Abstract: In the endoscopic examination support apparatus, the image acquisition means acquires an endoscopic image taken by an endoscope. The first lesion detection means detects a lesion candidate from the endoscopic image, using a machine learning model that learned a relationship between the lesion candidate and a normal state of a large intestine. The second lesion detection means detects a lesion candidate from the endoscopic image, using a machine learning model that learned a relationship between the lesion candidate and a predetermined state of the large intestine. The output means outputs at least one of a detection result of the first lesion detection means and a detection result of the second lesion detection means.

Abstract: In the endoscopic examination support apparatus, the image acquisition means acquires an endoscopic image taken by an endoscope. The first lesion detection means detects a lesion candidate from the endoscopic image, using a machine learning model that learned a relationship between the lesion candidate and a normal state of a large intestine. The second lesion detection means detects a lesion candidate from the endoscopic image, using a machine learning model that learned a relationship between the lesion candidate and a predetermined state of the large intestine. The output means outputs at least one of a detection result of the first lesion detection means and a detection result of the second lesion detection means.

Abstract: The image processing device 1X includes a first acquisition means 30X, a second acquisition means 31X, and an inference means 33X. The first acquisition means 30X acquires a set value of a first index indicating an accuracy relating to a lesion analysis. The second acquisition means 31X acquires, for each of plural models which make inference regarding a lesion, a predicted value of a second index, which is an index of the accuracy other than the first index, on an assumption that the set value of the first index is satisfied. The inference means 33X makes inference regarding the lesion included in an endoscopic image of an examination target, based on the predicted value of the second index and the plural models.

Abstract: The image processing device 1X includes an acquisition means 31X, a selection means 32X, and a determination means 33X. The acquisition means 31X is configured to acquire data obtained by applying Fourier transform to an endoscopic image of an examination target photographed by a photographing unit provided in an endoscope. The selection means 32X is configured to select partial data that is a part of the data. The determination means 33X is configured to make a determination regarding an attention point to be noticed in the examination target based on the partial data.

Abstract: The image processing device 1X includes an acquisition means 30X, a variation detection means 311X, a selection means 312X, and a lesion detection means 34X. The acquisition means 30X acquires an endoscopic image obtained by photographing an examination target by a photographing unit provided in an endoscope. The variation detection means 311X detects a degree of variation between the endoscopic images. The selection means 312X selects either one of a first model or a second model based on the degree of variation, the first model making an inference regarding a lesion of the examination target based on a predetermined number of the endoscopic images, the second model making an inference regarding the lesion based on a variable number of the endoscopic images. The lesion detection means 34X detects the lesion based on a selection model that is either the first model or the second model selected.

Abstract: The image processing device 1X includes an acquisition means 31X, a selection means 32X, and a determination means 33X. The acquisition means 31X is configured to acquire data obtained by applying Fourier transform to an endoscopic image of an examination target photographed by a photographing unit provided in an endoscope. The selection means 32X is configured to select partial data that is a part of the data. The determination means 33X is configured to make a determination regarding an attention point to be noticed in the examination target based on the partial data.

Abstract: The image processing device 1X includes an acquisition means 30X and a lesion detection means 34X. The acquisition means 30X acquires an endoscopic image obtained by photographing an examination target by a photographing unit provided in an endoscope. The lesion detection means 34X detects a lesion based on a selection model which is selected from a first model and a second model, the first model being configured to make an inference regarding a lesion of the examination target based on a predetermined number of endoscopic images, the second model being configured to make an inference regarding a lesion of the examination target based on a variable number of endoscopic images. Besides, the lesion detection means 34X changes a parameter to be used for detection of the lesion based on a non-selection model that is the first model or the second model other than the selection model.

Abstract: The image processing device 1X includes an acquisition means 30X and a lesion detection means 34X. The acquisition means 30X acquires an endoscopic image obtained by photographing an examination target by a photographing unit provided in an endoscope. The lesion detection means 34X detects a lesion based on a selection model which is selected from a first model and a second model, the first model being configured to make an inference regarding a lesion of the examination target based on a predetermined number of endoscopic images, the second model being configured to make an inference regarding a lesion of the examination target based on a variable number of endoscopic images. Besides, the lesion detection means 34X changes a parameter to be used for detection of the lesion based on a non-selection model that is the first model or the second model other than the selection model.

Abstract: The image processing device 1X includes an acquisition means 30X, a variation detection means 311X, a selection means 312X, and a lesion detection means 34X. The acquisition means 30X acquires an endoscopic image obtained by photographing an examination target by a photographing unit provided in an endoscope. The variation detection means 311X detects a degree of variation between the endoscopic images. The selection means 312X selects either one of a first model or a second model based on the degree of variation, the first model making an inference regarding a lesion of the examination target based on a predetermined number of the endoscopic images, the second model making an inference regarding the lesion based on a variable number of the endoscopic images. The lesion detection means 34X detects the lesion based on a selection model that is either the first model or the second model selected.

Abstract: An MRI apparatus comprising a signal analyzing unit for determining a feature quantity of a navigator signal, and obtaining data representing a temporal change of the feature quantity for each coil element; a transforming unit for transforming the data obtained for each coil element into frequency spectra FS1 to FS16; and a selecting unit for selecting a coil element for determining a signal value of a body-motion signal for the subject from among coil elements E1 to E16 based on the frequency spectra FS1 to FS16.

Abstract: An MRI system for performing time resolved MR imaging of an object with grouped data acquisition is provided. The MRI system includes an MRI controller in electronic communication with a magnet assembly and operative to sample a group of data points within a first region of a k-space. The first region includes a central sub-region and a first peripheral sub-region. The MRI controller is further operative to sample a group of data points within a second region of the k-space. The second region includes the central sub-region and a second peripheral sub-region different from the first peripheral sub-region.

Abstract: Methods and systems are disclosed herein for selecting a channel adapted to detection of the position of a liver. The position “m” of the border between the liver and the lung is obtained from a profile. A sum Sliver of signal intensities in a liver region R1 and a sum Slung of signal intensities in a lung region R2 are calculated. Sliver and Slung are compared. In the case where Sliver is equal to or less than Slung (Sliver?Slung), a channel is not selected as a channel used at the time of detecting the position of the edge of the liver. On the other hand, in the case where Sliver is larger than Slung (Sliver>Slung), a channel is selected as a channel used at the time of detecting the position of the edge of the liver.

Abstract: An MRI apparatus comprising a signal analyzing unit for determining a feature quantity of a navigator signal, and obtaining data representing a temporal change of the feature quantity for each coil element; a transforming unit for transforming the data obtained for each coil element into frequency spectra FS1 to FS16; and a selecting unit for selecting a coil element for determining a signal value of a body-motion signal for the subject from among coil elements E1 to E16 based on the frequency spectra FS1 to FS16.

Abstract: An MRI system for performing time resolved MR imaging of an object with grouped data acquisition is provided. The MRI system includes an MRI controller in electronic communication with a magnet assembly and operative to sample a group of data points within a first region of a k-space. The first region includes a central sub-region and a first peripheral sub-region. The MRI controller is further operative to sample a group of data points within a second region of the k-space. The second region includes the central sub-region and a second peripheral sub-region different from the first peripheral sub-region.

Abstract: To provide an imaging technique suitable for acquiring an image with reduced artifacts due to differences in signal intensity. An MR apparatus 100 acquires, in data acquisition periods A1, A2, and A3, data at part of grid points lying closer to a high-frequency region RH within a low-frequency region RL, and data at part of grid points lying closer to the low-frequency region RL within the high-frequency region RH. On the other hand, in a data acquisition period B, it acquires data at another part of grid points lying closer to the high-frequency region RH within the low-frequency region RL, and data at another part of grid points lying closer to the low-frequency region RL within the high-frequency region RH.

Abstract: A magnetic resonance apparatus which acquires navigator signals generated from a navigator region including a first portion and a second portion, the navigator signals acquired using a coil having a plurality of channels, is provided. The magnetic resonance apparatus includes a scan unit configured to execute a first navigator sequence for acquiring first navigator signals generated from the navigator region, a profile generating unit configured to generate first profiles each including position information on the first portion for every channel, a determining unit configured to determine correlations between the first profiles and a template, and a selecting unit configured to select each channel used to acquire the position information on the first portion based on the correlations.

Abstract: A magnetic resonance imaging apparatus includes a scan section for executing a navigator sequence which transmits an RF pulse to a subject to obtain each magnetic resonance signal as navigator data. Upon execution of the navigator sequence, the scan section excites both a navigator area having two regions from which intensities of different navigator data signals are obtained, the two regions containing a body-moved region of the subject, and a region different from the two regions simultaneously, and transmits the RF pulse in such a manner that the phase of navigator data obtained from the different region differs from the phase of at least one region of navigator data obtained from the two regions.

Abstract: To provide an imaging technique suitable for acquiring an image with reduced artifacts due to differences in signal intensity. An MR apparatus 100 acquires, in data acquisition periods A1, A2, and A3, data at part of grid points lying closer to a high-frequency region RH within a low-frequency region RL, and data at part of grid points lying closer to the low-frequency region RL within the high-frequency region RH. On the other hand, in a data acquisition period B, it acquires data at another part of grid points lying closer to the high-frequency region RH within the low-frequency region RL, and data at another part of grid points lying closer to the low-frequency region RL within the high-frequency region RH.

Abstract: A magnetic resonance apparatus for performing a scan for generating a first magnetic resonance signal from an imaged part including a moving part is provided. The magnetic resonance apparatus includes a coil having a plurality of channels configured to receive the first magnetic resonance signal, a channel selecting unit configured to select a first channel disposed near an end of the moving part from the plurality of channels, and a generating unit configured to generate a biological signal including motion information indicating a movement of the imaged part in the scan, based on the first magnetic resonance signal received by the first channel.

Abstract: A magnetic resonance apparatus which acquires navigator signals generated from a navigator region including a first portion and a second portion, the navigator signals acquired using a coil having a plurality of channels, is provided. The magnetic resonance apparatus includes a scan unit configured to execute a first navigator sequence for acquiring first navigator signals generated from the navigator region, a profile generating unit configured to generate first profiles each including position information on the first portion for every channel, a determining unit configured to determine correlations between the first profiles and a template, and a selecting unit configured to select each channel used to acquire the position information on the first portion based on the correlations.