PViT-6D: Overclocking Vision Transformers for 6D Pose Estimation with Confidence-Level Prediction and Pose Tokens

In the current state of 6D pose estimation, top-performing techniques depend on complex intermediate correspondences, specialized architectures, and non-end-to-end algorithms. In contrast, our research reframes the problem as a straightforward regression task by exploring the capabilities of Vision Transformers for direct 6D pose estimation through a tailored use of classification tokens. We also introduce a simple method for determining pose confidence, which can be readily integrated into most 6D pose estimation frameworks. This involves modifying the transformer architecture by decreasing the number of query elements based on the network's assessment of the scene complexity. Our method that we call Pose Vision Transformer or PViT-6D provides the benefits of simple implementation and being end-to-end learnable while outperforming current state-of-the-art methods by +0.3% ADD(-S) on Linemod-Occlusion and +2.7% ADD(-S) on the YCB-V dataset. Moreover, our method enhances both the model's interpretability and the reliability of its performance during inference

OC-0549: Improving the clinical applicability of markerless lung tumour tracking with contrast-enhanced kV imaging

In-room kV imaging is widely applied for intrafraction motion compensation in image-guided radiation therapy (IGRT). The low contrast of lung tumours in kV images and the overlap of high-intensity surrounding structures, such as the mediastinum, may limit the applicability of IGRT techniques in lung cancer treatments. The aim of this study is to apply a CT-based contrast enhancement method to improve markerless lung tumour tracking in kV images, thus enhancing the potential of X-raybased image guidance in lung cancer patients

Contactless Sensing of Water Properties for Smart Monitoring of Pipelines

A key milestone for the pervasive diffusion of wireless sensing nodes for smart monitoring of water quality and quantity in distribution networks is the simplification of the installation of sensors. To address this aspect, we demonstrate how two basic contactless sensors, such as piezoelectric transducers and strip electrodes (in a longitudinal interdigitated configuration to sense impedance inside and outside of the pipe with potential for impedimetric leak detection), can be easily clamped on plastic pipes to enable the measurement of multiple parameters without contact with the fluid and, thus, preserving the integrity of the pipe. Here we report the measurement of water flow rate (up to 24 m(3)/s) and temperature with ultrasounds and of the pipe filling fraction (capacitance at 1 MHz with similar to cm(3) resolution) and ionic conductivity (resistance at 20 MHz from 700 to 1400 mu S/cm) by means of impedance. The equivalent impedance model of the sensor is discussed in detail. Numerical finite-element simulations, carried out to optimize the sensing parameters such as the sensing frequency, confirm the lumped models and are matched by experimental results. In fact, a 6 m long, 30 L demonstration hydraulic loop was built to validate the sensors in realistic conditions (water speed of 1 m/s) monitoring a pipe segment of 0.45 m length and 90 mm diameter (one of the largest ever reported in the literature). Tradeoffs in sensors accuracy, deployment, and fabrication, for instance, adopting single-sided flexible PCBs as electrodes protected by Kapton on the external side and experimentally validated, are discussed as well

integration of enhanced optical tracking techniques and imaging in igrt

Patient setup/Optical tracking/IGRT/Treatment surveillance. In external beam radiotherapy, modern technologies for dynamic dose delivery and beam conformation provide high selectivity in radiation dose administration to the pathological volume. A comparable accuracy level is needed in the 3-D localization of tumor and organs at risk (OARs), in order to accomplish the planned dose distribution in the reality of each irradiation session. In-room imaging techniques for patient setup verification and tumor targeting may benefit of the combined daily use of optical tracking technologies, supported by techniques for the detection and compensation of organ motion events. Multiple solutions to enhance the use of optical tracking for the on-line correction of target localization uncertainties are described, with specific emphasis on the compensation of setup errors, breathing movements and non-rigid deformations. The final goal is the implementation of customized protocols where appropriate external landmarks, to be tracked in real-time by means of noninvasive optical devices, are selected as a function of inner target localization. The presented methodology features high accuracy in patient setup optimization, also providing a valuable tool for on-line patient surveillance, taking into account both breathing and deformation effects. The methodic application of optical tracking is put forward to represent a reliable and low cost procedure for the reduction of safety margins, once the patient-specific correlation between external landmarks and inner structures has been established. Therefore, the integration of optical tracking with in-room imaging devices is proposed as a way to gain higher confidence in the framework of Image Guided Radiation Therapy (IGRT) treatments

X-ray CT adaptation based on a 2D–3D deformable image registration framework using simulated in-room proton radiographies

The aim of this work is to investigate in-room proton radiographies to compensate realistic rigid and non-rigid transformations in clinical-like scenarios based on 2D–3D deformable image registration (DIR) framework towards future clinical implementation of adaptive radiation therapy (ART). Monte Carlo simulations of proton radiographies (pRads) based on clinical x-ray CT of a head and neck, and a brain tumor patients are simulated for two different detector configurations (i.e. integration-mode and list-mode detectors) including high and low proton statistics. A realistic deformation, derived from cone beam CT of the patient, is applied to the treatment planning CT. Rigid inaccuracies in patient positioning are also applied and the effect of small, medium and large fields of view (FOVs) is investigated. A stopping criterion, as desirable in realistic scenarios devoid of ground truth proton CT (pCT), is proposed and investigated. Results show that rigid and non-rigid transformations can be compensated based on a limited number of low dose pRads. The root mean square error with respect to the pCT shows that the 2D–3D DIR of the treatment planning CT based on 10 pRads from integration-mode data and 2 pRads from list-mode data is capable of achieving comparable accuracy (∼90% and >90%, respectively) to conventional 3D–3D DIR. The dice similarity coefficient over the segmented regions of interest also verifies the improvement in accuracy prior to and after 2D–3D DIR. No relevant changes in accuracy are found between high and low proton statistics except for 2 pRads from integration-mode data. The impact of FOV size is negligible. The convergence of the metric adopted for the stopping criterion indicates the optimal convergence of the 2D–3D DIR. This work represents a further step towards the potential implementation of ART in proton therapy. Further computational optimization is however required to enable extensive clinical validation

On the robustness of multilateration of ionoacoustic signals for localization of the Bragg peak at pre-clinical proton beam energies in water

Objectives. The energy deposited in a medium by a pulsed proton beam results in the emission of thermoacoustic waves, also called ionoacoustics (IA). The proton beam stopping position (Bragg peak) can be retrieved from a time-of-flight analysis (ToF) of IA signals acquired at different sensor locations (multilateration). This work aimed to assess the robustness of multilateration methods in proton beams at pre-clinical energies for the development of a small animal irradiator. Approach. The accuracy of multilateration performed using different algorithms; namely, time of arrival and time difference of arrival, was investigated in-silico for ideal point sources in the presence of realistic uncertainties on the ToF estimation and ionoacoustic signals generated by a 20 MeV pulsed proton beam stopped in a homogeneous water phantom. The localisation accuracy was further investigated experimentally based on two different measurements with pulsed monoenergetic proton beams at energies of 20 and 22 MeV. Main results. It was found that the localisation accuracy mainly depends on the position of the acoustic detectors relative to the proton beam due to spatial variation of the error on the ToF estimation. By optimally positioning the sensors to reduce the ToF error, the Bragg peak could be located in-silico with an accuracy better than 90 μm (2 error). Localisation errors going up to 1 mm were observed experimentally due to inaccurate knowledge of the sensor positions and noisy ionoacoustic signals. Significance. This study gives a first overview of the implementation of different multilateration methods for ionoacoustics-based Bragg peak localisation in two- and three-dimensions at pre-clinical energies. Different sources of uncertainty were investigated, and their impact on the localisation accuracy was quantified in-silico and experimentally

Integration of Spatial Distortion Effects in a 4D Computational Phantom for Simulation Studies in Extra-Cranial MRI-guided Radiation Therapy: Initial Results.

PurposeSpatial distortions in magnetic resonance imaging (MRI) are mainly caused by inhomogeneities of the static magnetic field, nonlinearities in the applied gradients, and tissue‐specific magnetic susceptibility variations. These factors may significantly alter the geometrical accuracy of the reconstructed MR image, thus questioning the reliability of MRI for guidance in image‐guided radiation therapy. In this work, we quantified MRI spatial distortions and created a quantitative model where different sources of distortions can be separated. The generated model was then integrated into a four‐dimensional (4D) computational phantom for simulation studies in MRI‐guided radiation therapy at extra‐cranial sites.MethodsA geometrical spatial distortion phantom was designed in four modules embedding laser‐cut PMMA grids, providing 3520 landmarks in a field of view of (345 × 260 × 480) mm3. The construction accuracy of the phantom was verified experimentally. Two fast MRI sequences for extra‐cranial imaging at 1.5 T were investigated, considering axial slices acquired with online distortion correction, in order to mimic practical use in MRI‐guided radiotherapy. Distortions were separated into their sources by acquisition of images with gradient polarity reversal and dedicated susceptibility calculations. Such a separation yielded a quantitative spatial distortion model to be used for MR imaging simulations. Finally, the obtained spatial distortion model was embedded into an anthropomorphic 4D computational phantom, providing registered virtual CT/MR images where spatial distortions in MRI acquisition can be simulated.ResultsThe manufacturing accuracy of the geometrical distortion phantom was quantified to be within 0.2 mm in the grid planes and 0.5 mm in depth, including thickness variations and bending effects of individual grids. Residual spatial distortions after MRI distortion correction were strongly influenced by the applied correction mode, with larger effects in the trans‐axial direction. In the axial plane, gradient nonlinearities caused the main distortions, with values up to 3 mm in a 1.5 T magnet, whereas static field and susceptibility effects were below 1 mm. The integration in the 4D anthropomorphic computational phantom highlighted that deformations can be severe in the region of the thoracic diaphragm, especially when using axial imaging with 2D distortion correction. Adaptation of the phantom based on patient‐specific measurements was also verified, aiming at increased realism in the simulation.ConclusionsThe implemented framework provides an integrated approach for MRI spatial distortion modeling, where different sources of distortion can be quantified in time‐dependent geometries. The computational phantom represents a valuable platform to study motion management strategies in extra‐cranial MRI‐guided radiotherapy, where the effects of spatial distortions can be modeled on synthetic images in a virtual environment