A position-signal processing method for flat panel gamma imaging probe includes a modeling phase and a use phase. In the modeling phase, a weight direction for an imaging detector is defined, position centers and weight ratios of the imaging detector in the weight direction are utilized to obtain a distribution graph of the weight ratios to the position centers, and curve fitting is performed upon the distribution graph to obtain a position estimation curve. In the use phase, the position estimation curve is utilized to derive a position estimation value of a probe trigger event in a 2D crystal diagram, a position value of the probe in the 2D crystal diagram with respect to the position estimation value of the probe trigger event is obtained, and a crystal code is located in a crystal code look-up table for the position value of the probe in the 2D crystal diagram.

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.

Systems and methods of PET attenuation correction using low-field MR image data includes receiving a first set of image data and a set of low-field magnetic resonance (MR) image data. An attenuation correction map is generated from the low-field MR image data using a first trained neural network. At least one attenuation correction process is applied to the first set of image data based on the attenuation correction map to generate at least one clinical attenuation-corrected image.

The disclosure is directed to a device that includes a cavity formed by a plurality of rails, the plurality of rails connected to both a first support and a second support, each at predetermined intervals about a circumference of the first support and the second support; and at least one particle detection device operably connected to each rail of the plurality of rails. The disclosure is also directed to a scanner that includes the device, and a processor.

In a gantry for a computed tomography device, a rotary frame is arranged on the tilt frame so that the rotary frame rotates relative to the tilt frame about an axis of rotation. The tilt frame is arranged on the support frame so that the tilt frame tilts about a tilt axis relative to the support frame, such that a tilt angle of the tilt frame relative to the support frame is changeable by a tilting movement of the tilt frame relative to the support frame about the tilt axis. The proximity switch has a proximity sensor and a reference mark, which interacts with the proximity sensor. The proximity switch is also coupled to the support frame and to the tilt frame such that the proximity switch is configured to react to an approach of the tilt angle to a reference angle.

Disclosed are a method and device for analyzing human tissue on the basis of a medical image. A tissue analysis device generates training data including a two-dimensional medical image and volume information of tissue by using a three-dimensional medical image, and trains, by using the training data, an artificial intelligence model that obtains a three-dimensional size, volume, or weight of tissue by dividing at least one or more normal or diseased tissues from a two-dimensional medical image in which a plurality of tissues are displayed overlapping on the same plane. In addition, the tissue analysis device obtains a three-dimensional size, volume, or weight of normal or diseased tissue from an X-ray medical image by using the artificial intelligence model.

A method for adaptive coincidence data processing is provided. The method includes detecting positron annihilation events with a detector array of a positron emission tomography (PET) scanner, wherein the PET scanner includes multiple detector rings disposed along a longitudinal axis of the PET scanner, and each detector ring includes multiple detectors. The method also includes, within a given time period, dynamically adjusting a number of positron annihilation events accepted and transmitted to acquisition circuitry for processing utilizing a numerical difference in detector rings along the longitudinal axis between a first detector and a second detector detecting respective annihilation photons from a positron annihilation event.

A method for forming a virtual 3D mathematical model of a dental system, including receiving DICOM files representing the dental system; identifying number and location of voxels of tissues of the dental system; combining the voxels of the tissues into voxels of organs of the dental system; combining the organs into the virtual 3D mathematical model of the dental system, wherein the virtual 3D mathematical models supports linear, non-linear and volumetric measurements of the dental system; and presenting the virtual 3D mathematical model to a user. The DICOM files can be cone beam or multispiral computed tomography, MRT, PET and/or ultrasonography. The tissues include enamel, dentin, pulp, cartilage, periodontium, and/or jaw bone. The organs include teeth, gums, temporomandibular joint and/or jaw. A size of the voxels is typically between 40 μm and 200 μm.

A method for adaptive coincidence data processing is provided. The method includes detecting positron annihilation events with a detector array of a positron emission tomography (PET) scanner, wherein the PET scanner includes multiple detector rings disposed along a longitudinal axis of the PET scanner, and each detector ring includes multiple detectors. The method also includes, within a given time period, dynamically adjusting a number of positron annihilation events accepted and transmitted to acquisition circuitry for processing utilizing a numerical difference in detector rings along the longitudinal axis between a first detector and a second detector detecting respective annihilation photons from a positron annihilation event.

Embodiments discussed herein facilitate determination of risk of recurrence of atrial fibrillation (AF) after ablation based on fractal features. One example embodiment is configured to generate a binary mask of at least a portion of a CT scan of a heart of a patient with AF; compute one or more radiomic fractal-based features from at least one of the binary mask or the portion of the CT scan; provide the one or more radiomic fractal-based features to a trained machine learning (ML) classifier; and receive a prediction from the trained ML classifier of whether or not the AF will recur after AF ablation, wherein the prediction is based at least in part on the one or more radiomic fractal-based features.