart supported with a LVAD. As a bridge to recovery treatment, it is important to maintain appropriate and active dynamics of the aortic valve and the cardiac output of the native heart, which requires that the LVAD pump must be adjusted so that a proper balance between the blood contributed through the aortic valve and the pump is maintained. In this paper, our objective is to identify a critical value of the pump power to ensure that the LVAD pump does not take over the pumping function in the cardiovascular-pump system and share the ejected blood with left ventricle to help the heart to recover. In addition, hemodynamic often involves variability due to patients heterogeneity and the stochastic nature of cardiovascular system. The variability poses significant challenges to understand dynamic behaviors of the aortic valve and cardiac output. A generalized polynomial chaos (gPC) expansion is used in this work to develop a stochastic cardiovascular-pump model for efficient uncertainty propagation, from which it is possible to rapidly calculate the variance in the aortic valve opening duration and the cardiac output in the presence of variability. The simulation results show that the gPC based cardiovascular-pump model is a reliable platform that can provide useful information to understand the effect of LVAD pump on the hemodynamic of the heart.

q-bio.TO↗

arXiv2019

ECGNET: Learning where to attend for detection of atrial fibrillation with deep visual attention

The complexity of the patterns associated with Atrial Fibrillation (AF) and the high level of noise affecting these patterns have significantly limited the current signal processing and shallow machine learning approaches to get accurate AF detection results. Deep neural networks have shown to be very powerful to learn the non-linear patterns in the data. While a deep learning approach attempts to learn complex pattern related to the presence of AF in the ECG, they can benefit from knowing which parts of the signal is more important to focus during learning. In this paper, we introduce a two-channel deep neural network to more accurately detect AF presented in the ECG signal. The first channel takes in a preprocessed ECG signal and automatically learns where to attend for detection of AF. The second channel simultaneously takes in the preprocessed ECG signal to consider all features of entire signals. The model shows via visualization that what parts of the given ECG signal are important to attend while trying to detect atrial fibrillation. In addition, this combination significantly improves the performance of the atrial fibrillation detection (achieved a sensitivity of 99.53%, specificity of 99.26% and accuracy of 99.40% on the MIT-BIH atrial fibrillation database with 5-s ECG segments.)

q-bio.QM↗

arXiv2018

Class Augmented Semi-Supervised Learning for Practical Clinical Analytics on Physiological Signals

Computational analysis on physiological signals would provide immense impact for enabling automated clinical analytics. However, the class imbalance issue where negative or minority class instances are rare in number impairs the robustness of the practical solution. The key idea of our approach is intelligent augmentation of minority class examples to construct smooth, unbiased decision boundary for robust semi-supervised learning. This solves the practical class imbalance problem in anomaly detection task for computational clinical analytics using physiological signals. We choose two critical cardiac marker physiological signals: Heart sound or Phonocardiogram (PCG) and Electrocardiogram (ECG) to validate our claim of robust anomaly detection of clinical events under augmented class learning, where intelligent synthesis of minority class instances attempt to balance the class distribution. We perform extensive experiments on publicly available expert-labelled MIT-Physionet PCG and ECG datasets that establish high performance merit of the proposed scheme, and our scheme fittingly performs better than the state-of-the-art algorithms.

physics.med-ph↗

arXiv2018

Model Based Iterative Reconstruction With Spatially Adaptive Sinogram Weights for Wide-Cone Cardiac CT

With the recent introduction of CT scanners with large cone angles, wide coverage detectors now provide a desirable scanning platform for cardiac CT that allows whole heart imaging in a single rotation. On these scanners, while half-scan data is strictly sufficient to produce images with the best temporal resolution, acquiring a full 360 degree rotation worth of data is beneficial for wide-cone image reconstruction at negligible additional radiation dose. Applying Model-Based Iterative Reconstruction (MBIR) algorithm to the heart has shown to yield significant enhancement in image quality for cardiac CT. But imaging the heart in large cone angle geometry leads to apparently conflicting data usage considerations. On the one hand, in addition to using the fastest available scanner rotation speed, a minimal complete data set of 180 degrees plus the fan angle is typically used to minimize both cardiac and respiratory motion. On the other hand, a full 360 degree acquisition helps better handle the challenges of missing frequencies and incomplete projections associated with wide-cone half-scan data acquisition. In this paper, we develop a Spatially Adaptive sinogram Weights MBIR algorithm (SAW-MBIR) that is designed to achieve the benefits of both half and full-scan reconstructions in order to maximize temporal resolution over the heart region while providing stable results over the whole volume covered with the wide-area detector. Spatially-adaptive sinogram weights applied to each projection measurement in SAW-MBIR are designed to selectively perform backprojection from the full and half-scan portion of the sinogram based on both projection angle and reconstructed voxel location. We demonstrate with experimental results of SAW-MBIR applied to whole-heart cardiac CT clinical data that overall temporal resolution matches half-scan while full volume image quality is on par with full-scan MBIR.

eess.IV↗

arXiv2018

Knowledge-based automated planning with three-dimensional generative adversarial networks

We develop a knowledge-based automated planning (KBAP) pipeline that generates treatment plans using deep neural network architectures for predicting 3D doses. Our pipeline consisted of a generative adversarial network (GAN) to predict dose from a CT image followed by two optimization models to learn objective function weights and generate fluence-based plans, respectively. We investigated three different GAN models. The first two models predicted dose for each axial slice independently. One predicted dose as a RGB color map, while the other predicted a scalar value for dose directly. The third GAN model predicted scalar doses for the full 3D CT image at once, considering correlations between adjacent CT slices. For all models, we also investigated the impact of scaling the GAN predictions before optimization. Each GAN model was trained on 130 previously delivered oropharyngeal treatment plans. Performance was tested on 87 out-of-sample plans, by evaluating using clinical planning criteria and compared to their corresponding clinical plans. The best performing KBAP plans were generated with the 3D GAN, which predicted dose values followed by scaling. These plans satisfied close to 77% of all clinical criteria, compared to the clinical plans, which satisfied 64% of all criteria. Additionally, these KBAP plans satisfied the same criteria as the clinical plans 84% more frequently compared to the 2D GAN model using RGB dose prediction. The 3D GAN predictions were also more similar to the final plan compared to the other approaches, as it better captured the vertical dosimetric relationship between adjacent axial slices. The deliverable plans better captured implicit constraints associated with physical deliverability. Overall, our final plans had superior performance in satisfying clinical criteria and generated more realistic predictions compared to the previous state-of-the-art.

physics.med-ph↗

arXiv2018

Deep optical neural network by living tumour brain cells

The new era of artificial intelligence demands large-scale ultrafast hardware for machine learning. Optical artificial neural networks process classical and quantum information at the speed of light, and are compatible with silicon technology, but lack scalability and need expensive manufacturing of many computational layers. New paradigms, as reservoir computing and the extreme learning machine, suggest that disordered and biological materials may realize artificial neural networks with thousands of computational nodes trained only at the input and at the readout. Here we employ biological complex systems, i.e., living three-dimensional tumour brain models, and demonstrate a random neural network (RNN) trained to detect tumour morphodynamics via image transmission. The RNN, with the tumour spheroid as a three-dimensional deep computational reservoir, performs programmed optical functions and detects cancer morphodynamics from laser-induced hyperthermia inaccessible by optical imaging. Moreover, the RNN quantifies the effect of chemotherapy inhibiting tumour growth. We realize a non-invasive smart probe for cytotoxicity assay, which is at least one order of magnitude more sensitive with respect to conventional imaging. Our random and hybrid photonic/living system is a novel artificial machine for computing and for the real-time investigation of tumour dynamics.

physics.med-ph↗

arXiv2018

Technical Note: A Novel Electromagnetic-Tracked Scintillation Dosimeter for Accurate In Vivo Dosimetry in HDR Brachytherapy

Since large dose are administrated to the patient in brachytherapy treatment, ensuring the right dose is delivered is highly critical. This study presents a first step in solving the standing issue of accurately knowing the dosimeter position at all time during in vivo dosimetry. In this work, an energy independent dosimeter, namely plastic scintillation detector, is coupled to an electromagnetic (EM) sensor having sub-mm positional accuracy for real-time tracking of the dosimeter position. However, adding an EM sensor adds materials in the path to the scintillator and thus could potentially perturb the dose measurements.To confirm the perturbation presence, 4 different sensors were placed in front of the scintillator so the radiation does not arrive to it directly. Variation of the distance between the sensor and the scintillator was used to quantify the effect on the signal at 0° and 90°.To test the dependence for each sensor, the signal measurement were taken from 0° to 90° with 10$°$ increment. The 5PCBDOF sensor showed an increased signal of almost $20{\%}$ with increasing beam angle. Sensor 5DOF, 5DOFthin and 6DOFshowed no significant angle dependance.The 6DOF and 5DOFthin sensor's cable revealed no extra signal attenuation. The latter gives a smaller overall attenuation. Therefore, the 5DOFthin is chosen to be part of the novel dosimeter construction. It has a jitter error of $\pm$ 0.06 mm and a reproductibility of$ {\pm}$ 0.008 mm. In the optimal operating range, the average positional uncertainty is less than 0.2 mm. Average angle errors are at most of 1.1$°$. It is feasible to integrate an EM tracking sensor to an energy independent plastic scintillation dosimeter with minimal impact to the collected signal as well as sufficient positional accuracy to keep dose uncertainty below 5${\%}$.

physics.med-ph↗

arXiv2018

Portable Low-cost MRI System based on Permanent Magnets/Magnet Arrays

Portable low-cost MRI systems have the potential to enable point-of-care and timely MRI diagnosis, and to make this imaging modality available to routine scans and to underdeveloped areas. With simplicity, no maintenance, no power consumption, and low cost, permanent magnets or arrays are attractive to use as a source of magnetic field to realize portability and low cost for a scanners. However, when taking the Fourier imaging approach and using linear gradient fields, homogeneous fields are required, thus either a bulky magnet is needed, or the imaging volume is too small to image an organ if the magnet is scaled down. Recently, with the progress on image reconstruction based on non-linear gradient field, field patterns without spatial-linearity can be used as spatial encoding magnetic fields to encode MRI signals for imaging. As a result, the requirements for the homogeneity of the field can be relaxed, which allows permanent magnets(arrays) with reduced sizes, reduced weight to image bigger volumes covering organs such as a head. It offers chances to construct a truly portable low-cost MRI scanner. For this exciting potential application, permanent magnets(arrays) have attracted increased attention. A magnet(array) is strongly associated with the imaging volume, reconstruction methods, and RF excitation and coils, etc. through field patterns and homogeneity. This paper offers a review on permanent magnets(arrays) of different kinds, especially those can be used for spatial encoding towards the development of portable and low-cost MRI systems. It is aimed to familiarize the readers with relevant knowledge, literature, and the latest updates of the development on permanent magnets for MRI. Perspectives on and challenges of using permanent magnets to supply a patterned magnetic field, without spatial-linearity nor high homogeneity, for reconstruction in a portable setup are discussed.

physics.med-ph↗

arXiv2018

Optical detection of fractal dimensions of MUC13 stained pancreatic tissues for cancer diagnostics

Transmission intensity of a thin tissue sample produce intensity distribution that is proportional to the refractive index pattern in the thin slice, in turn reflects mass density pattern, and using the intensity pattern, fractal dimension can be calculated. In this paper, we report fractal dimension analyses of MUC13 stained pancreatic cancer tissues. Pancreatic cancer is deadliest due to its physical orientation, and no prominent chemical change till the late stages of cancer. The presence of pancreatic cancer can be detected using MUC13, however this fails to explain about the stages of cancer. Here we studied MUC13 expressed cancer tissues and observe their cancer stages by fractal dimension of light transmission intensity. Our results show that an early stage of pancreatic cancer for MUC13 expressed tissue is accurately distinguished by fractal dimension analysis. Further, their application to cancer diagnostics is also discussed in detail.

physics.med-ph↗

arXiv2018

Biosensing-by-learning Direct Targeting Strategy for Enhanced Tumor Sensitization

Objective:We propose a novel iterative-optimizationinspired direct targeting strategy (DTS) for smart nanosystems, which harness swarms of externally manipulable nanoswimmers assembled by magnetic nanoparticles (MNPs) for knowledgeaided tumor sensitization and targeting. We aim to demonstrate through computational experiments that the proposed DTS can significantly enhance the accumulation of MNPs in the tumor site, which serve as a contrast agent in various medical imaging modalities, by using the shortest possible physiological routes and with minimal systemic exposure. Methods: The epicenter of a tumor corresponds to the global maximum of an externally measurable objective function associated with an in vivo tumor-triggered biophysical gradient; the domain of the objective function is the tissue region at a high risk of malignancy; swarms of externally controllable magnetic nanoswimmers for tumor sensitization are modeled as the guess inputs. The objective function may be resulted from a passive phenomenon such as reduced blood flow or increased kurtosis of microvasculature due to tumor angiogenesis; otherwise, the objective function may involve an active phenomenon such as the fibrin formed during the coagulation cascade activated by tumortargeted "activator" nanoparticles. Subsequently, the DTS can be interpreted from the iterative optimization perspective: guess inputs (i.e., swarms of nanoswimmers) are continuously updated according to the gradient of the objective function in order to find the optimum (i.e., tumor) by moving through the domain (i.e., tissue under screening). Along this line of thought, we propose the computational model based on the gradient descent (GD) iterative method to describe the GD-inspired DTS, which takes into account the realistic in vivo propagation scenario of nanoswimmers.

physics.med-ph↗

arXiv2019

A novel approach to medical radioisotope production using inverse kinematics: a successful production test of the theranostic radionuclide 67Cu

A novel method for the production of important medical radioisotopes has been developed. The approach is based on performing the nuclear reaction in inverse kinematics, namely sending a heavy-ion beam of appropriate energy on a light target (e.g. H, d, He) and collecting the isotope of interest. In this work, as a proof-of-concept, we studied the production of the theranostic radionuclide 67Cu (T 1/2 =62 h) via the reaction of a 70Zn beam at 15 MeV/nucleon with a hydrogen gas target. The 67Cu radionuclide, alongside other coproduced isotopes, was collected after the gas target on an Al catcher foil and their radioactivity was measured by off-line γ-ray analysis. After 36 h from the end of the irradiation, apart from the product of interest 67Cu, the main radioimpurity coming from the 70Zn+p reaction was 69mZn (T 1/2 =13.8 h) that can be reduced by further radio-cooling. Moreover, along with the radionuclide of interest produced in inverse kinematics, the production of additional radioisotopes is possible by making use of the forward-focused neutrons from the reaction and letting them interact with a secondary target. A preliminary successful test of this concept was realized in the present study. The main requirement to obtain activities appropriate for preclinical studies is the development of high-intensity heavy-ion primary beams.

physics.med-ph↗

arXiv2019

An Irregular-shaped Ring-Pair Magnet Array with a Monotonic Field Gradient for 2D Head Imaging in Low-field Portable MRI

We present a design and the optimization of an irregular-shaped ring-pair magnet array that generates a 1D monotonic field pattern for 2D head imaging in a low-field portable MRI system. The magnet rings are discretized into fan-shaped ring segments with varying outer diameters for the design and optimization. Besides, the inner radii of ring-pairs are tapered from outside in to provide the controlled field inhomogeneity. Genetic algorithm (GA) was used, and a current model for a fan-shaped ring segment was derived to have a fast forward calculation in the optimization. A monotonic field pattern is successfully obtained along the x-direction in a cylindrical field of view (FoV), with a relatively strong magnetic field (132.98 mT) and the homogeneity of 151840 ppm. The proposed array was further evaluated by applying its field as a spatial encoding magnetic field (SEM) for imaging numerically. Due to the field monotonicity, the reconstructed image by applying the fields of the proposed array shows clearer features (a higher structural similarity index) with a reduced error rate compared to that using a sparse dipolar Halbach array. The proposed magnet array is a promising alternative to supply SEM for imaging in a permanent-magnet-based low-field portable MRI system.

physics.med-ph↗

arXiv2019

Evaluation of central corneal thickness in keratoconus and normal corneas during air puff indentation

Purpose: This study aimed to investigate the actual changes of central corneal thickness (CCT) in keratoconus and normal corneas during air puff indentation, by using corneal visualization Scheimpflug technology (Corvis ST). Methods: A total of 32 keratoconic eyes and 46 normal eyes were included in this study. Three parameters of CCTinitial, CCTfinal and CCTpeak were selected to represent the CCT at initial time, final time and highest corneal concavity, respectively, during air puff indentation. Wilcoxon signed rank test (paired sample test) was used to assess the differences between these 3 parameters in both keratoconus and normal groups. Univariate linear regression analysis was performed to determine the effect of CCTinitial on CCTpeak and CCTfinal, as well as the impact of air puff force on CCT in each group. Receiver operating characteristic (ROC) curves were constructed to evaluate the discriminative ability of the 3 parameters. Results: The results demonstrated that CCTpeak and CCTfinal were significantly decreased (p<0.01) compared to CCTinitial in both keratoconus and normal groups. Regression analysis indicated a significant positive correlation between CCTpeak and CCTinitial in normal cornea group (R2=0.337, p<0.01), but not in keratoconus group (R2=0.029, p=0.187). Likewise, regression models of air puff force and CCT revealed the different patterns of CCT changes between keratoconus and normal cornea groups. Furthermore, ROC curves showed that CCTpeak exhibited the greatest AUC (area under ROC curve) of 0.940, with accuracy, sensitivity and specificity of 94.9%, 87.5% and 100%, respectively. Conclusions: CCT may change during air puff indentation, and is significantly different between keratoconus and normal cornea groups. The changing pattern is useful for the diagnosis of keratoconus, and lays the foundation for corneal biomechanics.

physics.med-ph↗