Abstract: The present invention relates to sphingolipids, a method for the production of sphingoid bases, especially sphingosine, and novel derivatives thereof. The sphingoid bases are herein produced by making use of a vinylogous amide-type protecting group. The resulting vinylogous amide compounds enable an easy and effective production of sphingoid bases, especially sphingosine.

Abstract: In one embodiment, the method includes obtaining a plurality of sensor data items, each specifying a set of values determined from at least one sensor; and training, based on the plurality of sensor data items, a machine learning model including a data adaptation part configured for determining a modified sensor data item based on an input sensor data item, an encoder configured for determining encoded features based on the modified sensor data item, a decoder configured for determining a decoded sensor data item based on the encoded features, representing an estimation of the input sensor data item, and a classifier configured for determining a class associated with the input sensor data item. The training the machine learning model includes updating parameters of the data adaptation part based on the input sensor data item and the corresponding decoded sensor data item, while maintaining the encoder, the decoder and the classifier frozen.

Abstract: In one embodiment, the method includes obtaining a plurality of sensor data items, each specifying a set of values determined from at least one sensor; and training, based on the plurality of sensor data items, a machine learning model including a data adaptation part configured for determining a modified sensor data item based on an input sensor data item, an encoder configured for determining encoded features based on the modified sensor data item, a decoder configured for determining a decoded sensor data item based on the encoded features, representing an estimation of the input sensor data item, and a classifier configured for determining a class associated with the input sensor data item. The training the machine learning model includes updating parameters of the data adaptation part based on the input sensor data item and the corresponding decoded sensor data item, while maintaining the encoder, the decoder and the classifier frozen.

Abstract: The invention is a method for processing a phonocardiographic signal characterising fetal breathing movement (FBM), wherein in initial start point (SP) determination (S100) in frequency filtering (S110), bandpass-filtered signals of frequency subbands by first and second bandpass filters are generated from the phonocardiographic signal, and first identified SP of an FBM episode is determined in a frequency subband in SP search (S120), in episode discovering (S130) further SP is searched applying the SP search (S120) in episode search time period, and if found at smaller distance from identified SP than a clustering threshold, it is merged with identified SP, if found at larger distance from identified SP than the clustering threshold, an SP closest to identified SP is identified as second identified SP. The invention is, furthermore, a system for processing a phonocardiographic signal characterising FBM. (FIG.

Abstract: In one embodiment, the method includes obtaining a plurality of sensor data items, each specifying a set of values determined from at least one sensor; and training, based on the plurality of sensor data items, a machine learning model configured for classifying sensor data items. The machine learning model includes a data adaptation part configured for determining a modified sensor data item based on an input sensor data item, an encoder configured for determining encoded features based on the modified sensor data item, a decoder configured for determining a decoded sensor data item based on the encoded features, representing an estimation of the input sensor data item, and a classifier configured for determining a class associated with the input sensor data item.

Abstract: In one embodiment, the method includes obtaining a plurality of sensor data items, each specifying a set of values determined from at least one sensor; and training, based on the plurality of sensor data items, a machine learning model configured for classifying sensor data items. The machine learning model includes a data adaptation part configured for determining a modified sensor data item based on an input sensor data item, an encoder configured for determining encoded features based on the modified sensor data item, a decoder configured for determining a decoded sensor data item based on the encoded features, representing an estimation of the input sensor data item, and a classifier configured for determining a class associated with the input sensor data item.

Abstract: Respective location data of a plurality of users are distributed into respective buckets for each of the users. Each of the location data indicates a location of the respective user, and each of the buckets includes a respective time interval. The time intervals cover the time period, and there is a bucket for each of the users for each of the time intervals. For each pair of the users and for each of the time intervals, a respective sub-distance is calculated between the users of the respective pair based on the location data. For each of the pairs of the users, the sub-distances are aggregated over the time period to obtain a distance between the users of the respective pair. The users having a closest distance from each other are clustered.

Abstract: Respective location data of a plurality of users are distributed into respective buckets for each of the users. Each of the location data indicates a location of the respective user, and each of the buckets includes a respective time interval. The time intervals cover the time period, and there is a bucket for each of the users for each of the time intervals. For each pair of the users and for each of the time intervals, a respective sub-distance is calculated between the users of the respective pair based on the location data. For each of the pairs of the users, the sub-distances are aggregated over the time period to obtain a distance between the users of the respective pair. The users having a closest distance from each other are clustered.

Abstract: Method of generating a multi-modality anatomical atlas. The method includes receiving first and second medical images of a region-of-interest (ROI) of a same individual. The first and second medical images are acquired by different first and second imaging modalities. The method includes generating first and second feature images based on the first and second medical images. The first and second feature images include a same designated anatomical feature of the ROI. The method includes determining a transformation function by registering the first and second feature images and applying the transformation function to the first and second medical images to register the medical images. The method includes generating a multi-modality anatomical atlas. The multi-modality atlas has the first and second medical images. The first and second medical images are first and second reference images. The multi-modality anatomical atlas includes an organ model that corresponds to an organ in the ROI.

Abstract: The invention is an imaging apparatus comprising a detector device (12) for determining points of incidence of photons and having an impact surface (13), and an aperture (16) suitable for projecting the photons to the detector device (12) having an inlet surface (17) and an outlet surface facing the impact surface (13), and comprising pinholes (18?, 18?, 22) connecting the inlet surface (17) and the outlet surface. The pinholes (18?, 18?, 22) comprise one or more central pinholes (18?, 22) and one or more peripheral pinholes (18?), and at least one central pinhole (18?, 22) and at least one peripheral pinhole (18?) are formed with focal opening (20?, 20?, 23) depth or focal opening (20?, 20?, 23) sizes different from each other. Furthermore, the invention is an aperture for the imaging apparatus and a method of manufacturing an aperture of an imaging apparatus.

Abstract: Method of generating a multi-modality anatomical atlas. The method includes receiving first and second medical images of a region-of-interest (ROI) of a same individual. The first and second medical images are acquired by different first and second imaging modalities. The method includes generating first and second feature images based on the first and second medical images. The first and second feature images include a same designated anatomical feature of the ROI. The method includes determining a transformation function by registering the first and second feature images and applying the transformation function to the first and second medical images to register the medical images. The method includes generating a multi-modality anatomical atlas. The multi-modality atlas has the first and second medical images. The first and second medical images are first and second reference images. The multi-modality anatomical atlas includes an organ model that corresponds to an organ in the ROI.

Abstract: A method that includes receiving an input image of a region of interest (ROI) of an individual. The input image is a medical image acquired by a first imaging modality. The method also includes generating a first feature image based on the input image. The first feature image includes a designated anatomical feature of the ROI. The method also includes obtaining an anatomical atlas. The atlas has a reference image of the ROI of at least one other individual and an organ model. The reference image is a medical image that is acquired by a second imaging modality that is different from the first imaging modality. The method also includes determining a transformation function by registering the first feature image with a second feature image that is based on the reference image and includes the designated anatomical feature.

Abstract: A method for automatic segmentation of a medical image is provided. The method comprises registering a reference image associated with an object to the medical image, determining a transformation function on the basis of the registration, applying the transformation function to a probability map associated with the object; carrying out a probability thresholding on the transformed probability map by selecting a first area of the medical image in which the probability of the object is within a probability range, carrying out an intensity thresholding on the medical image by selecting a second area of the medical image in which the intensity is within an intensity range, selecting a common part of the first and second areas and carrying out on the common part a morphological opening resulting in separate sub-areas, selecting the largest sub-area as a seed, and segmenting the medical image on the basis of the seed.

Abstract: The invention is an imaging apparatus comprising a detector device (12) for determining points of incidence of photons and having an impact surface (13), and an aperture (16) suitable for projecting the photons to the detector device (12) having an inlet surface (17) and an outlet surface facing the impact surface (13), and comprising pinholes (18?, 18?, 22) connecting the inlet surface (17) and the outlet surface. The pinholes (18?, 18?, 22) comprise one or more central pinholes (18?, 22) and one or more peripheral pinholes (18?), and at least one central pinhole (18?, 22) and at least one peripheral pinhole (18?) are formed with focal opening (20?, 20?, 23) depth or focal opening (20?, 20?, 23) sizes different from each other. Furthermore, the invention is an aperture for the imaging apparatus and a method of manufacturing an aperture of an imaging apparatus.

Abstract: A method for automatically displaying an organ of interest includes accessing a series of medical images acquired from a first imaging modality, receiving an input that indicates an organ of interest, automatically detecting the organ of interest within at least one image in the series of medical images, automatically placing a visual indicator in the region of interest, and automatically propagating the visual indicator to at least one image that is acquired using a second different imaging modality. A medical imaging system and a non-transitory computer readable medium are also described herein.

Abstract: A method for determining image information for an image of an object includes obtaining at least one image of an object of interest, automatically determining an image type and a presence or absence of a contrast agent, automatically generating a label that indicates the image type and the presence or absence of the contrast agent, and modifying the at least one image to include the label. A system and non-transitory computer readable medium are also described herein.

Abstract: An apparatus for multi-energy tissue quantification includes an x-ray imaging system comprises an x-ray source configured to emit a beam of x-rays toward an object to be imaged, a detector configured to receive the x-rays attenuated by the object, and a data acquisition system (DAS) operably coupled to the detector. A computer operably connected to the x-ray source and the DAS is programmed to cause the x-ray source to emit x-rays at each of a first kVp and a second kVp toward the detector, acquire x-ray data from x-rays emitted at the first and second kVp through a region of interest (ROI), and perform a first multi-material decomposition based on the acquired x-ray data. The computer is also programmed to quantify a volume fraction of a first material in the ROI based on the first multi-material decomposition and display the volume fraction of the first material to a user.

Abstract: A method for automatically detecting an organ of interest that includes accessing a medical image dataset using a processor, automatically segmenting the medical image dataset to identify an outline of a body of a patient, automatically determining an axial reference image slice and a axial center point using the segmented body of the patient, automatically determining a location of the organ of interest using the axial reference image slice and the axial center point, and automatically placing a visual indicator in the organ of interest based on the determined location. A medical imaging system and a non-transitory computer readable medium are also described herein.

Abstract: A method that includes receiving an input image of a region of interest (ROI) of an individual. The input image is a medical image acquired by a first imaging modality. The method also includes generating a first feature image based on the input image. The first feature image includes a designated anatomical feature of the ROI. The method also includes obtaining an anatomical atlas. The atlas has a reference image of the ROI of at least one other individual and an organ model. The reference image is a medical image that is acquired by a second imaging modality that is different from the first imaging modality. The method also includes determining a transformation function by registering the first feature image with a second feature image that is based on the reference image and includes the designated anatomical feature.

Abstract: A method for determining image information for an image of an object includes obtaining at least one image of an object of interest, automatically determining an image type and a presence or absence of a contrast agent, automatically generating a label that indicates the image type and the presence or absence of the contrast agent, and modifying the at least one image to include the label. A system and non-transitory computer readable medium are also described herein.