Computer-accessible medium for determining arterial input function

    公开(公告)号:US10849528B2

    公开(公告)日:2020-12-01

    申请号:US15574474

    申请日:2016-05-16

    摘要: An exemplary system, method and computer-accessible medium for determining an arterial input function (AIF) of a mammal(s) can be provided, which can include, for example, receiving information related to a global circulatory system of the mammal(s), and determining the AIF based on the information by modeling a blood flow in the global circulatory system of the mammal(s) in terms of an input response function(s). The input response function(s) can include a delayed input response function(s). In certain exemplary embodiments of the present disclosure, the input response function(s) can include at least three input response functions, and each of the input response functions can be from a different part of a body of the mammal(s). The AIF can be determined by coupling the input response functions. The AIF can be further determined based on a total tracer amount in an organ(s) of the mammal(s).

    SYSTEM, METHOD AND COMPUTER ACCESSIBLE MEDIUM FOR NOISE ESTIMATION, NOISE REMOVAL AND GIBBS RINGING REMOVAL

    公开(公告)号:US20180120404A1

    公开(公告)日:2018-05-03

    申请号:US15574467

    申请日:2016-05-16

    摘要: An exemplary system, method and computer-accessible medium for removing noise and Gibbs ringing from a magnetic resonance (“MR”) image(s), can be provided, which can include, for example, receiving information related to the MR image(s), receiving information related to the MR image(s), and removing the Gibbs ringing from the information by extrapolating data in a k-space from the MR image(s) beyond an edge(s) of a measured portion of the k-space. The data can be extrapolated by formatting the data as a regularized minimization problem(s). A first weighted term of the regularized minimization problem(s) can preserve a fidelity of the extrapolated data, and a second weighted term of the regularized minimization problem(s) can be a penalty term that can be based a norm(s) of the MR image(s), which can be presumed to be sparse