An iterative image-based inter-frame motion compensation method for dynamic brain PET imaging

As a non-invasive imaging tool, Positron Emission Tomography (PET) plays an important role in brain science and disease research. Dynamic acquisition is one way of brain PET imaging. Its wide application in clinical research has often been hindered by practical challenges, such as patient involuntary movement, which could degrade both image quality and the accuracy of the quantification. This is even more obvious in scans of patients with neurodegeneration or mental disorders. Conventional motion compensation methods were either based on images or raw measured data, were shown to be able to reduce the effect of motion on the image quality. As for a dynamic PET scan, motion compensation can be challenging as tracer kinetics and relatively high noise can be present in dynamic frames. In this work, we propose an image-based inter-frame motion compensation approach specifically designed for dynamic brain PET imaging. Our method has an iterative implementation that only requires reconstructed images, based on which the inter-frame subject movement can be estimated and compensated. The method utilized tracer-specific kinetic modelling and can deal with simple and complex movement patterns. The synthesized phantom study showed that the proposed method can compensate for the simulated motion in scans with 18F-FDG, 18F-Fallypride and 18F-AV45. Fifteen dynamic 18F-FDG patient scans with motion artifacts were also processed. The quality of the recovered image was superior to the one of the non-corrected images and the corrected images with other image-based methods. The proposed method enables retrospective image quality control for dynamic brain PET imaging, hence facilitates the applications of dynamic PET in clinics and research.

An Articulated Robotic Forceps Design with a Parallel Wrist-Gripper Mechanism and Parasitic Motion Compensation

In this paper, a novel 4 degrees-of-freedom articulated parallel forceps mechanism with a large orientation workspace (±/−90deg in pitch and yaw, 360deg in roll rotations) is presented for robotic minimally invasive surgery. The proposed 3RSR-1UUP parallel mechanism utilizes a UUP center-leg which can convert thrust motion of the 3RSR mechanism into gripping motion. This design eliminates the need for an additional gripper actuator, but also introduces the problem of unintentional gripper opening/closing due to parasitic motion of the 3RSR mechanism. Here, position kinematics of the proposed mechanism, including the workspace, is analyzed in detail, and a solution to the parasitic motion problem is provided. Human in the loop simulations with a haptic interface are also performed to confirm the feasibility of the proposed design.

Optical-flow-based motion compensation algorithm in thermoelastic stress analysis using single-infrared video

<p>Thermoelastic stress analysis (TSA) is a non-contact measurement technique for stress distribution evaluation. A common issue related to this technique is the rigid-displacement of the specimen during the test phase, that can compromise the reliability of the measurement. For this purpose, several motion compensation techniques have been implemented over the years, but none of them is provided through a single measurement and a single sample surface conditioning. Due to this, a motion compensation technique based on Optical-Flow has been implemented, which greatly increases the strength and the effectiveness of the methodology through a single measurement and single specimen preparation. The proposed approach is based on measuring the displacement field of the specimen directly from the thermal video, through optical flow. This displacement field is then used to compensate for the specimen’s displacement on the infrared video, which will then be used for thermoelastic stress analysis. Firstly, the algorithm was validated by a comparison with synthetic videos, created ad hoc, and the quality of the motion compensation approach was evaluated on video acquired in the visible range. The research moved into infrared acquisitions, where the application of TSA gave reliable and accurate results. Finally, the quality of the stress map obtained was verified by comparison with a numerical model.</p>

Axial Motion Compensation Of Optical Coherence Tomography With A Collaborative Medical Robot

This thesis presents an imaging tool consisting of an Optical Coherence Tomography (OCT) imaging system mounted on a collaborative robotic arm to enable axial motion compensation. Optical Coherence Tomography is a subsurface, high-resolution imaging modality used in neuroimaging to differentiate between pathological and non-pathological tissue. The motivation behind this project is to bring Optical Coherence Tomography to the operating room for neuroimaging to help with cancerous tissue differentiation and maximize the extent of tumor resection. However, neurosurgeons have expressed concern with respect to intracranial pressure (ICP) pulsation displacing the brain far off the optic axis of the imaging system so as to not be visible. The collaborative robotic arm compensates for sample motion along the optic axis using a Proportional controller to track the position of the peak intensity of the sample’s intensity profile, which generally corresponds to the sample surface. Collaborative robots have changed the robot industry paradigm becoming increasingly functional and safer than the previous generations of robotic arms. We present an OCT robot end-effector to test the feasibility of performing OCT imaging with the collaborative robot.

Axial Motion Compensation Of Optical Coherence Tomography With A Collaborative Medical Robot

This thesis presents an imaging tool consisting of an Optical Coherence Tomography (OCT) imaging system mounted on a collaborative robotic arm to enable axial motion compensation. Optical Coherence Tomography is a subsurface, high-resolution imaging modality used in neuroimaging to differentiate between pathological and non-pathological tissue. The motivation behind this project is to bring Optical Coherence Tomography to the operating room for neuroimaging to help with cancerous tissue differentiation and maximize the extent of tumor resection. However, neurosurgeons have expressed concern with respect to intracranial pressure (ICP) pulsation displacing the brain far off the optic axis of the imaging system so as to not be visible. The collaborative robotic arm compensates for sample motion along the optic axis using a Proportional controller to track the position of the peak intensity of the sample’s intensity profile, which generally corresponds to the sample surface. Collaborative robots have changed the robot industry paradigm becoming increasingly functional and safer than the previous generations of robotic arms. We present an OCT robot end-effector to test the feasibility of performing OCT imaging with the collaborative robot.

Temperature measurement of intracranial cerebrospinal fluid using second-order motion compensation diffusion tensor imaging

AbstractTo reduce the determination errors of CSF pulsation in diffusion-weighted image (DWI) thermometry, we investigated whether applying second-order motion compensation diffusion tensor imaging (2nd-MC DTI) and fractional anisotropy (FA) processing improves the measurement of intracranial cerebrospinal fluid (CSF) temperature. In a phantom study, we investigated the relationship between temperature and FA in artificial CSF (ACSF) to determine the threshold for FA processing. The temperatures of ACSF were compared with those of water. In a human study, 18 healthy volunteers were scanned using conventional DTI (c-DTI) and 2nd-MC DTI on a 3.0T magnetic resonance imaging (MRI) system. A temperature map was created using diffusion coefficients from each DWI with/without FA processing. The temperatures of intracranial CSF were compared between each DTI image using Welch’s analysis of variance and Games–Howell’s multiple comparisons. In the phantom study, FA did not exceed 0.1 at any temperature. Consequently, pixels exceeding the threshold of 0.1 were removed from the temperature map. Intracranial CSF temperatures significantly differed between the four methods (p < 0.0001). The lowest temperature was 2nd-MC DTI with FA processing (mean, 35.62℃), followed in order by c-DTI with FA processing (mean, 36.16℃), 2nd-MC DTI (mean, 37.08℃), and c-DTI (mean, 39.08℃; p < 0.01 for each). Because the temperature of ACSF was estimated to be lower than that of water, the temperature of 2nd-DTI with FA processing was considered reasonable. The method of 2nd-MC DTI with FA processing enabled determining intracranial CSF temperature with a reduction in CSF pulsation.

Application of Digital Image Based on Machine Learning in Media Art Design

In digital media art, expressive force is an important art form of media. This paper studies digital images that have the same effect when applied to media art. The research object is media art images, and the application effect of the proposed algorithm is related to the media art images. The development of digital image technology has brought revolutionary changes to traditional media art expression techniques. In this paper, a partial-pixel interpolation technique based on convolutional neural network is proposed. Supervised training of convolutional neural networks requires predetermining the input and target output of the network, namely, integer image and fractional image in this paper. To solve the problem that the subpixel sample cannot be obtained, this paper first analyzes the imaging principle of digital image and proposes a subpixel sample generation algorithm based on Gaussian low-pass filter and polyphase sampling. From the perspective of rate distortion optimization, the purpose of pixel motion compensation is to improve the accuracy of interframe prediction. Therefore, this paper defines pixel motion compensation as an interframe regression problem, that is, the mapping process of the reference image integral pixel sample to the current image sample to be encoded. In this paper, a generalized partial-pixel interpolation model is proposed for bidirectional prediction. The partial-pixel interpolation of bidirectional prediction is regarded as a binary regression model; that is, the integral pixel reference block in two directions is mapped to the current block to be coded. It further studies how to apply the trained digital images to media art design more flexibly and efficiently.