Motion Deformation Research Articles

Fast Deformable Image Registration for Real-Time Target Tracking During Radiation Therapy Using Cine MRI and Deep Learning

We developed a deep learning (DL) model for fast deformable image registration using 2-dimensional sagittal cine magnetic resonance imaging (MRI) acquired during radiation therapy and evaluated its potential for real-time target tracking compared with conventional image registration methods. Our DL model uses a pair of cine MRI images as input and provides a motion vector field (MVF) as output. The MVF is then applied to align the input images. A retrospective study was conducted to train and evaluate our model using cine MRI data from patients undergoing treatment for abdominal and thoracic tumors. For each treatment fraction, MR-linear accelerator delivery log files, tracking videos, and cine image files were analyzed. Individual MRI frames were temporally sampled to construct a large set of image registration pairs used to evaluate multiple methods. The DL model was optimized using 5-fold cross validation, and model outputs (transformed images and MVFs) using test set images were saved for comparison with 3 conventional registration methods (affine, b-spline, and demons). Evaluation metrics were 3-fold: (1) registration error, (2) MVF stability (both spatial and temporal), and (3) average computation time. We analyzed >21 hours of cine MRI (>629,000 frames) acquired during 86 treatment fractions from 21 patients. In a test set of 10,320 image registration pairs, DL registration outperformed conventional methods in both registration error (affine, b-spline, demons, DL; root mean square error: 0.067, 0.040, 0.036, 0.032; paired t test demons vs DL: t[20]=4.2, P < .001) and computation time per frame (51, 1150, 4583, 8 ms). Among deformable methods, spatial stability of resulting MVFs was comparable; however, the DL model had significantly improved temporal consistency. DL-based image registration can leverage large-scale MR cine data sets to outperform conventional registration methods and is a promising solution for real-time deformable motion estimation in radiation therapy.

Deep Learning-Driven Real-Time Liver Tumor Localization via Optical Surface Imaging and Biomechanical Modeling

<h3>Purpose/Objective(s)</h3> To track liver tumors in real time via a deep learning-based framework, by exploring patient-specific correlations between patients' body surface maps and internal anatomical motion. <h3>Materials/Methods</h3> Real-time liver tumor localization was achieved via deformable registration-based motion estimation, in a two-step framework: (a) liver boundary motion estimation via deep features learnt from optical body surface imaging; and (b) intra-liver motion propagation via deep learning-based biomechanical modeling from liver boundary motion. In step (a), a patient-specific, fully-connected convolutional neural network (SurfCNN) predicts the motion of nodes residing on the boundary of a patient-specific reference liver mesh, by extracting saliency features from curvature entropy maps derived out of real-time optical body surface images. Subsequently, step (b) uses the liver boundary motion solved in (a) to infer intra-liver tumor motion, using a U-Net-style model (UNet-Bio) inspired by biomechanical modeling via finite element analysis (FEA). The cascaded framework was evaluated using a dataset of 8 liver cancer patients from our institute. Each patient had a 10-phase, contrast-enhanced 4D-CT set with liver and liver tumor contoured. To generate sufficient motion variations to train and test the patient-specific SurfCNN model, we augmented the dataset of each patient by simulating varying real-time motion patterns and magnitudes. The augmentation was achieved via a principal component analysis-based statistical model, expanding the nine inter-phase deformation-vector-fields (DVFs) of each 4D-CT into 1,728 different motion states including both rigid and deformable motion. Optical surface imaging was simulated from each motion state via extracted body surface maps, while corresponding liver boundary motion was derived via the augmented DVFs to supervise the training of SurfCNN. UNet-Bio, on the other hand, could be trained as a population-based model using supervisions from intra-liver DVFs solved by FEA-based biomechanical modeling. The liver tumor localization accuracy was assessed through Dice similarity coefficient (DSC), Hausdorff distance (HD), and center-of-mass-error (COME), by comparing the augmentation DVF-propagated ‘ground-truth' liver tumor volumes against ones deformed by the cascaded framework. <h3>Results</h3> Tested using 576 unseen scenarios for each patient case, the cascaded SurfCNN and UNet-Bio scheme can localize liver tumors to 0.791(mean) ± 0.141 (s.d.) in DSC, 3.6 ± 1.9 mm in HD, and 2.3 ± 1.7 mm in COME. In comparison, the prior reference image without deformable registration yielded 0.534 ± 0.277 in DSC, 8.3 ± 6.1 mm in HD and 6.8 ± 5.8 mm in COME. The whole model inference time was less than 100 milliseconds, satisfying the temporal constraints of real-time imaging. <h3>Conclusion</h3> The deep learning-based framework allows accurate 3D liver tumor tracking in real-time via non-ionizing, marker-less, and high frame-rate optical surface imaging.

Real-Time Motion Monitoring Using Orthogonal Cine MRI during MR-Guided Radiation Therapy for Liver Cancer

<h3>Purpose/Objective(s)</h3> This work investigates the feasibility of real-time motion monitoring (MM) using orthogonal cine MRI during MR guided Adaptative Radiation Therapy (MRgART) for liver tumors that are normally invisible on cine MRI with no contrast agent. <h3>Materials/Methods</h3> An investigational package, Motion Monitoring Research Package (MMRP), was developed to measure real-time target motion between orthogonal cine MRI and the daily baseline 3D MRI. The MMRP package was evaluated on 8 liver cancer patients treated on a 1.5T MR-Linac. For all patients, a 3D mid-position (MidP) image derived from a daily 4D MRI was used to delineate a sub-liver volume (SLV) encompassing the tumor. During the same session, real-time 2D T2/T1-weighted bFFE cine MRI were collected with free breathing during MRgART. The cine images were acquired with a temporal resolution of 200 ms interleaved between coronal and sagittal orientations. MMRP workflow included two steps: (1) creating a 2D template in each orientation using cine data acquired over the first 12 s, followed by a rigid registration between the combined templates and the MidP image over the SLV region; (2) measuring the SLV motion by registering the rest of the 2D cine series with the corresponding template. To evaluate the MM accuracy from MMRP, the ground-truth contours on cine MRI were created manually. Common visible vessels and segments of liver boundaries in proximity to the tumor were used as anatomical landmarks for reproducible delineations on both the 3D and cine images. MM accuracy was measured using the standard deviation of the error (SDE) to compare the center of mass position of the ground-truth and the monitored motion. Maximum of tumor motion was assessed on 4D MRI. <h3>Results</h3> In 6 of the 8 cases, the assessed mean (range) centroid motions were 8.8 (4.6-13.8), 1.5 (0.7-2.8) and 3.2 (1.5-5) mm with mean SDE (range) values of 1.2 (0.7-1.6), 0.94 (0.4-1.7), and 1 (0.4-1.4) mm in superior inferior (SI), left right, and anterior posterior (AP) directions, respectively. The mean (range) of the maximum tumor motion from the 4D MRI was 8 (5-11) mm in SI direction, smaller than the centroid MM, indicating the importance of motion capture using the MM in real-time. For the remaining two cases, quantitative MM was challenging due to target deformation and large through-plane motion in the AP direction. <h3>Conclusion</h3> We have demonstrated the feasibility of accurate real-time MM of a liver tumor using a sub-liver volume as a tumor surrogate, based on real-time orthogonal cine MRI during MRgART. The approach overcomes difficulty due to the invisibility of liver tumor on the T2/T1-w 2D cine without using a contrast agent and the need of implanting radio-opaque clips or the approximation of the whole liver motion as the tumor motion. With further investigation with more patient data, the real-time MM technique may be implemented in MRgART to manage intrafraction liver motion.

Real-Time Internal Motion Monitoring during Radiation Therapy Using Cine MRI and Deep Learning

<h3>Purpose/Objective(s)</h3> Fast and accurate internal motion estimation is fundamental to enabling real-time MRI-guided radiation therapy (RT). We hypothesized that a deep learning (DL) model for fast deformable image registration using 2D sagittal cine MRI is feasible. Herein, we report performance of a DL-based image registration model as compared to conventional image registration methods. <h3>Materials/Methods</h3> Our DL model uses a pair of cine MRI images acquired using an MR-LINAC as inputs and outputs a motion vector field (MVF) which is applied to align the images. Cine delivery data for patients (pts) undergoing treatment for abdominal and thoracic tumors were retrospectively analyzed. Cine MRI scans were sampled, constructing a large set of image registration pairs capturing pt respiratory motion to train the DL model. Data were partitioned by pt using five-fold cross validation. Model outputs (transformed images and MVFs) on test set images were saved for comparison with three conventional registration methods (affine, b-spline, and demons). Performance was quantified via average registration error and computation time. Paired t-tests determined whether differences in performance were statistically significant. <h3>Results</h3> We analyzed >21 hours of cine MRI (>629,000 frames) acquired during 86 treatment fractions from 21 pts. In a test set of 10,320 image registration pairs, DL registration outperformed conventional methods in both registration error (affine, b-spline, demons vs. DL; RMSE: 0.067, 0.040, 0.036 vs. 0.032; paired t-test demons vs DL: t(20) = 4.2, p<0.001) and computation time per frame (51 ms, 1150 ms, 4583 ms vs. 8 ms). <h3>Conclusion</h3> DL-based image registration can leverage large-scale cine MRI datasets to learn to predict pt internal motion fields using a single reference and a single moving frame as inputs. Low registration error and fast computation speed indicate that DL-based registration is a promising solution for real-time deformable motion tracking in RT. Further inter-institutional validation is underway.

The kinematics and micro mechanism of creep in sand based on DEM simulations

In this study, biaxial shear and creep numerical simulations were carried out on both dense and loose sands based on discrete element method to examine the kinematics and micro mechanism of creep. The simulations qualitatively reproduced the macroscopic behavior of sand during shearing and creep. Contact forces, contact normals and voids were used to describe the evolution of internal structure. And three independent modes of contact motion, i.e., contact deformation (normal and tangential), Type4 rolling and rigid-body motion, were utilized to explore the contributions of contact motions to sample deformation. It is found that (1) the change of creep stage can be charactered by the anisotropy evolution of internal structure, and the increase of internal structure anisotropy would induce the rise of creep rate (i.e., the creep rupture); (2) contributions from different modes of contact motion to macro sand deformation during creep and shearing process is complex, that is, the contact deformation is the dominant mode in both shearing and creep process, whereas the contributions from tangential contact deformation during creep is complete opposite to its effect in shearing process, which leads to contract during creep; the possible reason for this is that the creep behavior is deformation-path dependent; the creep rupture can occur only under the condition which the strain localization is developed; (3) the contributions of normal and tangential contact deformation to volumetric stain are also different in strong and weak contact force chains; the deformation in weak contact force chain is more similar to the macro deformation, which means that the macro deformation is controlled by the weak contact force chain.

Context-Aware, Reference-Free Local Motion Metric for CBCT Deformable Motion Compensation.

Deformable motion is one of the main challenges to image quality in interventional cone beam CT (CBCT). Autofocus methods have been successfully applied for deformable motion compensation in CBCT, using multi-region joint optimization approaches that leverage the moderately smooth spatial variation motion of the deformable motion field with a local neighborhood. However, conventional autofocus metrics enforce images featuring sharp image-appearance, but do not guarantee the preservation of anatomical structures. Our previous work (DL-VIF) showed that deep convolutional neural networks (CNNs) can reproduce metrics of structural similarity (visual information fidelity - VIF), removing the need for a matched motion-free reference, and providing quantification of motion degradation and structural integrity. Application of DL-VIF within local neighborhoods is challenged by the large variability of local image content across a CBCT volume, and requires global context information for successful evaluation of motion effects. In this work, we propose a novel deep autofocus metric, based on a context-aware, multi-resolution, deep CNN design. In addition to the inclusion of contextual information, the resulting metric generates a voxel-wise distribution of reference-free VIF values. The new metric, denoted CADL-VIF, was trained on simulated CBCT abdomen scans with deformable motion at random locations and with amplitude up to 30 mm. The CADL-VIF achieved good correlation with the ground truth VIF map across all test cases with R2 = 0.843 and slope = 0.941. When integrated into a multi-ROI deformable motion compensation method, CADL-VIF consistently reduced motion artifacts, yielding an average increase in SSIM of 0.129 in regions with severe motion and 0.113 in regions with mild motion. This work demonstrated the capability of CADL-VIF to recognize anatomical structures and penalize unrealistic images, which is a key step in developing reliable autofocus for complex deformable motion compensation in CBCT.

Simulation of Random Deformable Motion in Soft-Tissue Cone-Beam CT with Learned Models.

Cone-beam CT (CBCT) is widely used for guidance in interventional radiology but it is susceptible to motion artifacts. Motion in interventional CBCT features a complex combination of diverse sources including quasi-periodic, consistent motion patterns such as respiratory motion, and aperiodic, quasi-random, motion such as peristalsis. Recent developments in image-based motion compensation methods include approaches that combine autofocus techniques with deep learning models for extraction of image features pertinent to CBCT motion. Training of such deep autofocus models requires the generation of large amounts of realistic, motion-corrupted CBCT. Previous works on motion simulation were mostly focused on quasi-periodic motion patterns, and reliable simulation of complex combined motion with quasi-random components remains an unaddressed challenge. This work presents a framework aimed at synthesis of realistic motion trajectories for simulation of deformable motion in soft-tissue CBCT. The approach leveraged the capability of conditional generative adversarial network (GAN) models to learn the complex underlying motion present in unlabeled, motion-corrupted, CBCT volumes. The approach is designed for training with unpaired clinical CBCT in an unsupervised fashion. This work presents a first feasibility study, in which the model was trained with simulated data featuring known motion, providing a controlled scenario for validation of the proposed approach prior to extension to clinical data. Our proof-of-concept study illustrated the potential of the model to generate realistic, variable simulation of CBCT deformable motion fields, consistent with three trends underlying the designed training data: i) the synthetic motion induced only diffeomorphic deformations - with Jacobian Determinant larger than zero; ii) the synthetic motion showed median displacement of 0. 5 mm in regions predominantly static in the training (e.g., the posterior aspect of the patient laying supine), compared to a median displacement of 3.8 mm in regions more prone to motion in the training; and iii) the synthetic motion exhibited predominant directionality consistent with the training set, resulting in larger motion in the superior-inferior direction (median and maximum amplitude of 4.58 mm and 20 mm, > 2x larger than the two remaining direction). Together, the proposed framework shows the feasibility for realistic motion simulation and synthesis of variable CBCT data.

Primary accelerometer calibration with two-axis automatic positioning stage

In this study, we developed an automated, multipoint primary accelerometer calibration system using a two-axis positioning stage and a heterodyne laser interferometer. The proposed system offers low-cost, convenient, and automated multipoint accelerometer calibration, enabling less calibration lead time. The positioning stage also offers better positioning repeatability of 1μm, which is impossible through manual alignments. We measured the surface deformation of a laser reflection adaptor for a single-ended accelerometer by measuring more than 450 measurement positions. Visualizing the deformation of laser reflection surfaces facilitates understanding the effects of deformation or non-linear motion, which are among the most significant uncertainty components in high-frequency accelerometer calibrations.

Quaternary E-W Extension Uplifts Kythira Island and Segments the Hellenic Arc.

Several crustal and lithospheric mechanisms lead to deformation and vertical motion of the upper plate during subduction, but their relative contribution is often enigmatic. Multiple areas of the Hellenic Forearc have been uplifting since Plio-Quaternary times, yet spatiotemporal characteristics and sources of this uplift are poorly resolved. The remarkable geology and geomorphology of Kythira Island, in the southwestern Hellenic forearc, allow for a detailed tectonic reconstruction since the Late Miocene. We present a morphotectonic map of the island, together with new biostratigraphic dating and detailed analyses of active fault strikes and marine terraces. We find that the Tortonian-Pliocene stratigraphy in Kythira records ∼100m of subsidence, and a wide coastal rasa marks the ∼2.8-2.4Ma maximum transgression. Subsequent marine regression of ∼300-400m and minor E-W tilt are recorded in ∼12 marine terrace levels for which we estimate uplift rates of ∼0.2-0.4mm/yr. Guided by simple landscape evolution models, we interpret the coastal morphology as the result of initial stability or of slow, gradual sea-level drop since ∼2.8-2.4Ma, followed by faster uplift since ∼1.5-0.7Ma. Our findings on- and offshore suggest that E-W extension is the dominant mode of regional active upper crustal deformation, and N-S normal faults accommodate most, if not all of the uplift on Kythira. We interpret the initiation of E-W extension as the result of a change in plate boundary conditions, in response to either propagation of the North Anatolian Fault, incipient collision with the African plate, mantle dynamics or a combination thereof.

Mechanically ductile, ionically conductive and low-temperature tolerant hydrogel enabled by high-concentration saline towards flexible strain sensor

Achieving a good trade-off between high mechanical performance and long-term strain sensing of hydrogel materials in cold environmental conditions remains a great challenge in the engineering fields, such as wearable electronics and human-machine interfaces. Herein, we propose a mechanically ductile, ionically conductive, anti-freezing ionic-type nanocomposite hydrogel for strain sensing under low-temperature environments. Typically, the combination use of chain-entanglement structure induced by saturated sodium chloride and nano-reinforcement produces the resultant hydrogel with the advantages of highly enhanced and balanced mechanical properties, reliable freezing-tolerance (−56.8 °C) and improved electric performance. Notably, the strain sensor based on such ionic-type nanocomposite hydrogels exhibits intriguing sensing performance, including high sensitivity (gauge factor: 6.67), fast response (≈120 ms) as well as wide detection range (0–1216%). Owing to exceptional low-temperature tolerance of the hydrogels, the optimized sensor reveals a highly enhanced low-temperature adaptability and splendid sensing performance with good capacity retention (97.6% and 90.5% for electrical conductivity and gauge factor, respectively) even after storing for 30 days at − 20 °C. Furthermore, the strain sensor can accurately detect and distinguish both large mechanical deformation and human motions under harsh environment, reflected by the unique characteristic signal with stable repeatability (e.g., a strain of 200% with 200 cycles). Clearly, the versatile multi-functionalities of high-concentration ionic nanocomposite hydrogels prepared herein could provide a new perspective for the design and fabrication of advanced all-round ionic sensor for promising applications in extremely harsh low-temperature environments.

Ti3C2Tx MXene-Polymeric Strain Sensor with Huge Gauge Factor for Body Movement Detection

In this work, a composite strain sensor is fabricated by synthesizing MXene and deposition of polypyrrole on top of the flexible electrospun PVDF nanofibers. The fabricated sensor exhibits a conductive network constructed with MXene and polypyrrole of microcracks network structure, demonstrating its strain sensing properties. The presence of these microcracks serves as mechanical weak points, which leads to sensitivity enhancement, while the electrospun fiber substrate act as a cushion for strain loading under large deformations. The as-prepared MXene@Polypyrrole PVDF sensor has a gauge factor range of 78–355 with a sensing range between 0–100%. Besides strain deformations, the sensor can operate in torsional deformation and human motion, indicating the sensor’s potential as a wearable health monitoring device.

Three-Dimensional Printed Carbon Black/PDMS Composite Flexible Strain Sensor for Human Motion Monitoring

High-performance flexible strain sensors with a low cost, simple structure, and large-scale fabrication methods have a high demand in soft robotics, wearable devices, and health monitoring. Here, a direct-ink-writing-based 3D printing method, which fabricates structural layers in an efficient, layered manner, was developed to fabricate a stretchable and flexible strain sensor composed of carbon black/silicone elastomer (CB/PDMS) composites as the strain-sensing elements and electrodes. As the sensing element, the CB/PDMS composite had a sensitivity of 5.696 in the linear strain detection range of 0 to 60%, with good stability and low hysteresis. The flexible strain sensor demonstrates potential in monitoring various human motions, including large deformation motions of the human body, and muscle motions with facial micro-expressions.

Time-domain model identification of structural dynamics from spatially dense 3D vision-based measurements

A novel approach is presented for the time-domain system identification of structural dynamic components exploiting the high spatial density of vision-based measurements. By using spatially dense measurements, the number of spatial measurement points is much larger than the dimensionality of the underlying dominant dynamics (i.e., the number of measurement points needed to meet observability of the targeted dynamics). This opens up the potential to develop new experimental identification methods that use this spatial overdetermination which were out of reach with conventional discrete sensors. The new approach presented in this paper directly extracts a relatively noise-free, low-order set of dynamic states by projecting the spatially dense time-domain measurements on a low-order dominant deformation motion basis. The basis is constructed by applying an over-complete singular value decomposition on the measurements. These dynamic states together with their numerically calculated first and second order time-derivatives are then used in a two-step identification of the structural dynamic parameters. Here, the structure of the model to identify is based on a-priori physical knowledge of the underlying set of partial differential equations, unlike a purely data-driven method. This approach has the benefits of providing a mathematical formulation that is straightforward to understand and implement and is time-efficient to solve. The presented approach is experimentally validated on a clamped plate as well as on a flexibly suspended plate which requires a correction of the rigid body motion. The identified models have shown to provide an accuracy up to 1 × 10−5 m with respect to the dominant measured motion components in both validation cases.

Visual Object Tracking Based on Deep Neural Network

Computer vision systems cannot function without visual target tracking. Intelligent video monitoring, medical treatment, human-computer interaction, and traffic management all stand to benefit greatly from this technology. Although many new algorithms and methods emerge every year, the reality is complex. Targets are often disturbed by factors such as occlusion, illumination changes, deformation, and rapid motion. Solving these problems has also become the main task of visual target tracking researchers. As with the development for deep neural networks and attention mechanisms, object-tracking methods with deep learning show great research potential. This paper analyzes the abovementioned difficult factors, uses the tracking framework based on deep learning, and combines the attention mechanism model to accurately model the target, aiming to improve tracking algorithm. In this work, twin network tracking strategy with dual self-attention is designed. A dual self-attention mechanism is used to enhance feature representation of the target from the standpoint of space and channel, with the goal of addressing target deformation and other problems. In addition, adaptive weights and residual connections are used to enable adaptive attention feature selection. A Siamese tracking network is used in conjunction with the proposed dual self-attention technique. Massive experimental results show our proposed method improves tracking performance, and tracking strategy achieves an excellent tracking effect.

Fully implicit, stabilised, three-field material point method for dynamic coupled problems

This study presents the formulation and implementation of a fully implicit stabilised Material Point Method (MPM) for dynamic problems in two-phase porous media. In particular, the proposed method is built on a three-field formulation of the governing conservation laws, which uses solid displacement, pore pressure and fluid displacement as primary variables (u–p–U formulation). Stress oscillations associated with grid-crossing and pore pressure instabilities near the undrained/incompressible limit are mitigated by implementing enhanced shape functions according to the Generalised Interpolation Material Point (GIMP) method, as well as a patch recovery of pore pressures – from background nodes to material points – based on the same Moving Least Square Approximation (MLSA) approach investigated by Zheng et al. [1]. The accuracy and computational convenience of the proposed method are discussed with reference to several poroelastic verification examples, spanning different regimes of material deformation (small versus large) and dynamic motion (slow versus fast). The computational performance of the proposed method in combination with the PARDISO solver for the discrete linear system is also compared to explicit MPM modelling [1] in terms of accuracy, convergence rate, and computation time.

Flexible Intelligent Sensing System for Plane Complex Strain Monitoring

AbstractFlexible strain sensing system has a broad application prospect in the field of personalized monitoring. Here, a flexible intelligent sensing system based on the synergistic effect of sensor technology, communication technology, and computer technology has been designed to monitor the strain distribution of the measured objects directly. The system consists of several sensing modules that can be customized according to actual needs, each of which is composed of flexible sensing arrays that can be closely attached to the surface of the object in different directions to obtain tiny strain signals. The single sensor using PDMS as the flexible substrate and Ni‐Au metal thin film as the sensing layer is easy to prepare and adjust the performance. By artificially fabricating bioinspired parallel cut‐through channel cracks on the metal thin film, high sensitivity in a small sensing range (gauge factor value up to 4500 for 1.4% strain), fast response time (25 ms), and excellent mechanical stability of nearly 2000 cycles of strain sensors are achieved. Meanwhile, combined with a PI‐based ultra‐thin flexible signal processing circuit, the system exhibits promoted flexibility, higher sensitivity, better adaptability to complex environments, and finally displays the 2D mapping of strain distribution precisely in each part of the measured object in real‐time on a computer. This flexible intelligent strain sensing system can work for deformation detection, damage monitoring, motion tracking, and other strain sensing fields.

State of the Art and Future Opportunities in MRI-Guided Robot-Assisted Surgery and Interventions.

Magnetic resonance imaging (MRI) can provide high-quality 3-D visualization of target anatomy, surrounding tissue, and instrumentation, but there are significant challenges in harnessing it for effectively guiding interventional procedures. Challenges include the strong static magnetic field, rapidly switching magnetic field gradients, high-power radio frequency pulses, sensitivity to electrical noise, and constrained space to operate within the bore of the scanner. MRI has a number of advantages over other medical imaging modalities, including no ionizing radiation, excellent soft-tissue contrast that allows for visualization of tumors and other features that are not readily visible by other modalities, true 3-D imaging capabilities, including the ability to image arbitrary scan plane geometry or perform volumetric imaging, and capability for multimodality sensing, including diffusion, dynamic contrast, blood flow, blood oxygenation, temperature, and tracking of biomarkers. The use of robotic assistants within the MRI bore, alongside the patient during imaging, enables intraoperative MR imaging (iMRI) to guide a surgical intervention in a closed-loop fashion that can include tracking of tissue deformation and target motion, localization of instrumentation, and monitoring of therapy delivery. With the ever-expanding clinical use of MRI, MRI-compatible robotic systems have been heralded as a new approach to assist interventional procedures to allow physicians to treat patients more accurately and effectively. Deploying robotic systems inside the bore synergizes the visual capability of MRI and the manipulation capability of robotic assistance, resulting in a closed-loop surgery architecture. This article details the challenges and history of robotic systems intended to operate in an MRI environment and outlines promising clinical applications and associated state-of-the-art MRI-compatible robotic systems and technology for making this possible.

Stretchy Electrochemical Harvesters for Binarized Self-Powered Strain Gauge-Based Static Motion Sensors.

The human monitoring system has motivated the search for new technology, leading to the development of a self-powered strain sensor. We report on the stretchable and soft stretchy electrochemical harvester (SECH) bilayer for a binarized self-powered strain gauge in dynamic and static motion. The active surface area participating in the electrochemical reaction was enhanced after stretching the SECH in the electrolyte, leading to an increase in the electrochemical double-layer capacitance. A change in the capacitance induced a change in the electrical potential of the bilayer, generating electrical energy. The SECH overcomes several challenges of the previous mechano-electrochemical harvester: The harvester had high elasticity (50%), which satisfied the required strain during human motion. The harvester was highly soft (modulus of 5.8 MPa), 103 times lower than that of the previous harvester. The SECH can be applied to a self-powered strain gauge, capable of measuring stationary deformation and low-speed motion. The SECH created a system to examine the configuration of the human body, as demonstrated by the human monitoring sensor from five independent SECH assembled on the hand. Furthermore, the sensing information was simplified through the binarized signal. It can be used to assess the hand configuration for hand signals and sign language.

Fluid mechanics of the zebrafish embryonic heart trabeculation.

Embryonic heart development is a mechanosensitive process, where specific fluid forces are needed for the correct development, and abnormal mechanical stimuli can lead to malformations. It is thus important to understand the nature of embryonic heart fluid forces. However, the fluid dynamical behaviour close to the embryonic endocardial surface is very sensitive to the geometry and motion dynamics of fine-scale cardiac trabecular surface structures. Here, we conducted image-based computational fluid dynamics (CFD) simulations to quantify the fluid mechanics associated with the zebrafish embryonic heart trabeculae. To capture trabecular geometric and motion details, we used a fish line that expresses fluorescence at the endocardial cell membrane, and high resolution 3D confocal microscopy. Our endocardial wall shear stress (WSS) results were found to exceed those reported in existing literature, which were estimated using myocardial rather than endocardial boundaries. By conducting simulations of single intra-trabecular spaces under varied scenarios, where the translational or deformational motions (caused by contraction) were removed, we found that a squeeze flow effect was responsible for most of the WSS magnitude in the intra-trabecular spaces, rather than the shear interaction with the flow in the main ventricular chamber. We found that trabecular structures were responsible for the high spatial variability of the magnitude and oscillatory nature of WSS, and for reducing the endocardial deformational burden. We further found cells attached to the endocardium within the intra-trabecular spaces, which were likely embryonic hemogenic cells, whose presence increased endocardial WSS. Overall, our results suggested that a complex multi-component consideration of both anatomic features and motion dynamics were needed to quantify the trabeculated embryonic heart fluid mechanics.

Deep Affine Motion Compensation Network for Inter Prediction in VVC

In video coding, it is a challenge to deal with scenes with complex motions, such as rotation and zooming. Although affine motion compensation (AMC) is employed in Versatile Video Coding (VVC), it is still difficult to handle non-translational motions due to the adopted hand-craft block-based motion compensation. In this paper, we propose a deep affine motion compensation network (DAMC-Net) for inter prediction in video coding to effectively improve the prediction accuracy. To the best of our knowledge, our work is the first attempt to deal with the deformable motion compensation based on CNN in VVC. Specifically, a deformable motion-compensated prediction (DMCP) module is proposed to compensate the current encoding block through a learnable way to estimate accurate motion fields. Meanwhile, the spatial neighboring information and the temporal reference block as well as the initial motion field are fully exploited. By effectively fusing the multi-channel feature maps from DMCP, an attention-based fusion and reconstruction (AFR) module is designed to reconstruct the output block. The proposed DAMC-Net is integrated into VVC and the experimental results demonstrate that the proposed method considerably enhances the coding performance.