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arXiv2018

Sparse-View CT Reconstruction via Convolutional Sparse Coding

Traditional dictionary learning based CT reconstruction methods are patch-based and the features learned with these methods often contain shifted versions of the same features. To deal with these problems, the convolutional sparse coding (CSC) has been proposed and introduced into various applications. In this paper, inspired by the successful applications of CSC in the field of signal processing, we propose a novel sparse-view CT reconstruction method based on CSC with gradient regularization on feature maps. By directly working on whole image, which need not to divide the image into overlapped patches like dictionary learning based methods, the proposed method can maintain more details and avoid the artifacts caused by patch aggregation. Experimental results demonstrate that the proposed method has better performance than several existing algorithms in both qualitative and quantitative aspects.

physics.med-ph↗

arXiv2018

Universal atrial coordinates applied to visualisation, registration and construction of patient specific meshes

Integrating spatial information about atrial physiology and anatomy in a single patient from multimodal datasets, as well as generalizing these data across patients, requires a common coordinate system. In the atria, this is challenging due to the complexity and variability of the anatomy. We aimed to develop and validate a universal atrial coordinate system for the following applications: combination and assessment of multimodal data; comparison of spatial data across patients; 2D visualization; and construction of patient specific geometries to test mechanistic hypotheses. Left and right atrial LGE-MRI data were segmented and meshed. Two coordinates were calculated for each atrium by solving Laplace's equation, with boundary conditions assigned using five landmark points. The coordinate system was used to map spatial information between atrial meshes, including atrial anatomic structures and fibre directions from a reference geometry. Average error in point transfer from a source mesh to a destination mesh and back again was less than 6% of the average mesh element edge length. Scalar mapping from electroanatomic to MRI geometries was compared to an affine registration technique (mean difference in bipolar voltage: <10% of voltage range). Patient specific meshes were constructed using UACs and phase singularity density maps from arrhythmia simulations were visualised in 2D. We have developed a universal atrial coordinate system allowing automatic registration of imaging and electroanatomic mapping data, 2D visualisation, and patient specific model creation, using just five landmark points.

physics.med-ph↗

arXiv2018

Lesion Focused Super-Resolution

Super-resolution (SR) for image enhancement has great importance in medical image applications. Broadly speaking, there are two types of SR, one requires multiple low resolution (LR) images from different views of the same object to be reconstructed to the high resolution (HR) output, and the other one relies on the learning from a large amount of training datasets, i.e., LR-HR pairs. In real clinical environment, acquiring images from multi-views is expensive and sometimes infeasible. In this paper, we present a novel Generative Adversarial Networks (GAN) based learning framework to achieve SR from its LR version. By performing simulation based studies on the Multimodal Brain Tumor Segmentation Challenge (BraTS) datasets, we demonstrate the efficacy of our method in application of brain tumor MRI enhancement. Compared to bilinear interpolation and other state-of-the-art SR methods, our model is lesion focused, which is not only resulted in better perceptual image quality without blurring, but also more efficient and directly benefit for the following clinical tasks, e.g., lesion detection and abnormality enhancement. Therefore, we can envisage the application of our SR method to boost image spatial resolution while maintaining crucial diagnostic information for further clinical tasks.

eess.IV↗

arXiv2018

Super-resolution MRI through Deep Learning

Magnetic resonance imaging (MRI) is extensively used for diagnosis and image-guided therapeutics. Due to hardware, physical and physiological limitations, acquisition of high-resolution MRI data takes long scan time at high system cost, and could be limited to low spatial coverage and also subject to motion artifacts. Super-resolution MRI can be achieved with deep learning, which is a promising approach and has a great potential for preclinical and clinical imaging. Compared with polynomial interpolation or sparse-coding algorithms, deep learning extracts prior knowledge from big data and produces superior MRI images from a low-resolution counterpart. In this paper, we adapt two state-of-the-art neural network models for CT denoising and deblurring, transfer them for super-resolution MRI, and demonstrate encouraging super-resolution MRI results toward two-fold resolution enhancement.

physics.med-ph↗

arXiv2018

Development of Carbon-Fiber Electrodes Modified for Electrochemical ATP Detection

Adenosine triphosphate (ATP) is a highly energetic molecule used as a coenzyme in the transfer of chemical energy required for the continuation of cellular metabolism. In addition, extracellular ATP has an important role in cell-to-cell communication in immune, nervous and vascular systems. ATP is detected mainly using commercially available luciferin-luciferase kits and this method does not allow local measurement. In this study, first carbon-fiber microelectrodes capable of local detection were fabricated and then, the surface of these electrodes were modified with enzymes for detection of non-electroactive ATP molecules.

physics.med-ph↗

arXiv2018

The NOVO project neutron detection for real-time range verification in proton therapy, A Monte Carlo feasibility study

Uncertainties in the proton range in tissue during proton therapy limit the precision in treatment delivery. These uncertainties result in expanded treatment margins, thereby increasing radiation dose to healthy tissue. Real-time range verification techniques aim to reduce these uncertainties in order to take full advantage of the finite range of the primary protons. In this paper, we propose a novel concept for real-time range verification based on detection of secondary neutrons produced in nuclear interactions during proton therapy. The proposed detector concept is simple; consisting of a hydrogen-rich converter material followed by two charged particle tracking detectors, mimicking a proton recoil telescopic arrangement. Neutrons incident on the converter material are converted into protons through elastic and inelastic (n,p) interactions. The protons are subsequently detected in the tracking detectors. The information on the direction and position of these protons is then utilized in a new reconstruction algorithm to estimate the depth distribution of neutron production by the proton beam, which in turn is correlated with the primary proton range. In this paper, we present the results of a Monte Carlo feasibility study and show that the proposed concept could be used for real-time range verification with millimetric precision in proton therapy.

physics.med-ph↗

arXiv2018

On tradeoffs between treatment time and plan quality of volumetric-modulated arc therapy with sliding-window delivery

The purpose of this study is to give an exact formulation of optimization of volumetric-modulated arc therapy (VMAT) with sliding-window delivery, and to investigate the plan quality effects of decreasing the number of sliding-window sweeps made on the 360-degree arc for a faster VMAT treatment. In light of the exact formulation, we interpret an algorithm previously suggested in the literature as a heuristic method for solving this optimization problem. By first making a generalization, we suggest a modification of this algorithm for better handling of plans with fewer sweeps. In a numerical study involving one prostate and one lung case, plans with varying treatment times and number of sweeps are generated. It is observed that, as the treatment time restrictions become tighter, fewer sweeps may lead to better plan quality. Performance of the original and the modified version of the algorithm is evaluated in parallel. Applying the modified version results in better objective function values and less dose discrepancies between optimized and accurate dose, and the advantages are pronounced with decreasing number of sweeps.

physics.med-ph↗

arXiv2018

A High Accuracy and High Sensitivity System Architecture for Electrical Impedance Tomography System

A high accuracy and high sensitivity system architecture is proposed for the read-out circuit of electrical impedance tomography system-on-chip. The switched ratiometric technique is applied in the proposed architecture. The proposed system architecture minimizes the device noise by processing signals from both read-out electrodes and the stimulus. The quantized signals are post-processed in the digital processing unit for proper signal demodulation and impedance ratio calculation. Our proposed architecture improves the sensitivity of the read-out circuit, cancels out the gain fluctuations in the system, and overcomes the effects of motion artifacts on measurements.

physics.med-ph↗

arXiv2018

Active Decoupling of Transmit and Receive Coils for Full-Duplex MRI

Objective: Concurrent excitation and acquisition in MRI is a method to acquire MRI signal from tissues with very short transverse relaxation time. Since transmit power is many orders of magnitude larger than receive signal, a weak coupling dominates the MR signal during CEA. Thus, appropriate decoupling between transmit and receive coils is required. In this study, two controllable decoupling designs are investigated for achieving isolation between coils. Methods: A modified version of isolation concept used in the full-duplex radios in communication systems is applied to acquire MRI signal using CEA. In our new method, a small copy of RF transmit signal is attenuated and delayed to generate the same coupling signal which is available in the receiver coil. Then it is subtracted from the receive signal to detect the MRI signal. The proposed decoupling method is developed and implemented in two designs: Semi-Automatic and Fully-Automatic Controllable Decoupling Designs. Results: Using Semi-Automatic Controllable Decoupling Design, decoupling of more than 75 dB is achieved. Fully-Automatic Controllable Decoupling Design provides more than 100 dB decoupling between coils which is good enough for detecting MRI signals during excitation from tissues with very short transverse relaxation time. Conclusion: This study shows feasibility of applying full duplex electronics to decouple transmit and receive coils for CEA in a clinical MRI system. Significance: These designs can automatically tune the cancellation circuit and it is a potential tool for recovering signal from tissues with very short T2 in clinical MR systems with a minor hardware modification.

physics.med-ph↗

arXiv2018

Bio-inspired method based on bone architecture to optimize the structure of mechanical workspieces

Nowadays, additive manufacturing processes greatly simplify the production of openwork workpiece providing new opportunities for workpieces design. Based on Nature knowledge, a new bio-inspired workpiece structural optimization approach is presented in this paper. This approach is derived from bones structure. The aim of this method is to reduce the workpiece weight maintaining an acceptable resistance. Like in bones, the porosity of the part to optimize was controlled by a bio-inspired method as function of the local stress field. Shape, size and orientation of the porosities were derived from bone structure; two main strategies were used: one inspired of avian species and other inspired of terrestrial mammalian. Subsequently, to validate this method, an experimental test was carried out for comparing a topological optimization and the proposed bio-inspired designs. This test was conducted on a beam part in 2.5D subjected to a static three-point bending with 65% of density. Three beams were manufactured by 3D metal printing: two bio-inspired beams (terrestrial mammalian and avian species) and the last designed using a topological optimization method. Experimental test results demonstrated the usefulness of the proposed method. This bio-inspired structural optimization approach opens up new prospects in design of openwork workpiece.

physics.med-ph↗

arXiv2018

Skin optical attenuation coefficient modulation with arterial pulsation

Video plethysmographic (vPPG) signals arise from subtle color modulations in reflected light. vPPG signals can be used to monitor vital signs remotely in a noninvasive manner. We have used optical coherence tomography (OCT) imaging to find whether vPPG signals may result from skin optical properties being modulated by arterial transmural pressure propagation. Our results confirm this hypothesis and show that the attenuation coefficient of even the most external, non-vascularized skin, changes at the same frequency as the arterial pulsation.

physics.bio-ph↗

arXiv2018

Low dose CT reconstruction assisted by an image manifold prior

X-ray Computed Tomography (CT) is an important tool in medical imaging to obtain a direct visualization of patient anatomy. However, the x-ray radiation exposure leads to the concern of lifetime cancer risk. Low-dose CT scan can reduce the radiation exposure to patient while the image quality is usually degraded due to the appearance of noise and artifacts. Numerous studies have been conducted to regularize CT image for better image quality. Yet, exploring the underlying manifold where real CT images residing on is still an open problem. In this paper, we propose a fully data-driven manifold learning approach by incorporating the emerging deep-learning technology. An encoder-decoder convolutional neural network has been established to map a CT image to the inherent low-dimensional manifold, as well as to restore the CT image from its corresponding manifold representation. A novel reconstruction algorithm assisted by the leant manifold prior has been developed to achieve high quality low-dose CT reconstruction. In order to demonstrate the effectiveness of the proposed framework, network training, testing, and comprehensive simulation study have been performed using patient abdomen CT images. The trained encoder-decoder CNN is capable of restoring high-quality CT images with average error of ~20 HU. Furthermore, the proposed manifold prior assisted reconstruction scheme achieves high-quality low-dose CT reconstruction, with average reconstruction error of < 30 HU, more than five times and two times lower than that of filtered back projection method and total-variation based iterative reconstruction method, respectively.

physics.med-ph↗

arXiv2018

Diffusive Mobile MC for Controlled-Release Drug Delivery with Absorbing Receiver

Nanoparticle drug carriers play an important role in facilitating efficient targeted drug delivery, i.e., improving treatment success and reducing drug costs and side effects. However, the mobility of nanoparticle drug carriers poses a challenge in designing drug delivery systems. Moreover, healing results critically depend on the rate and time duration of drug absorption. Therefore, in this paper, we aim to design a controlled-release drug delivery system with a mobile drug carrier that minimizes the total amount of released drugs while ensuring a desired rate of drug absorption during a prescribed time period. We model the mobile drug carrier as a mobile transmitter, the targeted diseased cells as an absorbing receiver, and the channel between the transceivers as a time-variant channel since the carrier mobility results in a time-variant absorption rate of the drug molecules. Based on this, we develop a molecular communication (MC) framework to design the controlled-release drug delivery system. In particular, we develop new analytical expressions for the mean, variance, probability density function, and cumulative distribution function of the channel impulse response (CIR). Equipped with the statistical analysis of the CIR, we design and evaluate the performance of the controlled-release drug delivery system. Numerical results show significant savings in the amount of released drugs compared to a constant-release rate design and reveal the necessity of accounting for drug carrier mobility for reliable drug delivery.

physics.med-ph↗

arXiv2018

Comparative Study of Simulated Nebulized and Spray Particle Deposition in Chronic Rhinosinusitis Patients

Introduction: Topical intranasal drugs are widely prescribed for Chronic Rhinosinusitis (CRS), although delivery can vary with device type and droplet size. The study objective was to compare nebulized and sprayed droplet deposition in the paranasal sinuses and ostiomeatal complex (OMC) across multiple droplet sizes in CRS patients using computational fluid dynamics (CFD). Methods: Three-dimensional models of sinonasal cavities were constructed from computed tomography (CT) scans of three subjects with CRS refractory to medical therapy using imaging software. Assuming steady-state inspiratory airflow at resting rate, CFD was used to simulate 1-120 μm sprayed droplet deposition in the left and right sinuses and OMC with spray nozzle positioning as in current nasal spray use instructions. Zero-velocity nebulization simulations were performed for 1-30 μm droplet sizes, maximal sinus and OMC deposition fractions (MSDF) were obtained, and sizes that achieved at least 50% of MSDF were identified. Nebulized MSDF was compared to sprayed droplet deposition. We also validated CFD framework through in vitro experiments. Results: Among nebulized droplet sizes, 11-14 μm droplets achieved at least 50% of MSDF in all six sinonasal cavities. Five of six sinonasal cavities had greater sinus and OMC deposition with nebulized droplets than with sprayed droplets at optimal sizes. Conclusions: Nebulized droplets may target the sinuses and OMC more effectively than sprayed particles at sizes achieving best deposition. Further studies are needed to confirm our preliminary findings. Several commercial nasal nebulizers have average particle sizes outside the optimal nebulized droplet size range found here, suggesting potential for product enhancement.

physics.med-ph↗

arXiv2018

Comparative Analysis of the Main Nasal Cavity and the Paranasal Sinuses in Chronic Rhinosinusitis: An Anatomic Study of Maximal Medical Therapy

Objective: Minimal literature exists investigating changes in inflammation with respect to the main nasal cavity (MNC) and paranasal sinuses (PS) before and after maximal medical therapy (MMT) for chronic rhinosinusitis (CRS). We hypothesized that MMT produces a differential level of change in the volume of air space in the MNC and PS, and that resolution of mucosal disease associated with the osteomeatal complex (OMC) influences clinical response to MMT. Study Design: Retrospective study of 12 pre- and post-MMT sinus-CT scans from 6 subjects with CRS, of which three succeeded and three failed therapy. Methods: Mimics was used to create 3D-models of the MNC and PS, and then analysis of the models was performed. Results: Mean differences in the sinonasal volume were 7866.5+/-4339.9 mm3 and 17869.10+/-19472.70 mm3, amongst the failures and successes, respectively. There is wide variability in the contribution of PS and MNC airspace volume change to the overall change in the sinonasal volume. In two subjects, the direction of volume change in the MNC and PS diverged with respect to the overall change in volume. Line-of-sight analysis demonstrated that successful responders to MMT had more patent MNC with direct access to the OMC. Conclusions: There is a differential contribution to sinonasal, airspace volume change after MMT, when comparing the MNC and PS. Response to MMT may not be solely attributable to PS change and may include a function of MNC change. Line-of-sight models suggest that direct access to the OMC may impact response to MMT.

physics.med-ph↗

arXiv2018

Prior Knowledge Helps Improve Beam Angle Optimization Efficiency in Radiotherapy Planning

One of the grand challenges in intensity-modulated radiotherapy (IMRT) planning is to optimize the beam angles in a reasonable computation time. To demonstrate the value of prior knowledge on improving the efficiency of beam angle optimization (BAO) in IMRT, this study proposes a modified genetic algorithm (GA) by incorporating prior knowledge into the evolutionary procedure. In the so-called nowGABAO (kNOWledge guided GA for BAO) approach, two types of prior knowledge are incorporated into the general flowchart of GA: (1) beam angle constraints, which define the angle scopes through which no beam is allowed to pass, and (2) plan templates, which define the most possible beam configurations suitable to the studied case and are used to guide the evolutionary progress of GA. Optimization tasks with different prior knowledge were tested, and the results on two complicated clinical cases at the head-and-neck and lung regions show that suitable defining of angle constraints and plan templates may obviously speedup the optimization. Furthermore, a moderate number of quite bad templates produce negligible influence on the degradation of optimization efficiency. In conclusion, prior knowledge helps improve the beam angle optimization efficiency, and can be suitably incorporated by a GA. It is resistant to the inclusion of bad templates.

physics.med-ph↗

arXiv2018

A simple algorithm to implement jump boundary conditions within the lattice Boltzmann method

An algorithm is proposed to implement unsteady jump boundary conditions, presenting discontinuity in physical quantities, within the lattice Boltzmann method (LBM). This is useful to tackle problems involving mass or heat transfer through interfaces or membranes that exhibit resistance to transport. The algorithm is simple to implement within an existing LBM based code that computes diffusion and advection of a scalar field, either temperature or solute concentration. Analytical solutions are recovered numerically with acceptable accuracy for the limiting case of a planar membrane. The algorithm is adapted for moving-free boundaries and adopting arbitrary geometries when combined with the immersed boundary method. This is demonstrated by simulating controlled release of a solute from a stationary rigid particle and from a moving deformable particle.

physics.comp-ph↗

arXiv2018

Optical Wireless Cochlear Implants

In the present contribution, we introduce a wireless optical communication-based system architecture which is shown to significantly improve the reliability and the spectral and power efficiency of the transcutaneous link in cochlear implants (CIs). We refer to the proposed system as optical wireless cochlear implant (OWCI).In order to provide a quantified understanding of its design parameters, we establish a theoretical framework that takes into account the channel particularities, the integration area of the internal unit, the transceivers misalignment, and the characteristics of the optical units. To this end, we derive explicit expressions for the corresponding average signal-to-noise-ratio, outage probability, ergodic spectral efficiency and capacity of the transcutaneous optical link (TOL). These expressions are subsequently used to assess the dependence of the TOL's communication quality on the transceivers design parameters and the corresponding channels characteristics. The offered analytic results are corroborated with respective results from Monte Carlo simulations. Our findings reveal that OWCI is a particularly promising architecture that drastically increases the reliability and effectiveness of the CI TOL, whilst it requires considerably lower transmit power compared to the corresponding widely-used radio frequency (RF) solution.

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