An Assessment of a Low-Cost Visual Tracking System (VTS) to Detect and Compensate for Patient Motion during SPECT

Patient motion is inevitable in SPECT and PET due to the lengthy period of time patients are imaged and patient motion can degrade diagnostic accuracy. The goal of our studies is to perfect a methodology for tracking and correcting patient motion when it occurs. In this paper we report on enhancements to the calibration, camera stability, accuracy of motion tracking, and temporal synchronization of a low-cost visual tracking system (VTS) we are developing. The purpose of the VTS is to track the motion of retro-reflective markers on stretchy bands wrapped about the chest and abdomen of patients. We have improved the accuracy of 3D spatial calibration by using a MATLAB optical camera calibration package with a planar calibration pattern. This allowed us to determine the intrinsic and extrinsic parameters for stereo-imaging with our CCD cameras. Locations in the VTS coordinate system are transformed to the SPECT coordinate system by a VTS/SPECT mapping using a phantom of 7 retro-reflective spheres each filled with a drop of Tc(99m). We switched from pan, tilt and zoom (PTZ) network cameras to fixed network cameras to reduce the amount of camera drift. The improved stability was verified by tracking the positions of fixed retro-reflective markers on a wall. The ability of our VTS to track movement, on average, with sub-millimeter and sub-degree accuracy was established with the 7-sphere phantom for 1 cm vertical and axial steps as well as for an arbitrary rotation and translation. The difference in the time of optical image acquisition as decoded from the image headers relative to synchronization signals sent to the SPECT system was used to establish temporal synchrony between optical and list-mode SPECT acquisition. Two experiments showed better than 100 ms agreement between VTS and SPECT observed motion for three axial translations. We were able to track 3 reflective markers on an anthropomorphic phantom with a precision that allowed us to correct motion such that no loss in visual quality was noted in motion corrected slices relative to motion free slices

Transcript-indexed ATAC-seq for precision immune profiling.

T cells create vast amounts of diversity in the genes that encode their T cell receptors (TCRs), which enables individual clones to recognize specific peptide-major histocompatibility complex (MHC) ligands. Here we combined sequencing of the TCR-encoding genes with assay for transposase-accessible chromatin with sequencing (ATAC-seq) analysis at the single-cell level to provide information on the TCR specificity and epigenomic state of individual T cells. By using this approach, termed transcript-indexed ATAC-seq (T-ATAC-seq), we identified epigenomic signatures in immortalized leukemic T cells, primary human T cells from healthy volunteers and primary leukemic T cells from patient samples. In peripheral blood CD4+ T cells from healthy individuals, we identified cis and trans regulators of naive and memory T cell states and found substantial heterogeneity in surface-marker-defined T cell populations. In patients with a leukemic form of cutaneous T cell lymphoma, T-ATAC-seq enabled identification of leukemic and nonleukemic regulatory pathways in T cells from the same individual by allowing separation of the signals that arose from the malignant clone from the background T cell noise. Thus, T-ATAC-seq is a new tool that enables analysis of epigenomic landscapes in clonal T cells and should be valuable for studies of T cell malignancy, immunity and immunotherapy

Systems, Software, and Simulation: Meeting the DARPA Robotics Challenge

Presented on April 23, 2014 at 12:00 PM at the TSRB Auditorium.Michael A. Gennert is the director of the Robotics Engineering Program at Worcester Polytechnic Institute, where he is a professor of both computer science and electrical and computer engineering. Previously, he has worked at the University of Massachusetts Medical Center, the University of California–Riverside, General Electric Ordnance Systems, and PAR Technology Corporation.Runtime: 59:22 minutesThis talk describes the WPI-CMU DRC Team entry, the hardware choices, and software architecture that enable human-in-the-loop control of a 28 degree-of-freedom Atlas humanoid robot over a limited bandwidth link. We discuss our methods, results, and lessons learned from the DRC Trials tasks, focusing on driving. We were one of only seven teams to attempt the driving task, and the only team with an Atlas robot to successfully drive the course. The effectiveness of our system architecture was demonstrated as the WPI-CMU DRC Team scored 11 out of a possible 32 points, ranked 7th at the Trials, and was selected as a finalist for the DRC Finals

Any Dimensional Reconstruction from Hyperplanar Projections

In this paper we examine the reconstruction of functions of any dimension from hyperplanar projections. This is a generalization of a problem that has generated much interest recently, especially in the field of medical imaging. Computed Axial Tomography (CAT) and Nuclear Magnetic Resonance (NMR) are two medical techniques that fall in this framework. CAT scans measure the hydrogen density along planes through the body. Here we will examine reconstruction methods that involve backprojecting the projection data and summing this over the entire region of interest. There are two methods for doing this. One method is to filter the projection data first, and then backproject this filtered data and sum over all projection directions. The other method is to backproject and sum the projection data first, and then filter. The two methods are mathematically equivalent, producing very similar equations. We will derive the reconstruction formulas for both methods for any number of dimensions. We will examine the cases of two and three dimensions, since these are the only ones encountered in practice. The equations are very different for these cases. In general, the equations are very different for even and odd dimensionality. We will discuss why this is so, and show that the equations for even and odd dimensionality are related by the Hilbert Transform

Relaxing the Brightness Constancy Assumption in Computing Optical Flow

Optical flow is the apparent (or perceived) motion of image brightness patterns arising from relative motion of objects and observer. Estimation of the optical flow requires the application of two kinds of constraint: the flow field smoothness constraint and the brightness constancy constraint. The brightness constancy constraint permits one to match image brightness values across images, but is very restrictive. We propose replacing this constraint with a more general constraint, which permits a linear transformation between image brightness values. The transformation parameters are allowed to vary smoothly so that inexact matching is allowed. We describe the implementation on a highly parallel computer and present sample results

Multiscale Relaxation Labeling of Fractal Images

This paper describes the application of multi-scale relaxation to automatically detect pavement distress. Pavement distress detection is a difficult task which simple edge detection schemes perform poorly. We have chosen to use relaxation labeling to improve upon an initial edge-based segmentation. This work is based upon a fractal model of pavement distress. The scale-invariance property of fractals suggests that information at different scales of resolution may be combined to improve segmentation. Thus, wehave developed a multi-scale relaxation technique for use in a pavement distress detection system. Straightforward linear interactions fail to capture the complexity of pixel interactions for this problem. To better model pixel interactions, we have included non-linear terms in the relaxation process. Symmetry arguments and careful engineering allow a 93% reduction in the complexity of this approach. To demonstrate the necessity of the multi-scale approach, examples with and without..