제2회 막수 포럼 (The 2nd Last Wednesday Forum)
일시: 2013년 5월 1일, 12:00~13:00
장소: 전산학과(E3-1) 4층 오상수 영상강의실
Many-view under-sampling(MVUS) technique for low dose CT
Presenter: 이태원 (조승룡 교수님 연구실)
In computed tomography (CT) imaging, radiation dose delivered to the patient is one of the major concerns. Sparse-view CT is a viable option to low-dose CT. However, a fast power switching of an x- ray tube, which is needed for the sparse-view sampling, can be challenging in many CT systems. We have recently proposed a novel alternative approach to sparse-view CT that can be readily incorporated in the existing CT systems, and have successfully shown its feasibility. Instead of switching the x-ray tube power, one can place an oscillating multi-slit collimator between the x-ray tube and the patient to partially block the x-ray beam. A simulation study was performed based on experimentally acquired micro CT data of a mouse to demonstrate the feasibility of the proposed method. MicroCT projection data of a mouse were used and a numerical collimation was applied in the form of multi-slits. For image reconstruction, we used a total-variation minimization (TV) algorithm which has shown its out-performance in many sparse-view CT applications.
Method of ROI Selection Affects Resting State Intrinsic Connectivity: The Necessity for Subject Specific ROIs
Presenter: William Sohn (정용 교수님 연구실)
Resting state functional analysis has become a popular method is analyzing brain connectivity over the past decade. Current methods to construct resting state connectivity node graphs use a seed based analysis. This method has a variety of inherent problems which have yet to be addressed. For example, by slightly varying the seed location or changing the size can have drastic changes in correlation with other regions of the brain. Thus it has become debatable whether or not this seed region can be considered a true representation of a desired time series. In this presentation we propose creating subject specific functional regions of interest (ROIs). We show that we are able to obtain higher correlations which may more accurately reflect intrinsic resting state connectivity. In addition, we will apply this method for classification of Alzheimer's disease using machine learning and compare accuracy with conventional methods.