Elekta Ab (publ)

Okinawa Institute of Science and Technology Promotion Corporation

Ozan Öktem

Jonas Anders Adler

Hans Rullgàrd

Lars-Göran Öfverstedt

Anders Edin

Johan Lagerros

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 Asuch 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 Acharacterised by a parameter set Θand the second process (for example, segmentation) is expressed via a second reconstruction operator Acharacterised by a parameter set Θ, then the result of the two steps applied consecutively is A(A(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.

Analysis of medical images

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.

Posterior image sampling to detect errors in medical imaging

Posterior image sampling using statistical learning model

The present invention relates to a solution for solving an ill-posed inverse problem in image analysis, e.g. in an electron tomography application in order to recover a structure of a sample. The solution is provided for instance as a method comprising steps of determining reliable prior knowledge about the solution, determining initial guess for the solution and determining the corresponding forward operator, deciding upon model of stochasticity, deciding on suitable regularization method, deciding on updating scheme, and producing a sequence using the set configuration.

Iterated variational regularization combined with componentwise regularization

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Sundbyberg, Sweden

Ozan Öktem