An injection system has: an injection device that injects a drug solution; and a sound detection device that detects sound while a subject is being imaged and generates first data based on the sound, wherein the injection device has a data generation unit that acquires the first data, and a history generation unit that generates injection history data of the drug solution, and the data generation unit generates second data in which the first data is associated with time data in common with the injection history data.

An injection system has: an injection device that injects a drug solution; and a sound detection device that detects sound while a subject is being imaged and generates first data based on the sound, wherein the injection device has a data generation unit that acquires the first data, and a history generation unit that generates injection history data of the drug solution, and the data generation unit generates second data in which the first data is associated with time data in common with the injection history data.

The invention relates to the field of computer engineering for processing images that provides increased accuracy of finding and classifying a similar object . The technical result is achieved by: downloading files of a radiographic image which comprise metadata including information about the object or subject of the image and information about the image itself; encrypting the downloaded files if the above-mentioned files comprise personal data about a person; decrypting the above-mentioned, encrypted, downloaded files; and processing the radiographic image, wherein, as a result of the processing, the following occurs: finding and capturing a relevant region of the radiographic image; removing noise from the captured, relevant region of the radiographic image, wherein a region with a found object is meant by a relevant region of the radiographic image; compressing or unzipping a previously processed radiographic image; and finding a similar object in two previously processed images, and processing said object.

The present disclosure relates to a system and a method for recommending at least one imaging protocol for scanning a patient. The method includes receiving, by a processor, an imaging protocol, wherein the imaging protocol includes information pertaining to set of imaging parameters for imaging a patient, determining at least one imaging identifier for the received imaging protocol based on a clinical intent of the received imaging protocol, determining at least one harmonized imaging protocol out of the plurality of harmonized imaging protocols based on the determined at least one imaging identifier, and providing the determined at least one harmonized imaging protocol on a graphical user interface.

The present disclosure relates to a system and a method for recommending at least one imaging protocol for scanning a patient. The method includes receiving, by a processor, an imaging protocol, wherein the imaging protocol includes information pertaining to set of imaging parameters for imaging a patient, determining at least one imaging identifier for the received imaging protocol based on a clinical intent of the received imaging protocol, determining at least one harmonized imaging protocol out of the plurality of harmonized imaging protocols based on the determined at least one imaging identifier, and providing the determined at least one harmonized imaging protocol on a graphical user interface.

The present disclosure provides methods, systems, and non-transitory computer-readable media for assessment of disease treatment or progression on a lesion-by-lesion level. The systems and methods are based on measurements of a variety of features including total number of lesions, total number and proportion of lesions regressing or progressing, changes in dimensions of a lesion over time, and uptake values of a molecular imaging agent.

Accurate tissue segmentation is performed without a priori knowledge of tissue type or other extrinsic information not found within the subject image, and may be combined with classification analysis so that diseased tissue is not only delineated within an image but also characterized in terms of disease type. In various embodiments, a source image is decomposed into smaller overlapping subimages such as square or rectangular tiles. A predictor such as a convolutional neural network produces tile-level classifications that are aggregated to produce a tissue segmentation and, in some embodiments, to classify the source image or a subregion thereof.

Accurate tissue segmentation is performed without a priori knowledge of tissue type or other extrinsic information not found within the subject image, and may be combined with classification analysis so that diseased tissue is not only delineated within an image but also characterized in terms of disease type. In various embodiments, a source image is decomposed into smaller overlapping subimages such as square or rectangular tiles. A predictor such as a convolutional neural network produces tile-level classifications that are aggregated to produce a tissue segmentation and, in some embodiments, to classify the source image or a subregion thereof.

The present disclosure provides systems and methods for digital imaging and communications in medicine (DICOM) file processing. The methods may include receiving a request for processing a DICOM file. The DICOM file may include data of metadata and pixel data. The methods may also include parsing at least part of the metadata of the DICOM file. The methods may further include writing the data of the DICOM file to one or more data streams based on the parsed metadata.

The present disclosure provides systems and methods for digital imaging and communications in medicine (DICOM) file processing. The methods may include receiving a request for processing a DICOM file. The DICOM file may include data of metadata and pixel data. The methods may also include parsing at least part of the metadata of the DICOM file. The methods may further include writing the data of the DICOM file to one or more data streams based on the parsed metadata.