Automatic reconstruction of 3D neuron structures using a graph-augmented deformable model

Motivation: Digital reconstruction of 3D neuron structures is an important step toward reverse engineering the wiring and functions of a brain. However, despite a number of existing studies, this task is still challenging, especially when a 3D microscopic image has low single-to-noise ratio and discontinued segments of neurite patterns

Low Cost Shear and Pressure Sensor

Elevated pressure and shearing stresses at the foot-shoe interface are believed to play a role in diabetic foot ulceration. The main goal of this project was to design a simple insole that could distinguish between sites of high pressure and sites of excessive shearing stresses at the foot-shoe interface. Wear patterns that relate specifically to shear and pressure acting on the plantar surface of a patient’s foot also needed to be exhibited. The team used reflective tape that was applied to the surface to various types of insoles to look for visible wear patterns. These wear patterns were identified by the use of a MATLAB code. Pressure sensitive Fujifilm Prescale was used to detect sites of excessive shear stresses at the skin-shoe interface, by being inserted into slits in an insole. Based on the color and color-density of the Fujifilm sites, high and low shear stresses can be identified. Areas of excessive wear from the reflective tape and the Fujifilm results can be correlated to the control sample step we obtained from Dr. Davis’s shear detection machine

Birational cobordism invariance of uniruled symplectic manifolds

A symplectic manifold $(M,\omega)$ is called {\em (symplectically) uniruled} if there is a nonzero genus zero GW invariant involving a point constraint. We prove that symplectic uniruledness is invariant under symplectic blow-up and blow-down. This theorem follows from a general Relative/Absolute correspondence for a symplectic manifold together with a symplectic submanifold. A direct consequence is that symplectic uniruledness is a symplectic birational invariant. Here we use Guillemin and Sternberg's notion of cobordism as the symplectic analogue of the birational equivalence.Comment: To appear in Invent. Mat

Real-Time Profiling of Respiratory Motion: Baseline Drift, Frequency Variation and Fundamental Pattern Change

To precisely ablate tumor in radiation therapy, it is important to locate the tumor position in real time during treatment. However, respiration-induced tumor motions are difficult to track. They are semi-periodic and exhibit variations in baseline, frequency and fundamental pattern (oscillatory amplitude and shape). In this study, we try to decompose the above-mentioned components from discrete observations in real time. Baseline drift, frequency (equivalently phase) variation and fundamental pattern change characterize different aspects of respiratory motion and have distinctive clinical indications. Furthermore, smoothness is a valid assumption for each one of these components in their own spaces, and facilitates effective extrapolation for the purpose of estimation and prediction. We call this process 'profiling' to reflect the integration of information extraction, decomposition, processing and recovery. The proposed method has three major ingredients: (1) real-time baseline and phase estimation based on elliptical shape tracking in augmented state space and Poincaré sectioning principle; (2) estimation of the fundamental pattern by unwarping the observation with phase estimate from the previous step; (3) filtering of individual components and assembly in the original temporal-displacement signal space. We tested the proposed method with both simulated and clinical data. For the purpose of prediction, the results are comparable to what one would expect from a human operator. The proposed approach is fully unsupervised and data driven, making it ideal for applications requiring economy, efficiency and flexibility.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85908/1/Fessler14.pd

PCA-based lung motion model

Organ motion induced by respiration may cause clinically significant targeting errors and greatly degrade the effectiveness of conformal radiotherapy. It is therefore crucial to be able to model respiratory motion accurately. A recently proposed lung motion model based on principal component analysis (PCA) has been shown to be promising on a few patients. However, there is still a need to understand the underlying reason why it works. In this paper, we present a much deeper and detailed analysis of the PCA-based lung motion model. We provide the theoretical justification of the effectiveness of PCA in modeling lung motion. We also prove that under certain conditions, the PCA motion model is equivalent to 5D motion model, which is based on physiology and anatomy of the lung. The modeling power of PCA model was tested on clinical data and the average 3D error was found to be below 1 mm.Comment: 4 pages, 1 figure. submitted to International Conference on the use of Computers in Radiation Therapy 201