Fei Comapny

Other

Andrew M Kingston

Adrian Paul Sheppard

Trond Karsten Varslot

Shane Jamie Latham

Glenn Robert Myers

Benoit Recur

Heyang Li

Robert Martin Sok

Mark Alexander Knackstedt

Mahsa Paziresh

Olaf Delgado-Friedrichs

Petr Strelec

Wilfred Kelsham Fullagar

Jeremy James Holt

Benoit Mathieu Baptiste Recur

Arthur Sakellariou

Qiheng Yang

Shane Latham

Methods and systems for generation and use of an accelerated tomographic reconstruction preconditioner (ATRP) for accelerated iterative tomographic reconstruction are disclosed. An example method for generating an ATRP for accelerated iterative tomographic reconstruction includes accessing data for a tomography investigation of a sample and determining a trajectory of the tomography investigation of a sample. At least one toy model sample depicting a feature characteristic of the sample are accessed and at least one candidate preconditioner is selected. A first performance of each of the at least one candidate preconditioner on the one or more toy samples is determined, where the candidate preconditioners are then updated to create updated candidate preconditioners. A second performance of each of the updated candidate preconditioners on the one or more toy samples is determined determining. An ATRP is then generated based on at least the first performance and the second performance.

System and method for accelerated convergence of iterative tomographic reconstruction

A method of investigating a specimen using tomographic imaging, comprising the steps of providing a specimen and a source, directing a beam of radiation from said source to said specimen, and detecting a flux of radiation transmitted through said specimen. The method further comprises the steps of moving at least one of said specimen and said source for providing relative motion of the source with respect to the specimen; and imaging the specimen along a series of different viewing axes, which intersect a virtual reference surface that surrounds the specimen and is substantially centered thereon, wherein said combined steps of moving and imaging generate a sampling geometry on said virtual reference surface. As defined herein, the steps of moving and imaging are coordinated in such a way that said sampling geometry comprises a plurality of spaced apart line segments.

Method of investigating a specimen using a tomographic imaging apparatus

A method of investigating a specimen using a tomographic imaging apparatus comprising:

Acquisition and processing of data in a tomographic imaging apparatus

Methods and apparatuses are disclosed herein to correct for inconsistencies in CT scans based on pi-lines. An example method at least includes acquiring a plurality of projections of a sample, each projection of the plurality of projections acquired at a different location around the sample based on a trajectory, determining pairs of opposing projections from the plurality of projections based on a respective pi-line, and determining an amount of inconsistency between respective pi-line data for each pair of opposing projections, where the pi-line data is based, at least in part, on attenuation data.

Geometric alignment, sample motion correction, and intensity normalization of computed tomography projections using pi-line optimization

Apparatuses and methods for implementing scanning trajectories for ROI tomography are disclosed herein. An example method includes determining a first focus object distance based on a circumradius of a sample, the sample including a region of interest, determining a second focus object distance based on a radius of a smallest cylinder that contains the region of interest, determining a plurality of viewing angles from a plurality of possible viewing angles in response to the first focus object distance, where each viewing angle of the plurality of viewing angles has an associated focus object distance measured from the region of interest, and where the associated focus object distance of each of the plurality of viewing angles is less than the first focus object distance and greater than the second focus object distance, and scanning the region of interest using at least the plurality of viewing angles.

Scanning trajectories for region-of-interest tomograph

Methods and apparatuses disclosed herein provide beam hardening correction to tomographic reconstruction using a simplification to the Alvarez-Macovski attenuation model. An example method includes simplifying a forward projection model, the forward projection model based on an Alvarez-Macovski (AM) attenuation model, wherein the simplification of the forward projection model simplifies the AM attenuation model for one of photoelectric effect only, constant density, constant atomic number, and density proportional to atomic number, and performing an iterative reconstruction of a sample using the simplified forward projection model, the iterative reconstruction weighted by a first spectrum, wherein measured image data of the sample used in the iterative reconstruction is obtained at a first energy, and wherein a reverse operation of the iterative reconstruction is a non-adjoint to the simplified forward projection model.

X-ray beam-hardening correction in tomographic reconstruction using the Alvarez-Macovski attenuation model

A method of analyzing a specimen using X-rays, comprising the steps of:

Statistical analysis in X-ray imaging

Some embodiments of the invention provide a method of determining a material characteristic of material in a sample by iterative tomographic reconstruction. The method conducts one or more X-ray tomography scans of a sample, and then determines one or more estimated material characteristics, such as atomic number and density, for multiple volume elements in the sample using a tomographic reconstruction algorithm. These estimated material characteristics are then modified by reference to stored known material characteristic data. Preferably, determining the composition of the sample volume during reconstruction includes segmenting the sample into regions of common composition, the segmenting being performed during iterative reconstruction instead of being based on the voxel characteristics determined upon the completion of iterative reconstruction. Preferred versions will perform one or more additional iterations of the tomographic reconstruction algorithm, where each iteration updates the one or more estimated material characteristics for the volume elements.

Tomographic reconstruction for material characterization

Provided are improved referenceless multi-material beam hardening correction methods, with an emphasis on maintaining data quality for real-world imaging of geologic materials with a view towards automation. A referenceless post reconstruction (RPC) correction technique is provided that applies the corrections in integrated attenuation space. A container-only pre-correction technique also is provided to allow automation of the segmentation process required for beam hardening correction methods.

Correction of beam hardening artifacts in microtomography for samples imaged in containers

A method of investigating a specimen using a tomographic imaging apparatus using a stage for producing relative motion of a source with respect to a specimen, so as to allow the source and a detector to image the specimen along a series of different viewing axes and a processing apparatus for assembling a tomographic image of at least part of the specimen. The investigation is carried out by considering a virtual reference surface that surrounds the specimen and is substantially centered thereon, considering an incoming point of intersection of each of said viewing axes with this reference surface, thereby generating a set of such intersection points corresponding to the series of viewing axes, choosing discrete viewing axes in the series so as to cause the set to comprise a two-dimensional lattice of points located areally on the reference surface in a substantially uniform distribution.

A method for investigating a specimen using a tomographic imaging apparatus, comprising acquiring a set of input images of the specimen taken at a corresponding set of source positions that are intended to lie on an ideal locus but are instead caused by positioning errors to lie on a distorted locus, using the images to construct an initial tomographic image, dissociating the initial tomographic image into a set of reference images referenced to said ideal locus, comparing the given input images to the corresponding reference images, calculating a set of transformations necessary to map the input images onto the reference images, and using the set of transformations to construct a modified tomographic image.

Positional error correction in a tomographic imaging apparatus

A method of investigating a specimen using a tomographic imaging apparatus, by performing, in multiple iterations, the following steps:

Data processing in a tomographic imaging apparatus

A method for processing image data of a sample is disclosed. The method comprises registering a first and a second images of at least partially overlapping spatial regions of the sample and processing data from the registered images to obtain integrated image data comprising information about the sample, said information being additional to that available from said first and second images.

Image data processing

A computed tomography imaging process, including: acquiring projection images of an object by detecting radiation that has passed through the object for respective different relative orientations of the object and the radiation; and processing the projection images to generate a tomogram of the object; wherein the radiation passes through the object in the form of a diverging beam, and the different relative orientations of the object and the beam of radiation define two or more complete trajectories of the beam along the object, the complete trajectories being mutually offset to reduce the degradation of spatial resolution in portions of the generated tomogram due to the divergence of the beam through the object.

Computed tomography imaging process and system

Disclosed is a method of reconstructing a multi-dimensional data set representing a dynamic sample at a series of reconstruction instants. The multi-dimensional data set comprises a static component and a dynamic component. The method comprises acquiring a plurality of projection images of the dynamic sample; reconstructing a static component of the multi-dimensional data set from the acquired projection images; acquiring a further plurality of projection images of the dynamic sample; and reconstructing a dynamic component of the multi-dimensional data set at each reconstruction instant from the further plurality of acquired projection images using a priori information about the sample. The multi-dimensional data set is the sum of the static component and the dynamic component at each reconstruction instant.

Growing community of inventors

Kambah, Australia

Andrew M Kingston