Maximum Likelihood Estimation of Head Motion Using Epipolar Consistency

Open gantry C-arm systems that are placed within the interventional room enable 3-D imaging and guidance for stroke therapy without patient transfer. This can profit in drastically reduced time-totherapy, however, due to the interventional setting, the data acquisition is comparatively slow. Thus, involuntary patient motion needs to be estimated and compensated to achieve high image quality. Patient motion results in a misalignment of the geometry and the acquired image data. Consistency measures can be used to restore the correct mapping to compensate the motion. They describe constraints on an idealized imaging process which makes them also sensitive to beam hardening, scatter, truncation or overexposure. We propose a probabilistic approach based on the Student’s t-distribution to model image artifacts that affect the consistency measure without sourcing from motion

Pediatric Patient Surface Model Atlas Generation and X-Ray Skin Dose Estimation

Fluoroscopy is used in a wide variety of examinations and procedures to diagnose or treat patients in modern pediatric medicine. Although these image guided interventions have many advantages in treating pediatric patients, understanding the deterministic and long term stochastic effects of ionizing radiation are of particular importance for this patient demographic. Therefore, quantitative estimation and visualization of radiation exposure distribution, and dose accumulation over the course of a procedure, is crucial for intra-procedure dose tracking and long term monitoring for risk assessment. Personalized pediatric models are necessary for precise determination of patient-X-ray interactions. One way to obtain such a model is to collect data from a population of pediatric patients, establish a population based generative pediatric model and use the latter for skin dose estimation. In this paper, we generate a population model for pediatric patient using data acquired by two RGB-D cameras from different views. A generative atlas was established using template registration. We evaluated the registered templates and generative atlas by computing the mean vertex error to the associated point cloud. The evaluation results show that the mean vertex error reduced from 25.2 ± 12.9 mm using an average surface model to 18.5 ± 9.4mm using specifically estimated pediatric surface model using the generated atlas. Similarly, the dose estimation error was halved from 10.6 ± 8.5% using the average surface model to 5.9 ± 9.3% using the personalized surface estimates

Methods for controlling of the laser-induced absorption in a BTO crystal by using of cw-laser radiation

A photorefractive BTO crystal is exposed with cw-laser beams (430 mW/cm2 at 514 nm), and the photoinduced absorption between 480 and 900 nm is studied. A method of controlling this absorption by low intensity laser radiation of “green” and “red” wavelengths is demonstrated. The physical mechanism can be explained by the redistribution of electrons on the long-lived energy levels into the forbidden band. Dynamical characteristics of the redistribution are estimated

Deep action learning enables robust 3D segmentation of body organs in various CT and MRI images

In this study, we propose a novel point cloud based 3D registration and segmentation framework using reinforcement learning. An artificial agent, implemented as a distinct actor based on value networks, is trained to predict the optimal piece-wise linear transformation of a point cloud for the joint tasks of registration and segmentation. The actor network estimates a set of plausible actions and the value network aims to select the optimal action for the current observation. Point-wise features that comprise spatial positions (and surface normal vectors in the case of structured meshes), and their corresponding image features, are used to encode the observation and represent the underlying 3D volume. The actor and value networks are applied iteratively to estimate a sequence of transformations that enable accurate delineation of object boundaries. The proposed approach was extensively evaluated in both segmentation and registration tasks using a variety of challenging clinical datasets. Our method has fewer trainable parameters and lower computational complexity compared to the 3D U-Net, and it is independent of the volume resolution. We show that the proposed method is applicable to mono- and multi-modal segmentation tasks, achieving significant improvements over the state-of-the-art for the latter. The flexibility of the proposed framework is further demonstrated for a multi-modal registration application. As we learn to predict actions rather than a target, the proposed method is more robust compared to the 3D U-Net when dealing with previously unseen datasets, acquired using different protocols or modalities. As a result, the proposed method provides a promising multi-purpose segmentation and registration framework, particular in the context of image-guided interventions

Automatic guide-wire detection for neurointerventions using low-rank sparse matrix decomposition and denoising.

In neuro-interventional surgeries, physicians rely on fluoroscopic video sequences to guide tools through the vascular system to the region of interest. Due to the low signal-to-noise ratio of low-dose images and the presence of many line-like structures in the brain, the guide-wire and other tools are difficult to see. In this work we propose an effective method to detect guide-wires in fluoroscopic videos that aims at enhancing the visualization for better intervention guidance. In contrast to prior work, we do not rely on a specific modeling of the catheter (e.g. shape, intensity, etc.), nor on prior statistical learning. Instead, we base our approach on motion cues by making use of recent advances in low-rank and sparse matrix decomposition, which we then combine with denoising. An evaluation on 651 X-ray images from 5 patient shows that our guide-wire tip detection is precise and within clinical tolerance for guide-wire inter-frame motions as high as 6 mm

Optical measurements with phase-conjugate mirrors

Phase-conjugate mirrors on the basis of photorefractive crystals offer interesting possibilities of application for optical measurement systems. In this paper we report on the application of phase-conjugate mirrors (PCMs) for optical measurement techniques. We show different schemes for realizing PCNIs and discuss characteristics such as reflectivity, fidelity, and time behavior. We demonstrate that two-beam interferometers with PCNIs and with a suitable phase-shifting unit in the reference arm have general advantages compared with conventional systems. It is shown that self-pumped PCNIs (SPPCNIs) could also be successfully used for novelty filtering systems comparing a present state of a signal phase with a reference phase. It is found that SPPCNIs can improve both the axial and the lateral resolution of a confocal microscope. A 30% better resolution of the PCM system is established. We demonstrate that two-beam interferometers with PCNIs and with a suitable phase-shifting unit in the reference arm have general advantages compared with conventional systems. It is shown that self-pumped PCNIs (SPPCNIs) could also be successfully used for novelty filtering systems comparing a present state of a signal phase with a reference phase. It is found that SPPCNIs can improve both the axial and the lateral resolution of a confocal microscope. A 30% better resolution of the PCM system is established