Xiaofeng Liu

Shortest Path Refinement for Motion Estimation From Tagged MR Images

Magnetic resonance tagging makes it possible to measure the motion of tissues such as muscles in the heart and tongue. The harmonic phase (HARP) method largely automates the process of tracking points within tagged MR images, permitting many motion properties to be computed. However, HARP tracking can yield erroneous motion estimates due to 1) large deformations between image frames, 2) through-plane motion, and 3) tissue boundaries. Methods that incorporate the spatial continuity of motion-so-called refinement or flood-filling methods-have previously been reported to reduce tracking errors. This paper presents a new refinement method based on shortest path computations. The method uses a graph representation of the image and seeks an optimal tracking order from a specified seed to each point in the image by solving a single source shortest path problem. This minimizes the potential errors for those path dependent solutions that are found in other refinement methods. In addition to this, tracking in the presence of through-plane motion is improved by introducing synthetic tags at the reference time (when the tissue is not deformed). Experimental results on both tongue and cardiac images show that the proposed method can track the whole tissue more robustly and is also computationally efficient.

Incompressible Deformation Estimation Algorithm (IDEA) From Tagged MR Images

Measuring the 3D motion of muscular tissues, e.g., the heart or the tongue, using magnetic resonance (MR) tagging is typically carried out by interpolating the 2D motion information measured on orthogonal stacks of images. The incompressibility of muscle tissue is an important constraint on the reconstructed motion field and can significantly help to counter the sparsity and incompleteness of the available motion information. Previous methods utilizing this fact produced incompressible motions with limited accuracy. In this paper, we present an incompressible deformation estimation algorithm (IDEA) that reconstructs a dense representation of the 3D displacement field from tagged MR images and the estimated motion field is incompressible to high precision. At each imaged time frame, the tagged images are first processed to determine components of the displacement vector at each pixel relative to the reference time. IDEA then applies a smoothing, divergence-free, vector spline to interpolate velocity fields at intermediate discrete times such that the collection of velocity fields integrate over time to match the observed displacement components. Through this process, IDEA yields a dense estimate of a 3D displacement field that matches our observations and also corresponds to an incompressible motion. The method was validated with both numerical simulation and in vivo human experiments on the heart and the tongue.

Prostate Brachytherapy Seed Reconstruction With Gaussian Blurring and Optimal Coverage Cost

Intraoperative dosimetry in prostate brachytherapy requires localization of the implanted radioactive seeds. A tomosynthesis-based seed reconstruction method is proposed. A three-dimensional volume is reconstructed from Gaussian-blurred projection images and candidate seed locations are computed from the reconstructed volume. A false positive seed removal process, formulated as an optimal coverage problem, iteratively removes ldquoghostrdquo seeds that are created by tomosynthesis reconstruction. In an effort to minimize pose errors that are common in conventional C-arms, initial pose parameter estimates are iteratively corrected by using the detected candidate seeds as fiducials, which automatically ldquofocusesrdquo the collected images and improves successive reconstructed volumes. Simulation results imply that the implanted seed locations can be estimated with a detection rate of ges97.9% and ges99.3% from three and four images, respectively, when the C-arm is calibrated and the pose of the C-arm is known. The algorithm was also validated on phantom data sets successfully localizing the implanted seeds from four or five images. In a Phase-1 clinical trial, we were able to localize the implanted seeds from five intraoperative fluoroscopy images with 98.8% (STD=1.6) overall detection rate.

A preliminary application of principal components and cluster analysis to internal tongue deformation patterns

Complex patterns of muscle contractions create gross tongue motion during speech. It is of scientific and medical importance to better understand speech motor strategies and variations due to language or disorders. Dense patterns of tongue motion can be imaged using tagged magnetic resonance imaging, but characterisation of motion strategies is difficult using visualisation alone. This paper explores the use of principal component analysis for dimensionality reduction and cluster analysis for tongue motion categorisation. Velocity fields were acquired and analysed from midsagittal tongue slices during motion from /i/ to /u/ for eight datasets containing multiple languages and a glossectomy patient. The analyses were carried out on the tongue-only and tongue-plus-floor of the mouth regions. The results showed that both the analyses were sensitive to region size and that cluster analysis was harder to interpret. Both the analyses grouped the Japanese speaker with the glossectomy patient, which although explicable with biologically plausible reasons, highlights the limitations of extensive data reduction.

HARP TRACKING REFINEMENT USING SEEDED REGION GROWING

Tagged magnetic resonance (MR) imaging makes it possible to image the motion of tissues such as the muscles found in the heart and tongue. The harmonic phase (HARP) method largely automates the process of tracking points within tagged MR images. It works by finding spatial points in successive images that retain the same two harmonic phase values throughout the entire image sequence. Given a set of tracked points, many interesting and useful motion properties such as regional displacement or rotation, elongation, strain, and twist, can be computed. When there is a large motion between successive image frames, HARP tracking can fail, and this results in mistracked points and erroneous motion estimates. In this paper, we present a novel HARP refinement method based on seeded region growing that addresses this problem. Starting from a given seed point which is determined by the user to be correctly tracked throughout the entire sequence, this method can reliably track the motion of the whole tissue. A novel cost function is used in the region growing to assure that points that can be most reliably tracked are tracked first. Experimental results on tagged MR images of the tongue demonstrate very reliable tracking

Tomosynthesis-based radioactive seed localization in prostate brachytherapy using modified distance map images

We have developed a tomosynthesis-based radioactive seed localization method for prostate brachytherapy. In contrast to the projection image-based matching approach, our method does not involve explicit segmentation of seeds and can recover hidden seeds. Modified distance map images are computed from a limited number of x-ray projection images, and are backprojected to reconstuct a 3-D volume of interest. Candidate seed locations are extracted from the reconstructed volume and false positive seeds are eliminated by solving an optimal geometry coverage problem. The simulation results indicate that the implanted seed locations can be estimated from three or four images depending on the number of seeds if the pose of a C-arm is known. The algorithm was validated using phantom and clinical patient data.

Tracking tongue motion in three dimensions using tagged MR image

Harmonic phase (HARP) analysis has been used in tagged magnetic resonance imaging (MRI) to measure two-dimensional (2D) in-plane motion and strain, and was recently applied in characterizing the motion of tongue during speech. The 3D-HARP method extended the HARP method to track three-dimensional (3D) cardiac motion from short- and long-axis tagged MR images by diffusing 2D in-plane motion on a sparse 3D mesh. In this paper, we propose a new 3D-HARP method to calculate the 3D tongue motion from three orthogonal tag orientations imaged within sagittal, coronal, and axial image planes. Our method iteratively tracks 2D in-plane motions on points on a compact mesh using 2D-HARP followed by thin-plate spline (TPS) interpolation to extend the 2D motion to the whole mesh. Experiments on real tongue data show that our method is capable of accurate motion tracking in simple utterances

Incompressible Cardiac Motion Estimation of the Left Ventricle Using Tagged MR Images

Interpolation from sparse imaging data is typically required to achieve dense, three-dimensional quantification of left ventricular function. Although the heart muscle is known to be incompressible, this fact is ignored by most previous approaches that address this problem. In this paper, we present a method to reconstruct a dense representation of the three-dimensional, incompressible deformation of the left ventricle from tagged MR images acquired in both short-axis and long axis orientations. The approach applies a smoothing, divergence-free, vector spline to interpolate velocity fields at intermediate discrete times such that the collection of velocity fields integrate over time to match the observed displacement components. Through this process, the method yields a dense estimate of a displacement field that matches our observations and also corresponds to an incompressible motion.

Prostate Brachytherapy Seed Localization with Gaussian Blurring and Camera Self-calibration

A tomosynthesis-based prostate brachytherapy seed localization method is described. Gaussian-blurred images are computed from a limited number of X-ray images, and a 3-D volume is reconstructed by backprojection. Candidate seed locations are extracted from the reconstructed volume and false positive seeds are removed by optimizing a local cost function. In case where the estimated pose error is large, a self-calibration process corrects the estimation error of the intrinsic camera parameters and the translation of the pose in order to improve the reconstruction. Simulation and phantom experiment results imply that the implanted seed locations can be estimated from four or five images depending on the number of seeds. The algorithm was also validated using patient data, successfully localizing the implanted seeds.

Tracking muscle deformation during speech from tagged and diffusion tensor magnetic resonance imaging.

Oral head and neck cancer is the sixth most common cancer worldwide. These tumors are usually treated by surgical removal of the affected tissue. The result of the surgery is a loss of muscle tissue, accompanied by scarring, reduced strength, and often reduced function. This paper explores the relationship between tongue muscle orientation and muscle deformation pattern in normal and post‐cancer surgery speakers. Our motivation is the need to better understand the mechanisms that underlie tongue motion, in order to better interpret clinical observations and to provide data that can help predict optimal surgical outcomes. This study uses diffusion tensor imaging (DTI) to extract muscle fiber orientation, but it is not possible to image the muscle deformation in real‐time using DTI. On the other hand, tagged MRI can track muscle fiber bundles in real time, but the original fiber bundle is not visible. Therefore, this work develops a method to overlay muscle bundles from the DTI onto the corresponding locati...