Siemens Healthineers

The invention relates to processing multiple cardiac images depicting a portion of coronary arteries within an anatomical region of interest. A respective set of lumen radius measurements is determined based on each of the multiple cardiac images and comprises multiple lumen radius measurements respectively associated with multiple locations of the portion of the coronary arteries. One or more locations of the multiple locations, at each of which at least two sets of the respective sets of lumen radius measurements all have a local or relative extremum, e.g., either a local maximum or a local minimum, are determined based on the respective sets of lumen radius measurements. Rigid-motion artifacts in the multiple cardiac images are corrected based on the one or more locations.

The invention relates to a system (100) and method (200) for generating an MR-based-virtual X-Ray, hereafter MRXR, image, said method (200) comprising: - acquiring (201) an initial set of one or several MRI images, wherein the MRI images comprised within said initial set are configured for enabling one or several 3D representations of a biological object; - processing (202) said one or several MRI images for increasing, notably for each of said one or several images, a visibility of each image region, if any, that represents a bone of the biological object - called hereafter "bone-region" - with respect to a visibility of another region that represents tissue of the biological object that is free of bone - called hereafter "non-bone region", and creating an image input set comprising the processed one or several images; - feeding (203) an improved bone visibility - hereafter IBV - 3D image into a cone-beam projection algorithm configured for performing an X-Ray-like projection, wherein the IBV 3D image is obtained from the processed one or several images of said input set, and wherein the cone-beam projection algorithm is configured for outputting said MRXR image; - providing (204) the outputted MRXR image via an interface.

Die Erfindung betrifft eine Detektorhalterungseinrichtung (1) für eine Röntgeneinrichtung (30, 38), aufweisend einen Röntgendetektor (2) und eine Stativanordnung (9), von welcher der Röntgendetektor (2) innerhalb eines Gehäuses (4) eines Detektormoduls (3) getragen wird, wobei der Röntgendetektor (2) eine sich in einer Detektorebene erstreckende Detektorfläche, die hinter einer röntgendurchlässigen Wand (5) des Gehäuses (4) angeordnet ist, aufweist, dadurch gekennzeichnet, dass der Röntgendetektor (2) mittels wenigstens eines ansteuerbaren Verschiebeaktors (8) in wenigstens einer Richtung (6, 7) in der Detektorebene innerhalb des Gehäuses (4) bewegbar ist.

The invention relates to a method for generating MR images of a patient at an MR imaging system comprising sampling a k-space with a 3-dimensional acquisition schema of k-space with 2 phase encoding directions and one read out direction. The sampling schema includes applying a sequence of inversion pulses to the patient to invert a magnetisation of the patient, wherein sampling the k space is performed with a uniform undersampling of the k space and n different EPI readout shots in the read-out direction after each inversion pulse, with n being => 5. The MR images are reconstructed based on the sampled k space.

The invention relates to a computer-implemented method for characterizing a lesion in a blood vessel of a brain depicted in a radiography image comprising identifying a void, in the radiography image, wherein the void is representative for a presence of the lesion, determining a distance of the identified void to a region of interest in the radiography image, determining a size of a nidus in the radiography image, determining a presence of a deep venous drainage in the radiography image and determining a grading of the lesion based on the determined distance of the void to the region of interest, based on the size of the nidus and/or based on the determined presence of the deep venous drainage.

The invention relates to a computer-implemented method to determine at least one feature of a predetermined pixel (22) of an image, wherein the computer-implemented method comprises: receiving a first image information (10) comprising at least one image that comprises the predetermined pixel (22); receiving at least one second image information (13) that corresponds to the first image information (10) but has a different resolution; for the first image information (10) and the at least one second image information (13) individually: determining multiple features (18) by applying a first trained machine learning model (16) to the respective image information (10, 13); selecting pixel features (20) that are assigned to the predetermined pixel (22); and determining interpolated features (25) for the respective image information (10, 13). The computer-implemented method also comprises determining a pixel information (29) by applying a second trained machine learning model (28) to the determined interpolated features (25).

For determining object motion information based on time-resolved medical imaging data, a time-resolved 3D image reconstruction (4) representing an imaged object (6) during an imaging period is received. A first amplitude of a motion of a first region of interest in the time-resolved 3D image reconstruction (4), which corresponds to a surface region (7) on a surface of the object (6), as a function of time during the imaging period is determined based on the time-resolved 3D image reconstruction (4). A first motion trace (5), which represents an amplitude of a motion of the surface region (7) as a function of time during the imaging period, is determined based on the first amplitude.

Computer-implemented method for operating a magnetic resonance device (1) for acquiring spatially resolved magnetic resonance data (7),- wherein a segmented turbo spin echo sequence (12), that comprises multiple subsequences (13-16), is used to acquire the magnetic resonance data (7),- wherein each one of the subsequences (13-16) acquires data for a different segment (34, 35) of a sampled k-space (33),- wherein each subsequence (13-16) comprises multiple pulse trains (17-20), each pulse train (17-20) comprising an excitation pulse (21) preceding multiple refocusing pulses (22, 23), and- wherein the multiple pulse trains (17-20) of the respective subsequence (13-16) use mutually different excitation pulses (21) to acquire data for different spectral bins (24-27) and/or different spatial slices of the magnetic resonance data (7),- wherein a repetition time (28-30), that is defined for the respective subsequence (13-16) as the time between the first excitation pulse (21) within that subsequence (13-16) and the first excitation pulse (21) of the subsequence (13-16) that precedes the respective subsequence (13-16) in the segmented turbo spin echo sequence (12), is different for at least two of the subsequences (13-16).

Die Erfindung betrifft ein Verfahren zum Verarbeiten eines Bilddatensatzes, welcher auf einer multispektralen Computertomographie-Bildgebung basiert und welcher ein erstes Bildwerte-Tupel und ein zweites Bildwerte-Tupel aufweist, wobei das erste Bildwerte-Tupel einem ersten Volumenelement eines abzubildenden Bereichs zugeordnet ist, wobei das zweite Bildwerte-Tupel einem zweiten Volumenelement des abzubildenden Bereichs zugeordnet ist, das Verfahren umfassend: - ein Berechnen eines ersten Anteils eines Basismaterials, indem eine Abbildung auf das erste Bildwerte-Tupel angewendet wird, wobei der erste Anteil des Basismaterials dem ersten Volumenelement des abzubildenden Bereichs zugeordnet ist, wobei die Abbildung an dem ersten Bildwerte-Tupel eine erste Steigung aufweist, wobei die erste Steigung ungleich Null ist, - ein Berechnen eines zweiten Anteils des Basismaterials, indem die Abbildung auf das zweite Bildwerte-Tupel angewendet wird, wobei der zweite Anteil des Basismaterials dem zweiten Volumenelement des abzubildenden Bereichs zugeordnet ist, wobei die Abbildung an dem zweiten Bildwerte-Tupel eine zweite Steigung aufweist, wobei die zweite Steigung ungleich Null und ungleich der ersten Steigung ist.

A system (1) for determining a number N1 of optimal paths (2a - 2c) for a lesion ablation treatment of a lesion (3) of a patient, with N1 ≥ 1, the system (1) comprising: a medical imaging unit (4) for acquiring a medical image (5) of the patient; a computer-implemented path determination unit (6) for an automatic determination of the N1 optimal paths (2a - 2c) based on the acquired medical image (5); and a user interface (7) for outputting the determined N1 optimal paths (2a - 2c) to a user of the system (1).