Image-based Motion Research Articles

Impact of subject head motion on quantitative brain 15O PET and its correction by image-based registration algorithm

Subject head motion during sequential (15)O positron emission tomography (PET) scans can result in artifacts in cerebral blood flow (CBF) and oxygen metabolism maps. However, to our knowledge, there are no systematic studies examining this issue. Herein, we investigated the effect of head motion on quantification of CBF and oxygen metabolism, and proposed an image-based motion correction method dedicated to (15)O PET study, correcting for transmission-emission mismatch and inter-scan mismatch of emission scans. We analyzed (15)O PET data for patients with major arterial steno-occlusive disease (n = 130) to determine the occurrence frequency of head motion during (15)O PET examination. Image-based motion correction without and with realignment between transmission and emission scans, termed simple and 2-step method, respectively, was applied to the cases that showed severe inter-scan motion. Severe inter-scan motion (>3 mm translation or >5° rotation) was observed in 27 of 520 adjacent scan pairs (5.2 %). In these cases, unrealistic values of oxygen extraction fraction (OEF) or cerebrovascular reactivity (CVR) were observed without motion correction. Motion correction eliminated these artifacts. The volume-of-interest (VOI) analysis demonstrated that the motion correction changed the OEF on the middle cerebral artery territory by 17.3 % at maximum. The inter-scan motion also affected CBV, CMRO2 and CBF, which were improved by the motion correction. A difference of VOI values between the simple and 2-step method was also observed. These data suggest that image-based motion correction is useful for accurate measurement of CBF and oxygen metabolism by (15)O PET.

Robust vision-aided inertial navigation algorithm via entropy-like relative pose estimation

The paper presents a loosely coupled approach for the improvement of state estimation in autonomous inertial navigation, augmented via image-based relative motion estimation. The proposed approach uses a novel pose estimation technique based on the minimization of an entropy-like cost function which is robust by nature to the presence of noise and outliers in the visual features. Experimental evidence of the performance of this approach is given and compared to a state-of-the-art algorithm. An indirect Kalman filter is used for navigation in the framework of stochastic cloning. The robust relative pose estimation given by our novel technique is used to feed a relative position fix to the navigation filter. The simulation results are presented and compared with the results obtained via the classical RANSAC-based direct linear transform approach.

Motion corrected sensitivity encoded isotropic projection reconstruction (SNIPR) for whole-heart coronary MRA

Background As has been shown recently, the use of undersampled 3D projection reconstruction (3DPR) and image-based motion correction with 100% acquisition efficiency [1,2] enables highly accelerated coronary MRA (CMRA) with high isotropic resolution. However, streaking artifact from undersampling significantly reduces the apparent signal-to-noise ratio (SNR). Moreover, affine motion correction distorts the k-space trajectory, hence introducing more streaking. In this work, the SNIPR reconstruction [3], a self-calibrated sensitivity encoding scheme for 3DPR, is integrated with image-based motion correction to reduce streaking artifacts and thus improve image quality.

Assessing Bradykinesia in Parkinsonian Disorders

Objective: Bradykinesia is one of the clinical hallmarks of Parkinson’s disease (PD) and atypical Parkinsonian syndromes. Clinical ratings scales and technology-based assessments have been developed to measure bradykinesia. We review the different tools that exist for measurement of bradykinesia and analyze their reliability and applicability to PD and atypical Parkinsonian syndromes.Methods: We summarize data on the factor structure of the two primary scales used to assess PD, the Unified Parkinson’s Disease Rating Scale (UPDRS) and Movement Disorder Society revision of the UPDRS, the MDS-UPDRS. We review how these scales have been used in atypical Parkinsonian syndromes, specifically Progressive Supranuclear Palsy (PSP). Finally, we report on the different technology-based tools being used to assess bradykinesia.Results: The UPDRS is a useful measure of PD function and disability with six clinically distinct factors, three of which pertain to bradykinesia. The MDS-UPDRS has shown high internal consistency and correlation with the original UPDRS. Factor analysis of the UPDRS in PSP reveals five clinically distinct factors, two of which are independent bradykinesia factors. Thus the UPDRS and MDS-UPDRS are reliable and applicable scales for PD and the UPDRS can be used to assess bradykinesia in PSP. Technology-based tools for measuring bradykinesia include gyrosensors, Coordination Ability Test System, Brain Test, quantitative digitography, Motus motion analysis system, precision real-time image-based motion analysis, and the At-Home Testing Device. These tools have been compared to the UPDRS motor subscale and are effective in assessing bradykinesia.Conclusion: The UPDRS and MDS-UPDRS are well-established measures of bradykinesia that are applicable and useful in PD. The UPDRS is also been shown to be applicable to PSP. Different technologies exist to measure bradykinesia, though further work is needed to validate these assessment tools and bring them to clinical practice.

Measurement of patient imaging dose for real-time kilovoltage x-ray intrafraction tumour position monitoring in prostate patients

The dose for image-based motion monitoring of prostate tumours during radiotherapy delivery has not been established. This study aimed to provide quantitative analysis and optimization of the fluoroscopic patient imaging dose during radiotherapy for IMRT and VMAT treatments using standard and hypofractionated treatment schedules. Twenty-two patients with type T1c N0/M0 prostate cancer and three implanted fiducial markers were considered. Minimum field sizes encompassing all fiducial markers plus a 7.5 mm motion margin were determined for each treatment beam, each patient and the complete cohort. Imaging doses were measured for different field sizes and depths in a phantom at 75 and 120 kV. Based on these measurements, the patient imaging doses were then estimated according to beam-on time for clinical settings. The population minimum field size was 5.3 × 6.1 cm2, yielding doses of 406 and 185 mGy over the course of an IMRT treatment for 75 kV (10 mAs) and 120 kV (1.04 mAs) imaging respectively, at 1 Hz. The imaging dose was reduced by an average of 28% and 32% by adopting patient-specific and treatment-beam-specific field sizes respectively. Standard fractionation VMAT imaging doses were 37% lower than IMRT doses over a complete treatment. Hypofractionated IMRT stereotactic body radiotherapy (SBRT) and VMAT SBRT imaging doses were 58% and 76% lower than IMRT doses respectively. The patient dose for kilovoltage intrafraction monitoring of the prostate was quantified. Tailoring imaging field sizes to specific patients yielded a significant reduction in the imaging dose, as did adoption of faster treatment modalities such as VMAT.

Robust Real-Time-Constrained Estimation of Respiratory Motion for Interventional MRI on Mobile Organs

Real-time magnetic resonance imaging is a promising tool for image-guided interventions. For applications such as thermotherapy on moving organs, a precise image-based compensation of motion is required in real time to allow quantitative analysis, retrocontrol of the interventional device, or determination of the therapy endpoint. Reduced field-of-view imaging represents a promising way to improve spatial and/or temporal resolution. However, it introduces new challenges for target motion estimation, since structures near the target may appear transiently due to the respiratory motion and the limited spatial coverage. In this paper, a new image-based motion estimation method is proposed combining a global motion estimation with a novel optical flow approach extending the initial Horn and Schunck (H&S) method by an additional regularization term. This term integrates the displacement of physiological landmarks into the variational formulation of the optical flow problem. This allowed for a better control of the optical flow in presence of transient structures. The method was compared to the same registration pipeline employing the H&S approach on a synthetic dataset and in vivo image sequences. Compared to the H&S approach, a significant improvement (p<0.05) of the Dice's similarity criterion computed between the reference and the registered organ positions was achieved.

Correction of head movements in positron emission tomography using point source tracking system: a simulation study

The motion of the head during brain positron emission tomography (PET) acquisitions has been identified as a source of artifact in the reconstructed image. In this study, a method is described to develop an image-based motion correction technique for correcting the post-acquisition data without using external optical motion-tracking system such as POLARIS. In this technique, GATE has been used to simulate PET brain scan using point sources mounted around the head to accurately monitor the position of the head during the time frames. The measurement of head motion in each frame showed a transformation in the image frame matrix, resulting in a fully corrected data set. Using different kinds of phantoms and motions, the accuracy of the correction method is tested and its applicability to experimental studies is demonstrated as well.

Robust bronchoscope motion tracking using sequential Monte Carlo methods in navigated bronchoscopy: dynamic phantom and patient validation

Accurate and robust estimates of camera position and orientation in a bronchoscope are required for navigation. Fusion of pre-interventional information (e.g., CT, MRI, or US) and intra-interventional information (e.g., bronchoscopic video) were incorporated into a navigation system to provide physicians with an augmented reality environment for bronchoscopic interventions. Two approaches were used to predict bronchoscope movements by incorporating sequential Monte Carlo (SMC) simulation including (1) image-based tracking techniques and (2) electromagnetic tracking (EMT) methods. SMC simulation was introduced to model ambiguities or uncertainties that occurred in image- and EMT-based bronchoscope tracking. Scale invariant feature transform (SIFT) features were employed to overcome the limitations of image-based motion tracking methods. Validation was performed on five phantom and ten human case datasets acquired in the supine position. For dynamic phantom validation, the EMT-SMC simulation method improved the tracking performance of the successfully registered bronchoscopic video frames by 12.7% compared with a hybrid-based method. In comparisons between tracking results and ground truth, the accuracy of the EMT-SMC simulation method was 1.51 mm (positional error) and 5.44° (orientation error). During patient assessment, the SIFT-SMC simulation scheme was more stable or robust than a previous image-based approach for bronchoscope motion estimation, showing 23.6% improvement of successfully tracked frames. Comparing the estimates of our method to ground truth, the position and orientation errors are 3.72 mm and 10.2°, while those of our previous image-based method were at least 7.77 mm and 19.3°. The computational times of our EMT- and SIFT-SMC simulation methods were 0.9 and 1.2 s per frame, respectively. The SMC simulation method was developed to model ambiguities that occur in bronchoscope tracking. This method more stably and accurately predicts the bronchoscope camera position and orientation parameters, reducing uncertainties due to problematic bronchoscopic video frames and airway deformation during intra-bronchoscopy navigation.

Image-based motion planning for two mobile robots in an environment with obstacles by using cellular neural networks

Abstract The paper presents an image-based algorithm for motion-planning of two mobile robots moving to the same target in an environment with obstacles. Due to parallel computing, the Cellular Neural Networks (CNN) techniques ensure the images processing in real-time and represent an advantageous solution for autonomous mobile robots guidance. The path planning algorithm can be improved increasing the speed of image processing, using advanced type of the CNN implementation and it can be extended for three or more robots.

Real‐time optical motion correction for diffusion tensor imaging

Head motion is a fundamental problem in brain MRI. The problem is further compounded in diffusion tensor imaging because of long acquisition times, and the sensitivity of the tensor computation to even small misregistration. To combat motion artifacts in diffusion tensor imaging, a novel real-time prospective motion correction method was introduced using an in-bore monovision system. The system consists of a camera mounted on the head coil and a self-encoded checkerboard marker that is attached to the patient's forehead. Our experiments showed that optical prospective motion correction is more effective at removing motion artifacts compared to image-based retrospective motion correction. Statistical analysis revealed a significant improvement in similarity between diffusion data acquired at different time points when prospective correction was used compared to retrospective correction (P<0.001).

Navigation Aiding Based on Coupled Online Mosaicking and Camera Scanning

This paper presents a new method for vision-aided navigation of airborne platforms. The method is based on online mosaicking using images acquired by an onboard gimballed camera, which scans ground regions in the vicinity of the flight trajectory. The coupling of the scanning and mosaicking processes improves image-based motion estimation when operating in challenging scenarios such as narrow field-of-view cameras observing low-texture scenes. These improved motion estimations are fused with an inertial navigation system. The mosaic used for navigation is constructed in two levels. A small mosaic based on recently captured images is computed in real-time, and a larger mosaic including all the images is computed in a background process. The low-level mosaic is used for immediate motion estimation, while the higher-level mosaic is used for global navigation. The correlation terms between the navigation system and the mosaic construction process are not maintained in the proposed approach. The advantage of this architecture is the low computational load required for navigation aiding. However, the accuracy of the proposed method could be compromised compared with bearing-only simultaneous localization and mapping. The new method was examined using statistical simulation runs and experiments based on Google Earth imagery, showing its superior performance compared with traditional methods for two-view navigation aiding.

Adaptive Radiation for Lung Cancer

The challenges of lung cancer radiotherapy are intra/inter-fraction tumor/organ anatomy/motion changes and the need to spare surrounding critical structures. Evolving radiotherapy technologies, such as four-dimensional (4D) image-based motion management, daily on-board imaging and adaptive radiotherapy based on volumetric images over the course of radiotherapy, have enabled us to deliver higher dose to target while minimizing normal tissue toxicities. The image-guided radiotherapy adapted to changes of motion and anatomy has made the radiotherapy more precise and allowed ablative dose delivered to the target using novel treatment approaches such as intensity-modulated radiation therapy, stereotactic body radiation therapy, and proton therapy in lung cancer, techniques used to be considered very sensitive to motion change. Future clinical trials using real time tracking and biological adaptive radiotherapy based on functional images are proposed.

Data Assimilation for Convective-Cell Tracking on Meteorological Image Sequences

This paper focuses on the tracking and analysis of convective cloud systems from Meteosat Second Generation images. The highly deformable nature of convective clouds, the complexity of the physical processes involved, and also the partially hidden measurements available from image data make difficult the direct use of conventional image-analysis techniques for tasks of detection, tracking, and characterization. In this paper, we face these issues using variational-data-assimilation tools. Such techniques enable us to perform the estimation of an unknown state function according to a given dynamical model and to noisy and incomplete measurements. The system state we are setting in this study for the cloud representation is composed of two nested curves corresponding to the exterior frontiers of the clouds and to the interior coldest parts (core) of the convective clouds. Since no reliable simple dynamical model exists for such phenomena at the image grid scale, the dynamics on which we are relying has been directly defined from image-based motion measurements and takes into account an uncertainty modeling of the curve dynamics along time. In addition to this assimilation technique, we show in the Appendix how each cell of the recovered cloud system can be labeled and associated to characteristic parameters (birth or death time, mean temperature, velocity, growth, etc.) of great interest for meteorologists.

Respiratory motion compensation by model-based catheter tracking during EP procedures

In many cases, radio-frequency catheter ablation of the pulmonary veins attached to the left atrium still involves fluoroscopic image guidance. Two-dimensional X-ray navigation may also take advantage of overlay images derived from static pre-operative 3D volumetric data to add anatomical details otherwise not visible under X-ray. Unfortunately, respiratory motion may impair the utility of static overlay images for catheter navigation. We developed a novel approach for image-based 3D motion estimation and compensation as a solution to this problem. It is based on 3D catheter tracking which, in turn, relies on 2D/3D registration. To this end, a bi-plane C-arm system is used to take X-ray images of a special circumferential mapping catheter from two directions. In the first step of the method, a 3D model of the device is reconstructed. Three-dimensional respiratory motion at the site of ablation is then estimated by tracking the reconstructed catheter model in 3D based on bi-plane fluoroscopy. Phantom data and clinical data were used to assess model-based catheter tracking. Our phantom experiments yielded an average 2D tracking error of 1.4mm and an average 3D tracking error of 1.1mm. Our evaluation of clinical data sets comprised 469 bi-plane fluoroscopy frames (938 monoplane fluoroscopy frames). We observed an average 2D tracking error of 1.0 + or - 0.4mm and an average 3D tracking error of 0.8 + or - 0.5mm. These results demonstrate that model-based motion-compensation based on 2D/3D registration is both feasible and accurate.

Image-Based Motion Detection: Using the Concept of Weighted Directional Descriptors

The use of image guidance in medical applications is constantly growing because of its tremendous impact on the future of health care. Although image-based tissue tracking has been thoroughly explored in the academic literature for years, it has not yet matured to become widely accepted by clinicians. Undetected tissue movements in image-based clinical procedures may cause safety and efficacy difficulties. We introduce an image-based approach for detecting tissue movements during clinical procedures. Our method has been validated in more than 600 true clinical cases. The results show that our algorithm agrees with an expert analysis in 98% of the cases, showing zero events of false alarms and zero events of undetected motion. The results show that the approach provides a clinically ready motion detection algorithm. These robust results are achieved by introducing the concept of weighted directional descriptors (WDDs). The technique analyzes the directivity and confidence level of each anatomical feature and uses it to weight local inputs resulting in a robust motion vector. The robustness is further increased by a novel preprocess that screens out features that may be misleading or are repeated in the adjacent search zone. The technique meets the requirements, as defined by our clinicians, and is now integrated in true medical systems. In particular, our approach has been uniquely developed and integrated into a clinical product. ExAblate is the first Food and Drug Administration (FDA)-approved magnetic resonance (MR)-guided noninvasive surgical device using focused ultrasound therapy. It is used in commercial clinics and in leading medical academic research institutions, attesting to the success of our method and its practical clinical value.

사실적인 컴퓨터 애니메이션 구현을 위한 증분형 영상 기반 운동 렌더링 기법

사실적인 컴퓨터 애니메이션 제작 시 종종 모션 캡쳐 기술이 사용된다. 모션 캡쳐 기술은 대상체의 운동을 측정해서 모델링한 운동 렌더링 결과를 그래픽스로 표현한다. 본 논문에서는 카메라를 사용해서 얻어진 2차원 영상 정보로부터 대상체의 3차원 운동을 측정하여 모델링하는 영상 기반 운동 렌더링 문제를 다룬다. 기존의 영상 기반 운동 렌더링 알고리즘은 계산량이 너무 많거나 정확도가 떨어지는 등의 단점이 있었다. 첫 번 째 단점은 장편 애니메이션 제작시 제작 시간이 너무 길어서 문제가 되고, 두 번 째 단점은 사실적인 애니메이션 구현시 사실감이 저하되는 문제를 야기 시킨다. 이와 같은 기존 방식의 단점을 보완하기 위하여 본 논문에서는 계산량이 적고 정확도가 높은 영상 기반 운동 렌더링 알고리즘을 제안한다. 본 논문에서 제안한 방식에서는 계산량이 적은 증분형 운동렌더링 알고리즘을 최적제어 이론의 시각에서 분석하여 정확도를 향상시키도록 개조한다. 본 방식을 광학식 모션 캡쳐 기술에 적용할 경우 표시자(marker)의 부착 없이도 모션 캡쳐가 가능하다는 부가적 인 장점 또한 얻을 수 있다. Image-based motion capture technology is often used in making realistic computer animation. In this paper we try to implement image-based motion rendering by fixing a camera to a PC. Existing image-based rendering algorithms have disadvantages of high computational burden or low accuracy. The former disadvantage causes too long making-time of an animation. The latter disadvantage degrades reality in making realistic animation. To compensate for those disadvantages of the existing approaches, this paper presents an image-based motion rendering algorithm with low computational load and high estimation accuracy. In the proposed approach, an incremental motion rendering algorithm with low computational load is analyzed in the respect of optimal control theory and revised so that its estimation accuracy is enhanced. If we apply this proposed approach to optic motion capture systems, we can obtain additional advantages that motion capture can be performed without any markers, and with low cost in the respect of equipments and spaces.

Drug‐screening platform based on the contractility of tissue‐engineered muscle

A tissue-based approach to in vitro drug screening allows for determination of the cumulative positive and negative effects of a drug at the tissue rather than the cellular or subcellular level. Skeletal muscle myoblasts were tissue-engineered into three-dimensional muscle with parallel myofibers generating directed forces. When grown attached to two flexible micro-posts (mu posts) acting as artificial tendons in a 96-well plate format, the miniature bioartificial muscles (mBAMs) generated tetanic (active) forces upon electrical stimulation measured with a novel image-based motion detection system. mBAM myofiber hypertrophy and active force increased in response to insulin-like growth factor 1. In contrast, mBAM deterioration and weakness was observed with a cholesterol-lowering statin. The results described in this study demonstrate the integration of tissue engineering and biomechanical testing into a single platform for the screening of compounds affecting muscle strength.

Image-based motion compensation for structured light scanning of dynamic surfaces

Many structured light scanning systems based on temporal pattern codification produce dense and robust results on static scenes but behave very poorly when applied to dynamic scenes in which objects are allowed to move or to deform during the acquisition process. The main reason for this lies in the wrong combination of encoded correspondence information because the same point in the projector pattern sequence can map to different points within the camera images due to depth changes over time. We present a novel approach suitable for measuring and compensating such kind of pattern motion. The described technique can be combined with existing active range scanning systems designed for static surface reconstruction making them applicable for the dynamic case. We demonstrate the benefits of our method by integrating it into a gray code-based structured light scanner, which runs at 30 3D scans per second.

The Elastic View Graph Framework for Autonomous, Surface-based 3D-SLAM Part II: Global Layer and Experiments (Schritthaltendes flächenbasiertes 3D-SLAM mit elastisch gekoppelten Teilkarten)

This paper covers the global aspects of a new SLAM framework introduced in [9]. The map is a graph of overlapping range views, linked by multiple uncertain hypotheses of motion and correspondence. They form the interface between local pose estimation and globally consistent mapping. In order to prune the hypotheses and reduce the brittleness of the local algorithms, we propose a novel cooperation between image-based and odometric motion estimates, and a geometric-probabilistic visibility model for oriented surface features which can also discern moving objects. Global loop closing works by exchanging hypotheses, priorized on a node ambiguity measure to bound the update complexity. The cycle error in frame space is used as a consistency criterion. The new concepts were tested and evaluated during several indoor exploration tours.

Estimation of Errors in Force Platform Data

Force platforms (FPs) are regularly used in the biomechanical analysis of sport and exercise techniques, often in combination with image-based motion analysis (e.g., Begg & Kamruzzaman, 2005; Kuitunen, Komi, & Kyrolainen, 2002; Rahmani, Dalleau, Viale, Hautier, & Lacour, 2000; Rodano & Squadrone, 2002). Force time data, particularly when combined with joint positions and segmental inertia parameters, can be used to evaluate the effectiveness of a wide range of movement patterns in sport and exercise (Bartlett, Messenger, & Lindsay, 1997). According to Dainty and Norman (1987) and Bartlett et al. (1997), valid and reliable force measures depend on low threshold, hysteresis and cross-talk, high linearity, adequate sensitivity and the elimination of cable interference, electrical inductance, and temperature and humidity variations. Moreover, a platform must possess high stiffness and high natural frequency and be located such that extraneous vibrations are excluded. Given that FPs are regularly used in sport and exercise research not only for data collection but also for evaluating other biomechanical equipment (e.g., Rahmani et al., 2000), the lack of attention paid to the likelihood of errors in their measurements is somewhat surprising. Although the scientific literature for kinematic data has established and consistently reported the estimation and propagation of errors, FP data are often taken as error-free. For example, Johnson and Buckley (2001) used a FP to measure ground reaction forces during sprinting, without calculating or reporting possible errors in FP data. Reporting FP data to an unjustifiably high precision and assuming they are acceptably accurate is potentially problematic. For example, when the force data are used for further calculations, such as estimating joint moments from external forces, the error in the force measures will propagate through the calculation--especially in the absence of postprocessing techniques--and directly affect the final result. Some investigators, however, have tried to assess the accuracy and reliability of their measurements. Bobbert and Schamhardt (1990) and Mita et al. (1993) found inaccuracies in estimates of the center of pressure position, especially toward the platform edges, and identified poor calibration and differences in the individual characteristics among the load cell amplifiers as possible causes. Although FP calibration data are usually available from manufacturers, researchers should not assume the manufacturer-quoted values are retained following installation and over time. Experimental error is inevitable in a study that uses FP as a data collection tool and can arise from a variety of sources, which may influence the reliability of the findings and the study's validity. Despite this, neither calibration of FP nor estimation of potential errors in FP measurements is reported in most investigations. Furthermore, because the existing methods reported in studies for FP error calculation require sophisticated equipment and are time-consuming, their application in other FP studies would be rather complicated and, in many laboratories, not possible. Therefore, the purpose of the present study was to establish the magnitude of possible measurement errors from a FP typically used in sport and exercise biomechanics, by applying an easy-to-use, rime-efficient method.