Motion Traces Research Articles

Whole-brain T2 mapping with radial sampling and retrospective motion correction at 3T.

Several barriers prevent the use of whole-brain T2 mapping in routine use despite increasing interest in this parameter. One of the main barriers is the long scan time resulting in patient discomfort and motion corrupted data. To address this challenge, a method for accurate whole-brain T2 mapping with a limited acquisition time and motion correction capabilities is investigated. A 3D radial multi-echo spin-echo sequence was implemented with optimized sampling trajectory enabling the estimation of intra-scan motion, subsequently used to correct the raw data. Motion corrected echo images are then reconstructed with linear subspace constrained reconstruction. Experiments were carried out on phantom and volunteers at 3T to evaluate the accuracy of the T2 estimation, the sensitivity to lesions and the efficiency of the correction on motion corrupted data. Whole-brain T2 mapping acquired in less than 7 min enabled the depiction of lesions in the white matter with longer T2. Data retrospectively corrupted with typical motion traces of pediatric patients highly benefited from the motion correction by reducing the error in T2 estimates within the lesions. All datasets acquired on seven volunteers, with deliberate motion, also showed that motion corrupted T2 maps could be improved with the retrospective motion correction both at the voxel level and the structure level. A whole-brain T2 mapping sequence with retrospective intra-scan motion correction and reasonable acquisition time is proposed. The method necessitates advanced iterative reconstruction strategies but no additional navigator, external device, or increased scan time is required.

Clinical implementation and patient-specific quality assurance solutions for real-time target tracking and dynamic delivery in Radixact synchrony.

The installation and testing of the first Radixact with Synchrony system in Colombia marked a significant milestone in Latin America's medical landscape. There was a need to devise a robust quality assurance protocol to comprehensively evaluate both dose delivery and motion tracking accuracy. However, testing experiences under clinical conditions have not been extensively reported. Additionally, there are limited recommended measuring devices for Synchrony evaluation. To validate and implement an alternative setup for dynamic-PSQA while testing Synchrony's functionality under clinical scenarios, including real-patient motion traces, and to provide guidance to new centers undergoing clinical implementation of Helical Synchrony. This approach involves using the Iba miniPhantomR with strategically placed fiducial markers for configuring Gafchromic-films and array-based setups. When paired with the CIRS Dynamic Platform, this enables an innovative dynamic setup with trackable features for Synchrony delivery testing. Assessment scenarios, including compensation (M1S1) and no-motion compensation (M1S0), were evaluated using 2D-gamma pass rate analysis with multiple clinical gamma criteria. The Synchrony-Simulation feature was used to assess pre-treatment performance and capture the patient's target motion pattern. Synchrony for common clinical cases with patient's motion-traces was validated. The results for M1S0 and M1S1 demonstrated consistency with previous studies evaluating Synchrony functionality. Analysis using different gamma criteria unveiled dosimetric differences and impacts across various motion ranges. The application of effective kV-dose subtraction for array-based methods is of upmost importance when evaluating dynamic-PSQA with stringent gamma-criteria. However, no significant kV-dose impact on EBT3-Film was detectable. Two implemented configurations for dynamic-PSQA setups were validated and successfully integrated into our clinic. We addressed both the benefits and limitations of array-based and film-based methods. The functionality and limitations of Synchrony were evaluated using the proposed setups. The potential utility of Synchrony-Simulation, along with the proposed patient-case classification table, can offer valuable support for new users during the clinical implementation of Synchrony treatments.

Reduction of Postradiation Therapy Urinary Toxicity Via Intrafractional Megavoltage-Kilovoltage Prostate Location Monitoring

We hypothesized that an in-house developed system using megavoltage and kilovoltage image guidance (MKIG) to ensure correct prostate positioning during stereotactic body radiation therapy (SBRT) could potentially avoid unwanted doses to nontarget tissues, leading to reduced toxicities. We built a 3-dimensional MKIG platform that accurately tracks prostate implanted fiducials in real time and clinically translated the system to replace a commercial approach, intrafraction motion review (IMR), which only tracks fiducials in the 2-dimensional kilovoltage views. From 2017 to 2019, 150 patients with prostate cancer were treated with SBRT and monitored using MKIG. The motion trace of the fiducials alerts therapists to interrupt and reposition the prostate when displacement exceeds a 1.5 mm threshold. A comparison cohort of 121 patients was treated with the same dose regimen and treatment technique but managed by IMR. Statistics of intrafractional patient shifts and delivery time were collected to evaluate the workflow efficacy. The incidence of grade ≥2 urinary toxicities was analyzed to assess clinical complications. The median follow-up time was 3.7 years (0.2-8.2 years). MKIG treatments had more treatment shifts (1.09 vs 0.28) and a longer average delivery time per fraction (579 ± 205 seconds vs 357 ± 117 seconds) than IMR treatments. Three-quarters (75%) of shifts resulting from MKIG were ≤3 mm, versus 51% in IMR, indicating that MKIG detected and corrected smaller deviations. The incidence of grade ≥2 urinary toxicity was lower in the MKIG than the IMR cohort: 10.7% versus 19.8% (P = .047). On multivariate analysis of late urinary toxicity, only high (>7) preradiation therapy international prostate symptom score (P < .043) and the use of MKIG were selected (P < .029). Automated and quantitative MKIG introduced minimal workflow impact and was superior to IMR in localizing the prostate during SBRT, which correlated with a clinically significant reduction in late urinary toxicity. Further clinical testing using randomized trials will be required to validate the impact on outcomes.

Characterizing the consistency of motion of spermatozoa through nanoscale motion tracing

To demonstrate nanoscale motion tracing of spermatozoa and present analysis of the motion traces to characterize the consistency of motion of spermatozoa as a complement to progressive motility analysis. Anonymized sperm samples were videographed under a quantitative phase microscope, followed by generating and analyzing superresolution motion traces of individual spermatozoa. Not applicable. Centrifuged human sperm samples. Not applicable. Precision of motion trace of individual sperms, presence of a helical pattern in the motion trace, mean and standard deviations of helical periods and radii of sperm motion traces, speed of progression. Spatially sensitive quantitative phase imaging with a superresolution computational technique MUltiple SIgnal Classification ALgorithm allowed achieving motion precision of 340 nm using ×10, 0.25 numerical aperture lens whereas the diffraction-limited resolution at this setting was 1,320 nm. The motion traces thus derived facilitated new kinematic features of sperm, namely the statistics of helix period and radii per sperm. Through the analysis, 47 sperms with a speed >25 μm/s were randomly selected from the same healthy donor semen sample, it is seen that the kinematic features did not correlate with the speed of the sperms. In addition, it is noted that spermatozoa may experience changes in the periodicity and radius of the helical path over time. Further, some very fast sperms (e.g., >70 μm/s) may demonstrate irregular motion and need further investigation. Presented computational analysis can be used directly for sperm samples from both fertility patients with normal and abnormal sperm cell conditions. We note that MUltiple SIgnal Classification ALgorithm is an image analysis technique that may vaguely fall under the machine learning category, but the conventional metrics for reporting found in Enhancing the QUAlity and Transparency Of health Research network do not apply. Alternative suitable metrics are reported, and bias is avoided through random selection of regions for analysis. Detailed methods are included for reproducibility. Kinematic features derived from nanoscale motion traces of spermatozoa contain information complementary to the speed of the sperms, allowing further distinction among the progressively motile sperms. Some highly progressive spermatozoa may have irregular motion patterns, and whether irregularity of motion indicates poor quality regarding artificial insemination needs further investigation. The presented technique can be generalized for sperm analysis for a variety of fertility conditions.

Feasibility of patient-specific quality assurance (PSQA) for real-time robotic stereotactic body radiotherapy (SBRT) based on tumor motion traces.

To design a patient specific quality assurance (PSQA) process for the CyberKnife Synchrony system and quantify its dosimetric accuracy using a motion platform driven by patient tumor traces with rotation. The CyberKnife Synchrony system was evaluated using a motion platform (MODUSQA) and a SRS MapCHECK phantom. The platform was programed to move in the superior-inferior (SI) direction based on tumor traces. The detector array housed by the StereoPhan was placed on the platform. Extra rotational angles in pitch (head down, 4.0°±0.15° or 1.2°±0.1°) were added to the moving phantom to examine robot capability of angle correction during delivery. A total of 15 Synchrony patients were performed SBRT PSQA on the moving phantom. All the results were benchmarked by the PSQA results based on static phantom. For smaller pitch angles, the mean gamma passing rates were 99.75%±0.87%, 98.63%±2.05%, and 93.11%±5.52%, for 3%/1mm, 2%/1mm, and 1%/1mm, respectively. Large discrepancy in the passing rates was observed for different pitch angles due to limited angle correction by the robot. For larger pitch angles, the corresponding mean passing rates were dropped to 93.00%±10.91%, 88.05%±14.93%, and 80.38%±17.40%. When comparing with the static phantom, no significant statistic difference was observed for smaller pitch angles (p=0.1 for 3%/1mm), whereas a larger statistic difference was observed for larger pitch angles (p<0.02 for all criteria). All the gamma passing rates were improved, if applying shift and rotation correction. The significance of this work is that it is the first study to benchmark PSQA for the CyberKnife Synchrony system using realistically moving phantoms with rotation. With reasonable delivery time, we found it may be feasible to perform PSQA for Synchrony patients with a realistic breathing pattern.

Respiratory Motion Prediction with Empirical Mode Decomposition-Based Random Vector Functional Link

The precise prediction of tumor motion for radiotherapy has proven challenging due to the non-stationary nature of respiration-induced motion, frequently accompanied by unpredictable irregularities. Despite the availability of numerous prediction methods for respiratory motion prediction, the prediction errors they generate often suffer from large prediction horizons, intra-trace variabilities, and irregularities. To overcome these challenges, we have employed a hybrid method, which combines empirical mode decomposition (EMD) and random vector functional link (RVFL), referred to as EMD-RVFL. In the initial stage, EMD is used to decompose respiratory motion into interpretable intrinsic mode functions (IMFs) and residue. Subsequently, the RVFL network is trained for each obtained IMF and residue. Finally, the prediction results of all the IMFs and residue are summed up to obtain the final predicted output. We validated this proposed method on the benchmark datasets of 304 respiratory motion traces obtained from 31 patients for various prediction lengths, which are equivalent to the latencies of radiotherapy systems. In direct comparison with existing prediction techniques, our hybrid architecture consistently delivers a robust and highly accurate prediction performance. This proof-of-concept study indicates that the proposed approach is feasible and has the potential to improve the accuracy and effectiveness of radiotherapy treatment.

The Remove-the-Mask Open-Source head and neck Surface-Guided radiation therapy system

Background and PurposeSurface Guided Radiotherapy (SGRT) for head and neck radiotherapy is challenging as obstructions are common and non-rigid facial motion can compromise surface accuracy. The purpose of this work was to develop and benchmark the Remove the Mask (RtM) SGRT system, an open-source system especially designed to address the challenges faced in radiotherapy of head and neck cancer. Materials and MethodsThe accuracy of the RtM SGRT system was benchmarked using a head phantom positioned on a robotic motion platform capable of sub-millimetre accuracy which was used to induce unidirectional shifts and to reproduce three real head motion traces. We also assessed the accuracy of the system in ten humans volunteers. The ground truth motion of the volunteers was obtained using a commercial motion capture system with an accuracy < 0.3 mm. ResultsThe mean tracking error of the RtM SGRT system for the ten volunteers was of −0.1 ± 0.4 mm −0.6 ± 0.6 mm and 0.3 ± 0.2 mm, and 0.0 ± 0.2° 0.0 ± 0.1° and 0.0 ± 0.2° for translations and rotations along the left–right, superior-inferior and anterior-posterior axes respectively and we also found similar results in measurements with the head phantom. Forced facial motion was associated with lower tracking accuracy. The RtM SGRT system achieved submillimetre accuracy. ConclusionThe RtM SGRT system is a low-cost, easy to build and open-source SGRT system that can achieve an accuracy that meets international commissioning guidelines. Its open-source and modular design allows for the development and easy translation of novel surface tracking techniques.

A Couch Mounted Smartphone-based Motion Monitoring System for Radiation Therapy

PurposeSurface-guided radiation-therapy (SGRT) systems are being adopted into clinical practice for patient setup and motion monitoring. However, commercial systems remain cost prohibitive to resource-limited clinics around the world. Our aim is to develop and validate a smartphone-based application using LiDAR cameras (such as on recent Apple iOS devices) for facilitating SGRT in low-resource centers. The proposed SGRT application was tested at multiple institutions, and validated using phantoms and volunteers against various commercial systems to demonstrate feasibility. Methods and MaterialsAn iOS application was developed in Xcode and written in Swift using the Augmented-Reality (AR) Kit and implemented on an Apple iPhone 13 Pro with a built-in LiDAR camera. The application contains multiple features: 1) visualization of both the camera and depth video feeds (at a ∼60Hz sample-frequency), 2) region-of-interest (ROI) selection over the patient's anatomy where motion is measured, 3) chart displaying the average motion over time in the ROI, and 4) saving/exporting the motion traces and surface map over the ROI for further analysis. The iOS application was tested to evaluate depth measurement accuracy for: 1) different angled surfaces, 2) different field-of-views over different distances, and 3) similarity to a commercially available SGRT systems (Vision RT AlignRT® and Varian IDENTIFY™) with motion phantoms and healthy volunteers across three institutions. Measurements were analyzed using linear-regressions and Bland-Altman analysis. ResultsCompared to the clinical system measurements (reference), the iOS application showed excellent agreement for depth (r=1.000,p<0.0001; bias=-0.07±0.24cm) and angle (r=1.000,p<0.0001; bias=0.02±0.69°) measurements. For free-breathing traces, the iOS application was significantly correlated to phantom motion (institute 1: r=0.99,p<0.0001; bias=-0.003±0.03cm; institute 2: r=0.98,p<0.0001; bias=-0.001±0.10cm; institute 3: r=0.97,p<0.0001; bias=0.04±0.06cm) and healthy volunteer motion (institute 1: r=0.98,p<0.0001; bias=-0.008±0.06cm; institute 2: r=0.99,p<0.0001; bias=-0.007±0.12cm; institute 3: r=0.99,p<0.0001; bias=-0.001±0.04cm). ConclusionThe proposed approach using a smartphone-based application provides a low-cost platform that could improve access to surface-guided radiation therapy accounting for motion.

Dynamic capture behavior of rotating matrix to paramagnetic particles in centrifugal HGMS process: Based on FEM

Centrifugal high gradient magnetic separation (CHGMS) was developed to produce a high-quality magnetic product from weakly magnetic materials. In this research, the dynamic behavior of particles in the CHGMS process and the magnetic capture of rotating wires to paramagnetic particles were analyzed through Finite Element Method (FEM). It was found that particles move spirally with slurry in the separation area of the separator, due to the stirring of magnetic wires, and the motion traces of paramagnetic particles are more tortuous and complicated than those of non-magnetic particles. The capture capacity of elliptical wire to paramagnetic particles is stronger than that of circular wire, but is weaker than that of square one. The distance from rotating wires to the center of matrix significantly influences the mass weight of paramagnetic particles captured onto the wires, and there is a clear interaction between wire layers during the capture process of rotating matrix.

Real-time motion-including dose estimation of simulated multi-leaf collimator-tracked magnetic resonance-guided radiotherapy.

Real-time dose estimation is a key-prerequisite to enable online intra-fraction treatment adaptation in magnetic resonance (MR)-guided radiotherapy (MRgRT). It is an essential component for the assessment of the dosimetric benefits and risks of online adaptive treatments, such as multi-leaf collimator (MLC)-tracking. We present a proof-of-concept for a software workflow for real-time dose estimation of MR-guided adaptive radiotherapy based on real-time data-streams of the linac delivery parameters and target positions. A software workflow, combining our in-house motion management software DynaTrack, a real-time dose calculation engine that connects to a research version of the treatment planning software (TPS) Monaco (v.6.09.00, Elekta AB, Stockholm, Sweden) was developed and evaluated. MR-guided treatment delivery on the Elekta Unity MR-linac was simulated with and without MLC-tracking for three prostate patients, previously treated on the Elekta Unity MR-linac (36.25Gy/five fractions). Three motion scenarios were used: no motion, regular motion, and erratic prostate motion. Accumulated monitor units (MUs), centre of mass target position and MLC-leaf positions, were forwarded from DynaTrack at a rate of 25Hz to a Monte Carlo (MC) based dose calculation engine which utilises the research GPUMCD-library (Elekta AB, Stockholm, Sweden). A rigid isocentre shift derived from the selected motion scenarios was applied to a bulk density-assigned session MR-image. The respective electron density used for treatment planning was accessed through the research Monaco TPS. The software workflow including the online dose reconstruction was validated against offline dose reconstructions. Our investigation showed that MC-based real-time dose calculations that account for all linac states (including MUs, MLC positions and target position) were infeasible, hence states were randomly sampled and used for calculation as follows; Once a new linac state was received, a dose calculation with 106 photons was started. Linac states that arrived during the time of the ongoing calculation were put into a queue. After completion of the ongoing calculation, one new linac state was randomly picked from the queue and assigned the MU accumulated from the previous state until the last sample in the queue. The queue was emptied, and the process repeated throughout treatment simulation. On average 27% (23%-30%) of received samples were used in the real-time calculation, corresponding to a calculation time for one linac state of 148ms. Median gamma pass rate (2%/3mm local) was 100.0% (99.9%-100%) within the PTV volume and 99.1% (90.1%-99.4.0%) with a 15% dose cut off. Differences in PTVDmean , CTVDmean , RectumD2% , and BladderD2% (offline-online, % of prescribed dose) were below 0.64%. Beam-by-beam comparisons showed deviations below 0.07Gy. Repeated simulations resulted in standard deviations below 0.31% and 0.12Gy for the investigated volume and dose criteria respectively. Real-time dose estimation was successfully performed using the developed software workflow for different prostate motion traces with and without MLC-tracking. Negligible dosimetric differences were seen when comparing online and offline reconstructed dose, enabling online intra-fraction treatment decisions based on estimates of the delivered dose.

Experimental comparison of linear regression and LSTM motion prediction models for MLC-tracking on an MRI-linac.

Magnetic resonance imaging (MRI)-guided radiotherapy with multileaf collimator (MLC)-tracking is a promising technique for intra-fractional motion management, achieving high dose conformality without prolonging treatment times. To improve beam-target alignment, the geometric error due to system latency should be reduced by using temporal prediction. To experimentally compare linear regression (LR) and long-short-term memory (LSTM) motion prediction models for MLC-tracking on an MRI-linac using multiple patient-derived traces with different complexities. Experiments were performed on a prototype 1.0T MRI-linac capable of MLC-tracking. A motion phantom was programmed to move a target in superior-inferior (SI) direction according to eight lung cancer patient respiratory motion traces. Target centroid positions were localized from sagittal 2D cine MRIs acquired at 4Hz using a template matching algorithm. The centroid positions were input to one of four motion prediction models. We used (1) a LSTM network which had been optimized in a previous study on patient data from another cohort (offline LSTM). We also used (2) the same LSTM model as a starting point for continuous re-optimization of its weights during the experiment based on recent motion (offline+online LSTM). Furthermore, we implemented (3) a continuously updated LR model, which was solely based on recent motion (online LR). Finally, we used (4) the last available target centroid without any changes as a baseline (no-predictor). The predictions of the models were used to shift the MLC aperture in real-time. An electronic portal imaging device (EPID) was used to visualize the target and MLC aperture during the experiments. Based on the EPID frames, the root-mean-square error (RMSE) between the target and the MLC aperture positions was used to assess the performance of the different motion predictors. Each combination of motion trace and prediction model was repeated twice to test stability, for a total of 64 experiments. The end-to-end latency of the system was measured to be (389 ± 15)ms and was successfully mitigated by both LR and LSTM models. The offline+online LSTM was found to outperform the other models for all investigated motion traces. It obtained a median RMSE over all traces of (2.8 ± 1.3)mm, compared to the (3.2 ± 1.9)mm of the offline LSTM, the (3.3 ± 1.4)mm of the online LR and the (4.4 ± 2.4)mm when using the no-predictor. According to statistical tests, differences were significant (p-value <0.05) among all models in a pair-wise comparison, but for the offline LSTM and online LR pair. The offline+online LSTM was found to be more reproducible than the offline LSTM and the online LR with a maximum deviation in RMSE between two measurements of 10%. This study represents the first experimental comparison of different prediction models for MRI-guided MLC-tracking using several patient-derived respiratory motion traces. We have shown that among the investigated models, continuously re-optimized LSTM networks are the most promising to account for the end-to-end system latency in MRI-guided radiotherapy withMLC-tracking.

Evaluation of Patient-Specific Quality Control (QC) for Markerless Dynamic Tumor Tracking (MDTT) Deliveries

To report on the patient-specific QC for MDTT for liver and lung tumors treated using the dome-of-liver/diaphragm as a surrogate for the target position and compare to the QC of conventional fiducial-based tracking. Vero4DRT is a linear accelerator that has a gimbal-mounted waveguide and collimation system allowing the radiation beam to perform dynamic tumor tracking based on a patient's respiratory motion. A correlation model is built between an external IR marker placed on the chest and an internal structure, which may be implanted fiducial surrogates or in the case of MDTT, an anatomic landmark. During treatment an orthogonal kV image pair is taken every second. The auto detected internal structure position versus the predicted position is recorded in treatment log files. Five IMRT tracking plans were delivered to two commercial motion phantoms with in-house additions for patient-specific tracking QC. For point dose measurements, a 0.6cc farmer chamber was placed inside the dynamic tracking phantom which contains fiducials for fiducial-based tracking delivery and an anatomic landmark for MDTT. For 2D dose distribution measurements, a radiographic film insert for the Quasar Respiratory Motion Phantom was constructed. The phantom contains both fiducials and has a liver-dome shape at one end to facilitate MDTT. Both motion platforms were interfaced with software that enabled patient-specific respiratory motion traces acquired during a 4DCT scan. Each plan was delivered three times on each phantom: 1) in static mode, 2) during fiducial-based tracking, and 3) during MDTT. Chamber measurements taken during static and tracked delivery were compared to the average chamber dose from the treatment planning software (TPS). Film distributions measured during tracked deliveries were compared to the static film distribution using gamma analysis with FILMQA PRO software. Finally, dynamic tracking treatment statistics, extracted from log files, were compared between fiducial and markerless tracking deliveries. All chamber measurements resulted in dose differences <2.5% compared with the TPS. Dose differences between fiducial-based and MDTT were 0.26% on average (range 0.03-0.62%). For film dose map analysis, gamma pass rates were >95% for all plans for all tracking methods. The average gamma pass rate for 3%/3mm was 99.4% (fiducial-based) vs 98.4% (markerless). For 2%/2mm, the average gamma pass rate was 96.4% (fiducial-based tracking) vs 95.6% (MDTT). Dynamic treatment statistics from logs files reported an average 3D absolute deviation from predicted position of 0.52mm (±0.24) for fiducial tracking and 1.19mm (±0.50) for MDTT. Both fiducial-based and MDTT plans meet passing criteria for patient-specific QC. 3D absolute deviation of detected versus predicted position are larger for MDTT compared to fiducial tracking, however no significant impact on dose delivery was measured.

A strategy for human safety monitoring in high-temperature environments by 3D-printed heat-resistant TENG sensors

Flexible wearable devices based on triboelectric nanogenerators (TENGs) can be used to track many parameters around the human body, including signals from motions, gestures, and contacts, to perform a variety of tasks. However, it is still a challenge to achieve wide working temperature and multi-parameter body motion sensing with TENG sensors. To address that, this work reports a facile 3D printing-based strategy for preparing functionalized heat-resistant TENG sensors. In detail, a printable heat-resistant ink and multiple 3D printing-based assembly methods are proposed. The as-fabricated ink, which could maintain up to 200 ℃, then serves as multiple components in the TENG sensors through patterning. Functionalized TENG sensors and human–machine interfaces are printed and subsequently networked on a human body to sense comprehensive motion information. The results indicate six typical motions can be recognized with 94.85% accuracy based on neural network algorithms. A simple digital twin for motion capture and trace tracking in virtual space is furthermore implemented. Combining the proposed TENG sensors with virtual reality (VR) platforms opens up a new road for human safety monitoring in high-temperature environments.

MagicInput: Virtual Handwriting Interface Using Ubiquitous WiFi Signals

Past few years have witnessed the great potential of exploiting WiFi signals for positioning. Prior work focus on discovering the absolute locations of a radio source, and have achieved promising accuracies of tens of centimeters. However, many applications such as aerial gesture or handwriting tracking are more concerned with the detailed motion shape of the target rather than its exact locations, which require a several fold higher accuracy. To this end, we present MagicInput, a virtual handwriting interface by tracking the motion traces of a WiFi source. Based on Channel State Information (CSI), MagicInput elaborately devises an incremental motion-based tracking model by correlating the motion traces with the angle and length variations of propagation paths. The model shifts the tracking task from the transceiver view to the antenna array-oriented view, and eliminates the need for prior knowledge of anchor locations. MagicInput proposes an end-to-end pipeline for tracking refinement, by interference suppression, motion segmentation, and an integrated grasp pressure sensorbased motion instance detection. We prototype MagicInput using off-the-shelf WiFi radios, and extensive experiments attest that MagicInput can achieve the accuracy of 8.5 mm confronting diverse users and environment conditions. With ubiquitous WiFi signals, MagicInput can transform any region into an interactive handwriting interface with millimeter accuracy.

Real-Time Motion Analysis With 4D Deep Learning for Ultrasound-Guided Radiotherapy.

Motion compensation in radiation therapy is a challenging scenario that requires estimating and forecasting motion of tissue structures to deliver the target dose. Ultrasound offers direct imaging of tissue in real-time and is considered for image guidance in radiation therapy. Recently, fast volumetric ultrasound has gained traction, but motion analysis with such high-dimensional data remains difficult. While deep learning could bring many advantages, such as fast data processing and high performance, it remains unclear how to process sequences of hundreds of image volumes efficiently and effectively. We present a 4D deep learning approach for real-time motion estimation and forecasting using long-term 4D ultrasound data. Using motion traces acquired during radiation therapy combined with various tissue types, our results demonstrate that long-term motion estimation can be performed markerless with a tracking error of 0.35±0.2 mm and with an inference time of less than 5 ms. Also, we demonstrate forecasting directly from the image data up to 900 ms into the future. Overall, our findings highlight that 4D deep learning is a promising approach for motion analysis during radiotherapy.

Advanced Predictive Tile Selection Using Dynamic Tiling for Prioritized 360° Video VR Streaming

The widespread availability of smart computing and display devices such as mobile phones, gaming consoles, laptops, and tethered/untethered head-mounted displays has fueled an increase in demand for omnidirectional (360°) videos. 360° video applications enable users to change their viewing angles while interacting with the video during playback. This allows users to have a more personalized and interactive viewing experience. Unfortunately, these applications require substantial network and computational resources that the conventional infrastructure and end devices cannot support. Recently proposed viewport adaptive fixed tiling solutions stream only relevant video tiles based on user interaction with the virtual reality (VR) space to use existing transmission resources more efficiently. However, achieving real-time accurate viewport extraction and transmission in response to both head movements and bandwidth dynamics can be challenging, which can impact the user’s Quality of Experience (QoE). This article proposes innovative dynamic tiling-based adaptive 360° video streaming solutions in order to achieve high viewer QoE. First, novel and easy-to-scale tiling layout selection methods are introduced, and the best tiling layouts are employed in each adaptation interval based on the prediction-assisted visual quality metric and the observed viewport divergence. Second, a novel proactive tile selection approach is presented, which adaptively extracts tiles for each selected tiling layout based on two low-complex viewport prediction mechanisms. Finally, a practical dynamic tile priority-oriented bitrate adaptation scheme is introduced, which uniformly distributes the bitrate budget among different tiles during 360° video streaming. Extensive trace-driven experiments are conducted to evaluate the proposed solutions using head motion traces from 48 VR users for five 360° videos with tiling layouts of 4 × 3, 6 × 4, and 8 × 6 and segment durations of 1s, 1.5s, and 2s. The experimental evaluations show that the dynamic video tiling solutions achieve up to 11.2% more viewport matches and an average improvement in QoE of 9.7% to 18% compared to state-of-the-art 360° streaming approaches.

Mesoscale Simulation of Shaped Charge Jet Forming and Free Flight Based on B-spline and Domain Interpolation Material Point Method

Shaped charge (SC) generates a fluid-like high-speed jet (SCJ) undergoing extremely large ductile stretching without fracture. It is a formidable challenge to accurately track and monitor the mesoscale deformation characteristics of materials using fluid simulation algorithms. To address this issue, the Material Point Method (MPM) is introduced as an efficient particle-based method that discretizes the continuum into Lagrangian particles moving through a fixed Eulerian grid. By possessing all material properties, these particles facilitate tracking throughout the deformation process and enable the implementation of history-dependent constitutive models. Regrettably, the utilization of MPM in the study of SCJ formation is restricted. The objective of this study is to assess the capability of 2D-axisymmetric MPMs in modeling SCJ formation and free flight at the mesoscale, thereby providing valuable guidelines for their application in SCJs. The MPMs employed in this study are based on the B-spline (BSMPM) and domain interpolations (generalized and convected particle domain interpolations in MPM). The numerical results indicate that BSMPM with cubic and quartic splines is the most suitable method for calculating SCJs due to its exceptional continuity and alignment with the experimental data. The mesoscale evolution of particles reveals that the material undergoes impact crushing and tensile tearing, transforming into a low-speed slug and a high-speed jet. The equivalent plastic strain (EPS) in SCJs exhibits a radial expansion from the exterior to the axis in a layered manner. Particles in the outer layer with a thickness of approximately 1/2 exhibit a 'laminar' distribution, while particles near the axis exhibit 'turbulent' distribution and undergo severe deformation. The hierarchical progression of EPS and particle motion traces provides insight into the underlying causes of mesoscale experimental phenomena, such as the axial elongation of voids in the SCJ slug and the radial distribution of the material in three concentric circles.

Technical note: Feasibility of gating for dynamic trajectory radiotherapy - Mechanical accuracy and dosimetric performance.

Dynamic trajectory radiotherapy (DTRT) extends state-of-the-art volumetric modulated arc therapy (VMAT) by dynamic table and collimator rotations during beam-on. The effects of intrafraction motion during DTRT delivery are unknown, especially regarding the possible interplay between patient and machine motion with additional dynamic axes. To experimentally assess the technical feasibility and quantify the mechanical and dosimetric accuracy of respiratory gating during DTRT delivery. A DTRT and VMAT plan are created for a clinically motivated lung cancer case and delivered to a dosimetric motion phantom (MP) placed on the table of a TrueBeam system using Developer Mode. The MP reproduces four different 3D motion traces. Gating is triggered using an external marker block, placed on the MP. Mechanical accuracy and delivery time of the VMAT and DTRT deliveries with and without gating are extracted from the logfiles. Dosimetric performance is assessed by means of gamma evaluation (3% global/2 mm, 10% threshold). The DTRT and VMAT plans are successfully delivered with and without gating for all motion traces. Mechanical accuracy is similar for all experiments with deviations<0.14° (gantry angle),<0.15° (table angle),<0.09° (collimator angle) and<0.08 mm (MLC leaf positions). For DTRT (VMAT), delivery times are 1.6-2.3 (1.6- 2.5) times longer with than without gating for all motion traces except one, where DTRT (VMAT) delivery is 5.0 (3.6) times longer due to a substantial uncorrected baseline drift affecting only DTRT delivery. Gamma passing rates with (without) gating for DTRT/VMAT were ≥96.7%/98.5% (≤88.3%/84.8%). For one VMAT arc without gating it was 99.6%. Gating is successfully applied during DTRT delivery on a TrueBeam system for the first time. Mechanical accuracy is similar for VMAT and DTRT deliveries with and without gating. Gating substantially improved dosimetric performance for DTRT and VMAT.

Quantifying MR head motion in the Rhineland Study – A robust method for population cohorts

Head motion during MR acquisition reduces image quality and has been shown to bias neuromorphometric analysis. The quantification of head motion, therefore, has both neuroscientific as well as clinical applications, for example, to control for motion in statistical analyses of brain morphology, or as a variable of interest in neurological studies. The accuracy of markerless optical head tracking, however, is largely unexplored. Furthermore, no quantitative analysis of head motion in a general, mostly healthy population cohort exists thus far. In this work, we present a robust registration method for the alignment of depth camera data that sensitively estimates even small head movements of compliant participants. Our method outperforms the vendor-supplied method in three validation experiments: 1. similarity to fMRI motion traces as a low-frequency reference, 2. recovery of the independently acquired breathing signal as a high-frequency reference, and 3. correlation with image-based quality metrics in structural T1-weighted MRI. In addition to the core algorithm, we establish an analysis pipeline that computes average motion scores per time interval or per sequence for inclusion in downstream analyses. We apply the pipeline in the Rhineland Study, a large population cohort study, where we replicate age and body mass index (BMI) as motion correlates and show that head motion significantly increases over the duration of the scan session. We observe weak, yet significant interactions between this within-session increase and age, BMI, and sex. High correlations between fMRI and camera-based motion scores of proceeding sequences further suggest that fMRI motion estimates can be used as a surrogate score in the absence of better measures to control for motion in statistical analyses.

An Automatic Motion-Based Artifact Reduction Algorithm for fNIRS in Concurrent Functional Magnetic Resonance Imaging Studies (AMARA–fMRI)

Multimodal functional near-infrared spectroscopy–functional magnetic resonance imaging (fNIRS–fMRI) studies have been highly beneficial for both the fNIRS and fMRI field as, for example, they shed light on the underlying mechanism of each method. However, several noise sources exist in both methods. Motion artifact removal is an important preprocessing step in fNIRS analysis. Several manual motion–artifact removal methods have been developed which require time and are highly dependent on expertise. Only a few automatic methods have been proposed. AMARA (acceleration-based movement artifact reduction algorithm) is one of the most promising automatic methods and was originally tested in an fNIRS sleep study with long acquisition times (~8 h). However, it relies on accelerometry data, which is problematic when performing concurrent fNIRS–fMIRI experiments. Most accelerometers are not MR compatible, and in any case, existing datasets do not have this data. Here, we propose a new way to retrospectively determine acceleration data for motion correction methods, such as AMARA in multimodal fNIRS–fMRI studies. We do so by considering the individual slice stack acquisition times of simultaneous multislice (SMS) acquisition and reconstructing high-resolution motion traces from each slice stack time. We validated our method on 10 participants during a memory task (2- and 3-back) with 6 fNIRS channels over the prefrontal cortex (limited field of view with fMRI). We found that this motion correction significantly improved the detection of activation in deoxyhemoglobin and outperformed up-sampled motion traces. However, we found no improvement in oxyhemoglobin. Furthermore, our data show a high overlap with fMRI activation when considering activation in channels according to both deoxyhemoglobin and oxyhemoglobin.