-
公开(公告)号：US11164346B2
公开(公告)日：2021-11-02
申请号：US15929940
申请日：2020-05-29
申请人： Elekta AB (publ)
发明人： Jonas Anders Adler , Ozan Öktem
摘要： Image reconstruction can include using a statistical or machine learning, MAP estimator, or other reconstruction technique to produce a reconstructed image from acquired imaging data. A Conditional Generative Adversarial Network (CGAN) technique can be used to train a Generator, using a Discriminator, to generate posterior distribution sampled images that can be displayed or further processed such as to help provide uncertainty information about a mean reconstruction image. Such uncertainty information can be useful to help understand or even visually modify the mean reconstruction image. Similar techniques can be used in a segmentation use-case, instead of a reconstruction use case. The uncertainty information can also be useful for other post-processing techniques.
-
公开(公告)号：US11847721B2
公开(公告)日：2023-12-19
申请号：US16623660
申请日：2018-06-21
申请人： Elekta AB (publ)
发明人： Jonas Anders Adler , Ozan Öktem
CPC分类号： G06T11/006 , G06T11/005 , G06T11/008 , G16H30/00 , G06T2210/41 , G06T2211/424
摘要： Much of the image processing that is applied to medical images is a form of “inverse problem”. This is a class of mathematical problems in which a “forward” model by which a signal is converted into dataset is known, to at least some degree, but where the aim is to reconstruct the signal given the resulting dataset. Thus, an inverse problem is essentially seeking to discover x given knowledge of A(x)+noise by finding an appropriate reconstruction operator A† such that A† (A(x)+noise)≈x, thereby enabling us to obtain x (or a close approximation) given knowledge of an output dataset consisting of A(x)+noise. Generally, several such processes (or their equivalents) are applied to the image dataset. If the first process (for example, noise reduction) is expressed via a first reconstruction operator A1† characterised by a parameter set Θ1 and the second process (for example, segmentation) is expressed via a second reconstruction operator A2† characterised by a parameter set Θ2, then the result of the two steps applied consecutively is A2† (A1†(y)). This can be expressed as an overall reconstruction operator P+, characterised by a parameter set Φ. If we then allow a machine learning process to optimise P+, then the steps previously carried out separately can be combined into a single optimisation. This yields advantages in terms of computational load and in the accuracy of the end result.
-
公开(公告)号：US20210150780A1
公开(公告)日：2021-05-20
申请号：US16623660
申请日：2018-06-21
申请人： Elekta AB (publ)
发明人： Jonas Anders Adler , Ozan Öktem
摘要： Much of the image processing that is applied to medical images is a form of “inverse problem”. This is a class of mathematical problems in which a “forward” model by which a signal is converted into dataset is known, to at least some degree, but where the aim is to reconstruct the signal given the resulting dataset. Thus, an inverse problem is essentially seeking to discover x given knowledge of A(x)+noise by finding an appropriate reconstruction operator A† such that A† (A(x)+noise)≈x, thereby enabling us to obtain x (or a close approximation) given knowledge of an output dataset consisting of A(x)+noise. Generally, several such processes (or their equivalents) are applied to the image dataset. If the first process (for example, noise reduction) is expressed via a first reconstruction operator A1† characterised by a parameter set Θ1 and the second process (for example, segmentation) is expressed via a second reconstruction operator A2† characterised by a parameter set Θ2, then the result of the two steps applied consecutively is A2† (A1†(y)). This can be expressed as an overall reconstruction operator P+, characterised by a parameter set Φ. If we then allow a machine learning process to optimise P+, then the steps previously carried out separately can be combined into a single optimisation. This yields advantages in terms of computational load and in the accuracy of the end result.
-
公开(公告)号：US10672153B2
公开(公告)日：2020-06-02
申请号：US16189480
申请日：2018-11-13
申请人： Elekta AB (publ)
发明人： Jonas Anders Adler , Ozan Öktem
摘要： Image reconstruction can include using a statistical or machine learning, MAP estimator, or other reconstruction technique to produce a reconstructed image from acquired imaging data. A Conditional Generative Adversarial Network (CGAN) technique can be used to train a Generator, using a Discriminator, to generate posterior distribution sampled images that can be displayed or further processed such as to help provide uncertainty information about a mean reconstruction image. Such uncertainty information can be useful to help understand or even visually modify the mean reconstruction image. Similar techniques can be used in a segmentation use-case, instead of a reconstruction use case. The uncertainty information can also be useful for other post-processing techniques.
-
公开(公告)号：US20190325620A1
公开(公告)日：2019-10-24
申请号：US16189480
申请日：2018-11-13
申请人： Elekta AB (publ)
发明人： Jonas Anders Adler , Ozan Öktem
摘要： Image reconstruction can include using a statistical or machine learning, MAP estimator, or other reconstruction technique to produce a reconstructed image from acquired imaging data. A Conditional Generative Adversarial Network (CGAN) technique can be used to train a Generator, using a Discriminator, to generate posterior distribution sampled images that can be displayed or further processed such as to help provide uncertainty information about a mean reconstruction image. Such uncertainty information can be useful to help understand or even visually modify the mean reconstruction image. Similar techniques can be used in a segmentation use-case, instead of a reconstruction use case. The uncertainty information can also be useful for other post-processing techniques.
-
-
-
-
搜索结果
5 条记录 (耗时 0.011 秒)
